Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.

PubMed

Goldman, Gretchen T; Mulholland, James A; Russell, Armistead G; Strickland, Matthew J; Klein, Mitchel; Waller, Lance A; Tolbert, Paige E

2011-06-22

Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta. Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits. Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed. For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.

Spatial-temporal modeling of the association between air pollution exposure and preterm birth: identifying critical windows of exposure.

PubMed

Warren, Joshua; Fuentes, Montserrat; Herring, Amy; Langlois, Peter

2012-12-01

Exposure to high levels of air pollution during the pregnancy is associated with increased probability of preterm birth (PTB), a major cause of infant morbidity and mortality. New statistical methodology is required to specifically determine when a particular pollutant impacts the PTB outcome, to determine the role of different pollutants, and to characterize the spatial variability in these results. We develop a new Bayesian spatial model for PTB which identifies susceptible windows throughout the pregnancy jointly for multiple pollutants (PM(2.5) , ozone) while allowing these windows to vary continuously across space and time. We geo-code vital record birth data from Texas (2002-2004) and link them with standard pollution monitoring data and a newly introduced EPA product of calibrated air pollution model output. We apply the fully spatial model to a region of 13 counties in eastern Texas consisting of highly urban as well as rural areas. Our results indicate significant signal in the first two trimesters of pregnancy with different pollutants leading to different critical windows. Introducing the spatial aspect uncovers critical windows previously unidentified when space is ignored. A proper inference procedure is introduced to correctly analyze these windows. © 2012, The International Biometric Society.

Linking sewage pollution and water quality to spatial patterns of Porites lobata growth anomalies in Puako, Hawaii.

PubMed

Yoshioka, Reyn M; Kim, Catherine J S; Tracy, Allison M; Most, Rebecca; Harvell, C Drew

2016-03-15

Sewage pollution threatens the health of coastal populations and ecosystems, including coral reefs. We investigated spatial patterns of sewage pollution in Puako, Hawaii using enterococci concentrations and δ(15)N Ulva fasciata macroalgal bioassays to assess relationships with the coral disease Porites lobata growth anomalies (PGAs). PGA severity and enterococci concentrations were high, spatially variable, and positively related. Bioassay algal δ(15)N showed low sewage pollution at the reef edge while high values of resident algae indicated sewage pollution nearshore. Neither δ(15)N metric predicted PGA measures, though bioassay δ(15)N was negatively related to coral cover. Furthermore, PGA prevalence was much higher than previously recorded in Hawaii and the greater Indo-Pacific, highlighting Puako as an area of concern. Although further work is needed to resolve the relationship between sewage pollution and coral cover and disease, these results implicate sewage pollution as a contributor to diminished reef health. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Spatial variability and evaluation of soil heavy metal contamination in the urban-transect of Shanghai].

PubMed

Liu, Yun-Long; Zhang, Li-Jia; Han, Xiao-Fei; Zhuang, Teng-Fei; Shi, Zhen-Xiang; Lu, Xiao-Zhe

2012-02-01

Soil heavy metal concentrations along the typical urban-transect in Shanghai were analyzed to indicate the effect of urbanization and industrialization on soil environment quality. Spatial variation structure and distribution of 5 heavy metals (Cu, Cr, Mn, Pb and Zn) in the top soil of urban-transect were analyzed. The single pollution index and the composite pollution index were used to evaluate the soil heavy metal pollution. The results showed that the average concentrations of the Cu, Pb, Zn, Cr, Mn were 27.80, 28.86, 99.36, 87.72, 556.97 mg x kg(-1), respectively. Cu, Cr, Mn, Pb and Zn were medium in variability, Mn was distributed lognormally, while Cu, Cr, Pb and Zn were distributed normally. The results of semivariance analysis showed that Mn was fit for the exponential model, Cr, Pb, Cu and Zn were fit for the linear model. The spatial distribution maps of heavy metal content of the topsoil in this city-transect were produced by means of the universal kriging interpolation. Cu was spatially distributed in ribbon, Cr and Mn were distributed in island, while the spatial distribution of Pb and Zn showed the mixed characteristic of ribbon and island. With the result of soil pollution evaluation, it showed that the pollution of Cr, Zn and Pb was relatively severe. Cr, Zn, Pb, Mn and Cu were significantly correlated, and heavy metal co-contamination existed in soil. Difference of soil heavy metals pollution along "Urban-suburban-rural" was obvious, the special variation of heavy metal concentrations in the soil closely related to the degree of industrialization and urbanization of the city.

A novel principal component analysis for spatially misaligned multivariate air pollution data.

PubMed

Jandarov, Roman A; Sheppard, Lianne A; Sampson, Paul D; Szpiro, Adam A

2017-01-01

We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used. We demonstrate the utility of predictive (sparse) PCA in simulated data and apply the approach to annual averages of particulate matter speciation data from national Environmental Protection Agency (EPA) regulatory monitors.

Spatial variability in levels of benzene, formaldehyde, and total benzene, toluene, ethylbenzene and xylenes in New York City: a land-use regression study.

PubMed

Kheirbek, Iyad; Johnson, Sarah; Ross, Zev; Pezeshki, Grant; Ito, Kazuhiko; Eisl, Holger; Matte, Thomas

2012-07-31

Hazardous air pollutant exposures are common in urban areas contributing to increased risk of cancer and other adverse health outcomes. While recent analyses indicate that New York City residents experience significantly higher cancer risks attributable to hazardous air pollutant exposures than the United States as a whole, limited data exist to assess intra-urban variability in air toxics exposures. To assess intra-urban spatial variability in exposures to common hazardous air pollutants, street-level air sampling for volatile organic compounds and aldehydes was conducted at 70 sites throughout New York City during the spring of 2011. Land-use regression models were developed using a subset of 59 sites and validated against the remaining 11 sites to describe the relationship between concentrations of benzene, total BTEX (benzene, toluene, ethylbenzene, xylenes) and formaldehyde to indicators of local sources, adjusting for temporal variation. Total BTEX levels exhibited the most spatial variability, followed by benzene and formaldehyde (coefficient of variation of temporally adjusted measurements of 0.57, 0.35, 0.22, respectively). Total roadway length within 100 m, traffic signal density within 400 m of monitoring sites, and an indicator of temporal variation explained 65% of the total variability in benzene while 70% of the total variability in BTEX was accounted for by traffic signal density within 450 m, density of permitted solvent-use industries within 500 m, and an indicator of temporal variation. Measures of temporal variation, traffic signal density within 400 m, road length within 100 m, and interior building area within 100 m (indicator of heating fuel combustion) predicted 83% of the total variability of formaldehyde. The models built with the modeling subset were found to predict concentrations well, predicting 62% to 68% of monitored values at validation sites. Traffic and point source emissions cause substantial variation in street-level exposures to common toxic volatile organic compounds in New York City. Land-use regression models were successfully developed for benzene, formaldehyde, and total BTEX using spatial indicators of on-road vehicle emissions and emissions from stationary sources. These estimates will improve the understanding of health effects of individual pollutants in complex urban pollutant mixtures and inform local air quality improvement efforts that reduce disparities in exposure.

Accounting for spatial effects in land use regression for urban air pollution modeling.

PubMed

Bertazzon, Stefania; Johnson, Markey; Eccles, Kristin; Kaplan, Gilaad G

2015-01-01

In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Spatial analysis of concentrations of multiple air pollutants using NASA DISCOVER-AQ aircraft measurements: Implications for exposure assessment.

PubMed

Lee, Hyung Joo; Chatfield, Robert B; Bell, Michelle L

2018-01-01

In recent years, multipollutant approaches have been employed to investigate the association with health outcomes to better represent real-world conditions than more traditional analysis that considers a single pollutant. With regard to the exposure assessment of a mixture of air pollutants, it is critical to understand the spatial variability in multipollutant relations in order to assess their potential health implications. In this study, we investigated the spatial relations of multiple pollutant concentrations (i.e., NO x , NO y , black carbon, carbon monoxide, acetaldehyde, formaldehyde, toluene, xylenes/ethylbenzene, ozone, water-soluble organic carbon, and aerosol extinction) observed from the P-3B aircraft in the 2011 NASA field campaign in Baltimore/Washington D.C. areas during July 2011. The between-pollutant Pearson correlations and Z-scores (calculated from log-transformed concentrations) between near-highways and non-highways and between near-urban centers and non-urban centers varied by pollutant pair and space. We found generally lower correlations between NO x and other pollutants for near-highways (average r = 0.36) than for non-highways (average r = 0.41) and also for non-urban centers (average r = 0.37) than for near-urban centers (average r = 0.41). This indicated that the temporal associations between NO x and health outcomes might be less affected by other pollutants, which were also related to same health outcomes, for near-highways and non-urban centers. The analysis of between-pollutant Z-scores showed varying spatial relations for popular traffic-related pollutants with the Z-score differences of 0.43 (NO x -carbon monoxide), 0.29 (NO x -black carbon), and 0.17 (black carbon-carbon monoxide) between near-highways and non-highways. This result exhibited heterogeneous traffic-related pollutant mixtures with the proximity to highways, potentially leading to the diverse extent of health associations. Furthermore, a mixed effects model presented pollutant-specific associations between the concentrations and the proximity to highways and urban centers, showing larger declines for NO x , xylenes/ethylbenzene, toluene, and NO y than those for the pollutants related to secondary pollutant formation. The model also demonstrated the different sensitivity of each pollutant to meteorological parameters, which may modify the spatial and temporal variability in the relations between the pollutants. Our findings provide insights for exposure assessment studies to better understand the cumulative health consequences associated with multiple air pollutants simultaneously. Copyright © 2017 Elsevier Inc. All rights reserved.

Simulation of population-based commuter exposure to NO₂ using different air pollution models.

PubMed

Ragettli, Martina S; Tsai, Ming-Yi; Braun-Fahrländer, Charlotte; de Nazelle, Audrey; Schindler, Christian; Ineichen, Alex; Ducret-Stich, Regina E; Perez, Laura; Probst-Hensch, Nicole; Künzli, Nino; Phuleria, Harish C

2014-05-12

We simulated commuter routes and long-term exposure to traffic-related air pollution during commute in a representative population sample in Basel (Switzerland), and evaluated three air pollution models with different spatial resolution for estimating commute exposures to nitrogen dioxide (NO2) as a marker of long-term exposure to traffic-related air pollution. Our approach includes spatially and temporally resolved data on actual commuter routes, travel modes and three air pollution models. Annual mean NO2 commuter exposures were similar between models. However, we found more within-city and within-subject variability in annual mean (±SD) NO2 commuter exposure with a high resolution dispersion model (40 ± 7 µg m(-3), range: 21-61) than with a dispersion model with a lower resolution (39 ± 5 µg m(-3); range: 24-51), and a land use regression model (41 ± 5 µg m(-3); range: 24-54). Highest median cumulative exposures were calculated along motorized transport and bicycle routes, and the lowest for walking. For estimating commuter exposure within a city and being interested also in small-scale variability between roads, a model with a high resolution is recommended. For larger scale epidemiological health assessment studies, models with a coarser spatial resolution are likely sufficient, especially when study areas include suburban and rural areas.

A long view of polluting industry and environmental justice in Baltimore

Treesearch

Christopher G. Boone; Michail Fragkias; Geoffrey L. Buckley; J. Morgan Grove

2014-01-01

This study examines the density of polluting industry by neighborhoods in Baltimore over the long term, from 1950 to 2010, to determine if high pollution burdens correspond spatially with expected demographic and housing variables predicted in the environmental justice literature. For 1960-1980 we use data on heavy industry from Dun and Bradstreet directories and for...

Development of a distributed air pollutant dry deposition modeling framework

Treesearch

Satoshi Hirabayashi; Charles N. Kroll; David J. Nowak

2012-01-01

A distributed air pollutant dry deposition modeling systemwas developed with a geographic information system (GIS) to enhance the functionality of i-Tree Eco (i-Tree, 2011). With the developed system, temperature, leaf area index (LAI) and air pollutant concentration in a spatially distributed form can be estimated, and based on these and other input variables, dry...

Spatial-temporal and cancer risk assessment of selected hazardous air pollutants in Seattle.

PubMed

Wu, Chang-fu; Liu, L-J Sally; Cullen, Alison; Westberg, Hal; Williamson, John

2011-01-01

In the Seattle Air Toxics Monitoring Pilot Program, we measured 15 hazardous air pollutants (HAPs) at 6 sites for more than a year between 2000 and 2002. Spatial-temporal variations were evaluated with random-effects models and principal component analyses. The potential health risks were further estimated based on the monitored data, with the incorporation of the bootstrapping technique for the uncertainty analysis. It is found that the temporal variability was generally higher than the spatial variability for most air toxics. The highest temporal variability was observed for tetrachloroethylene (70% temporal vs. 34% spatial variability). Nevertheless, most air toxics still exhibited significant spatial variations, even after accounting for the temporal effects. These results suggest that it would require operating multiple air toxics monitoring sites over a significant period of time with proper monitoring frequency to better evaluate population exposure to HAPs. The median values of the estimated inhalation cancer risks ranged between 4.3 × 10⁻⁵ and 6.0 × 10⁻⁵, with the 5th and 95th percentile levels exceeding the 1 in a million level. VOCs as a whole contributed over 80% of the risk among the HAPs measured and arsenic contributed most substantially to the overall risk associated with metals. Copyright © 2010 Elsevier Ltd. All rights reserved.

Near-Port Air Quality Assessment Utilizing a Mobile Monitoring Approach

EPA Science Inventory

Mobile monitoring is an emerging strategy to characterize spatially and temporally variable air pollution in areas near sources. EPA’s Geospatial Monitoring of Air Pollution (GMAP) vehicle – an all-electric vehicle measuring real-time concentrations of particulate and gaseous po...

Intra-urban and street scale variability of BTEX, NO 2 and O 3 in Birmingham, UK: Implications for exposure assessment

NASA Astrophysics Data System (ADS)

Vardoulakis, Sotiris; Solazzo, Efisio; Lumbreras, Julio

2011-09-01

Automatic monitoring networks have the ability of capturing air pollution episodes, as well as short- and long-term air quality trends in urban areas that can be used in epidemiological studies. However, due to practical constraints (e.g. cost and bulk of equipment), the use of automatic analysers is restricted to a limited number of roadside and background locations within a city. As a result, certain localised air pollution hotspots may be overlooked or overemphasised, especially near heavily trafficked street canyons and intersections. This has implications for compliance with regulatory standards and may cause exposure misclassification in epidemiological studies. Apart from automatic analysers, low cost passive diffusion tubes can be used to characterise the spatial variability of air pollution in urban areas. In this study, BTEX, NO 2 and O 3 data from a one-year passive sampling survey were used to characterise the intra-urban and street scale spatial variability of traffic-related pollutants in Birmingham (UK). In addition, continuous monitoring of NO 2, NO x, O 3, CO, SO 2, PM 10 and PM 2.5 from three permanent monitoring sites was used to identify seasonal and annual pollution patterns. The passive sampling measurements allowed us to evaluate the representativeness of a permanent roadside monitoring site that has recorded some of the highest NO 2 and PM 10 concentrations in Birmingham in recent years. Dispersion modelling was also used to gain further insight into pollutant sources and dispersion characteristics at this location. The strong spatial concentration gradients observed in busy streets, as well as the differences between roadside and urban background levels highlight the importance of appropriate positioning of air quality monitoring equipment in cities.

Spatial analysis of trace elements in a moss bio-monitoring data over France by accounting for source, protocol and environmental parameters.

PubMed

Lequy, Emeline; Saby, Nicolas P A; Ilyin, Ilia; Bourin, Aude; Sauvage, Stéphane; Leblond, Sébastien

2017-07-15

Air pollution in trace elements (TE) remains a concern for public health in Europe. For this reasons, networks of air pollution concentrations or exposure are deployed, including a moss bio-monitoring programme in Europe. Spatial determinants of TE concentrations in mosses remain unclear. In this study, the French dataset of TE in mosses is analyzed by spatial autoregressive model to account for spatial structure of the data and several variables proven or suspected to affect TE concentrations in mosses. Such variables include source (atmospheric deposition and soil concentrations), protocol (sampling month, collector, and moss species), and environment (forest type and canopy density, distance to the coast or the highway, and elevation). Modeled atmospheric deposition was only available for Cd and Pb and was one of the main explanatory variables of the concentrations in mosses. Predicted soil content was also an important explanatory variable except for Cr, Ni, and Zn. However, the moss species was the main factor for all the studied TE. The other environmental variables affected differently the TE. In particular, the forest type and canopy density were important in most cases. These results stress the need for further research on the effect of the moss species on the capture and retention of TE, as well as for accounting for several variables and the spatial structure of the data in statistical analyses. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network.

PubMed

Jia, Zhenyi; Zhou, Shenglu; Su, Quanlong; Yi, Haomin; Wang, Junxiao

2017-12-26

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution.

SPATIAL VARIABILITY IN POLLUTANTS: IMPLICATIONS FOR EXPOSURE ASSESSMENT

EPA Science Inventory

The efforts to evaluate the value of improved exposure metrics on the ability to relate those metrics with outcomes in complex systems have met with varying degrees of success. This work describes the results of recent efforts, mostly involving air pollutants, to improve the sop...

Simulation of Population-Based Commuter Exposure to NO2 Using Different Air Pollution Models

PubMed Central

Ragettli, Martina S.; Tsai, Ming-Yi; Braun-Fahrländer, Charlotte; de Nazelle, Audrey; Schindler, Christian; Ineichen, Alex; Ducret-Stich, Regina E.; Perez, Laura; Probst-Hensch, Nicole; Künzli, Nino; Phuleria, Harish C.

2014-01-01

We simulated commuter routes and long-term exposure to traffic-related air pollution during commute in a representative population sample in Basel (Switzerland), and evaluated three air pollution models with different spatial resolution for estimating commute exposures to nitrogen dioxide (NO2) as a marker of long-term exposure to traffic-related air pollution. Our approach includes spatially and temporally resolved data on actual commuter routes, travel modes and three air pollution models. Annual mean NO2 commuter exposures were similar between models. However, we found more within-city and within-subject variability in annual mean (±SD) NO2 commuter exposure with a high resolution dispersion model (40 ± 7 µg m−3, range: 21–61) than with a dispersion model with a lower resolution (39 ± 5 µg m−3; range: 24–51), and a land use regression model (41 ± 5 µg m−3; range: 24–54). Highest median cumulative exposures were calculated along motorized transport and bicycle routes, and the lowest for walking. For estimating commuter exposure within a city and being interested also in small-scale variability between roads, a model with a high resolution is recommended. For larger scale epidemiological health assessment studies, models with a coarser spatial resolution are likely sufficient, especially when study areas include suburban and rural areas. PMID:24823664

Characterizing spatial variability of air pollution from vehicle traffic around the Houston Ship Channel area

NASA Astrophysics Data System (ADS)

Zhang, Xueying; Craft, Elena; Zhang, Kai

2017-07-01

Mobile emissions are a major source of urban air pollution and have been associated with a variety of adverse health outcomes. The Houston Ship Channel area is the home of a large number of diesel-powered vehicles emitting fine particulate matter (PM2.5; ≤2.5 μm in aerodynamic diameter) and nitrogen oxides (NOx). However, the spatial variability of traffic-related air pollutants in the Houston Ship Channel area has rarely been investigated. The objective of this study is to characterize spatial variability of PM2.5 and NOx concentrations attributable to on-road traffic in the Houston Ship Channel area in the year of 2011. We extracted the road network from the Texas Department of Transportation Road Inventory, and calculated emission rates using the Motor Vehicle Emission Simulator version 2014a (MOVES2014a). These parameters and preprocessed meteorological parameters were entered into a Research LINE-source Dispersion Model (RLINE) to conduct a simulation. Receptors were placed at 50 m resolution within 300 m to major roads and at 150 m resolution in the rest of the area. Our findings include that traffic-related PM2.5 were mainly emitted from trucks, while traffic-related NOx were emitted from both trucks and cars. The traffic contributed 0.90 μg/m3 PM2.5 and 29.23 μg/m3 NOx to the annual average mass concentrations of on-road air pollution, and the concentrations of the two pollutants decreased by nearly 40% within 500 m distance to major roads. The pollution level of traffic-related PM2.5 and NOx was higher in winter than those in the other three seasons. The Houston Ship Channel has earlier morning peak hours and relative late afternoon hours, which indicates the influence of goods movement from port activity. The varied near-road gradients illustrate that proximities to major roads are not an accurate surrogate of traffic-related air pollution.

Spatial and temporal variability of fine particle composition and source types in five cities of Connecticut and Massachusetts

PubMed Central

Lee, Hyung Joo; Gent, Janneane F.; Leaderer, Brian P.; Koutrakis, Petros

2011-01-01

To protect public health from PM2.5 air pollution, it is critical to identify the source types of PM2.5 mass and chemical components associated with higher risks of adverse health outcomes. Source apportionment modeling using Positive Matrix Factorization (PMF), was used to identify PM2.5 source types and quantify the source contributions to PM2.5 in five cities of Connecticut and Massachusetts. Spatial and temporal variability of PM2.5 mass, components and source contributions were investigated. PMF analysis identified five source types: regional pollution as traced by sulfur, motor vehicle, road dust, oil combustion and sea salt. The sulfur-related regional pollution and traffic source type were major contributors to PM2.5. Due to sparse ground-level PM2.5 monitoring sites, current epidemiological studies are susceptible to exposure measurement errors. The higher correlations in concentrations and source contributions between different locations suggest less spatial variability, resulting in less exposure measurement errors. When concentrations and/or contributions were compared to regional averages, correlations were generally higher than between-site correlations. This suggests that for assigning exposures for health effects studies, using regional average concentrations or contributions from several PM2.5 monitors is more reliable than using data from the nearest central monitor. PMID:21429560

Spatial variations in estimated chronic exposure to traffic-related air pollution in working populations: A simulation

PubMed Central

Setton, Eleanor M; Keller, C Peter; Cloutier-Fisher, Denise; Hystad, Perry W

2008-01-01

Background Chronic exposure to traffic-related air pollution is associated with a variety of health impacts in adults and recent studies show that exposure varies spatially, with some residents in a community more exposed than others. A spatial exposure simulation model (SESM) which incorporates six microenvironments (home indoor, work indoor, other indoor, outdoor, in-vehicle to work and in-vehicle other) is described and used to explore spatial variability in estimates of exposure to traffic-related nitrogen dioxide (not including indoor sources) for working people. The study models spatial variability in estimated exposure aggregated at the census tracts level for 382 census tracts in the Greater Vancouver Regional District of British Columbia, Canada. Summary statistics relating to the distributions of the estimated exposures are compared visually through mapping. Observed variations are explored through analyses of model inputs. Results Two sources of spatial variability in exposure to traffic-related nitrogen dioxide were identified. Median estimates of total exposure ranged from 8 μg/m3 to 35 μg/m3 of annual average hourly NO2 for workers in different census tracts in the study area. Exposure estimates are highest where ambient pollution levels are highest. This reflects the regional gradient of pollution in the study area and the relatively high percentage of time spent at home locations. However, for workers within the same census tract, variations were observed in the partial exposure estimates associated with time spent outside the residential census tract. Simulation modeling shows that some workers may have exposures 1.3 times higher than other workers residing in the same census tract because of time spent away from the residential census tract, and that time spent in work census tracts contributes most to the differences in exposure. Exposure estimates associated with the activity of commuting by vehicle to work were negligible, based on the relatively short amount of time spent in this microenvironment compared to other locations. We recognize that this may not be the case for pollutants other than NO2. These results represent the first time spatially disaggregated variations in exposure to traffic-related air pollution within a community have been estimated and reported. Conclusion The results suggest that while time spent in the home indoor microenvironment contributes most to between-census tract variation in estimates of annual average exposures to traffic-related NO2, time spent in the work indoor microenvironment contributes most to within-census tract variation, and time spent in transit by vehicle makes a negligible contribution. The SESM has potential as a policy evaluation tool, given input data that reflect changes in pollution levels or work flow patterns due to traffic demand management and land use development policy. PMID:18638398

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

PubMed Central

Zhou, Shenglu; Su, Quanlong; Yi, Haomin

2017-01-01

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution. PMID:29278363

Profiles of environmental contaminants in hawksbill turtle egg yolks reflect local to distant pollution sources among nesting beaches in the Yucatán Peninsula, Mexico.

PubMed

Muñoz, Cynthia C; Vermeiren, Peter

2018-04-01

Knowledge of spatial variation in pollutant profiles among sea turtle nesting locations is limited. This poses challenges in identifying processes shaping this variability and sets constraints to the conservation management of sea turtles and their use as biomonitoring tools for environmental pollutants. We aimed to increase understanding of the spatial variation in polycyclic aromatic hydrocarbon (PAH), organochlorine pesticide (OCP) and polychlorinated biphenyl (PCB) compounds among nesting beaches. We link the spatial variation to turtle migration patterns and the persistence of these pollutants. Specifically, using gas chromatography, we confirmed maternal transfer of a large number of compounds (n = 68 out of 69) among 104 eggs collected from 21 nests across three nesting beaches within the Yucatán Peninsula, one of the world's most important rookeries for hawksbill turtles (Eretmochelys imbricata). High variation in PAH profiles was observed among beaches, using multivariate correspondence analysis and univariate Peto-Prentice tests, reflecting local acquisition during recent migration movements. Diagnostic PAH ratios reflected petrogenic origins in Celestún, the beach closest to petroleum industries in the Gulf of Mexico. By contrast, pollution profiles of OCPs and PCBs showed high similarity among beaches, reflecting the long-term accumulation of these pollutants at regional scales. Therefore, spatial planning of protected areas and the use of turtle eggs in biomonitoring needs to account for the spatial variation in pollution profiles among nesting beaches. Copyright © 2018 Elsevier Ltd. All rights reserved.

Temporal and spatial variation in allocating annual traffic activity across an urban region and implications for air quality assessments

PubMed Central

Batterman, Stuart

2015-01-01

Patterns of traffic activity, including changes in the volume and speed of vehicles, vary over time and across urban areas and can substantially affect vehicle emissions of air pollutants. Time-resolved activity at the street scale typically is derived using temporal allocation factors (TAFs) that allow the development of emissions inventories needed to predict concentrations of traffic-related air pollutants. This study examines the spatial and temporal variation of TAFs, and characterizes prediction errors resulting from their use. Methods are presented to estimate TAFs and their spatial and temporal variability and used to analyze total, commercial and non-commercial traffic in the Detroit, Michigan, U.S. metropolitan area. The variability of total volume estimates, quantified by the coefficient of variation (COV) representing the percentage departure from expected hourly volume, was 21, 33, 24 and 33% for weekdays, Saturdays, Sundays and holidays, respectively. Prediction errors mostly resulted from hour-to-hour variability on weekdays and Saturdays, and from day-to-day variability on Sundays and holidays. Spatial variability was limited across the study roads, most of which were large freeways. Commercial traffic had different temporal patterns and greater variability than noncommercial vehicle traffic, e.g., the weekday variability of hourly commercial volume was 28%. The results indicate that TAFs for a metropolitan region can provide reasonably accurate estimates of hourly vehicle volume on major roads. While vehicle volume is only one of many factors that govern on-road emission rates, air quality analyses would be strengthened by incorporating information regarding the uncertainty and variability of traffic activity. PMID:26688671

Utilizing NASA DISCOVER-AQ Data to Examine Spatial Gradients in Complex Emission Environments

NASA Astrophysics Data System (ADS)

Buzanowicz, M. E.; Moore, W.; Crawford, J. H.; Schroeder, J.

2017-12-01

Although many regulations have been enacted with the goal of improving air quality, many parts of the US are still classified as `non-attainment areas' because they frequently violate federal air quality standards. Adequately monitoring the spatial distribution of pollutants both within and outside of non-attainment areas has been an ongoing challenge for regulators. Observations of near-surface pollution from space-based platforms would provide an unprecedented view of the spatial distribution of pollution, but this goal has not yet been realized due to fundamental limitations of satellites, specifically because the footprint size of satellite measurements may not be sufficiently small enough to capture true gradients in pollution, and rather represents an average over a large area. NASA's DISCOVER-AQ was a multi-year field campaign aimed at improving our understanding of the role that remote sensing, including satellite-based remote sensing, could play in air quality monitoring systems. DISCOVER-AQ data will be utilized to create a metric to examine spatial gradients and how satellites can capture those gradients in areas with complex emission environments. Examining horizontal variability within a vertical column is critical to understanding mixing within the atmosphere. Aircraft spirals conducted during DISCOVER-AQ were divided into octants, and averages of a given a species were calculated, with certain points receiving a flag. These flags were determined by calculating gradients between subsequent octants. Initial calculations have shown that over areas with large point source emissions, such as Platteville and Denver-La Casa in Colorado, and Essex, Maryland, satellite retrievals may not adequately capture spatial variability in the atmosphere, thus complicating satellite inversion techniques and limiting our ability to understand human exposure on sub-grid scales. Further calculations at other locations and for other trace gases are necessary to determine the effects of vertical variability within the atmosphere.

Variability of hazardous air pollutants in an urban area

NASA Astrophysics Data System (ADS)

Spicer, Chester W.; Buxton, Bruce E.; Holdren, Michael W.; Smith, Deborah L.; Kelly, Thomas J.; Rust, Steven W.; Pate, Alan D.; Sverdrup, George M.; Chuang, Jane C.

The variability of hazardous air pollutants (HAPs) is an important factor in determining human exposure to such chemicals, and in designing HAP measurement programs. This study has investigated the factors which contribute to HAP variability in an urban area. Six measurement sites separated by up to 12 km collected data with 3 h time resolution to examine spatial variability within neighborhoods and between neighborhoods. The measurements were made in Columbus, OH. The 3 h results also were used to study temporal variability, and duplicate samples collected at each site were used to determine the component of variability attributable to the measurement process. Hourly samples collected over 10 days at one site provided further insight into the temporal resolution needed to capture short-term peak concentrations. Measurements at the 6 spatial sites focused on 78 chemicals. Twenty-three of these species were found in at least 95% of the 3 h samples, and 39 chemicals were present at least 60% of the time. The relative standard deviations for most of these 39 frequently detected chemicals was 1.0 or lower. Variability was segmented into temporal, spatial, and measurement components. Temporal variation was the major contributor to HAP variability for 19 of the 39 frequently detected compounds, based on the 3 h data. Measurement imprecision contributed less than 25% for most of the volatile organic species, but 30% or more of the variability for carbonyl compounds, trace elements, and particle-bound extractable organic mass. Interestingly, the spatial component contributed less than 20% of the total variability for all the chemicals except sulfur. Based on the data with hourly resolution, peak to median ratios (hourly peak to 24 h median) averaged between 2 and 4 for most of the volatile organic compounds, but there were two species with peak to median ratios of about 10.

Effect of spatial variability of storm on the optimal placement of best management practices (BMPs).

PubMed

Chang, C L; Chiueh, P T; Lo, S L

2007-12-01

It is significant to design best management practices (BMPs) and determine the proper BMPs placement for the purpose that can not only satisfy the water quantity and water quality standard, but also lower the total cost of BMPs. The spatial rainfall variability can have much effect on its relative runoff and non-point source pollution (NPSP). Meantime, the optimal design and placement of BMPs would be different as well. The objective of this study was to discuss the relationship between the spatial variability of rainfall and the optimal BMPs placements. Three synthetic rainfall storms with varied spatial distributions, including uniform rainfall, downstream rainfall and upstream rainfall, were designed. WinVAST model was applied to predict runoff and NPSP. Additionally, detention pond and swale were selected for being structural BMPs. Scatter search was applied to find the optimal BMPs placement. The results show that mostly the total cost of BMPs is higher in downstream rainfall than in upstream rainfall or uniform rainfall. Moreover, the cost of detention pond is much higher than swale. Thus, even though detention pond has larger efficiency for lowering peak flow and pollutant exports, it is not always the determined set in each subbasin.

Spatial variability of polycyclic aromatic hydrocarbon load of urban wet weather pollution in combined sewers.

PubMed

Gasperi, J; Moilleron, R; Chebbo, G

2006-01-01

In Paris, the OPUR research programme created an experimental on-site observatory of urban pollutant loads in combined sewer systems in order to characterise the dry and wet weather flows at different spatial scales. This article presents the first results on the spatial variability of the polycyclic aromatic hydrocarbon (PAH) load during wet weather flow (WWF). At the scale of a rain event, investigations revealed that (i) PAH concentrations were relatively homogenous whatever the spatial scale and were greater than those of the dry weather flow (DWF), (ii) PAH distributions between dissolved and particulate phases were constant, and (iii) PAH fingerprints exhibited a similar pattern for all catchments. Moreover, an evaluation of the contribution of DWF, runoff and erosion of sewer deposits to WWF load was established. According to the hypothesis on the runoff concentration, the contributions were evaluated at 14, 8 and 78%, respectively, at the scale of the Marais catchment. For all the catchments, the runoff contribution was found quite constant and evaluated at approximately 10%. The DWF contribution seems to increase with the catchment area, contrary to the sewer erosion contribution, which seems to decrease. However, this latter still remains an important source of pollution. These first trends should be confirmed and completed by more investigations of rain events.

Performance assessment of a solar-powered air quality and weather station placed on a school rooftop in Hong Kong

EPA Science Inventory

Emerging air pollution measurement technologies that require minimal infrastructure to deploy may lead to new insights on air pollution spatial variability in urban areas. Through a collaboration between the USEPA and HKEPD, this study evaluates the performance of a compact, roo...

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

NASA Astrophysics Data System (ADS)

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

2012-12-01

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

Development of a distributed air pollutant dry deposition modeling framework.

PubMed

Hirabayashi, Satoshi; Kroll, Charles N; Nowak, David J

2012-12-01

A distributed air pollutant dry deposition modeling system was developed with a geographic information system (GIS) to enhance the functionality of i-Tree Eco (i-Tree, 2011). With the developed system, temperature, leaf area index (LAI) and air pollutant concentration in a spatially distributed form can be estimated, and based on these and other input variables, dry deposition of carbon monoxide (CO), nitrogen dioxide (NO(2)), sulfur dioxide (SO(2)), and particulate matter less than 10 microns (PM10) to trees can be spatially quantified. Employing nationally available road network, traffic volume, air pollutant emission/measurement and meteorological data, the developed system provides a framework for the U.S. city managers to identify spatial patterns of urban forest and locate potential areas for future urban forest planting and protection to improve air quality. To exhibit the usability of the framework, a case study was performed for July and August of 2005 in Baltimore, MD. Copyright © 2012 Elsevier Ltd. All rights reserved.

Spatial and temporal variability of fine particle composition and source types in five cities of Connecticut and Massachusetts.

PubMed

Lee, Hyung Joo; Gent, Janneane F; Leaderer, Brian P; Koutrakis, Petros

2011-05-01

To protect public health from PM(2.5) air pollution, it is critical to identify the source types of PM(2.5) mass and chemical components associated with higher risks of adverse health outcomes. Source apportionment modeling using Positive Matrix Factorization (PMF), was used to identify PM(2.5) source types and quantify the source contributions to PM(2.5) in five cities of Connecticut and Massachusetts. Spatial and temporal variability of PM(2.5) mass, components and source contributions were investigated. PMF analysis identified five source types: regional pollution as traced by sulfur, motor vehicle, road dust, oil combustion and sea salt. The sulfur-related regional pollution and traffic source type were major contributors to PM(2.5). Due to sparse ground-level PM(2.5) monitoring sites, current epidemiological studies are susceptible to exposure measurement errors. The higher correlations in concentrations and source contributions between different locations suggest less spatial variability, resulting in less exposure measurement errors. When concentrations and/or contributions were compared to regional averages, correlations were generally higher than between-site correlations. This suggests that for assigning exposures for health effects studies, using regional average concentrations or contributions from several PM(2.5) monitors is more reliable than using data from the nearest central monitor. Copyright © 2011 Elsevier B.V. All rights reserved.

Spatial variability of characteristics and origins of urban wet weather pollution in combined sewers.

PubMed

Kafi-Benyahia, M; Gromaire, M G; Chebbo, G

2005-01-01

An experimental on-site observatory of urban pollutant loads in combined sewers was created in the centre of Paris to quantify and characterise the dry and wet weather flow in relation to spatial scale. Eight rainfall events were studied from April 2003 to May 2004. Samples were analysed for suspended solids, organic matter, nitrogen and heavy metals. Results confirm the extent of wet weather pollution. They have shown the relative homogeneity of SS and organic matter characteristics from one urban catchment area to another. Two groups of heavy metals were identified. The first one concerns Cu, which has a higher concentration in wet weather flow (WWF) than in dry weather flow (DWF), and runoff. The second includes Cd, Pb and Zn, where higher concentrations were measured in urban runoff than in WWF and DWF. A first evaluation of contribution of wastewater, urban runoff and sewer deposit erosion sources to wet weather pollution was established and has highlighted the contribution of wastewater and sewer deposits to this pollution. However, it has shown that sewer deposit erosion remains an important source of wet weather pollution at different spatial scales.

Saturation sampling for spatial variation in multiple air pollutants across an inversion-prone metropolitan area of complex terrain

PubMed Central

2014-01-01

Background Characterizing intra-urban variation in air quality is important for epidemiological investigation of health outcomes and disparities. To date, however, few studies have been designed to capture spatial variation during select hours of the day, or to examine the roles of meteorology and complex terrain in shaping intra-urban exposure gradients. Methods We designed a spatial saturation monitoring study to target local air pollution sources, and to understand the role of topography and temperature inversions on fine-scale pollution variation by systematically allocating sampling locations across gradients in key local emissions sources (vehicle traffic, industrial facilities) and topography (elevation) in the Pittsburgh area. Street-level integrated samples of fine particulate matter (PM2.5), black carbon (BC), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) were collected during morning rush and probable inversion hours (6-11 AM), during summer and winter. We hypothesized that pollution concentrations would be: 1) higher under inversion conditions, 2) exacerbated in lower-elevation areas, and 3) vary by season. Results During July - August 2011 and January - March 2012, we observed wide spatial and seasonal variability in pollution concentrations, exceeding the range measured at regulatory monitors. We identified elevated concentrations of multiple pollutants at lower-elevation sites, and a positive association between inversion frequency and NO2 concentration. We examined temporal adjustment methods for deriving seasonal concentration estimates, and found that the appropriate reference temporal trend differs between pollutants. Conclusions Our time-stratified spatial saturation approach found some evidence for modification of inversion-concentration relationships by topography, and provided useful insights for refining and interpreting GIS-based pollution source indicators for Land Use Regression modeling. PMID:24735818

Saturation sampling for spatial variation in multiple air pollutants across an inversion-prone metropolitan area of complex terrain.

PubMed

Shmool, Jessie Lc; Michanowicz, Drew R; Cambal, Leah; Tunno, Brett; Howell, Jeffery; Gillooly, Sara; Roper, Courtney; Tripathy, Sheila; Chubb, Lauren G; Eisl, Holger M; Gorczynski, John E; Holguin, Fernando E; Shields, Kyra Naumoff; Clougherty, Jane E

2014-04-16

Characterizing intra-urban variation in air quality is important for epidemiological investigation of health outcomes and disparities. To date, however, few studies have been designed to capture spatial variation during select hours of the day, or to examine the roles of meteorology and complex terrain in shaping intra-urban exposure gradients. We designed a spatial saturation monitoring study to target local air pollution sources, and to understand the role of topography and temperature inversions on fine-scale pollution variation by systematically allocating sampling locations across gradients in key local emissions sources (vehicle traffic, industrial facilities) and topography (elevation) in the Pittsburgh area. Street-level integrated samples of fine particulate matter (PM2.5), black carbon (BC), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) were collected during morning rush and probable inversion hours (6-11 AM), during summer and winter. We hypothesized that pollution concentrations would be: 1) higher under inversion conditions, 2) exacerbated in lower-elevation areas, and 3) vary by season. During July - August 2011 and January - March 2012, we observed wide spatial and seasonal variability in pollution concentrations, exceeding the range measured at regulatory monitors. We identified elevated concentrations of multiple pollutants at lower-elevation sites, and a positive association between inversion frequency and NO2 concentration. We examined temporal adjustment methods for deriving seasonal concentration estimates, and found that the appropriate reference temporal trend differs between pollutants. Our time-stratified spatial saturation approach found some evidence for modification of inversion-concentration relationships by topography, and provided useful insights for refining and interpreting GIS-based pollution source indicators for Land Use Regression modeling.

Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis

PubMed Central

Zhou, Ying; Levy, Jonathan I

2007-01-01

Background There has been growing interest among exposure assessors, epidemiologists, and policymakers in the concept of "hot spots", or more broadly, the "spatial extent" of impacts from traffic-related air pollutants. This review attempts to quantitatively synthesize findings about the spatial extent under various circumstances. Methods We include both the peer-reviewed literature and government reports, and focus on four significant air pollutants: carbon monoxide, benzene, nitrogen oxides, and particulate matter (including both ultrafine particle counts and fine particle mass). From the identified studies, we extracted information about significant factors that would be hypothesized to influence the spatial extent within the study, such as the study type (e.g., monitoring, air dispersion modeling, GIS-based epidemiological studies), focus on concentrations or health risks, pollutant under study, background concentration, emission rate, and meteorological factors, as well as the study's implicit or explicit definition of spatial extent. We supplement this meta-analysis with results from some illustrative atmospheric dispersion modeling. Results We found that pollutant characteristics and background concentrations best explained variability in previously published spatial extent estimates, with a modifying influence of local meteorology, once some extreme values based on health risk estimates were removed from the analysis. As hypothesized, inert pollutants with high background concentrations had the largest spatial extent (often demonstrating no significant gradient), and pollutants formed in near-source chemical reactions (e.g., nitrogen dioxide) had a larger spatial extent than pollutants depleted in near-source chemical reactions or removed through coagulation processes (e.g., nitrogen oxide and ultrafine particles). Our illustrative dispersion model illustrated the complex interplay of spatial extent definitions, emission rates, background concentrations, and meteorological conditions on spatial extent estimates even for non-reactive pollutants. Our findings indicate that, provided that a health risk threshold is not imposed, the spatial extent of impact for mobile sources reviewed in this study is on the order of 100–400 m for elemental carbon or particulate matter mass concentration (excluding background concentration), 200–500 m for nitrogen dioxide and 100–300 m for ultrafine particle counts. Conclusion First, to allow for meaningful comparisons across studies, it is important to state the definition of spatial extent explicitly, including the comparison method, threshold values, and whether background concentration is included. Second, the observation that the spatial extent is generally within a few hundred meters for highway or city roads demonstrates the need for high resolution modeling near the source. Finally, our findings emphasize that policymakers should be able to develop reasonable estimates of the "zone of influence" of mobile sources, provided that they can clarify the pollutant of concern, the general site characteristics, and the underlying definition of spatial extent that they wish to utilize. PMID:17519039

Determining Spatial Variability in PM2.5 Source Impacts across Detroit, MI

EPA Science Inventory

Intra-urban variability in air pollution source impacts was investigated using receptor modeling of daily speciated PM_2.5 measurements collected at residential outdoor locations across Detroit, MI (Wayne County) as part of the Detroit Exposure and Aerosol Research Stud...

Use of multiple functional traits of protozoa for bioassessment of marine pollution.

PubMed

Zhong, Xiaoxiao; Xu, Guangjian; Xu, Henglong

2017-06-30

Ecological parameters based on multiply functional traits have many advantages for monitoring programs by reducing "signal to noise" ratios of observed species data. To identify potential indicators for bioassessment of marine pollution in function space, the functional patterns of protozoan communities and relationships with environmental changes were studied in coastal waters of the Yellow Sea during a 1-year period. The results showed that: (1) the spatial variability in functional trait distributions of the protozoa was significantly associated with changes in environmental variables, especially chemical oxygen demand (COD) and nutrients on spatial scale; (2) the functional traits, especially food resources and feeding type, were significantly correlated with COD and nutrients; and (3) the functional diversity indices were generally related to nutrients or COD. Based on the results, we suggest that the functional traits and diversity indices of protozoan communities may be used as more effective indicators for bioassessment of marine pollution. Copyright © 2017 Elsevier Ltd. All rights reserved.

Temporal and spatial patterns of ambient endotoxin concentrations in Fresno, California.

PubMed

Tager, Ira B; Lurmann, Frederick W; Haight, Thaddeus; Alcorn, Siana; Penfold, Bryan; Hammond, S Katharine

2010-10-01

Endotoxins are found in indoor dust generated by human activity and pets, in soil, and adsorbed onto the surfaces of ambient combustion particles. Endotoxin concentrations have been associated with respiratory symptoms and the risk of atopy and asthma in children. We characterized the temporal and spatial variability of ambient endotoxin in Fresno/Clovis, California, located in California's Central Valley, to identify correlates and potential predictors of ambient endotoxin concentrations in a cohort of children with asthma [Fresno Asthmatic Children's Environment Study (FACES)]. Between May 2001 and October 2004, daily ambient endotoxin and air pollutants were collected at the central ambient monitoring site of the California Air Resources Board in Fresno and, for shorter time periods, at 10 schools and indoors and outdoors at 84 residences in the community. Analyses were restricted to May-October, the dry months during which endotoxin concentrations are highest. Daily endotoxin concentration patterns were determined mainly by meteorologic factors, particularly the degree of air stagnation. Overall concentrations were lowest in areas distant from agricultural activities. Highest concentrations were found in areas immediately downwind from agricultural/pasture land. Among three other measured air pollutants [fine particulate matter, elemental carbon (a marker of traffic in Fresno), and coarse particulate matter (PMc)], PMc was the only pollutant correlated with endotoxin. Endotoxin, however, was the most spatially variable. Our data support the need to evaluate the spatial/temporal variability of endotoxin concentrations, rather than relying on a few measurements made at one location, in studies of exposure and and respiratory health effects, particularly in children with asthma and other chronic respiratory diseases.

Assessment of near-source air pollution at a fine spatial scale ...

EPA Pesticide Factsheets

Mobile monitoring is an emerging strategy to characterize spatially and temporally variable air pollution in areas near sources. EPA’s Geospatial Monitoring of Air Pollution (GMAP) vehicle, an all-electric vehicle measuring real-time concentrations of particulate and gaseous pollutants, was utilized to map air pollution trends near the Port of Charleston in South Carolina. High-resolution monitoring was performed along driving routes near several port terminals and rail yard facilities, recording geospatial coordinates and measurements of pollutants including black carbon, size-resolved particle count ranging from ultrafine to coarse (6 nm to 20 µm), carbon monoxide, carbon dioxide, and nitrogen dioxide. Additionally, a portable meteorological station was used to characterize local meteorology. Port activity data was provided by the Port Authority of Charleston and includes counts of ships and trucks, and port service operations such as cranes and forklifts during the sampling time periods. Measurements are supplemented with modeling performed with AERMOD and RLINE in order to characterize the impact of the various terminals at the Port of Charleston on local air quality. Specifically, the data are used to determine the magnitude of the increase in local, near-port pollutant concentrations as well as the spatial extent to which concentration is elevated above background. These effects are studied in relation to a number of potentially significant factors such

Daily ambient air pollution metrics for five cities: Evaluation of data-fusion-based estimates and uncertainties

NASA Astrophysics Data System (ADS)

Friberg, Mariel D.; Kahn, Ralph A.; Holmes, Heather A.; Chang, Howard H.; Sarnat, Stefanie Ebelt; Tolbert, Paige E.; Russell, Armistead G.; Mulholland, James A.

2017-06-01

Spatiotemporal characterization of ambient air pollutant concentrations is increasingly relying on the combination of observations and air quality models to provide well-constrained, spatially and temporally complete pollutant concentration fields. Air quality models, in particular, are attractive, as they characterize the emissions, meteorological, and physiochemical process linkages explicitly while providing continuous spatial structure. However, such modeling is computationally intensive and has biases. The limitations of spatially sparse and temporally incomplete observations can be overcome by blending the data with estimates from a physically and chemically coherent model, driven by emissions and meteorological inputs. We recently developed a data fusion method that blends ambient ground observations and chemical-transport-modeled (CTM) data to estimate daily, spatially resolved pollutant concentrations and associated correlations. In this study, we assess the ability of the data fusion method to produce daily metrics (i.e., 1-hr max, 8-hr max, and 24-hr average) of ambient air pollution that capture spatiotemporal air pollution trends for 12 pollutants (CO, NO2, NOx, O3, SO2, PM10, PM2.5, and five PM2.5 components) across five metropolitan areas (Atlanta, Birmingham, Dallas, Pittsburgh, and St. Louis), from 2002 to 2008. Three sets of comparisons are performed: (1) the CTM concentrations are evaluated for each pollutant and metropolitan domain, (2) the data fusion concentrations are compared with the monitor data, (3) a comprehensive cross-validation analysis against observed data evaluates the quality of the data fusion model simulations across multiple metropolitan domains. The resulting daily spatial field estimates of air pollutant concentrations and uncertainties are not only consistent with observations, emissions, and meteorology, but substantially improve CTM-derived results for nearly all pollutants and all cities, with the exception of NO2 for Birmingham. The greatest improvements occur for O3 and PM2.5. Squared spatiotemporal correlation coefficients range between simulations and observations determined using cross-validation across all cities for air pollutants of secondary and mixed origins are R2 = 0.88-0.93 (O3), 0.81-0.89 (SO4), 0.67-0.83 (PM2.5), 0.52-0.72 (NO3), 0.43-0.80 (NH4), 0.32-0.51 (OC), and 0.14-0.71 (PM10). Results for relatively homogeneous pollutants of secondary origin, tend to be better than those for more spatially heterogeneous (larger spatial gradients) pollutants of primary origin (NOx, CO, SO2 and EC). Generally, background concentrations and spatial concentration gradients reflect interurban airshed complexity and the effects of regional transport, whereas daily spatial pattern variability shows intra-urban consistency in the fused data. With sufficiently high CTM spatial resolution, traffic-related pollutants exhibit gradual concentration gradients that peak toward the urban centers. Ambient pollutant concentration uncertainty estimates for the fused data are both more accurate and smaller than those for either the observations or the model simulations alone.

Global impacts of the meat trade on in-stream organic river pollution: the importance of spatially distributed hydrological conditions

NASA Astrophysics Data System (ADS)

Wen, Yingrong; Schoups, Gerrit; van de Giesen, Nick

2018-01-01

In many regions of the world, intensive livestock farming has become a significant source of organic river pollution. As the international meat trade is growing rapidly, the environmental impacts of meat production within one country can occur either domestically or internationally. The goal of this paper is to quantify the impacts of the international meat trade on global organic river pollution at multiple scales (national, regional and gridded). Using the biological oxygen demand (BOD) as an overall indicator of organic river pollution, we compute the spatially distributed organic pollution in global river networks with and without a meat trade, where the without-trade scenario assumes that meat imports are replaced by local production. Our analysis reveals a reduction in the livestock population and production of organic pollutants at the global scale as a result of the international meat trade. However, the actual environmental impact of trade, as quantified by in-stream BOD concentrations, is negative; i.e. we find a slight increase in polluted river segments. More importantly, our results show large spatial variability in local (grid-scale) impacts that do not correlate with local changes in BOD loading, which illustrates: (1) the significance of accounting for the spatial heterogeneity of hydrological processes along river networks, and (2) the limited value of looking at country-level or global averages when estimating the actual impacts of trade on the environment.

The pollutants from livestock and poultry farming in China-geographic distribution and drivers.

PubMed

Gan, Ling; Hu, Xisheng

2016-05-01

Livestock and poultry farming is a major source of agricultural pollution. However, our knowledge of the constraining factors of the geographic distribution of pollutants from livestock and poultry farming is still limited. In this study, using the optimized pollutant generation coefficients, we estimated the annual pollutant productions of eight livestock and poultry species at the provincial level in 2005 and 2013 and their growth rates during the study period in China; using canonical correlation analysis, we also explored the association between the eight pollutant measurements as dependent variables and 14 factors (including resource endowment, developmental level, and economic structure factors) as independent variables. Results indicate that there exist spatial disparity in the distribution of pollutants from livestock and poultry farming across regions, with provinces in the Huang-Huai-Hai region and the southwestern region accounting for approximately 50 % of the total productions in the nation. Cattle, pig, and poultry constitute the primary pollution sources in terms of livestock and poultry farming not only at the national level but also at the province level. While the species constitute and their respective growth rates of the pollutants can be also characterized by spatial disparity across regions, canonical correlation analysis shows that the observed regional patterns of the pollutants can be largely explained by the resource endowment factors (positive effects) and the developmental level factors (negative effects). In addition, we found that the development of livestock and poultry farming is negatively associated with the growing rate of both the resource endowment and the socioeconomic factors. This indicates that there exist different driving patterns in the gross and increment of the pollutant productions. Our research has significant implications for the appropriate environmental protection policy formulation and implementation in livestock sector.

Land use regression models to assess air pollution exposure in Mexico City using finer spatial and temporal input parameters.

PubMed

Son, Yeongkwon; Osornio-Vargas, Álvaro R; O'Neill, Marie S; Hystad, Perry; Texcalac-Sangrador, José L; Ohman-Strickland, Pamela; Meng, Qingyu; Schwander, Stephan

2018-05-17

The Mexico City Metropolitan Area (MCMA) is one of the largest and most populated urban environments in the world and experiences high air pollution levels. To develop models that estimate pollutant concentrations at fine spatiotemporal scales and provide improved air pollution exposure assessments for health studies in Mexico City. We developed finer spatiotemporal land use regression (LUR) models for PM 2.5 , PM 10 , O 3 , NO 2 , CO and SO 2 using mixed effect models with the Least Absolute Shrinkage and Selection Operator (LASSO). Hourly traffic density was included as a temporal variable besides meteorological and holiday variables. Models of hourly, daily, monthly, 6-monthly and annual averages were developed and evaluated using traditional and novel indices. The developed spatiotemporal LUR models yielded predicted concentrations with good spatial and temporal agreements with measured pollutant levels except for the hourly PM 2.5 , PM 10 and SO 2 . Most of the LUR models met performance goals based on the standardized indices. LUR models with temporal scales greater than one hour were successfully developed using mixed effect models with LASSO and showed superior model performance compared to earlier LUR models, especially for time scales of a day or longer. The newly developed LUR models will be further refined with ongoing Mexico City air pollution sampling campaigns to improve personal exposure assessments. Copyright © 2018. Published by Elsevier B.V.

[Distribution of soil heavy metal and pollution evaluation on the different sampling scales in farmland on Yellow River irrigation area of Ningxia: a case study in Xingqing County of Yinchuan City].

PubMed

Wang, You-Qi; Bai, Yi-Ru; Wang, Jian-Yu

2014-07-01

Determining spatial distributions and analyses contamination condition of soil heavy metals play an important role in evaluation of the quality of agricultural ecological environment and the protection of food safety and human health. Topsoil samples (0-20 cm) from 223 sites in farmland were collected at two scales of sampling grid (1 m x 1 m, 10 m x 10 m) in the Yellow River irrigation area of Ningxia. The objectives of this study were to investigate the spatial variability of total copper (Cu), total zinc (Zn), total chrome (Cr), total cadmium (Cd) and total lead (Pb) on the two sampling scales by the classical and geostatistical analyses. The single pollution index (P(i)) and the Nemerow pollution index (P) were used to evaluate the soil heavy metal pollution. The classical statistical analyses showed that all soil heavy metals demonstrated moderate variability, the coefficient of variation (CV) changed in the following sequence: Cd > Pb > Cr > Zn > Cu. Geostatistical analyses showed that the nugget coefficient of Cd on the 10 m x 10 m scale and Pb on the 1 m x 1 m scale were 100% with pure nugget variograms, which showed weak variability affected by random factors. The nugget coefficient of the other indexes was less than 25%, which showed a strong variability affected by structural factors. The results combined with P(i) and P indicated that most soil heavy metals have slight pollution except total copper, and in general there were the trend of heavy metal accumulation in the study area.

Characterizing Intra-Urban Air Quality Gradients with a Spatially-Distributed Network

NASA Astrophysics Data System (ADS)

Zimmerman, N.; Ellis, A.; Schurman, M. I.; Gu, P.; Li, H.; Snell, L.; Gu, J.; Subramanian, R.; Robinson, A. L.; Apte, J.; Presto, A. A.

2016-12-01

City-wide air pollution measurements have typically relied on regulatory or research monitoring sites with low spatial density to assess population-scale exposure. However, air pollutant concentrations exhibit significant spatial variability depending on local sources and features of the built environment, which may not be well captured by the existing monitoring regime. To better understand urban spatial and temporal pollution gradients at 1 km resolution, a network of 12 real-time air quality monitoring stations was deployed beginning July 2016 in Pittsburgh, PA. The stations were deployed at sites along an urban-rural transect and in urban locations with a range of traffic, restaurant, and tall building densities to examine the impact of various modifiable factors. Measurements from the stationary monitoring stations were further supported by mobile monitoring, which provided higher spatial resolution pollutant measurements on nearby roadways and enabled routine calibration checks. The stationary monitoring measurements comprise ultrafine particle number (Aerosol Dynamics "MAGIC" CPC), PM2.5 (Met One Neighborhood PM Monitor), black carbon (Met One BC 1050), and a new low-cost air quality monitor, the Real-time Affordable Multi-Pollutant (RAMP) sensor package for measuring CO, NO2, SO2, O3, CO2, temperature and relative humidity. High time-resolution (sub-minute) measurements across the distributed monitoring network enable insight into dynamic pollutant behaviour. Our preliminary findings show that our instruments are sensitive to PM2.5 gradients exceeding 2 micro-grams per cubic meter and ultrafine particle gradients exceeding 1000 particles per cubic centimeter. Additionally, we have developed rigorous calibration protocols to characterize the RAMP sensor response and drift, as well as multiple linear regression models to convert sensor response into pollutant concentrations that are comparable to reference instrumentation.

Binational school-based monitoring of traffic-related air pollutants in El Paso, Texas (USA) and Ciudad Juárez, Chihuahua (México).

PubMed

Raysoni, Amit U; Sarnat, Jeremy A; Sarnat, Stefanie Ebelt; Garcia, Jośe Humberto; Holguin, Fernando; Luèvano, Silvia Flores; Li, Wen-Whai

2011-10-01

Paired indoor and outdoor concentrations of fine and coarse particulate matter (PM), PM2.5 reflectance [black carbon(BC)], and nitrogen dioxide (NO(2)) were determined for sixteen weeks in 2008 at four elementary schools (two in high and two in low traffic density zones) in a U.S.-Mexico border community to aid a binational health effects study. Strong spatial heterogeneity was observed for all outdoor pollutant concentrations. Concentrations of all pollutants, except coarse PM, were higher in high traffic zones than in the respective low traffic zones. Black carbon and NO(2) appear to be better traffic indicators than fine PM. Indoor air pollution was found to be well associated with outdoor air pollution, although differences existed due to uncontrollable factors involving student activities and building/ventilation configurations. Results of this study indicate substantial spatial variability of pollutants in the region, suggesting that children's exposures to these pollutants vary based on the location of their school. Copyright © 2011 Elsevier Ltd. All rights reserved.

Linking Meteorology, Air Quality Models and Observations to ...

EPA Pesticide Factsheets

Epidemiologic studies are critical in establishing the association between exposure to air pollutants and adverse health effects. Results of epidemiologic studies are used by U.S. EPA in developing air quality standards to protect the public from the health effects of air pollutants. A major challenge in environmental epidemiology is adequate exposure characterization. Numerous health studies have used measurements from a few central-site ambient monitors to characterize air pollution exposures. Relying solely on central-site ambient monitors does not account for the spatial-heterogeneity of ambient air pollution patterns, the temporal variability in ambient concentrations, nor the influence of infiltration and indoor sources. Central-site monitoring becomes even more problematic for certain air pollutants that exhibit significant spatial heterogeneity. Statistical interpolation techniques and passive monitoring methods can provide additional spatial resolution in ambient concentration estimates. In addition, spatio-temporal models, which integrate GIS data and other factors, such as meteorology, have also been developed to produce more resolved estimates of ambient concentrations. Models, such as the Community Multi-Scale Air Quality (CMAQ) model, estimate ambient concentrations by combining information on meteorology, source emissions, and chemical-fate and transport. Hybrid modeling approaches, which integrate regional scale models with local scale dispersion

Temporal and Spatial Patterns of Ambient Endotoxin Concentrations in Fresno, California

PubMed Central

Tager, Ira B.; Lurmann, Frederick W.; Haight, Thaddeus; Alcorn, Siana; Penfold, Bryan; Hammond, S. Katharine

2010-01-01

Background Endotoxins are found in indoor dust generated by human activity and pets, in soil, and adsorbed onto the surfaces of ambient combustion particles. Endotoxin concentrations have been associated with respiratory symptoms and the risk of atopy and asthma in children. Objective We characterized the temporal and spatial variability of ambient endotoxin in Fresno/Clovis, California, located in California’s Central Valley, to identify correlates and potential predictors of ambient endotoxin concentrations in a cohort of children with asthma [Fresno Asthmatic Children’s Environment Study (FACES)]. Methods Between May 2001 and October 2004, daily ambient endotoxin and air pollutants were collected at the central ambient monitoring site of the California Air Resources Board in Fresno and, for shorter time periods, at 10 schools and indoors and outdoors at 84 residences in the community. Analyses were restricted to May–October, the dry months during which endotoxin concentrations are highest. Results Daily endotoxin concentration patterns were determined mainly by meteorologic factors, particularly the degree of air stagnation. Overall concentrations were lowest in areas distant from agricultural activities. Highest concentrations were found in areas immediately downwind from agricultural/pasture land. Among three other measured air pollutants [fine particulate matter, elemental carbon (a marker of traffic in Fresno), and coarse particulate matter (PMc)], PMc was the only pollutant correlated with endotoxin. Endotoxin, however, was the most spatially variable. Conclusions Our data support the need to evaluate the spatial/temporal variability of endotoxin concentrations, rather than relying on a few measurements made at one location, in studies of exposure and and respiratory health effects, particularly in children with asthma and other chronic respiratory diseases. PMID:20494854

Empirical Research on Spatial Diffusion Process of Knowledge Spillovers

NASA Astrophysics Data System (ADS)

Jin, Xuehui

2018-02-01

Firstly, this paper gave a brief review of the core issues of previous studies on spatial distribution of knowledge spillovers. That laid the theoretical foundation for further research. Secondly, this paper roughly described the diffusion process of solar patents in Bejing-Tianjin-Hebei and the Pearl River Delta regions by means of correlation analysis based on patent information of the application date and address of patentee. After that, this paper introduced the variables of spatial distance, knowledge absorptive capacity, knowledge gap and pollution control and built the empirical model of patent, and then collecting data to test them. The results showed that knowledge absorptive capacity was the most significant factor than the other three, followed by the knowledge gap. The influence of spatial distance on knowledge spillovers was limited and the most weak influence factor was pollution control.

Land-use regression panel models of NO2 concentrations in Seoul, Korea

NASA Astrophysics Data System (ADS)

Kim, Youngkook; Guldmann, Jean-Michel

2015-04-01

Transportation and land-use activities are major air pollution contributors. Since their shares of emissions vary across space and time, so do air pollution concentrations. Despite these variations, panel data have rarely been used in land-use regression (LUR) modeling of air pollution. In addition, the complex interactions between traffic flows, land uses, and meteorological variables, have not been satisfactorily investigated in LUR models. The purpose of this research is to develop and estimate nitrogen dioxide (NO2) panel models based on the LUR framework with data for Seoul, Korea, accounting for the impacts of these variables, and their interactions with spatial and temporal dummy variables. The panel data vary over several scales: daily (24 h), seasonally (4), and spatially (34 intra-urban measurement locations). To enhance model explanatory power, wind direction and distance decay effects are accounted for. The results show that vehicle-kilometers-traveled (VKT) and solar radiation have statistically strong positive and negative impacts on NO2 concentrations across the four seasonal models. In addition, there are significant interactions with the dummy variables, pointing to VKT and solar radiation effects on NO2 concentrations that vary with time and intra-urban location. The results also show that residential, commercial, and industrial land uses, and wind speed, temperature, and humidity, all impact NO2 concentrations. The R2 vary between 0.95 and 0.98.

Hybrid Modeling Approach to Estimate Exposures of Hazardous Air Pollutants (HAPs) for the National Air Toxics Assessment (NATA).

PubMed

Scheffe, Richard D; Strum, Madeleine; Phillips, Sharon B; Thurman, James; Eyth, Alison; Fudge, Steve; Morris, Mark; Palma, Ted; Cook, Richard

2016-11-15

A hybrid air quality model has been developed and applied to estimate annual concentrations of 40 hazardous air pollutants (HAPs) across the continental United States (CONUS) to support the 2011 calendar year National Air Toxics Assessment (NATA). By combining a chemical transport model (CTM) with a Gaussian dispersion model, both reactive and nonreactive HAPs are accommodated across local to regional spatial scales, through a multiplicative technique designed to improve mass conservation relative to previous additive methods. The broad scope of multiple pollutants capturing regional to local spatial scale patterns across a vast spatial domain is precedent setting within the air toxics community. The hybrid design exhibits improved performance relative to the stand alone CTM and dispersion model. However, model performance varies widely across pollutant categories and quantifiably definitive performance assessments are hampered by a limited observation base and challenged by the multiple physical and chemical attributes of HAPs. Formaldehyde and acetaldehyde are the dominant HAP concentration and cancer risk drivers, characterized by strong regional signals associated with naturally emitted carbonyl precursors enhanced in urban transport corridors with strong mobile source sector emissions. The multiple pollutant emission characteristics of combustion dominated source sectors creates largely similar concentration patterns across the majority of HAPs. However, reactive carbonyls exhibit significantly less spatial variability relative to nonreactive HAPs across the CONUS.

Patterns of household concentrations of multiple indoor air pollutants in China.

PubMed

He, Gongli; Ying, Bo; Liu, Jiang; Gao, Shirong; Shen, Shaolin; Balakrishnan, Kalpana; Jin, Yinlong; Liu, Fan; Tang, Ning; Shi, Kai; Baris, Enis; Ezzati, Majid

2005-02-15

Most previous studies on indoor air pollution from household use of solid fuels have used either indirect proxies for human exposure or measurements of individual pollutants at a single point, as indicators of (exposure to) the mixture of pollutants in solid fuel smoke. A heterogeneous relationship among pollutant-location pairs should be expected because specific fuel-stove technology and combustion and dispersion conditions such as temperature, moisture, and air flow are likely to affect the emissions and dispersion of the various pollutants differently. We report on a study for monitoring multiple pollutants--including respirable particles (RPM), carbon monoxide, sulfur dioxide, fluoride, and arsenic--at four points inside homes that used coal and/or biomass fuels in Guizhou and Shaanxi provinces of China. All pollutants exhibited large variability in emissions and spatial dispersion within and between provinces and were generally poorly correlated. RPM, followed by SO2, was generally higher than common health-based guidelines/standards and provided sufficient resolution for assessing variations within and between households in both provinces. Indoor heating played an important role in the level and spatial patterns of pollution inside homes, possibly to an extent more important than cooking. The findings indicate the need for monitoring of RPM and selected other pollutants in longer-term health studies, with focus on both cooking and living/sleeping areas.

High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data.

PubMed

Apte, Joshua S; Messier, Kyle P; Gani, Shahzad; Brauer, Michael; Kirchstetter, Thomas W; Lunden, Melissa M; Marshall, Julian D; Portier, Christopher J; Vermeulen, Roel C H; Hamburg, Steven P

2017-06-20

Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4-5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km 2 area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO 2 , and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5-8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.

Wireless Distributed Environmental Sensor Networks for Air Pollution Measurement-The Promise and the Current Reality.

PubMed

Broday, David M

2017-10-02

The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids of Wireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented.

Wireless Distributed Environmental Sensor Networks for Air Pollution Measurement—The Promise and the Current Reality

PubMed Central

2017-01-01

The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids of Wireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented. PMID:28974042

Assessing Spatial and Temporal Variability of VOCs and PM-Components in Outdoor Air during the Detroit Exposure and Aerosol Research Study (DEARS)

EPA Science Inventory

Exposure models for air pollutants often adjust for effects of the physical environment (e.g., season, urban vs. rural populations) in order to improve exposure and risk predictions. Yet attempts are seldom made to attribute variability in observed outdoor air measurements to spe...

Highly-resolved Modeling of Emissions and Concentrations of Carbon Monoxide, Carbon Dioxide, Nitrogen Oxides, and Fine Particulate Matter in Salt Lake City, Utah

NASA Astrophysics Data System (ADS)

Mendoza, D. L.; Lin, J. C.; Mitchell, L.; Ehleringer, J. R.

2014-12-01

Accurate, high-resolution data on air pollutant emissions and concentrations are needed to understand human exposures and for both policy and pollutant management purposes. An important step in this process is also quantification of uncertainties. We present a spatially explicit and highly resolved emissions inventory for Salt Lake County, Utah, and trace gas concentration estimates for carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxides (NOx) and fine particles (PM2.5) within Salt Lake City. We assess the validity of this approach by comparing measured concentrations against simulated values derived from combining the emissions inventory with an atmospheric model. The emissions inventory for the criteria pollutants was constructed using the 2011 National Emissions Inventory (NEI). The spatial and temporal allocation methods from the Emission Modeling Clearinghouse data set are used to downscale the NEI data from annual to hourly scales and from county-level to 500 m x 500 m resolution. Onroad mobile source emissions were estimated by combining a bottom-up emissions calculation approach for large roadway links with a top-down spatial allocation approach for other roadways. Vehicle activity data for road links were derived from automatic traffic responder data. The emissions inventory for CO2 was obtained from the Hestia emissions data product at an hourly, building, facility, and road link resolution. The AERMOD and CALPUFF dispersion models were used to transport emissions and estimate air pollutant concentrations at an hourly temporal and 500 m x 500 m spatial resolution. Modeled results were compared against measurements from a mobile lab equipped with trace gas measurement equipment traveling on pre-determined routes in the Salt Lake City area. The comparison between both approaches to concentration estimation highlights spatial locations and hours of high variability/uncertainty. Results presented here will inform understanding of variability and uncertainty in emissions and concentrations to better inform future policy. This work will also facilitate the development of a systematic approach to incorporate measurement data and models to better inform estimates of pollutant concentrations that determine the extent to which urban populations are exposed to adverse air quality.

Variability in impact of air pollution on subjective well-being

NASA Astrophysics Data System (ADS)

Du, Guodong; Shin, Kong Joo; Managi, Shunsuke

2018-06-01

This paper examines the impact of variability in impact of air pollution on life satisfaction (LS). Previous studies have shown robust negative impact of air pollution on subjective well-being (SWB). However, empirical studies that consider variability in air pollution effects through comparative city study are limited. This study provides comparative evaluation of two major Chinese cities: Beijing and Shanghai. We apply a geo-statistical spatial interpolation technique on pollution data from monitoring sites to estimate the Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), coarse particles with a diameter between 2.5 and 10 μm (PM10) and fine particles with a diameter of 2.5 μm or less (PM2.5) pollution exposure of respondents of a survey conducted in 2016. The results show that all pollutants have robust negative impacts on LS for Beijing residents, whereas only SO2 and NO2 have significant negative impacts on LS for Shanghai residents; Per unit impact of SO2 is greater in Shanghai, and that of NO2 is greater in Beijing. Beijing and Shanghai residents have almost same monetary valuation for SO2 reduction but Beijing residents place approximately 1.5 times valuation on NO2 reduction compared to Shanghai residents. Moreover, the LS of Beijing residents is sensitive to temporal changes in the pollution level, whereas Shanghai residents are unaffected by such changes.

Spatiotemporal comparison of highly-resolved emissions and concentrations of carbon dioxide and criteria pollutants in Salt Lake City, Utah for health and policy applications

NASA Astrophysics Data System (ADS)

Mendoza, D. L.; Lin, J. C.; Mitchell, L.; Gurney, K. R.; Patarasuk, R.; Fasoli, B.; Bares, R.; o'Keefe, D.; Song, T.; Huang, J.; Horel, J.; Crosman, E.; Ehleringer, J. R.

2015-12-01

This study addresses the need for robust highly-resolved emissions and concentration data required for planning purposes and policy development aimed at managing pollutant sources. Adverse health effects resulting from urban pollution exposure are dependent on proximity to emission sources and atmospheric mixing, necessitating models with high spatial and temporal resolution. As urban emission sources co-emit carbon dioxide (CO2) and criteria pollutants (CAPs), efforts to reduce specific pollutants would synergistically reduce others. We present emissions inventories and modeled concentrations for CO2 and CAPs: carbon monoxide (CO), lead (Pb), nitrogen oxides (NOx), particulate matter (PM2.5 and PM10), and sulfur oxides (SOx) for Salt Lake County, Utah. We compare the resulting concentrations against stationary and mobile measurement data and present a systematic quantification of uncertainties. The emissions inventory for CO2 is based on the Hestia emissions data inventory that resolves emissions at an hourly, building and road link resolution as well as hourly gridded emissions with a 0.002o x 0.002o spatial resolution. Two methods for deriving criteria pollutant emission inventories were compared. One was constructed using methods similar to Hestia but downscales total emissions based on the 2011 National Emissions Inventory (NEI). The other used Emission Modeling Clearinghouse spatial and temporal surrogates to downscale the NEI data from annual and county-level resolution to hourly and 0.002o x 0.002o grid cells. The gridded emissions from both criteria pollutant methods were compared against the Hestia CO2 gridded data to characterize spatial similarities and differences between them. Correlations were calculated at multiple scales of aggregation. The CALPUFF dispersion model was used to transport emissions and estimate air pollutant concentrations at an hourly 0.002o x 0.002o resolution. The resulting concentrations were spatially compared in the same manner as the emissions. Modeled results were compared against stationary measurements and from equipment mounted atop a light rail car in the Salt Lake City area. The comparison between both approaches to emissions estimation and resulting concentrations highlights spatial locations and hours of high variability and uncertainty.

Spatial variability and response to anthropogenic pressures of assemblages dominated by a habitat forming seaweed sensitive to pollution (northern coast of Alboran Sea).

PubMed

Bermejo, Ricardo; de la Fuente, Gina; Ramírez-Romero, Eduardo; Vergara, Juan J; Hernández, Ignacio

2016-04-15

The Cystoseira ericaefolia group is conformed by three species: C. tamariscifolia, C. mediterranea and C. amentacea. These species are among the most important habitat forming species of the upper sublittoral rocky shores of the Mediterranean Sea and adjacent Atlantic coast. This species group is sensitive to human pressures and therefore is currently suffering important losses. This study aimed to assess the influence of anthropogenic pressures, oceanographic conditions and local spatial variability in assemblages dominated by C. ericaefolia in the Alboran Sea. The results showed the absence of significant effects of anthropogenic pressures or its interactions with environmental conditions in the Cystoseira assemblages. This fact was attributed to the high spatial variability, which is most probably masking the impact of anthropogenic pressures. The results also showed that most of the variability occurred on at local levels. A relevant spatial variability was observed at regional level, suggesting a key role of oceanographic features in these assemblages. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Extent of Heavy Metal Pollution and Their Potential Health Risk in Topsoils of the Massively Urbanized District of Shanghai.

PubMed

Jaffar, Syed Taseer Abbas; Luo, Fan; Ye, Rong; Younas, Hassan; Hu, Xue-Feng; Chen, Long-Zhu

2017-10-01

Urbanization and industrialization increase the concentrations of heavy metals in soils, which affect human health. A total of 127 topsoil samples were collected from the massively urbanized and industrialized district of Shanghai: Baoshan District. The sampling sites were isolated based on the land-use practice: industrial area, roadside area, residential area, and agricultural area. The absolute concentrations of heavy metals (Zn, Cr, Ni, Mn, Cu, Pb, and Cd) were determined using atomic absorption spectrometry and compared with Shanghai and the National soil background values. The geoaccumulation index (Igeo) and Nemerow pollution index were used to determine the existence and severity of the pollution of heavy metals. Enrichment factor (EF) analysis, spatial variability of pollution, and multivariate statistical analyses also were employed to determine the anthropogenic loading of heavy metals, their spatial dependency, and correlation among their sources, respectively. Moreover, potential ecological risk and human health risk [carcinogenic risk (RI) and noncarcinogenic hazard (HI)] were evaluated. The average concentration of all the metals (accounted as 229, 128, 56, 719, 55, 119, and 0.3 mg kg -1 for Zn, Cr, Ni, Mn, Cu, Pb, and Cd, respectively) was many folds higher than the background values. The indices depicted that the pollution exists in all the sites and severity decreases in the following order: industrial soils > roadside soil > residential soils > agricultural soils. However, Zn, Pb, and Cd showed high levels of pollution in all the soils. The EF values suggested that the majority of heavy metals are anthropogenically loaded; spatial variability showed that the pollution is more concentrated in Songnan town; Pearson's correlation, principal component analysis (PCA), and cluster analysis suggested different sources of origin for the majority of the heavy metals. RI of Cr and Pb ranged between 2.8E-04 and 2.7E-07. However, HI was site-specific (only for Cr, Pb, Mn), and most of the sites were in Songnan town. This study could be used as a significant piece of information for management purposes to prevent heavy metal pollution and to protect human health.

Locational Determinants of Emissions from Pollution-Intensive Firms in Urban Areas

PubMed Central

Zhou, Min; Tan, Shukui; Guo, Mingjing; Zhang, Lu

2015-01-01

Industrial pollution has remained as one of the most daunting challenges for many regions around the world. Characterizing the determinants of industrial pollution should provide important management implications. Unfortunately, due to the absence of high-quality data, rather few studies have systematically examined the locational determinants using a geographical approach. This paper aimed to fill the gap by accessing the pollution source census dataset, which recorded the quantity of discharged wastes (waste water and solid waste) from 717 pollution-intensive firms within Huzhou City, China. Spatial exploratory analysis was applied to analyze the spatial dependency and local clusters of waste emissions. Results demonstrated that waste emissions presented significantly positive autocorrelation in space. The high-high hotspots generally concentrated towards the city boundary, while the low-low clusters approached the Taihu Lake. Their locational determinants were identified by spatial regression. In particular, firms near the city boundary and county road were prone to discharge more wastes. Lower waste emissions were more likely to be observed from firms with high proximity to freight transfer stations or the Taihu Lake. Dense populous districts saw more likelihood of solid waste emissions. Firms in the neighborhood of rivers exhibited higher waste water emissions. Besides, the control variables (firm size, ownership, operation time and industrial type) also exerted significant influence. The present methodology can be applicable to other areas, and further inform the industrial pollution control practices. Our study advanced the knowledge of determinants of emissions from pollution-intensive firms in urban areas. PMID:25927438

On the feasibility of measuring urban air pollution by wireless distributed sensor networks.

PubMed

Moltchanov, Sharon; Levy, Ilan; Etzion, Yael; Lerner, Uri; Broday, David M; Fishbain, Barak

2015-01-01

Accurate evaluation of air pollution on human-wellbeing requires high-resolution measurements. Standard air quality monitoring stations provide accurate pollution levels but due to their sparse distribution they cannot capture the highly resolved spatial variations within cities. Similarly, dedicated field campaigns can use tens of measurement devices and obtain highly dense spatial coverage but normally deployment has been limited to short periods of no more than few weeks. Nowadays, advances in communication and sensory technologies enable the deployment of dense grids of wireless distributed air monitoring nodes, yet their sensor ability to capture the spatiotemporal pollutant variability at the sub-neighborhood scale has never been thoroughly tested. This study reports ambient measurements of gaseous air pollutants by a network of six wireless multi-sensor miniature nodes that have been deployed in three urban sites, about 150 m apart. We demonstrate the network's capability to capture spatiotemporal concentration variations at an exceptional fine resolution but highlight the need for a frequent in-situ calibration to maintain the consistency of some sensors. Accordingly, a procedure for a field calibration is proposed and shown to improve the system's performance. Overall, our results support the compatibility of wireless distributed sensor networks for measuring urban air pollution at a sub-neighborhood spatial resolution, which suits the requirement for highly spatiotemporal resolved measurements at the breathing-height when assessing exposure to urban air pollution. Copyright © 2014 Elsevier B.V. All rights reserved.

Measurement error in epidemiologic studies of air pollution based on land-use regression models.

PubMed

Basagaña, Xavier; Aguilera, Inmaculada; Rivera, Marcela; Agis, David; Foraster, Maria; Marrugat, Jaume; Elosua, Roberto; Künzli, Nino

2013-10-15

Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locations. The process of LUR model development commonly includes a variable selection procedure. When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small while most of the effect of measurement error was on the variance. In this study, we show that in realistic cases where LUR models are applied to health data, bias in health-effect estimates can be substantial. This bias depends on the number of air pollution measurement sites, the number of available predictors for model selection, and the amount of explainable variability in the true exposure. These results should be taken into account when interpreting health effects from studies that used LUR models.

Spatial and Seasonal Dynamics of Water Environmental Capacity in Mountainous Rivers of the Southeastern Coast, China

PubMed Central

Jiang, Jingang; Jing, Changwei

2018-01-01

The south-east littoral is one of the most populous and developed regions in China suffering from serious water pollution problems, and the Xian-Jiang Basin in the mid of this region is among the most polluted watersheds. Critical information is needed but lacking for improved pollution control and water quality assessment, among which water environmental capacity (WEC) is the most important variable but is difficult to calculate. In this study, a one-dimensional water quality model combined with a matrix calculation algorithm was first developed and calibrated with in-situ observations in the Xian-Jiang basin. Then, the model was applied to analyze the spatial and temporal patterns of WEC of the entire basin. The results indicated that, in 2015, the total pollutant discharges into the river reached 6719.68 t/yr, 488.12 t/yr, and 128.57 t/yr for COD, NH3-N and TP, respectively. The spatial pattern suggested a strong correlation between these water contaminants and industrial enterprises, residential areas, and land-use types in the basin. Furthermore, it was noticed that there was a significant seasonal pattern in WEC that the dry season pollution is much greater than that in the plum season, while that in the typhoon season appears to be the weakest among all seasons. The WEC differed significantly among the 24 sub-basins during the dry season but varied to a smaller extent in other seasons, suggesting differential complex spatial-temporal dependency of the WEC. PMID:29315265

Land use regression models for the oxidative potential of fine particles (PM2.5) in five European areas.

PubMed

Gulliver, John; Morley, David; Dunster, Chrissi; McCrea, Adrienne; van Nunen, Erik; Tsai, Ming-Yi; Probst-Hensch, Nicoltae; Eeftens, Marloes; Imboden, Medea; Ducret-Stich, Regina; Naccarati, Alessio; Galassi, Claudia; Ranzi, Andrea; Nieuwenhuijsen, Mark; Curto, Ariadna; Donaire-Gonzalez, David; Cirach, Marta; Vermeulen, Roel; Vineis, Paolo; Hoek, Gerard; Kelly, Frank J

2018-01-01

Oxidative potential (OP) of particulate matter (PM) is proposed as a biologically-relevant exposure metric for studies of air pollution and health. We aimed to evaluate the spatial variability of the OP of measured PM 2.5 using ascorbate (AA) and (reduced) glutathione (GSH), and develop land use regression (LUR) models to explain this spatial variability. We estimated annual average values (m -3 ) of OP AA and OP GSH for five areas (Basel, CH; Catalonia, ES; London-Oxford, UK (no OP GSH ); the Netherlands; and Turin, IT) using PM 2.5 filters. OP AA and OP GSH LUR models were developed using all monitoring sites, separately for each area and combined-areas. The same variables were then used in repeated sub-sampling of monitoring sites to test sensitivity of variable selection; new variables were offered where variables were excluded (p > .1). On average, measurements of OP AA and OP GSH were moderately correlated (maximum Pearson's maximum Pearson's R = = .7) with PM 2.5 and other metrics (PM 2.5 absorbance, NO 2 , Cu, Fe). HOV (hold-out validation) R 2 for OP AA models was .21, .58, .45, .53, and .13 for Basel, Catalonia, London-Oxford, the Netherlands and Turin respectively. For OP GSH , the only model achieving at least moderate performance was for the Netherlands (R 2 = .31). Combined models for OP AA and OP GSH were largely explained by study area with weak local predictors of intra-area contrasts; we therefore do not endorse them for use in epidemiologic studies. Given the moderate correlation of OP AA with other pollutants, the three reasonably performing LUR models for OP AA could be used independently of other pollutant metrics in epidemiological studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Simulating air quality in the Netherlands with WRF-Chem 3.8.1 at high resolution

NASA Astrophysics Data System (ADS)

Hilboll, Andreas; Kuenen, Jeroen; Denier van der Gon, Hugo; Vrekoussis, Mihalis

2017-04-01

Air pollution is the single most important environmental hazard for public health. Especially nitrogen dioxide (NO(2)) plays a key role in air quality research, both due to its immediate importance for the production of tropospheric ozone and acid rain, and as a general indicator of fossil fuel burning. To improve the quality and reproducibility of measurements of NO(2) vertical distribution from MAX-DOAS instruments, the CINDI-2 campaign was held in Cabauw (NL) in September 2016, featuring instruments from many of the leading atmospheric research institutions in the world. The measurement site in Cabauw is located in a rather rural region, surrounded by several major pollution centers (Utrecht, Rotterdam, Amsterdam). Since the instruments measure in several azimuthal directions, the measurements are able to provide information about the high spatial and temporal variability in pollutant concentrations, caused by both the spatial heterogeneity of emissions and meteorological conditions. When using air quality models in the analysis of the measured data to identify pollution sources, this mandates high spatial resolution in order to resolve the expected fine spatial structure in NO(2) concentrations. In spite of constant advances in computing power, this remains a challenge, mostly due to the uncertainties and large spatial heterogeneity of emissions and the need to parameterize small-scale processes. In this study, we use the most recent version 3.8.1 of the Weather Research and Forecasting Model with Chemistry (WRF-Chem) to simulate air pollutant concentrations over the Netherlands, to facilitate the analysis of the CINDI-2 NO(2}) measurements. The model setup contains three nested domains with horizontal resolutions of 15, 3, and 1 km. Anthropogenic emissions are taken from the TNO-MACC III inventory and, where available, from the Dutch Pollutant Release and Transfer Register (Emissieregistratie), at a spatial resolution of 7 and 1 km, respectively. We use the Common Reactive Intermediates gas-phase chemical mechanism (CRIv2-R5) with the MOSAIC aerosol module. The high spatial resolution of model and emissions will allow us to resolve the strong spatial gradients in the NO(2) concentrations measured during the CINDI-2 campaign, allowing for an unprecedented level of detail in the analysis of individual pollution sources.

A novel mobile monitoring approach to characterize spatial and temporal variation in traffic-related air pollutants in an urban community

NASA Astrophysics Data System (ADS)

Yu, Chang Ho; Fan, Zhihua; Lioy, Paul J.; Baptista, Ana; Greenberg, Molly; Laumbach, Robert J.

2016-09-01

Air concentrations of traffic-related air pollutants (TRAPs) vary in space and time within urban communities, presenting challenges for estimating human exposure and potential health effects. Conventional stationary monitoring stations/networks cannot effectively capture spatial characteristics. Alternatively, mobile monitoring approaches became popular to measure TRAPs along roadways or roadsides. However, these linear mobile monitoring approaches cannot thoroughly distinguish spatial variability from temporal variations in monitored TRAP concentrations. In this study, we used a novel mobile monitoring approach to simultaneously characterize spatial/temporal variations in roadside concentrations of TRAPs in urban settings. We evaluated the effectiveness of this mobile monitoring approach by performing concurrent measurements along two parallel paths perpendicular to a major roadway and/or along heavily trafficked roads at very narrow scale (one block away each other) within short time period (<30 min) in an urban community. Based on traffic and particulate matter (PM) source information, we selected 4 neighborhoods to study. The sampling activities utilized real-time monitors, including battery-operated PM2.5 monitor (SidePak), condensation particle counter (CPC 3007), black carbon (BC) monitor (Micro-Aethalometer), carbon monoxide (CO) monitor (Langan T15), and portable temperature/humidity data logger (HOBO U12), and a GPS-based tracker (Trackstick). Sampling was conducted for ∼3 h in the morning (7:30-10:30) in 7 separate days in March/April and 6 days in May/June 2012. Two simultaneous samplings were made at 5 spatially-distributed locations on parallel roads, usually distant one block each other, in each neighborhood. The 5-min averaged BC concentrations (AVG ± SD, [range]) were 2.53 ± 2.47 [0.09-16.3] μg/m3, particle number concentrations (PNC) were 33,330 ± 23,451 [2512-159,130] particles/cm3, PM2.5 mass concentrations were 8.87 ± 7.65 [0.27-46.5] μg/m3, and CO concentrations were 1.22 ± 0.60 [0.22-6.29] ppm in the community. The traffic-related air pollutants, BC and PNC, but not PM2.5 or CO, varied spatially depending on proximity to local stationary/mobile sources. Seasonal differences were observed for all four TRAPs, significantly higher in colder months than in warmer months. The coefficients of variation (CVs) in concurrent measurements from two parallel routes were calculated around 0.21 ± 0.17, and variations were attributed by meteorological variation (25%), temporal variability (19%), concentration level (6%), and spatial variability (2%), respectively. Overall study findings suggest this mobile monitoring approach could effectively capture and distinguish spatial/temporal characteristics in TRAP concentrations for communities impacted by heavy motor vehicle traffic and mixed urban air pollution sources.

Validity of ambient levels of fine particles as surrogate for personal exposure to outdoor air pollution--results of the European EXPOLIS-EAS Study (Swiss Center Basel).

PubMed

Oglesby, L; Künzli, N; Röösli, M; Braun-Fahrländer, C; Mathys, P; Stern, W; Jantunen, M; Kousa, A

2000-07-01

To evaluate the validity of fixed-site fine particle levels as exposure surrogates in air pollution epidemiology, we considered four indicator groups: (1) PM2.5 total mass concentrations, (2) sulfur and potassium for regional air pollution, (3) lead and bromine for traffic-related particles, and (4) calcium for crustal particles. Using data from the European EXPOLIS (Air Pollution Exposure Distribution within Adult Urban Populations in Europe) study, we assessed the associations between 48-hr personal exposures and home outdoor levels of the indicators. Furthermore, within-city variability of fine particle levels was evaluated. Personal exposures to PM2.5 mass were not correlated to corresponding home outdoor levels (n = 44, rSpearman (Sp) = 0.07). In the group reporting neither relevant indoor sources nor relevant activities, personal exposures and home outdoor levels of sulfur were highly correlated (n = 40, rSp = 0.85). In contrast, the associations were weaker for traffic (Pb: n = 44, rSp = 0.53; Br: n = 44, rSp = 0.21) and crustal (Ca: n = 44, rSp = 0.12) indicators. This contrast is consistent with spatially homogeneous regional pollution and higher spatial variability of traffic and crustal indicators observed in Basel, Switzerland. We conclude that for regional air pollution, fixed-site fine particle levels are valid exposure surrogates. For source-specific exposures, however, fixed-site data are probably not the optimal measure. Still, in air pollution epidemiology, ambient PM2.5 levels may be more appropriate exposure estimates than total personal PM2.5 exposure, since the latter reflects a mixture of indoor and outdoor sources.

Validity of Ambient Levels of Fine Particles as Surrogate for Personal Exposure to Outdoor Air Pollution-Results of the European EXPOLIS-EAS Study (Swiss Center Basel).

PubMed

Oglesby, Lucy; Künzli, Nino; Röösli, Martin; Braun-Fahrländer, Charlotte; Mathys, Patrick; Stern, Willem; Jantunen, Matti; Kousa, Anu

2000-07-01

To evaluate the validity of fixed-site fine particle levels as exposure surrogates in air pollution epidemiology, we considered four indicator groups: (1) PM 25 total mass concentrations, (2) sulfur and potassium for regional air pollution, (3) lead and bromine for traffic-related particles, and (4) calcium for crustal particles. Using data from the European EXPOLIS (Air Pollution Exposure Distribution within Adult Urban Populations in Europe) study, we assessed the associations between 48-hr personal exposures and home outdoor levels of the indicators. Furthermore, within-city variability of fine particle levels was evaluated. Personal exposures to PM 2.5 mass were not correlated to corresponding home outdoor levels (n = 44, r S (S) =r o v ' Spearman (Sp) 0.07). In the group reporting neither relevant indoor sources nor relevant activities, personal exposures and home outdoor levels of sulfur were highly correlated (n = 40, r Sp = 0.85). In contrast, the associations were weaker for traffic (Pb: n = 44, r Sp = 0.53; Br: n = 44, r Sp = 0.21) and crustal (Ca: n = 44, r Sp = 0.12) indicators. This contrast is consistent with spatially homogeneous regional pollution and higher spatial variability of traffic and crustal indicators observed in Basel, Switzerland. We conclude that for regional air pollution, fixed-site fine particle levels are valid exposure surrogates. For source-specific exposures, however, fixed-site data are probably not the optimal measure. Still, in air pollution epidemiology, ambient PM 2.5 levels may be more appropriate exposure estimates than total personal PM 2.5 exposure, since the latter reflects a mixture of indoor and outdoor sources.

The temporal structure of pollution levels in developed cities.

PubMed

Barrigón Morillas, Juan Miguel; Ortiz-Caraballo, Carmen; Prieto Gajardo, Carlos

2015-06-01

Currently, the need for mobility can cause significant pollution levels in cities, with important effects on health and quality of life. Any approach to the study of urban pollution and its effects requires an analysis of spatial distribution and temporal variability. It is a crucial dilemma to obtain proven methodologies that allow an increase in the quality of the prediction and the saving of resources in the spatial and temporal sampling. This work proposes a new analytical methodology in the study of temporal structure. As a result, a model for estimating annual levels of urban traffic noise was proposed. The average errors are less than one decibel in all acoustics indicators. A new working methodology of urban noise has begun. Additionally, a general application can be found for the study of the impacts of pollution associated with traffic, with implications for urban design and possibly in economic and sociological aspects. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatial-temporal variation characteristics of air pollution in Henan of China: Localized emission inventory, WRF/Chem simulations and potential source contribution analysis.

PubMed

Liu, Shuhan; Hua, Shenbing; Wang, Kun; Qiu, Peipei; Liu, Huanjia; Wu, Bobo; Shao, Pangyang; Liu, Xiangyang; Wu, Yiming; Xue, Yifeng; Hao, Yan; Tian, Hezhong

2018-05-15

Henan is the most populous province and one of the most seriously polluted areas in China at present. In this study, we establish an integrated atmospheric emission inventory of primary air pollutants in Henan province for the target year of 2012. The inventory developed here accounts for detailed activity levels of 11 categories of primary anthropogenic emission sources, and determines the best available representation of emission factors. Further, we allocate the annual emissions into a high spatial resolution of 3km×3km with ArcGIS methodology and surrogate indices, such as regional population distribution and gross domestic product (GDP). Our results show that the emissions of VOCs, SO 2 , PM 10 , PM 2.5 , NO X , NH 3 , CO, BC and OC are about 1.15, 1.24, 1.29, 0.70, 1.93, 1.05, 7.92, 0.27 and 0.25milliontons, respectively. The majority of these pollutant emissions comes from the Central Plain Urban Agglomeration (CPUA) region, particularly Zhengzhou and Pingdingshan. By combining with the emission inventory with the WRF/Chem modeling and backward trajectory analysis, we investigate the temporal and spatial variability of air pollution in the province and explore the causes of higher pollutants concentrations in the region of CPUA during the heavily polluted period of January. The results demonstrate that intensive pollutants emissions and unfavorable meteorological conditions are the main causes of the heavy pollution. Besides, Weighted Potential Source Contribution Function (WPSCF) analysis indicates that local emissions remain the major contributor of PM 2.5 in Henan province, although emissions from the neighboring provinces (e.g. Shanxi, Shaanxi, Anhui, and Shandong) are also important contributors. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatial analysis of health risk assessment with arsenic intake of drinking water in the LanYang plain

NASA Astrophysics Data System (ADS)

Chen, C. F.; Liang, C. P.; Jang, C. S.; Chen, J. S.

2016-12-01

Groundwater is one of the most component water resources in Lanyang plain. The groundwater of the Lanyang Plain contains arsenic levels that exceed the current Taiwan Environmental Protection Administration (Taiwan EPA) limit of 10 μg/L. The arsenic of groundwater in some areas of the Lanyang Plain pose great menace for the safe use of groundwater resources. Therefore, poor water quality can adversely impact drinking water uses, leading to human health risks. This study analyzed the potential health risk associated with the ingestion of arsenic-affected groundwater in the arseniasis-endemic Lanyang plain. Geostatistical approach is widely used in spatial variability analysis and distributions of field data with uncertainty. The estimation of spatial distribution of the arsenic contaminant in groundwater is very important in the health risk assessment. This study used indicator kriging (IK) and ordinary kriging (OK) methods to explore the spatial variability of arsenic-polluted parameters. The estimated difference between IK and OK estimates was compared. The extent of arsenic pollution was spatially determined and the Target cancer risk (TR) and dose response were explored when the ingestion of arsenic in groundwater. Thus, a zonal management plan based on safe groundwater use is formulated. The research findings can provide a plan reference of regional water resources supplies for local government administrators and developing groundwater resources in the Lanyang Plain.

[Airports and air quality: a critical synthesis of the literature].

PubMed

Cattani, Giorgio; Di Menno di Bucchianico, Alessandro; Gaeta, Alessandra; Romani, Daniela; Fontana, Luca; Iavicoli, Ivo

2014-01-01

This work reviewed existing literature on airport related activities that could worsen surrounding air quality; its aim is to underline the progress coming from recent-year studies, the knowledge emerging from new approaches, the development of semi-empiric analytical methods as well as the questions still needing to be clarified. To estimate pollution levels, spatial and temporal variability, and the sources relative contributions integrated assessment, using both fixed point measurement and model outputs, are needed. The general picture emerging from the studies was a non-negligible and highly spatially variable (within 2-3 km from the fence line) airport contribution; even if it is often not dominant compared to other concomitant pollution sources. Results were highly airport-specific. Traffic volumes, landscape and meteorology were the key variables that drove the impacts. Results were thus hardly exportable to other contexts. Airport related pollutant sources were found to be characterized by unusual emission patterns (particularly ultrafine particles, black carbon and nitrogen oxides during take-off); high time-resolution measurements allow to depict the rapidly changing take-off effect on air quality that could not be adequately observed otherwise. Few studies used high time resolution data in a successful way as statistical models inputs to estimate the aircraft take-off contribution to the observed average levels. These findings should not be neglected when exposure of people living near airports is to be assessed.

Spatial Variability of AERONET Aerosol Optical Properties and Satellite Data in South Korea during NASA DRAGON-Asia Campaign.

PubMed

Lee, Hyung Joo; Son, Youn-Suk

2016-04-05

We investigated spatial variability in aerosol optical properties, including aerosol optical depth (AOD), fine-mode fraction (FMF), and single scattering albedo (SSA), observed at 21 Aerosol Robotic Network (AERONET) sites and satellite remote sensing data in South Korea during the spring of 2012. These dense AERONET networks established in a National Aeronautics and Space Administration (NASA) field campaign enabled us to examine the spatially detailed aerosol size distribution and composition as well as aerosol levels. The springtime particle air quality was characterized by high background aerosol levels and high contributions of coarse-mode aerosols to total aerosols. We found that between-site correlations and coefficient of divergence for AOD and FMF strongly relied on the distance between sites, particularly in the south-north direction. Higher AOD was related to higher population density and lower distance from highways, and the aerosol size distribution and composition reflected source-specific characteristics. The ratios of satellite NO2 to AOD, which indicate the relative contributions of local combustion sources to aerosol levels, represented higher local contributions in metropolitan Seoul and Pusan. Our study demonstrates that the aerosol levels were determined by both local and regional pollution and that the relative contributions of these pollutions to aerosols generated spatial heterogeneity in the particle air quality.

Development of a microscale land use regression model for predicting NO2 concentrations at a heavy trafficked suburban area in Auckland, NZ.

PubMed

Weissert, L F; Salmond, J A; Miskell, G; Alavi-Shoshtari, M; Williams, D E

2018-04-01

Land use regression (LUR) analysis has become a key method to explain air pollutant concentrations at unmeasured sites at city or country scales, but little is known about the applicability of LUR at microscales. We present a microscale LUR model developed for a heavy trafficked section of road in Auckland, New Zealand. We also test the within-city transferability of LUR models developed at different spatial scales (local scale and city scale). Nitrogen dioxide (NO 2 ) was measured during summer at 40 sites and a LUR model was developed based on standard criteria. The results showed that LUR models are able to capture the microscale variability with the model explaining 66% of the variability in NO 2 concentrations. Predictor variables identified at this scale were street width, distance to major road, presence of awnings and number of bus stops, with the latter three also being important determinants at the local scale. This highlights the importance of street and building configurations for individual exposure at the street level. However, within-city transferability was limited with the number of bus stops being the only significant predictor variable at all spatial scales and locations tested, indicating the strong influence of diesel emissions related to bus traffic. These findings show that air quality monitoring is necessary at a high spatial density within cities in capturing small-scale variability in NO 2 concentrations at the street level and assessing individual exposure to traffic related air pollutants. Copyright © 2017. Published by Elsevier B.V.

EXPOSURE MONITORING COMPONENT FOR DETROIT CHILDREN'S HEALTH STUDY ( DCHS )

EPA Science Inventory

Conventional, regulatory-based air monitoring is expensive and, thus, conducted at one or few locations in a city. This provides limited info on intra-urban variability and spatial distribution of air pollution. Research-oriented urban network monitoring has progressed with inc...

AIR QUALITY MODELING AT NEIGHBORHOOD SCALES TO IMPROVE HUMAN EXPOSURE ASSESSMENT

EPA Science Inventory

Air quality modeling is an integral component of risk assessment and of subsequent development of effective and efficient management of air quality. Urban areas introduce of fresh sources of pollutants into regional background producing significant spatial variability of the co...

The spatial-temporal distribution of the atmospheric polluting agents during the period 2000-2005 in the Urban Area of Guadalajara, Jalisco, Mexico.

PubMed

Sánchez, Hermes U Ramírez; García, María D Andrade; Bejaran, Rubén; Guadalupe, Mario E García; Vázquez, Antonio Wallo; Toledano, Ana C Pompa; Villasenor, Odila de la Torre

2009-06-15

In the large cities, the disordered urban development, the industrial activities, and the transport, have caused elevated concentrations of polluting agents and possible risks to the health of the population. The metropolises located in valleys with little ventilation (such as the Urban Area of Guadalajara: UAG) present low dispersion of polluting agents can cause high risk of respiratory and cardiovascular diseases. The objective of this work was to describe the spatial-temporal distribution of the atmospheric polluting agents: carbon monoxide (CO), nitrogen dioxide (NO(2)), sulfur dioxide (SO(2)), particles smaller than 10 microns (microm) (PM(10)) and ozone (O(3)) in the UAG during the period 2000-2005. A spatial-temporal distribution analysis was made by means of graphic interpolation (Kriging method) of the statistical parameters of CO, NO(2), SO(2), PM(10) and O(3) with the collected data from eight stations of atmospheric monitoring in the UAG. The results show that the distributions of the atmospheric polluting agents are variable during the analyzed years. The polluting agent with highest concentration is PM(10) (265.42 microg/m(3)), followed by O(3) (0.11 ppm), NO(2) (0.11 ppm), CO (9.17 ppm) and SO(2) (0.05 ppm). The most affected zone is the southeast of the UAG. The results showed that an important percentage of days exceed the Mexican norms of air quality (93-199 days/year).

Spatial variation of PM elemental composition between and within 20 European study areas--Results of the ESCAPE project.

PubMed

Tsai, Ming-Yi; Hoek, Gerard; Eeftens, Marloes; de Hoogh, Kees; Beelen, Rob; Beregszászi, Timea; Cesaroni, Giulia; Cirach, Marta; Cyrys, Josef; De Nazelle, Audrey; de Vocht, Frank; Ducret-Stich, Regina; Eriksen, Kirsten; Galassi, Claudia; Gražuleviciene, Regina; Gražulevicius, Tomas; Grivas, Georgios; Gryparis, Alexandros; Heinrich, Joachim; Hoffmann, Barbara; Iakovides, Minas; Keuken, Menno; Krämer, Ursula; Künzli, Nino; Lanki, Timo; Madsen, Christian; Meliefste, Kees; Merritt, Anne-Sophie; Mölter, Anna; Mosler, Gioia; Nieuwenhuijsen, Mark J; Pershagen, Göran; Phuleria, Harish; Quass, Ulrich; Ranzi, Andrea; Schaffner, Emmanuel; Sokhi, Ranjeet; Stempfelet, Morgane; Stephanou, Euripides; Sugiri, Dorothea; Taimisto, Pekka; Tewis, Marjan; Udvardy, Orsolya; Wang, Meng; Brunekreef, Bert

2015-11-01

An increasing number of epidemiological studies suggest that adverse health effects of air pollution may be related to particulate matter (PM) composition, particularly trace metals. However, we lack comprehensive data on the spatial distribution of these elements. We measured PM2.5 and PM10 in twenty study areas across Europe in three seasonal two-week periods over a year using Harvard impactors and standardized protocols. In each area, we selected street (ST), urban (UB) and regional background (RB) sites (totaling 20) to characterize local spatial variability. Elemental composition was determined by energy-dispersive X-ray fluorescence analysis of all PM2.5 and PM10 filters. We selected a priori eight (Cu, Fe, K, Ni, S, Si, V, Zn) well-detected elements of health interest, which also roughly represented different sources including traffic, industry, ports, and wood burning. PM elemental composition varied greatly across Europe, indicating different regional influences. Average street to urban background ratios ranged from 0.90 (V) to 1.60 (Cu) for PM2.5 and from 0.93 (V) to 2.28 (Cu) for PM10. Our selected PM elements were variably correlated with the main pollutants (PM2.5, PM10, PM2.5 absorbance, NO2 and NOx) across Europe: in general, Cu and Fe in all size fractions were highly correlated (Pearson correlations above 0.75); Si and Zn in the coarse fractions were modestly correlated (between 0.5 and 0.75); and the remaining elements in the various size fractions had lower correlations (around 0.5 or below). This variability in correlation demonstrated the distinctly different spatial distributions of most of the elements. Variability of PM10_Cu and Fe was mostly due to within-study area differences (67% and 64% of overall variance, respectively) versus between-study area and exceeded that of most other traffic-related pollutants, including NO2 and soot, signaling the importance of non-tailpipe (e.g., brake wear) emissions in PM. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling pollution potential input from the drainage basin into Barra Bonita reservoir, São Paulo - Brazil.

PubMed

Prado, R B; Novo, E M L M

2015-05-01

In this study multi-criteria modeling tools are applied to map the spatial distribution of drainage basin potential to pollute Barra Bonita Reservoir, São Paulo State, Brasil. Barra Bonita Reservoir Basin had undergone intense land use/land cover changes in the last decades, including the fast conversion from pasture into sugarcane. In this respect, this study answers to the lack of information about the variables (criteria) which affect the pollution potential of the drainage basin by building a Geographic Information System which provides their spatial distribution at sub-basin level. The GIS was fed by several data (geomorphology, pedology, geology, drainage network and rainfall) provided by public agencies. Landsat satellite images provided land use/land cover map for 2002. Ratings and weights of each criterion defined by specialists supported the modeling process. The results showed a wide variability in the pollution potential of different sub-basins according to the application of different criterion. If only land use is analyzed, for instance, less than 50% of the basin is classified as highly threatening to water quality and include sub basins located near the reservoir, indicating the importance of protection areas at the margins. Despite the subjectivity involved in the weighing processes, the multi-criteria analysis model allowed the simulation of scenarios which support rational land use polices at sub-basin level regarding the protection of water resources.

Spatial analysis and hazard assessment on soil total nitrogen in the middle subtropical zone of China

NASA Astrophysics Data System (ADS)

Lu, Peng; Lin, Wenpeng; Niu, Zheng; Su, Yirong; Wu, Jinshui

2006-10-01

Nitrogen (N) is one of the main factors affecting environmental pollution. In recent years, non-point source pollution and water body eutrophication have become increasing concerns for both scientists and the policy-makers. In order to assess the environmental hazard of soil total N pollution, a typical ecological unit was selected as the experimental site. This paper showed that Box-Cox transformation achieved normality in the data set, and dampened the effect of outliers. The best theoretical model of soil total N was a Gaussian model. Spatial variability of soil total N at NE60° and NE150° directions showed that it had a strip anisotropic structure. The ordinary kriging estimate of soil total N concentration was mapped. The spatial distribution pattern of soil total N in the direction of NE150° displayed a strip-shaped structure. Kriging standard deviations (KSD) provided valuable information that will increase the accuracy of total N mapping. The probability kriging method is useful to assess the hazard of N pollution by providing the conditional probability of N concentration exceeding the threshold value, where we found soil total N>2.0g/kg. The probability distribution of soil total N will be helpful to conduct hazard assessment, optimal fertilization, and develop management practices to control the non-point sources of N pollution.

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: Insights into spatial variability using high-resolution satellite data

PubMed Central

Alexeeff, Stacey E.; Schwartz, Joel; Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Coull, Brent A.

2016-01-01

Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1km x 1km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R2 yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with greater than 0.9 out-of-sample R2 yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the standard errors. Land use regression models performed better in chronic effects simulations. These results can help researchers when interpreting health effect estimates in these types of studies. PMID:24896768

Spatial Analysis of Ambient PM2.5 Exposure and Bladder Cancer Mortality in Taiwan

PubMed Central

Yeh, Hsin-Ling; Hsu, Shang-Wei; Chang, Yu-Chia; Chan, Ta-Chien; Tsou, Hui-Chen; Chang, Yen-Chen; Chiang, Po-Huang

2017-01-01

Fine particulate matter (PM2.5) is an air pollutant that is receiving intense regulatory attention in Taiwan. In previous studies, the effect of air pollution on bladder cancer has been explored. This study was conducted to elucidate the effect of atmospheric PM2.5 and other local risk factors on bladder cancer mortality based on available 13-year mortality data. Geographically weighted regression (GWR) was applied to estimate and interpret the spatial variability of the relationships between bladder cancer mortality and ambient PM2.5 concentrations, and other variables were covariates used to adjust for the effect of PM2.5. After applying a GWR model, the concentration of ambient PM2.5 showed a positive correlation with bladder cancer mortality in males in northern Taiwan and females in most of the townships in Taiwan. This is the first time PM2.5 has been identified as a risk factor for bladder cancer based on the statistical evidence provided by GWR analysis. PMID:28489042

Cross-comparison and evaluation of air pollution field estimation methods

NASA Astrophysics Data System (ADS)

Yu, Haofei; Russell, Armistead; Mulholland, James; Odman, Talat; Hu, Yongtao; Chang, Howard H.; Kumar, Naresh

2018-04-01

Accurate estimates of human exposure is critical for air pollution health studies and a variety of methods are currently being used to assign pollutant concentrations to populations. Results from these methods may differ substantially, which can affect the outcomes of health impact assessments. Here, we applied 14 methods for developing spatiotemporal air pollutant concentration fields of eight pollutants to the Atlanta, Georgia region. These methods include eight methods relying mostly on air quality observations (CM: central monitor; SA: spatial average; IDW: inverse distance weighting; KRIG: kriging; TESS-D: discontinuous tessellation; TESS-NN: natural neighbor tessellation with interpolation; LUR: land use regression; AOD: downscaled satellite-derived aerosol optical depth), one using the RLINE dispersion model, and five methods using a chemical transport model (CMAQ), with and without using observational data to constrain results. The derived fields were evaluated and compared. Overall, all methods generally perform better at urban than rural area, and for secondary than primary pollutants. We found the CM and SA methods may be appropriate only for small domains, and for secondary pollutants, though the SA method lead to large negative spatial correlations when using data withholding for PM2.5 (spatial correlation coefficient R = -0.81). The TESS-D method was found to have major limitations. Results of the IDW, KRIG and TESS-NN methods are similar. They are found to be better suited for secondary pollutants because of their satisfactory temporal performance (e.g. average temporal R2 > 0.85 for PM2.5 but less than 0.35 for primary pollutant NO2). In addition, they are suitable for areas with relatively dense monitoring networks due to their inability to capture spatial concentration variabilities, as indicated by the negative spatial R (lower than -0.2 for PM2.5 when assessed using data withholding). The performance of LUR and AOD methods were similar to kriging. Using RLINE and CMAQ fields without fusing observational data led to substantial errors and biases, though the CMAQ model captured spatial gradients reasonably well (spatial R = 0.45 for PM2.5). Two unique tests conducted here included quantifying autocorrelation of method biases (which can be important in time series analyses) and how well the methods capture the observed interspecies correlations (which would be of particular importance in multipollutant health assessments). Autocorrelation of method biases lasted longest and interspecies correlations of primary pollutants was higher than observations when air quality models were used without data fusing. Use of hybrid methods that combine air quality model outputs with observational data overcome some of these limitations and is better suited for health studies. Results from this study contribute to better understanding the strengths and weaknesses of different methods for estimating human exposures.

A study protocol to evaluate the relationship between outdoor air pollution and pregnancy outcomes

PubMed Central

2010-01-01

Background The present study protocol is designed to assess the relationship between outdoor air pollution and low birth weight and preterm births outcomes performing a semi-ecological analysis. Semi-ecological design studies are widely used to assess effects of air pollution in humans. In this type of analysis, health outcomes and covariates are measured in individuals and exposure assignments are usually based on air quality monitor stations. Therefore, estimating individual exposures are one of the major challenges when investigating these relationships with a semi-ecologic design. Methods/Design Semi-ecologic study consisting of a retrospective cohort study with ecologic assignment of exposure is applied. Health outcomes and covariates are collected at Primary Health Care Center. Data from pregnant registry, clinical record and specific questionnaire administered orally to the mothers of children born in period 2007-2010 in Portuguese Alentejo Litoral region, are collected by the research team. Outdoor air pollution data are collected with a lichen diversity biomonitoring program, and individual pregnancy exposures are assessed with spatial geostatistical simulation, which provides the basis for uncertainty analysis of individual exposures. Awareness of outdoor air pollution uncertainty will improve validity of individual exposures assignments for further statistical analysis with multivariate regression models. Discussion Exposure misclassification is an issue of concern in semi-ecological design. In this study, personal exposures are assigned to each pregnant using geocoded addresses data. A stochastic simulation method is applied to lichen diversity values index measured at biomonitoring survey locations, in order to assess spatial uncertainty of lichen diversity value index at each geocoded address. These methods assume a model for spatial autocorrelation of exposure and provide a distribution of exposures in each study location. We believe that variability of simulated exposure values at geocoded addresses will improve knowledge on variability of exposures, improving therefore validity of individual exposures to input in posterior statistical analysis. PMID:20950449

A study protocol to evaluate the relationship between outdoor air pollution and pregnancy outcomes.

PubMed

Ribeiro, Manuel C; Pereira, Maria J; Soares, Amílcar; Branquinho, Cristina; Augusto, Sofia; Llop, Esteve; Fonseca, Susana; Nave, Joaquim G; Tavares, António B; Dias, Carlos M; Silva, Ana; Selemane, Ismael; de Toro, Joaquin; Santos, Mário J; Santos, Fernanda

2010-10-15

The present study protocol is designed to assess the relationship between outdoor air pollution and low birth weight and preterm births outcomes performing a semi-ecological analysis. Semi-ecological design studies are widely used to assess effects of air pollution in humans. In this type of analysis, health outcomes and covariates are measured in individuals and exposure assignments are usually based on air quality monitor stations. Therefore, estimating individual exposures are one of the major challenges when investigating these relationships with a semi-ecologic design. Semi-ecologic study consisting of a retrospective cohort study with ecologic assignment of exposure is applied. Health outcomes and covariates are collected at Primary Health Care Center. Data from pregnant registry, clinical record and specific questionnaire administered orally to the mothers of children born in period 2007-2010 in Portuguese Alentejo Litoral region, are collected by the research team. Outdoor air pollution data are collected with a lichen diversity biomonitoring program, and individual pregnancy exposures are assessed with spatial geostatistical simulation, which provides the basis for uncertainty analysis of individual exposures. Awareness of outdoor air pollution uncertainty will improve validity of individual exposures assignments for further statistical analysis with multivariate regression models. Exposure misclassification is an issue of concern in semi-ecological design. In this study, personal exposures are assigned to each pregnant using geocoded addresses data. A stochastic simulation method is applied to lichen diversity values index measured at biomonitoring survey locations, in order to assess spatial uncertainty of lichen diversity value index at each geocoded address. These methods assume a model for spatial autocorrelation of exposure and provide a distribution of exposures in each study location. We believe that variability of simulated exposure values at geocoded addresses will improve knowledge on variability of exposures, improving therefore validity of individual exposures to input in posterior statistical analysis.

Quantifying co-benefits of source-specific CO2 emission reductions in Canada and the US: An adjoint sensitivity analysis

NASA Astrophysics Data System (ADS)

Zhao, S.; Soltanzadeh, M.; Pappin, A. J.; Hakami, A.; Turner, M. D.; Capps, S.; Henze, D. K.; Percell, P.; Bash, J. O.; Napelenok, S. L.; Pinder, R. W.; Russell, A. G.; Nenes, A.; Baek, J.; Carmichael, G. R.; Stanier, C. O.; Chai, T.; Byun, D.; Fahey, K.; Resler, J.; Mashayekhi, R.

2016-12-01

Scenario-based studies evaluate air quality co-benefits by adopting collective measures introduced under a climate policy scenario cannot distinguish between benefits accrued from CO2 reductions among sources of different types and at different locations. Location and sector dependencies are important factors that can be captured in an adjoint-based analysis of CO2 reduction co-benefits. The present study aims to quantify how the ancillary benefits of reducing criteria co-pollutants vary spatially and by sector. The adjoint of USEPA's CMAQ was applied to quantify the health benefits associated with emission reduction of criteria pollutants (NOX) in on-road mobile, Electric Generation Units (EGUs), and other select sectors on a location-by-location basis across the US and Canada. These health benefits are then converted to CO2 emission reduction co-benefits by accounting for source-specific emission rates of criteria pollutants in comparison to CO2. We integrate the results from the adjoint of CMAQ with emission estimates from 2011 NEI at the county level, and point source data from EPA's Air Markets Program Data and National Pollutant Release Inventory (NPRI) for Canada. Our preliminary results show that the monetized health benefits (due to averted chronic mortality) associated with reductions of 1 ton of CO2 emissions is up to 65/ton in Canada and 200/ton in US for mobile on-road sector. For EGU sources, co-benefits are estimated at up to 100/ton and 10/ton for the US and Canada respectively. For Canada, the calculated co-benefits through gaseous pollutants including NOx is larger than those through PM2.5 due to the official association between NO2 exposure and chronic mortality. Calculated co-benefits show a great deal of spatial variability across emission locations for different sectors and sub-sectors. Implications of such spatial variability in devising control policy options that effectively address both climate and air quality objectives will be discussed.

Role of highway traffic on spatial and temporal distributions of air pollutants in a Swiss Alpine valley.

PubMed

Ducret-Stich, Regina E; Tsai, Ming-Yi; Ragettli, Martina S; Ineichen, Alex; Kuenzli, Nino; Phuleria, Harish C

2013-07-01

Traffic-related air pollutants show high spatial variability near roads, posing a challenge to adequately assess exposures. Recent modeling approaches (e.g. dispersion models, land-use regression (LUR) models) have addressed this but mostly in urban areas where traffic is abundant. In contrast, our study area was located in a rural Swiss Alpine valley crossed by the main North-south transit highway of Switzerland. We conducted an extensive measurement campaign collecting continuous nitrogen dioxide (NO₂), particulate number concentrations (PN), daily respirable particulate matter (PM10), elemental carbon (EC) and organic carbon (OC) at one background, one highway and seven mobile stations from November 2007 to June 2009. Using these measurements, we built a hybrid model to predict daily outdoor NO₂ concentrations at residences of children participating in an asthma panel study. With the exception of OC, daily variations of the pollutants followed the temporal trends of heavy-duty traffic counts on the highway. In contrast, variations of weekly/seasonal means were strongly determined by meteorological conditions, e.g., winter inversion episodes. For pollutants related to primary exhaust emissions (i.e. NO₂, EC and PN) local spatial variation strongly depended on proximity to the highway. Pollutant concentrations decayed to background levels within 150 to 200 m from the highway. Two separate daily NO₂ prediction models were built using LUR approaches with (a) short-term traffic and weather data (model 1) and (b) subsequent addition of daily background NO₂ to previous model (model 2). Models 1 and 2 explained 70% and 91% of the variability in outdoor NO₂ concentrations, respectively. The biweekly averaged predictions from the final model 2 agreed very well with the independent biweekly integrated passive measurements taken at thirteen homes and nine community sites (validation R(2)=0.74). The excellent spatio-temporal performance of our model provides a very promising basis for the health effect assessment of the panel study. Copyright © 2013 Elsevier B.V. All rights reserved.

Annual and seasonal spatial models for nitrogen oxides in Tehran, Iran

NASA Astrophysics Data System (ADS)

Amini, Heresh; Taghavi-Shahri, Seyed-Mahmood; Henderson, Sarah B.; Hosseini, Vahid; Hassankhany, Hossein; Naderi, Maryam; Ahadi, Solmaz; Schindler, Christian; Künzli, Nino; Yunesian, Masud

2016-09-01

Very few land use regression (LUR) models have been developed for megacities in low- and middle-income countries, but such models are needed to facilitate epidemiologic research on air pollution. We developed annual and seasonal LUR models for ambient oxides of nitrogen (NO, NO2, and NOX) in the Middle Eastern city of Tehran, Iran, using 2010 data from 23 fixed monitoring stations. A novel systematic algorithm was developed for spatial modeling. The R2 values for the LUR models ranged from 0.69 to 0.78 for NO, 0.64 to 0.75 for NO2, and 0.61 to 0.79 for NOx. The most predictive variables were: distance to the traffic access control zone; distance to primary schools; green space; official areas; bridges; and slope. The annual average concentrations of all pollutants were high, approaching those reported for megacities in Asia. At 1000 randomly-selected locations the correlations between cooler and warmer season estimates were 0.64 for NO, 0.58 for NOX, and 0.30 for NO2. Seasonal differences in spatial patterns of pollution are likely driven by differences in source contributions and meteorology. These models provide a basis for understanding long-term exposures and chronic health effects of air pollution in Tehran, where such research has been limited.

Spatial variation of nitrogen pollution of the water table at Oued M'Zab (Northern Algerian Sahara)

NASA Astrophysics Data System (ADS)

Benhedid, H.; Bouhoun, M. Daddi

2018-05-01

The aim of our work is the study of spatial variations of the water table pollution of Oued M'Zab, in order to determine their abilities of use and the posed problems of degradation. The methodological approach we adopted is to make a spatial study of the variability of nitrogen pollution, as well as to classify water quality according to international standards. The main results obtained in this research show that NH4+ range from 0 to 0,143 mg.l-1 with an average of 0,048 ± 0,039 mg.l-1, the NO2- from 0 to 0,209 mg.l-1 give an average of 0,007 ± 0,033 mg.l-1, and the NO3- vary between 14,264 and 143,465 mg.l-1, with a mean value 54,594 ± 30,503 mg.l-1. According to W.H.O. standards, the majority of these waters are classified as polluted and not drinkable. Our research shows a degradation of the underground water resources in M'Zab Valley. It resulted that it is essential to regulate the use of water and set out other adjustments in order to safeguard the underground water resources so as to promote sustainable development in the valley of M'Zab.

Annual and seasonal spatial models for nitrogen oxides in Tehran, Iran

PubMed Central

Amini, Heresh; Taghavi-Shahri, Seyed-Mahmood; Henderson, Sarah B.; Hosseini, Vahid; Hassankhany, Hossein; Naderi, Maryam; Ahadi, Solmaz; Schindler, Christian; Künzli, Nino; Yunesian, Masud

2016-01-01

Very few land use regression (LUR) models have been developed for megacities in low- and middle-income countries, but such models are needed to facilitate epidemiologic research on air pollution. We developed annual and seasonal LUR models for ambient oxides of nitrogen (NO, NO2, and NOX) in the Middle Eastern city of Tehran, Iran, using 2010 data from 23 fixed monitoring stations. A novel systematic algorithm was developed for spatial modeling. The R2 values for the LUR models ranged from 0.69 to 0.78 for NO, 0.64 to 0.75 for NO2, and 0.61 to 0.79 for NOx. The most predictive variables were: distance to the traffic access control zone; distance to primary schools; green space; official areas; bridges; and slope. The annual average concentrations of all pollutants were high, approaching those reported for megacities in Asia. At 1000 randomly-selected locations the correlations between cooler and warmer season estimates were 0.64 for NO, 0.58 for NOX, and 0.30 for NO2. Seasonal differences in spatial patterns of pollution are likely driven by differences in source contributions and meteorology. These models provide a basis for understanding long-term exposures and chronic health effects of air pollution in Tehran, where such research has been limited. PMID:27622593

Spatial and Temporal Variability of Trace Gas Columns Derived from WRF/Chem Regional Model Output: Planning for Geostationary Observations of Atmospheric Composition

NASA Technical Reports Server (NTRS)

Follette-Cook, M. B.; Pickering, K.; Crawford, J.; Duncan, B.; Loughner, C.; Diskin, G.; Fried, A.; Weinheimer, A.

2015-01-01

We quantify both the spatial and temporal variability of column integrated O3, NO2, CO, SO2, and HCHO over the Baltimore / Washington, DC area using output from the Weather Research and Forecasting model with on-line chemistry (WRF/Chem) for the entire month of July 2011, coinciding with the first deployment of the NASA Earth Venture program mission Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ). Using structure function analyses, we find that the model reproduces the spatial variability observed during the campaign reasonably well, especially for O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument will be the first NASA mission to make atmospheric composition observations from geostationary orbit and partially fulfills the goals of the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission. We relate the simulated variability to the precision requirements defined by the science traceability matrices of these space-borne missions. Results for O3 from 0- 2 km altitude indicate that the TEMPO instrument would be able to observe O3 air quality events over the Mid-Atlantic area, even on days when the violations of the air quality standard are not widespread. The results further indicated that horizontal gradients in CO from 0-2 km would be observable over moderate distances (= 20 km). The spatial and temporal results for tropospheric column NO2 indicate that TEMPO would be able to observe not only the large urban plumes at times of peak production, but also the weaker gradients between rush hours. This suggests that the proposed spatial and temporal resolutions for these satellites as well as their prospective precision requirements are sufficient to answer the science questions they are tasked to address.

Spatial patterns of air pollutants and social groups: a distributive environmental justice study in the phoenix metropolitan region of USA

NASA Astrophysics Data System (ADS)

Pope, Ronald; Wu, Jianguo; Boone, Christopher

2016-11-01

Quantifying spatial distribution patterns of air pollutants is imperative to understand environmental justice issues. Here we present a landscape-based hierarchical approach in which air pollution variables are regressed against population demographics on multiple spatiotemporal scales. Using this approach, we investigated the potential problem of distributive environmental justice in the Phoenix metropolitan region, focusing on ambient ozone and particulate matter. Pollution surfaces (maps) are evaluated against the demographics of class, age, race (African American, Native American), and ethnicity (Hispanic). A hierarchical multiple regression method is used to detect distributive environmental justice relationships. Our results show that significant relationships exist between the dependent and independent variables, signifying possible environmental inequity. Although changing spatiotemporal scales only altered the overall direction of these relationships in a few instances, it did cause the relationship to become nonsignificant in many cases. Several consistent patterns emerged: people aged 17 and under were significant predictors for ambient ozone and particulate matter, but people 65 and older were only predictors for ambient particulate matter. African Americans were strong predictors for ambient particulate matter, while Native Americans were strong predictors for ambient ozone. Hispanics had a strong negative correlation with ambient ozone, but a less consistent positive relationship with ambient particulate matter. Given the legacy conditions endured by minority racial and ethnic groups, and the relative lack of mobility of all the groups, our findings suggest the existence of environmental inequities in the Phoenix metropolitan region. The methodology developed in this study is generalizable with other pollutants to provide a multi-scaled perspective of environmental justice issues.

A wide field-of-view imaging DOAS instrument for continuous trace gas mapping from aircraft

NASA Astrophysics Data System (ADS)

Schönhardt, A.; Altube, P.; Gerilowski, K.; Krautwurst, S.; Hartmann, J.; Meier, A. C.; Richter, A.; Burrows, J. P.

2014-04-01

For the purpose of trace gas measurements and pollution mapping, the Airborne imaging DOAS instrument for Measurements of Atmospheric Pollution (AirMAP) has been developed, characterised and successfully operated from aircraft. From the observations with the AirMAP instrument nitrogen dioxide (NO2) columns were retrieved. A major benefit of the pushbroom imaging instrument is the spatially continuous, gap-free measurement sequence independent of flight altitude, a valuable characteristic for mapping purposes. This is made possible by the use of a frame-transfer detector. With a wide-angle entrance objective, a broad field-of-view across track of around 48° is achieved, leading to a swath width of about the same size as the flight altitude. The use of fibre coupled light intake optics with sorted light fibres allows flexible positioning within the aircraft and retains the very good imaging capabilities. The measurements yield ground spatial resolutions below 100 m. From a maximum of 35 individual viewing directions (lines of sight, LOS) represented by 35 single fibres, the number of viewing directions is adapted to each situation by averaging according to signal-to-noise or spatial resolution requirements. Exploitation of all the viewing directions yields observations at 30 m spatial resolution, making the instrument a suitable tool for mapping trace gas point sources and small scale variability. For accurate spatial mapping the position and aircraft attitude are taken into account using the Attitude and Heading Reference System of the aircraft. A first demonstration mission using AirMAP was undertaken. In June 2011, AirMAP has been operated on the AWI Polar-5 aircraft in the framework of the AIRMETH2011 campaign. During a flight above a medium sized coal-fired power plant in North-West Germany, AirMAP clearly detects the emission plume downwind from the exhaust stack, with NO2 vertical columns around 2 × 1016 molecules cm-2 in the plume center. The emission estimates are consistent with reports in the pollutant transfer register. Strong spatial gradients and variability in NO2 amounts across and along flight direction are observed, and small-scale enhancements of NO2 above a motorway are detected. The present study reports on the experimental setup and characteristics of AirMAP, and the first measurements at high spatial resolution and wide spatial coverage are presented which meet the requirements for NO2 mapping to observe and account for the intrinsic variability of tropospheric NO2.

Identifying controlling factors of ground-level ozone levels over southwestern Taiwan using a decision tree

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Lin, Chuan-Yao; Liau, Churn-Jung; Kuo, Yi-Ming

2012-12-01

Kaohsiung City and the suburban region of southwestern Taiwan have suffered from severe air pollution since becoming the largest center of heavy industry in Taiwan. The complex process of ozone (O3) formation and its precursor compounds (the volatile organic compounds (VOCs) and nitrogen oxide (NOx) emissions), accompanied by meteorological conditions, make controlling ozone difficult. Using a decision tree is especially appropriate for analyzing time series data that contain ozone levels and meteorological and explanatory variables for ozone formation. Results show that dominant variables such as temperature, wind speed, VOCs, and NOx can play vital roles in describing ozone variations among observations. That temperature and wind speed are highly correlated with ozone levels indicates that these meteorological conditions largely affect ozone variability. The results also demonstrate that spatial heterogeneity of ozone patterns are in coastal and inland areas caused by sea-land breeze and pollutant sources during high ozone episodes over southwestern Taiwan. This study used a decision tree to obtain quantitative insight into spatial distributions of precursor compound emissions and effects of meteorological conditions on ozone levels that are useful for refining monitoring plans and developing management strategies.

An urban observatory for quantifying phosphorus and suspended solid loads in combined natural and stormwater conveyances.

PubMed

Melcher, Anthony A; Horsburgh, Jeffery S

2017-06-01

Water quality in urban streams and stormwater systems is highly dynamic, both spatially and temporally, and can change drastically during storm events. Infrequent grab samples commonly collected for estimating pollutant loadings are insufficient to characterize water quality in many urban water systems. In situ water quality measurements are being used as surrogates for continuous pollutant load estimates; however, relatively few studies have tested the validity of surrogate indicators in urban stormwater conveyances. In this paper, we describe an observatory aimed at demonstrating the infrastructure required for surrogate monitoring in urban water systems and for capturing the dynamic behavior of stormwater-driven pollutant loads. We describe the instrumentation of multiple, autonomous water quality and quantity monitoring sites within an urban observatory. We also describe smart and adaptive sampling procedures implemented to improve data collection for developing surrogate relationships and for capturing the temporal and spatial variability of pollutant loading events in urban watersheds. Results show that the observatory is able to capture short-duration storm events within multiple catchments and, through inter-site communication, sampling efforts can be synchronized across multiple monitoring sites.

Modeling spatial and temporal variability of residential air exchange rates for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS).

PubMed

Breen, Michael S; Burke, Janet M; Batterman, Stuart A; Vette, Alan F; Godwin, Christopher; Croghan, Carry W; Schultz, Bradley D; Long, Thomas C

2014-11-07

Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. The residential air exchange rate (AER), which is the rate of exchange of indoor air with outdoor air, is an important determinant for house-to-house (spatial) and temporal variations of air pollution infiltration. Our goal was to evaluate and apply mechanistic models to predict AERs for 213 homes in the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS), a cohort study of traffic-related air pollution exposures and respiratory effects in asthmatic children living near major roads in Detroit, Michigan. We used a previously developed model (LBL), which predicts AER from meteorology and questionnaire data on building characteristics related to air leakage, and an extended version of this model (LBLX) that includes natural ventilation from open windows. As a critical and novel aspect of our AER modeling approach, we performed a cross validation, which included both parameter estimation (i.e., model calibration) and model evaluation, based on daily AER measurements from a subset of 24 study homes on five consecutive days during two seasons. The measured AER varied between 0.09 and 3.48 h(-1) with a median of 0.64 h(-1). For the individual model-predicted and measured AER, the median absolute difference was 29% (0.19 h‑1) for both the LBL and LBLX models. The LBL and LBLX models predicted 59% and 61% of the variance in the AER, respectively. Daily AER predictions for all 213 homes during the three year study (2010-2012) showed considerable house-to-house variations from building leakage differences, and temporal variations from outdoor temperature and wind speed fluctuations. Using this novel approach, NEXUS will be one of the first epidemiology studies to apply calibrated and home-specific AER models, and to include the spatial and temporal variations of AER for over 200 individual homes across multiple years into an exposure assessment in support of improving risk estimates.

Spatial and temporal variability in desert dust and anthropogenic pollution in Iraq, 1997-2010.

PubMed

Chudnovsky, A Alexandra; Koutrakis, Petros; Kostinski, Alex; Proctor, Susan P; Garshick, Eric

2017-01-01

Satellite imaging has emerged as a method for monitoring regional air pollution and detecting areas of high dust concentrations. Unlike ground observations, continuous data monitoring is available with global coverage of terrestrial and atmospheric components. In this study we test the utility of different sources of satellite data to assess air pollution concentrations in Iraq. SeaWiFS and MODIS Deep Blue (DB) aerosol optical depth (AOD) products were evaluated and used to characterize the spatial and temporal pollution levels from the late 1990s through 2010. The AOD and Ångström exponent (an indicator of particle size, since smaller Ångström exponent values reflect a source that includes larger particles) were correlated on 50 × 50 km spatial resolution. Generally, AOD and Ångström exponent were inversely correlated, suggesting a significant contribution of coarse particles from dust storms to AOD maxima. Although the majority of grid cells exhibited this trend, a weaker relationship in other locations suggested an additional contribution of fine particles from anthropogenic sources. Tropospheric NO 2 densities from the OMI satellite were elevated over cities, also consistent with a contribution from anthropogenic sources. Our analysis demonstrates the use of satellite imaging data to estimate relative pollution levels and source contributions in areas of the world where direct measurements are not available. The authors demonstrated how satellite data can be used to characterize exposures to dust and to anthropogenic pollution for future health related studies. This approach is of a great potential to investigate the associations between subject-specific exposures to different pollution sources and their health effects in inaccessible regions and areas where ground monitoring is unavailable.

Spatial and temporal variability in desert dust and anthropogenic pollution in Iraq, 1997–2010

PubMed Central

Chudnovsky, A. Alexandra; Koutrakis, Petros; Kostinski, Alex; Proctor, Susan P.; Garshick, Eric

2016-01-01

Satellite imaging has emerged as a method for monitoring regional air pollution and detecting areas of high dust concentrations. Unlike ground observations, continuous data monitoring is available with global coverage of terrestrial and atmospheric components. In this study we test the utility of different sources of satellite data to assess air pollution concentrations in Iraq. SeaWiFS and MODIS Deep Blue (DB) aerosol optical depth (AOD) products were evaluated and used to characterize the spatial and temporal pollution levels from the late 1990s through 2010. The AOD and Ångström exponent (an indicator of particle size, since smaller Ångström exponent values reflect a source that includes larger particles) were correlated on 50 × 50 km spatial resolution. Generally, AOD and Ångström exponent were inversely correlated, suggesting a significant contribution of coarse particles from dust storms to AOD maxima. Although the majority of grid cells exhibited this trend, a weaker relationship in other locations suggested an additional contribution of fine particles from anthropogenic sources. Tropospheric NO2 densities from the OMI satellite were elevated over cities, also consistent with a contribution from anthropogenic sources. Our analysis demonstrates the use of satellite imaging data to estimate relative pollution levels and source contributions in areas of the world where direct measurements are not available. Implications The authors demonstrated how satellite data can be used to characterize exposures to dust and to anthropogenic pollution for future health related studies. This approach is of a great potential to investigate the associations between subject-specific exposures to different pollution sources and their health effects in inaccessible regions and areas where ground monitoring is unavailable. PMID:28001122

Impacts of ozone air pollution and temperature extremes on crop yields: Spatial variability, adaptation and implications for future food security

NASA Astrophysics Data System (ADS)

Tai, Amos P. K.; Val Martin, Maria

2017-11-01

Ozone air pollution and climate change pose major threats to global crop production, with ramifications for future food security. Previous studies of ozone and warming impacts on crops typically do not account for the strong ozone-temperature correlation when interpreting crop-ozone or crop-temperature relationships, or the spatial variability of crop-to-ozone sensitivity arising from varietal and environmental differences, leading to potential biases in their estimated crop losses. Here we develop an empirical model, called the partial derivative-linear regression (PDLR) model, to estimate the spatial variations in the sensitivities of wheat, maize and soybean yields to ozone exposures and temperature extremes in the US and Europe using a composite of multidecadal datasets, fully correcting for ozone-temperature covariation. We find generally larger and more spatially varying sensitivities of all three crops to ozone exposures than are implied by experimentally derived concentration-response functions used in most previous studies. Stronger ozone tolerance is found in regions with high ozone levels and high consumptive crop water use, reflecting the existence of spatial adaptation and effect of water constraints. The spatially varying sensitivities to temperature extremes also indicate stronger heat tolerance in crops grown in warmer regions. The spatial adaptation of crops to ozone and temperature we find can serve as a surrogate for future adaptation. Using the PDLR-derived sensitivities and 2000-2050 ozone and temperature projections by the Community Earth System Model, we estimate that future warming and unmitigated ozone pollution can combine to cause an average decline in US wheat, maize and soybean production by 13%, 43% and 28%, respectively, and a smaller decline for European crops. Aggressive ozone regulation is shown to offset such decline to various extents, especially for wheat. Our findings demonstrate the importance of considering ozone regulation as well as ozone and climate change adaptation (e.g., selecting heat- and ozone-tolerant cultivars, irrigation) as possible strategies to enhance future food security in response to imminent environmental threats.

FINE SCALE AIR QUALITY MODELING USING DISPERSION AND CMAQ MODELING APPROACHES: AN EXAMPLE APPLICATION IN WILMINGTON, DE

EPA Science Inventory

Characterization of spatial variability of air pollutants in an urban setting at fine scales is critical for improved air toxics exposure assessments, for model evaluation studies and also for air quality regulatory applications. For this study, we investigate an approach that su...

Impacts of terrain attributes on economics and the environment: Costs of reducing potential nitrogen pollution in wheat production

USDA-ARS?s Scientific Manuscript database

The economic cost of achieving desired environmental outcomes from uniform and variable rate fertilizer application technologies depends both on market forces and agronomic properties. Using spatial econometric methods, we analyze the impact of nitrogen fertilizer supply by terrain attribute on the...

A spatial-temporal regression model to predict daily outdoor residential PAH concentrations in an epidemiologic study in Fresno, CA

NASA Astrophysics Data System (ADS)

Noth, Elizabeth M.; Hammond, S. Katharine; Biging, Gregory S.; Tager, Ira B.

2011-05-01

BackgroundPolycyclic aromatic hydrocarbons (PAHs) are generated as a byproduct of combustion, and are associated with respiratory symptoms and increased risk of asthma attacks. ObjectivesTo assign daily, outdoor exposures to participants in the Fresno Asthmatic Children's Environment Study (FACES) using land use regression models for the sum of 4-, 5- and 6-ring PAHs (PAH456). MethodsPAH data were collected daily at the EPA Supersite in Fresno, CA from 10/2000 through 2/2007. From 2/2002 to 2/2003, intensive air pollution sampling was conducted at 83 homes of participants in the FACES study. These measurement data were combined with meteorological data, source data, and other spatial variables to form a land use regression model to assign daily exposure at all FACES homes for all years of the study (2001-2008). ResultsThe model for daily, outdoor residential PAH456 concentrations accounted for 80% of the between-home variability and 18% of the within-home variability. Both temporal and spatial variables were significant in the model. Traffic characteristics and home heating fuel were the main spatial explanatory variables. ConclusionsBecause spatial and temporal distributions of PAHs vary on an intra-urban scale, the location of the child's home within the urban setting plays an important role in the level of exposure that each child has to PAHs.

[Air pollution in an urban area nearby the Rome-Ciampino city airport].

PubMed

Di Menno di Bucchianico, Alessandro; Cattani, Giorgio; Gaeta, Alessandra; Caricchia, Anna Maria; Troiano, Francesco; Sozzi, Roberto; Bolignano, Andrea; Sacco, Fabrizio; Damizia, Sesto; Barberini, Silvia; Caleprico, Roberta; Fabozzi, Tina; Ancona, Carla; Ancona, Laura; Cesaroni, Giulia; Forastiere, Francesco; Gobbi, Gian Paolo; Costabile, Francesca; Angelini, Federico; Barnaba, Francesca; Inglessis, Marco; Tancredi, Francesco; Palumbo, Lorenzo; Fontana, Luca; Bergamaschi, Antonio; Iavicoli, Ivo

2014-01-01

to assess air pollution spatial and temporal variability in the urban area nearby the Ciampino International Airport (Rome) and to investigate the airport-related emissions contribute. the study domain was a 64 km2 area around the airport. Two fifteen-day monitoring campaigns (late spring, winter) were carried out. Results were evaluated using several runs outputs of an airport-related sources Lagrangian particle model and a photochemical model (the Flexible Air quality Regional Model, FARM). both standard and high time resolution air pollutant concentrations measurements: CO, NO, NO2, C6H6, mass and number concentration of several PM fractions. 46 fixed points (spread over the study area) of NO2 and volatile organic compounds concentrations (fifteen days averages); deterministic models outputs. standard time resolution measurements, as well as model outputs, showed the airport contribution to air pollution levels being little compared to the main source in the area (i.e. vehicular traffic). However, using high time resolution measurements, peaks of particles associated with aircraft takeoff (total number concentration and soot mass concentration), and landing (coarse mass concentration) were observed, when the site measurement was downwind to the runway. the frequently observed transient spikes associated with aircraft movements could lead to a not negligible contribute to ultrafine, soot and coarse particles exposure of people living around the airport. Such contribute and its spatial and temporal variability should be investigated when assessing the airports air quality impact.

Comparison of the common spatial interpolation methods used to analyze potentially toxic elements surrounding mining regions.

PubMed

Ding, Qian; Wang, Yong; Zhuang, Dafang

2018-04-15

The appropriate spatial interpolation methods must be selected to analyze the spatial distributions of Potentially Toxic Elements (PTEs), which is a precondition for evaluating PTE pollution. The accuracy and effect of different spatial interpolation methods, which include inverse distance weighting interpolation (IDW) (power = 1, 2, 3), radial basis function interpolation (RBF) (basis function: thin-plate spline (TPS), spline with tension (ST), completely regularized spline (CRS), multiquadric (MQ) and inverse multiquadric (IMQ)) and ordinary kriging interpolation (OK) (semivariogram model: spherical, exponential, gaussian and linear), were compared using 166 unevenly distributed soil PTE samples (As, Pb, Cu and Zn) in the Suxian District, Chenzhou City, Hunan Province as the study subject. The reasons for the accuracy differences of the interpolation methods and the uncertainties of the interpolation results are discussed, then several suggestions for improving the interpolation accuracy are proposed, and the direction of pollution control is determined. The results of this study are as follows: (i) RBF-ST and OK (exponential) are the optimal interpolation methods for As and Cu, and the optimal interpolation method for Pb and Zn is RBF-IMQ. (ii) The interpolation uncertainty is positively correlated with the PTE concentration, and higher uncertainties are primarily distributed around mines, which is related to the strong spatial variability of PTE concentrations caused by human interference. (iii) The interpolation accuracy can be improved by increasing the sample size around the mines, introducing auxiliary variables in the case of incomplete sampling and adopting the partition prediction method. (iv) It is necessary to strengthen the prevention and control of As and Pb pollution, particularly in the central and northern areas. The results of this study can provide an effective reference for the optimization of interpolation methods and parameters for unevenly distributed soil PTE data in mining areas. Copyright © 2018 Elsevier Ltd. All rights reserved.

Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the Swiss SAPALDIA regions.

PubMed

Eeftens, Marloes; Meier, Reto; Schindler, Christian; Aguilera, Inmaculada; Phuleria, Harish; Ineichen, Alex; Davey, Mark; Ducret-Stich, Regina; Keidel, Dirk; Probst-Hensch, Nicole; Künzli, Nino; Tsai, Ming-Yi

2016-04-18

Land Use Regression (LUR) is a popular method to explain and predict spatial contrasts in air pollution concentrations, but LUR models for ultrafine particles, such as particle number concentration (PNC) are especially scarce. Moreover, no models have been previously presented for the lung deposited surface area (LDSA) of ultrafine particles. The additional value of ultrafine particle metrics has not been well investigated due to lack of exposure measurements and models. Air pollution measurements were performed in 2011 and 2012 in the eight areas of the Swiss SAPALDIA study at up to 40 sites per area for NO2 and at 20 sites in four areas for markers of particulate air pollution. We developed multi-area LUR models for biannual average concentrations of PM2.5, PM2.5 absorbance, PM10, PMcoarse, PNC and LDSA, as well as alpine, non-alpine and study area specific models for NO2, using predictor variables which were available at a national level. Models were validated using leave-one-out cross-validation, as well as independent external validation with routine monitoring data. Model explained variance (R(2)) was moderate for the various PM mass fractions PM2.5 (0.57), PM10 (0.63) and PMcoarse (0.45), and was high for PM2.5 absorbance (0.81), PNC (0.87) and LDSA (0.91). Study-area specific LUR models for NO2 (R(2) range 0.52-0.89) outperformed combined-area alpine (R (2)  = 0.53) and non-alpine (R (2)  = 0.65) models in terms of both cross-validation and independent external validation, and were better able to account for between-area variability. Predictor variables related to traffic and national dispersion model estimates were important predictors. LUR models for all pollutants captured spatial variability of long-term average concentrations, performed adequately in validation, and could be successfully applied to the SAPALDIA cohort. Dispersion model predictions or area indicators served well to capture the between area variance. For NO2, applying study-area specific models was preferable over applying combined-area alpine/non-alpine models. Correlations between pollutants were higher in the model predictions than in the measurements, so it will remain challenging to disentangle their health effects.

Ventilation Processes in a Three-Dimensional Street Canyon

NASA Astrophysics Data System (ADS)

Nosek, Štěpán; Kukačka, Libor; Kellnerová, Radka; Jurčáková, Klára; Jaňour, Zbyněk

2016-05-01

The ventilation processes in three different street canyons of variable roof geometry were investigated in a wind tunnel using a ground-level line source. All three street canyons were part of an urban-type array formed by courtyard-type buildings with pitched roofs. A constant roof height was used in the first case, while a variable roof height along the leeward or windward walls was simulated in the two other cases. All street-canyon models were exposed to a neutrally stratified flow with two approaching wind directions, perpendicular and oblique. The complexity of the flow and dispersion within the canyons of variable roof height was demonstrated for both wind directions. The relative pollutant removals and spatially-averaged concentrations within the canyons revealed that the model with constant roof height has higher re-emissions than models with variable roof heights. The nomenclature for the ventilation processes according to quadrant analysis of the pollutant flux was introduced. The venting of polluted air (positive fluctuations of both concentration and velocity) from the canyon increased when the wind direction changed from perpendicular to oblique, irrespective of the studied canyon model. Strong correlations (>0.5) between coherent structures and ventilation processes were found at roof level, irrespective of the canyon model and wind direction. This supports the idea that sweep and ejection events of momentum bring clean air in and detrain the polluted air from the street canyon, respectively.

Health and household air pollution from solid fuel use: the need for improved exposure assessment.

PubMed

Clark, Maggie L; Peel, Jennifer L; Balakrishnan, Kalpana; Breysse, Patrick N; Chillrud, Steven N; Naeher, Luke P; Rodes, Charles E; Vette, Alan F; Balbus, John M

2013-10-01

Nearly 3 billion people worldwide rely on solid fuel combustion to meet basic household energy needs. The resulting exposure to air pollution causes an estimated 4.5% of the global burden of disease. Large variability and a lack of resources for research and development have resulted in highly uncertain exposure estimates. We sought to identify research priorities for exposure assessment that will more accurately and precisely define exposure-response relationships of household air pollution necessary to inform future cleaner-burning cookstove dissemination programs. As part of an international workshop in May 2011, an expert group characterized the state of the science and developed recommendations for exposure assessment of household air pollution. The following priority research areas were identified to explain variability and reduce uncertainty of household air pollution exposure measurements: improved characterization of spatial and temporal variability for studies examining both short- and long-term health effects; development and validation of measurement technology and approaches to conduct complex exposure assessments in resource-limited settings with a large range of pollutant concentrations; and development and validation of biomarkers for estimating dose. Addressing these priority research areas, which will inherently require an increased allocation of resources for cookstove research, will lead to better characterization of exposure-response relationships. Although the type and extent of exposure assessment will necessarily depend on the goal and design of the cookstove study, without improved understanding of exposure-response relationships, the level of air pollution reduction necessary to meet the health targets of cookstove interventions will remain uncertain.

NO2 and Cancer Incidence in Saudi Arabia

PubMed Central

Al-Ahmadi, Khalid; Al-Zahrani, Ali

2013-01-01

Air pollution exposure has been shown to be associated with an increased risk of specific cancers. This study investigated whether the number and incidence of the most common cancers in Saudi Arabia were associated with urban air pollution exposure, specifically NO2. Overall, high model goodness of fit (GOF) was observed in the Eastern, Riyadh and Makkah regions. The significant coefficients of determination (r2) were higher at the regional level (r2 = 0.32–0.71), weaker at the governorate level (r2 = 0.03–0.43), and declined slightly at the city level (r2 = 0.17–0.33), suggesting that an increased aggregated spatial level increased the explained variability and the model GOF. However, the low GOF at the lowest spatial level suggests that additional variation remains unexplained. At different spatial levels, associations between NO2 concentration and the most common cancers were marginally improved in geographically weighted regression (GWR) analysis, which explained both global and local heterogeneity and variations in cancer incidence. High coefficients of determination were observed between NO2 concentration and lung and breast cancer incidences, followed by prostate, bladder, cervical and ovarian cancers, confirming results from other studies. These results could be improved using individual explanatory variables such as environmental, demographic, behavioral, socio-economic, and genetic risk factors. PMID:24192792

High-Resolution Atmospheric Emission Inventory of the Argentine Enery Sector

NASA Astrophysics Data System (ADS)

Puliafito, Salvador Enrique; Castesana, Paula; Allende, David; Ruggeri, Florencia; Pinto, Sebastián; Pascual, Romina; Bolaño Ortiz, Tomás; Fernandez, Rafael Pedro

2017-04-01

This study presents a high-resolution spatially disaggregated inventory (2.5 km x 2.5 km), updated to 2014, of the main emissions from energy activities in Argentina. This inventory was created with the purpose of improving air quality regional models. The sub-sectors considered are public electricity and heat production, cement production, domestic aviation, road and rail transportation, inland navigation, residential and commercial, and fugitive emissions from refineries and fuel expenditure. The pollutants considered include greenhouse gases and ozone precursors: CO2, CH4, NOx, N2O VOC; and other gases specifically related to air quality including PM10, PM2.5, SOx, Pb and POPs. The uncertainty analysis of the inventories resulted in a variability of 3% for public electricity generation, 3-6% in the residential, commercial sector, 6-12% terrestrial transportation sector, 10-20% in oil refining and cement production according to the considered pollutant. Aviation and maritime navigation resulted in a higher variability reaching more than 60%. A comparison with the international emission inventory EDGAR shows disagreements in the spatial distribution of emissions, probably due to the finer resolution of the map presented here, particularly as a result of the use of new spatially disaggregated data of higher resolution that is currently available.

Geospatial Modeling of Asthma Population in Relation to Air Pollution

NASA Technical Reports Server (NTRS)

Kethireddy, Swatantra R.; Tchounwou, Paul B.; Young, John H.; Luvall, Jeffrey C.; Alhamdan, Mohammad

2013-01-01

Current observations indicate that asthma is growing every year in the United States, specific reasons for this are not well understood. This study stems from an ongoing research effort to investigate the spatio-temporal behavior of asthma and its relatedness to air pollution. The association between environmental variables such as air quality and asthma related health issues over Mississippi State are investigated using Geographic Information Systems (GIS) tools and applications. Health data concerning asthma obtained from Mississippi State Department of Health (MSDH) for 9-year period of 2003-2011, and data of air pollutant concentrations (PM2.5) collected from USEPA web resources, and are analyzed geospatially to establish the impacts of air quality on human health specifically related to asthma. Disease mapping using geospatial techniques provides valuable insights into the spatial nature, variability, and association of asthma to air pollution. Asthma patient hospitalization data of Mississippi has been analyzed and mapped using quantitative Choropleth techniques in ArcGIS. Patients have been geocoded to their respective zip codes. Potential air pollutant sources of Interstate highways, Industries, and other land use data have been integrated in common geospatial platform to understand their adverse contribution on human health. Existing hospitals and emergency clinics are being injected into analysis to further understand their proximity and easy access to patient locations. At the current level of analysis and understanding, spatial distribution of Asthma is observed in the populations of Zip code regions in gulf coast, along the interstates of south, and in counties of Northeast Mississippi. It is also found that asthma is prevalent in most of the urban population. This GIS based project would be useful to make health risk assessment and provide information support to the administrators and decision makers for establishing satellite clinics in future.

Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

NASA Astrophysics Data System (ADS)

Li, Lianfa; Wu, Anna H.; Cheng, Iona; Chen, Jiu-Chiuan; Wu, Jun

2017-10-01

Monitoring of fine particulate matter with diameter <2.5 μm (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

USE OF GIS AND ANCILLARY VARIABLES TO PREDICT VOLATILE ORGANIC COMPOUND AND NITROGEN DIOXIDE LEVELS AT UNMONITORED LOCATIONS

EPA Science Inventory

This paper presents a GIS-based regression spatial method, known as land-use regression (LUR) modeling, to estimate ambient air pollution exposures used in the EPA El Paso Children's Health Study. Passive measurements of select volatile organic compounds (VOC) and nitrogen dioxi...

Spatial And Temporal Variability Of Wildland Fire Emissions Over The U.S.

Treesearch

Yongqiang Liu

2003-01-01

Wildland fires release large amounts of particulate matter (PM), CO, S02, NOx,, and Volatile Organic Carbon (VOC), which can cause serious consequence of regional and local air quality (Sandberg et al., 1999). All these components except VOC are the principal pollutants whose emissions are subject to the National Ambient...

GIS Based Distributed Runoff Predictions in Variable Source Area Watersheds Employing the SCS-Curve Number

NASA Astrophysics Data System (ADS)

Steenhuis, T. S.; Mendoza, G.; Lyon, S. W.; Gerard Marchant, P.; Walter, M. T.; Schneiderman, E.

2003-04-01

Because the traditional Soil Conservation Service Curve Number (SCS-CN) approach continues to be ubiquitously used in GIS-BASED water quality models, new application methods are needed that are consistent with variable source area (VSA) hydrological processes in the landscape. We developed within an integrated GIS modeling environment a distributed approach for applying the traditional SCS-CN equation to watersheds where VSA hydrology is a dominant process. Spatial representation of hydrologic processes is important for watershed planning because restricting potentially polluting activities from runoff source areas is fundamental to controlling non-point source pollution. The methodology presented here uses the traditional SCS-CN method to predict runoff volume and spatial extent of saturated areas and uses a topographic index to distribute runoff source areas through watersheds. The resulting distributed CN-VSA method was incorporated in an existing GWLF water quality model and applied to sub-watersheds of the Delaware basin in the Catskill Mountains region of New York State. We found that the distributed CN-VSA approach provided a physically-based method that gives realistic results for watersheds with VSA hydrology.

Does foreign direct investment affect environmental pollution in China's cities? A spatial econometric perspective.

PubMed

Liu, Qianqian; Wang, Shaojian; Zhang, Wenzhong; Zhan, Dongsheng; Li, Jiaming

2018-02-01

Environmental pollution has aroused extensive concern worldwide. Existing literature on the relationship between foreign direct investment (FDI) and environmental pollution has, however, seldom taken into account spatial effects. Addressing this gap, this paper investigated the spatial agglomeration effects and dynamics at work in FDI and environmental pollution (namely, in waste soot and dust, sulfur dioxide, and wastewater) in 285 Chinese cities during the period 2003-2014, using global and local measures of spatial autocorrelation. Our results showed significant spatial autocorrelation in FDI and environmental pollution levels, both of which demonstrated obvious path dependence characteristics in their geographical distribution. A range of agglomeration regions were observed. The high-value and low-value agglomeration areas of FDI were not fully consistent with those of environmental pollution. This result indicates that higher inflows of FDI did not necessarily lead to greater environmental pollution from a geographic perspective, and vice versa. Spatial panel data models were further adopted to explore the impact of FDI on environmental pollution. The results of a spatial lag model (SLM) and a spatial error model (SEM) revealed that the inflow of FDI had distinct effects on different environmental pollutants, thereby confirming the Pollution Heaven Hypothesis and Pollution Halo Hypothesis. The inflow of FDI was found to have reduced waste soot and dust pollution to a certain extent, while it increased the degree of wastewater and sulfur dioxide pollution. The findings set out in this paper hold significant implications for Chinese environmental pollution protection. Copyright © 2017 Elsevier B.V. All rights reserved.

Diversity, composition, and geographical distribution of microbial communities in California salt marsh sediments

USGS Publications Warehouse

Cordova-Kreylos, A. L.; Cao, Y.; Green, P.G.; Hwang, H.-M.; Kuivila, K.M.; LaMontagne, M.G.; Van De Werfhorst, L. C.; Holden, P.A.; Scow, K.M.

2006-01-01

The Pacific Estuarine Ecosystem Indicators Research Consortium seeks to develop bioindicators of toxicant-induced stress and bioavailability for wetland biota. Within this framework, the effects of environmental and pollutant variables on microbial communities were studied at different spatial scales over a 2-year period. Six salt marshes along the California coastline were characterized using phospholipid fatty acid (PLFA) analysis and terminal restriction fragment length polymorphism (TRFLP) analysis. Additionally, 27 metals, six currently used pesticides, total polychlorinated biphenyls and polycyclic aromatic hydrocarbons, chlordanes, nonachlors, dichlorodiphenyldichloroethane, and dichlorodiphenyldichloroethylene were analyzed. Sampling was performed over large (between salt marshes), medium (stations within a marsh), and small (different channel depths) spatial scales. Regression and ordination analysis suggested that the spatial variation in microbial communities exceeded the variation attributable to pollutants. PLFA analysis and TRFLP canonical correspondence analysis (CCA) explained 74 and 43% of the variation, respectively, and both methods attributed 34% of the variation to tidal cycles, marsh, year, and latitude. After accounting for spatial variation using partial CCA, we found that metals had a greater effect on microbial community composition than organic pollutants had. Organic carbon and nitrogen contents were positively correlated with PLFA biomass, whereas total metal concentrations were positively correlated with biomass and diversity. Higher concentrations of heavy metals were negatively correlated with branched PLFAs and positively correlated with methyl- and cyclo-substituted PLFAs. The strong relationships observed between pollutant concentrations and some of the microbial indicators indicated the potential for using microbial community analyses in assessments of the ecosystem health of salt marshes. Copyright ?? 2006, American Society for Microbiology. All Rights Reserved.

Diversity, Composition, and Geographical Distribution of Microbial Communities in California Salt Marsh Sediments

PubMed Central

Córdova-Kreylos, Ana Lucía; Cao, Yiping; Green, Peter G.; Hwang, Hyun-Min; Kuivila, Kathryn M.; LaMontagne, Michael G.; Van De Werfhorst, Laurie C.; Holden, Patricia A.; Scow, Kate M.

2006-01-01

The Pacific Estuarine Ecosystem Indicators Research Consortium seeks to develop bioindicators of toxicant-induced stress and bioavailability for wetland biota. Within this framework, the effects of environmental and pollutant variables on microbial communities were studied at different spatial scales over a 2-year period. Six salt marshes along the California coastline were characterized using phospholipid fatty acid (PLFA) analysis and terminal restriction fragment length polymorphism (TRFLP) analysis. Additionally, 27 metals, six currently used pesticides, total polychlorinated biphenyls and polycyclic aromatic hydrocarbons, chlordanes, nonachlors, dichlorodiphenyldichloroethane, and dichlorodiphenyldichloroethylene were analyzed. Sampling was performed over large (between salt marshes), medium (stations within a marsh), and small (different channel depths) spatial scales. Regression and ordination analysis suggested that the spatial variation in microbial communities exceeded the variation attributable to pollutants. PLFA analysis and TRFLP canonical correspondence analysis (CCA) explained 74 and 43% of the variation, respectively, and both methods attributed 34% of the variation to tidal cycles, marsh, year, and latitude. After accounting for spatial variation using partial CCA, we found that metals had a greater effect on microbial community composition than organic pollutants had. Organic carbon and nitrogen contents were positively correlated with PLFA biomass, whereas total metal concentrations were positively correlated with biomass and diversity. Higher concentrations of heavy metals were negatively correlated with branched PLFAs and positively correlated with methyl- and cyclo-substituted PLFAs. The strong relationships observed between pollutant concentrations and some of the microbial indicators indicated the potential for using microbial community analyses in assessments of the ecosystem health of salt marshes. PMID:16672478

[Distribution Characteristics and Influencing Factors of Nitrate Pollution in Shallow Groundwater of Liujiang Basin].

PubMed

Wang, He; Gu, Hong-biao; Chi, Bao-ming; Li, Hai-jun; Jiang, Hai-ning

2016-05-15

Taking the nitrate in shallow groundwater of Liujiang basin as the research object, a total of 215 groups of shallow groundwater samples were collected during the wet period in July 2014 and the drought period in April 2015 on the basis of groundwater pollution investigation. The characteristics of spatial and temporal variability and the account of nitrate pollution were analyzed based on the model of semivariogram, the geostatistics of ArcGIS and factor analysis, respectively. The results showed that the study region in the southeast was the main nitrate-polluted area, with concentrations of up to 30-120 mg · L⁻¹, in both wet and drought periods, while the nitrate-contaminated area in drought period was about 1. 4 times higher than that in wet period. The spatial distribution of nitrate was primarily influenced by human activities and the geological conditions, and secondarily by Eh, DO, pH and landform conditions. The nitrate concentration was less than 20 mg · L⁻¹ in north. Pollution in local middle area was rather serious, due to human activities and the loss of nitrogen fertilizer in agricultural cultivation; the area to the south, which was confined by impervious boundary, was seriously contaminated, as indicated by the nitrate accumulation effects.

Assessing environmental inequalities in ambient air pollution across urban Australia.

PubMed

Knibbs, Luke D; Barnett, Adrian G

2015-04-01

Identifying inequalities in air pollution levels across population groups can help address environmental justice concerns. We were interested in assessing these inequalities across major urban areas in Australia. We used a land-use regression model to predict ambient nitrogen dioxide (NO2) levels and sought the best socio-economic and population predictor variables. We used a generalised least squares model that accounted for spatial correlation in NO2 levels to examine the associations between the variables. We found that the best model included the index of economic resources (IER) score as a non-linear variable and the percentage of non-Indigenous persons as a linear variable. NO2 levels decreased with increasing IER scores (higher scores indicate less disadvantage) in almost all major urban areas, and NO2 also decreased slightly as the percentage of non-Indigenous persons increased. However, the magnitude of differences in NO2 levels was small and may not translate into substantive differences in health. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spatial associations between social groups and ozone air pollution exposure in the Beijing urban area.

PubMed

Zhao, Xinyi; Cheng, Hongguang; He, Siyuan; Cui, Xiangfen; Pu, Xiao; Lu, Lu

2018-07-01

Few studies have linked social factors to air pollution exposure in China. Unlike the race or minority concepts in western countries, the Hukou system (residential registration system) is a fundamental reason for the existence of social deprivation in China. To assess the differences in ozone (O 3 ) exposure among social groups, especially groups divided by Hukou status, we assigned estimates of O 3 exposure to the latest census data of the Beijing urban area using a kriging interpolation model. We developed simultaneous autoregressive (SAR) models that account for spatial autocorrelation to identify the associations between O 3 exposure and social factors. Principal component regression was used to control the multicollinearity bias as well as explore the spatial structure of the social data. The census tracts (CTs) with higher proportions of persons living alone and migrants with non-local Hukou were characterized by greater exposure to ambient O 3 . The areas with greater proportions of seniors had lower O 3 exposure. The spatial distribution patterns were similar among variables including migrants, agricultural population and household separation (population status with separation between Hukou and actual residences), which fit the demographic characteristics of the majority of migrants. Migrants bore a double burden of social deprivation and O 3 pollution exposure due to city development planning and the Hukou system. Copyright © 2018 Elsevier Inc. All rights reserved.

Analysis of field-scale spatial correlations and variations of soil nutrients using geostatistics.

PubMed

Liu, Ruimin; Xu, Fei; Yu, Wenwen; Shi, Jianhan; Zhang, Peipei; Shen, Zhenyao

2016-02-01

Spatial correlations and soil nutrient variations are important for soil nutrient management. They help to reduce the negative impacts of agricultural nonpoint source pollution. Based on the sampled available nitrogen (AN), available phosphorus (AP), and available potassium (AK), soil nutrient data from 2010, the spatial correlation, was analyzed, and the probabilities of the nutrient's abundance or deficiency were discussed. This paper presents a statistical approach to spatial analysis, the spatial correlation analysis (SCA), which was originally developed for describing heterogeneity in the presence of correlated variation and based on ordinary kriging (OK) results. Indicator kriging (IK) was used to assess the susceptibility of excess of soil nutrients based on crop needs. The kriged results showed there was a distinct spatial variability in the concentration of all three soil nutrients. High concentrations of these three soil nutrients were found near Anzhou. As the distance from the center of town increased, the concentration of the soil nutrients gradually decreased. Spatially, the relationship between AN and AP was negative, and the relationship between AP and AK was not clear. The IK results showed that there were few areas with a risk of AN and AP overabundance. However, almost the entire study region was at risk of AK overabundance. Based on the soil nutrient distribution results, it is clear that the spatial variability of the soil nutrients differed throughout the study region. This spatial soil nutrient variability might be caused by different fertilizer types and different fertilizing practices.

Topsoil pollution forecasting using artificial neural networks on the example of the abnormally distributed heavy metal at Russian subarctic

NASA Astrophysics Data System (ADS)

Tarasov, D. A.; Buevich, A. G.; Sergeev, A. P.; Shichkin, A. V.; Baglaeva, E. M.

2017-06-01

Forecasting the soil pollution is a considerable field of study in the light of the general concern of environmental protection issues. Due to the variation of content and spatial heterogeneity of pollutants distribution at urban areas, the conventional spatial interpolation models implemented in many GIS packages mostly cannot provide appreciate interpolation accuracy. Moreover, the problem of prediction the distribution of the element with high variability in the concentration at the study site is particularly difficult. The work presents two neural networks models forecasting a spatial content of the abnormally distributed soil pollutant (Cr) at a particular location of the subarctic Novy Urengoy, Russia. A method of generalized regression neural network (GRNN) was compared to a common multilayer perceptron (MLP) model. The proposed techniques have been built, implemented and tested using ArcGIS and MATLAB. To verify the models performances, 150 scattered input data points (pollutant concentrations) have been selected from 8.5 km2 area and then split into independent training data set (105 points) and validation data set (45 points). The training data set was generated for the interpolation using ordinary kriging while the validation data set was used to test their accuracies. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. The predictive accuracy of both models was confirmed to be significantly higher than those achieved by the geostatistical approach (kriging). It is shown that MLP could achieve better accuracy than both kriging and even GRNN for interpolating surfaces.

Levels of Viable Enterococci Fecal Indicator Bacteria at a Marine Subtropical Beach: Assessing Temporal and Spatial Variability

EPA Science Inventory

Beach water quality monitoring is an important tool to inform the public of health risks from recreational beach use, as well as to assess the impacts of land-based sources of pollution on coastal ecosystems. Many beach monitoring programs in the US currently utilize a strategy o...

Spatial and temporal distribution of ambient nitric acid and ammonia in the Athabasca Oil Sands Region, Alberta

Treesearch

A. Bytnerowicz; W. Fraczek; S. Schilling; D. Alexander

2010-01-01

Monthly average ambient concentrations of gaseous nitric acid (HNO3) and ammonia (NH3) were monitored at the Athabasca Oils Sands Region (AOSR), Alberta, Canada, between May 2005 and September 2008. Generally, concentrations of both pollutants were elevated and highly variable in space and time. The highest atmospheric...

Multivariate-Statistical Assessment of Heavy Metals for Agricultural Soils in Northern China

PubMed Central

Yang, Pingguo; Yang, Miao; Mao, Renzhao; Shao, Hongbo

2014-01-01

The study evaluated eight heavy metals content and soil pollution from agricultural soils in northern China. Multivariate and geostatistical analysis approaches were used to determine the anthropogenic and natural contribution of soil heavy metal concentrations. Single pollution index and integrated pollution index could be used to evaluate soil heavy metal risk. The results show that the first factor explains 27.3% of the eight soil heavy metals with strong positive loadings on Cu, Zn, and Cd, which indicates that Cu, Zn, and Cd are associated with and controlled by anthropic activities. The average value of heavy metal is lower than the second grade standard values of soil environmental quality standards in China. Single pollution index is lower than 1, and the Nemerow integrated pollution index is 0.305, which means that study area has not been polluted. The semivariograms of soil heavy metal single pollution index fitted spherical and exponential models. The variable ratio of single pollution index showed moderately spatial dependence. Heavy metal contents showed relative safety in the study area. PMID:24892058

Microplastic and tar pollution on three Canary Islands beaches: An annual study.

PubMed

Herrera, A; Asensio, M; Martínez, I; Santana, A; Packard, T; Gómez, M

2018-04-01

Marine debris accumulation was analyzed from three exposed beaches of the Canary Islands (Lambra, Famara and Las Canteras). Large microplastics (1-5mm), mesoplastics (5-25mm) and tar pollution were assessed twice a month for a year. There was great spatial and temporal variability in the Canary Island coastal pollution. Seasonal patterns differed at each location, marine debris concentration depended mainly of local-scale wind and wave conditions. The most polluted beach was Lambra, a remote beach infrequently visited. The types of debris found were mainly preproduction resin pellets, plastic fragments and tar, evidencing that pollution was not of local origin, but it cames from the open sea. The levels of pollution were similar to those of highly industrialized and contaminated regions. This study corroborates that the Canary Islands are an area of accumulation of microplastics and tar rafted from the North Atlantic Ocean by the southward flowing Canary Current. Copyright © 2017 Elsevier Ltd. All rights reserved.

Variability of intra-urban exposure to particulate matter and CO from Asian-type community pollution sources

NASA Astrophysics Data System (ADS)

Lung, Shih-Chun Candice; Hsiao, Pao-Kuei; Wen, Tzu-Yao; Liu, Chun-Hu; Fu, Chi Betsy; Cheng, Yu-Ting

2014-02-01

Asian residential communities are usually dotted with various spot pollution sources (SPS), such as restaurants, temples, and home factories, with traffic arteries passing through, resulting in higher intra-urban pollution variability compared with their western counterparts. Thus, it is important to characterize spatial variability of pollutant levels in order to assess accurately residents' exposures in their communities. The objectives of this study are to assess the actual pollutant levels and variability within an Asian urban area and to evaluate the influence of vehicle emission and various SPS on the exposure levels within communities. Real-time monitoring was conducted for a total of 123 locations for particulate matter (PM) and CO in Taipei metropolitan, Taiwan. The mean concentrations for PM1, PM2.5, PM10, and CO are 29.8 ± 22.7, 36.0 ± 25.5, 61.9 ± 35.0 μg m-3 and 4.0 ± 2.5 ppm, respectively. The mean values of PM1/PM2.5 and PM2.5/PM10 are 0.80 ± 0.10 and 0.57 ± 0.15, respectively. PM and CO levels at locations near SPS could be increased by 3.5-4.9 times compared with those at background locations. Regression results show that restaurants contribute significantly 6.18, 6.33, 7.27 μg m-3, and 1.64 ppm to community PM1, PM2.5, PM10, and CO levels, respectively; while the contribution from temples are 13.2, 15.1, and 17.2 μg m-3 for PM1, PM2.5 and PM10, respectively. Additionally, construction sites elevate nearby PM10 levels by 14.2 μg m-3. At bus stops and intersections, vehicle emissions increased PM1 and PM2.5 levels by 5 μg m-3. These results demonstrate significant contribution of community sources to air pollution, and thus the importance of assessing intra-community variability in Asian cities for air pollution and health studies. The methodology used is applicable to other Asian countries with similar features.

Factors affecting pollutant concentrations in the near-road environment

NASA Astrophysics Data System (ADS)

Baldwin, Nichole; Gilani, Owais; Raja, Suresh; Batterman, Stuart; Ganguly, Rajiv; Hopke, Philip; Berrocal, Veronica; Robins, Thomas; Hoogterp, Sarah

2015-08-01

An improved understanding of traffic-related air pollutants is needed to estimate exposures and adverse health impacts in traffic corridors and near-road environments. In this study, concentrations of black carbon (BC), nitrogen oxides (NO, NO2, NOx), sulfur dioxide (SO2), and particulate matter (PM2.5, PM10, ultrafine particles, and accumulation mode particles, AMP) were measured using a mobile air pollutant laboratory along nine transects across major roads in Detroit, MI in winter 2012. Repeated measurements were taken during rush-hour periods at sites in residential neighborhoods located 50-500 m from both sides of the road. Concentration gradients attributable to on-road emissions were estimated by accounting for traffic volume and mix, wind speed, wind direction, and background concentrations. BC, NO, NOx, and UFP had the strongest gradients, and elevated concentrations of NOx, NO2, PM2.5 and PM10, as well as decreased particle size, were found at the 50 m sites compared to background levels. Exponential models incorporating effects of road size, wind speed, and up- and downwind distance explained from 31 to 53% of the variability in concentration gradients for BC, NO, NOx, UFP and particle size. The expected concentration increments 50 m from the study roads were 17.0 ppb for NO, 17.7 ppb for NOx, 2245 particles/cm3 for UFP, and 0.24 μg/m3 for BC, and the expected distance to decrease increments by half was 89-129 m in the downwind direction, and 14-20 m in the upwind direction. While accounting for portion of the temporal and spatial variability across transects and measurement periods, these results highlight the influence of road-to-road differences and other locally-varying factors important in urban and industrial settings. The study demonstrates a methodology to quantify near-road concentrations and influences on these concentrations while accounting for temporal and spatial variability, and it provides information useful for estimating exposures of traffic-related air pollutants in urban environments.

Health risks from large-scale water pollution: trends in Central Asia.

PubMed

Törnqvist, Rebecka; Jarsjö, Jerker; Karimov, Bakhtiyor

2011-02-01

Limited data on the pollution status of spatially extensive water systems constrain health-risk assessments at basin-scales. Using a recipient measurement approach in a terminal water body, we show that agricultural and industrial pollutants in groundwater-surface water systems of the Aral Sea Drainage Basin (covering the main part of Central Asia) yield cumulative health hazards above guideline values in downstream surface waters, due to high concentrations of copper, arsenic, nitrite, and to certain extent dichlorodiphenyltrichloroethane (DDT). Considering these high-impact contaminants, we furthermore perform trend analyses of their upstream spatial-temporal distribution, investigating dominant large-scale spreading mechanisms. The ratio between parent DDT and its degradation products showed that discharges into or depositions onto surface waters are likely to be recent or ongoing. In river water, copper concentrations peak during the spring season, after thawing and snow melt. High spatial variability of arsenic concentrations in river water could reflect its local presence in the top soil of nearby agricultural fields. Overall, groundwaters were associated with much higher health risks than surface waters. Health risks can therefore increase considerably, if the downstream population must switch to groundwater-based drinking water supplies during surface water shortage. Arid regions are generally vulnerable to this problem due to ongoing irrigation expansion and climate changes. Copyright © 2010 Elsevier Ltd. All rights reserved.

Interaction of Soil Heavy Metal Pollution with Industrialisation and the Landscape Pattern in Taiyuan City, China

PubMed Central

Liu, Yong; Su, Chao; Zhang, Hong; Li, Xiaoting; Pei, Jingfei

2014-01-01

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning. PMID:25251460

Interaction of soil heavy metal pollution with industrialisation and the landscape pattern in Taiyuan city, China.

PubMed

Liu, Yong; Su, Chao; Zhang, Hong; Li, Xiaoting; Pei, Jingfei

2014-01-01

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning.

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

PubMed Central

Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

2015-01-01

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas. PMID:26426030

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.

PubMed

Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

2015-09-29

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

Evaluating agricultural nonpoint-source pollution using integrated geographic information systems and hydrologic/water quality model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tim, U.S.; Jolly, R.

1994-01-01

Considerable progress has been made in developing physically based, distributed parameter, hydrologic/water quality (HIWQ) models for planning and control of nonpoint-source pollution. The widespread use of these models is often constrained by the excessive and time-consuming input data demands and the lack of computing efficiencies necessary for iterative simulation of alternative management strategies. Recent developments in geographic information systems (GIS) provide techniques for handling large amounts of spatial data for modeling nonpoint-source pollution problems. Because a GIS can be used to combine information from several sources to form an array of model input data and to examine any combinations ofmore » spatial input/output data, it represents a highly effective tool for HiWQ modeling. This paper describes the integration of a distributed-parameter model (AGNPS) with a GIS (ARC/INFO) to examine nonpoint sources of pollution in an agricultural watershed. The ARC/INFO GIS provided the tools to generate and spatially organize the disparate data to support modeling, while the AGNPS model was used to predict several water quality variables including soil erosion and sedimentation within a watershed. The integrated system was used to evaluate the effectiveness of several alternative management strategies in reducing sediment pollution in a 417-ha watershed located in southern Iowa. The implementation of vegetative filter strips and contour buffer (grass) strips resulted in a 41 and 47% reduction in sediment yield at the watershed outlet, respectively. In addition, when the integrated system was used, the combination of the above management strategies resulted in a 71% reduction in sediment yield. In general, the study demonstrated the utility of integrating a simulation model with GIS for nonpoini-source pollution control and planning. Such techniques can help characterize the diffuse sources of pollution at the landscape level. 52 refs., 6 figs., 1 tab.« less

Deployment Considerations for Low-cost Air Quality Sensor Networks; Examining Spatial Variability of Gas-Phase Pollutants Around a Building in Los Angeles

NASA Astrophysics Data System (ADS)

Collier-Oxandale, A. M.; Hannigan, M.; Casey, J. G.; Johnston, J.; Coffey, E.; Thorson, J.

2017-12-01

The field of low-cost air quality sensing technologies is growing rapidly through the continual development of new sensors, increased research into sensor performance, and more and more community groups utilizing sensors to investigate local issues. However, as this technology is still in an exploratory phase, there are few `best-practices' available to serve as guidelines for these projects and the standardization of some procedures could benefit the research community as a whole. For example, deployment considerations such as where and how to place a monitor at a given location are often determined by accessibility and safety, power-requirements, and what is an ideal for sampling the target pollutant. Using data from multiple gas-phase sensors, we will examine the importance of siting considerations for low-cost monitoring systems. During a sampling campaign in Los Angeles, a subset of monitors was deployed at one field site to explore the variability in air quality sensor data around a single building. The site is a three story, multi-family housing unit in a primarily residential neighborhood that is near two major roadways and other potential sources of pollution. Five low-cost monitors were co-located prior to and following the field deployment. During the approximately 2.5-month deployment, these monitors were placed at various heights above street level, on different sides of the building, and on the roof. In our analysis, we will examine how monitor placement affects a sensor's ability to detect local verses more regional trends and how this building-scale spatial variability changes over time. Additionally, examining data from VOC sensors (quantified for methane and total non-methane hydrocarbon signals) and O3 sensors will allow us to compare the variability of primary and secondary pollutants. An outcome of this analysis may include guidelines or `best practices' for siting sensors that could aid in ensuring the collection of high quality field data. These may be particularly useful in community-based projects where monitor siting is typically a collaborative process.

Assessment of semi-volatile organic compounds in drinking water sources in Jiangsu, China.

PubMed

Wu, Yifeng; Jia, Yongzhi; Lu, Xiwu

2013-08-01

Many xenobiotic compounds, especially organic pollutants in drinking water, can cause threats to human health and natural ecosystems. The ability to predict the level of pollutants and identify their source is crucial for the design of pollutant risk reduction plans. In this study, 25 semi-volatile organic compounds (SVOCs) were assessed at 16 monitoring sites of drinking water sources in Jiangsu, east China, to evaluate water quality conditions and source of pollutants. Four multivariate statistical techniques were used for this analysis. The correlation test indicated that 25 SVOCs parameters variables had a significant spatial variability (P<0.05). The results of correlation analysis, principal component analysis (PCA) and cluster analysis (CA) suggested that at least four sources, i.e., agricultural residual pesticides, industrial sewage, water transportation vehicles and miscellaneous sources, were responsible for the presence of SVOCs in the drinking water sites examined, accounting for 89.6% of the total variance in the dataset. The analysis of site similarity showed that 16 sites could be divided into high, moderate, and low pollutant level groups at (D(link)/D(max))×25<10, and each group had primary typical SVOCs. These results provide useful information for developing appropriate strategies for contaminants control in drinking water sources. Copyright © 2013 Elsevier Inc. All rights reserved.

Validation of WRF-Chem air quality simulations in the Netherlands at high resolution

NASA Astrophysics Data System (ADS)

Hilboll, A.; Lowe, D.; Kuenen, J. J. P.; Denier Van Der Gon, H.; Vrekoussis, M.

2017-12-01

Air pollution is the single most important environmental hazard for publichealth, and especially nitrogen dioxide (NO2) plays a key role in air qualityresearch. With the aim of improving the quality and reproducibility ofmeasurements of NO2 vertical distribution from MAX-DOAS instruments, theCINDI-2 campaign was held in Cabauw (NL) in September 2016.The measurement site was rural, but surrounded by several major pollutioncenters. Due to this spatial heterogeneity of emissions, as well as themeteorological conditions, high spatial and temporal variability in NO2 mixingratios were observed.Air quality models used in the analysis of the measured data must have highspatial resolution in order to resolve this fine spatial structure. Thisremains a challenge even today, mostly due to the uncertainties and largespatial heterogeneity of emission data, and the need to parameterize small-scaleprocesses.In this study, we use the state-of-the-art version 3.9 of the Weather Researchand Forecasting Model with Chemistry (WRF-Chem) to simulate air pollutantconcentrations over the Netherlands, to facilitate the analysis of the CINDI-2NO2 measurements. The model setup contains three nested domains withhorizontal resolutions of 15, 3, and 1 km. Anthropogenic emissions are takenfrom the TNO-MACC III inventory and, where available, from the Dutch PollutantRelease and Transfer Register (Emissieregistratie), at a spatial resolution of 7and 1 km, respectively. We use the Common Reactive Intermediates gas-phasechemical mechanism (CRIv2-R5) with the MOSAIC aerosol module.The high spatial resolution of model and emissions will allow us to resolve thestrong spatial gradients in the NO2 concentrations measured during theCINDI-2 campaign, allowing for an unprecedented level of detail in theanalysis of individual pollution sources.

Air Pollution from Livestock Farms Is Associated with Airway Obstruction in Neighboring Residents.

PubMed

Borlée, Floor; Yzermans, C Joris; Aalders, Bernadette; Rooijackers, Jos; Krop, Esmeralda; Maassen, Catharina B M; Schellevis, François; Brunekreef, Bert; Heederik, Dick; Smit, Lidwien A M

2017-11-01

Livestock farm emissions may not only affect respiratory health of farmers but also of neighboring residents. To explore associations between spatial and temporal variation in pollutant emissions from livestock farms and lung function in a general, nonfarming, rural population in the Netherlands. We conducted a cross-sectional study in 2,308 adults (age, 20-72 yr). A pulmonary function test was performed measuring prebronchodilator and post-bronchodilator FEV 1 , FVC, FEV 1 /FVC, and maximum mid-expiratory flow (MMEF). Spatial exposure was assessed as (1) number of farms within 500 m and 1,000 m of the home, (2) distance to the nearest farm, and (3) modeled annual average fine dust emissions from farms within 500 m and 1,000 m of the home address. Temporal exposure was assessed as week-average ambient particulate matter <10 μm in diameter and ammonia (NH 3 ) concentrations before lung function measurements. Data were analyzed with generalized additive models (smoothing). A negative association was found between the number of livestock farms within a 1,000-m buffer from the home address and MMEF, which was more pronounced in participants without atopy. No associations were found with other spatial exposure variables. Week-average particulate matter <10 μm in diameter and NH 3 levels were negatively associated with FEV 1 , FEV 1 /FVC, and MMEF. In a two-pollutant model, only NH 3 remained associated. A 25-μg/m 3 increase in NH 3 was associated with a 2.22% lower FEV 1 (95% confidence interval, -3.69 to -0.74), FEV 1 /FVC of -1.12% (-1.96 to -0.28), and MMEF of -5.67% (-8.80 to -2.55). Spatial and temporal variation in livestock air pollution emissions are associated with lung function deficits in nonfarming residents.

A Low Cost High Density Sensor Network for Air Quality at London Heathrow Airport

NASA Astrophysics Data System (ADS)

Bright, V.; Mead, M. I.; Popoola, O. A.; Baron, R. P.; Saffell, J.; Stewart, G.; Kaye, P.; Jones, R.

2012-12-01

Atmospheric composition within urban areas has a direct effect on the air quality of an environment in which a large majority of people live and work. Atmospheric pollutants including ozone (O3), nitrogen dioxide (NO2), volatile organic compounds (VOCs) and particulate matter (PM) can have a significant effect on human health. As such it is important to determine the potential exposure of individuals to these atmospheric constituents and investigate the processes that lead to the degradation of air quality within the urban environment. Whilst modelled pollutant levels on the local scale often suggest high degrees of spatial and temporal variability, the relatively sparse fixed site automated urban networks only provide low spatial resolution data that do not appear adequate in detecting such small scale variability. In this paper we demonstrate that measurements can now be made using networks of low-cost sensors that utilise a variety of techniques, including electrochemical and optical, to measure concentrations of atmospheric species. Once equipped with GPS and GPRS to determine position and transmit data respectively, these networks have the potential to provide valuable insights into pollutant variability inherent on the local or micro-scale. The methodology has been demonstrated successfully in field campaigns carried out in cities including London and Valencia, and is now being deployed as part of the Sensor Networks for Air Quality currently deployed at London Heathrow airport (SNAQ-Heathrow) which is outlined in the partner paper presented by Mead et al. (this conference). The SNAQ-Heathrow network of 50 sensor nodes will provide an unprecedented data set that includes measurements of O3, NO, NO2, CO, CO2, SO2, total VOCs, size-speciated PM as well as meteorological variables that include temperature, relative humidity, wind speed and direction. This network will provide high temporal (20 second intervals) and spatial (50 sites within the airport area) resolution data over a 12 month period with data transmitted back to a server every 2 hours. In this paper we present the data capture and storage, data accessibility, data mining and visualisation techniques applied to the measurements of the SNAQ Heathrow high density sensor network, the preliminary results of which provide an insight into the potential use of such networks in characterising air quality, emissions and validating dispersion models on local scales. We also present a web based interface developed for the sensor network that allows users to access archived data and assess meteorological conditions, atmospheric dispersion, pollutant levels and emission rates.

Spatial and temporal associations of road traffic noise and air pollution in London: Implications for epidemiological studies.

PubMed

Fecht, Daniela; Hansell, Anna L; Morley, David; Dajnak, David; Vienneau, Danielle; Beevers, Sean; Toledano, Mireille B; Kelly, Frank J; Anderson, H Ross; Gulliver, John

2016-03-01

Road traffic gives rise to noise and air pollution exposures, both of which are associated with adverse health effects especially for cardiovascular disease, but mechanisms may differ. Understanding the variability in correlations between these pollutants is essential to understand better their separate and joint effects on human health. We explored associations between modelled noise and air pollutants using different spatial units and area characteristics in London in 2003-2010. We modelled annual average exposures to road traffic noise (LAeq,24h, Lden, LAeq,16h, Lnight) for ~190,000 postcode centroids in London using the UK Calculation of Road Traffic Noise (CRTN) method. We used a dispersion model (KCLurban) to model nitrogen dioxide, nitrogen oxide, ozone, total and the traffic-only component of particulate matter ≤2.5μm and ≤10μm. We analysed noise and air pollution correlations at the postcode level (~50 people), postcodes stratified by London Boroughs (~240,000 people), neighbourhoods (Lower layer Super Output Areas) (~1600 people), 1km grid squares, air pollution tertiles, 50m, 100m and 200m in distance from major roads and by deprivation tertiles. Across all London postcodes, we observed overall moderate correlations between modelled noise and air pollution that were stable over time (Spearman's rho range: |0.34-0.55|). Correlations, however, varied considerably depending on the spatial unit: largest ranges were seen in neighbourhoods and 1km grid squares (both Spearman's rho range: |0.01-0.87|) and was less for Boroughs (Spearman's rho range: |0.21-0.78|). There was little difference in correlations between exposure tertiles, distance from road or deprivation tertiles. Associations between noise and air pollution at the relevant geographical unit of analysis need to be carefully considered in any epidemiological analysis, in particular in complex urban areas. Low correlations near roads, however, suggest that independent effects of road noise and traffic-related air pollution can be reliably determined within London. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Spatiotemporal Patterns of Urban Trace Gases and Pollutants Observed with a Light Rail Vehicle Platform in Salt Lake City, UT

NASA Astrophysics Data System (ADS)

Mitchell, L.; Crosman, E.; Fasoli, B.; Leclair-Marzolf, L.; Jacques, A.; Horel, J.; Lin, J. C.; Bowling, D. R.; Ehleringer, J. R.

2015-12-01

Urban environments are characterized by both spatial complexity and temporal variability, each of which present challenges for measurement strategies aimed at constraining estimates of greenhouse gas emissions and air quality. To address these challenges we initiated a project in December 2014 to measure trace species (CO2, CH4, O3, and Particulate Matter) by way of a Utah Transit Authority (UTA) light rail vehicle whose route traverses the Salt Lake Valley in Utah on an hourly basis, retracing the same route through commercial, residential, suburban, and rural typologies. Light rail vehicles present advantages as a measurement platform, including the absence of in-situ fossil fuel emissions, repeated transects across a urban region that provides both spatial and temporal information, and relatively low operating costs. We present initial results from the first year of operations including the spatiotemporal patterns of greenhouse gases and pollutants across Salt Lake City, UT with an emphasis on criteria pollutants, identification of sources, and future applications of this measurement platform.

Schools, Air Pollution, and Active Transportation: An Exploratory Spatial Analysis of Calgary, Canada.

PubMed

Bertazzon, Stefania; Shahid, Rizwan

2017-07-25

An exploratory spatial analysis investigates the location of schools in Calgary (Canada) in relation to air pollution and active transportation options. Air pollution exhibits marked spatial variation throughout the city, along with distinct spatial patterns in summer and winter; however, all school locations lie within low to moderate pollution levels. Conversely, the study shows that almost half of the schools lie in low walkability locations; likewise, transitability is low for 60% of schools, and only bikability is widespread, with 93% of schools in very bikable locations. School locations are subsequently categorized by pollution exposure and active transportation options. This analysis identifies and maps schools according to two levels of concern: schools in car-dependent locations and relatively high pollution; and schools in locations conducive of active transportation, yet exposed to relatively high pollution. The findings can be mapped and effectively communicated to the public, health practitioners, and school boards. The study contributes with an explicitly spatial approach to the intra-urban public health literature. Developed for a moderately polluted city, the methods can be extended to more severely polluted environments, to assist in developing spatial public health policies to improve respiratory outcomes, neurodevelopment, and metabolic and attention disorders in school-aged children.

Examination of spotted sand bass (Paralabrax maculatofasciatus) pollutant bioaccumulation in San Diego Bay, San Diego, California

PubMed Central

2013-01-01

The spotted sand bass (Paralabrax maculatofasciatus) is an important recreational sport and subsistence food fish within San Diego Bay, a large industrialized harbor in San Diego, California. Despite this importance, few studies examining the species life history relative to pollutant tissue concentrations and the consumptive fishery exist. This study utilized data from three independent spotted sand bass studies from 1989 to 2002 to investigate PCB, DDT, and mercury tissue concentrations relative to spotted sand bass age and growth in San Diego Bay, with subsequent comparisons to published pollutant advisory levels and fishery regulations for recreational and subsistence consumption of the species. Subsequent analysis focused on examining temporal and spatial differences for different regions of San Diego Bay. Study results for growth confirmed previous work, finding the species to exhibit highly asymptotic growth, making tissue pollutant concentrations at initial take size difficult if not impossible to predict. This was corroborated by independent tissue concentration results for mercury, which found no relationship between fish size and pollutant bioaccumulation observed. However, a positive though highly variable relationship was observed between fish size and PCB tissue concentration. Despite these findings, a significant proportion of fish exhibited pollutant levels above recommended state recreational angler consumption advisory levels for PCBs and mercury, especially for fish above the minimum take size, making the necessity of at-size predictions less critical. Lastly, no difference in tissue concentration was found temporally or spatially within San Diego Bay. PMID:24282672

Examination of spotted sand bass (Paralabrax maculatofasciatus) pollutant bioaccumulation in San Diego Bay, San Diego, California.

PubMed

Loflen, Chad L

2013-01-01

The spotted sand bass (Paralabrax maculatofasciatus) is an important recreational sport and subsistence food fish within San Diego Bay, a large industrialized harbor in San Diego, California. Despite this importance, few studies examining the species life history relative to pollutant tissue concentrations and the consumptive fishery exist. This study utilized data from three independent spotted sand bass studies from 1989 to 2002 to investigate PCB, DDT, and mercury tissue concentrations relative to spotted sand bass age and growth in San Diego Bay, with subsequent comparisons to published pollutant advisory levels and fishery regulations for recreational and subsistence consumption of the species. Subsequent analysis focused on examining temporal and spatial differences for different regions of San Diego Bay. Study results for growth confirmed previous work, finding the species to exhibit highly asymptotic growth, making tissue pollutant concentrations at initial take size difficult if not impossible to predict. This was corroborated by independent tissue concentration results for mercury, which found no relationship between fish size and pollutant bioaccumulation observed. However, a positive though highly variable relationship was observed between fish size and PCB tissue concentration. Despite these findings, a significant proportion of fish exhibited pollutant levels above recommended state recreational angler consumption advisory levels for PCBs and mercury, especially for fish above the minimum take size, making the necessity of at-size predictions less critical. Lastly, no difference in tissue concentration was found temporally or spatially within San Diego Bay.

The identification of 'hotspots' of heavy metal pollution in soil-rice systems at a regional scale in eastern China.

PubMed

Li, Wanlu; Xu, Binbin; Song, Qiujin; Liu, Xingmei; Xu, Jianming; Brookes, Philip C

2014-02-15

Chinese agricultural soils and crops are suffering from increasing damage from heavy metals, which are introduced from various pollution sources including agriculture, traffic, mining and especially the flourishing private metal recycling industry. In this study, 219 pairs of rice grain and corresponding soil samples were collected from Wenling in Zhejiang Province to identify the spatial relationship and pollution hotspots of Cd, Cu, Ni and Zn in the soil-rice system. The mean soil concentrations of heavy metals were 0.316 mg kg(-1) for Cd, 47.3 mg kg(-1) for Cu, 31.7 mg kg(-1) for Ni and 131 mg kg(-1) for Zn, and the metal concentrations in rice grain were 0.132 mg kg(-1) for Cd, 2.46 mg kg(-1) for Cu, 0.223 mg kg(-1) for Ni and 17.4 mg kg(-1) for Zn. The coefficient of variability (CV) of soil Cd, Cu and rice Cd were 147%, 146% and 180%, respectively, indicating an extensive variability. While the CVs of other metals ranged from 23.4% to 84.3% with a moderate variability. Kriging interpolation procedure and the Local Moran's I index detected the locations of pollution hotspots of these four metals. Cd and Cu had a very similar spatial pattern, with contamination hotspots located simultaneously in the northwestern part of the study area, and there were obvious hotspots for soil Zn in the north area, while in the northeast for soil Ni. The existence of hotspots may be due to industrialization and other anthropogenic activities. An Enrichment Index (EI) was employed to measure the uptake of heavy metals by rice. The results indicated that the accumulation and availability of heavy metals in the soil-rice system may be influenced by both soil heavy metal concentrations and soil physico-chemical properties. Cross-correlograms quantitatively illustrated that EIs were significantly correlated with soil properties. Soil pH and organic matter were the most important factors controlling the uptake of heavy metals by rice. As results, positive measures should be taken into account to control soil pollution and to curtail metal contamination to the food chain in the areas of Wenling, which were the most polluted by toxic metals. Copyright © 2013 Elsevier B.V. All rights reserved.

How robust are the estimated effects of air pollution on health? Accounting for model uncertainty using Bayesian model averaging.

PubMed

Pannullo, Francesca; Lee, Duncan; Waclawski, Eugene; Leyland, Alastair H

2016-08-01

The long-term impact of air pollution on human health can be estimated from small-area ecological studies in which the health outcome is regressed against air pollution concentrations and other covariates, such as socio-economic deprivation. Socio-economic deprivation is multi-factorial and difficult to measure, and includes aspects of income, education, and housing as well as others. However, these variables are potentially highly correlated, meaning one can either create an overall deprivation index, or use the individual characteristics, which can result in a variety of pollution-health effects. Other aspects of model choice may affect the pollution-health estimate, such as the estimation of pollution, and spatial autocorrelation model. Therefore, we propose a Bayesian model averaging approach to combine the results from multiple statistical models to produce a more robust representation of the overall pollution-health effect. We investigate the relationship between nitrogen dioxide concentrations and cardio-respiratory mortality in West Central Scotland between 2006 and 2012. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Testing a high resolution CO2 and CO emission inventory against atmospheric observations in Salt Lake City, Utah for policy applications

NASA Astrophysics Data System (ADS)

Mendoza, D. L.; Lin, J. C.; Mitchell, L.; Gurney, K. R.; Patarasuk, R.; Mallia, D. V.; Fasoli, B.; Bares, R.; Catharine, D.; O'Keeffe, D.; Song, Y.; Huang, J.; Horel, J.; Crosman, E.; Hoch, S.; Ehleringer, J. R.

2016-12-01

We address the need for robust highly-resolved emissions and trace gas concentration data required for planning purposes and policy development aimed at managing pollutant sources. Adverse health effects resulting from urban pollution exposure are the result of proximity to emission sources and atmospheric mixing, necessitating models with high spatial and temporal resolution. As urban emission sources co-emit carbon dioxide (CO2) and criteria air pollutants (CAPs), efforts to reduce specific pollutants would synergistically reduce others. We present a contemporary (2010-2015) emissions inventory and modeled CO2 and carbon monoxide (CO) concentrations for Salt Lake County, Utah. We compare emissions transported by a dispersion model against stationary measurement data and present a systematic quantification of uncertainties. The emissions inventory for CO2 is based on the Hestia emissions data inventory that resolves emissions at hourly, building and road-link resolutions, as well as on an hourly gridded scale. The emissions were scaled using annual Energy Information Administration (EIA) fuel consumption data. We derived a CO emissions inventory using methods similar to Hestia, downscaling total county emissions from the 2011 Environmental Protection Agency's (EPA) National Emissions Inventory (NEI). The gridded CO emissions were compared against the Hestia CO2 gridded data to characterize spatial similarities and differences between them. Correlations were calculated at multiple scales of aggregation. The Stochastic Time-Inverted Lagrangian Trasport (STILT) dispersion model was used to transport emissions and estimate pollutant concentrations at an hourly resolution. Modeled results were compared against stationary measurements in the Salt Lake County area. This comparison highlights spatial locations and hours of high variability and uncertainty. Sensitivity to biological fluxes as well as to specific economic sectors was tested by varying their contributions to modeled concentrations and calibrating their emissions.

Tropospheric emissions: monitoring of pollution (TEMPO)

NASA Astrophysics Data System (ADS)

Chance, Kelly; Liu, Xiong; Suleiman, Raid M.; Flittner, David E.; Al-Saadi, Jassim; Janz, Scott J.

2013-09-01

TEMPO was selected in 2012 by NASA as the first Earth Venture Instrument, for launch circa 2018. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO measures from Mexico City to the Canadian tar sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (~2 km N/S×4.5 km E/W at 36.5°N, 100°W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with European Sentinel-4 and Korean GEMS.

High Spatial Resolution of Atmospheric Particle Mixing State and Its Links to Particle Evolution in a Metropolitan Area

NASA Astrophysics Data System (ADS)

Ye, Q.; Gu, P.; Li, H.; Robinson, E. S.; Apte, J.; Sullivan, R. C.; Robinson, A. L.; Presto, A. A.; Donahue, N.

2017-12-01

Traditional air quality studies in urban areas have mostly relied on very few monitoring locations either at urban background sites or at roadside sites.However, air pollution is highly complex and dynamic and will undergo complicated transformations. Therefore, results from one or two monitoring sites may not be sufficient to address the spatial gradients of pollutants and their evolution after atmosphere processing on a local scale. Our study, as part of the Center for Air, Climate, and Energy Solutions, performed stratified mobile sampling of atmospheric particulate matter with high spatial resolution to address intra-city variability of atmospheric particle composition and mixing state. A suite of comprehensive real-time instrumentations including a state-of-the-art aerosol mass spectrometer with single particle measurement capability are deployed on the mobile platform. Our sampling locations covered a wide variety of places with substantial differences in emissions and land use types including tunnels, inter-state highways, commercial areas, residential neighborhood, parks, as well as locations upwind and downwind of the city center. Our results show that particles from traffic emissions and restaurant cookings are two major contributors to fresh particles in the urban environment. In addition, there are large spatial variabilities of source-specific particles and we identify the relevant physicochemical processes governing transformation of particle composition, size and mixing state. We also combine our results with demographic data to study population exposure to particles of specific sources. This work will help evaluate the performance of existing modeling tools for air quality and population exposure studies.

Health and Household Air Pollution from Solid Fuel Use: The Need for Improved Exposure Assessment

PubMed Central

Peel, Jennifer L.; Balakrishnan, Kalpana; Breysse, Patrick N.; Chillrud, Steven N.; Naeher, Luke P.; Rodes, Charles E.; Vette, Alan F.; Balbus, John M.

2013-01-01

Background: Nearly 3 billion people worldwide rely on solid fuel combustion to meet basic household energy needs. The resulting exposure to air pollution causes an estimated 4.5% of the global burden of disease. Large variability and a lack of resources for research and development have resulted in highly uncertain exposure estimates. Objective: We sought to identify research priorities for exposure assessment that will more accurately and precisely define exposure–response relationships of household air pollution necessary to inform future cleaner-burning cookstove dissemination programs. Data Sources: As part of an international workshop in May 2011, an expert group characterized the state of the science and developed recommendations for exposure assessment of household air pollution. Synthesis: The following priority research areas were identified to explain variability and reduce uncertainty of household air pollution exposure measurements: improved characterization of spatial and temporal variability for studies examining both short- and long-term health effects; development and validation of measurement technology and approaches to conduct complex exposure assessments in resource-limited settings with a large range of pollutant concentrations; and development and validation of biomarkers for estimating dose. Addressing these priority research areas, which will inherently require an increased allocation of resources for cookstove research, will lead to better characterization of exposure–response relationships. Conclusions: Although the type and extent of exposure assessment will necessarily depend on the goal and design of the cookstove study, without improved understanding of exposure–response relationships, the level of air pollution reduction necessary to meet the health targets of cookstove interventions will remain uncertain. Citation: Clark ML, Peel JL, Balakrishnan K, Breysse PN, Chillrud SN, Naeher LP, Rodes CE, Vette AF, Balbus JM. 2013. Health and household air pollution from solid fuel use: the need for improved exposure assessment. Environ Health Perspect 121:1120–1128; http://dx.doi.org/10.1289/ehp.1206429 PMID:23872398

Comparing Multipollutant Emissions-Based Mobile Source Indicators to Other Single Pollutant and Multipollutant Indicators in Different Urban Areas

PubMed Central

Oakes, Michelle M.; Baxter, Lisa K.; Duvall, Rachelle M.; Madden, Meagan; Xie, Mingjie; Hannigan, Michael P.; Peel, Jennifer L.; Pachon, Jorge E.; Balachandran, Siv; Russell, Armistead; Long, Thomas C.

2014-01-01

A variety of single pollutant and multipollutant metrics can be used to represent exposure to traffic pollutant mixtures and evaluate their health effects. Integrated mobile source indicators (IMSIs) that combine air quality concentration and emissions data have recently been developed and evaluated using data from Atlanta, Georgia. IMSIs were found to track trends in traffic-related pollutants and have similar or stronger associations with health outcomes. In the current work, we apply IMSIs for gasoline, diesel and total (gasoline + diesel) vehicles to two other cities (Denver, Colorado and Houston, Texas) with different emissions profiles as well as to a different dataset from Atlanta. We compare spatial and temporal variability of IMSIs to single-pollutant indicators (carbon monoxide (CO), nitrogen oxides (NOx) and elemental carbon (EC)) and multipollutant source apportionment factors produced by Positive Matrix Factorization (PMF). Across cities, PMF-derived and IMSI gasoline metrics were most strongly correlated with CO (r = 0.31–0.98), while multipollutant diesel metrics were most strongly correlated with EC (r = 0.80–0.98). NOx correlations with PMF factors varied across cities (r = 0.29–0.67), while correlations with IMSIs were relatively consistent (r = 0.61–0.94). In general, single-pollutant metrics were more correlated with IMSIs (r = 0.58–0.98) than with PMF-derived factors (r = 0.07–0.99). A spatial analysis indicated that IMSIs were more strongly correlated (r > 0.7) between two sites in each city than single pollutant and PMF factors. These findings provide confidence that IMSIs provide a transferable, simple approach to estimate mobile source air pollution in cities with differing topography and source profiles using readily available data. PMID:25405595

Characterizing the spatial variability of local and background concentration signals for air pollution at the neighbourhood scale

NASA Astrophysics Data System (ADS)

Shairsingh, Kerolyn K.; Jeong, Cheol-Heon; Wang, Jonathan M.; Evans, Greg J.

2018-06-01

Vehicle emissions represent a major source of air pollution in urban districts, producing highly variable concentrations of some pollutants within cities. The main goal of this study was to identify a deconvolving method so as to characterize variability in local, neighbourhood and regional background concentration signals. This method was validated by examining how traffic-related and non-traffic-related sources influenced the different signals. Sampling with a mobile monitoring platform was conducted across the Greater Toronto Area over a seven-day period during summer 2015. This mobile monitoring platform was equipped with instruments for measuring a wide range of pollutants at time resolutions of 1 s (ultrafine particles, black carbon) to 20 s (nitric oxide, nitrogen oxides). The monitored neighbourhoods were selected based on their land use categories (e.g. industrial, commercial, parks and residential areas). The high time-resolution data allowed pollutant concentrations to be separated into signals representing background and local concentrations. The background signals were determined using a spline of minimums; local signals were derived by subtracting the background concentration from the total concentration. Our study showed that temporal scales of 500 s and 2400 s were associated with the neighbourhood and regional background signals respectively. The percent contribution of the pollutant concentration that was attributed to local signals was highest for nitric oxide (NO) (37-95%) and lowest for ultrafine particles (9-58%); the ultrafine particles were predominantly regional (32-87%) in origin on these days. Local concentrations showed stronger associations than total concentrations with traffic intensity in a 100 m buffer (ρ:0.21-0.44). The neighbourhood scale signal also showed stronger associations with industrial facilities than the total concentrations. Given that the signals show stronger associations with different land use suggests that resolving the ambient concentrations differentiates which emission sources drive the variability in each signal. The benefit of this deconvolution method is that it may reduce exposure misclassification when coupled with predictive models.

NO2 and HCHO variability in Mexico City from MAX-DOAS measurements

NASA Astrophysics Data System (ADS)

Grutter, M.; Friedrich, M. M.; Rivera, C. I.; Arellano, E. J.; Stremme, W.

2015-12-01

Atmospheric studies in large cities are of great relevance since pollution affects air quality and human health. A network of Multi Axis Differential Optical Absorption Spectrometers (MAX-DOAS) has been established in strategic sites within the Mexico City metropolitan area. Four instruments are now in operation with the aim to study the variability and spatial distribution of key pollutants, providing results of O4, NO2 and HCHO slant column densities (SCD). A numerical code has been written to retrieve gas profiles of NO2 and HCHO using radiative transfer simulations. We present the first results of the variability of these trace gases which will bring new insight in the current knowledge of transport patterns, emissions as well as frequency and origin of extraordinary events. Results of the vertical column densities (VCD) valiability of NO2 and HCHO in Mexico City are presented. These studies are useful to validate current and future satellite observatopns such as OMI, TROPOMI and TEMPO.

Spatial patterns in community response to aircraft noise associated with non-noise factors

NASA Astrophysics Data System (ADS)

Hall, F. L.; Taylor, S. M.; Birnie, S. E.

1980-08-01

Non-noise aspects of airport operations may affect individuals' responses to aircraft noise. Fear of crashes, other forms of pollution, and proximity to the flight path are three such non-noise aspects which have spatial patterns that are closely related to the pattern of noise contours around an airport. If these variables affect response to aircraft noise, they may therefore confound attempts to understand relationships between noise level and community response. Analyses based on data from 673 individuals around Toronto International Airport suggest that these factors do affect annoyance responses, but do not affect reported activity interference. Hence it may prove fruitful, in aggregate analyses of community response data, to control for these variables in order to better understand the noise-annoyance relationships.

A wide field-of-view imaging DOAS instrument for two-dimensional trace gas mapping from aircraft

NASA Astrophysics Data System (ADS)

Schönhardt, A.; Altube, P.; Gerilowski, K.; Krautwurst, S.; Hartmann, J.; Meier, A. C.; Richter, A.; Burrows, J. P.

2015-12-01

The Airborne imaging differential optical absorption spectroscopy (DOAS) instrument for Measurements of Atmospheric Pollution (AirMAP) has been developed for the purpose of trace gas measurements and pollution mapping. The instrument has been characterized and successfully operated from aircraft. Nitrogen dioxide (NO2) columns were retrieved from the AirMAP observations. A major benefit of the push-broom imaging instrument is the spatially continuous, gap-free measurement sequence independent of flight altitude, a valuable characteristic for mapping purposes. This is made possible by the use of a charge coupled device (CCD) frame-transfer detector. A broad field of view across track of around 48° is achieved with wide-angle entrance optics. This leads to a swath width of about the same size as the flight altitude. The use of fibre coupled light intake optics with sorted light fibres allows flexible instrument positioning within the aircraft and retains the very good imaging capabilities. The measurements yield ground spatial resolutions below 100 m depending on flight altitude. The number of viewing directions is chosen from a maximum of 35 individual viewing directions (lines of sight, LOS) represented by 35 individual fibres. The selection is adapted to each situation by averaging according to signal-to-noise or spatial resolution requirements. Observations at 30 m spatial resolution are obtained when flying at 1000 m altitude and making use of all 35 viewing directions. This makes the instrument a suitable tool for mapping trace gas point sources and small-scale variability. The position and aircraft attitude are taken into account for accurate spatial mapping using the Attitude and Heading Reference System of the aircraft. A first demonstration mission using AirMAP was undertaken in June 2011. AirMAP was operated on the AWI Polar-5 aircraft in the framework of the AIRMETH-2011 campaign. During a flight above a medium-sized coal-fired power plant in north-west Germany, AirMAP clearly detected the emission plume downwind from the exhaust stack, with NO2 vertical columns around 2 × 1016 molecules cm-2 in the plume centre. NOx emissions estimated from the AirMAP observations are consistent with reports in the European Pollutant Release and Transfer Register. Strong spatial gradients and variability in NO2 amounts across and along flight direction are observed, and small-scale enhancements of NO2 above a motorway are detected.

Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures.

PubMed

Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent

2016-04-01

Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Schools, Air Pollution, and Active Transportation: An Exploratory Spatial Analysis of Calgary, Canada

PubMed Central

Bertazzon, Stefania; Shahid, Rizwan

2017-01-01

An exploratory spatial analysis investigates the location of schools in Calgary (Canada) in relation to air pollution and active transportation options. Air pollution exhibits marked spatial variation throughout the city, along with distinct spatial patterns in summer and winter; however, all school locations lie within low to moderate pollution levels. Conversely, the study shows that almost half of the schools lie in low walkability locations; likewise, transitability is low for 60% of schools, and only bikability is widespread, with 93% of schools in very bikable locations. School locations are subsequently categorized by pollution exposure and active transportation options. This analysis identifies and maps schools according to two levels of concern: schools in car-dependent locations and relatively high pollution; and schools in locations conducive of active transportation, yet exposed to relatively high pollution. The findings can be mapped and effectively communicated to the public, health practitioners, and school boards. The study contributes with an explicitly spatial approach to the intra-urban public health literature. Developed for a moderately polluted city, the methods can be extended to more severely polluted environments, to assist in developing spatial public health policies to improve respiratory outcomes, neurodevelopment, and metabolic and attention disorders in school-aged children. PMID:28757577

Geostatistical methods in the assessment of the spatial variability of the quality of river water

NASA Astrophysics Data System (ADS)

Krasowska, Małgorzata; Banaszuk, Piotr

2017-11-01

The research was conducted in the agricultural catchment in north-eastern Poland. The aim of this study was to check how geostatistical analysis can be useful for the detection zones and forms of supply stream by water from different sources. The work was included the implementation of hydrochemical profiles. These profiles were made by measuring the electrical conductivity (EC) values and temperature along the river. On the basis of these results, the authors calculated the coefficient of Moran I and performed semivariogram and found that the EC values are correlated on a stretch of about 140 m. This means that the spatial correlation between samples of water in the stream is readable over a distance of about 140 meters. Therefore it is believed that the degree of water mineralization on this section is shaped by water entering the river channel migration in different ways: through tributaries, leachate drainage and surface runoff. In the case of the analyzed catchment, the potential sources of pollution were drainage systems. Therefore, the spatial analysis allowed the identification pollution sources in a catchment, especially in drained agricultural catchments.

Seasonal comparison of moss bag technique against vertical snow samples for monitoring atmospheric pollution.

PubMed

Salo, Hanna; Berisha, Anna-Kaisa; Mäkinen, Joni

2016-03-01

This is the first study seasonally applying Sphagnum papillosum moss bags and vertical snow samples for monitoring atmospheric pollution. Moss bags, exposed in January, were collected together with snow samples by early March 2012 near the Harjavalta Industrial Park in southwest Finland. Magnetic, chemical, scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDX), K-means clustering, and Tomlinson pollution load index (PLI) data showed parallel spatial trends of pollution dispersal for both materials. Results strengthen previous findings that concentrate and slag handling activities were important (dust) emission sources while the impact from Cu-Ni smelter's pipe remained secondary at closer distances. Statistically significant correlations existed between the variables of snow and moss bags. As a summary, both methods work well for sampling and are efficient pollutant accumulators. Moss bags can be used also in winter conditions and they provide more homogeneous and better controlled sampling method than snow samples. Copyright © 2015. Published by Elsevier B.V.

Soil fauna and soil microflora as possible indicators of soil pollution.

PubMed

Eijsackers, H

1983-09-01

Research on biological indicators of soil pollution is hampered by soil variability and temporal and spatial fluctuations of numbers of soil animals. These characters on the other hand promote a high biological diversity in the soil. A high diversity combined with persistent soil pollutants increases the chance to select good indicators. However research on these topics is still limited. Examples of specific indicators are the changed arthropod species patterns due to pesticide influence and the changed soil enzyme activity under the influence of specific heavy metals. Another approach is to look for organisms that give a general indication of soil pollution. In this respect the earthworm species Allolobophora caliginosa proved to be sensitive for different types of manure especially pig manure with copper, for sewage sludge, for municipal waste compost and for fly ash. A third way of indication is by organisms accumulating pollutants. For some heavy metals (Cd, Zn), earthworms are very efficient accumulators. More research is needed especially on the specific relation between biological responses and abiotic soil characteristics.

Explaining nitrate pollution pressure on the groundwater resource in Kinshasa using a multivariate statistical modelling approach

NASA Astrophysics Data System (ADS)

Mfumu Kihumba, Antoine; Vanclooster, Marnik

2013-04-01

Drinking water in Kinshasa, the capital of the Democratic Republic of Congo, is provided by extracting groundwater from the local aquifer, particularly in peripheral areas. The exploited groundwater body is mainly unconfined and located within a continuous detrital aquifer, primarily composed of sedimentary formations. However, the aquifer is subjected to an increasing threat of anthropogenic pollution pressure. Understanding the detailed origin of this pollution pressure is important for sustainable drinking water management in Kinshasa. The present study aims to explain the observed nitrate pollution problem, nitrate being considered as a good tracer for other pollution threats. The analysis is made in terms of physical attributes that are readily available using a statistical modelling approach. For the nitrate data, use was made of a historical groundwater quality assessment study, for which the data were re-analysed. The physical attributes are related to the topography, land use, geology and hydrogeology of the region. Prior to the statistical modelling, intrinsic and specific vulnerability for nitrate pollution was assessed. This vulnerability assessment showed that the alluvium area in the northern part of the region is the most vulnerable area. This area consists of urban land use with poor sanitation. Re-analysis of the nitrate pollution data demonstrated that the spatial variability of nitrate concentrations in the groundwater body is high, and coherent with the fragmented land use of the region and the intrinsic and specific vulnerability maps. For the statistical modeling use was made of multiple regression and regression tree analysis. The results demonstrated the significant impact of land use variables on the Kinshasa groundwater nitrate pollution and the need for a detailed delineation of groundwater capture zones around the monitoring stations. Key words: Groundwater , Isotopic, Kinshasa, Modelling, Pollution, Physico-chemical.

Water toxicity assessment and spatial pollution patterns identification in a Mediterranean River Basin District. Tools for water management and risk analysis.

PubMed

Carafa, Roberta; Faggiano, Leslie; Real, Montserrat; Munné, Antoni; Ginebreda, Antoni; Guasch, Helena; Flo, Monica; Tirapu, Luís; von der Ohe, Peter Carsten

2011-09-15

In compliance with the requirements of the EU Water Framework Directive, monitoring of the ecological and chemical status of Catalan river basins (NE Spain) is carried out by the Catalan Water Agency. The large amount of data collected and the complex relationships among the environmental variables monitored often mislead data interpretation in terms of toxic impact, especially considering that even pollutants at very low concentrations might contribute to the total toxicity. The total dataset of chemical monitoring carried out between 2007 and 2008 (232 sampling stations and 60 pollutants) has been analyzed using sequential advanced modeling techniques. Data on concentrations of contaminants in water were pre-treated in order to calculate the bioavailable fraction, depending on substance properties and local environmental conditions. The resulting values were used to predict the potential impact of toxic substances in complex mixtures on aquatic biota and to identify hot spots. Exposure assessment with Species Sensitivity Distribution (SSD) and mixture toxicity rules were used to compute the multi-substances Potentially Affected Fraction (msPAF). The combined toxicity of the pollutants analyzed in the Catalan surface waters might potentially impact more than 50% of the species in 10% of the sites. In order to understand and visualize the spatial distribution of the toxic risk, Self Organising Map (SOM), based on the Kohonen's Artificial Neural Network (ANN) algorithm, was applied on the output data of these models. Principal Component Analysis (PCA) was performed on top of Neural Network results in order to identify main influential variables which account for the pollution trends. Finally, predicted toxic impacts on biota have been linked and correlated to field data on biological quality indexes using macroinvertebrate and diatom communities (IBMWP and IPS). The methodology presented could represent a suitable tool for water managers in environmental risk assessment and management. Copyright © 2011 Elsevier B.V. All rights reserved.

A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai, China.

PubMed

Liu, Chao; Henderson, Barron H; Wang, Dongfang; Yang, Xinyuan; Peng, Zhong-Ren

2016-09-15

Intra-urban assessment of air pollution exposure has become a priority study while international attention was attracted to PM2.5 pollution in China in recent years. Land Use Regression (LUR), which has previously been proved to be a feasible way to describe the relationship between land use and air pollution level in European and American cities, was employed in this paper to explain the correlations and spatial variations in Shanghai, China. PM2.5 and NO2 concentrations at 35-45 monitoring locations were selected as dependent variables, and a total of 44 built environmental factors were extracted as independent variables. Only five factors showed significant explanatory value for both PM2.5 and NO2 models: longitude, distance from monitors to the ocean, highway intensity, waterbody area, and industrial land area for PM2.5 model; residential area, distance to the coast, industrial area, urban district, and highway intensity for NO2 model. Respectively, both PM2.5 and NO2 showed anti-correlation with coastal proximity (an indicator of clean air dilution) and correlation with highway and industrial intensity (source indicators). NO2 also showed significant correlation with local indicators of population density (residential intensity and urban classification), while PM2.5 showed significant correlation with regional dilution (longitude as a indicator of distance from polluted neighbors and local water features). Both adjusted R squared values were strong with PM2.5 (0.88) being higher than NO2 (0.62). The LUR was then used to produce continuous concentration fields for NO2 and PM2.5 to illustrate the features and, potentially, for use by future studies. Comparison to PM2.5 studies in New York and Beijing show that Shanghai PM2.5 pollutant distribution was more sensitive to geographic location and proximity to neighboring regions. Copyright © 2015. Published by Elsevier B.V.

Benthic foraminifers in the regional monitoring program’s San Francisco Estuary samples

USGS Publications Warehouse

McGann, Mary; Sloan, Doris

1999-01-01

For over three decades, sand-sized protozoans known as foraminifers have made contributions to our understanding of environmental problems in urban areas (Alve, 1991; Clark, 1971; Ellison et al., 1986; Watkins, 1961). Benthic foraminiferal assemblages are particularly sensitive pollution indicators in estuarine and coastal areas (Alve, 1995) because they vary spatially and temporally in relation to environmental variables and can respond to almost imperceptible physical change in the environment due to pollutants. Foraminifers also have similar distributions to those of shallow marine invertebrates (Buzas and Culver, 1991, 1993), and can therefore act as proxies for larger organisms in polluted environments. In addition, the ability of foraminifers to respond to environmental degradation is enhanced because they reproduce quickly, as often as every three months to one year (Murray, 1991).

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

NASA Technical Reports Server (NTRS)

Zoogman, P.; Liu, X.; Suleiman, R. M.; Pennington, W. F.; Flittner, D. E.; Al-Saadi, J. A.; Hilton, B. B.; Nicks, D. K.; Newchurch, M. J.; Carr, J. L.;

Implementation of Tropospheric Emissions: Monitoring of Pollution (TEMPO)

NASA Astrophysics Data System (ADS)

Chance, K.; Liu, X.; Suleiman, R. M.; Flittner, D. E.; Al-Saadi, J. A.; Janz, S. J.

2014-12-01

The updated status of TEMPO, as it proceeds from formulation phase into implementation phase is presented. TEMPO, the first NASA Earth Venture Instrument, will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO measures from Mexico City to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution. TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, reducing uncertainty in air quality predictions by 50%. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available. TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. GEO-CAPE is not planned for implementation this decade. However, instruments from Europe (Sentinel 4) and Asia (GEMS) will form parts of a global GEO constellation for pollution monitoring later this decade, with a major focus on intercontinental pollution transport. TEMPO will launch at a prime time to be a component of this constellation

Chemical characterization and spatial distribution of PAHs and heavy hydrocarbons in rural sites of Campania Region, South Italy.

PubMed

Monaco, D; Riccio, A; Chianese, E; Adamo, P; Di Rosa, S; Fagnano, M

2015-10-01

In this paper, the behaviour and distribution patterns of heavy hydrocarbons and several polycyclic aromatic hydrocarbon (PAH) priority pollutants, as listed by the US Environmental Protection Agency, were evaluated in 891 soil samples. The samples were collected in three expected polluted rural sites in Campania (southern Italy) as part of the LIFE11 ECOREMED project, funded by the European Commission, to test innovative agriculture-based soil restoration techniques. These sites have been selected because they have been used for the temporary storage of urban and building waste (Teverola), subject to illicit dumping of unknown material (Trentola-Ducenta), or suspected to be polluted by metals due to agricultural practices (Giugliano). Chemical analysis of soil samples allowed the baseline pollution levels to be determined prior to any intervention. It was found that these areas can be considered contaminated for residential use, in accordance with Italian environmental law (Law Decree 152/2006). Statistical analysis applied to the data proved that average mean concentrations of heavy hydrocarbons could be as high as 140 mg/kg of dry soil with peaks of 700 mg/kg of dry soil, for the Trentola-Ducenta site; the median concentration of analytical results for hydrocarbon (HC) concentration for the Trentola-Ducenta and Giugliano sites was 63 and 73.4 mg/kg dry soil, respectively; for Teverola, the median level was 35 mg/kg dry soil. Some PAHs (usually benzo(a)pyrene) also exceeded the maximum allowed level in all sites. From the principal component analysis applied to PAH concentrations, it emerged that pollutants can be supposed to derive from a single source for the three sites. Diagnostic ratios calculated to determine possible PAH sources suggest petroleum combustion or disposal practice. Our sampling protocol also showed large dishomogeneity in soil pollutant spatial distribution, even at a scale as small as 3.3 m, indicating that variability could emerge at very short spatial scales.

Impacts of Realistic Urban Heating, Part I: Spatial Variability of Mean Flow, Turbulent Exchange and Pollutant Dispersion

NASA Astrophysics Data System (ADS)

Nazarian, Negin; Martilli, Alberto; Kleissl, Jan

2018-03-01

As urbanization progresses, more realistic methods are required to analyze the urban microclimate. However, given the complexity and computational cost of numerical models, the effects of realistic representations should be evaluated to identify the level of detail required for an accurate analysis. We consider the realistic representation of surface heating in an idealized three-dimensional urban configuration, and evaluate the spatial variability of flow statistics (mean flow and turbulent fluxes) in urban streets. Large-eddy simulations coupled with an urban energy balance model are employed, and the heating distribution of urban surfaces is parametrized using sets of horizontal and vertical Richardson numbers, characterizing thermal stratification and heating orientation with respect to the wind direction. For all studied conditions, the thermal field is strongly affected by the orientation of heating with respect to the airflow. The modification of airflow by the horizontal heating is also pronounced for strongly unstable conditions. The formation of the canyon vortices is affected by the three-dimensional heating distribution in both spanwise and streamwise street canyons, such that the secondary vortex is seen adjacent to the windward wall. For the dispersion field, however, the overall heating of urban surfaces, and more importantly, the vertical temperature gradient, dominate the distribution of concentration and the removal of pollutants from the building canyon. Accordingly, the spatial variability of concentration is not significantly affected by the detailed heating distribution. The analysis is extended to assess the effects of three-dimensional surface heating on turbulent transfer. Quadrant analysis reveals that the differential heating also affects the dominance of ejection and sweep events and the efficiency of turbulent transfer (exuberance) within the street canyon and at the roof level, while the vertical variation of these parameters is less dependent on the detailed heating of urban facets.

Spatio-temporal modelling for assessing air pollution in Santiago de Chile

NASA Astrophysics Data System (ADS)

Nicolis, Orietta; Camaño, Christian; Mařın, Julio C.; Sahu, Sujit K.

2017-01-01

In this work, we propose a space-time approach for studying the PM2.5 concentration in the city of Santiago de Chile. In particular, we apply the autoregressive hierarchical model proposed by [1] using the PM2.5 observations collected by a monitoring network as a response variable and numerical weather forecasts from the Weather Research and Forecasting (WRF) model as covariate together with spatial and temporal (periodic) components. The approach is able to provide short-term spatio-temporal predictions of PM2.5 concentrations on a fine spatial grid (at 1km × 1km horizontal resolution.)

Two Methods to Derive Ground-level Concentrations of PM2.5 with Improved Accuracy in the North China, Calibrating MODIS AOD and CMAQ Model Predictions

NASA Astrophysics Data System (ADS)

Lyu, Baolei; Hu, Yongtao; Chang, Howard; Russell, Armistead; Bai, Yuqi

2016-04-01

Reliable and accurate characterizations of ground-level PM2.5 concentrations are essential to understand pollution sources and evaluate human exposures etc. Monitoring network could only provide direct point-level observations at limited locations. At the locations without monitors, there are generally two ways to estimate the pollution levels of PM2.5. One is observations of aerosol properties from the satellite-based remote sensing, such as Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD). The other one is from deterministic atmospheric chemistry models, such as the Community Multi-Scale Air Quality Model (CMAQ). In this study, we used a statistical spatio-temporal downscaler to calibrate the two datasets to monitor observations to derive fine-scale ground-level concentrations of PM2.5 with improved accuracy. We treated both MODIS AOD and CMAQ model predictions as biased proxy estimations of PM2.5 pollution levels. The downscaler proposed a Bayesian framework to model the spatially and temporally varying coefficients of the two types of estimations in the linear regression setting, in order to correct biases. Especially for calibrating MODIS AOD, a city-specific linear model was established to fill the missing AOD values, and a novel interpolation-based variable, i.e. PM2.5 Spatial Interpolator, was introduced to account for the spatial dependence among grid cells. We selected the heavy polluted and populated North China as our study area, in a grid setting of 81×81 12-km cells. For the evaluation of calibration performance for retrieved MODIS AOD, the R2 was 0.61 by the full model with PM2.5 Spatial Interpolator being presented, and was 0.48 with PM2.5 Spatial Interpolator not being presented. The constructed AOD values effectively predicted PM2.5 concentrations under our model structure, with R2=0.78. For the evaluation of calibrated CMAQ predictions, the R2 was 0.51, a little less than that of calibrated AOD. Finally we obtained two sets of calibrated estimations of ground-level PM2.5 concentrations with complete spatial coverage. By comparing the two datasets, we found that the prediction from AOD have a little smoother texture than that from CMAQ. The former also predicted larger heavy pollution area in the southern Hebei province than the latter, but in a small margin. In general, they have pretty similar spatial patterns, indicating the reliability of our data fusion method. In summary, the statistical spatio-temporal downscaler could provide improvements on MODIS AOD and CMAQ's predictions on PM2.5 pollution levels. Future work would focus on fusing three datasets, as aforementioned monitor observations, MODIS AOD and CMAQ predictions, to derive predictions of ground-level PM2.5 pollution levels with even increased accuracy.

Evaluating methods for estimating space-time paths of individuals in calculating long-term personal exposure to air pollution

NASA Astrophysics Data System (ADS)

Schmitz, Oliver; Soenario, Ivan; Vaartjes, Ilonca; Strak, Maciek; Hoek, Gerard; Brunekreef, Bert; Dijst, Martin; Karssenberg, Derek

2016-04-01

Air pollution is one of the major concerns for human health. Associations between air pollution and health are often calculated using long-term (i.e. years to decades) information on personal exposure for each individual in a cohort. Personal exposure is the air pollution aggregated along the space-time path visited by an individual. As air pollution may vary considerably in space and time, for instance due to motorised traffic, the estimation of the spatio-temporal location of a persons' space-time path is important to identify the personal exposure. However, long term exposure is mostly calculated using the air pollution concentration at the x, y location of someone's home which does not consider that individuals are mobile (commuting, recreation, relocation). This assumption is often made as it is a major challenge to estimate space-time paths for all individuals in large cohorts, mostly because limited information on mobility of individuals is available. We address this issue by evaluating multiple approaches for the calculation of space-time paths, thereby estimating the personal exposure along these space-time paths with hyper resolution air pollution maps at national scale. This allows us to evaluate the effect of the space-time path and resulting personal exposure. Air pollution (e.g. NO2, PM10) was mapped for the entire Netherlands at a resolution of 5×5 m2 using the land use regression models developed in the European Study of Cohorts for Air Pollution Effects (ESCAPE, http://escapeproject.eu/) and the open source software PCRaster (http://www.pcraster.eu). The models use predictor variables like population density, land use, and traffic related data sets, and are able to model spatial variation and within-city variability of annual average concentration values. We approximated space-time paths for all individuals in a cohort using various aggregations, including those representing space-time paths as the outline of a persons' home or associated parcel of land, the 4 digit postal code area or neighbourhood of a persons' home, circular areas around the home, and spatial probability distributions of space-time paths during commuting. Personal exposure was estimated by averaging concentrations over these space-time paths, for each individual in a cohort. Preliminary results show considerable differences of a persons' exposure using these various approaches of space-time path aggregation, presumably because air pollution shows large variation over short distances.

Weather Condition dominates the Regional PM2.5 Pollutions in the Eastern Coastal Provinces of China during winter

NASA Astrophysics Data System (ADS)

Cai, Zhe; Jiang, Fei; Chen, Jingming; Jiang, Ziqiang

2017-04-01

China has been suffering from severe particulate matter (PM) pollution in recent years. Both pollution area and pollution levels are increasing gradually. The PM pollution episodes not only occur in the traditional developed areas like Yangtze River Delta (YRD) and Beijing-Tianjin-Hebei (BTH) region, but also frequently happen in the whole eastern coastal provinces (ECPs) of China. Based on hourly PM2.5 concentrations during December 2013 February 2014 of 55 cities located in the ECPs, we investigated the spatial and temporal variabilities of PM2.5 concentrations and the corresponding meteorological conditions during winter. The results shown that basically the seasonal mean concentrations over the whole ECPs exceeded the China's national standard of 75 μg/m3, and the most polluted area with mean concentrations greater than 150 μg/m3 were located in the southwest of Hebei and the west of Shandong provinces. From December to February, there was a decrease trend for the PM2.5 pollution in most areas, especially in the YRD region, while the PM2.5 concentrations over north of Hebei province increased. The spatial distributions and monthly variations are strongly related to the weather conditions. Overall, severe PM pollution was corresponding to a stable weather condition, i.e., small Sea Level Pressure (SLP) gradient, lower Planetary Boundary Layer (PBL) height and weaker wind fields. Statistics shown that the changes of mean PM2.5 concentrations over the ECPs region usually lagged behind the variations of PBL height and wind speeds about 12 18 hours. The variations of weather conditions could explain about 71% (R2) of the overall changes of PM2.5 concentrations in the ECPs region. This study gives a full insight into the PM2.5 pollution in the area of eastern coastal provinces of China during winter, which would be helpful to predict and control the PM2.5 pollution for this area in the future.

Spatial Distribution and Effects of Sewage in Coastal Hawaiian Waters

NASA Astrophysics Data System (ADS)

Abaya, L.; Wiegner, T. N.; Colbert, S.; Lindsey, K.; Beets, J.

2016-02-01

Sewage pollution is a worldwide threat to marine ecosystems and human health through the release of pathogens and nutrients into nearshore waters. Goals of this study were to document hydrological connections between cesspools and nearshore waters, detect the presence of sewage through biological and chemical tracers, and determine the spatial extent of sewage offshore. Puakō, located on Hawaíi Island, was the focus of this study as most homes have cesspools. Fluorescein dye injected into cesspools was detected at the shoreline in as little as three days. Elevated δ 15N signatures in macroalgae and high Enterococcus counts further confirmed presence of sewage in nearshore waters. Offshore sampling revealed significant differences among distances from shore for fecal indicator bacteria and δ 15N signatures in macroalgae. Results indicated distance from shore and stations are important factors of variability. Additionally, nutrient concentrations and macroalgal cover were higher in areas with high groundwater discharge. Surprisingly, δ15N macroalgal signatures and Enterococcus were not correlated with salinity. These results suggest that possibly the location of cesspools, subsurface geology, and/or nearshore circulation may affect sewage transport to the coastline and offshore. Spatial analysis techniques helped visualize potential hot spots of sewage pollution using δ15N macroalgal and Enterococcus data. The combination of tools used here to document sewage pollution presence may be useful for communities facing similar environmental problems.

High resolution spatio-temporal mapping of NO2 pollution for estimating personal exposures of the Dutch population

NASA Astrophysics Data System (ADS)

Soenario, Ivan; Helbich, Marco; Schmitz, Oliver; Strak, Maciek; Hoek, Gerard; Karssenberg, Derek

2017-04-01

Air pollution has been associated with adverse health effects (e.g., cardiovascular and respiration diseases) in the urban environments. Therefore, the assessment of people's exposure to air pollution is central in epidemiological studies. The estimation of exposures on an individual level can be done by combining location information across space and over time with spatio-temporal data on air pollution concentrations. When detailed information on peoples' space-time paths (e.g. commuting patterns calculated by means of spatial routing algorithms or tracked through GPS) and peoples' major activity locations (e.g. home location, work location) are available, it is possible to calculate more precise personal exposure levels depending on peoples' individual space-time mobility patterns. This requires air pollution values not only at a high level of spatial accuracy and high temporal granularity but such data also needs to be available on a nation-wide scale. As current data is seriously limited in this respect, we introduce a novel data set of NO2 levels across the Netherlands. The provided NO2 concentrations are accessible on hourly timestamps on a 5 meter grid cell resolution for weekdays and weekends, and each month of the year. We modeled a single Land Use Regression model using a five year average of NO2 data from the Dutch NO2 measurement network consisting of N=46 sampling locations distributed over the country. Predictor variables for this model were selected in a data-driven manner using an Elastic Net and Best Subset Selection procedure from 70 candidate predictors including traffic, industry, infrastructure and population-based variables. Subsequently, to model NO2 for each time scale (hour, week, month), the LUR coefficients were fitted using the NO2 data, aggregated per time scale. Model validation was grounded on independent data collected in an ad hoc measurement campaign. Our results show a considerable difference in urban concentrations between weekdays and weekend-days. We observe a diurnal variation in concentrations particularly during weekdays related to traffic intensity and considerable differences in concentrations between seasons. Considerable spatial variation occurs both within cities and urban areas where concentrations on roads are high and decrease rapidly with distance to roads. Both on-road and far-from-road concentrations are consistently higher in urban areas than in rural areas.

The use of alternative pollutant metrics in time-series studies of ambient air pollution and respiratory emergency department visits.

PubMed

Darrow, Lyndsey A; Klein, Mitchel; Sarnat, Jeremy A; Mulholland, James A; Strickland, Matthew J; Sarnat, Stefanie E; Russell, Armistead G; Tolbert, Paige E

2011-01-01

Various temporal metrics of daily pollution levels have been used to examine the relationships between air pollutants and acute health outcomes. However, daily metrics of the same pollutant have rarely been systematically compared within a study. In this analysis, we describe the variability of effect estimates attributable to the use of different temporal metrics of daily pollution levels. We obtained hourly measurements of ambient particulate matter (PM₂.₅), carbon monoxide (CO), nitrogen dioxide (NO₂), and ozone (O₃) from air monitoring networks in 20-county Atlanta for the time period 1993-2004. For each pollutant, we created (1) a daily 1-h maximum; (2) a 24-h average; (3) a commute average; (4) a daytime average; (5) a nighttime average; and (6) a daily 8-h maximum (only for O₃). Using Poisson generalized linear models, we examined associations between daily counts of respiratory emergency department visits and the previous day's pollutant metrics. Variability was greatest across O₃ metrics, with the 8-h maximum, 1-h maximum, and daytime metrics yielding strong positive associations and the nighttime O₃ metric yielding a negative association (likely reflecting confounding by air pollutants oxidized by O₃). With the exception of daytime metric, all of the CO and NO₂ metrics were positively associated with respiratory emergency department visits. Differences in observed associations with respiratory emergency room visits among temporal metrics of the same pollutant were influenced by the diurnal patterns of the pollutant, spatial representativeness of the metrics, and correlation between each metric and copollutant concentrations. Overall, the use of metrics based on the US National Ambient Air Quality Standards (for example, the use of a daily 8-h maximum O₃ as opposed to a 24-h average metric) was supported by this analysis. Comparative analysis of temporal metrics also provided insight into underlying relationships between specific air pollutants and respiratory health.

Space-Time Urban Air Pollution Forecasts

NASA Astrophysics Data System (ADS)

Russo, A.; Trigo, R. M.; Soares, A.

2012-04-01

Air pollution, like other natural phenomena, may be considered a space-time process. However, the simultaneous integration of time and space is not an easy task to perform, due to the existence of different uncertainties levels and data characteristics. In this work we propose a hybrid method that combines geostatistical and neural models to analyze PM10 time series recorded in the urban area of Lisbon (Portugal) for the 2002-2006 period and to produce forecasts. Geostatistical models have been widely used to characterize air pollution in urban areas, where the pollutant sources are considered diffuse, and also to industrial areas with localized emission sources. It should be stressed however that most geostatistical models correspond basically to an interpolation methodology (estimation, simulation) of a set of variables in a spatial or space-time domain. The temporal prediction of a pollutant usually requires knowledge of the main trends and complex patterns of physical dispersion phenomenon. To deal with low resolution problems and to enhance reliability of predictions, an approach based on neural network short term predictions in the monitoring stations which behave as a local conditioner to a fine grid stochastic simulation model is presented here. After the pollutant concentration is predicted for a given time period at the monitoring stations, we can use the local conditional distributions of observed values, given the predicted value for that period, to perform the spatial simulations for the entire area and consequently evaluate the spatial uncertainty of pollutant concentration. To attain this objective, we propose the use of direct sequential simulations with local distributions. With this approach one succeed to predict the space-time distribution of pollutant concentration that accounts for the time prediction uncertainty (reflecting the neural networks efficiency at each local monitoring station) and the spatial uncertainty as revealed by the spatial variograms. The dataset used consists of PM10 concentrations recorded hourly by 12 monitoring stations within the Lisbon's area, for the period 2002-2006. In addition, meteorological data recorded at 3 monitoring stations and boundary layer height (BLH) daily values from the ECMWF (European Centre for Medium Weather Forecast), ERA Interim, were also used. Based on the large-scale standard pressure fields from the ERA40/ECMWF, prevailing circulation patterns at regional scale where determined and used on the construction of the models. After the daily forecasts were produced, the difference between the average maps based on real observations and predicted values were determined and the model's performance was assessed. Based on the analysis of the results, we conclude that the proposed approach shows to be a very promising alternative for urban air quality characterization because of its good results and simplicity of application.

Evaluating the role of soil variability on groundwater pollution and recharge at regional scale by integrating a process-based vadose zone model in a stochastic approach

NASA Astrophysics Data System (ADS)

Coppola, Antonio; Comegna, Alessandro; Dragonetti, Giovanna; Lamaddalena, Nicola; Zdruli, Pandi

2013-04-01

Interpreting and predicting the evolution of water resources and soils at regional scale are continuing challenges for natural scientists. Examples include non-point source (NPS) pollution of soil and surface and subsurface water from agricultural chemicals and pathogens, as well as overexploitation of groundwater resources. The presence and build up of NPS pollutants may be harmful for both soil and groundwater resources. The accumulation of salts and trace elements in soils can significantly impact crop productivity, while loading of salts, nitrates, trace elements and pesticides into groundwater supplies can deteriorate a source of drinking and irrigation water. Consequently, predicting the spatial distribution and fate of NPS pollutants in soils at applicative scales is now considered crucial for maintaining the fragile balance between crop productivity and the negative environmental impacts of NPS pollutants, which is a basis of sustainable agriculture. Soil scientists and hydrologists are regularly asked to assist state agencies to understand these critical environmental issues. The most frequent inquiries are related to the development of mathematical models needed for analyzing the impacts of alternative land-use and best management use and management of soil and water resources. Different modelling solutions exist, mainly differing on the role of the vadose zone and its horizontal and vertical variability in the predictive models. The vadose zone (the region from the soil surface to the groundwater surface) is a complex physical, chemical and biological ecosystem that controls the passage of NPS pollutants from the soil surface where they have been deposited or accumulated due to agricultural activities, to groundwater. Physically based distributed hydrological models require the internal variability of the vadose zone be explored at a variety of scales. The equations describing fluxes and storage of water and solutes in the unsaturated zone used in these modelling approaches have been developed at small space scales. Their extension to the applicative macroscale of the regional model is not a simple task mainly because of the heterogeneity of vadose zone properties, as well as of non-linearity of hydrological processes. Besides, one of the problems when applying distributed models is that spatial and temporal scales for data to be used as input in the models vary on a wide range of scales and are not always consistent with the model structure. Under these conditions, a strictly deterministic response to questions about the fate of a pollutant in the soil is impossible. At best, one may answer "this is the average behaviour within this uncertainty band". Consequently, the extension of these equations to account for regional-scale processes requires the uncertainties of the outputs be taken into account if the pollution vulnerability maps that may be drawn are to be used as agricultural management tools. A map generated without a corresponding map of associated uncertainties has no real utility. The stochastic stream tube approach is a frequently used to the water flux and solute transport through the vadose zone at applicative scales. This approach considers the field soil as an ensemble of parallel and statistically independent tubes, assuming only vertical flow. The stream tubes approach is generally used in a probabilistic framework. Each stream tube defines local flow properties that are assumed to vary randomly between the different stream tubes. Thus, the approach allows average water and solute behaviour be described, along with the associated uncertainty bands. These stream tubes are usually considered to have parameters that are vertically homogeneous. This would be justified by the large difference between the horizontal and vertical extent of the spatial applicative scale. Vertical is generally overlooked. Obviously, all the model outputs are conditioned by this assumption. The latter, in turn, is more dictated by the lack of information on vertical variability of soil properties. It is our opinion that, with sufficient information on soil horizonation and with an appropriate horizontal resolution, it may be demonstrated that model outputs may be largely sensitive to the vertical variability of stream tubes, even at applicative scales. Horizon differentiation is one of the main observations made by pedologists while describing soils and most analytical data are given according to soil horizons. Over the last decades, soil horizonation has been subjected to regular monitoring for mapping soil variation at regional scales. Accordingly, this study mainly aims to developing a regional-scale simulation approach for vadose zone flow and transport that use real soil profiles data based on information on vertical variability of soils. As to the methodology, the parallel column concept was applied to account for the effect of vertical heterogeneity on variability of water flow and solute transport in the vadose zone. Even if the stream tube approach was mainly introduced for (unrealistic) vertically homogeneous soils, we extended their use to real vertically variable soils. The approach relies on available datasets coming from different sources and offers quantitative answers to soil and groundwater vulnerability to non-point source of chemicals and pathogens at regional scale within a defined confidence interval. This result will be pursued through the design and building up of a spatial database containing 1). Detailed pedological information, 2). Hydrological properties mainly measured in the investigated area in different soil horizons, 3). Water table depth, 4). Spatially distributed climatic temporal series, and 5). Land use. The area of interest for the study is located in the sub-basin of Metaponto agricultural site, located in southern Basilicata Region in Italy, covering approximately 11,698 hectares, crossed by two main rivers, Sinni and Agri and from many secondary water bodies. Distributed output of soil pollutant leaching behaviour, with corresponding statistical uncertainties, will be provided and finally visualized in GIS maps. The example pollutants considered cover much of the practical pollution conditions one may found in the reality. Nevertheless, this regional- scale methodology may be applied to any specific pollutants for any soil, climatic and land use conditions. Also, as the approach is built on physically based equations, it may be extended to the predictions of any water and solute storage and fluxes (i.e., groundwater recharge) in the vadose zone. By integrating the scientific results with economic and political considerations, and with advanced information technologies, the NPS-pollution assessment may become a powerful decision support tool for guiding activities involving soil and groundwater resources and, more in general, for managing environmental resources.

Spatiotemporal analysis of particulate matter, sulfur dioxide and carbon monoxide concentrations over the city of Rio de Janeiro, Brazil

NASA Astrophysics Data System (ADS)

Zeri, Marcelo; Oliveira-Júnior, José Francisco; Lyra, Gustavo Bastos

2011-09-01

Time series of pollutants and weather variables measured at four sites in the city of Rio de Janeiro, Brazil, between 2002 and 2004, were used to characterize temporal and spatial relationships of air pollution. Concentrations of particulate matter (PM10), sulfur dioxide (SO2) and carbon monoxide (CO) were compared to national and international standards. The annual median concentration of PM10 was higher than the standard set by the World Health Organization (WHO) on all sites and the 24 h means exceeded the standards on several occasions on two sites. SO2 and CO did not exceed the limits, but the daily maximum of CO in one of the stations was 27% higher on weekends compared to weekdays, due to increased activity in a nearby Convention Center. Air temperature and vapor pressure deficit have both presented the highest correlations with pollutant's concentrations. The concentrations of SO2 and CO were not correlated between sites, suggesting that local sources are more important to those pollutants compared to PM10. The time series of pollutants and air temperature were decomposed in time and frequency by wavelet analysis. The results revealed that the common variability of air temperature and PM10 is dominated by temporal scales of 1-8 days, time scales that are associated with the passage of weather events, such as cold fronts.

Soil quality assessment using GIS-based chemometric approach and pollution indices: Nakhlak mining district, Central Iran.

PubMed

Moore, Farid; Sheykhi, Vahideh; Salari, Mohammad; Bagheri, Adel

2016-04-01

This paper is a comprehensive assessment of the quality of soil in the Nakhlak mining district in Central Iran with special reference to potentially toxic metals. In this regard, an integrated approach involving geostatistical, correlation matrix, pollution indices, and chemical fractionation measurement is used to evaluate selected potentially toxic metals in soil samples. The fractionation of metals indicated a relatively high variability. Some metals (Mo, Ag, and Pb) showed important enrichment in the bioavailable fractions (i.e., exchangeable and carbonate), whereas the residual fraction mostly comprised Sb and Cr. The Cd, Zn, Co, Ni, Mo, Cu, and As were retained in Fe-Mn oxide and oxidizable fractions, suggesting that they may be released to the environment by changes in physicochemical conditions. The spatial variability patterns of 11 soil heavy metals (Ag, As, Cd, Co, Cr, Cu, Mo, Ni, Pb, Sb, and Zn) were identified and mapped. The results demonstrated that Ag, As, Cd, Mo, Cu, Pb, Sb, and Zn pollution are associated with mineralized veins and mining operations in this area. Further environmental monitoring and remedial actions are required for management of soil heavy metals in the study area. The present study not only enhanced our knowledge regarding soil pollution in the study area but also introduced a better technique to analyze pollution indices by multivariate geostatistical methods.

Spatio-temporal variation analysis of hydrochemical characteristics in the Luanhe River Basin, China.

PubMed

Xie, Ying; Li, Xuyong; Wang, Huiliang; Li, Wenzan

2013-01-01

The analysis of river pollution and assessment of spatial and temporal variation in hydrochemistry are essential to river water pollution control in the context of rapid economic growth and growing pollution threats in China. In this study, we focused on hydrochemical characteristics of the Luanhe River Basin (China) and evaluation of 12 hydrochemical variables obtained from 32 monitoring stations during 2001-2010. In each study year, the streams were monitored in the three hydrological periods (April, August, and October) to observe differences in the impacts of agricultural activity and rainfall pattern. Multivariate statistical methods were applied to the data set, and the river water hydrochemical characteristics were assessed using the water quality identification index (WQIIM). The results showed that parameters had variable contribution to water quality status in different months except for ammonia nitrogen (NH4-N) and total nitrogen (TN), which were the most important parameters in contributing to water quality variations for all three periods. Results of WQIIM revealed that 18 sites were classified as 'meeting standard' while the other 14 sites were classified as 'not meeting standard', with most of the seriously polluted sites located in urban area, mainly due to discharge of wastewater from domestic and industrial sources. Sites with low pollution level were located primarily in smaller tributaries, whereas sites of medium and high pollution levels were in the main river channel and the larger tributaries. Our findings provide valuable information and guidance for water pollution control and water resource management in the Luanhe River Basin.

Multivariate analysis of water quality and environmental variables in the Great Barrier Reef catchments

NASA Astrophysics Data System (ADS)

Ryu, D.; Liu, S.; Western, A. W.; Webb, J. A.; Lintern, A.; Leahy, P.; Wilson, P.; Watson, M.; Waters, D.; Bende-Michl, U.

2016-12-01

The Great Barrier Reef (GBR) lagoon has been experiencing significant water quality deterioration due in part to agricultural intensification and urban settlement in adjacent catchments. The degradation of water quality in rivers is caused by land-derived pollutants (i.e. sediment, nutrient and pesticide). A better understanding of dynamics of water quality is essential for land management to improve the GBR ecosystem. However, water quality is also greatly influenced by natural hydrological processes. To assess influencing factors and predict the water quality accurately, selection of the most important predictors of water quality is necessary. In this work, multivariate statistical techniques - cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) - are used to reduce the complexity derived from the multidimensional water quality monitoring data. Seventeen stations are selected across the GBR catchments, and the event-based measurements of 12 variables monitored during 9 years (2006 - 2014) were analysed by means of CA and PCA/FA. The key findings are: (1) 17 stations can be grouped into two clusters according to the hierarchical CA, and the spatial dissimilarity between these sites is characterised by the different climatic and land use in the GBR catchments. (2) PCA results indicate that the first 3 PCs explain 85% of the total variance, and FA on the entire data set shows that the varifactor (VF) loadings can be used to interpret the sources of spatial variation in water quality on the GBR catchments level. The impact of soil erosion and non-point source of pollutants from agriculture contribution to VF1 and the variability in hydrological conditions and biogeochemical processes can explain the loadings in VF2. (3) FA is also performed on two groups of sites identified in CA individually, to evaluate the underlying sources that are responsible for spatial variability in water quality in the two groups. For the Cluster 1 sites, spatial variations in water quality are likely from the agricultural inputs (fertilises) and for the Cluster 2 sites, the differences in hydrological transport is responsible for large spatial variations in water quality. These findings can be applied to water quality assessment along with establish effective water and land management in the future.

[Multiple time scales analysis of spatial differentiation characteristics of non-point source nitrogen loss within watershed].

PubMed

Liu, Mei-bing; Chen, Xing-wei; Chen, Ying

2015-07-01

Identification of the critical source areas of non-point source pollution is an important means to control the non-point source pollution within the watershed. In order to further reveal the impact of multiple time scales on the spatial differentiation characteristics of non-point source nitrogen loss, a SWAT model of Shanmei Reservoir watershed was developed. Based on the simulation of total nitrogen (TN) loss intensity of all 38 subbasins, spatial distribution characteristics of nitrogen loss and critical source areas were analyzed at three time scales of yearly average, monthly average and rainstorms flood process, respectively. Furthermore, multiple linear correlation analysis was conducted to analyze the contribution of natural environment and anthropogenic disturbance on nitrogen loss. The results showed that there were significant spatial differences of TN loss in Shanmei Reservoir watershed at different time scales, and the spatial differentiation degree of nitrogen loss was in the order of monthly average > yearly average > rainstorms flood process. TN loss load mainly came from upland Taoxi subbasin, which was identified as the critical source area. At different time scales, land use types (such as farmland and forest) were always the dominant factor affecting the spatial distribution of nitrogen loss, while the effect of precipitation and runoff on the nitrogen loss was only taken in no fertilization month and several processes of storm flood at no fertilization date. This was mainly due to the significant spatial variation of land use and fertilization, as well as the low spatial variability of precipitation and runoff.

Participatory measurements of individual exposure to air pollution in urban areas

NASA Astrophysics Data System (ADS)

Madelin, Malika; Duché, Sarah; Dupuis, Vincent

2016-04-01

Air pollution is a major environmental issue in urban areas. Chronic and high concentration exposure presents a health risk with cardiovascular and respiratory problems and longer term nervous, carcinogenic and endocrine problems. In addition to the estimations based on simulations of both background and regional pollution and of the pollution induced by the traffic, knowing exposure of each individual is a key issue. This exposure reflects the high variability of pollution at fine spatial and time scales, according to the proximity of emission sources and the urban morphology outside. The emergence of citizen science and the progress of miniaturized electronics, low-cost and accessible to (almost) everyone, offers new opportunities for the monitoring of air pollution, but also for the citizens' awareness of their individual exposure to air pollution. In this communication, we propose to present a participatory research project 'What is your air?' (project funded by the Île-de-France region), which aims at raising awareness on the theme of air quality, its monitoring with sensors assembled in a FabLab workshop and an online participatory mapping. Beyond the discussion on technical choices, the stages of manufacture or the sensor calibration procedures, we discuss the measurements made, in this case the fine particle concentration measurements, which are dated and georeferenced (communication via a mobile phone). They show high variability between the measurements (in part linked to the substrates, land use, traffic) and low daily contrasts. In addition to the analysis of the measurements and their comparison with the official data, we also discuss the choice of representation of information, including mapping, and therefore the message about pollution to communicate.

Comparison of spatiotemporal prediction models of daily exposure of individuals to ambient nitrogen dioxide and ozone in Montreal, Canada.

PubMed

Buteau, Stephane; Hatzopoulou, Marianne; Crouse, Dan L; Smargiassi, Audrey; Burnett, Richard T; Logan, Travis; Cavellin, Laure Deville; Goldberg, Mark S

2017-07-01

In previous studies investigating the short-term health effects of ambient air pollution the exposure metric that is often used is the daily average across monitors, thus assuming that all individuals have the same daily exposure. Studies that incorporate space-time exposures of individuals are essential to further our understanding of the short-term health effects of ambient air pollution. As part of a longitudinal cohort study of the acute effects of air pollution that incorporated subject-specific information and medical histories of subjects throughout the follow-up, the purpose of this study was to develop and compare different prediction models using data from fixed-site monitors and other monitoring campaigns to estimate daily, spatially-resolved concentrations of ozone (O 3 ) and nitrogen dioxide (NO 2 ) of participants' residences in Montreal, 1991-2002. We used the following methods to predict spatially-resolved daily concentrations of O 3 and NO 2 for each geographic region in Montreal (defined by three-character postal code areas): (1) assigning concentrations from the nearest monitor; (2) spatial interpolation using inverse-distance weighting; (3) back-extrapolation from a land-use regression model from a dense monitoring survey, and; (4) a combination of a land-use and Bayesian maximum entropy model. We used a variety of indices of agreement to compare estimates of exposure assigned from the different methods, notably scatterplots of pairwise predictions, distribution of differences and computation of the absolute agreement intraclass correlation (ICC). For each pairwise prediction, we also produced maps of the ICCs by these regions indicating the spatial variability in the degree of agreement. We found some substantial differences in agreement across pairs of methods in daily mean predicted concentrations of O 3 and NO 2 . On a given day and postal code area the difference in the concentration assigned could be as high as 131ppb for O 3 and 108ppb for NO 2 . For both pollutants, better agreement was found between predictions from the nearest monitor and the inverse-distance weighting interpolation methods, with ICCs of 0.89 (95% confidence interval (CI): 0.89, 0.89) for O 3 and 0.81 (95%CI: 0.80, 0.81) for NO 2 , respectively. For this pair of methods the maximum difference on a given day and postal code area was 36ppb for O 3 and 74ppb for NO 2 . The back-extrapolation method showed a higher degree of disagreement with the nearest monitor approach, inverse-distance weighting interpolation, and the Bayesian maximum entropy model, which were strongly constrained by the sparse monitoring network. The maps showed that the patterns of agreement differed across the postal code areas and the variability depended on the pair of methods compared and the pollutants. For O 3 , but not NO 2 , postal areas showing greater disagreement were mostly located near the city centre and along highways, especially in maps involving the back-extrapolation method. In view of the substantial differences in daily concentrations of O 3 and NO 2 predicted by the different methods, we suggest that analyses of the health effects from air pollution should make use of multiple exposure assessment methods. Although we cannot make any recommendations as to which is the most valid method, models that make use of higher spatially resolved data, such as from dense exposure surveys or from high spatial resolution satellite data, likely provide the most valid estimates. Copyright © 2017 Elsevier Inc. All rights reserved.

Lessons Learned from OMI Observations of Point Source SO2 Pollution

NASA Technical Reports Server (NTRS)

Krotkov, N.; Fioletov, V.; McLinden, Chris

2011-01-01

The Ozone Monitoring Instrument (OMI) on NASA Aura satellite makes global daily measurements of the total column of sulfur dioxide (SO2), a short-lived trace gas produced by fossil fuel combustion, smelting, and volcanoes. Although anthropogenic SO2 signals may not be detectable in a single OMI pixel, it is possible to see the source and determine its exact location by averaging a large number of individual measurements. We describe new techniques for spatial and temporal averaging that have been applied to the OMI SO2 data to determine the spatial distributions or "fingerprints" of SO2 burdens from top 100 pollution sources in North America. The technique requires averaging of several years of OMI daily measurements to observe SO2 pollution from typical anthropogenic sources. We found that the largest point sources of SO2 in the U.S. produce elevated SO2 values over a relatively small area - within 20-30 km radius. Therefore, one needs higher than OMI spatial resolution to monitor typical SO2 sources. TROPOMI instrument on the ESA Sentinel 5 precursor mission will have improved ground resolution (approximately 7 km at nadir), but is limited to once a day measurement. A pointable geostationary UVB spectrometer with variable spatial resolution and flexible sampling frequency could potentially achieve the goal of daily monitoring of SO2 point sources and resolve downwind plumes. This concept of taking the measurements at high frequency to enhance weak signals needs to be demonstrated with a GEOCAPE precursor mission before 2020, which will help formulating GEOCAPE measurement requirements.

Abundance and fragmentation patterns of the ecosystem engineer Lithophyllum byssoides (Lamarck) Foslie along the Iberian Peninsula Atlantic coast. Conservation and management implications

NASA Astrophysics Data System (ADS)

Veiga, Puri; Rubal, Marcos; Cacabelos, Eva; Moreira, Juan; Sousa-Pinto, Isabel

2013-10-01

The crustose calcareous red macroalgae Lithophyllum byssoides (Lamarck) Foslie is a common ecosystem engineer along the Atlantic and Mediterranean coast of the Iberian Peninsula. This species is threatened by several anthropogenic impacts acting at different spatial scales, such as pollution or global warming. The aim of this study is to identify scales of spatial variation in the abundance and fragmentation patterns of L. byssoides along the Atlantic coast of the Iberian Peninsula. For this aim we used a hierarchical sampling design considering four spatial scales (from metres to 100s of kilometres). Results of the present study indicated no significant variability among regions investigated whereas significant variability was found at the scales of shore and site in spatial patterns of abundance and fragmentation of L. byssoides. Variance components were higher at the spatial scale of shore for abundance and fragmentation of L. byssoides with the only exception of percentage cover and thus, processes acting at the scale of 10s of kilometres seem to be more relevant in shaping the spatial variability both in abundance and fragmentation of L. byssoides. These results provided quantitative estimates of abundance and fragmentation of L. byssoides at the Atlantic coast of the Iberian Peninsula establishing the observational basis for future assessment, monitoring and experimental investigations to identify the processes and anthropogenic impacts affecting L. byssoides populations. Finally we have also identified percentage cover and patch density as the best variables for long-term monitoring programs aimed to detect future anthropogenic impacts on L. byssoides. Therefore, our results have important implications for conservation and management of this valuable ecosystem engineer along the Atlantic coast of the Iberian Peninsula.

Using a Data-Driven Approach to Understand the Interaction between Catchment Characteristics and Water Quality Responses

NASA Astrophysics Data System (ADS)

Western, A. W.; Lintern, A.; Liu, S.; Ryu, D.; Webb, J. A.; Leahy, P.; Wilson, P.; Waters, D.; Bende-Michl, U.; Watson, M.

2016-12-01

Many streams, lakes and estuaries are experiencing increasing concentrations and loads of nutrient and sediments. Models that can predict the spatial and temporal variability in water quality of aquatic systems are required to help guide the management and restoration of polluted aquatic systems. We propose that a Bayesian hierarchical modelling framework could be used to predict water quality responses over varying spatial and temporal scales. Stream water quality data and spatial data of catchment characteristics collected throughout Victoria and Queensland (in Australia) over two decades will be used to develop this Bayesian hierarchical model. In this paper, we present the preliminary exploratory data analysis required for the development of the Bayesian hierarchical model. Specifically, we present the results of exploratory data analysis of Total Nitrogen (TN) concentrations in rivers in Victoria (in South-East Australia) to illustrate the catchment characteristics that appear to be influencing spatial variability in (1) mean concentrations of TN; and (2) the relationship between discharge and TN throughout the state. These important catchment characteristics were identified using: (1) monthly TN concentrations measured at 28 water quality gauging stations and (2) climate, land use, topographic and geologic characteristics of the catchments of these 28 sites. Spatial variability in TN concentrations had a positive correlation to fertiliser use in the catchment and average temperature. There were negative correlations between TN concentrations and catchment forest cover, annual runoff, runoff perenniality, soil erosivity and catchment slope. The relationship between discharge and TN concentrations showed spatial variability, possibly resulting from climatic and topographic differences between the sites. The results of this study will feed into the hierarchical Bayesian model of river water quality.

Outlier Detection in Urban Air Quality Sensor Networks.

PubMed

van Zoest, V M; Stein, A; Hoek, G

2018-01-01

Low-cost urban air quality sensor networks are increasingly used to study the spatio-temporal variability in air pollutant concentrations. Recently installed low-cost urban sensors, however, are more prone to result in erroneous data than conventional monitors, e.g., leading to outliers. Commonly applied outlier detection methods are unsuitable for air pollutant measurements that have large spatial and temporal variations as occur in urban areas. We present a novel outlier detection method based upon a spatio-temporal classification, focusing on hourly NO 2 concentrations. We divide a full year's observations into 16 spatio-temporal classes, reflecting urban background vs. urban traffic stations, weekdays vs. weekends, and four periods per day. For each spatio-temporal class, we detect outliers using the mean and standard deviation of the normal distribution underlying the truncated normal distribution of the NO 2 observations. Applying this method to a low-cost air quality sensor network in the city of Eindhoven, the Netherlands, we found 0.1-0.5% of outliers. Outliers could reflect measurement errors or unusual high air pollution events. Additional evaluation using expert knowledge is needed to decide on treatment of the identified outliers. We conclude that our method is able to detect outliers while maintaining the spatio-temporal variability of air pollutant concentrations in urban areas.

Quantifying Neighborhood-Scale Spatial Variations of Ozone at Open Space and Urban Sites in Boulder, Colorado Using Low-Cost Sensor Technology.

PubMed

Cheadle, Lucy; Deanes, Lauren; Sadighi, Kira; Gordon Casey, Joanna; Collier-Oxandale, Ashley; Hannigan, Michael

2017-09-10

Recent advances in air pollution sensors have led to a new wave of low-cost measurement systems that can be deployed in dense networks to capture small-scale spatio-temporal variations in ozone, a pollutant known to cause negative human health impacts. This study deployed a network of seven low-cost ozone metal oxide sensor systems (UPods) in both an open space and an urban location in Boulder, Colorado during June and July of 2015, to quantify ozone variations on spatial scales ranging from 12 m between UPods to 6.7 km between open space and urban measurement sites with a measurement uncertainty of ~5 ppb. The results showed spatial variability of ozone at both deployment sites, with the largest differences between UPod measurements occurring during the afternoons. The peak median hourly difference between UPods was 6 ppb at 1:00 p.m. at the open space site, and 11 ppb at 4:00 p.m. at the urban site. Overall, the urban ozone measurements were higher than in the open space measurements. This study evaluates the effectiveness of using low-cost sensors to capture microscale spatial and temporal variation of ozone; additionally, it highlights the importance of field calibrations and measurement uncertainty quantification when deploying low-cost sensors.

Evaluation of Land Use Regression Models for Nitrogen Dioxide and Benzene in Four US Cities

PubMed Central

Mukerjee, Shaibal; Smith, Luther; Neas, Lucas; Norris, Gary

2012-01-01

Spatial analysis studies have included the application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO2) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks in El Paso and Dallas, TX, Detroit, MI, and Cleveland, OH to assess spatial variability and source influences. LURs were successfully developed to estimate pollutant concentrations throughout the study areas. Comparisons of development and predictive capabilities of LURs from these four cities are presented to address this issue of uniform application of LURs across study areas. Traffic and other urban variables were important predictors in the LURs although city-specific influences (such as border crossings) were also important. In addition, transferability of variables or LURs from one city to another may be problematic due to intercity differences and data availability or comparability. Thus, developing common predictors in future LURs may be difficult. PMID:23226985

Determinants of the Spatial Distributions of Elemental Carbon and Particulate Matter in Eight Southern Californian Communities

PubMed Central

Urman, Robert; Gauderman, James; Fruin, Scott; Lurmann, Fred; Liu, Feifei; Hosseini, Reza; Franklin, Meredith; Avol, Edward; Penfold, Bryan; Gilliland, Frank; Brunekreef, Bert; McConnell, Rob

2014-01-01

Emerging evidence indicates that near-roadway pollution (NRP) in ambient air has adverse health effects. However, specific components of the NRP mixture responsible for these effects have not been established. A major limitation for health studies is the lack of exposure models that estimate NRP components observed in epidemiological studies over fine spatial scale of tens to hundreds of meters. In this study, exposure models were developed for fine-scale variation in biologically relevant elemental carbon (EC). Measurements of particulate matter (PM) and EC less than 2.5 μm in aerodynamic diameter (EC2.5) and of PM and EC of nanoscale size less than 0.2 μm were made at up to 29 locations in each of eight Southern California Children's Health Study communities. Regression-based prediction models were developed using a guided forward selection process to identify traffic variables and other pollutant sources, community physical characteristics and land use as predictors of PM and EC variation in each community. A combined eight-community model including only CALINE4 near-roadway dispersion-estimated vehicular emissions accounting for distance, distance-weighted traffic volume, and meteorology, explained 51% of the EC0.2 variability. Community-specific models identified additional predictors in some communities; however, in most communities the correlation between predicted concentrations from the eight-community model and observed concentrations stratified by community were similar to those for the community-specific models. EC2.5 could be predicted as well as EC0.2. EC2.5 estimated from CALINE4 and population density explained 53% of the within-community variation. Exposure prediction was further improved after accounting for between-community heterogeneity of CALINE4 effects associated with average distance to Pacific Ocean shoreline (to 61% for EC0.2) and for regional NOx pollution (to 57% for EC2.5). PM fine spatial scale variation was poorly predicted in both size fractions. In conclusion, models of exposure that include traffic measures such as CALINE4 can provide useful estimates for EC0.2 and EC2.5 on a spatial scale appropriate for health studies of NRP in selected Southern California communities. PMID:25313293

Landcover change and light pollution in Kota Bandarlampung

NASA Astrophysics Data System (ADS)

Rohman, Akmal F.; Hafidz, Muhammad; Hazairin, Azra Q.; Riadini, Fitri

2016-10-01

Excessive emission of light or light pollution at night is one of the elements of environmental pollution. Indirectly light pollution causes increase of fossil fuel use, greenhouse gasses and pollution in the atmosphere. Direct effects of light pollution including: disturbance of animals life, human's psychology and environmental degradation. Light pollution in an area is related with the existence of built-up area and the lack of vegetation as a manifestation of economic and population growth. This research aims to know the relation of land cover changes with light pollution in Bandar Lampung and surrounding with 40 km radius over the last ten years. This research used satellite imagery to obtained data and later does the verification and accuracy tests on the field. The variables used in this research include light pollution radiance value, percentages in the built-up area and vegetation density. Light pollution radiance value is obtained from DMSP-OLS Version 4 satellite images, while the changes of built up and vegetation density data obtained from NDBI dan NDVI from Landsat 8 satellite images. The research area is divided into a grid with a size of 30"×30" which is the same as spatial resolution of DMSP. From sample grids, regression analysis between the percentage of light pollution radiance value with the percentage of NDVI and NDBI index on each grids. The percentages of built up areas and vegetation has 58 % of fair correlation with light emission.

Biomonitoring approach with mussel Mytilus galloprovincialis (Lmk) and clam Ruditapes philippinarum (Adams and Reeve, 1850) in the Lagoon of Venice.

PubMed

Moschino, Vanessa; Delaney, Eugenia; Meneghetti, Francesca; Ros, Luisa Da

2011-06-01

Transplanted Mytilus galloprovincialis and native Ruditapes philippinarum were deployed in 10 sampling stations with different pollution impact within the Lagoon of Venice to evaluate the temporal variations and the suitability of the following cytochemical and histochemical biomarkers just as indicators of environmental stress: lysosomal membrane stability, lipofuscins, neutral lipids and lysosome to cytoplasm volume ratio. The physiological status of the organisms was also investigated by determining the survival in air capability and the reburrowing rate (clams). The biological parameters were assessed in June and October. Furthermore, for a better definition of the environmental aspects of the study sites, heavy metal, PAH and PCB concentrations were also evaluated in the sediments. As a whole, the biological responses examined in both species from all the sampling sites showed significant differences between the two seasonal campaigns, only lysosomal membrane stability exhibited less variability. Pollutants in sediments generally showed low-intermediate contamination levels, few hotspots persisting mostly in the inner areas of the lagoon, the most influenced by the industrial zone. Transplanted mussels were more responsive than native clams and the biological responses of both species varied temporally. The range of the spatial variability was always narrow and reflected only partially the broader variability shown by the chemical content in the sediments. In this sense, biological responses seemed to be particularly influenced by the high temporal and spatial heterogeneity that characterise the Lagoon of Venice, as well as most of the transitional environments.

Combine the soil water assessment tool (SWAT) with sediment geochemistry to evaluate diffuse heavy metal loadings at watershed scale.

PubMed

Jiao, Wei; Ouyang, Wei; Hao, Fanghua; Huang, Haobo; Shan, Yushu; Geng, Xiaojun

2014-09-15

Assessing the diffuse pollutant loadings at watershed scale has become increasingly important when formulating effective watershed water management strategies, but the process was seldom achieved for heavy metals. In this study, the overall temporal-spatial variability of particulate Pb, Cu, Cr and Ni losses within an agricultural watershed was quantitatively evaluated by combining SWAT with sediment geochemistry. Results showed that the watershed particulate heavy metal loadings displayed strong variability in the simulation period 1981-2010, with an obvious increasing trend in recent years. The simulated annual average loadings were 20.21 g/ha, 21.75 g/ha, 47.35 g/ha and 21.27 g/ha for Pb, Cu, Cr and Ni, respectively. By comparison, these annual average values generally matched the estimated particulate heavy metal loadings at field scale. With spatial interpolation of field loadings, it was found that the diffuse heavy metal pollution mainly came from the sub-basins dominated with cultivated lands, accounting for over 70% of total watershed loadings. The watershed distribution of particulate heavy metal losses was very similar to that of soil loss but contrary to that of heavy metal concentrations in soil, highlighting the important role of sediment yield in controlling the diffuse heavy metal loadings. Copyright © 2014 Elsevier B.V. All rights reserved.

Long-term diffuse phosphorus pollution dynamics under the combined influence of land use and soil property variations.

PubMed

Huang, Haobo; Ouyang, Wei; Wu, Haotian; Liu, Hongbin; Andrea, Critto

2017-02-01

Analyses of the spatial-temporal distribution of diffuse pollution in agricultural regions are essential to the sustained management of water resources. Although nutrients, such as phosphorus fertilizers, can promote crop growth while improving soil fertility, excessive nutrient inputs can produce diffuse pollution, which may results in water quality degradation. The objective of this paper is to employ the SWAT (Soil and Water Assessment Tool) to estimate diffuse P effects on temporal and spatial distributions for a typical agricultural watershed and to identify the conjunct and independent influences of long-term land use and soil properties variation on diffuse P. With the validated model, the four-period simulation results (from 1979 to 2009) indicate that land use changes from agricultural development increased diffuse P yields. However, regarding updated soil properties, no significant differences of P yield were found between 1979 and 2009, demonstrating that impact of the cropland expansion were naturalized with soil property variations. An F-test was employed to assess the essentiality of all of the variables examined during the simulation period, and the test results indicated that diffuse P loading was more sensitive to soil properties than to land use. Before the P pollution control project about the land use optimization planning, it is more effective to distinguish the impacts of land use and soil properties. Copyright © 2016 Elsevier B.V. All rights reserved.

Analysis of a long-term measurement of air pollutants (2007-2011) in North China Plain (NCP); Impact of emission reduction during the Beijing Olympic Games.

PubMed

Xu, Ruiguang; Tang, Guiqian; Wang, Yuesi; Tie, Xuexi

2016-09-01

Five years measurements were used to evaluate the effect of emission controls on the changes of air pollutants in Beijing and its surroundings in the NCP during 2008 Olympic Games (2008OG). The major challenge of this study was to filter out the effect of variability of meteorological conditions, when compared the air pollutants during the game to non-game period. We used four-year (2007, 2009-2011) average as the Non-2008OG to smooth the temporal variability caused by meteorological parameters. To study the spatial variability and regional transport, 6 sites (urban, rural, a mega city, a heavy industrial city, and a remote site) were selected. The result showed that the annually meteorological variability was significantly reduced. Such as, in BJ the differences between 2008OG and 5-years averaged values were 2.7% for relative humidity and 0.6% for wind speed. As a result, the anomaly of air pollutants between 2008OG and Non-2008OG can largely attribute to the emission control. The comparison showed that the major pollutants (PM10, PM2.5, NO, NOx) at the 6 sites in 2008OG were consistently lowered. For example, PM2.5 in BJ decreased from 75 to 45 μg/m(3) (40% reduction). However, the emission controls had minor effect on O3 concentrations (1% reduction). In contrast, the O3 precursor (NOx) reduced from 19.7 to 13.2 ppb (33% reduction). The in-sensitivity between NOx and O3 suggested that the O3 formation was under VOCs control condition in NCP, showing that strong VOC emission control is needed in order to significantly reduce O3 concentration in the region. Copyright © 2016 Elsevier Ltd. All rights reserved.

Surface NO2 fields derived from joint use of OMI and GOME-2A observations with EMEP model output

NASA Astrophysics Data System (ADS)

Schneider, Philipp; Svendby, Tove; Stebel, Kerstin

2016-04-01

Nitrogen dioxide (NO2) is one of the most prominent air pollutants. Emitted primarily by transport and industry, NO2 has a major impact on health and economy. In contrast to the very sparse network of air quality monitoring stations, satellite data of NO2 is ubiquitous and allows for quantifying the NO2 levels worldwide. However, one drawback of satellite-derived NO2 products is that they provide solely an estimate of the entire tropospheric column, whereas what is generally needed for air quality applications are the concentrations of NO2 near the surface. Here we derive surface NO2 concentration fields from OMI and GOME-2A tropospheric column products using the EMEP chemical transport model as auxiliary information. The model is used for providing information of the boundary layer contribution to the total tropospheric column. For preparation of deriving the surface product, a comprehensive model-based analysis of the spatial and temporal patterns of the NO2 surface-to-column ratio in Europe was carried out for the year 2011. The results from this analysis indicate that the spatial patterns of the surface-to-column ratio vary only slightly. While the highest ratio values can be found in some shipping lanes, the spatial variability of the ratio in some of the most polluted areas of Europe is not very high. Some but not all urban agglomeration shows high ratio values. Focusing on the temporal behavior, the analysis showed that the European-wide average ratio varies throughout the year. The surface-to-column ratio increases from January all the way through April when it reaches its maximum, then decreases relatively rapidly to average levels and then stays mostly constant throughout the summer. The minimum ratio is observed in December. The knowledge gained from analyzing the spatial and temporal patterns of the surface-to-column ratio was then used to produce surface NO2 products from the daily NO2 data for OMI and GOME-2A. This was carried out using two methods, namely using 1) hourly surface-to-column ratio at the time of the satellite overpass as well as 2) using annual average ratios thus eliminating the temporal variability and focusing solely on the spatial patterns. A validation of the resulting surface NO2 fields was performed using station observations of NO2 as provided by the Airbase database maintained by the European Environment Agency. First results indicate that the methodology is capable of producing surface concentration fields that reproduce the station-observed surface NO2 levels significantly better than the model surface fields as measured by the root mean squared error. The results also show that the spatial patterns of the surface-to-column ratio are more significant than its temporal variability. In addition to deriving satellite-based surface NO2, we further present initial results of a geostatistical methodology for downscaling satellite products of NO2 to spatial scales that are more relevant for applications in urban air quality. This is being carried out by applying area-to-point kriging techniques while using high-resolution (1-2 km spatial resolution) runs of a chemical transport model as a spatial proxy. In combination, these two techniques for deriving surface NO2 and spatially downscaling satellite-based NO2 fields have significant potential for improving satellite-based monitoring and mapping of regional and local-scale air pollution.

Spatio-temporal Variations of Nitrogen Dioxide Pollution in China, 2005-2015

NASA Astrophysics Data System (ADS)

Cui, Yuanzheng

China has experienced rapid economic growth in recent decades, accompanied with intensive urbanization and industrialization. This economic growth has led to many significant environmental problems, including deteriorating nitrogen dioxide (NO2) pollution. NO2 is a key air pollutant, and it plays a major role in the tropospheric chemistry. This thesis investigates the extent to which the characteristics of NO2 pollution changes at different spatial and temporal scales reflects regional inequality in economic and environmental policies enforced by Chinese governments, which has important implications for future emission control. The objective of this thesis is to investigate the spatial and temporal variability and trends of tropospheric NO2 pollution over China, by analyzing the NO2 vertical column density (VCD) data over 2005 to 2015 retrieved from the space-borne Ozone Monitoring Instrument (OMI). It is found that over most polluted regions in China, the NO2 columns increased rapidly since 2005, reached their peaks around 2011, and started to decline afterwards. Over parts of Eastern China, the NO2 levels in 2015 were close to the levels in 2005. Furthermore, severe pollution has extended from the traditional highly developed regions in Eastern China to newly emerged cities clusters in the west. Further comparisons with GEOS-Chem model simulations for 2005-2012 confirm that the OMI observed NO2 trends were driven mainly by changes in anthropogenic emissions. Then the long-term trends of NO2 over 2005-2013 from other scales of temporal variability over provincial-level regions of Western China were further distinguished, by using a wavelet decomposition analysis. The anthropogenic NO2 grew rapidly over Western China at a regional average rate of 8.6 +/- 0.9% yr-1 between 2005 and 2013. The rate of NO2 growth during 2005-2013 reached 11.3 +/- 1.0% yr-1 over Northwestern China, exceeding the rates over Southwestern China (5.9 +/- 0.6 % yr-1) and the three well-known polluted regions in the east (5.3 +/- 0.8% yr-1 over Beijing-Tianjin-Hebei, 4.0 +/- 0.6% yr-1 over the Yangtze River Delta, and -3.3 +/- 0.3% yr-1 over the Pearl River Delta). Subsequent socioeconomic analyses suggest that the rapid NO 2 growth over Northwestern China is likely related to the fast developing resource- and pollution-intensive industries along with the "Go West" movement as well as relatively weak emission controls. Finally, the signal of urbanization between 2005 and 2015 in the prefectural-level cities was identified from the OMI NO2 data, by using a high-resolution NO2 VCD dataset (with a spatial resolution of 0.125° x 0.125°) to derive three relevant metrics ("size relative trend" as a metric for urban area expansion, "mean relative trend" for mean urban pollution, and "max relative trend" for peak intensity). Additionally, the comparisons of the spatial distribution of annual mean NO 2 VCDs over China with night light data and population density data in 2010 show the similar spatial patterns of NO2, nighttime light and population density: the correlation coefficients get to 0.74 between NO 2 and night light and 0.66 between NO2 and population density. This highlights the consequences of urbanization for pollution and health impacts. Finally, a geographically and temporally weighted regression (GTWR) model is employed to explore the spatio-temporal relationship between NO 2 pollution and GDP at a city level over 2005-2013. The GTWR model results show that cities with highest positive estimated parameters were mainly those less developed cities from inland China by providing energy sources and semi-products to coastal cities in the east. For most cities, the NO2 pollution per unit of GDP has declined over 2005-2013, reflecting to some extent the success of local emission control that is accompanied with relocation of production and emissions from the east to the west and from richer to poorer areas. In summary, our results suggest that the central and local governments should move to achieve and maintain sustainable development both in Eastern China and in the west, perhaps starting by recognizing the importance of removing regional inequality in economic and environmental policies and by optimizing the eco-compensation mechanism and energy structure. More efforts should be made to alleviate NOx and other pollution to achieve sustainable development in Western China, in addition to reducing pollution in the east.

Chronic air pollution and social deprivation as modifiers of the association between high temperature and daily mortality

PubMed Central

2014-01-01

Background Heat and air pollution are both associated with increases in mortality. However, the interactive effect of temperature and air pollution on mortality remains unsettled. Similarly, the relationship between air pollution, air temperature, and social deprivation has never been explored. Methods We used daily mortality data from 2004 to 2009, daily mean temperature variables and relative humidity, for Paris, France. Estimates of chronic exposure to air pollution and social deprivation at a small spatial scale were calculated and split into three strata. We developed a stratified Poisson regression models to assess daily temperature and mortality associations, and tested the heterogeneity of the regression coefficients of the different strata. Deaths due to ambient temperature were calculated from attributable fractions and mortality rates were estimated. Results We found that chronic air pollution exposure and social deprivation are effect modifiers of the association between daily temperature and mortality. We found a potential interactive effect between social deprivation and chronic exposure with regards to air pollution in the mortality-temperature relationship. Conclusion Our results may have implications in considering chronically polluted areas as vulnerable in heat action plans and in the long-term measures to reduce the burden of heat stress especially in the context of climate change. PMID:24941876

Chronic air pollution and social deprivation as modifiers of the association between high temperature and daily mortality.

PubMed

Benmarhnia, Tarik; Oulhote, Youssef; Petit, Claire; Lapostolle, Annabelle; Chauvin, Pierre; Zmirou-Navier, Denis; Deguen, Séverine

2014-06-18

Heat and air pollution are both associated with increases in mortality. However, the interactive effect of temperature and air pollution on mortality remains unsettled. Similarly, the relationship between air pollution, air temperature, and social deprivation has never been explored. We used daily mortality data from 2004 to 2009, daily mean temperature variables and relative humidity, for Paris, France. Estimates of chronic exposure to air pollution and social deprivation at a small spatial scale were calculated and split into three strata. We developed a stratified Poisson regression models to assess daily temperature and mortality associations, and tested the heterogeneity of the regression coefficients of the different strata. Deaths due to ambient temperature were calculated from attributable fractions and mortality rates were estimated. We found that chronic air pollution exposure and social deprivation are effect modifiers of the association between daily temperature and mortality. We found a potential interactive effect between social deprivation and chronic exposure with regards to air pollution in the mortality-temperature relationship. Our results may have implications in considering chronically polluted areas as vulnerable in heat action plans and in the long-term measures to reduce the burden of heat stress especially in the context of climate change.

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

NASA Astrophysics Data System (ADS)

Chance, Kelly; Liu, Xiong; Suleiman, Raid M.; Flittner, David E.; Al-Saadi, Jassim; Janz, Scott J.

2014-06-01

TEMPO, selected by NASA as the first Earth Venture Instrument, will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO measures from Mexico City to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution. TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a GEO host spacecraft to provide a modest-cost mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, reducing uncertainty in air quality predictions by 50 %. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available. TEMPO makes the first tropospheric trace gas measurements from GEO, by building on the heritage of five spectrometers flown in low-earth-orbit (LEO). These LEO instruments measure the needed spectra, although at coarse spatial and temporal resolutions, to the precisions required for TEMPO and use retrieval algorithms developed for them by TEMPO Science Team members and currently running in operational environments. This makes TEMPO an innovative use of a well-proven technique, able to produce a revolutionary data set. TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. GEO-CAPE is not planned for implementation this decade. However, instruments from Europe (Sentinel 4) and Asia (GEMS) will form parts of a global GEO constellation for pollution monitoring later this decade, with a major focus on intercontinental pollution transport. TEMPO will launch at a prime time to be a component of this constellation.

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

NASA Astrophysics Data System (ADS)

Chance, K.; Liu, X.; Suleiman, R. M.; Flittner, D. E.; Janz, S. J.

2012-12-01

TEMPO is a proposed concept to measure pollution for greater North America using ultraviolet/visible spectroscopy. TEMPO measures from Mexico City to the Canadian tar/oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (9 km2). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, reducing uncertainty in air quality predictions by 50%. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available. TEMPO makes the first tropospheric trace gas measurements from GEO, by building on the heritage of five spectrometers flown in low-earth-orbit (LEO). These LEO instruments measure the needed spectra, although at coarse spatial and temporal resolutions, to the precisions required for TEMPO and use retrieval algorithms developed for them by TEMPO Science Team members and currently running in operational environments. This makes TEMPO an innovative use of a well proven technique, able to produce a revolutionary data set. TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. GEO-CAPE is not planned for implementation this decade. However, instruments from Europe (Sentinel 4) and Asia (GEMS) will form parts of a global GEO constellation for pollution monitoring later this decade, with a major focus on intercontinental pollution transport. TEMPO will launch at a prime time to be a component of this constellation.

Spatial-temporal migration laws of Cd in Jiaozhou Bay

NASA Astrophysics Data System (ADS)

Yang, Dongfang; Li, Haixia; Zhang, Xiaolong; Wang, Qi; Miao, Zhenqing

2018-02-01

Many marine bays have been polluted by various pollutants, and understanding the migration laws is essential to scientific research and pollution control. This paper analyzed the spatial and temporal migration laws of Cd in waters in Jiaozhou Bay during 1979—1983. Results showed that there were twenty spatial-temporal migration law for the migration processes of Cd. These laws were helpful for better understanding the migration of Cd in marine bay, providing basis for scientific research and pollution control.

Detection of spatial fluctuations of non-point source fecal pollution in coral reef surrounding waters in southwestern Puerto Rico using PCR-based assays.

PubMed

Bonkosky, M; Hernández-Delgado, E A; Sandoz, B; Robledo, I E; Norat-Ramírez, J; Mattei, H

2009-01-01

Human fecal contamination of coral reefs is a major cause of concern. Conventional methods used to monitor microbial water quality cannot be used to discriminate between different fecal pollution sources. Fecal coliforms, enterococci, and human-specific Bacteroides (HF183, HF134), general Bacteroides-Prevotella (GB32), and Clostridium coccoides group (CP) 16S rDNA PCR assays were used to test for the presence of non-point source fecal contamination across the southwestern Puerto Rico shelf. Inshore waters were highly turbid, consistently receiving fecal pollution from variable sources, and showing the highest frequency of positive molecular marker signals. Signals were also detected at offshore waters in compliance with existing microbiological quality regulations. Phylogenetic analysis showed that most isolates were of human fecal origin. The geographic extent of non-point source fecal pollution was large and impacted extensive coral reef systems. This could have deleterious long-term impacts on public health, local fisheries and in tourism potential if not adequately addressed.

Dynamic Analysis and Research on Environmental Pollution in China from 1992 to 2014

NASA Astrophysics Data System (ADS)

Sun, Fei; Yuan, Peng; Li, Huiting; Zhang, Moli

2018-01-01

The regular pattern of development of the environmental pollution events was analyzed from the perspective of statistical analysis of pollution events in recent years. The Moran, s I and spatial center-of-gravity shift curve of China, s environmental emergencies were calculated by ARCGIS software. And the method is global spatial analysis and spatial center of gravity shift. The results showed that the trend of China, s environmental pollution events from 1992 to 2014 was the first dynamic growth and then gradually reduced. Environmental pollution events showed spatial aggregation distribution in 1992-1994, 2001-2006, 2008-2014, and the rest of year was a random distribution of space. There were two stages in China, s environmental pollution events: The transition to the southwest from 1992 to 2006 and the transition to the northeast from the year of 2006 to 2014.

Satellite-aided evaluation of population exposure to air pollution

USGS Publications Warehouse

Todd, William J.; George, Anthony J.; Bryant, Nevin A.

1979-01-01

The Clean Air Act Amendments of 1977 set schedules for states to implement regional, spatial assessments of air quality impacts. Accordingly, the U.S. Environmental Protection Agency recently published guidelines for quantifying population exposure to adverse air quality impact by using air quality and population data by census tracts. Our research complements the EPA guidelines in that it demonstrates the ability to determine population exposure to air pollution through computer processing that utilizes Landsat satellite-derived land use information. Three variables-a 1985 estimate of total suspended particulates for 2-km2 grid cells, Landsat-derived residential land cover data for 0.45-ha cells, and population totals for census tracts-were spatially registered and cross-tabulated to produce tabular and map products illustrating relative air quality exposure for residential population by 2-km2 cells. It would cost $20,000 to replicate our analysis for an area similar in size to the 4000-km2 Portland area. Once completed, the spatially fine, computer-compatible air quality and population data are amenable to the timely and efficient generation of population-at-risk tabular and map information on a continuous or periodic basis.

COMPARISON OF SPATIAL PATTERNS OF POLLUTANT DISTRIBUTION WITH CMAQ PREDICTIONS

EPA Science Inventory

One indication of model performance is the comparison of spatial patterns of pollutants, either as concentration or deposition, predicted by the model with spatial patterns derived from measurements. If the spatial patterns produced by the model are similar to the observations i...

Multiscale Framework for Assessing Critical Loads of Atmospheric Nitrogen Deposition for Aquatic Ecosystems in Wilderness Areas of the Western United States

NASA Astrophysics Data System (ADS)

Nanus, Leora; Clow, David; Saros, Jasmine; McMurray, Jill; Blett, Tamara; Sickman, James

2017-04-01

High-elevation aquatic ecosystems in Wilderness areas of the western United States are impacted by current and historic atmospheric nitrogen (N) deposition associated with local and regional air pollution. Documented effects include elevated surface water nitrate concentrations, increased algal productivity, and changes in diatom species assemblages. A predictive framework was developed for sensitive high-elevation basins across the western United States at multiple spatial scales including the Rocky Mountain Region (Rockies), the Greater Yellowstone Area (GYA), and Yosemite (YOSE) and Sequoia & Kings Canyon (SEKI) National Parks. Spatial trends in critical loads of N deposition for nutrient enrichment of aquatic ecosystems were quantified and mapped using a geostatistical approach, with modeled N deposition, topography, vegetation, geology, and climate as potential explanatory variables. Multiple predictive models were created using various combinations of explanatory variables; this approach allowed for better quantification of uncertainty and identification of areas most sensitive to high atmospheric N deposition (> 3 kg N ha-1 yr-1). For multiple spatial scales, the lowest critical loads estimates (<1.5 + 1 kg N ha-1 yr-1) occurred in high-elevation basins with steep slopes, sparse vegetation, and exposed bedrock and talus. Based on a nitrate threshold of 1 μmol L-1, estimated critical load exceedances (>1.5 + 1 kg N ha-1 yr-1) correspond with areas of high N deposition and vary spatially ranging from less than 20% to over 40% of the study area for the Rockies, GYA, YOSE, and SEKI. These predictive models and maps identify sensitive aquatic ecosystems that may be impacted by excess atmospheric N deposition and can be used to help protect against future anthropogenic disturbance. The approach presented here may be transferable to other remote and protected high-elevation ecosystems at multiple spatial scales that are sensitive to adverse effects of pollutant loading in the US and around the world.

Measuring the value of air quality: application of the spatial hedonic model.

PubMed

Kim, Seung Gyu; Cho, Seong-Hoon; Lambert, Dayton M; Roberts, Roland K

2010-03-01

This study applies a hedonic model to assess the economic benefits of air quality improvement following the 1990 Clean Air Act Amendment at the county level in the lower 48 United States. An instrumental variable approach that combines geographically weighted regression and spatial autoregression methods (GWR-SEM) is adopted to simultaneously account for spatial heterogeneity and spatial autocorrelation. SEM mitigates spatial dependency while GWR addresses spatial heterogeneity by allowing response coefficients to vary across observations. Positive amenity values of improved air quality are found in four major clusters: (1) in East Kentucky and most of Georgia around the Southern Appalachian area; (2) in a few counties in Illinois; (3) on the border of Oklahoma and Kansas, on the border of Kansas and Nebraska, and in east Texas; and (4) in a few counties in Montana. Clusters of significant positive amenity values may exist because of a combination of intense air pollution and consumer awareness of diminishing air quality.

Risk perception of aquatic pollution originated from chemical industry clusters in the coastal area of Jiangsu province, China.

PubMed

Yao, Hong; Liu, Bo; You, Zhen; Zhao, Li

2018-02-01

According to "the Layout Scheme of the Chemical Industry in Jiangsu Province From 2016 to 2030" and "the Development Planning in the Coastal Area of Jiangsu Province, China," several chemical industry clusters will be located in the coastal area of Jiangsu province, China, and the risk of surface water pollution will be inevitably higher in the densely populated region. To get to know the risk acceptance level of the residents near the clusters, public perception was analyzed from the five risk factors: the basic knowledge about the pollution, the negative effects on aquatic environment imposed by the clusters, the positive effects brought by the clusters, the trust of controlling aquatic pollution, and the acceptance of the clusters. Twenty-four statements were screened out to describe the five factors, and about 600 residents were covered in three typical clusters surveyed. On the whole, the youth showed a higher interest on the survey, and middle-aged people were likely to be more concerned about aquatic pollution incident. There was no significant difference on risk perception of the three clusters. The respondents investigated had good knowledge background on aquatic pollution and the residents identified with the benefits brought by the clusters. They were weak in risk awareness of pollution originated from the chemical enterprises' groups. Although the respondents regarded that chemical industry clusters did not expose all points of pollutants' generation to the public, they inclined to trust the administration agencies on controlling the pollution and welcome the construction of chemical clusters in their dwelling cities. Besides, risk perception showed obvious spatial distribution. The closer were the samples' sites to the clusters and the rivers receiving pollutants, the higher were the residents' perceived risk, benefit, and trust. However, there was no identical spatial difference on risk acceptance, which might be comprehensively influenced by various factors. Demographic variables on diverse risk acceptance levels were further illustrated, and some useful conclusions might be provided for managing the response of residents to aquatic pollution and helping identify effective precautionary measures in the vicinity of chemical industry clusters.

InMAP: a new model for air pollution interventions

NASA Astrophysics Data System (ADS)

Tessum, C. W.; Hill, J. D.; Marshall, J. D.

2015-10-01

Mechanistic air pollution models are essential tools in air quality management. Widespread use of such models is hindered, however, by the extensive expertise or computational resources needed to run most models. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations - the air pollution outcome generally causing the largest monetized health damages - attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model (WRF-Chem) within an Eulerian modeling framework, to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. InMAP uses a variable resolution grid that focuses on human exposures by employing higher spatial resolution in urban areas and lower spatial resolution in rural and remote locations and in the upper atmosphere; and by directly calculating steady-state, annual average concentrations. In comparisons run here, InMAP recreates WRF-Chem predictions of changes in total PM2.5 concentrations with population-weighted mean fractional error (MFE) and bias (MFB) < 10 % and population-weighted R2 ~ 0.99. Among individual PM2.5 species, the best predictive performance is for primary PM2.5 (MFE: 16 %; MFB: 13 %) and the worst predictive performance is for particulate nitrate (MFE: 119 %; MFB: 106 %). Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. Features planned for future model releases include a larger spatial domain, more temporal information, and the ability to predict ground-level ozone (O3) concentrations. The InMAP model source code and input data are freely available online.

Tropospheric emissions: Monitoring of pollution (TEMPO)

NASA Astrophysics Data System (ADS)

Zoogman, P.; Liu, X.; Suleiman, R. M.; Pennington, W. F.; Flittner, D. E.; Al-Saadi, J. A.; Hilton, B. B.; Nicks, D. K.; Newchurch, M. J.; Carr, J. L.; Janz, S. J.; Andraschko, M. R.; Arola, A.; Baker, B. D.; Canova, B. P.; Chan Miller, C.; Cohen, R. C.; Davis, J. E.; Dussault, M. E.; Edwards, D. P.; Fishman, J.; Ghulam, A.; González Abad, G.; Grutter, M.; Herman, J. R.; Houck, J.; Jacob, D. J.; Joiner, J.; Kerridge, B. J.; Kim, J.; Krotkov, N. A.; Lamsal, L.; Li, C.; Lindfors, A.; Martin, R. V.; McElroy, C. T.; McLinden, C.; Natraj, V.; Neil, D. O.; Nowlan, C. R.; O`Sullivan, E. J.; Palmer, P. I.; Pierce, R. B.; Pippin, M. R.; Saiz-Lopez, A.; Spurr, R. J. D.; Szykman, J. J.; Torres, O.; Veefkind, J. P.; Veihelmann, B.; Wang, H.; Wang, J.; Chance, K.

2017-01-01

TEMPO was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution ( 2.1 km N/S×4.4 km E/W at 36.5°N, 100°W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), bromine monoxide (BrO), IO (iodine monoxide), water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

The effects of changes in cadmium and lead air pollution on cancer incidence in children.

PubMed

Absalon, Damian; Slesak, Barbara

2010-09-15

This article presents the results of research on the effects of air pollution on cancer incidence in children in the region of Silesia (Poland), which has undergone one of the most profound anthropogenic transformations in Europe. The main objective of the research was to specify the impact of changes in cadmium and lead pollution in the years 1990-2005 on the incidence of cancers reported in children. Lead concentration ranged from 0 to 1490 x 10(-9) G m(-2)/year, and cadmium concentration ranged from 0 to 33.7 x 10(-9) G m(-2)/year. There was no strong significant correlation (max 0.3) between air pollution and incidence rate (IR) in the general population of children in any particular year. Alongside the cartographic presentation of dependences, correlation coefficients between the variables in question were calculated. This made it possible to determine the relationship between the pollution levels and incidence rates in the area. There was a significant reduction in the level of pollution during the investigated period. The study of the relationship between the number of cancers reported and the condition of the natural environment revealed increased sensitivity to toxins in boys (correlation coefficient 0.3). In addition, the spatial distribution of the number of cases reported in boys suggests a correlation with the spatial distribution of the coefficients for the entire group of children included in the study. The yearly average IR of childhood cancer in specific districts ranged from 0 to 61.48/100,000 children under 18 years of age during the 1995-2004 period. Copyright 2010 Elsevier B.V. All rights reserved.

Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia.

PubMed

Pearce, John L; Waller, Lance A; Sarnat, Stefanie E; Chang, Howard H; Klein, Mitch; Mulholland, James A; Tolbert, Paige E

2016-08-01

Exposure metrics that identify spatial contrasts in multipollutant air quality are needed to better understand multipollutant geographies and health effects from air pollution. Our aim is to improve understanding of: (1) long-term spatial distributions of multiple pollutants; and (2) demographic characteristics of populations residing within areas of differing air quality. We obtained average concentrations for ten air pollutants (p=10) across a 12 km grid (n=253) covering Atlanta, Georgia for 2002-2008. We apply a self-organizing map (SOM) to our data to derive multipollutant patterns observed across our grid and classify locations under their most similar pattern (i.e, multipollutant spatial type (MST)). Finally, we geographically map classifications to delineate regions of similar multipollutant characteristics and characterize associated demographics. We found six MSTs well describe our data, with profiles highlighting a range of combinations, from locations experiencing generally clean air to locations experiencing conditions that were relatively dirty. Mapping MSTs highlighted that downtown areas were dominated by primary pollution and that suburban areas experienced relatively higher levels of secondary pollution. Demographics show the largest proportion of the overall population resided in downtown locations experiencing higher levels of primary pollution. Moreover, higher proportions of nonwhites and children in poverty reside in these areas when compared to suburban populations that resided in areas exhibiting relatively lower pollution. Our approach reveals the nature and spatial distribution of differential pollutant combinations across urban environments and provides helpful insights for identifying spatial exposure and demographic contrasts for future health studies. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Heavy metal assessment using geochemical and statistical tools in the surface sediments of Vembanad Lake, Southwest Coast of India.

PubMed

Selvam, A Paneer; Priya, S Laxmi; Banerjee, Kakolee; Hariharan, G; Purvaja, R; Ramesh, R

2012-10-01

The geochemical distribution and enrichment of ten heavy metals in the surface sediments of Vembanad Lake, southwest coast of India was evaluated. Sediment samples from 47 stations in the Lake were collected during dry and wet seasons in 2008 and examined for heavy metal content (Al, Fe, Mn, Cr, Zn, Ni, Pb, Cu, Co, Cd), organic carbon, and sediment texture. Statistically significant spatial variation was observed among all sediment variables, but negligible significant seasonal variation was observed. Correlation analysis showed that the metal content of sediments was mainly regulated by organic carbon, Fe oxy-hydroxides, and grain size. Principal component analysis was used to reduce the 14 sediment variables into three factors that reveal distinct origins or accumulation mechanisms controlling the chemical composition in the study area. Pollution intensity of the Vembanad Lake was measured using the enrichment factor and the pollution load index. Severe and moderately severe enrichment of Cd and Zn in the north estuary with minor enrichment of Pb and Cr were observed, which reflects the intensity of the anthropogenic inputs related to industrial discharge into this system. The results of pollution load index reveal that the sediment was heavily polluted in northern arm and moderately polluted in the extreme end and port region of the southern arm of the lake. A comparison with sediment quality guideline quotient was also made, indicating that there may be some ecotoxicological risk to benthic organisms in these sediments.

Oak tree-rings record spatial-temporal pollution trends from different sources in Terni (Central Italy).

PubMed

Perone, A; Cocozza, C; Cherubini, P; Bachmann, O; Guillong, M; Lasserre, B; Marchetti, M; Tognetti, R

2018-02-01

Monitoring atmospheric pollution in industrial areas near urban center is essential to infer past levels of contamination and to evaluate the impact for environmental health and safety. The main aim of this study was to understand if the chemical composition of tree-ring wood can be used for monitoring spatial-temporal variability of pollutants in Terni, Central Italy, one of the most polluted towns in Italy. Tree cores were taken from 32 downy oaks (Quercus pubescens) located at different distances from several pollutant sources, including a large steel factory. Trace element (Cr, Co, Cu, Pb, Hg, Mo, Ni, Tl, W, U, V, and Zn) index in tree-ring wood was determined using high-resolution laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). We hypothesized that the presence of contaminants detected in tree-rings reflected industrial activities over time. The accumulation of contaminants in tree-rings was affected by anthropogenic activities in the period 1958-2009, though signals varied in intensity with the distance of trees from the industrial plant. A stronger limitation of tree growth was observed in the proximity of the industrial plant in comparison with other pollutant sources. Levels of Cr, Ni, Mo, V, U and W increased in tree-ring profiles of trees close to the steel factory, especially during the 80's and 90's, in correspondence to a peak of pollution in this period, as recorded by air quality monitoring stations. Uranium contents in our tree-rings were difficult to explain, while the higher contents of Cu, Hg, Pb, and Tl could be related to the contaminants released from an incinerator located close to the industrial plant. The accumulation of contaminants in tree-rings reflected the historical variation of environmental pollution in the considered urban context. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing.

PubMed

Qiao, Pengwei; Lei, Mei; Yang, Sucai; Yang, Jun; Guo, Guanghui; Zhou, Xiaoyong

2018-06-01

Spatial interpolation method is the basis of soil heavy metal pollution assessment and remediation. The existing evaluation index for interpolation accuracy did not combine with actual situation. The selection of interpolation methods needs to be based on specific research purposes and research object characteristics. In this paper, As pollution in soils of Beijing was taken as an example. The prediction accuracy of ordinary kriging (OK) and inverse distance weighted (IDW) were evaluated based on the cross validation results and spatial distribution characteristics of influencing factors. The results showed that, under the condition of specific spatial correlation, the cross validation results of OK and IDW for every soil point and the prediction accuracy of spatial distribution trend are similar. But the prediction accuracy of OK for the maximum and minimum is less than IDW, while the number of high pollution areas identified by OK are less than IDW. It is difficult to identify the high pollution areas fully by OK, which shows that the smoothing effect of OK is obvious. In addition, with increasing of the spatial correlation of As concentration, the cross validation error of OK and IDW decreases, and the high pollution area identified by OK is approaching the result of IDW, which can identify the high pollution areas more comprehensively. However, because the semivariogram constructed by OK interpolation method is more subjective and requires larger number of soil samples, IDW is more suitable for spatial prediction of heavy metal pollution in soils.

Fine-Scale Spatial Variability of Pedestrian-Level Particulate Matters in Compact Urban Commercial Districts in Hong Kong

PubMed Central

Ng, Edward

2017-01-01

Particulate matters (PM) at the pedestrian level significantly raises the health impacts in the compact urban environment of Hong Kong. A detailed investigation of the fine-scale spatial variation of pedestrian-level PM is necessary to assess the health risk to pedestrians in the outdoor environment. However, the collection of PM data is difficult in the compact urban environment of Hong Kong due to the limited amount of roadside monitoring stations and the complicated urban context. In this study, we measured the fine-scale spatial variability of the PM in three of the most representative commercial districts of Hong Kong using a backpack outdoor environmental measuring unit. Based on the measurement data, 13 types of geospatial interpolation methods were examined for the spatial mapping of PM2.5 and PM10 with a group of building geometrical covariates. Geostatistical modelling was adopted as the basis of spatial interpolation of the PM. The results show that the original cokriging with the exponential kernel function provides the best performance in the PM mapping. Using the fine-scale building geometrical features as covariates slightly improves the interpolation performance. The study results also imply that the fine-scale, localized pollution emission sources heavily influence pedestrian exposure to PM. PMID:28869527

A GIS-based spatial correlation analysis for ambient air pollution and AECOPD hospitalizations in Jinan, China.

PubMed

Wang, Wenqiao; Ying, Yangyang; Wu, Quanyuan; Zhang, Haiping; Ma, Dedong; Xiao, Wei

2015-03-01

Acute exacerbations of COPD (AECOPD) are important events during disease procedure. AECOPD have negative effect on patients' quality of life, symptoms and lung function, and result in high socioeconomic costs. Though previous studies have demonstrated the significant association between outdoor air pollution and AECOPD hospitalizations, little is known about the spatial relationship utilized a spatial analyzing technique- Geographical Information System (GIS). Using GIS to investigate the spatial association between ambient air pollution and AECOPD hospitalizations in Jinan City, 2009. 414 AECOPD hospitalization cases in Jinan, 2009 were enrolled in our analysis. Monthly concentrations of five monitored air pollutants (NO2, SO2, PM10, O3, CO) during January 2009-December 2009 were provided by Environmental Protection Agency of Shandong Province. Each individual was geocoded in ArcGIS10.0 software. The spatial distribution of five pollutants and the temporal-spatial specific air pollutants exposure level for each individual was estimated by ordinary Kriging model. Spatial autocorrelation (Global Moran's I) was employed to explore the spatial association between ambient air pollutants and AECOPD hospitalizations. A generalized linear model (GLM) using a Poisson distribution with log-link function was used to construct a core model. At residence, concentrations of SO2, PM10, NO2, CO, O3 and AECOPD hospitalization cases showed statistical significant spatially clustered. The Z-score of SO2, PM10, CO, O3, NO2 at residence is 15.88, 13.93, 12.60, 4.02, 2.44 respectively, while at workplace, concentrations of PM10, SO2, O3, CO and AECOPD hospitalization cases showed statistical significant spatially clustered. The Z-score of PM10, SO2, O3, CO at workplace is 11.39, 8.07, 6.10, and 5.08 respectively. After adjusting for potential confounders in the model, only the PM10 concentrations at workplace showed statistical significance, with a 10 μg/m(3) increase of PM10 at workplace associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD. Ambient air pollution is correlated with AECOPD hospitalizations spatially. A 10 μg/m(3) increase of PM10 at workplace was associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD in Jinan, 2009. As a spatial data processing tool, GIS has novel and great potential on air pollutants exposure assessment and spatial analysis in AECOPD research. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development and Application of Nonlinear Land-Use Regression Models

NASA Astrophysics Data System (ADS)

Champendal, Alexandre; Kanevski, Mikhail; Huguenot, Pierre-Emmanuel

2014-05-01

The problem of air pollution modelling in urban zones is of great importance both from scientific and applied points of view. At present there are several fundamental approaches either based on science-based modelling (air pollution dispersion) or on the application of space-time geostatistical methods (e.g. family of kriging models or conditional stochastic simulations). Recently, there were important developments in so-called Land Use Regression (LUR) models. These models take into account geospatial information (e.g. traffic network, sources of pollution, average traffic, population census, land use, etc.) at different scales, for example, using buffering operations. Usually the dimension of the input space (number of independent variables) is within the range of (10-100). It was shown that LUR models have some potential to model complex and highly variable patterns of air pollution in urban zones. Most of LUR models currently used are linear models. In the present research the nonlinear LUR models are developed and applied for Geneva city. Mainly two nonlinear data-driven models were elaborated: multilayer perceptron and random forest. An important part of the research deals also with a comprehensive exploratory data analysis using statistical, geostatistical and time series tools. Unsupervised self-organizing maps were applied to better understand space-time patterns of the pollution. The real data case study deals with spatial-temporal air pollution data of Geneva (2002-2011). Nitrogen dioxide (NO2) has caught our attention. It has effects on human health and on plants; NO2 contributes to the phenomenon of acid rain. The negative effects of nitrogen dioxides on plants are the reduction of the growth, production and pesticide resistance. And finally, the effects on materials: nitrogen dioxide increases the corrosion. The data used for this study consist of a set of 106 NO2 passive sensors. 80 were used to build the models and the remaining 36 have constituted the testing set. Missing data have been completed using multiple linear regression and annual average values of pollutant concentrations were computed. All sensors are dispersed homogeneously over the central urban area of Geneva. The main result of the study is that the nonlinear LUR models developed have demonstrated their efficiency in modelling complex phrenomena of air pollution in urban zones and significantly reduced the testing error in comparison with linear models. Further research deals with the development and application of other non-linear data-driven models (Kanevski et al. 2009). References Kanevski M., Pozdnoukhov A. and Timonin V. (2009). Machine Learning for Spatial Environmental Data. Theory, Applications and Software. EPLF Press, Lausanne.

Spatial distribution of vehicle emission inventories in the Federal District, Brazil

NASA Astrophysics Data System (ADS)

Réquia, Weeberb João; Koutrakis, Petros; Roig, Henrique Llacer

2015-07-01

Air pollution poses an important public health risk, especially in large urban areas. Information about the spatial distribution of air pollutants can be used as a tool for developing public policies to reduce source emissions. Air pollution monitoring networks provide information about pollutant concentrations; however, they are not available in every urban area. Among the 5570 cities in Brazil, for example, only 1.7% of them have air pollution monitoring networks. In this study we assess vehicle emissions for main traffic routes of the Federal District (state of Brazil) and characterize their spatial patterns. Toward this end, we used a bottom-up method to predict emissions and to characterize their spatial patterns using Global Moran's (Spatial autocorrelation analysis) and Getis-Ord General G (High/Low cluster analysis). Our findings suggested that light duty vehicles are primarily responsible for the vehicular emissions of CO (68.9%), CH4 (93.6%), and CO2 (57.9%), whereas heavy duty vehicles are primarily responsible for the vehicular emissions of NMHC (92.9%), NOx (90.7%), and PM (97.4%). Furthermore, CO2 is the pollutant with the highest emissions, over 30 million tons/year. In the spatial autocorrelation analysis was identified cluster (p < 0.01) for all types of vehicles and for all pollutants. However, we identified high cluster only for the light vehicles.

Controlling for unmeasured confounding and spatial misalignment in long-term air pollution and health studies.

PubMed

Lee, Duncan; Sarran, Christophe

2015-11-01

The health impact of long-term exposure to air pollution is now routinely estimated using spatial ecological studies, owing to the recent widespread availability of spatial referenced pollution and disease data. However, this areal unit study design presents a number of statistical challenges, which if ignored have the potential to bias the estimated pollution-health relationship. One such challenge is how to control for the spatial autocorrelation present in the data after accounting for the known covariates, which is caused by unmeasured confounding. A second challenge is how to adjust the functional form of the model to account for the spatial misalignment between the pollution and disease data, which causes within-area variation in the pollution data. These challenges have largely been ignored in existing long-term spatial air pollution and health studies, so here we propose a novel Bayesian hierarchical model that addresses both challenges and provide software to allow others to apply our model to their own data. The effectiveness of the proposed model is compared by simulation against a number of state-of-the-art alternatives proposed in the literature and is then used to estimate the impact of nitrogen dioxide and particulate matter concentrations on respiratory hospital admissions in a new epidemiological study in England in 2010 at the local authority level. © 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd.

Multivariate Analysis and Modeling of Sediment Pollution Using Neural Network Models and Geostatistics

NASA Astrophysics Data System (ADS)

Golay, Jean; Kanevski, Mikhaïl

2013-04-01

The present research deals with the exploration and modeling of a complex dataset of 200 measurement points of sediment pollution by heavy metals in Lake Geneva. The fundamental idea was to use multivariate Artificial Neural Networks (ANN) along with geostatistical models and tools in order to improve the accuracy and the interpretability of data modeling. The results obtained with ANN were compared to those of traditional geostatistical algorithms like ordinary (co)kriging and (co)kriging with an external drift. Exploratory data analysis highlighted a great variety of relationships (i.e. linear, non-linear, independence) between the 11 variables of the dataset (i.e. Cadmium, Mercury, Zinc, Copper, Titanium, Chromium, Vanadium and Nickel as well as the spatial coordinates of the measurement points and their depth). Then, exploratory spatial data analysis (i.e. anisotropic variography, local spatial correlations and moving window statistics) was carried out. It was shown that the different phenomena to be modeled were characterized by high spatial anisotropies, complex spatial correlation structures and heteroscedasticity. A feature selection procedure based on General Regression Neural Networks (GRNN) was also applied to create subsets of variables enabling to improve the predictions during the modeling phase. The basic modeling was conducted using a Multilayer Perceptron (MLP) which is a workhorse of ANN. MLP models are robust and highly flexible tools which can incorporate in a nonlinear manner different kind of high-dimensional information. In the present research, the input layer was made of either two (spatial coordinates) or three neurons (when depth as auxiliary information could possibly capture an underlying trend) and the output layer was composed of one (univariate MLP) to eight neurons corresponding to the heavy metals of the dataset (multivariate MLP). MLP models with three input neurons can be referred to as Artificial Neural Networks with EXternal drift (ANNEX). Moreover, the exact number of output neurons and the selection of the corresponding variables were based on the subsets created during the exploratory phase. Concerning hidden layers, no restriction were made and multiple architectures were tested. For each MLP model, the quality of the modeling procedure was assessed by variograms: if the variogram of the residuals demonstrates pure nugget effect and if the level of the nugget exactly corresponds to the nugget value of the theoretical variogram of the corresponding variable, all the structured information has been correctly extracted without overfitting. Finally, it is worth mentioning that simple MLP models are not always able to remove all the spatial correlation structure from the data. In that case, Neural Network Residual Kriging (NNRK) can be carried out and risk assessment can be conducted with Neural Network Residual Simulations (NNRS). Finally, the results of the ANNEX models were compared to those of ordinary (co)kriging and (co)kriging with an external drift. It was shown that the ANNEX models performed better than traditional geostatistical algorithms when the relationship between the variable of interest and the auxiliary predictor was not linear. References Kanevski, M. and Maignan, M. (2004). Analysis and Modelling of Spatial Environmental Data. Lausanne: EPFL Press.

Deriving Surface NO2 Mixing Ratios from DISCOVER-AQ ACAM Observations: A Method to Assess Surface NO2 Spatial Variability

NASA Astrophysics Data System (ADS)

Silverman, M. L.; Szykman, J.; Chen, G.; Crawford, J. H.; Janz, S. J.; Kowalewski, M. G.; Lamsal, L. N.; Long, R.

2015-12-01

Studies have shown that satellite NO2 columns are closely related to ground level NO2 concentrations, particularly over polluted areas. This provides a means to assess surface level NO2 spatial variability over a broader area than what can be monitored from ground stations. The characterization of surface level NO2 variability is important to understand air quality in urban areas, emissions, health impacts, photochemistry, and to evaluate the performance of chemical transport models. Using data from the NASA DISCOVER-AQ campaign in Baltimore/Washington we calculate NO2 mixing ratios from the Airborne Compact Atmospheric Mapper (ACAM), through four different methods to derive surface concentration from column measurements. High spectral resolution lidar (HSRL) mixed layer heights, vertical P3B profiles, and CMAQ vertical profiles are used to scale ACAM vertical column densities. The derived NO2 mixing ratios are compared to EPA ground measurements taken at Padonia and Edgewood. We find similar results from scaling with HSRL mixed layer heights and normalized P3B vertical profiles. The HSRL mixed layer heights are then used to scale ACAM vertical column densities across the DISCOVER-AQ flight pattern to assess spatial variability of NO2 over the area. This work will help define the measurement requirements for future satellite instruments.

Spatial Variability in Biodegradation Rates as Evidenced by Methane Production from an Aquifer

PubMed Central

Adrian, Neal R.; Robinson, Joseph A.; Suflita, Joseph M.

1994-01-01

Accurate predictions of carbon and energy cycling rates in the environment depend on sampling frequencies and on the spatial variability associated with biological activities. We examined the variability associated with anaerobic biodegradation rates at two sites in an alluvial sand aquifer polluted by municipal landfill leachate. In situ rates of methane production were measured for almost a year, using anaerobic wells installed at two sites. Methane production ranged from 0 to 560 μmol · m-2 · day-1 at one site (A), while a range of 0 to 120,000 μmol · m-2 · day-1 was measured at site B. The mean and standard deviations associated with methane production at site A were 17 and 57 μmol · m-2 · day-1, respectively. The comparable summary statistics for site B were 2,000 and 9,900 μmol · m-2 · day-1. The coefficients of variation at sites A and B were 340 and 490%, respectively. Despite these differences, the two sites had similar seasonal trends, with the maximal rate of methane production occurring in summer. However, the relative variability associated with the seasonal rates changed very little. Our results suggest that (i) two spatially distinct sites exist in the aquifer, (ii) methanogenesis is a highly variable process, (iii) the coefficient of variation varied little with the rate of methane production, and (iv) in situ anaerobic biodegradation rates are lognormally distributed. PMID:16349410

Status of Tropospheric Emissions: Monitoring of Pollution (TEMPO)

NASA Astrophysics Data System (ADS)

Suleiman, R. M.; Chance, K.; Liu, X.; Flittner, D. E.; Al-Saadi, J. A.; Janz, S. J.

2015-12-01

TEMPO is now well into its implementation phase, having passed both its Key Decision Point C and the Critical Design Review (CDR) for the instrument. The CDR for the ground systems will occur in March 2016 and the CDR for the Mission component at a later date, after the host spacecraft has been selected. TEMPO is on schedule to measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO measures from Mexico City to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution. TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies.TEMPO takes advantage of a GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions by 50%. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available.TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. Instruments from Europe (Sentinel 4) and Asia (GEMS) will form parts of a global GEO constellation for pollution monitoring later this decade, with a major focus on intercontinental pollution transport. TEMPO will launch at a prime time to be a component of this constellation

Status of Tropospheric Emissions: Monitoring of Pollution (TEMPO)

NASA Astrophysics Data System (ADS)

Chance, K.; Liu, X.; Suleiman, R. M.; Flittner, D. E.; Al-Saadi, J. A.; Janz, S. J.

2016-12-01

TEMPO is now in the Assembly, Integration and Test (AI&T) phase, having passed its Key Decision Point C, Critical Design Reviews (CDRs) for the instrument and the ground systems, and the Test Readiness Review (TRR). The TEMPO instrument is scheduled for delivery in August 2017. The request for proposals to host TEMPO on a commercial geostationary satellite is scheduled for release by May 2017, with host selection hopefully completed by the end of calendar 2017. TEMPO is thus on schedule to measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO measures from Mexico City and Cuba to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution. It provides a measurement suite that includes the key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the high variability in the diurnal cycle of emissions and chemistry. The small spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies.TEMPO takes advantage of a GEO host spacecraft to provide a mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available.TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. Instruments from Europe (Sentinel 4) and Asia (GEMS) will form parts of a global GEO constellation for pollution monitoring later this decade, with a major focus on intercontinental pollution transport. TEMPO will launch at a prime time to be a component of this constellation.

Tropospheric Emissions: Monitoring of Pollution Overview

NASA Technical Reports Server (NTRS)

Chance, Kelly; Liu, Xiong; Suleiman, Raid M.; Flittner, David; Al-Saadi, Jay; Janz, Scott

2015-01-01

TEMPO is now well into its implementation phase, having passed both its Key Decision Point C and the Critical Design Review (CDR) for the instrument. The CDR for the ground systems will occur in March 2016 and the CDR for the Mission component at a later date, after the host spacecraft has been selected. TEMPO is on schedule to measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO measures from Mexico City to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution. TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions by 50 percent. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available. TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. Instruments from Europe (Sentinel 4) and Asia (GEMS) will form parts of a global GEO constellation for pollution monitoring later this decade, with a major focus on intercontinental pollution transport. TEMPO will launch at a prime time to be a component of this constellation.

Temporal and spatial correlation patterns of air pollutants in Chinese cities

PubMed Central

Dai, Yue-Hua

2017-01-01

As a huge threat to the public health, China’s air pollution has attracted extensive attention and continues to grow in tandem with the economy. Although the real-time air quality report can be utilized to update our knowledge on air quality, questions about how pollutants evolve across time and how pollutants are spatially correlated still remain a puzzle. In view of this point, we adopt the PMFG network method to analyze the six pollutants’ hourly data in 350 Chinese cities in an attempt to find out how these pollutants are correlated temporally and spatially. In terms of time dimension, the results indicate that, except for O3, the pollutants have a common feature of the strong intraday patterns of which the daily variations are composed of two contraction periods and two expansion periods. Besides, all the time series of the six pollutants possess strong long-term correlations, and this temporal memory effect helps to explain why smoggy days are always followed by one after another. In terms of space dimension, the correlation structure shows that O3 is characterized by the highest spatial connections. The PMFGs reveal the relationship between this spatial correlation and provincial administrative divisions by filtering the hierarchical structure in the correlation matrix and refining the cliques as the tinny spatial clusters. Finally, we check the stability of the correlation structure and conclude that, except for PM10 and O3, the other pollutants have an overall stable correlation, and all pollutants have a slight trend to become more divergent in space. These results not only enhance our understanding of the air pollutants’ evolutionary process, but also shed lights on the application of complex network methods into geographic issues. PMID:28832599

Comparison of Satellite Observations of Nitrogen Dioxide to Surface Monitor Nitrogen Dioxide Concentration

NASA Technical Reports Server (NTRS)

Kleb, Mary M.; Pippin, Margaret R.; Pierce, R. Bradley; Neil, Doreen O.; Lingenfelser, Gretchen; Szykman, James J.

2006-01-01

Nitrogen dioxide is one of the U. S. EPA s criteria pollutants, and one of the main ingredients needed for the production of ground-level ozone. Both ozone and nitrogen dioxide cause severe public health problems. Existing satellites have begun to produce observational data sets for nitrogen dioxide. Under NASAs Earth Science Applications Program, we examined the relationship between satellite observations and surface monitor observations of this air pollutant to examine if the satellite data can be used to facilitate a more capable and integrated observing network. This report provides a comparison of satellite tropospheric column nitrogen dioxide to surface monitor nitrogen dioxide concentration for the period from September 1996 through August 1997 at more than 300 individual locations in the continental US. We found that the spatial resolution and observation time of the satellite did not capture the variability of this pollutant as measured at ground level. The tools and processes developed to conduct this study will be applied to the analysis of advanced satellite observations. One advanced instrument has significantly better spatial resolution than the measurements studied here and operates with an afternoon overpass time, providing a more representative distribution for once-per-day sampling of this photochemically active atmospheric constituent.

A spatial model to aggregate point-source and nonpoint-source water-quality data for large areas

USGS Publications Warehouse

White, D.A.; Smith, R.A.; Price, C.V.; Alexander, R.B.; Robinson, K.W.

1992-01-01

More objective and consistent methods are needed to assess water quality for large areas. A spatial model, one that capitalizes on the topologic relationships among spatial entities, to aggregate pollution sources from upstream drainage areas is described that can be implemented on land surfaces having heterogeneous water-pollution effects. An infrastructure of stream networks and drainage basins, derived from 1:250,000-scale digital-elevation models, define the hydrologic system in this spatial model. The spatial relationships between point- and nonpoint pollution sources and measurement locations are referenced to the hydrologic infrastructure with the aid of a geographic information system. A maximum-branching algorithm has been developed to simulate the effects of distance from a pollutant source to an arbitrary downstream location, a function traditionally employed in deterministic water quality models. ?? 1992.

Investigation of priorities in water quality management based on correlations and variations.

PubMed

Boyacıoğlu, Hülya; Gündogdu, Vildan; Boyacıoğlu, Hayal

2013-04-15

The development of water quality assessment strategies investigating spatial and temporal changes caused by natural and anthropogenic phenomena is an important tool in management practices. This paper used cluster analysis, water quality index method, sensitivity analysis and canonical correlation analysis to investigate priorities in pollution control activities. Data sets representing 22 surface water quality parameters were subject to analysis. Results revealed that organic pollution was serious threat for overall water quality in the region. Besides, oil and grease, lead and mercury were the critical variables violating the standard. In contrast to inorganic variables, organic and physical-inorganic chemical parameters were influenced by variations in physical conditions (discharge, temperature). This study showed that information produced based on the variations and correlations in water quality data sets can be helpful to investigate priorities in water management activities. Moreover statistical techniques and index methods are useful tools in data - information transformation process. Copyright © 2013 Elsevier Ltd. All rights reserved.

Land use regression modeling of ultrafine particles, ozone, nitrogen oxides and markers of particulate matter pollution in Augsburg, Germany.

PubMed

Wolf, Kathrin; Cyrys, Josef; Harciníková, Tatiana; Gu, Jianwei; Kusch, Thomas; Hampel, Regina; Schneider, Alexandra; Peters, Annette

2017-02-01

Important health relevance has been suggested for ultrafine particles (UFP) and ozone, but studies on long-term effects are scarce, mainly due to the lack of appropriate spatial exposure models. We designed a measurement campaign to develop land use regression (LUR) models to predict the spatial variability focusing on particle number concentration (PNC) as indicator for UFP, ozone and several other air pollutants in the Augsburg region, Southern Germany. Three bi-weekly measurements of PNC, ozone, particulate matter (PM 10 , PM 2.5 ), soot (PM 2.5 abs) and nitrogen oxides (NO x , NO 2 ) were performed at 20 sites in 2014/15. Annual average concentration were calculated and temporally adjusted by measurements from a continuous background station. As geographic predictors we offered several traffic and land use variables, altitude, population and building density. Models were validated using leave-one-out cross-validation. Adjusted model explained variance (R 2 ) was high for PNC and ozone (0.89 and 0.88). Cross-validation adjusted R 2 was slightly lower (0.82 and 0.81) but still indicated a very good fit. LUR models for other pollutants performed well with adjusted R 2 between 0.68 (PM coarse ) and 0.94 (NO 2 ). Contrary to previous studies, ozone showed a moderate correlation with NO 2 (Pearson's r=-0.26). PNC was moderately correlated with ozone and PM 2.5 , but highly correlated with NO x (r=0.91). For PNC and NO x , LUR models comprised similar predictors and future epidemiological analyses evaluating health effects need to consider these similarities. Copyright © 2016 Elsevier B.V. All rights reserved.

Temporal and spatial variations in fly ash quality

USGS Publications Warehouse

Hower, J.C.; Trimble, A.S.; Eble, C.F.

2001-01-01

Fly ash quality, both as the amount of petrographically distinguishable carbons and in chemistry, varies in both time and space. Temporal variations are a function of a number of variables. Variables can include variations in the coal blend organic petrography, mineralogy, and chemistry; variations in the pulverization of the coal, both as a function of the coal's Hardgrove grindability index and as a function of the maintenance and settings of the pulverizers; and variations in the operating conditions of the boiler, including changes in the pollution control system. Spatial variation, as an instantaneous measure of fly ash characteristics, should not involve changes in the first two sets of variables listed above. Spatial variations are a function of the gas flow within the boiler and ducts, certain flow conditions leading to a tendency for segregation of the less-dense carbons in one portion of the gas stream. Caution must be applied in sampling fly ash. Samples from a single bin, or series of bins, m ay not be representative of the whole fly ash, providing a biased view of the nature of the material. Further, it is generally not possible to be certain about variation until the analysis of the ash is complete. ?? 2001 Elsevier Science B.V. All rights reserved.

Mixing weight determination for retrieving optical properties of polluted dust with MODIS and AERONET data

NASA Astrophysics Data System (ADS)

Chang, Kuo-En; Hsiao, Ta-Chih; Hsu, N. Christina; Lin, Neng-Huei; Wang, Sheng-Hsiang; Liu, Gin-Rong; Liu, Chian-Yi; Lin, Tang-Huang

2016-08-01

In this study, an approach in determining effective mixing weight of soot aggregates from dust-soot aerosols is proposed to improve the accuracy of retrieving properties of polluted dusts by means of satellite remote sensing. Based on a pre-computed database containing several variables (such as wavelength, refractive index, soot mixing weight, surface reflectivity, observation geometries and aerosol optical depth (AOD)), the fan-shaped look-up tables can be drawn out accordingly for determining the mixing weights, AOD and single scattering albedo (SSA) of polluted dusts simultaneously with auxiliary regional dust properties and surface reflectivity. To validate the performance of the approach in this study, 6 cases study of polluted dusts (dust-soot aerosols) in Lower Egypt and Israel were examined with the ground-based measurements through AErosol RObotic NETwork (AERONET). The results show that the mean absolute differences could be reduced from 32.95% to 6.56% in AOD and from 2.67% to 0.83% in SSA retrievals for MODIS aerosol products when referenced to AERONET measurements, demonstrating the soundness of the proposed approach under different levels of dust loading, mixing weight and surface reflectivity. Furthermore, the developed algorithm is capable of providing the spatial distribution of the mixing weights and removing the requirement to assume that the dust plume properties are uniform. The case study further shows the spatially variant dust-soot mixing weight would improve the retrieval accuracy in AODmixture and SSAmixture about 10.0% and 1.4% respectively.

A novel approach to water polution monitoring by combining ion exchange resin and XRF-scanning technique

NASA Astrophysics Data System (ADS)

Huang, J. J.; Lin, S. C.; Löwemark, L.; Liou, Y. H.; Chang, Q. M.; Chang, T. K.; Wei, K. Y.; Croudace, I. W. C.

2017-12-01

Due to the rapid industrial expansion, environments are subject to irregular fluctuations and spatial distributions in pollutant concentrations. This study proposes to use ion exchange resin accompanied with the XRF-scanning technique to monitor environmental pollution. As a passive sampling sorbent, the use of ion exchange resin provides a rapid, low cost and simple method to detect episodic pollution signals with a high spatial sampling density. In order to digest large quantities of samples, the fast and non-destructive Itrax-XRF core scanner has been introduced to assess elemental concentrations in the resin samples. Although the XRF scanning results are often considered as a semi-quantitative measurement due to possible absorption or scattering caused by the physical variabilities of scanned materials, the use of resin can minimize such influences owing to the standarization of the sample matrix. In this study, 17 lab-prepared standard resin samples were scanned with the Itrax-XRF core scanner (at 100 s exposure time with the Mo-tube) and compared with the absolute elemental concentrations. Six elements generally used in pollution studies (Cr, Mn, Ni, Cu, Zn, and Pb) were selected, and their regression lines and correlation coefficients were determined. In addition, 5 standard resin samples were scanned at different exposure time settings (1 s, 5 s, 15 s, 30 s, 100 s) to address the influence of exposure time on the accuracy of the measurements. The results show that within the test range (from few ppm to thousands ppm), the correlation coefficients are higher than 0.97, even at the shortest exposure time (1 s). Furthermore, a pilot field survey with 30 resin samples has been conducted in a potentially polluted farm area in central Taiwan to demonstrate the feasibility of this novel approach. The polluted hot zones could be identified and the properties and sources of wastewater pollution can therefore be traced over large areas for the purposes of environmental monitoring and environmental forensics.

Assessment of microscale spatio-temporal variation of air pollution at an urban hotspot in Madrid (Spain) through an extensive field campaign

NASA Astrophysics Data System (ADS)

Borge, Rafael; Narros, Adolfo; Artíñano, Begoña; Yagüe, Carlos; Gómez-Moreno, Francisco Javier; de la Paz, David; Román-Cascón, Carlos; Díaz, Elías; Maqueda, Gregorio; Sastre, Mariano; Quaassdorff, Christina; Dimitroulopoulou, Chrysanthi; Vardoulakis, Sotiris

2016-09-01

Poor urban air quality is one of the main environmental concerns worldwide due to its implications for population exposure and health-related issues. However, the development of effective abatement strategies in cities requires a consistent and holistic assessment of air pollution processes, taking into account all the relevant scales within a city. This contribution presents the methodology and main results of an intensive experimental campaign carried out in a complex pollution hotspot in Madrid (Spain) under the TECNAIRE-CM research project, which aimed at understanding the microscale spatio-temporal variation of ambient concentration levels in areas where high pollution values are recorded. A variety of instruments were deployed during a three-week field campaign to provide detailed information on meteorological and micrometeorological parameters and spatio-temporal variations of the most relevant pollutants (NO2 and PM) along with relevant information needed to simulate pedestrian fluxes. The results show the strong dependence of ambient concentrations on local emissions and meteorology that turns out in strong spatial and temporal variations, with gradients up to 2 μg m-3 m-1 for NO2 and 55 μg m-3 min-1 for PM10. Pedestrian exposure to these pollutants also presents strong variations temporally and spatially but it concentrates on pedestrian crossings and bus stops. The analysis of the results show that the high concentration levels found in urban hotspots depend on extremely complex dynamic processes that cannot be captured by routinely measurements made by air quality monitoring stations used for regulatory compliance assessment. The large influence from local traffic in the concentration fields highlights the need for a detailed description of specific variables that determine emissions and dispersion at microscale level. This also indicates that city-scale interventions may be complemented with local control measures and exposure management, to improve air quality and reduce air pollution health effects more effectively.

Predicting diffuse microbial pollution risk across catchments: The performance of SCIMAP and recommendations for future development.

PubMed

Porter, Kenneth D H; Reaney, Sim M; Quilliam, Richard S; Burgess, Chris; Oliver, David M

2017-12-31

Microbial pollution of surface waters in agricultural catchments can be a consequence of poor farm management practices, such as excessive stocking of livestock on vulnerable land or inappropriate handling of manures and slurries. Catchment interventions such as fencing of watercourses, streamside buffer strips and constructed wetlands have the potential to reduce faecal pollution of watercourses. However these interventions are expensive and occupy valuable productive land. There is, therefore, a requirement for tools to assist in the spatial targeting of such interventions to areas where they will have the biggest impact on water quality improvements whist occupying the minimal amount of productive land. SCIMAP is a risk-based model that has been developed for this purpose but with a focus on diffuse sediment and nutrient pollution. In this study we investigated the performance of SCIMAP in predicting microbial pollution of watercourses and assessed modelled outputs of E. coli, a common faecal indicator organism (FIO), against observed water quality information. SCIMAP was applied to two river catchments in the UK. SCIMAP uses land cover risk weightings, which are routed through the landscape based on hydrological connectivity to generate catchment scale maps of relative in-stream pollution risk. Assessment of the model's performance and derivation of optimum land cover risk weightings was achieved using a Monte-Carlo sampling approach. Performance of the SCIMAP framework for informing on FIO risk was variable with better performance in the Yealm catchment (r s =0.88; p<0.01) than the Wyre (r s =-0.36; p>0.05). Across both catchments much uncertainty was associated with the application of optimum risk weightings attributed to different land use classes. Overall, SCIMAP showed potential as a useful tool in the spatial targeting of FIO diffuse pollution management strategies; however, improvements are required to transition the existing SCIMAP framework to a robust FIO risk-mapping tool. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Fine Particulate Matter Predictions Using High Resolution Aerosol Optical Depth (AOD) Retrievals

NASA Technical Reports Server (NTRS)

Chudnovsky, Alexandra A.; Koutrakis, Petros; Kloog, Itai; Melly, Steven; Nordio, Francesco; Lyapustin, Alexei; Wang, Jujie; Schwartz, Joel

2014-01-01

To date, spatial-temporal patterns of particulate matter (PM) within urban areas have primarily been examined using models. On the other hand, satellites extend spatial coverage but their spatial resolution is too coarse. In order to address this issue, here we report on spatial variability in PM levels derived from high 1 km resolution AOD product of Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm developed for MODIS satellite. We apply day-specific calibrations of AOD data to predict PM(sub 2.5) concentrations within the New England area of the United States. To improve the accuracy of our model, land use and meteorological variables were incorporated. We used inverse probability weighting (IPW) to account for nonrandom missingness of AOD and nested regions within days to capture spatial variation. With this approach we can control for the inherent day-to-day variability in the AOD-PM(sub 2.5) relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance among others. Out-of-sample "ten-fold" cross-validation was used to quantify the accuracy of model predictions. Our results show that the model-predicted PM(sub 2.5) mass concentrations are highly correlated with the actual observations, with out-of- sample R(sub 2) of 0.89. Furthermore, our study shows that the model captures the pollution levels along highways and many urban locations thereby extending our ability to investigate the spatial patterns of urban air quality, such as examining exposures in areas with high traffic. Our results also show high accuracy within the cities of Boston and New Haven thereby indicating that MAIAC data can be used to examine intra-urban exposure contrasts in PM(sub 2.5) levels.

High Incidence of Breast Cancer in Light-Polluted Areas with Spatial Effects in Korea.

PubMed

Kim, Yun Jeong; Park, Man Sik; Lee, Eunil; Choi, Jae Wook

2016-01-01

We have reported a high prevalence of breast cancer in light-polluted areas in Korea. However, it is necessary to analyze the spatial effects of light polluted areas on breast cancer because light pollution levels are correlated with region proximity to central urbanized areas in studied cities. In this study, we applied a spatial regression method (an intrinsic conditional autoregressive [iCAR] model) to analyze the relationship between the incidence of breast cancer and artificial light at night (ALAN) levels in 25 regions including central city, urbanized, and rural areas. By Poisson regression analysis, there was a significant correlation between ALAN, alcohol consumption rates, and the incidence of breast cancer. We also found significant spatial effects between ALAN and the incidence of breast cancer, with an increase in the deviance information criterion (DIC) from 374.3 to 348.6 and an increase in R2 from 0.574 to 0.667. Therefore, spatial analysis (an iCAR model) is more appropriate for assessing ALAN effects on breast cancer. To our knowledge, this study is the first to show spatial effects of light pollution on breast cancer, despite the limitations of an ecological study. We suggest that a decrease in ALAN could reduce breast cancer more than expected because of spatial effects.

Mapping Exposure to Multi-Pollutants Using Environmental Biomonitors-A Multi-Exposure Index.

PubMed

Serrano, Helena C; Köbel, Melanie; Palma-Oliveira, José; Pinho, Pedro; Branquinho, Cristina

2017-01-01

Atmosphere is a major pathway for transport and deposition of pollutants in the environment. In industrial areas, organic compounds are released or formed as by-products, such as polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F's). Inorganic chemical elements, including lead and arsenic, are also part of the pollutants mixture, and even in low concentrations may potentially be toxic and carcinogenic. However, assessing the spatial pattern of their deposition is difficult due to high spatial and temporal heterogeneity. Lichens have been used as biomonitors of atmospheric deposition, because these organisms encompass greater spatial detail than air monitoring stations and provide an integration of overall pollution. Based upon the ability of lichens to concentrate pollutants such as PCDD/F and chemical elements, the main objectives of this study were to develop a new semi-quantitative multi-pollutant toxicity exposure index (TEQ-like), derived from risk estimates, in an attempt to correlate several atmospheric pollutants to human exposure levels. The actual pollutant concentrations were measured in the environment, from biomonitors (organisms that integrate multi-pollutants), enabling interpolation and mapping of contaminant deposition within the region. Thus, the TEQ-like index provides a spatial representation not from absolute accumulation of the different pollutants, but from the accumulation weighted by their relative risk. The assessment of environmental human exposure to multi-pollutants through atmospheric deposition may be applied to industries to improve mitigation processes or to health stakeholders to target populations for a comprehensive risk assessment, epidemiological studies, and health recommendations.

Deriving spatial trends of air pollution at a neighborhood-scale through mobile monitoring

EPA Science Inventory

Abstract: Measuring air pollution in real-time using an instrumented vehicle platform has been an emerging strategy to resolve air pollution trends at a very fine spatial scale (10s of meters). Achieving second-by-second data representative of urban air quality trends requires a...

Spatial variations of storm runoff pollution and their correlation with land-use in a rapidly urbanizing catchment in China.

PubMed

Qin, Hua-Peng; Khu, Soon-Thiam; Yu, Xiang-Ying

2010-09-15

The composition of land use for a rapidly urbanizing catchment is usually heterogeneous, and this may result in significant spatial variations of storm runoff pollution and increase the difficulties of water quality management. The Shiyan Reservoir catchment, a typical rapidly urbanizing area in China, is chosen as a study area, and temporary monitoring sites were set at the downstream of its 6 sub-catchments to synchronously measure rainfall, runoff and water quality during 4 storm events in 2007 and 2009. Due to relatively low frequency monitoring, the IHACRES and exponential pollutant wash-off simulation models are used to interpolate the measured data to compensate for data insufficiency. Three indicators, event pollutant loads per unit area (EPL), event mean concentration (EMC) and pollutant loads transported by the first 50% of runoff volume (FF50), were used to describe the runoff pollution for different pollutants in each sub-catchment during the storm events, and the correlations between runoff pollution spatial variations and land-use patterns were tested by Spearman's rank correlation analysis. The results indicated that similar spatial variation trends were found for different pollutants (EPL or EMC) in light storm events, which strongly correlate with the proportion of residential land use; however, they have different trends in heavy storm events, which correlate with not only the residential land use, but also agricultural and bare land use. And some pairs of pollutants (such as COD/BOD, NH(3)-N/TN) might have the similar source because they have strong or moderate positive spatial correlation. Moreover, the first flush intensity (FF50) varies with impervious land areas and different interception ratio of initial storm runoff volume should be adopted in different sub-catchments. Copyright 2010 Elsevier B.V. All rights reserved.

Modeling temporal and spatial variability of traffic-related air pollution: Hourly land use regression models for black carbon

NASA Astrophysics Data System (ADS)

Dons, Evi; Van Poppel, Martine; Kochan, Bruno; Wets, Geert; Int Panis, Luc

2013-08-01

Land use regression (LUR) modeling is a statistical technique used to determine exposure to air pollutants in epidemiological studies. Time-activity diaries can be combined with LUR models, enabling detailed exposure estimation and limiting exposure misclassification, both in shorter and longer time lags. In this study, the traffic related air pollutant black carbon was measured with μ-aethalometers on a 5-min time base at 63 locations in Flanders, Belgium. The measurements show that hourly concentrations vary between different locations, but also over the day. Furthermore the diurnal pattern is different for street and background locations. This suggests that annual LUR models are not sufficient to capture all the variation. Hourly LUR models for black carbon are developed using different strategies: by means of dummy variables, with dynamic dependent variables and/or with dynamic and static independent variables. The LUR model with 48 dummies (weekday hours and weekend hours) performs not as good as the annual model (explained variance of 0.44 compared to 0.77 in the annual model). The dataset with hourly concentrations of black carbon can be used to recalibrate the annual model, resulting in many of the original explaining variables losing their statistical significance, and certain variables having the wrong direction of effect. Building new independent hourly models, with static or dynamic covariates, is proposed as the best solution to solve these issues. R2 values for hourly LUR models are mostly smaller than the R2 of the annual model, ranging from 0.07 to 0.8. Between 6 a.m. and 10 p.m. on weekdays the R2 approximates the annual model R2. Even though models of consecutive hours are developed independently, similar variables turn out to be significant. Using dynamic covariates instead of static covariates, i.e. hourly traffic intensities and hourly population densities, did not significantly improve the models' performance.

Toward verifying fossil fuel CO2 emissions with the CMAQ model: motivation, model description and initial simulation.

PubMed

Liu, Zhen; Bambha, Ray P; Pinto, Joseph P; Zeng, Tao; Boylan, Jim; Huang, Maoyi; Lei, Huimin; Zhao, Chun; Liu, Shishi; Mao, Jiafu; Schwalm, Christopher R; Shi, Xiaoying; Wei, Yaxing; Michelsen, Hope A

2014-04-01

Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO2 spatiotemporal variability and verify CO2 fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO2. This paper presents methods, input data, and initial results for CO2 simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere-atmosphere exchange, and meteorology in regulating the spatial distribution of CO2 near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO2 concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver CO, the CMAQ model using hourly varying, high-resolution CO2 fossil-fuel emissions from the Vulcan inventory and Carbon Tracker optimized NEE reproduced the observed diurnal profile of CO2 reasonably well but with a low bias in the early morning. The spatial distribution of CO2 was found to correlate with NO(x), SO2, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO2 observations and understand CO2 variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO2 source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO2 spatiotemporal variability and various uncertainties in the future. Atmospheric CO2 has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO2 variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO2 emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO2 as an air pollutant.

Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models.

PubMed

Adams, Matthew D; Kanaroglou, Pavlos S

2016-03-01

Air pollution poses health concerns at the global scale. The challenge of managing air pollution is significant because of the many air pollutants, insufficient funds for monitoring and abatement programs, and political and social challenges in defining policy to limit emissions. Some governments provide citizens with air pollution health risk information to allow them to limit their exposure. However, many regions still have insufficient air pollution monitoring networks to provide real-time mapping. Where available, these risk mapping systems either provide absolute concentration data or the concentrations are used to derive an Air Quality Index, which provides the air pollution risk for a mix of air pollutants with a single value. When risk information is presented as a single value for an entire region it does not inform on the spatial variation within the region. Without an understanding of the local variation residents can only make a partially informed decision when choosing daily activities. The single value is typically provided because of a limited number of active monitoring units in the area. In our work, we overcome this issue by leveraging mobile air pollution monitoring techniques, meteorological information and land use information to map real-time air pollution health risks. We propose an approach that can provide improved health risk information to the public by applying neural network models within a framework that is inspired by land use regression. Mobile air pollution monitoring campaigns were conducted across Hamilton from 2005 to 2013. These mobile air pollution data were modelled with a number of predictor variables that included information on the surrounding land use characteristics, the meteorological conditions, air pollution concentrations from fixed location monitors, and traffic information during the time of collection. Fine particulate matter and nitrogen dioxide were both modelled. During the model fitting process we reserved twenty percent of the data to validate the predictions. The models' performances were measured with a coefficient of determination at 0.78 and 0.34 for PM2.5 and NO2, respectively. We apply a relative importance measure to identify the importance of each variable in the neural network to partially overcome the black box issues of neural network models. Copyright © 2015 Elsevier Ltd. All rights reserved.

Robust geographically weighted regression of modeling the Air Polluter Standard Index (APSI)

NASA Astrophysics Data System (ADS)

Warsito, Budi; Yasin, Hasbi; Ispriyanti, Dwi; Hoyyi, Abdul

2018-05-01

The Geographically Weighted Regression (GWR) model has been widely applied to many practical fields for exploring spatial heterogenity of a regression model. However, this method is inherently not robust to outliers. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression model. One of solution to handle the outliers in the regression model is to use the robust models. So this model was called Robust Geographically Weighted Regression (RGWR). This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the RGWR approach. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. The best model is determined by the smallest AIC value. There are significance differences between Regression and RGWR in this case, but Basic GWR using the Gaussian kernel is the best model to modeling APSI because it has smallest AIC.

Multi-pollutant mobile platform measurements of air pollutants adjacent to a major roadway

NASA Astrophysics Data System (ADS)

Riley, Erin A.; Banks, Lyndsey; Fintzi, Jonathan; Gould, Timothy R.; Hartin, Kris; Schaal, LaNae; Davey, Mark; Sheppard, Lianne; Larson, Timothy; Yost, Michael G.; Simpson, Christopher D.

2014-12-01

A mobile monitoring platform developed at the University of Washington Center for Clean Air Research (CCAR) measured 10 pollutant metrics (10 s measurements at an average speed of 22 km/h) in two neighborhoods bordering a major interstate in Albuquerque, NM, USA from April 18-24 2012. 5 days of data sharing a common downwind orientation with respect to the roadway were analyzed. The aggregate results show a three-fold increase in black carbon (BC) concentrations within 10 m of the edge of roadway, in addition to elevated nanoparticle concentration and particulate matter with aerodynamic diameter <1 μm (PN1) concentrations. A 30% reduction in ozone concentration near the roadway was observed, anti-correlated with an increase in the oxides of nitrogen (NOx). In this study, the pollutants measured have been expanded to include polycyclic aromatic hydrocarbons (PAH), particle size distribution (0.25-32 μm), and ultra-violet absorbing particulate matter (UVPM). The raster sampling scheme combined with spatial and temporal measurement alignment provide a measure of variability in the near roadway concentrations, and allow us to use a principal component analysis to identify multi-pollutant features and analyze their roadway influences.

Spatial and Temporal Distribution of Polycyclic Aromatic Hydrocarbons and Elemental Carbon in Bakersfield, California

PubMed Central

Noth, Elizabeth M.; Lurmann, Fred; Northcross, Amanda; Perrino, Charles; Vaughn, David; Hammond, S. Katharine

2016-01-01

Despite increasing evidence that airborne polycyclic aromatic hydrocarbon (PAH) exposures contribute to adverse health outcomes for sensitive populations, limited data are available on short-term intraurban spatial distributions for use in epidemiologic research. Exposure assessments for airborne PAHs are uncommon because air sampling for PAHs is a labor-, equipment-, and time-intensive task. To address this gap we measured wintertime PAH concentrations during 2010-2011 in Bakersfield, California, USA, a major city in the Southern San Joaquin Valley. Specifically, 58 96-hour integrated PAH samples were collected during 4 time periods at 14 locations from November 2010 to January 2011; duplicates were collected at two sites. We also collected elemental carbon (EC) at the same 14 sites and analyzed the two time periods with the highest ambient PAH pollution. We used linear regression models to quantify the relationship between potential spatial and temporal predictors of PAH concentrations. We found that wintertime PAH concentrations in Bakersfield, CA, are best predicted by meteorological variables and traffic proximity. Our model explains a moderate amount of the variability in the data (R2=0.58), likely reflecting the major sources of PAHs in Bakersfield. We also observed that PAH concentrations were more spatially variable than EC concentrations. Comparing our data to historical monitoring data at one location in Bakersfield showed that the relatively low PAH concentrations during the 2010-2011 winter in Bakersfield is part of a long-term trend in decreasing PAH concentrations. PMID:28083077

Spatio-temporal dynamics of species richness in coastal fish communities

USGS Publications Warehouse

Lekve, K.; Boulinier, T.; Stenseth, N.C.; Gjøsaeter, J.; Fromentin, J-M.; Hines, J.E.; Nichols, J.D.

2002-01-01

Determining patterns of change in species richness and the processes underlying the dynamics of biodiversity are of key interest within the field of ecology, but few studies have investigated the dynamics of vertebrate communities at a decadal temporal scale. Here, we report findings on the spado-temporal variability in the richness and composition of fish communities along the Norwegian Skagerrak coast having been surveyed for more than half a century. Using statistical models incorporating non-detection and associated sampling variance, we estimate local species richness and changes in species composition allowing us to compute temporal variability in species richness. We tested whether temporal variation could be related to distance to the open sea and to local levels of pollution. Clear differences in mean species richness and temporal variability are observed between fjords that were and were not exposed to the effects of pollution. Altogether this indicates that the fjord is an appropriate scale for studying changes in coastal fish communities in space and time. The year-to-year rates of local extinction and turnover were found to be smaller than spatial differences in community composition. At the regional level, exposure to the open sea plays a homogenizing role, possibly due to coastal currents and advection.

Seasonal and Spatial Variability of Anthropogenic and Natural Factors Influencing Groundwater Quality Based on Source Apportionment

PubMed Central

Guo, Xueru; Zuo, Rui; Meng, Li; Wang, Jinsheng; Teng, Yanguo; Liu, Xin; Chen, Minhua

2018-01-01

Globally, groundwater resources are being deteriorated by rapid social development. Thus, there is an urgent need to assess the combined impacts of natural and enhanced anthropogenic sources on groundwater chemistry. The aim of this study was to identify seasonal characteristics and spatial variations in anthropogenic and natural effects, to improve the understanding of major hydrogeochemical processes based on source apportionment. 34 groundwater points located in a riverside groundwater resource area in northeast China were sampled during the wet and dry seasons in 2015. Using principal component analysis and factor analysis, 4 principal components (PCs) were extracted from 16 groundwater parameters. Three of the PCs were water-rock interaction (PC1), geogenic Fe and Mn (PC2), and agricultural pollution (PC3). A remarkable difference (PC4) was organic pollution originating from negative anthropogenic effects during the wet season, and geogenic F enrichment during the dry season. Groundwater exploitation resulted in dramatic depression cone with higher hydraulic gradient around the water source area. It not only intensified dissolution of calcite, dolomite, gypsum, Fe, Mn and fluorine minerals, but also induced more surface water recharge for the water source area. The spatial distribution of the PCs also suggested the center of the study area was extremely vulnerable to contamination by Fe, Mn, COD, and F−. PMID:29415516

InMAP: A model for air pollution interventions

DOE PAGES

Tessum, Christopher W.; Hill, Jason D.; Marshall, Julian D.; ...

2017-04-19

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. We present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations—the air pollution outcome generally causing the largest monetized health damages–attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical informationmore » from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons we run, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of -17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.« less

InMAP: A model for air pollution interventions

PubMed Central

Hill, Jason D.; Marshall, Julian D.

2017-01-01

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations—the air pollution outcome generally causing the largest monetized health damages–attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons run here, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of −17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license. PMID:28423049

InMAP: A model for air pollution interventions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tessum, Christopher W.; Hill, Jason D.; Marshall, Julian D.

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. We present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations—the air pollution outcome generally causing the largest monetized health damages–attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical informationmore » from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons we run, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of -17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.« less

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

NASA Astrophysics Data System (ADS)

Chance, K.; Liu, X.; Suleiman, R. M.; Flittner, D. E.; Al-Saadi, J. A.; Janz, S. J.; Tempo Science Team

2013-05-01

TEMPO has been selected by NASA as the first Earth Venture Instrument. It will measure atmospheric pollution for greater North America from space using ultraviolet/visible spectroscopy. TEMPO measures from Mexico City to the Canadian tar/oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (Mexico City is measured at 1.6 km N/S by 4.5 km E/W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, reducing uncertainty in air quality predictions by 50%. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available. TEMPO makes the first tropospheric trace gas measurements from GEO, by building on the heritage of five spectrometers flown in low-earth-orbit (LEO). These LEO instruments measure the needed spectra, although at coarse spatial and temporal resolutions, to the precisions required for TEMPO and use retrieval algorithms developed for them by TEMPO Science Team members and currently running in operational environments. This makes TEMPO an innovative use of a well proven technique, able to produce a revolutionary data set. TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. GEO-CAPE is not planned for implementation this decade. However, instruments from Europe (Sentinel 4) and Asia (GEMS) will form parts of a global GEO constellation for pollution monitoring later this decade, with a major focus on intercontinental pollution transport. TEMPO will launch at a prime time to be a component of this constellation.

Source Identification and Apportionment of Trace Elements in Soils in the Yangtze River Delta, China.

PubMed

Shao, Shuai; Hu, Bifeng; Fu, Zhiyi; Wang, Jiayu; Lou, Ge; Zhou, Yue; Jin, Bin; Li, Yan; Shi, Zhou

2018-06-12

Trace elements pollution has attracted a lot of attention worldwide. However, it is difficult to identify and apportion the sources of multiple element pollutants over large areas because of the considerable spatial complexity and variability in the distribution of trace elements in soil. In this study, we collected total of 2051 topsoil (0⁻20 cm) samples, and analyzed the general pollution status of soils from the Yangtze River Delta, Southeast China. We applied principal component analysis (PCA), a finite mixture distribution model (FMDM), and geostatistical tools to identify and quantitatively apportion the sources of seven kinds of trace elements (chromium (Cr), cadmium (Cd), mercury (Hg), copper (Cu), zinc (Zn), nickel (Ni), and arsenic (As)) in soil. The PCA results indicated that the trace elements in soil in the study area were mainly from natural, multi-pollutant and industrial sources. The FMDM also fitted three sub log-normal distributions. The results from the two models were quite similar: Cr, As, and Ni were mainly from natural sources caused by parent material weathering; Cd, Cu, and Zu were mainly from mixed sources, with a considerable portion from anthropogenic activities such as traffic pollutants, domestic garbage, and agricultural inputs, and Hg was mainly from industrial wastes and pollutants.

Investigating the effects of point source and nonpoint source pollution on the water quality of the East River (Dongjiang) in South China

USGS Publications Warehouse

Wu, Yiping; Chen, Ji

2013-01-01

Understanding the physical processes of point source (PS) and nonpoint source (NPS) pollution is critical to evaluate river water quality and identify major pollutant sources in a watershed. In this study, we used the physically-based hydrological/water quality model, Soil and Water Assessment Tool, to investigate the influence of PS and NPS pollution on the water quality of the East River (Dongjiang in Chinese) in southern China. Our results indicate that NPS pollution was the dominant contribution (>94%) to nutrient loads except for mineral phosphorus (50%). A comprehensive Water Quality Index (WQI) computed using eight key water quality variables demonstrates that water quality is better upstream than downstream despite the higher level of ammonium nitrogen found in upstream waters. Also, the temporal (seasonal) and spatial distributions of nutrient loads clearly indicate the critical time period (from late dry season to early wet season) and pollution source areas within the basin (middle and downstream agricultural lands), which resource managers can use to accomplish substantial reduction of NPS pollutant loadings. Overall, this study helps our understanding of the relationship between human activities and pollutant loads and further contributes to decision support for local watershed managers to protect water quality in this region. In particular, the methods presented such as integrating WQI with watershed modeling and identifying the critical time period and pollutions source areas can be valuable for other researchers worldwide.

Chemical climatology of the southeastern United States, 1999-2013

NASA Astrophysics Data System (ADS)

Hidy, G. M.; Blanchard, C. L.; Baumann, K.; Edgerton, E.; Tanenbaum, S.; Shaw, S.; Knipping, E.; Tombach, I.; Jansen, J.; Walters, J.

2014-06-01

A series of experiments (the Southern Oxidant and Aerosol Study-SOAS) took place in central Alabama in June-July 2013 as part of the broader Southern Atmosphere Study (SAS). These projects were aimed at studying oxidant photochemistry and formation and impacts of aerosols at a detailed process level in a location where high biogenic organic vapor emissions interact with anthropogenic emissions, and the atmospheric chemistry occurs in a subtropical climate in North America. The majority of the ground-based experiments were located at the Southeastern Aerosol Research and Characterization (SEARCH) Centreville (CTR) site near Brent, Alabama, where extensive, unique aerometric measurements of meteorology, trace gases and particles have been made from the early 1990s through 2013. The SEARCH network data permits a characterization of temporal and spatial context of the SOAS findings. The long-term measurements show that the SOAS experiments took place during the second wettest and coolest year in the 2000-2013 period, with lower than average solar radiation. The pollution levels at CTR and other SEARCH sites were the lowest since full measurements began in 1999. This dataset provides a perspective for the SOAS program in terms of long-term average chemistry (chemical climatology) and short-term comparisons of summer average spatial variability across the Southeast at high temporal (hourly) resolution. Changes in anthropogenic gas and particle emissions between 1999 and 2013, account for the decline in pollutant concentrations at the monitoring sites in the region. The long-term and short-term data provide an opportunity to contrast SOAS results with temporally and spatially variable conditions in support for the development of tests for the robustness of SOAS findings.

Spatial distribution and source apportionment of water pollution in different administrative zones of Wen-Rui-Tang (WRT) river watershed, China.

PubMed

Yang, Liping; Mei, Kun; Liu, Xingmei; Wu, Laosheng; Zhang, Minghua; Xu, Jianming; Wang, Fan

2013-08-01

Water quality degradation in river systems has caused great concerns all over the world. Identifying the spatial distribution and sources of water pollutants is the very first step for efficient water quality management. A set of water samples collected bimonthly at 12 monitoring sites in 2009 and 2010 were analyzed to determine the spatial distribution of critical parameters and to apportion the sources of pollutants in Wen-Rui-Tang (WRT) river watershed, near the East China Sea. The 12 monitoring sites were divided into three administrative zones of urban, suburban, and rural zones considering differences in land use and population density. Multivariate statistical methods [one-way analysis of variance, principal component analysis (PCA), and absolute principal component score-multiple linear regression (APCS-MLR) methods] were used to investigate the spatial distribution of water quality and to apportion the pollution sources. Results showed that most water quality parameters had no significant difference between the urban and suburban zones, whereas these two zones showed worse water quality than the rural zone. Based on PCA and APCS-MLR analysis, urban domestic sewage and commercial/service pollution, suburban domestic sewage along with fluorine point source pollution, and agricultural nonpoint source pollution with rural domestic sewage pollution were identified to the main pollution sources in urban, suburban, and rural zones, respectively. Understanding the water pollution characteristics of different administrative zones could put insights into effective water management policy-making especially in the area across various administrative zones.

Modeling the intraurban variation in nitrogen dioxide in urban areas in Kathmandu Valley, Nepal.

PubMed

Gurung, Anobha; Levy, Jonathan I; Bell, Michelle L

2017-05-01

With growing urbanization, traffic has become one of the main sources of air pollution in Nepal. Understanding the impact of air pollution on health requires estimation of exposure. Land use regression (LUR) modeling is widely used to investigate intraurban variation in air pollution for Western cities, but LUR models are relatively scarce in developing countries. In this study, we developed LUR models to characterize intraurban variation of nitrogen dioxide (NO 2 ) in urban areas of Kathmandu Valley, Nepal, one of the fastest urbanizing areas in South Asia. Over the study area, 135 monitoring sites were selected using stratified random sampling based on building density and road density along with purposeful sampling. In 2014, four sampling campaigns were performed, one per season, for two weeks each. NO 2 was measured using duplicate Palmes tubes at 135 sites, with additional information on nitric oxide (NO), NO 2 , and nitrogen oxide (NOx) concentrations derived from Ogawa badges at 28 sites. Geographical variables (e.g., road network, land use, built area) were used as predictor variables in LUR modeling, considering buffers 25-400m around each monitoring site. Annual average NO 2 by site ranged from 5.7 to 120ppb for the study area, with higher concentrations in the Village Development Committees (VDCs) of Kathmandu and Lalitpur than in Kirtipur, Thimi, and Bhaktapur, and with variability present within each VDC. In the final LUR model, length of major road, built area, and industrial area were positively associated with NO 2 concentration while normalized difference vegetation index (NDVI) was negatively associated with NO 2 concentration (R 2 =0.51). Cross-validation of the results confirmed the reliability of the model. The combination of passive NO 2 sampling and LUR modeling techniques allowed for characterization of nitrogen dioxide patterns in a developing country setting, demonstrating spatial variability and high pollution levels. Copyright © 2017 Elsevier Inc. All rights reserved.

Long term effects of traffic noise on mortality in the city of Barcelona, 2004-2007.

PubMed

Barceló, Maria Antònia; Varga, Diego; Tobias, Aurelio; Diaz, Julio; Linares, Cristina; Saez, Marc

2016-05-01

Numerous studies showing statistically significant associations between environmental noise and adverse health effects already exist for short-term (over one day at most) and long-term (over a year or more) noise exposure, both for morbidity and (albeit to a lesser extent) mortality. Recently, several studies have shown this association to be independent from confounders, mainly those of air pollutants. However, what has not been addressed is the problem of misalignment (i.e. the exposure data locations and health outcomes have different spatial locations). Without any explicit control of such misalignment inference is seriously compromised. Our objective is to assess the long-term effects of traffic noise on mortality in the city of Barcelona (Spain) during 2004-2007. We take into account the control of confounding, for both air pollution and socioeconomic factors at a contextual level and, in particular, we explicitly address the problem of misalignment. We employed a case-control design with individual data. We used deaths resulting from myocardial infarction, hypertension, or Type II diabetes mellitus in Barcelona between 2004 and 2007 as cases for the study, while for controls we used deaths (likewise in Barcelona and over the same period of time) resulting from AIDS or external causes (e.g. accidental falls, accidental poisoning by psychotropic drugs, drugs of abuse, suicide and self-harm, or injuries resulting from motor vehicle accidents). The controls were matched with the cases by sex and age. We used the annual average equivalent A-weighted sound pressure levels for daytime (7-21h), evening-time (21-23h) and night-time (23-7h), and controlled for the following confounders: i) air pollutants (NO2, PM10 and benzene), ii) material deprivation (at a census tract level) and iii) land use and other spatial variables. We explicitly controlled for heterogeneity (uneven distribution of both response and environmental exposures within an area), spatial dependency (of the observations of the response variables), temporal trends (long-term behaviour of the response variables) and spatial misalignment (between response and environmental exposure locations). We used a fully Bayesian method, through the Integrated Nested Laplace Approximation (INLA). Specifically, we plugged the whole model for the exposure into the health model and obtained a linear predictor defined on the entire spatial domain. Separate analyses were carried out for men and for women. After adjusting for confounders, we found that traffic noise was associated with myocardial infarction mortality along with Type II diabetes mellitus in men (in both cases, odds ratios (OR) were around 1.02) and mortality from hypertension in women (ORs around 1.01). Nevertheless, only in the case of hypertension in women, does the association remain statistically significant for all age groups considered (all ages, ≥65 years and ≥75 years). Copyright © 2016 Elsevier Inc. All rights reserved.

Global Land Use Regression Model for Nitrogen Dioxide Air Pollution.

PubMed

Larkin, Andrew; Geddes, Jeffrey A; Martin, Randall V; Xiao, Qingyang; Liu, Yang; Marshall, Julian D; Brauer, Michael; Hystad, Perry

2017-06-20

Nitrogen dioxide is a common air pollutant with growing evidence of health impacts independent of other common pollutants such as ozone and particulate matter. However, the worldwide distribution of NO 2 exposure and associated impacts on health is still largely uncertain. To advance global exposure estimates we created a global nitrogen dioxide (NO 2 ) land use regression model for 2011 using annual measurements from 5,220 air monitors in 58 countries. The model captured 54% of global NO 2 variation, with a mean absolute error of 3.7 ppb. Regional performance varied from R 2 = 0.42 (Africa) to 0.67 (South America). Repeated 10% cross-validation using bootstrap sampling (n = 10,000) demonstrated a robust performance with respect to air monitor sampling in North America, Europe, and Asia (adjusted R 2 within 2%) but not for Africa and Oceania (adjusted R 2 within 11%) where NO 2 monitoring data are sparse. The final model included 10 variables that captured both between and within-city spatial gradients in NO 2 concentrations. Variable contributions differed between continental regions, but major roads within 100 m and satellite-derived NO 2 were consistently the strongest predictors. The resulting model can be used for global risk assessments and health studies, particularly in countries without existing NO 2 monitoring data or models.

Assessing the Variability of Heavy Metal Concentrations in Liquid-Solid Two-Phase and Related Environmental Risks in the Weihe River of Shaanxi Province, China

PubMed Central

Song, Jinxi; Yang, Xiaogang; Zhang, Junlong; Long, Yongqing; Zhang, Yan; Zhang, Taifan

2015-01-01

Accurate estimation of the variability of heavy metals in river water and the hyporheic zone is crucial for pollution control and environmental management. The biotoxicities and potential ecological risks of heavy metals (Cu, Zn, Pb, Cd) in a solid-liquid two-phase system were estimated using the Geo-accumulation Index, Potential Ecological Risk Assessment and Quality Standard Index methods in the Weihe River of Shaanxi Province, China. Water and sediment samples were collected from five study sites during spring, summer and winter, 2013. The dominant species in the streambed sediments were chironomids and flutter earthworm, whose bioturbation mainly ranged from 0 to 20 cm. The concentrations of heavy metals in surface water and pore water varied obviously in spring and summer. The degrees of concentration of Cu and Cd in spring and summer were higher than the U.S. water quality Criteria Maximum Concentrations. Furthermore, the biotoxicities of Pb and Zn demonstrated season-spatial variations. The concentrations of Cu, Zn, Pb and Cd in spring and winter were significantly higher than those in summer, and the pollution levels also varied obviously in different layers of the sediments. Moreover, the pollution level of Cd was the most serious, as estimated by all three assessment methods. PMID:26193293

Worldwide estimation of river concentrations of any chemical originating from sewage-treatment plants using dilution factors.

PubMed

Keller, Virginie D J; Williams, Richard J; Lofthouse, Caryn; Johnson, Andrew C

2014-02-01

Dilution factors are a critical component in estimating concentrations of so-called "down-the-drain" chemicals (e.g., pharmaceuticals) in rivers. The present study estimated the temporal and spatial variability of dilution factors around the world using geographically referenced data sets at 0.5° × 0.5° resolution. Domestic wastewater effluents were derived from national per capita domestic water use estimates and gridded population. Monthly and annual river flows were estimated by accumulating runoff estimates using topographically derived flow directions. National statistics, including the median and interquartile range, were generated to quantify dilution factors. Spatial variability of the dilution factor was found to be considerable; for example, there are 4 orders of magnitude in annual median dilution factor between Canada and Morocco. Temporal variability within a country can also be substantial; in India, there are up to 9 orders of magnitude between median monthly dilution factors. These national statistics provide a global picture of the temporal and spatial variability of dilution factors and, hence, of the potential exposure to down-the-drain chemicals. The present methodology has potential for a wide international community (including decision makers and pharmaceutical companies) to assess relative exposure to down-the-drain chemicals released by human pollution in rivers and, thus, target areas of potentially high risk. © 2013 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Air Pollution Measurements by Citizen Scientists and NASA Satellites: Data Integration and Analysis

NASA Astrophysics Data System (ADS)

Gupta, P.; Maibach, J.; Levy, R. C.; Doraiswamy, P.; Pikelnaya, O.; Feenstra, B.; Polidori, A.

2017-12-01

PM2.5, or fine particulate matter, is a category of air pollutant consisting of solid particles with effective aerodynamic diameter of less than 2.5 microns. These particles are hazardous to human health, as their small size allows them to penetrate deep into the lungs. Since the late 1990's, the US Environmental Protection Agency has been monitoring PM2.5 using a network of ground-level sensors. Due to cost and space restrictions, the EPA monitoring network remains spatially sparse. That is, while the network spans the extent of the US, the distance between sensors is large enough that significant spatial variation in PM concentration can go undetected. To increase the spatial resolution of monitoring, previous studies have used satellite data to estimate ground-level PM concentrations. From imagery, one can create a measure of haziness due to aerosols, called aerosol optical depth (AOD), which then can be used to estimate PM concentrations using statistical and physical modeling. Additionally, previous research has identified a number of meteorological variables, such as relative humidity and mixing height, which aide in estimating PM concentrations from AOD. Although the high spatial resolution of satellite data is valuable alone for forecasting air quality, higher resolution ground-level data is needed to effectively study the relationship between PM2.5 concentrations and AOD. To this end, we discuss a citizen-science PM monitoring network deployed in California. Using low-cost PM sensors, this network achieves higher spatial resolution. We additionally discuss a software pipeline for integrating resulting PM measurements with satellite data, as well as initial data analysis.

Spatial variation of butyltins in an intermittent French River

NASA Astrophysics Data System (ADS)

Chahinian, N.; Bancon-Montigny, C.; Brunel, V.; Tournoud, M. G.

2009-04-01

Organotins (OTs) have been increasingly used in industrial applications because of their thermoresistant and biocidal properties: in the 1970s and 1980s Tributyltin (TBT) based anti-fouling paints were used on ships and vessels of all kinds. However, studies pointed out the highly toxic nature of these compounds and their active role as an endoctrine perturbator. This lead the EU to ban the use of TBT paints in 2003. The toxicity of OTs combined to their widespread use has lead then to be included in the priority list of the EU water framework directive. Organotins are prone to adsorption, can bond easily to particulate matter and "migrate" from the water column unto the sediments where their half-life can extend to a few decades. Consequently sediments can become important organotin stores and release OT compounds during dredging operations, storms, tides or floods. The main objective of this work was to investigate the presence of organotin compounds in the sediments and water column of an intermittent river reach i.e. to establish the presence of organotins in regular flow conditions and assess their spatial variability. The study zone is a reach of the Vène River located in southern France. The Vène is a major tributary of the Thau lagoon which is an important shellfish farming site and thus very vulnerable to OT contamination. Butyltin, trace metal and dissolved organic carbon (DOC) concentrations were measured on water and sediment samples collected over a 3 month period stretching from March to May 2008. The results revealed the presence of butyltins at concentrations exceeding the EU and French pollution limits. In terms of spatial variability, by combining the butyltin and the trace metal results at 16 locations along the reach, two point pollutions spots were identified, namely a sewage treatment plant and a drainage ditch. However, the later are not the only OT input sources, the results of the sediment analysis pointed out to a diffuse pollution throughout the length of the reach. Finally, flow and concentration values were used to calculate the potential export rate of butyltins from the reach during the spring months.

The pink shrimp Farfantepenaeus duorarum, its symbionts and helminths as bioindicators of chemical pollution in Campeche Sound, Mexico.

PubMed

Vidal-Martínez, V M; Aguirre-Macedo, M L; Del Rio-Rodríguez, R; Gold-Bouchot, G; Rendón-von Osten, J; Miranda-Rosas, G A

2006-06-01

The pink shrimp Farfantepenaeus duorarum may acquire pollutants, helminths and symbionts from their environment. Statistical associations were studied between the symbionts and helminths of F. duorarum and pollutants in sediments, water and shrimps in Campeche Sound, Mexico. The study area spatially overlapped between offshore oil platforms and natural shrimp mating grounds. Spatial autocorrelation of data was controlled with spatial analysis using distance indices (SADIE) which identifies parasite or pollutant patches (high levels) and gaps (low levels), expressing them as clustering indices compared at each point to produce a measure of spatial association. Symbionts included the peritrich ciliates Epistylis sp. and Zoothamnium penaei and all symbionts were pooled. Helminths included Hysterothylacium sp., Opecoeloides fimbriatus, Prochristianella penaei and an unidentified cestode. Thirty-five pollutants were identified, including polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), pesticides and heavy metals. The PAHs (2-3 ring) in water, unresolved complex mixture (UCM), Ni and V in sediments, and Zn, Cr and heptachlor in shrimps were significantly clustered. The remaining pollutants were randomly distributed in the study area. Juvenile shrimps acquired pesticides, PAHs (2-3 rings) and Zn, while adults acquired PAHs (4-5 rings), Cu and V. Results suggest natural PAH spillovers, and continental runoff of dichlorodiphenyltrichloroethane (DDT), PCBs and PAHs (2-3 ring). There were no significant associations between pollutants and helminths. However, there were significant negative associations of pesticides, UCM and PCBs with symbiont numbers after controlling shrimp size and spatial autocorrelation. Shrimps and their symbionts appear to be promising bioindicators of organic chemical pollution in Campeche Sound.

Measurement of spatial and temporal variation in volatile hazardous air pollutants in Tacoma, Washington, using a mobile membrane introduction mass spectrometry (MIMS) system.

PubMed

Davey, Nicholas G; Fitzpatrick, Cole T E; Etzkorn, Jacob M; Martinsen, Morten; Crampton, Robert S; Onstad, Gretchen D; Larson, Timothy V; Yost, Michael G; Krogh, Erik T; Gilroy, Michael; Himes, Kathy H; Saganić, Erik T; Simpson, Christopher D; Gill, Christopher G

2014-09-19

The objective of this study was to use membrane introduction mass spectrometry (MIMS), implemented on a mobile platform, in order to provide real-time, fine-scale, temporally and spatially resolved measurements of several hazardous air pollutants. This work is important because there is now substantial evidence that fine-scale spatial and temporal variations of air pollutant concentrations are important determinants of exposure to air pollution and adverse health outcomes. The study took place in Tacoma, WA during periods of impaired air quality in the winter and summer of 2008 and 2009. Levels of fine particles were higher in winter compared to summer, and were spatially uniform across the study area. Concentrations of vapor phase pollutants measured by membrane introduction mass spectrometry (MIMS), notably benzene and toluene, had relatively uniform spatial distributions at night, but exhibited substantial spatial variation during the day-daytime levels were up to 3-fold higher at traffic-impacted locations compared to a reference site. Although no direct side-by-side comparison was made between the MIMS system and traditional fixed site monitors, the MIMS system typically reported higher concentrations of specific VOCs, particularly benzene, ethylbenzene and naphthalene, compared to annual average concentrations obtained from SUMA canisters and gas chromatographic analysis at the fixed sites.

Weather elements, chemical air pollutants and airborne pollen influencing asthma emergency room visits in Szeged, Hungary: performance of two objective weather classifications

NASA Astrophysics Data System (ADS)

Makra, László; Puskás, János; Matyasovszky, István; Csépe, Zoltán; Lelovics, Enikő; Bálint, Beatrix; Tusnády, Gábor

2015-09-01

Weather classification approaches may be useful tools in modelling the occurrence of respiratory diseases. The aim of the study is to compare the performance of an objectively defined weather classification and the Spatial Synoptic Classification (SSC) in classifying emergency department (ED) visits for acute asthma depending from weather, air pollutants, and airborne pollen variables for Szeged, Hungary, for the 9-year period 1999-2007. The research is performed for three different pollen-related periods of the year and the annual data set. According to age and gender, nine patient categories, eight meteorological variables, seven chemical air pollutants, and two pollen categories were used. In general, partly dry and cold air and partly warm and humid air aggravate substantially the symptoms of asthmatics. Our major findings are consistent with this establishment. Namely, for the objectively defined weather types favourable conditions for asthma ER visits occur when an anticyclonic ridge weather situation happens with near extreme temperature and humidity parameters. Accordingly, the SSC weather types facilitate aggravating asthmatic conditions if warm or cool weather occur with high humidity in both cases. Favourable conditions for asthma attacks are confirmed in the extreme seasons when atmospheric stability contributes to enrichment of air pollutants. The total efficiency of the two classification approaches is similar in spite of the fact that the methodology for derivation of the individual types within the two classification approaches is completely different.

Weather elements, chemical air pollutants and airborne pollen influencing asthma emergency room visits in Szeged, Hungary: performance of two objective weather classifications.

PubMed

Makra, László; Puskás, János; Matyasovszky, István; Csépe, Zoltán; Lelovics, Enikő; Bálint, Beatrix; Tusnády, Gábor

2015-09-01

Weather classification approaches may be useful tools in modelling the occurrence of respiratory diseases. The aim of the study is to compare the performance of an objectively defined weather classification and the Spatial Synoptic Classification (SSC) in classifying emergency department (ED) visits for acute asthma depending from weather, air pollutants, and airborne pollen variables for Szeged, Hungary, for the 9-year period 1999-2007. The research is performed for three different pollen-related periods of the year and the annual data set. According to age and gender, nine patient categories, eight meteorological variables, seven chemical air pollutants, and two pollen categories were used. In general, partly dry and cold air and partly warm and humid air aggravate substantially the symptoms of asthmatics. Our major findings are consistent with this establishment. Namely, for the objectively defined weather types favourable conditions for asthma ER visits occur when an anticyclonic ridge weather situation happens with near extreme temperature and humidity parameters. Accordingly, the SSC weather types facilitate aggravating asthmatic conditions if warm or cool weather occur with high humidity in both cases. Favourable conditions for asthma attacks are confirmed in the extreme seasons when atmospheric stability contributes to enrichment of air pollutants. The total efficiency of the two classification approaches is similar in spite of the fact that the methodology for derivation of the individual types within the two classification approaches is completely different.

Spatial/Temporal Variations of Elemental Carbon, Organic Carbon, and Trace Elements in PM10 and the Impact of Land-Use Patterns on Community Air Pollution in Paterson, NJ

PubMed Central

Yu, Chang Ho; Fan, Zhi-Hua; Meng, Qingyu; Zhu, Xianlei; Korn, Leo; Bonanno, Linda J.

2014-01-01

An urban community PM10 (particulate matter ≤ 10 μm in aerodynamic diameter) air pollution study was conducted in Paterson, NJ, a mixed land-use community that is interspersed with industrial, commercial, mobile, and residential land-use types. This paper examines (1) the spatial/temporal variation of PM10, elemental carbon (EC), organic carbon (OC), and nine elements; and (2) the impact of land-use type on those variations. Air samples were collected from three community-oriented locations in Paterson that attempted to capture industrial, commercial, and mobile source-dominated emissions. Sampling was conducted for 24 hr every 6 days from November 2005 through December 2006. Samples were concurrently collected at the New Jersey Department of Environmental Protection-designated air toxics background site in Chester, NJ. PM10 mass, EC, OC, and nine elements (Ca, Cu, Fe, Pb, Mn, Ni, S, Ti, and Zn) that had more than 50% of samples above detection and known sources or are toxic were selected for spatial/temporal analysis in this study. The concentrations of PM10, EC, OC, and eight elements (except S) were significantly higher in Paterson than in Chester (P < 0.05). The concentrations of these elements measured in Paterson were also found to be higher during winter than the other three seasons (except S), and higher on weekdays than on weekends (except Pb). The concentrations of EC, Cu, Fe, and Zn at the commercial site in Paterson were significantly higher than the industrial and mobile sites; however, the other eight species were not significantly different within the city (P > 0.05). These results indicated that anthropogenic sources of air pollution were present in Paterson. The source apportionment confirmed the impact of vehicular and industrial emissions on the PM10 ambient air pollution in Paterson. The multiple linear regression analysis showed that categorical land-use type was a significant predictor for all air pollution levels, explaining up to 42% of the variability in concentration by land-use type only. PMID:21751583

Possible influence of atmospheric circulations on winter haze pollution in Beijing-Tianjin-Hebei region, northern China

NASA Astrophysics Data System (ADS)

Zhang, Z. Y.

2016-12-01

Using the daily records derived from the synoptic weather stations and the NCEP/NCAR and ERA-Interim reanalysis data, the variability of the winter haze pollutions (indicated by the mean visibility and number of hazy days) in Beijing-Tianjin-Hebei (BTH) region during the period 1981 to 2015 and its relationship to the atmospheric circulations in middle-high latitude were analyzed in this study. The winter haze pollution in BTH had distinct inter-annual and inter-decadal variabilities without a significant long-term trend. According to the spatial distribution of correlation coefficients, six atmospheric circulation indices (I1 to I6) were defined from the key areas in sea level pressure (SLP), zonal and meridional winds at 850 hPa (U850, V850), geopotential height field at 500 hPa (H500), zonal wind at 200 hPa (U200), and air temperature at 200 hPa (T200), respectively. All of the six indices have significant and stable correlations with the winter visibility and number of hazy days in BTH. The six circulation indices together can explain 77.7% (78.7%) and 61.7% (69.1%) variances of the winter visibility and number of hazy days in the year-to-year (inter-annual) variability, srespectively. The increase of Ic(a comprehensive index derived from the six individual circulation indices) can cause a shallowing of the East Asian trough at the middle troposphere and a weakening of the Siberian high pressure field at sea level, and then accompanied by a reduction (increase) of horizontal advection and vertical convection (relative humidity) in the lowest troposphere and a reduced boundary layer height in BTH and its neighboring areas, which are favorable for the formation of haze pollutions in BTH winter, and vice versa. The high level of the prediction statistics and the reasonable mechanism suggested that the winter haze pollutions in BTH can be forecasted or estimated credibly based on the optimized atmospheric circulation indices. Thus it is helpful for government decision-making departments to take actions in advance in dealing with probably severe haze pollutions in BTH indicated by the atmospheric circulation conditions.

Mortality and long-term exposure to ambient air pollution: ongoing analyses based on the American Cancer Society cohort.

PubMed

Krewski, Daniel; Burnett, Richard; Jerrett, Michael; Pope, C Arden; Rainham, Daniel; Calle, Eugenia; Thurston, George; Thun, Michael

This article provides an overview of previous analysis and reanalysis of the American Cancer Society (ACS) cohort, along with an indication of current ongoing analyses of the cohort with additional follow-up information through to 2000. Results of the first analysis conducted by Pope et al. (1995) showed that higher average sulfate levels were associated with increased mortality, particularly from cardiopulmonary disease. A reanalysis of the ACS cohort, undertaken by Krewski et al. (2000), found the original risk estimates for fine-particle and sulfate air pollution to be highly robust against alternative statistical techniques and spatial modeling approaches. A detailed investigation of covariate effects found a significant modifying effect of education with risk of mortality associated with fine particles declining with increasing educational attainment. Pope et al. (2002) subsequently reported results of a subsequent study using an additional 10 yr of follow-up of the ACS cohort. This updated analysis included gaseous copollutant and new fine-particle measurements, more comprehensive information on occupational exposures, dietary variables, and the most recent developments in statistical modeling integrating random effects and nonparametric spatial smoothing into the Cox proportional hazards model. Robust associations between ambient fine particulate air pollution and elevated risks of cardiopulmonary and lung cancer mortality were clearly evident, providing the strongest evidence to date that long-term exposure to fine particles is an important health risk. Current ongoing analysis using the extended follow-up information will explore the role of ecologic, economic, and, demographic covariates in the particulate air pollution and mortality association. This analysis will also provide insight into the role of spatial autocorrelation at multiple geographic scales, and whether critical instances in time of exposure to fine particles influence the risk of mortality from cardiopulmonary and lung cancer. Information on the influence of covariates at multiple scales and of critical exposure time windows can assist policymakers in establishing timelines for regulatory interventions that maximize population health benefits.

Performance of European chemistry transport models as function of horizontal resolution

NASA Astrophysics Data System (ADS)

Schaap, M.; Cuvelier, C.; Hendriks, C.; Bessagnet, B.; Baldasano, J. M.; Colette, A.; Thunis, P.; Karam, D.; Fagerli, H.; Graff, A.; Kranenburg, R.; Nyiri, A.; Pay, M. T.; Rouïl, L.; Schulz, M.; Simpson, D.; Stern, R.; Terrenoire, E.; Wind, P.

2015-07-01

Air pollution causes adverse effects on human health as well as ecosystems and crop yield and also has an impact on climate change trough short-lived climate forcers. To design mitigation strategies for air pollution, 3D Chemistry Transport Models (CTMs) have been developed to support the decision process. Increases in model resolution may provide more accurate and detailed information, but will cubically increase computational costs and pose additional challenges concerning high resolution input data. The motivation for the present study was therefore to explore the impact of using finer horizontal grid resolution for policy support applications of the European Monitoring and Evaluation Programme (EMEP) model within the Long Range Transboundary Air Pollution (LRTAP) convention. The goal was to determine the "optimum resolution" at which additional computational efforts do not provide increased model performance using presently available input data. Five regional CTMs performed four runs for 2009 over Europe at different horizontal resolutions. The models' responses to an increase in resolution are broadly consistent for all models. The largest response was found for NO2 followed by PM10 and O3. Model resolution does not impact model performance for rural background conditions. However, increasing model resolution improves the model performance at stations in and near large conglomerations. The statistical evaluation showed that the increased resolution better reproduces the spatial gradients in pollution regimes, but does not help to improve significantly the model performance for reproducing observed temporal variability. This study clearly shows that increasing model resolution is advantageous, and that leaving a resolution of 50 km in favour of a resolution between 10 and 20 km is practical and worthwhile. As about 70% of the model response to grid resolution is determined by the difference in the spatial emission distribution, improved emission allocation procedures at high spatial and temporal resolution are a crucial factor for further model resolution improvements.

Using mobile monitoring to visualise diurnal variation of traffic pollutants across two near-highway neighbourhoods

NASA Astrophysics Data System (ADS)

Pattinson, Woodrow; Longley, Ian; Kingham, Simon

2014-09-01

It is widely accepted that concentrations of primary traffic pollutants can vary substantially across relatively small urban areas. Fixed-site monitors have been shown to be largely inadequate for representing concentrations at nearby locations, resulting in the increasing use of spatial modelling or mobile sampling methods to achieve spatial saturation. In this study, we employ the use of a simple bicycle to sample concentrations of ultrafine particles (UFPs), carbon monoxide (CO) and particulate matter (PM10) at two small areas (<2.5 km2) in South Auckland, New Zealand. Portable instruments were mounted inside a custom-built casing at the front of the bicycle and every street within each study area was sampled in a grid-like fashion, at four times of day (07:00, 12:00, 17:00 and 22:00). Each area has a six-lane highway running through its centre and the core aim was to visualise and describe spatial variability of pollutant levels about the highway, main arterials and quieter streets, at periods of contrasting meteorological and traffic conditions. A total of 20 sampling runs in each area (five at each of the four timings) were conducted. Meteorological data were logged continuously at background sites within each study area. Results show that the influence of highway traffic (UFPs, CO) was strongest during the mornings and late evenings when wind speeds were low, while for the midday and afternoon timings, concentrations were highest at the arterial and shopping zones. Concentrations of PM10 appeared to be strongest in the residential areas during mornings and late evenings, suggesting an influence of wood burning for home heating. For all timings combined, for all three pollutants, it appears the arterial roads featuring shops and numerous intersections with traffic lights, had a stronger influence on concentrations than the busier but more free-flowing highways. This study provides not only an insight into microspatial hotspot variation across suburbs, but also how this variation shifts diurnally.

Can tintinnids be used for discriminating water quality status in marine ecosystems?

PubMed

Feng, Meiping; Zhang, Wuchang; Wang, Weiding; Zhang, Guangtao; Xiao, Tian; Xu, Henglong

2015-12-30

Ciliated protozoa have many advantages in bioassessment of water quality. The ability of tintinnids for assessing water quality status was studied during a 7-yearcycle in Jiaozhou Bay of the Yellow Sea, northern China. The samples were collected monthly at four sites with a spatial gradient of environmental pollution. Environmental variables, e.g., temperature, salinity, chlorophyll a (Chl a), dissolved inorganic nitrogen, soluble reactive phosphate (SRP), and soluble active silicate (SRSi), were measured synchronously for comparison with biotic parameters. Results showed that: (1) tintinnid community structures represented significant differences among the four sampling sites; (2) spatial patterns of the tintinnid communities were significantly correlated with environmental variables, especially SRSi and nutrients; and (3) the community structural parameters and the five dominant species were significantly correlated with SRSi and nutrients. We suggested that tintinnids may be used as a potential bioindicator for discriminating water quality status in marine ecosystems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spatial Resolution Requirements for Traffic-Related Air Pollutant Exposure Evaluations

PubMed Central

Batterman, Stuart; Chambliss, Sarah; Isakov, Vlad

2014-01-01

Vehicle emissions represent one of the most important air pollution sources in most urban areas, and elevated concentrations of pollutants found near major roads have been associated with many adverse health impacts. To understand these impacts, exposure estimates should reflect the spatial and temporal patterns observed for traffic-related air pollutants. This paper evaluates the spatial resolution and zonal systems required to estimate accurately intraurban and near-road exposures of traffic-related air pollutants. The analyses use the detailed information assembled for a large (800 km2) area centered on Detroit, Michigan, USA. Concentrations of nitrogen oxides (NOx) due to vehicle emissions were estimated using hourly traffic volumes and speeds on 9,700 links representing all but minor roads in the city, the MOVES2010 emission model, the RLINE dispersion model, local meteorological data, a temporal resolution of 1 hr, and spatial resolution as low as 10 m. Model estimates were joined with the corresponding shape files to estimate residential exposures for 700,000 individuals at property parcel, census block, census tract, and ZIP code levels. We evaluate joining methods, the spatial resolution needed to meet specific error criteria, and the extent of exposure misclassification. To portray traffic-related air pollutant exposure, raster or inverse distance-weighted interpolations are superior to nearest neighbor approaches, and interpolations between receptors and points of interest should not exceed about 40 m near major roads, and 100 m at larger distances. For census tracts and ZIP codes, average exposures are overestimated since few individuals live very near major roads, the range of concentrations is compressed, most exposures are misclassified, and high concentrations near roads are entirely omitted. While smaller zones improve performance considerably, even block-level data can misclassify many individuals. To estimate exposures and impacts of traffic-related pollutants accurately, data should be geocoded or estimated at the most-resolved spatial level; census tract and larger zones have little if any ability to represent intraurban variation in traffic-related air pollutant concentrations. These results are based on one of the most comprehensive intraurban modeling studies in the literature and results are robust. Recommendations address the value of dispersion models to portray spatial and temporal variation of air pollutants in epidemiology and other studies; techniques to improve accuracy and reduce the computational burden in urban scale modeling; the necessary spatial resolution for health surveillance, demographic, and pollution data; and the consequences of low resolution data in terms of exposure misclassification. PMID:25132794

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran.

PubMed

Ghaemi, Z; Alimohammadi, A; Farnaghi, M

2018-04-20

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.

Exploring relationships between outdoor air particulate-associated polycyclic aromatic hydrocarbon and PM 2.5: A case study of benzo(a)pyrene in California metropolitan regions

NASA Astrophysics Data System (ADS)

Lobscheid, Agnes B.; McKone, Thomas E.; Vallero, Daniel A.

Polycyclic aromatic hydrocarbons (PAHs) and particulate matter (PM) are co-pollutants emitted as by-products of combustion processes. Convincing evidence exists for PAHs as a primary toxic component of fine PM (PM 2.5). Because PM 2.5 is listed by the US EPA as a "Criteria Pollutant", it is monitored regularly at sites nationwide. In contrast, very limited data is available on measured ambient air concentrations of PAHs. However, between 1999 and 2001, ambient air concentrations of PM 2.5 and benzo(a)pyrene (BaP) are available for California locations. We use multivariate linear regression models (MLRMs) to predict ambient air levels of BaP in four air basins based on reported PM 2.5 concentrations and spatial, temporal and meteorological variables as variates. We obtain an R2 ranging from 0.57 to 0.72 among these basins. Significant variables ( p<0.05) include the average daily PM 2.5 concentration, wind speed, temperature and relative humidity, and the coastal distance as well as season, and holiday or weekend. Combining the data from all sites and using only these variables to estimate ambient BaP levels, we obtain an R2 of 0.55. These R2-values, combined with analysis of the residual error and cross validation using the PRESS-statistic, demonstrate the potential of our method to estimate reported outdoor air PAH exposure levels in metropolitan regions. These MLRMs provide a first step towards relating outdoor ambient PM 2.5 and PAH concentrations for epidemiological studies when PAH measurements are unavailable, or limited in spatial coverage, based on publicly available meteorological and PM 2.5 data.

Frontiers in Atmospheric Chemistry Modelling

NASA Astrophysics Data System (ADS)

Colette, Augustin; Bessagnet, Bertrand; Meleux, Frederik; Rouïl, Laurence

2013-04-01

The first pan-European kilometre-scale atmospheric chemistry simulation is introduced. The continental-scale air pollution episode of January 2009 is modelled with the CHIMERE offline chemistry-transport model with a massive grid of 2 million horizontal points, performed on 2000 CPU of a high performance computing system hosted by the Research and Technology Computing Center at the French Alternative Energies and Atomic Energy Commission (CCRT/CEA). Besides the technical challenge, which demonstrated the robustness of the selected air quality model, we discuss the added value in terms of air pollution modelling and decision support. The comparison with in-situ observations shows that model biases are significantly improved despite some spurious added spatial variability attributed to shortcomings in the emission downscaling process and coarse resolution of the meteorological fields. The increased spatial resolution is clearly beneficial for the detection of exceedances and exposure modelling. We reveal small scale air pollution patterns that highlight the contribution of city plumes to background air pollution levels. Up to a factor 5 underestimation of the fraction of population exposed to detrimental levels of pollution can be obtained with a coarse simulation if subgrid scale correction such as urban increments are ignored. This experiment opens new perspectives for environmental decision making. After two decades of efforts to reduce air pollutant emissions across Europe, the challenge is now to find the optimal trade-off between national and local air quality management strategies. While the first approach is based on sectoral strategies and energy policies, the later builds upon new alternatives such as urban development. The strategies, the decision pathways and the involvement of individual citizen differ, and a compromise based on cost and efficiency must be found. We illustrated how high performance computing in atmospheric science can contribute to this aim. Although further developments are still needed to secure the results for routine policy use, the door is now open...

Contamination characteristics, ecological risk and source identification of trace metals in sediments of the Le'an River (China).

PubMed

Chen, Haiyang; Chen, Ruihui; Teng, Yanguo; Wu, Jin

2016-03-01

Recognizing the pollution characteristics of trace metals in river sediments and targeting their potential sources are of key importance for proposing effective strategies to protect watershed ecosystem health. In this study, a comprehensive investigation was conducted to identify the contamination and risk characteristics of trace metals in sediments of Le'an River which is a main tributary of the largest freshwater lake in China, Poyang Lake. To attain this objective, several tools and models were considered. Geoaccumulation index and enrichment factor were used to understand the general pollution characteristic of trace metals in sediments. Discriminant analysis was applied to identify the spatial variability of sediment metals. Sediment quality guidelines and potential ecological risk index were employed for ecological risk evaluation. Multivariate curve resolution-alternating least square was proposed to extract potential pollution sources, as well as the application of Monte-Carlo simulation for uncertainty analysis of source identification. Results suggested that the sediments in Le'an River were considerably polluted by the investigated trace metals (Cd, Cr, As, Hg, Pb, Cu, Zn and Ni). Sediment concentrations of these metals showed significant spatial variations. The potential ecological risk lay in high level. Comparatively speaking, the metals of Cd, Cu and Hg were likely to result in more harmful effects. Mining activities and the application of fertilizers and agrochemicals were identified as the main anthropogenic sources. To protect the ecological system of Le'an River and Poyang Lake watershed, industrial mining and agricultural activities in this area should to be strictly regulated. Copyright © 2015 Elsevier Inc. All rights reserved.

Asian Dust Storm Outbreaks: A Satellite-Surface Perspective

NASA Technical Reports Server (NTRS)

Tsay, Si-Chee

2006-01-01

Airborne dusts from northern China contribute a significant part of the air quality problem and, to some extent, regional climatic impact in Asia during springtime. Asian dust typically originates in desert areas far from polluted urban regions. During the transport, dust layers can interact with anthropogenic sulfate and soot aerosols from heavily polluted urban areas. Added to the complex effects of clouds and natural marine aerosols, dust particles reaching the marine environment can have drastically different properties than those from the source. Thus, understanding the unique temporal and spatial variations of Asian dust is of special importance in regional-to-global climate issues (e.g., radiative forcing, hydrological cycle, and primary biological productivity in the mid-Pacific Ocean, etc.), as well as societal concerns (e.g., adverse health effects to humans). The Asian dust and air pollution aerosols can be detected by its colored appearance on current Earth observing satellites (e.g., MODIS, SeaWiFS, TOMS, etc.) and its evolution monitored by satellites and surface network (e.g. AERONET, SKY NET, MPLNET, etc.). Recently, many field campaigns (e.g., ACE-Asia-2001, TRACEP-2001, ADE-2002 & -2003, APEX-2001 & -2003, etc.) were designed and executed to study the compelling variability in spatial and temporal scale of both pollution-derived and naturally occurring aerosols, which often exist in high concentrations over eastern Asia and along the rim of the western Pacific. I will present an overview of the outbreak of Asian dust storms from space and surface observations and to address the climatic effects and societal impacts.

Spatial distribution and risk assessment of radionuclides in soils around a coal-fired power plant: A case study from the city of Baoji, China

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dai Lijun; Wei Haiyan; Wang Lingqing

2007-06-15

Coal burning may enhance human exposure to the natural radionuclides that occur around coal-fired power plants (CFPP). In this study, the spatial distribution and hazard assessment of radionuclides found in soils around a CFPP were investigated using statistics, geostatistics, and geographic information system (GIS) techniques. The concentrations of {sup 226}Ra, {sup 232}Th, and {sup 40}K in soils range from 12.54 to 40.18, 38.02 to 72.55, and 498.02 to 1126.98 Bq kg{sup -1}, respectively. Ordinary kriging was carried out to map the spatial patterns of radionuclides, and disjunctive kriging was used to quantify the probability of radium equivalent activity (Ra{sub eq})more » higher than the threshold. The maps show that the spatial variability of the natural radionuclide concentrations in soils was apparent. The results of this study could provide valuable information for risk assessment of environmental pollution and decision support.« less

Spatial distribution and risk assessment of radionuclides in soils around a coal-fired power plant: A case study from the city of Baoji, China

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dai, L.J.; Wei, H.Y.; Wang, L.Q.

2007-06-15

Coal burning may enhance human exposure to the natural radionuclides that occur around coal-fired power plants (CFPP). In this study, the spatial distribution and hazard assessment of radionuclides found in soils around a CFPP were investigated using statistics, geostatistics, and geographic information system (GIS) techniques. The concentrations of Ra-226, Th-232, and K-40 in soils range from 12.54 to 40.18, 38.02 to 72.55, and 498.02 to 1126.98 Bq kg{sup -1}, respectively. Ordinary kriging was carried out to map the spatial patterns of radionuclides, and disjunctive kriging was used to quantify the probability of radium equivalent activity (Ra{sub eq}) higher than themore » threshold. The maps show that the spatial variability of the natural radionuclide concentrations in soils was apparent. The results of this study could provide valuable information for risk assessment of environmental pollution and decision support.« less

Review of modelling air pollution from traffic at street-level - The state of the science.

PubMed

Forehead, H; Huynh, N

2018-06-13

Traffic emissions are a complex and variable cocktail of toxic chemicals. They are the major source of atmospheric pollution in the parts of cities where people live, commute and work. Reducing exposure requires information about the distribution and nature of emissions. Spatially and temporally detailed data are required, because both the rate of production and the composition of emissions vary significantly with time of day and with local changes in wind, traffic composition and flow. Increasing computer processing power means that models can accept highly detailed inputs of fleet, fuels and road networks. The state of the science models can simulate the behaviour and emissions of all the individual vehicles on a road network, with resolution of a second and tens of metres. The chemistry of the simulated emissions is also highly resolved, due to consideration of multiple engine processes, fuel evaporation and tyre wear. Good results can be achieved with both commercially available and open source models. The extent of a simulation is usually limited by processing capacity; the accuracy by the quality of traffic data. Recent studies have generated real time, detailed emissions data by using inputs from novel traffic sensing technologies and data from intelligent traffic systems (ITS). Increasingly, detailed pollution data is being combined with spatially resolved demographic or epidemiological data for targeted risk analyses. Copyright © 2018 Elsevier Ltd. All rights reserved.

Assessing the hydrogeochemical processes affecting groundwater pollution in arid areas using an integration of geochemical equilibrium and multivariate statistical techniques.

PubMed

El Alfy, Mohamed; Lashin, Aref; Abdalla, Fathy; Al-Bassam, Abdulaziz

2017-10-01

Rapid economic expansion poses serious problems for groundwater resources in arid areas, which typically have high rates of groundwater depletion. In this study, integration of hydrochemical investigations involving chemical and statistical analyses are conducted to assess the factors controlling hydrochemistry and potential pollution in an arid region. Fifty-four groundwater samples were collected from the Dhurma aquifer in Saudi Arabia, and twenty-one physicochemical variables were examined for each sample. Spatial patterns of salinity and nitrate were mapped using fitted variograms. The nitrate spatial distribution shows that nitrate pollution is a persistent problem affecting a wide area of the aquifer. The hydrochemical investigations and cluster analysis reveal four significant clusters of groundwater zones. Five main factors were extracted, which explain >77% of the total data variance. These factors indicated that the chemical characteristics of the groundwater were influenced by rock-water interactions and anthropogenic factors. The identified clusters and factors were validated with hydrochemical investigations. The geogenic factors include the dissolution of various minerals (calcite, aragonite, gypsum, anhydrite, halite and fluorite) and ion exchange processes. The anthropogenic factors include the impact of irrigation return flows and the application of potassium, nitrate, and phosphate fertilizers. Over time, these anthropogenic factors will most likely contribute to further declines in groundwater quality. Copyright © 2017 Elsevier Ltd. All rights reserved.

Measuring restoration in intertidal macrophyte assemblages following sewage treatment upgrade.

PubMed

Díez, I; Santolaria, A; Muguerza, N; Gorostiaga, J M

2013-03-01

Understanding the effectiveness of pollution mitigation actions in terms of biological recovery is essential if the environmental protection goals of management policies are to be achieved. Few studies, however, have evaluated the restoration of seaweed assemblages following pollution abatement. This study aimed to investigate the response of macroalgal vegetation to the upgrade of a wastewater treatment plant using a "Beyond BACI" experimental design. Temporal differences in vegetation structure between the outfall and two control locations over a 10-year period were assessed. Improvement in sewage treatment was found to lead to increases in diversity, cover of morphologically complex algae and spatial heterogeneity. The multivariate composition of assemblages at the outfall location became more similar to that at the controls; however, their complete recovery may depend on factors other than pollution removal. Our findings also suggest that the extent of restoration and the time required to detect it are largely predetermined by the response variables we choose to assess recovery. Copyright © 2012 Elsevier Ltd. All rights reserved.

Modeling effects of traffic and landscape characteristics on ambient nitrogen dioxide levels in Connecticut

NASA Astrophysics Data System (ADS)

Skene, Katherine J.; Gent, Janneane F.; McKay, Lisa A.; Belanger, Kathleen; Leaderer, Brian P.; Holford, Theodore R.

2010-12-01

An integrated exposure model was developed that estimates nitrogen dioxide (NO 2) concentration at residences using geographic information systems (GIS) and variables derived within residential buffers representing traffic volume and landscape characteristics including land use, population density and elevation. Multiple measurements of NO 2 taken outside of 985 residences in Connecticut were used to develop the model. A second set of 120 outdoor NO 2 measurements as well as cross-validation were used to validate the model. The model suggests that approximately 67% of the variation in NO 2 levels can be explained by: traffic and land use primarily within 2 km of a residence; population density; elevation; and time of year. Potential benefits of this model for health effects research include improved spatial estimations of traffic-related pollutant exposure and reduced need for extensive pollutant measurements. The model, which could be calibrated and applied in areas other than Connecticut, has importance as a tool for exposure estimation in epidemiological studies of traffic-related air pollution.

Spatial Variability of Perchlorate along a Traverse Route from Zhongshan Station to Dome A, East Antarctica

NASA Astrophysics Data System (ADS)

Jiang, S.; Cole-Dai, J.; Li, Y.; An, C.

2016-12-01

Snow deposition and accumulation on the Antarctic ice sheet preserve records of climatic change, as well as those of chemical characteristics of the environment. Chemical composition of snow and ice cores can be used to track the sources of important substances including pollutants and to investigate relationships between atmospheric chemistry and climatic conditions. Recent development in analytical methodology has enabled the determination of ultra-trace levels of perchlorate in polar snow. We have measured perchlorate concentrations in surface snow samples collected along a traverse route from Zhongshan Station to Dome A in East Antarctica to determine the level of atmospheric perchlorate in East Antarctica and to assess the spatial variability of perchlorate along the traverse route. Results show that the perchlorate concentrations vary between 32 and 200 ng kg-1, with an average of 104.3 ng kg-1. And perchlorate concentration profile presents regional variation patterns along the traverse route. In the coastal region, perchlorate concentration displays an apparent decreasing relationship with increasing distance inland; it exhibits no apparent trend in the intermediate region from 200 to 1000 km. The inland region from 1000 to 1244 km presents a generally increasing trend of perchlorate concentration approaching the dome. Different rates of atmospheric production, dilution by snow accumulation and re-deposition of snow-emitted perchlorate (post-depositional change) are the three possible factors influencing the spatial variability of perchlorate over Antarctica.

Spatial Assessment of the Association between Long-Term Exposure to Environmental Factors and the Occurrence of Amyotrophic Lateral Sclerosis in Catalonia, Spain: A Population-Based Nested Case-Control Study.

PubMed

Povedano, Mònica; Saez, Marc; Martínez-Matos, Juan-Antonio; Barceló, Maria Antònia

2018-05-31

It is believed that an interaction between genetic and non-genetic factors may be involved in the development of amyotrophic lateral sclerosis (ALS). With the exception of exposure to agricultural chemicals like pesticides, evidence of an association between environmental risk factors and ALS is inconsistent. Our objective here was to investigate the association between long-term exposure to environmental factors and the occurrence of ALS in Catalonia, Spain, and to provide evidence that spatial clusters of ALS related to these environmental factors exist. We carried out a nested case-control study constructed from a retrospective population-based cohort, covering the entire region. Environmental variables were the explanatory variables of interest. We controlled for both observed and unobserved confounders. We have found some spatial clusters of ALS. The results from the multivariate model suggest that these clusters could be related to some of the environmental variables, in particular agricultural chemicals. In addition, in high-risk clusters, besides corresponding to agricultural areas, key road infrastructures with a high density of traffic are also located. Our results indicate that some environmental factors, in particular those associated with exposure to pesticides and air pollutants as a result of urban traffic, could be associated with the occurrence of ALS. © 2018 S. Karger AG, Basel.

Use of local statistics to reveal hidden information of pollution hotspots in urban soil geochemistry

NASA Astrophysics Data System (ADS)

Zhang, Chaosheng

2017-04-01

The identification of pollution hotspots is an important approach for a better understanding of spatial distribution patterns and the exploration for their influencing factors in environmental studies. One of the most often asked questions in an environmental investigation is: Where are the pollution hotspots? This presentation explains one of the popularly used methodologies called local index of spatial association (LISA) and its applications in urban geochemical studies in Galway, Ireland and London of the UK. The LISA is a useful tool for identifying pollution hotspots and classifying them into spatial clusters and spatial outliers. The results were affected by the definition of weight function, data transformation and existence of extreme values, and it is suggested that all these influencing factors should be considered until reasonable and reliable results are obtained. This method has been applied to identify Pb pollution in Galway, polluted areas in bonfires sites, elevated P and REE concentrations in London. Hotspots in identified in urban soils are related to locations of high road density, traditional festival bonfires, industries and other human activities. The results of hotspots analysis provide useful information for the management of urban soils.

Development of land-use regression models for fine particles and black carbon in peri-urban South India.

PubMed

Sanchez, Margaux; Ambros, Albert; Milà, Carles; Salmon, Maëlle; Balakrishnan, Kalpana; Sambandam, Sankar; Sreekanth, V; Marshall, Julian D; Tonne, Cathryn

2018-09-01

Land-use regression (LUR) has been used to model local spatial variability of particulate matter in cities of high-income countries. Performance of LUR models is unknown in less urbanized areas of low-/middle-income countries (LMICs) experiencing complex sources of ambient air pollution and which typically have limited land use data. To address these concerns, we developed LUR models using satellite imagery (e.g., vegetation, urbanicity) and manually-collected data from a comprehensive built-environment survey (e.g., roads, industries, non-residential places) for a peri-urban area outside Hyderabad, India. As part of the CHAI (Cardiovascular Health effects of Air pollution in Telangana, India) project, concentrations of fine particulate matter (PM 2.5 ) and black carbon were measured over two seasons at 23 sites. Annual mean (sd) was 34.1 (3.2) μg/m 3 for PM 2.5 and 2.7 (0.5) μg/m 3 for black carbon. The LUR model for annual black carbon explained 78% of total variance and included both local-scale (energy supply places) and regional-scale (roads) predictors. Explained variance was 58% for annual PM 2.5 and the included predictors were only regional (urbanicity, vegetation). During leave-one-out cross-validation and cross-holdout validation, only the black carbon model showed consistent performance. The LUR model for black carbon explained a substantial proportion of the spatial variability that could not be captured by simpler interpolation technique (ordinary kriging). This is the first study to develop a LUR model for ambient concentrations of PM 2.5 and black carbon in a non-urban area of LMICs, supporting the applicability of the LUR approach in such settings. Our results provide insights on the added value of manually-collected built-environment data to improve the performance of LUR models in settings with limited data availability. For both pollutants, LUR models predicted substantial within-village variability, an important feature for future epidemiological studies. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Spatial Variability of Co On The Scale of The Street According To Street Morphology, Car Traffic and Local Climate In Paris

NASA Astrophysics Data System (ADS)

Quenol, H.; Bridier, S.; Beltrando, G.; Frangi, J. P.

The purpose of this study is to observe CO distribution on street level according to variations of car traffic, weather situation and street morphology. The experimentation use an electronical CO sensor connected to a data logger. Two kinds of observations are carried out : - measurements along an itinerary from the n´ butte de Montmartre z to the n´ Seine z highlights the spatial variability of CO distribution according to car traffic and weather characteristics (radiativ, advectiv, cloudy, rainy) ; - measurements in fixed stations underlines the differences in concentration according the situation (in or out of buildings) and according to level of the vertical measure (from 1.5 to 10 m). This approach of pollution distribution at the street and the district scale is made possible by using low cost sensors. They allow an very fine approach of CO distribution by multiplying measurements points. All of these data are integrated in a GIS with a DEM, a cartography of building implantation and width of the streets. This study shows that the CO concentration is in relation to street morphology (width, height, topography), intensity of car traffic and local meteorology (breezes and wind channeled by street network). This method leads to the constitution of a mobil network used to produce data on horizontal and vertical CO distribution inside the n´ meshs z of the present network of pollution observation.

Tropospheric Emissions: Monitoring of Pollution (TEMPO) - Status and Potential Science Studies

NASA Astrophysics Data System (ADS)

Chance, Kelly

2016-05-01

TEMPO is the first NASA Earth Venture Instrument, to launch between 2019 and 2021. It measures atmospheric pollution from Mexico City and Cuba to the Canadian oil sands, and from the Atlantic to the Pacific, hourly at high spatial resolution, ~ 10 km2. It measures the key elements of air pollution chemistry. Geostationary (GEO) measurements capture the variability in the diurnal cycle of emissions and chemistry at sub-urban scale to improve emission inventories, monitor population exposure, and enable emission-control strategies. TEMPO measures the UV/visible spectra to retrieve O3, NO2, SO2, H2 CO, C2 H2 O2, H2 O, aerosols, cloud parameters, and UVB radiation. It tracks aerosol loading. It provides near-real-time air quality products. TEMPO is the North American component of the global geostationary constellation for pollution monitoring, with the European Sentinel-4 and the Korean GEMS. TEMPO studies may include: Solar-induced fluorescence from chlorophyll over land and in the ocean to study tropical dynamics, primary productivity, carbon uptake, to detect red tides, and to study phytoplankton; Measurements of stratospheric intrusions that cause air quality exceedances; Measurements at peaks in vehicle travel to capture the variability in emissions from mobile sources; Measurements of thunderstorm activity, including outflow regions to better quantify lightning NOx and O3 production; Cropland measurements follow the temporal evolution of emissions after fertilizer application and from rain-induced emissions from semi-arid soils; Measurements investigate the chemical processing of primary fire emissions and the secondary formation of VOCs and ozone; Measurements examine ocean halogen emissions and their impact on the oxidizing capacity of coastal environments; Spectra of nighttime lights are markers for human activity, energy conservation, and compliance with outdoor lighting standards intended to reduce light pollution.

Investigating the Potential of Land Use Modifications to Mitigate the Respiratory Health Impacts of NO2: A Case Study in the Portland-Vancouver Metropolitan Area

NASA Astrophysics Data System (ADS)

Rao, Meenakshi

The health impacts of urban air pollution are a growing concern in our rapidly urbanizing world. Urban air pollutants show high intra-urban spatial variability linked to urban land use and land cover (LULC). This correlation of air pollutants with LULC is widely recognized; LULC data is an integral input into a wide range of models, especially land use regression models developed by epidemiologists to study the impact of air pollution on human health. Given the demonstrated links between LULC and urban air pollution, and between urban air pollution and health, an interesting question arises: what is the potential of LULC modifications to mitigate the health impacts of urban air pollution? In this dissertation we assess the potential of LULC modifications to mitigate the health impacts of NO2, a respiratory irritant and strong marker for combustion-related air pollution, in the Portland-Vancouver metropolitan area in northwestern USA. We begin by measuring summer and winter NO2 in the area using a spatially dense network of passive NO 2 samplers. We next develop an annual average model for NO2 based on the observational data, using random forest--for the first time in the realm of urban air pollution--to disentangle the effects of highly correlated LULC variables on ambient NO2 concentrations. We apply this random forest (LURF) model to a 200m spatial grid covering the study area, and use this 200m LURF model to quantify the effect of different urban land use categories on ambient concentrations of NO2. Using the changes in ambient NO2 concentrations resulting from land use modifications as input to BenMAP (a health benefits assessment tool form the US EPA), we assess the NO2-related health impact associated with each land use category and its modifications. We demonstrate how the LURF model can be used to assess the respiratory health benefits of competing land use modifications, including city-wide and local-scale mitigation strategies based on modifying tree canopy and vehicle miles traveled (VMT). Planting trees is a common land cover modification strategy undertaken by cities to reduce air pollution. Statistical models such as LUR and LURF demonstrate a correlation between tree cover and reduced air pollution, but they cannot demonstrate causation. Hence, we run the atmospheric chemistry and transport model CMAQ to examine to what extent the dry deposition mechanism can explain the reduction of NO2 which statistical models associate with tree canopy. Results from our research indicate that even though the Portland-Vancouver area is in compliance with the US EPA NO2 standards, ambient concentrations of NO2 still create an annual health burden of at least 40 million USD. Our model suggests that NO2 associated with high intensity development and VMT may be creating an annual health burden of 7 million and 3.3 million USD respectively. Existing tree canopy, on the other hand, is associated with an annual health benefit of 1.4 million USD. LULC modifications can mitigate some fraction of this health burden. A 2% increase in tree canopy across the study area may reduce incidence rates of asthma exacerbation by as much as 7%. We also find that increasing tree canopy is a more effective strategy than reducing VMT in terms of mitigating the health burden of NO 2. CMAQ indicates that the amount of NO2 removed by dry deposition is an order of magnitude smaller than that predicted by our statistical model. About one-third of the difference can be explained by the lower NO2 values predicted by CMAQ, and one-third may be attributable to parameterization of stomatal uptake.

Mobile measurements of air pollutants with an instrumented car in populated areas

NASA Astrophysics Data System (ADS)

Weber, Konradin; Scharifi, Emad; Fischer, Christian; Pohl, Tobias; Lange, Martin; Boehlke, Christoph

2017-04-01

Detailed mobile measurement of gases and fine particulate matter has been reported in the literature to be suitable to exhibit the air pollutants concentration in populated areas. This concentration is linked to the increase of number of cars, construction areas, industries and other emission sources. However, fixed measurement stations, mostly operated by environmental agencies, are limited in numbers and cannot cover a large area in monitoring. For this reason, to overcome this drawback, mobile measurements of the variability of gases (such as O3, NO, NO2) and particulate matter concentration were carried out during this study using an instrumented car. This car was able to deliver measurement results of all these compounds in a large area. The experimental results in this work demonstrate a large spatial variability of gases and fine particulate matters mainly depended on the traffic density and the location. These effects are especially obvious in the city core and the high traffic roads. In terms of fine particulate matter, this becomes evident for PM 10 and PM 2.5, where the mass and number concentration increases with arriving these zones.

Development of a process-oriented vulnerability concept for water travel time in karst aquifers-case study of Tanour and Rasoun springs catchment area.

NASA Astrophysics Data System (ADS)

Hamdan, Ibraheem; Sauter, Martin; Ptak, Thomas; Wiegand, Bettina; Margane, Armin; Toll, Mathias

2017-04-01

Key words: Karst aquifer, water travel time, vulnerability assessment, Jordan. The understanding of the groundwater pathways and movement through karst aquifers, and the karst aquifer response to precipitation events especially in the arid to semi-arid areas is fundamental to evaluate pollution risks from point and non-point sources. In spite of the great importance of the karst aquifer for drinking purposes, karst aquifers are highly sensitive to contamination events due to the fast connections between the land-surface and the groundwater (through the karst features) which is makes groundwater quality issues within karst systems very complicated. Within this study, different methods and approaches were developed and applied in order to characterise the karst aquifer system of the Tanour and Rasoun springs (NW-Jordan) and the flow dynamics within the aquifer, and to develop a process-oriented method for vulnerability assessment based on the monitoring of different multi-spatially variable parameters of water travel time in karst aquifer. In general, this study aims to achieve two main objectives: 1. Characterization of the karst aquifer system and flow dynamics. 2. Development of a process-oriented method for vulnerability assessment based on spatially variable parameters of travel time. In order to achieve these aims, different approaches and methods were applied starting from the understanding of the geological and hydrogeological characteristics of the karst aquifer and its vulnerability against pollutants, to using different methods, procedures and monitored parameters in order to determine the water travel time within the aquifer and investigate its response to precipitation event and, finally, with the study of the aquifer response to pollution events. The integrated breakthrough signal obtained from the applied methods and procedures including the using of stable isotopes of oxygen and hydrogen, the monitoring of multi qualitative and quantitative parameters using automated probes and data loggers, and the development of travel time physics-based vulnerability assessment method shows good agreement as an applicable methods to determine the water travel time in karst aquifers, and to investigate its response to precipitation and pollution events.

Evaluation of high-resolution GRAMM-GRAL (v15.12/v14.8) NOx simulations over the city of Zürich, Switzerland

NASA Astrophysics Data System (ADS)

Berchet, Antoine; Zink, Katrin; Oettl, Dietmar; Brunner, Jürg; Emmenegger, Lukas; Brunner, Dominik

2017-09-01

Hourly NOx concentrations were simulated for the city of Zürich, Switzerland, at 10 m resolution for the years 2013-2014. The simulations were generated with the nested mesoscale meteorology and micro-scale dispersion model system GRAMM-GRAL (versions v15.12 and v14.8) by applying a catalogue-based approach. This approach was specifically designed to enable long-term city-wide building-resolving simulations with affordable computation costs. It relies on a discrete set of possible weather situations and corresponding steady-state flow and dispersion patterns that are pre-computed and then matched hourly with actual meteorological observations. The modelling system was comprehensively evaluated using eight sites continuously monitoring NOx concentrations and 65 passive samplers measuring NO2 concentrations on a 2-weekly basis all over the city. The system was demonstrated to fulfil the European Commission standards for air pollution modelling at nearly all sites. The average spatial distribution was very well represented, despite a general tendency to overestimate the observed concentrations, possibly due to a crude representation of traffic-induced turbulence and to underestimated dispersion in the vicinity of buildings. The temporal variability of concentrations explained by varying emissions and weather situations was accurately reproduced on different timescales. The seasonal cycle of concentrations, mostly driven by stronger vertical dispersion in summer than in winter, was very well captured in the 2-year simulation period. Short-term events, such as episodes of particularly high and low concentrations, were detected in most cases by the system, although some unrealistic pollution peaks were occasionally generated, pointing at some limitations of the steady-state approximation. The different patterns of the diurnal cycle of concentrations observed in the city were generally well captured as well. The evaluation confirmed the adequacy of the catalogue-based approach in the context of city-scale air pollution modelling. The ability to reproduce not only the spatial gradients but also the hourly temporal variability over multiple years makes the model system particularly suitable for investigating individualized air pollution exposure in the city.

A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes

DOE PAGES

Kaufeld, Kimberly Ann; Fuentes, Montse; Reich, Brian J.; ...

2017-09-11

Evidence suggests that exposure to elevated concentrations of air pollution during pregnancy is associated with increased risks of birth defects and other adverse birth outcomes. While current regulations put limits on total PM2.5 concentrations, there are many speciated pollutants within this size class that likely have distinct effects on perinatal health. However, due to correlations between these speciated pollutants, it can be difficult to decipher their effects in a model for birth outcomes. To combat this difficulty, we develop a multivariate spatio-temporal Bayesian model for speciated particulate matter using dynamic spatial factors. These spatial factors can then be interpolated tomore » the pregnant women’s homes to be used to model birth defects. The birth defect model allows the impact of pollutants to vary across different weeks of the pregnancy in order to identify susceptible periods. Here, the proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.« less

A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaufeld, Kimberly Ann; Fuentes, Montse; Reich, Brian J.

Evidence suggests that exposure to elevated concentrations of air pollution during pregnancy is associated with increased risks of birth defects and other adverse birth outcomes. While current regulations put limits on total PM2.5 concentrations, there are many speciated pollutants within this size class that likely have distinct effects on perinatal health. However, due to correlations between these speciated pollutants, it can be difficult to decipher their effects in a model for birth outcomes. To combat this difficulty, we develop a multivariate spatio-temporal Bayesian model for speciated particulate matter using dynamic spatial factors. These spatial factors can then be interpolated tomore » the pregnant women’s homes to be used to model birth defects. The birth defect model allows the impact of pollutants to vary across different weeks of the pregnancy in order to identify susceptible periods. Here, the proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.« less

Do migratory or demographic disruptions rule the population impact of pollution in spatial networks?

PubMed

Chaumot, A; Charles, S; Flammarion, P; Auger, P

2003-12-01

Ecotoxicology supplies environmental quality criteria mainly based on the potential effects of contaminants on demographic rates of natural populations. Possible impacts through pollutant-induced disruptions of spatial behaviors are totally neglected. Should it be significant to take into account this "second way"? We developed the example of a hypothetical brown trout population living in a river network. We analyzed how behaviors of toxic avoidance or attraction during the spawning migration alter the impact of pollution. Attraction behaviors basically enhanced the bad effect of pollution. More interesting, avoidance behaviors can weakly lift the asymptotic population growth rate, while if there is density-dependent effects on recruitment, pollutant avoidance can actually lead to a substantial drop in equilibrium size. Our model allowed comparing the relative significance of migratory and demographic disruptions for explaining the population impacts of pollution; we thus stress on the need of increasing efforts to develop knowledge relative to toxicant-induced spatial behaviors and to integrate such effects in the definition of environmental quality criteria.

Scales of Spatial Heterogeneity of Plastic Marine Debris in the Northeast Pacific Ocean

PubMed Central

Goldstein, Miriam C.; Titmus, Andrew J.; Ford, Michael

2013-01-01

Plastic debris has been documented in many marine ecosystems, including remote coastlines, the water column, the deep sea, and subtropical gyres. The North Pacific Subtropical Gyre (NPSG), colloquially called the “Great Pacific Garbage Patch,” has been an area of particular scientific and public concern. However, quantitative assessments of the extent and variability of plastic in the NPSG have been limited. Here, we quantify the distribution, abundance, and size of plastic in a subset of the eastern Pacific (approximately 20–40°N, 120–155°W) over multiple spatial scales. Samples were collected in Summer 2009 using surface and subsurface plankton net tows and quantitative visual observations, and Fall 2010 using surface net tows only. We documented widespread, though spatially variable, plastic pollution in this portion of the NPSG and adjacent waters. The overall median microplastic numerical concentration in Summer 2009 was 0.448 particles m−2 and in Fall 2010 was 0.021 particles m−2, but plastic concentrations were highly variable over the submesoscale (10 s of km). Size-frequency spectra were skewed towards small particles, with the most abundant particles having a cross-sectional area of approximately 0.01 cm2. Most microplastic was found on the sea surface, with the highest densities detected in low-wind conditions. The numerical majority of objects were small particles collected with nets, but the majority of debris surface area was found in large objects assessed visually. Our ability to detect high-plastic areas varied with methodology, as stations with substantial microplastic did not necessarily also contain large visually observable objects. A power analysis of our data suggests that high variability of surface microplastic will make future changes in abundance difficult to detect without substantial sampling effort. Our findings suggest that assessment and monitoring of oceanic plastic debris must account for high spatial variability, particularly in regards to the evaluation of initiatives designed to reduce marine debris. PMID:24278233

Scales of spatial heterogeneity of plastic marine debris in the northeast pacific ocean.

PubMed

Goldstein, Miriam C; Titmus, Andrew J; Ford, Michael

2013-01-01

Plastic debris has been documented in many marine ecosystems, including remote coastlines, the water column, the deep sea, and subtropical gyres. The North Pacific Subtropical Gyre (NPSG), colloquially called the "Great Pacific Garbage Patch," has been an area of particular scientific and public concern. However, quantitative assessments of the extent and variability of plastic in the NPSG have been limited. Here, we quantify the distribution, abundance, and size of plastic in a subset of the eastern Pacific (approximately 20-40°N, 120-155°W) over multiple spatial scales. Samples were collected in Summer 2009 using surface and subsurface plankton net tows and quantitative visual observations, and Fall 2010 using surface net tows only. We documented widespread, though spatially variable, plastic pollution in this portion of the NPSG and adjacent waters. The overall median microplastic numerical concentration in Summer 2009 was 0.448 particles m(-2) and in Fall 2010 was 0.021 particles m(-2), but plastic concentrations were highly variable over the submesoscale (10 s of km). Size-frequency spectra were skewed towards small particles, with the most abundant particles having a cross-sectional area of approximately 0.01 cm(2). Most microplastic was found on the sea surface, with the highest densities detected in low-wind conditions. The numerical majority of objects were small particles collected with nets, but the majority of debris surface area was found in large objects assessed visually. Our ability to detect high-plastic areas varied with methodology, as stations with substantial microplastic did not necessarily also contain large visually observable objects. A power analysis of our data suggests that high variability of surface microplastic will make future changes in abundance difficult to detect without substantial sampling effort. Our findings suggest that assessment and monitoring of oceanic plastic debris must account for high spatial variability, particularly in regards to the evaluation of initiatives designed to reduce marine debris.

Spatial prediction of water quality variables along a main river channel, in presence of pollution hotspots.

PubMed

Rizo-Decelis, L D; Pardo-Igúzquiza, E; Andreo, B

2017-12-15

In order to treat and evaluate the available data of water quality and fully exploit monitoring results (e.g. characterize regional patterns, optimize monitoring networks, infer conditions at unmonitored locations, etc.), it is crucial to develop improved and efficient methodologies. Accordingly, estimation of water quality along fluvial ecosystems is a frequent task in environment studies. In this work, a particular case of this problem is examined, namely, the estimation of water quality along a main stem of a large basin (where most anthropic activity takes place), from observational data measured along this river channel. We adapted topological kriging to this case, where each watershed contains all the watersheds of the upstream observed data ("nested support effect"). Data analysis was additionally extended by taking into account the upstream distance to the closest contamination hotspot as an external drift. We propose choosing the best estimation method by cross-validation. The methodological approach in spatial variability modeling may be used for optimizing the water quality monitoring of a given watercourse. The methodology presented is applied to 28 water quality variables measured along the Santiago River in Western Mexico. Copyright © 2017 Elsevier B.V. All rights reserved.

Association of weather and air pollution interactions on daily mortality in 12 Canadian cities.

PubMed

Vanos, J K; Cakmak, S; Kalkstein, L S; Yagouti, Abderrahmane

It has been well established that both meteorological attributes and air pollution concentrations affect human health outcomes. We examined all cause nonaccident mortality relationships for 28 years (1981-2008) in relation to air pollution and synoptic weather type (encompassing air mass) data in 12 Canadian cities. This study first determines the likelihood of summertime extreme air pollution events within weather types using spatial synoptic classification. Second, it examines the modifying effect of weather types on the relative risk of mortality (RR) due to daily concentrations of air pollution (nitrogen dioxide, ozone, sulfur dioxide, and particulate matter <2.5 μm). We assess both single- and two-pollutant interactions to determine dependent and independent pollutant effects using the relatively new time series technique of distributed lag nonlinear modeling (DLNM). Results display dry tropical (DT) and moist tropical plus (MT+) weathers to result in a fourfold and twofold increased likelihood, respectively, of an extreme pollution event (top 5 % of pollution concentrations throughout the 28 years) occurring. We also demonstrate statistically significant effects of single-pollutant exposure on mortality ( p  < 0.05) to be dependent on summer weather type, where stronger results occur in dry moderate (fair weather) and DT or MT+ weather types. The overall average single-effect RR increases due to pollutant exposure within DT and MT+ weather types are 14.9 and 11.9 %, respectively. Adjusted exposures (two-way pollutant effect estimates) generally results in decreased RR estimates, indicating that the pollutants are not independent. Adjusting for ozone significantly lowers 67 % of the single-pollutant RR estimates and reduces model variability, which demonstrates that ozone significantly controls a portion of the mortality signal from the model. Our findings demonstrate the mortality risks of air pollution exposure to differ by weather type, with increased accuracy obtained when accounting for interactive effects through adjustment for dependent pollutants using a DLNM.

Isotopic Recorders of Pollution in Heterogeneous Urban Areas

NASA Astrophysics Data System (ADS)

Pataki, D. E.; Cobley, L.; Smith, R. M.; Ehleringer, J. R.; Chritz, K.

2017-12-01

A significant difficulty in quantifying urban pollution lies in the extreme spatial and temporal heterogeneity of cities. Dense sources of both point and non-point source pollution as well as the dynamic role of human activities, which vary over very short time scales and small spatial scales, complicate efforts to establish long-term urban monitoring networks that are relevant at neighborhood, municipal, and regional scales. Fortunately, the natural abundance of isotopes of carbon, nitrogen, and other elements provides a wealth of information about the sources and fate of urban atmospheric pollution. In particular, soils and plant material integrate pollution sources and cycling over space and time, and have the potential to provide long-term records of pollution dynamics that extend back before atmospheric monitoring data are available. Similarly, sampling organic material at high spatial resolution can provide "isoscapes" that shed light on the spatial heterogeneity of pollutants in different urban parcels and neighborhoods, along roads of varying traffic density, and across neighborhoods of varying affluence and sociodemographic composition. We have compiled numerous datasets of the isotopic composition of urban organic matter that illustrate the potential for isotopic monitoring of urban areas as a means of understanding hot spots and hot moments in urban atmospheric biogeochemistry. Findings to date already reveal the critical role of affluence, economic activity, demographic change, and land management practices in influencing urban pollution sources and sinks, and suggest an important role of stable isotope and radioisotope measurements in urban atmospheric and biogeochemical monitoring.

Potentially toxic trace element contamination, sources, and pollution assessment in farmlands, Bijie City, southwestern China.

PubMed

Yuan, Zhimin; Yao, Jun; Wang, Fei; Guo, Zunwei; Dong, Zeqin; Chen, Feng; Hu, Yu; Sunahara, Geoffrey

2017-01-01

Artisanal zinc smelting activities, which had been widely applied in Bijie City, Guizhou Province, southwestern of China, can pollute surrounding farmlands. In the present study, 177 farmland topsoil samples of Bijie City were collected and 11 potentially toxic trace elements (PTEs), namely Pb, Zn, Cu, Ni, Co, Mn, Cr, V, Hg, As, and Cd were tested to characterize the concentrations, sources, and ecological risks. Mean concentrations of these PTEs in soils were (mg/kg) as follows: Pb (127), Zn (379), Cu (93.1), Ni (54.6), Co (26.2), Mn (1095), Cr (133), V (206), Hg (0.15), As (16.2), and Cd (3.08). Pb, Zn, and Cd had coefficients of variation greater than 100% and showed a high uneven distribution and spatial variability in the study area. Correlation coefficient analysis and principal component analysis (PCA) were used to quantify potential pollution sources. Results showed that Cu, Ni, Co, Mn, and V came from natural sources, whereas Pb, Zn, Hg, As, and Cd came from anthropogenic pollution sources. Geoaccumulation index and potential ecological risk indices were employed to study the pollution degree of PTEs, which revealed that Pb and Cd shared the greatest contamination and would pose serious ecological risks to the surrounding environment. The results of this study could help the local government managers to establish pollution control strategies and to secure food safety.

The relationship between odour annoyance scores and modelled ambient air pollution in Sarnia, "Chemical Valley", Ontario.

PubMed

Atari, Dominic Odwa; Luginaah, Isaac N; Fung, Karen

2009-10-01

This study aimed at establishing the relationship between annoyance scores and modelled air pollution in "Chemical Valley", Sarnia, Ontario (Canada). Annoyance scores were taken from a community health survey (N = 774); and respondents' exposure to nitrogen dioxide (NO(2)) and sulphur dioxide (SO(2)) were estimated using land use regression (LUR) models. The associations were examined by univariate analysis while multivariate logistic regression was used to examine the determinants of odour annoyance. The results showed that odour annoyance was significantly correlated to modelled pollutants at the individual (NO(2), r = 0.15; SO(2), r = 0.13) and census tract (NO(2), r = 0.56; SO(2), r = 0.67) levels. The exposure-response relationships show that residents of Sarnia react to very low pollution concentrations levels even if they are within the Ontario ambient air quality criteria. The study found that exposure to high NO(2) and SO(2) concentrations, gender, and perception of health effects were significant determinants of individual odour annoyance reporting. The observed association between odour annoyance and modelled ambient pollution suggest that individual and census tract level annoyance scores may serve as proxies for air quality in exposed communities because they capture the within area spatial variability of pollution. However, questionnaire-based odour annoyance scores need to be validated longitudinally and across different scales if they are to be adopted for use at the national level.

Do outdoor environmental noise and atmospheric NO2 levels spatially overlap in urban areas?

PubMed

Tenailleau, Quentin M; Bernard, Nadine; Pujol, Sophie; Parmentier, Anne-Laure; Boilleaut, Mathieu; Houot, Hélène; Joly, Daniel; Mauny, Frédéric

2016-07-01

The urban environment holds numerous emission sources for air and noise pollution, creating optimum conditions for environmental multi-exposure situations. Evaluation of the joint-exposure levels is the main obstacle for multi-exposure studies and one of the biggest challenges of the next decade. The present study aims to describe the noise/NO2 multi-exposure situations in the urban environment by exploring the possible discordant and concordant situations of both exposures. Fine-scale diffusion models were developed in the European medium-sized city of Besançon (France), and a classification method was used to evaluate the multi-exposure situations in the façade perimeter of 10,825 buildings. Although correlated (Pearson's r = 0.64, p < 0.01), urban spatial distributions of the noise and NO2 around buildings do not overlap, and 30% of the buildings were considered to be discordant in terms of the noise and NO2 exposure levels. This discrepancy is spatially structured and associated with variables describing the building's environment. Our results support the presence of several co-existing, multi-exposure situations across the city impacted by both the urban morphology and the emission and diffusion/propagation phases of each pollutant. Identifying the mechanisms of discrepancy and convergence of multi-exposure situations could help improve the health risk assessment and public health. Copyright © 2016 Elsevier Ltd. All rights reserved.

Atmospheric deposition maps for the Rocky Mountains

USGS Publications Warehouse

Nanus, L.; Campbell, D.H.; Ingersoll, G.P.; Clow, D.W.; Mast, M.A.

2003-01-01

Variability in atmospheric deposition across the Rocky Mountains is influenced by elevation, slope, aspect, and precipitation amount and by regional and local sources of air pollution. To improve estimates of deposition in mountainous regions, maps of average annual atmospheric deposition loadings of nitrate, sulfate, and acidity were developed for the Rocky Mountains by using spatial statistics. A parameter-elevation regressions on independent slopes model (PRISM) was incorporated to account for variations in precipitation amount over mountainous regions. Chemical data were obtained from the National Atmospheric Deposition Program/National Trends Network and from annual snowpack surveys conducted by the US Geological Survey and National Park Service, in cooperation with other Federal, State and local agencies. Surface concentration maps were created by ordinary kriging in a geographic information system, using a local trend and mathematical model to estimate the spatial variance. Atmospheric-deposition maps were constructed at 1-km resolution by multiplying surface concentrations from the kriged grid and estimates of precipitation amount from the PRISM model. Maps indicate an increasing spatial trend in concentration and deposition of the modeled constituents, particularly nitrate and sulfate, from north to south throughout the Rocky Mountains and identify hot-spots of atmospheric deposition that result from combined local and regional sources of air pollution. Highest nitrate (2.5-3.0kg/ha N) and sulfate (10.0-12.0kg/ha SO4) deposition is found in northern Colorado.

Evaluation of agricultural nonpoint source pollution potential risk over China with a Transformed-Agricultural Nonpoint Pollution Potential Index method.

PubMed

Yang, Fei; Xu, Zhencheng; Zhu, Yunqiang; He, Chansheng; Wu, Genyi; Qiu, Jin Rong; Fu, Qiang; Liu, Qingsong

2013-01-01

Agricultural nonpoint source (NPS) pollution has been the most important threat to water environment quality. Understanding the spatial distribution of NPS pollution potential risk is important for taking effective measures to control and reduce NPS pollution. A Transformed-Agricultural Nonpoint Pollution Potential Index (T-APPI) model was constructed for evaluating the national NPS pollution potential risk in this study; it was also combined with remote sensing and geographic information system techniques for evaluation on the large scale and at 1 km2 spatial resolution. This model considers many factors contributing to the NPS pollution as the original APPI model, summarized as four indicators of the runoff, sediment production, chemical use and the people and animal load. These four indicators were analysed in detail at 1 km2 spatial resolution throughout China. The T-APPI model distinguished the four indicators into pollution source factors and transport process factors; it also took their relationship into consideration. The studied results showed that T-APPI is a credible and convenient method for NPS pollution potential risk evaluation. The results also indicated that the highest NPS pollution potential risk is distributed in the middle-southern Jiangsu province. Several other regions, including the North China Plain, Chengdu Basin Plain, Jianghan Plain, cultivated lands in Guangdong and Guangxi provinces, also showed serious NPS pollution potential. This study can provide a scientific reference for predicting the future NPS pollution risk throughout China and may be helpful for taking reasonable and effective measures for preventing and controlling NPS pollution.

The Specific Features of Pollution Transport in the Northwest Pacific Ocean

NASA Astrophysics Data System (ADS)

Diansky, Nikolay; Fomin, Vladimir; Gusev, Anatoly

2013-04-01

Two calculations of pollutant dispersal in the Northwest Pacific Ocean are presented: (1) during possible shipwrecks in the process of spent nuclear fuel transportation from Petropavlovsk-Kamchatsky and (2) pollutant spread from the Japanese coast after the Fukushima 1 nuclear disaster on March 11, 2011. The circulation was simulated using a σ - coordinate ocean model INMOM (Institute of Numerical Mathematics Ocean Model) developed at the INM RAS. The INMOM is based on the primitive equations using the spherical σ - coordinate system with a free ocean surface. The INMOM was realized for the Pacific Ocean basin from the equator to the Bering Strait with a high 1/8° spatial resolution for reproducing the mesoscale ocean variability. The pollutant dispersal in the case of possible shipwrecks was estimated for currents for a statistically average year with atmospheric forcing from Common Ocean-ice Reference Experiments (CORE) for normal year data. The pollution spread from the Fukushima 1 nuclear power plant (NPP) was estimated for currents calculated with the real atmospheric forcing in accordance with the NCEP GFS (0.5 degree grid). The simulation period of pollutant dispersal from Fukushima 1 was 17 days: from March 11 to 28, 2011. The results of numerical simulation show that pollutant dispersal from the Fukushima 1 spread eastward according to the Kuroshio. Moreover, exceeding of natural background radiation level was simulated in the narrow region of the Japanese coast with width of less than 50 km.

Coupled Effects of Natural and Anthropogenic Controls on Seasonal and Spatial Variations of River Water Quality during Baseflow in a Coastal Watershed of Southeast China

PubMed Central

Huang, Jinliang; Huang, Yaling; Zhang, Zhenyu

2014-01-01

Surface water samples of baseflow were collected from 20 headwater sub-watersheds which were classified into three types of watersheds (natural, urban and agricultural) in the flood, dry and transition seasons during three consecutive years (2010–2012) within a coastal watershed of Southeast China. Integrating spatial statistics with multivariate statistical techniques, river water quality variations and their interactions with natural and anthropogenic controls were examined to identify the causal factors and underlying mechanisms governing spatiotemporal patterns of water quality. Anthropogenic input related to industrial effluents and domestic wastewater, agricultural activities associated with the precipitation-induced surface runoff, and natural weathering process were identified as the potential important factors to drive the seasonal variations in stream water quality for the transition, flood and dry seasons, respectively. All water quality indicators except SRP had the highest mean concentrations in the dry and transition seasons. Anthropogenic activities and watershed characteristics led to the spatial variations in stream water quality in three types of watersheds. Concentrations of NH4 +-N, SRP, K+, CODMn, and Cl− were generally highest in urban watersheds. NO3 –N Concentration was generally highest in agricultural watersheds. Mg2+ concentration in natural watersheds was significantly higher than that in agricultural watersheds. Spatial autocorrelations analysis showed similar levels of water pollution between the neighboring sub-watersheds exhibited in the dry and transition seasons while non-point source pollution contributed to the significant variations in water quality between neighboring sub-watersheds. Spatial regression analysis showed anthropogenic controls played critical roles in variations of water quality in the JRW. Management implications were further discussed for water resource management. This research demonstrates that the coupled effects of natural and anthropogenic controls involved in watershed processes, contribute to the seasonal and spatial variation of headwater stream water quality in a coastal watershed with high spatial variability and intensive anthropogenic activities. PMID:24618771

Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination

PubMed Central

Ha, Hoehun; Rogerson, Peter A.; Olson, James R.; Han, Daikwon; Bian, Ling; Shao, Wanyun

2016-01-01

Heavy industrialization has resulted in the contamination of soil by metals from anthropogenic sources in Anniston, Alabama. This situation calls for increased public awareness of the soil contamination issue and better knowledge of the main factors contributing to the potential sources contaminating residential soil. The purpose of this spatial epidemiology research is to describe the effects of physical factors on the concentration of lead (Pb) in soil in Anniston AL, and to determine the socioeconomic and demographic characteristics of those residing in areas with higher soil contamination. Spatial regression models are used to account for spatial dependencies using these explanatory variables. After accounting for covariates and multicollinearity, results of the analysis indicate that lead concentration in soils varies markedly in the vicinity of a specific foundry (Foundry A), and that proximity to railroads explained a significant amount of spatial variation in soil lead concentration. Moreover, elevated soil lead levels were identified as a concern in industrial sites, neighborhoods with a high density of old housing, a high percentage of African American population, and a low percent of occupied housing units. The use of spatial modelling allows for better identification of significant factors that are correlated with soil lead concentrations. PMID:27649221

Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination.

PubMed

Ha, Hoehun; Rogerson, Peter A; Olson, James R; Han, Daikwon; Bian, Ling; Shao, Wanyun

2016-09-14

Heavy industrialization has resulted in the contamination of soil by metals from anthropogenic sources in Anniston, Alabama. This situation calls for increased public awareness of the soil contamination issue and better knowledge of the main factors contributing to the potential sources contaminating residential soil. The purpose of this spatial epidemiology research is to describe the effects of physical factors on the concentration of lead (Pb) in soil in Anniston AL, and to determine the socioeconomic and demographic characteristics of those residing in areas with higher soil contamination. Spatial regression models are used to account for spatial dependencies using these explanatory variables. After accounting for covariates and multicollinearity, results of the analysis indicate that lead concentration in soils varies markedly in the vicinity of a specific foundry (Foundry A), and that proximity to railroads explained a significant amount of spatial variation in soil lead concentration. Moreover, elevated soil lead levels were identified as a concern in industrial sites, neighborhoods with a high density of old housing, a high percentage of African American population, and a low percent of occupied housing units. The use of spatial modelling allows for better identification of significant factors that are correlated with soil lead concentrations.

Modeling spatial effects of PM{sub 2.5} on term low birth weight in Los Angeles County

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coker, Eric, E-mail: cokerer@onid.orst.edu; Ghosh, Jokay; Jerrett, Michael

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM{sub 2.5}) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure–response of PM{sub 2.5} on TLBW to be the same throughout a large geographical area. Health effects related to PM{sub 2.5} exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure–response relationship between individual-level exposure to PM{sub 2.5} and TLBW. Here, we examine the overall and spatially varying exposure–response relationship between PM{sub 2.5} and TLBW throughout urbanmore » Los Angeles (LA) County, California. We estimated PM{sub 2.5} from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM{sub 2.5} level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure–response for PM{sub 2.5} and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure–response estimates for PM{sub 2.5} on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective. - Highlights: • We model the spatial dependency of PM{sub 2.5} effects on term low birth weight (TLBW). • PM{sub 2.5} effects on TLBW are shown to vary spatially across urban LA County. • Modeling spatial dependency of PM{sub 2.5} health effects may identify effect 'hotspots'. • Birth outcomes studies should consider the spatial dependency of PM{sub 2.5} effects.« less

Development of water environment information management and water pollution accident response system

NASA Astrophysics Data System (ADS)

Zhang, J.; Ruan, H.

2009-12-01

In recent years, many water pollution accidents occurred with the rapid economical development. In this study, water environment information management and water pollution accident response system are developed based on geographic information system (GIS) techniques. The system integrated spatial database, attribute database, hydraulic model, and water quality model under a user-friendly interface in a GIS environment. System ran in both Client/Server (C/S) and Browser/Server (B/S) platform which focused on model and inquiry respectively. System provided spatial and attribute data inquiry, water quality evaluation, statics, water pollution accident response case management (opening reservoir etc) and 2D and 3D visualization function, and gave assistant information to make decision on water pollution accident response. Polluted plume in Huaihe River were selected to simulate the transport of pollutes.

Advantages of a city-scale emission inventory for urban air quality research and policy: the case of Nanjing, a typical industrial city in the Yangtze River Delta, China

NASA Astrophysics Data System (ADS)

Zhao, Y.; Qiu, L. P.; Xu, R. Y.; Xie, F. J.; Zhang, Q.; Yu, Y. Y.; Nielsen, C. P.; Qin, H. X.; Wang, H. K.; Wu, X. C.; Li, W. Q.; Zhang, J.

2015-11-01

With most eastern Chinese cities facing major air quality challenges, there is a strong need for city-scale emission inventories for use in both chemical transport modeling and the development of pollution control policies. In this paper, a high-resolution emission inventory (with a horizontal resolution of 3 × 3 km) of air pollutants and CO2 for Nanjing, a typical large city in the Yangtze River Delta, is developed, incorporating the best available information on local sources. Emission factors and activity data at the unit or facility level are collected and compiled using a thorough on-site survey of major sources. Over 900 individual plants, which account for 97 % of the city's total coal consumption, are identified as point sources, and all of the emission-related parameters including combustion technology, fuel quality, and removal efficiency of air pollution control devices (APCD) are analyzed. New data-collection approaches including continuous emission monitoring systems and real-time monitoring of traffic flows are employed to improve spatiotemporal distribution of emissions. Despite fast growth of energy consumption between 2010 and 2012, relatively small interannual changes in emissions are found for most air pollutants during this period, attributed mainly to benefits of growing APCD deployment and the comparatively strong and improving regulatory oversight of the large point sources that dominate the levels and spatial distributions of Nanjing emissions overall. The improvement of this city-level emission inventory is indicated by comparisons with observations and other inventories at larger spatial scale. Relatively good spatial correlations are found for SO2, NOx, and CO between the city-scale emission estimates and concentrations at nine state-operated monitoring sites (R = 0.58, 0.46, and 0.61, respectively). The emission ratios of specific pollutants including BC to CO, OC to EC, and CO2 to CO compare well to top-down constraints from ground observations. The interannual variability and spatial distribution of NOx emissions are consistent with NO2 vertical column density measured by the Ozone Monitoring Instrument (OMI). In particular, the Nanjing city-scale emission inventory correlates better with satellite observations than the downscaled Multi-resolution Emission Inventory for China (MEIC) does when emissions from power plants are excluded. This indicates improvement in emission estimation for sectors other than power generation, notably industry and transportation. A high-resolution emission inventory may also provide a basis to consider the quality of instrumental observations. To further improve emission estimation and evaluation, more measurements of both emission factors and ambient levels of given pollutants are suggested; the uncertainties of emission inventories at city scale should also be fully quantified and compared with those at national scale.

Advantages of city-scale emission inventory for urban air quality research and policy: the case of Nanjing, a typical industrial city in the Yangtze River Delta, China

NASA Astrophysics Data System (ADS)

Zhao, Y.; Qiu, L.; Xu, R.; Xie, F.; Zhang, Q.; Yu, Y.; Nielsen, C. P.; Qin, H.; Wang, H.; Wu, X.; Li, W.; Zhang, J.

2015-07-01

With most eastern Chinese cities facing major air quality challenges, there is a strong need for city-scale emission inventories for use in both chemical transport modeling and the development of pollution control policies. In this paper, a high-resolution emission inventory of air pollutants and CO2 for Nanjing, a typical large city in the Yangtze River Delta, is developed incorporating the best available information on local sources. Emission factors and activity data at the unit or facility level are collected and compiled using a thorough onsite survey of major sources. Over 900 individual plants, which account for 97 % of the city's total coal consumption, are identified as point sources, and all of the emission-related parameters including combustion technology, fuel quality, and removal efficiency of air pollution control devices (APCD) are analyzed. New data-collection approaches including continuous emission monitoring systems and real-time monitoring of traffic flows are employed to improve spatiotemporal distribution of emissions. Despite fast growth of energy consumption between 2010 and 2012, relatively small inter-annual changes in emissions are found for most air pollutants during this period, attributed mainly to benefits of growing APCD deployment and the comparatively strong and improving regulatory oversight of the large point sources that dominate the levels and spatial distributions of Nanjing emissions overall. The improvement of this city-level emission inventory is indicated by comparisons with observations and other inventories at larger spatial scale. Relatively good spatial correlations are found for SO2, NOx, and CO between the city-scale emission estimates and concentrations at 9 state-opertated monitoring sites (R = 0.58, 0.46, and 0.61, respectively). The emission ratios of specific pollutants including BC to CO, OC to EC, and CO2 to CO compare well to top-down constraints from ground observations. The inter-annual variability and spatial distribution of NOx emissions are consistent with NO2 vertical column density measured by the Ozone Monitoring Instrument (OMI). In particular, the Nanjing city-scale emission inventory correlates better with satellite observations than the downscaled Multi-resolution Emission Inventory for China (MEIC) does when emissions from power plants are excluded. This indicates improvement in emission estimation for sectors other than power generation, notably industry and transportation. High-resolution emission inventory may also provide a basis to consider the quality of instrumental observations. To further improve emission estimation and evaluation, more measurements of both emission factors and ambient levels of given pollutants are suggested; the uncertainties of emission inventories at city scale should also be fully quantified and compared with those at national scale.

Status of the first NASA EV-I Project, Tropospheric Emissions: Monitoring of Pollution (TEMPO)

NASA Astrophysics Data System (ADS)

Chance, K.; Liu, X.; Suleiman, R. M.; Flittner, D. E.; Al-Saadi, J. A.; Janz, S. J.

2013-12-01

TEMPO is the first NASA Earth Venture Instrument. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO measures from Mexico City to the Canadian tar sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (2 km N/S × 4.5 km E/W at the center of its field of regard). The status of TEMPO including progress in instrument definition and implementation of the ground system will be presented. TEMPO provides a minimally-redundant measurement suite that includes all key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO will be delivered in 2017 for integration onto a NASA-selected GEO host spacecraft for launch as early as 2018. It will provide the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available. Additional gases not central to air quality, including BrO, OClO, and IO will also be measured. TEMPO and its Asian (GEMS) and European (Sentinel-4) constellation partners make the first tropospheric trace gas measurements from GEO, building on the heritage of six spectrometers flown in low-earth-orbit (LEO). These LEO instruments measure the needed spectra, although at coarse spatial and temporal resolutions, to the precisions required for TEMPO and use retrieval algorithms developed for them by TEMPO Science Team members and currently running in operational environments. This makes TEMPO an innovative use of a well-proven technique, able to produce a revolutionary data set. TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, 'Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond.' TEMPO, GEMS, and Sentinel-4 will form parts of a global GEO constellation for pollution monitoring later this decade, with a major focus on intercontinental pollution transport.

Spatial And Temporal Trends Of Organic Pollutants In Vegetation From Remote And Rural Areas

NASA Astrophysics Data System (ADS)

Bartrons, Mireia; Catalan, Jordi; Penuelas, Josep

2016-05-01

Persistent organic pollutants (POPs) and polycyclic aromatic hydrocarbons (PAHs) used in agricultural, industrial, and domestic applications are widely distributed and bioaccumulate in food webs, causing adverse effects to the biosphere. A review of published data for 1977-2015 for a wide range of vegetation around the globe indicates an extensive load of pollutants in vegetation. On a global perspective, the accumulation of POPs and PAHs in vegetation depends on the industrialization history across continents and distance to emission sources, beyond organism type and climatic variables. International regulations initially reduced the concentrations of POPs in vegetation in rural areas, but concentrations of HCB, HCHs, and DDTs at remote sites did not decrease or even increased over time, pointing to a remobilization of POPs from source areas to remote sites. The concentrations of compounds currently in use, PBDEs and PAHs, are still increasing in vegetation. Differential congener specific accumulation is mostly determined by continent—in accordance to the different regulations of HCHs, PCBs and PBDEs in different countries—and by plant type (PAHs). These results support a concerning general accumulation of toxic pollutants in most ecosystems of the globe that for some compounds is still far from being mitigated in the near future.

Underwater lidar system: design challenges and application in pollution detection

NASA Astrophysics Data System (ADS)

Gupta, Pradip; Sankolli, Swati; Chakraborty, A.

2016-05-01

The present remote sensing techniques have imposed limitations in the applications of LIDAR Technology. The fundamental sampling inadequacy of the remote sensing data obtained from satellites is that they cannot resolve in the third spatial dimension, the vertical. This limits our possibilities of measuring any vertical variability in the water column. Also the interaction between the physical and biological process in the oceans and their effects at subsequent depths cannot be modeled with present techniques. The idea behind this paper is to introduce underwater LIDAR measurement system by using a LIDAR mounted on an Autonomous Underwater Vehicle (AUV). The paper introduces working principles and design parameters for the LIDAR mounted AUV (AUV-LIDAR). Among several applications the papers discusses the possible use and advantages of AUV-LIDAR in water pollution detection through profiling of Dissolved Organic Matter (DOM) in water bodies.

Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland.

PubMed

Zhang, Chaosheng; Luo, Lin; Xu, Weilin; Ledwith, Valerie

2008-07-15

Pollution hotspots in urban soils need to be identified for better environmental management. It is important to know if there are hotspots and if the hotspots are statistically significant. In this study identification of pollution hotspots was investigated using Pb concentrations in urban soils of Galway City in Ireland as an example, and the influencing factors on results of hotspot identification were investigated. The index of local Moran's I is a useful tool for identifying pollution hotspots of Pb pollution in urban soils, and for classifying them into spatial clusters and spatial outliers. The results were affected by the definition of weight function, data transformation and existence of extreme values. Compared with the results for the positively skewed raw data, the transformed data and data with extreme values excluded revealed a larger area for the high value spatial clusters in the city centre. While it is hard to decide the best way of using this index, it is suggested that all these influencing factors should be considered until reasonable and reliable results are obtained. GIS mapping can be applied to help evaluate the results via visualization of the spatial patterns. Meanwhile, selected pollution hotspots (extreme values) in this study were confirmed by re-analyses and re-sampling.

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies.

PubMed

Vedal, Sverre; Han, Bin; Xu, Jia; Szpiro, Adam; Bai, Zhipeng

2017-12-15

No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources.

Spatial and temporal trends from an air quality sensor network near a heavily trafficked intersection

NASA Astrophysics Data System (ADS)

Orlando, P.; Vo, D.; Giossi, C.; George, L.

2017-12-01

With the world-wide increase in urbanization and the increasing usage of combustion vehicles in urban areas, traffic-related air pollution is a growing health hazard. However, there are limited studies that examine the spatial and temporal impacts of traffic-related pollutants within cities. In particular, there are few studies that look at traffic management and its potential for pollution mitigation. In a previous study we examined roadway pollution and traffic parameters with one roadway station instrumented with standard measurement instruments. With the advent of low-cost air pollution sensors, we have expanded our work by observing multiple sites within a neighborhood to understand spatial and temporal exposures. We have deployed a high-density sensor network around urban arterial corridors in SE Portland, Oregon. This network consisted of ten nodes measuring CO, NO, NO2 and O3, and ten nodes measuring CO, CO2, VOC and PM2.5. The co-location of standard measurement instruments provided insight towards the utility of our low-cost sensor network, as the different nodes varied in cost, and potentially in quality. We have identified near-real-time temporal trends and local-scale spatial patterns during the summer of 2017. Meteorological and traffic data were included to further characterize these patterns, exploring the potential for pollution mitigation.

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies

PubMed Central

Vedal, Sverre; Han, Bin; Szpiro, Adam; Bai, Zhipeng

2017-01-01

No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources. PMID:29244738

Assessment of nitrogen oxides and ground-level ozone behavior in a dense air quality station network: case study in the lesser antilles arc.

PubMed

Plocoste, Thomas; Dorville, Jean-François; Monjoly, Stéphanie; Jacoby-Koaly, Sandra; André, Maïna

2018-04-30

This paper presents a study on ground-level ozone (O 3 ), nitrogen oxides (NO x  = NO + NO 2 ) concentration and their variabilities in the ambient air of three sites of a tropical archipelago moderately urbanized. Statistical analysis were performed on quite complete (> 80%) five years of measurements (2008-2012). There are few studies on those pollutants and their seasonal behavior in the Caribbean area where pollution level and cities configuration are different from megacities. Analyses are focus on pollutant variations at the scale of the day, the week and the seasons using hourly data. The observations show that NO x concentrations are more elevated during wet season whereas O 3 concentrations are higher in dry season. Amplitudes of ozone cycles are strongly influenced by meteorological conditions (temperature, global radiation and wind speed) and prevailing levels of NO x . An ozone weekend effect is detected with the highest amplitude in the city where anthropogenic activity is the lowest during the weekend. Due to the nature and the origin of pollutants, NO x shows higher variability than O 3 on time series. Our results evince the need of continuous measurements of Volatile Organic Compounds (VOCs) in order to better quantify their contribution in O 3 formation in insular context where numerous natural sources have been identified. Implication Statement Statistical analysis of observed NO x and O 3 concentrations for five years for a typical low industrialized site of the Caribbean area have been done. Air quality for those components is correct based on the standards of the WHO, pollutant source spatial distributions and level of industrialization. Observations show the same patterns as in megacities but also a strong impact of weather conditions and road traffic. Behaviors of O 3 cannot be fully explained without VOCs monitoring. Localization and type of AQS should be reconsidered to improve the accuracy of concentrations of the pollutant and better understand their behaviors.

The quality of rivers: From pristine stage to global pollution

NASA Astrophysics Data System (ADS)

Meybeck, Michel; Helmer, Richard

1989-12-01

River water quality is highly variable by nature due to environmental conditions such as basin lithology, vegetation and climate. In small watersheds spatial variations extend over orders of magnitude for most major elements and nutrients, while this variability is an order of magnitude lower for major basins. A standard river water for use as reference is therefore not applicable. As a consequence natural waters can possibly be unfit for various human uses, even including drinking. The Water Quality (WQ) concept has greatly evolved since the beginning of the century in accordance with expanding water uses and analytical developments. Even in well developed countries the dissolved heavy metal measurements in rivers are not very reliable while dissolved organic micro-pollutants are even rarely analysed routinely. Major WQ problems have been identified according to river basin size, including organic pollution, salinity, total suspended solids, heavy metals, eutrophication, nitrate, organic micro-pollutants, acidification. They generally occurred in this order over a period of about 100 years in the industrialized countries. Historical records of WQ are rare but can be established indirectly through studies of lake sediments. When proper control action is taken at an early stage, numerous examples of WQ recovery have been found in rivers for most of the common pollution problems. Future WQ problems will mostly derive from mine tailings and toxic waste disposal in both developed and developing countries, industrial accidents and organic micropollutants which emerge faster than our analytical capacities. The newly industrializing countries will face all the above-mentioned problems within a very short time period without having the means to cope with them one at a time. River studies point out the global alteration of the biogeochemical cycles of many major elements and nutrients (S, Na, K, N, P). For heavy metals such as lead, present estimates of global river loads emphasize the role of interim storage on land, thus delaying downstream pollution problems.

Spatial Surface PM2.5 Concentration Estimates for Wildfire Smoke Plumes in the Western U.S. Using Satellite Retrievals and Data Assimilation Techniques

NASA Astrophysics Data System (ADS)

Loria Salazar, S. M.; Holmes, H.

2015-12-01

Health effects studies of aerosol pollution have been extended spatially using data assimilation techniques that combine surface PM2.5 concentrations and Aerosol Optical Depth (AOD) from satellite retrievals. While most of these models were developed for the dark-vegetated eastern U.S. they are being used in the semi-arid western U.S. to remotely sense atmospheric aerosol concentrations. These models are helpful to understand the spatial variability of surface PM2.5concentrations in the western U.S. because of the sparse network of surface monitoring stations. However, the models developed for the eastern U.S. are not robust in the western U.S. due to different aerosol formation mechanisms, transport phenomena, and optical properties. This region is a challenge because of complex terrain, anthropogenic and biogenic emissions, secondary organic aerosol formation, smoke from wildfires, and low background aerosol concentrations. This research concentrates on the use and evaluation of satellite remote sensing to estimate surface PM2.5 concentrations from AOD satellite retrievals over California and Nevada during the summer months of 2012 and 2013. The aim of this investigation is to incorporate a spatial statistical model that uses AOD from AERONET as well as MODIS, surface PM2.5 concentrations, and land-use regression to characterize spatial surface PM2.5 concentrations. The land use regression model uses traditional inputs (e.g. meteorology, population density, terrain) and non-traditional variables (e.g. FIre Inventory from NCAR (FINN) emissions and MODIS albedo product) to account for variability related to smoke plume trajectories and land use. The results will be used in a spatially resolved health study to determine the association between wildfire smoke exposure and cardiorespiratory health endpoints. This relationship can be used with future projections of wildfire emissions related to climate change and droughts to quantify the expected health impact.

A Global Land Use Regression Model for Nitrogen Dioxide Air Pollution

PubMed Central

Larkin, Andrew; Geddes, Jeffrey A.; Martin, Randall V.; Xiao, Qingyang; Liu, Yang; Marshall, Julian D.; Brauer, Michael; Hystad, Perry

2017-01-01

Nitrogen dioxide is a common air pollutant with growing evidence of health impacts independent of other common pollutants such as ozone and particulate matter. However, the global distribution of NO2 exposure and associated impacts on global health is still largely uncertain. To advance global exposure estimates we created a global nitrogen dioxide (NO2) land use regression model for 2011 using annual measurements from 5,220 air monitors in 58 countries. The model captured 54% of global NO2 variation, with a mean absolute error of 3.7 ppb. Regional performance varied from R2 = 0.42 (Africa) to 0.67 (South America). Repeated 10% cross-validation using bootstrap sampling (n=10,000) demonstrated robust performance with respect to air monitor sampling in North America, Europe, and Asia (adjusted R2 within 2%) but not for Africa and Oceania (adjusted R2 within 11%) where NO2 monitoring data are sparse. The final model included 10 variables that captured both between and within-city spatial gradients in NO2 concentrations. Variable contributions differed between continental regions but major roads within 100m and satellite-derived NO2 were consistently the strongest predictors. The resulting model will be made available and can be used for global risk assessments and health studies, particularly in countries without existing NO2 monitoring data or models. PMID:28520422

Small-scale variability in peatland pore-water biogeochemistry, Hudson Bay Lowland, Canada.

PubMed

Ulanowski, T A; Branfireun, B A

2013-06-01

The Hudson Bay Lowland (HBL) of northern Ontario, Manitoba and Quebec, Canada is the second largest contiguous peatland complex in the world, currently containing more than half of Canada's soil carbon. Recent concerns about the ecohydrological impacts to these large northern peatlands resulting from climate change and resource extraction have catalyzed a resurgence in scientific research into this ecologically important region. However, the sheer size, heterogeneity and elaborate landscape arrangements of this ecosystem raise important questions concerning representative sampling of environmental media for chemical or physical characterization. To begin to quantify such variability, this study assessed the small-scale spatial (1m) and short temporal (21 day) variability of surface pore-water biogeochemistry (pH, dissolved organic carbon, and major ions) in a Sphagnum spp.-dominated, ombrotrophic raised bog, and a Carex spp.-dominated intermediate fen in the HBL. In general, pore-water pH and concentrations of dissolved solutes were similar to previously reported literature values from this region. However, systematic sampling revealed consistent statistically significant differences in pore-water chemistries between the bog and fen peatland types, and large within-site spatiotemporal variability. We found that microtopography in the bog was associated with consistent differences in most biogeochemical variables. Temporal changes in dissolved solute chemistry, particularly base cations (Na(+), Ca(2+) and Mg(2+)), were statistically significant in the intermediate fen, likely a result of a dynamic connection between surficial waters and mineral-rich deep groundwater. In both the bog and fen, concentrations of SO4(2-) showed considerable spatial variability, and a significant decrease in concentrations over the study period. The observed variability in peatland pore-water biogeochemistry over such small spatial and temporal scales suggests that under-sampling in northern peatland environments could lead to erroneous conclusions concerning the abundance and distribution of natural elements and pollutants alike. Copyright © 2013 Elsevier B.V. All rights reserved.

[Spatial heterogeneity and classified control of agricultural non-point source pollution in Huaihe River Basin].

PubMed

Zhou, Liang; Xu, Jian-Gang; Sun, Dong-Qi; Ni, Tian-Hua

2013-02-01

Agricultural non-point source pollution is of importance in river deterioration. Thus identifying and concentrated controlling the key source-areas are the most effective approaches for non-point source pollution control. This study adopts inventory method to analysis four kinds of pollution sources and their emissions intensity of the chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) in 173 counties (cities, districts) in Huaihe River Basin. The four pollution sources include livestock breeding, rural life, farmland cultivation, aquacultures. The paper mainly addresses identification of non-point polluted sensitivity areas, key pollution sources and its spatial distribution characteristics through cluster, sensitivity evaluation and spatial analysis. A geographic information system (GIS) and SPSS were used to carry out this study. The results show that: the COD, TN and TP emissions of agricultural non-point sources were 206.74 x 10(4) t, 66.49 x 10(4) t, 8.74 x 10(4) t separately in Huaihe River Basin in 2009; the emission intensity were 7.69, 2.47, 0.32 t.hm-2; the proportions of COD, TN, TP emissions were 73%, 24%, 3%. The paper achieves that: the major pollution source of COD, TN and TP was livestock breeding and rural life; the sensitivity areas and priority pollution control areas among the river basin of non-point source pollution are some sub-basins of the upper branches in Huaihe River, such as Shahe River, Yinghe River, Beiru River, Jialu River and Qingyi River; livestock breeding is the key pollution source in the priority pollution control areas. Finally, the paper concludes that pollution type of rural life has the highest pollution contribution rate, while comprehensive pollution is one type which is hard to control.

Impacts from Land Use Pattern on Spatial Distribution of Cultivated Soil Heavy Metal Pollution in Typical Rural-Urban Fringe of Northeast China

PubMed Central

Li, Wenbo; Wang, Dongyan; Wang, Qing; Liu, Shuhan; Zhu, Yuanli; Wu, Wenjun

2017-01-01

Under rapid urban sprawl in Northeast China, land conversions are not only encroaching on the quantity of cultivated lands, but also posing a great threat to black soil conservation and food security. This study’s aim is to explore the spatial relationship between comprehensive cultivated soil heavy metal pollution and peri-urban land use patterns in the black soil region. We applied spatial lag regression to analyze the relationship between PLI (pollution load index) and influencing factors of land use by taking suburban cultivated land of Changchun Kuancheng District as an empirical case. The results indicate the following: (1) Similar spatial distribution characteristics are detected between Pb, Cu, and Zn, between Cr and Ni, and between Hg and Cd. The Yitong River catchment in the central region, and the residential community of Lanjia County in the west, are the main hotspots for eight heavy metals and PLI. Beihu Wetland Park, with a larger-area distribution of ecological land in the southeast, has low level for both heavy metal concentrations and PLI values. Spatial distribution characteristics of cultivated heavy metals are related to types of surrounding land use and industry; (2) Spatial lag regression has a better fit for PLI than the ordinary least squares regression. The regression results indicate the inverse relationship between heavy metal pollution degree and distance from long-standing residential land and surface water. Following rapid urban land expansion and a longer accumulation period, residential land sprawl is going to threaten cultivated land with heavy metal pollution in the suburban black soil region, and cultivated land irrigated with urban river water in the suburbs will have a higher tendency for heavy metal pollution. PMID:28327541

Impacts from Land Use Pattern on Spatial Distribution of Cultivated Soil Heavy Metal Pollution in Typical Rural-Urban Fringe of Northeast China.

PubMed

Li, Wenbo; Wang, Dongyan; Wang, Qing; Liu, Shuhan; Zhu, Yuanli; Wu, Wenjun

2017-03-22

Under rapid urban sprawl in Northeast China, land conversions are not only encroaching on the quantity of cultivated lands, but also posing a great threat to black soil conservation and food security. This study's aim is to explore the spatial relationship between comprehensive cultivated soil heavy metal pollution and peri-urban land use patterns in the black soil region. We applied spatial lag regression to analyze the relationship between PLI (pollution load index) and influencing factors of land use by taking suburban cultivated land of Changchun Kuancheng District as an empirical case. The results indicate the following: (1) Similar spatial distribution characteristics are detected between Pb, Cu, and Zn, between Cr and Ni, and between Hg and Cd. The Yitong River catchment in the central region, and the residential community of Lanjia County in the west, are the main hotspots for eight heavy metals and PLI. Beihu Wetland Park, with a larger-area distribution of ecological land in the southeast, has low level for both heavy metal concentrations and PLI values. Spatial distribution characteristics of cultivated heavy metals are related to types of surrounding land use and industry; (2) Spatial lag regression has a better fit for PLI than the ordinary least squares regression. The regression results indicate the inverse relationship between heavy metal pollution degree and distance from long-standing residential land and surface water. Following rapid urban land expansion and a longer accumulation period, residential land sprawl is going to threaten cultivated land with heavy metal pollution in the suburban black soil region, and cultivated land irrigated with urban river water in the suburbs will have a higher tendency for heavy metal pollution.

First experiment on retrieval of tropospheric NO2 over polluted areas with 2.4-km spatial resolution basing on satellite spectral measurements

NASA Astrophysics Data System (ADS)

Postylyakov, Oleg V.; Borovski, Alexander N.; Makarenkov, Aleksandr A.

2017-11-01

Three satellites of the Resurs-P series (№1, №2, №3) aimed for remote sensing of the Earth began to operate in Russia in 2013-2016. Hyperspectral instruments GSA onboard Resurs-P perform routine imaging of the Earth surface in the spectral range of 400-1000 nm with the spectral resolution better than 10 nm and the spatial resolution of 30 m. In a special regime the GSA/Resurs-P may reach higher spectral resolution with the spatial resolution of 120 m and be used for retrieval of the tropospheric NO2 spatial distribution. We developed the first GSA/Resurs-P algorithm for the tropospheric NO2 retrieval and shortly analyze the first results for the most polluted Hebei province of China. The developed GSA/Resurs-P algorithm shows the spatial resolution of about 2.4 km for tropospheric NO2 pollution what significantly exceed resolution of other available now satellite instruments and considered as a target for future geostationary (GEO) missions for monitoring of tropospheric NO2 pollution. Differ to the currently operated low-Earth orbit (LEO) instruments, which may provide global distribution of NO2 every one or two days, GSA performs NO2 measurement on request. The precision of the NO2 measurements with 2.4 km resolution is about 2.5x1015 mol/cm2 (for DSCD) therefore it is recommended to use it for investigation of the tropospheric NO2 in polluted areas. Thus GSA/Resurs-P is the interesting and unique tool for NO2 pollution investigations and testing methods of interpretation of future high-resolution satellite data on pollutions and their emissions.

Methodological approach in determination of small spatial units in a highly complex terrain in atmospheric pollution research: the case of Zasavje region in Slovenia.

PubMed

Kukec, Andreja; Boznar, Marija Z; Mlakar, Primoz; Grasic, Bostjan; Herakovic, Andrej; Zadnik, Vesna; Zaletel-Kragelj, Lijana; Farkas, Jerneja; Erzen, Ivan

2014-05-01

The study of atmospheric air pollution research in complex terrains is challenged by the lack of appropriate methodology supporting the analysis of the spatial relationship between phenomena affected by a multitude of factors. The key is optimal design of a meaningful approach based on small spatial units of observation. The Zasavje region, Slovenia, was chosen as study area with the main objective to investigate in practice the role of such units in a test environment. The process consisted of three steps: modelling of pollution in the atmosphere with dispersion models, transfer of the results to geographical information system software, and then moving on to final determination of the function of small spatial units. A methodology capable of designing useful units for atmospheric air pollution research in highly complex terrains was created, and the results were deemed useful in offering starting points for further research in the field of geospatial health.

A longitudinal study of mortality and air pollution for São Paulo, Brazil.

PubMed

Botter, Denise A; Jørgensen, Bent; Peres, Antonieta A Q

2002-09-01

We study the effects of various air-pollution variables on the daily death counts for people over 65 years in São Paulo, Brazil, from 1991 to 1993, controlling for meteorological variables. We use a state space model where the air-pollution variables enter via the latent process, and the meteorological variables via the observation equation. The latent process represents the potential mortality due to air pollution, and is estimated by Kalman filter techniques. The effect of air pollution on mortality is found to be a function of the variation in the sulphur dioxide level for the previous 3 days, whereas the other air-pollution variables (total suspended particulates, nitrogen dioxide, carbon monoxide, ozone) are not significant when sulphur dioxide is in the equation. There are significant effects of humidity and up to lag 3 of temperature, and a significant seasonal variation.

Combined multivariate statistical techniques, Water Pollution Index (WPI) and Daniel Trend Test methods to evaluate temporal and spatial variations and trends of water quality at Shanchong River in the Northwest Basin of Lake Fuxian, China.

PubMed

Wang, Quan; Wu, Xianhua; Zhao, Bin; Qin, Jie; Peng, Tingchun

2015-01-01

Understanding spatial and temporal variations in river water quality and quantitatively evaluating the trend of changes are important in order to study and efficiently manage water resources. In this study, an analysis of Water Pollution Index (WPI), Daniel Trend Test, Cluster Analysis and Discriminant Analysis are applied as an integrated approach to quantitatively explore the spatial and temporal variations and the latent sources of water pollution in the Shanchong River basin, Northwest Basin of Lake Fuxian, China. We group all field surveys into 2 clusters (dry season and rainy season). Moreover, 14 sampling sites have been grouped into 3 clusters for the rainy season (highly polluted, moderately polluted and less polluted sites) and 2 clusters for the dry season (highly polluted and less polluted sites) based on their similarities and the level of pollution during the two seasons. The results show that the main trend of pollution was aggravated during the transition from the dry to the rainy season. The Water Pollution Index of Total Nitrogen is the highest of all pollution parameters, whereas the Chemical Oxygen Demand (Chromium) is the lowest. Our results also show that the main sources of pollution are farming activities alongside the Shanchong River, soil erosion and fish culture at Shanchong River reservoir area and domestic sewage from scattered rural residential area. Our results suggest that strategies to prevent water pollutionat the Shanchong River basin need to focus on non-point pollution control by employing appropriate fertilizer formulas in farming, and take the measures of soil and water conservation at Shanchong reservoir area, and purifying sewage from scattered villages.

Combined Multivariate Statistical Techniques, Water Pollution Index (WPI) and Daniel Trend Test Methods to Evaluate Temporal and Spatial Variations and Trends of Water Quality at Shanchong River in the Northwest Basin of Lake Fuxian, China

PubMed Central

Wang, Quan; Wu, Xianhua; Zhao, Bin; Qin, Jie; Peng, Tingchun

2015-01-01

Understanding spatial and temporal variations in river water quality and quantitatively evaluating the trend of changes are important in order to study and efficiently manage water resources. In this study, an analysis of Water Pollution Index (WPI), Daniel Trend Test, Cluster Analysis and Discriminant Analysis are applied as an integrated approach to quantitatively explore the spatial and temporal variations and the latent sources of water pollution in the Shanchong River basin, Northwest Basin of Lake Fuxian, China. We group all field surveys into 2 clusters (dry season and rainy season). Moreover, 14 sampling sites have been grouped into 3 clusters for the rainy season (highly polluted, moderately polluted and less polluted sites) and 2 clusters for the dry season (highly polluted and less polluted sites) based on their similarities and the level of pollution during the two seasons. The results show that the main trend of pollution was aggravated during the transition from the dry to the rainy season. The Water Pollution Index of Total Nitrogen is the highest of all pollution parameters, whereas the Chemical Oxygen Demand (Chromium) is the lowest. Our results also show that the main sources of pollution are farming activities alongside the Shanchong River, soil erosion and fish culture at Shanchong River reservoir area and domestic sewage from scattered rural residential area. Our results suggest that strategies to prevent water pollutionat the Shanchong River basin need to focus on non-point pollution control by employing appropriate fertilizer formulas in farming, and take the measures of soil and water conservation at Shanchong reservoir area, and purifying sewage from scattered villages. PMID:25837673

A GIS-based multi-source and multi-box modeling approach (GMSMB) for air pollution assessment--a North American case study.

PubMed

Wang, Bao-Zhen; Chen, Zhi

2013-01-01

This article presents a GIS-based multi-source and multi-box modeling approach (GMSMB) to predict the spatial concentration distributions of airborne pollutant on local and regional scales. In this method, an extended multi-box model combined with a multi-source and multi-grid Gaussian model are developed within the GIS framework to examine the contributions from both point- and area-source emissions. By using GIS, a large amount of data including emission sources, air quality monitoring, meteorological data, and spatial location information required for air quality modeling are brought into an integrated modeling environment. It helps more details of spatial variation in source distribution and meteorological condition to be quantitatively analyzed. The developed modeling approach has been examined to predict the spatial concentration distribution of four air pollutants (CO, NO(2), SO(2) and PM(2.5)) for the State of California. The modeling results are compared with the monitoring data. Good agreement is acquired which demonstrated that the developed modeling approach could deliver an effective air pollution assessment on both regional and local scales to support air pollution control and management planning.

Scale-dependency of macroinvertebrate communities: responses to contaminated sediments within run-of-river dams.

PubMed

Colas, Fanny; Archaimbault, Virginie; Devin, Simon

2011-03-01

Due to their nutrient recycling function and their importance in food-webs, macroinvertebrates are essential for the functioning of aquatic ecosystems. These organisms also constitute an important component of biodiversity. Sediment evaluation and monitoring is an essential aspect of ecosystem monitoring since sediments represent an important component of aquatic habitats and are also a potential source of contamination. In this study, we focused on macroinvertebrate communities within run-of-river dams, that are prime areas for sediment and pollutant accumulation. Little is known about littoral macroinvertebrate communities within run-of-river dam or their response to sediment levels and pollution. We therefore aimed to evaluate the following aspects: the functional and structural composition of macroinvertebrate communities in run-of-river dams; the impact of pollutant accumulation on such communities, and the most efficient scales and tools needed for the biomonitoring of contaminated sediments in such environments. Two run-of-river dams located in the French alpine area were selected and three spatial scales were examined: transversal (banks and channel), transversal x longitudinal (banks/channel x tail/middle/dam) and patch scale (erosion, sedimentation and vegetation habitats). At the patch scale, we noted that the heterogeneity of littoral habitats provided many available niches that allow for the development of diversified macroinvertebrate communities. This implies highly variable responses to contamination. Once combined on a global 'banks' spatial scale, littoral habitats can highlight the effects of toxic disturbances. Copyright © 2011 Elsevier B.V. All rights reserved.

Global inequities between polluters and the polluted: climate change impacts on coral reefs.

PubMed

Wolff, Nicholas H; Donner, Simon D; Cao, Long; Iglesias-Prieto, Roberto; Sale, Peter F; Mumby, Peter J

2015-11-01

For many ecosystem services, it remains uncertain whether the impacts of climate change will be mostly negative or positive and how these changes will be geographically distributed. These unknowns hamper the identification of regional winners and losers, which can influence debate over climate policy. Here, we use coral reefs to explore the spatial variability of climate stress by modelling the ecological impacts of rising sea temperatures and ocean acidification, two important coral stressors associated with increasing greenhouse gas (GHG) emissions. We then combine these results with national per capita emissions to quantify inequities arising from the distribution of cause (CO2 emissions) and effect (stress upon reefs) among coral reef countries. We find pollution and coral stress are spatially decoupled, creating substantial inequity of impacts as a function of emissions. We then consider the implications of such inequity for international climate policy. Targets for GHG reductions are likely to be tied to a country's emissions. Yet within a given level of GHG emissions, our analysis reveals that some countries experience relatively high levels of impact and will likely experience greater financial cost in terms of lost ecosystem productivity and more extensive adaptation measures. We suggest countries so disadvantaged be given access to international adaptation funds proportionate with impacts to their ecosystem. We raise the idea that funds could be more equitably allocated by formally including a metric of equity within a vulnerability framework. © 2015 John Wiley & Sons Ltd.

Heavy Metal Pollution Delineation Based on Uncertainty in a Coastal Industrial City in the Yangtze River Delta, China

PubMed Central

Zhao, Ruiying; Chen, Songchao; Zhou, Yue; Jin, Bin; Li, Yan

2018-01-01

Assessing heavy metal pollution and delineating pollution are the bases for evaluating pollution and determining a cost-effective remediation plan. Most existing studies are based on the spatial distribution of pollutants but ignore related uncertainty. In this study, eight heavy-metal concentrations (Cr, Pb, Cd, Hg, Zn, Cu, Ni, and Zn) were collected at 1040 sampling sites in a coastal industrial city in the Yangtze River Delta, China. The single pollution index (PI) and Nemerow integrated pollution index (NIPI) were calculated for every surface sample (0–20 cm) to assess the degree of heavy metal pollution. Ordinary kriging (OK) was used to map the spatial distribution of heavy metals content and NIPI. Then, we delineated composite heavy metal contamination based on the uncertainty produced by indicator kriging (IK). The results showed that mean values of all PIs and NIPIs were at safe levels. Heavy metals were most accumulated in the central portion of the study area. Based on IK, the spatial probability of composite heavy metal pollution was computed. The probability of composite contamination in the central core urban area was highest. A probability of 0.6 was found as the optimum probability threshold to delineate polluted areas from unpolluted areas for integrative heavy metal contamination. Results of pollution delineation based on uncertainty showed the proportion of false negative error areas was 6.34%, while the proportion of false positive error areas was 0.86%. The accuracy of the classification was 92.80%. This indicated the method we developed is a valuable tool for delineating heavy metal pollution. PMID:29642623

Heavy Metal Pollution Delineation Based on Uncertainty in a Coastal Industrial City in the Yangtze River Delta, China.

PubMed

Hu, Bifeng; Zhao, Ruiying; Chen, Songchao; Zhou, Yue; Jin, Bin; Li, Yan; Shi, Zhou

2018-04-10

Assessing heavy metal pollution and delineating pollution are the bases for evaluating pollution and determining a cost-effective remediation plan. Most existing studies are based on the spatial distribution of pollutants but ignore related uncertainty. In this study, eight heavy-metal concentrations (Cr, Pb, Cd, Hg, Zn, Cu, Ni, and Zn) were collected at 1040 sampling sites in a coastal industrial city in the Yangtze River Delta, China. The single pollution index (PI) and Nemerow integrated pollution index (NIPI) were calculated for every surface sample (0-20 cm) to assess the degree of heavy metal pollution. Ordinary kriging (OK) was used to map the spatial distribution of heavy metals content and NIPI. Then, we delineated composite heavy metal contamination based on the uncertainty produced by indicator kriging (IK). The results showed that mean values of all PIs and NIPIs were at safe levels. Heavy metals were most accumulated in the central portion of the study area. Based on IK, the spatial probability of composite heavy metal pollution was computed. The probability of composite contamination in the central core urban area was highest. A probability of 0.6 was found as the optimum probability threshold to delineate polluted areas from unpolluted areas for integrative heavy metal contamination. Results of pollution delineation based on uncertainty showed the proportion of false negative error areas was 6.34%, while the proportion of false positive error areas was 0.86%. The accuracy of the classification was 92.80%. This indicated the method we developed is a valuable tool for delineating heavy metal pollution.

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression.

PubMed

Chen, Qiang; Mei, Kun; Dahlgren, Randy A; Wang, Ting; Gong, Jian; Zhang, Minghua

2016-12-01

As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R 2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R 2 ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to optimize mitigation strategies for contrasting land-use characteristics and seasonal variations. Copyright © 2016 Elsevier B.V. All rights reserved.

Influence of Social-economic Activities on Air Pollutants in Beijing, China

NASA Astrophysics Data System (ADS)

Li, Xiaolu; Zheng, Wenfeng; Yin, Lirong; Yin, Zhengtong; Song, Lihong; Tian, Xia

2017-08-01

With the rapid economic development, the serious air pollution in Beijing attracts increasing attention in the last decade. Seen as one whole complex and grey system, the causal relationship between the social development and the air pollution in Beijing has been quantitatively analyzed in this paper. By using the grey relational model, the aim of this study is to explore how the socio-economic and human activities affect on the air pollution in the city of Beijing, China. Four air pollutants, as the particulate matter with size 2.5 micrometers or less (PM2.5), particulate matter with size 10 micrometers or less (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NOx), are selected as the indicators of air pollution. Additionally, fifteen socio-economic indicators are selected to account for the regional socio-economic characteristics (economy variables, energy consumption variables, pollution emissions variables, environment and construction activity variables). The results highlight that all variables are associated with the concentrations of the four selected air pollutants, but with notable differences between the air pollutants. Most of the socio-economic indicators, such as industrial output, total energy consumption are highly correlated with PM2.5, while PM10, SO2, and NOx present in general moderate correlations with most of the socio-economic variables. Contrary to other studies and reports this study reveals that vehicles and life energy do not have the strongest effect on air pollution in Beijing. This study provides useful information to reduce air pollution and support decision-making for sustainable development.

Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis.

PubMed

Alam, Md Saniul; McNabola, Aonghus

2015-05-01

Estimation of daily average exposure to PM10 (particulate matter with an aerodynamic diameter<10 μm) using the available fixed-site monitoring stations (FSMs) in a city poses a great challenge. This is because typically FSMs are limited in number when considering the spatial representativeness of their measurements and also because statistical models of citywide exposure have yet to be explored in this context. This paper deals with the later aspect of this challenge and extends the widely used land use regression (LUR) approach to deal with temporal changes in air pollution and the influence of transboundary air pollution on short-term variations in PM10. Using the concept of multiple linear regression (MLR) modeling, the average daily concentrations of PM10 in two European cities, Vienna and Dublin, were modeled. Models were initially developed using the standard MLR approach in Vienna using the most recently available data. Efforts were subsequently made to (i) assess the stability of model predictions over time; (ii) explores the applicability of nonparametric regression (NPR) and artificial neural networks (ANNs) to deal with the nonlinearity of input variables. The predictive performance of the MLR models of the both cities was demonstrated to be stable over time and to produce similar results. However, NPR and ANN were found to have more improvement in the predictive performance in both cities. Using ANN produced the highest result, with daily PM10 exposure predicted at R2=66% for Vienna and 51% for Dublin. In addition, two new predictor variables were also assessed for the Dublin model. The variables representing transboundary air pollution and peak traffic count were found to account for 6.5% and 12.7% of the variation in average daily PM10 concentration. The variable representing transboundary air pollution that was derived from air mass history (from back-trajectory analysis) and population density has demonstrated a positive impact on model performance. The implications of this research would suggest that it is possible to produce a model of ambient air quality on a citywide scale using the readily available data. Most European cities typically have a limited FSM network with average daily concentrations of air pollutants as well as available meteorological, traffic, and land-use data. This research highlights that using these data in combination with advanced statistical techniques such as NPR or ANNs will produce reasonably accurate predictions of ambient air quality across a city, including temporal variations. Therefore, this approach reduces the need for additional measurement data to supplement existing historical records and enables a lower-cost method of air pollution model development for practitioners and policy makers.

PM2.5 and gaseous pollutants in New York State during 2005-2016: Spatial variability, temporal trends, and economic influences

NASA Astrophysics Data System (ADS)

Squizzato, Stefania; Masiol, Mauro; Rich, David Q.; Hopke, Philip K.

2018-06-01

Over the past decades, mitigation strategies have been adopted both by federal and state agencies in the United States (US) to improve air quality. Between 2007 and 2009, the US faced a financial/economic crisis that lowered activity and reduced emissions. At the same time, changes in the prices of coal and natural gas drove a shift in fuels used for electricity generation. Seasonal patterns, diel cycles, spatial gradients, and trends in PM2.5 and gaseous pollutants concentrations (NOx, SO2, CO and O3) monitored in New York State (NYS) from 2005 to 2016 were examined. Relationships between ambient concentrations, changes in NYS emissions retrieved from the US EPA trends inventory, and economic indicators were studied. PM2.5 and primary gaseous pollutants concentrations decreased across NYS. By 2016, PM2.5 and SO2 attained relatively homogeneous concentrations across the state. PM2.5 concentrations decreased significantly at all sites. Similarly, SO2 concentrations declined at all sites within this period, with the highest slopes observed at the urban sites. Reductions in NOx emissions likely contributed to summertime average ozone reductions. NOx and VOCs controls reduced O3 peak concentrations as seen in significant relationships between the annual O3 4th-highest daily maximum 8-h concentrations and estimated NOx emissions at rural and suburban sites (r2 ∼ 0.7). Spring maxima were not reduced with most sites showing insignificant slopes or significant positive slopes (e.g., +2.6% y-1 and +2% y-1, at CCNY and PFI, respectively). Increases in autumn and winter ozone concentrations were found (e,g., 6.6 ± 0.4% y-1 on average in New York City). Significant relationships were observed between PM2.5, primary pollutants, and economic indicators. Overall, a decrease in electricity generation with coal, and the simultaneous increase in natural gas consumption for power generation, led to a decrease in PM2.5 and gaseous pollutants concentrations.

Urban airborne matter in central and southern Chile: Effects of meteorological conditions on fine and coarse particulate matter

NASA Astrophysics Data System (ADS)

Yáñez, Marco A.; Baettig, Ricardo; Cornejo, Jorge; Zamudio, Francisco; Guajardo, Jorge; Fica, Rodrigo

2017-07-01

Air pollution is one of the major global environmental problems affecting human health and life quality. Many cities of Chile are heavily polluted with PM2.5 and PM10, mainly in the cold season, and there is little understanding of how the variation in particle matter differs between cities and how this is affected by the meteorological conditions. The objective of this study was to assess the effect of meteorological variables on respirable particulate matter (PM) of the main cities in the central-south valley of Chile during the cold season (May to August) between 2014 and 2016. We used hourly PM2.5 and PMcoarse (PM10- PM2.5) information along with wind speed, temperature and relative humidity, and other variables derived from meteorological parameters. Generalized additive models (GAMs) were fitted for each of the eight cities selected, covering a latitudinal range of 929 km, from Santiago to Osorno. Great variation in PM was found between cities during the cold months, and that variation exhibited a marked latitudinal pattern. Overall, the more northerly cities tended to be less polluted in PM2.5 and more polluted in PMcoarse than the more southerly cities, and vice versa. The results show that other derived variables from meteorology were better related with PM than the use of traditional daily means. The main variables selected with regard to PM2.5 content were mean wind speed and minimum temperature (negative relationship). Otherwise, the main variables selected with regard to PMcoarse content were mean wind speed (negative), and the daily range in temperature (positive). Variables derived from relative humidity contributed differently to the models, having a higher effect on PMcoarse than PM2.5, and exhibiting both negative and positive effects. For the different cities the deviance explained by the GAMs ranged from 37.6 to 79.1% for PM2.5 and from 18.5 to 63.7% for PMcoarse. The percentage of deviance explained by the models for PM2.5 exhibited a latitudinal pattern, which was not observed in PMcoarse. This highlights the greater predictability of PM2.5 according to meteorological parameters in the cities to the south. Southern cities located spatially close to one another had similar patterns in both the selected variables for the models and the trends. The meteorological factor influencing the cities had a major impact on PM concentrations. The findings of this study may aid understanding of PM variation across the country, in the way of improving forecasting models.

Feasibility of Assessing Public Health Impacts of Air Pollution Reduction Programs on a Local Scale: New Haven Case Study

PubMed Central

Lobdell, Danelle T.; Isakov, Vlad; Baxter, Lisa; Touma, Jawad S.; Smuts, Mary Beth; Özkaynak, Halûk

2011-01-01

Background New approaches to link health surveillance data with environmental and population exposure information are needed to examine the health benefits of risk management decisions. Objective We examined the feasibility of conducting a local assessment of the public health impacts of cumulative air pollution reduction activities from federal, state, local, and voluntary actions in the City of New Haven, Connecticut (USA). Methods Using a hybrid modeling approach that combines regional and local-scale air quality data, we estimated ambient concentrations for multiple air pollutants [e.g., PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter), NOx (nitrogen oxides)] for baseline year 2001 and projected emissions for 2010, 2020, and 2030. We assessed the feasibility of detecting health improvements in relation to reductions in air pollution for 26 different pollutant–health outcome linkages using both sample size and exploratory epidemiological simulations to further inform decision-making needs. Results Model projections suggested decreases (~ 10–60%) in pollutant concentrations, mainly attributable to decreases in pollutants from local sources between 2001 and 2010. Models indicated considerable spatial variability in the concentrations of most pollutants. Sample size analyses supported the feasibility of identifying linkages between reductions in NOx and improvements in all-cause mortality, prevalence of asthma in children and adults, and cardiovascular and respiratory hospitalizations. Conclusion Substantial reductions in air pollution (e.g., ~ 60% for NOx) are needed to detect health impacts of environmental actions using traditional epidemiological study designs in small communities like New Haven. In contrast, exploratory epidemiological simulations suggest that it may be possible to demonstrate the health impacts of PM reductions by predicting intraurban pollution gradients within New Haven using coupled models. PMID:21335318

An innovative expression model of human health risk based on the quantitative analysis of soil metals sources contribution in different spatial scales.

PubMed

Zhang, Yimei; Li, Shuai; Wang, Fei; Chen, Zhuang; Chen, Jie; Wang, Liqun

2018-09-01

Toxicity of heavy metals from industrialization poses critical concern, and analysis of sources associated with potential human health risks is of unique significance. Assessing human health risk of pollution sources (factored health risk) concurrently in the whole and the sub region can provide more instructive information to protect specific potential victims. In this research, we establish a new expression model of human health risk based on quantitative analysis of sources contribution in different spatial scales. The larger scale grids and their spatial codes are used to initially identify the level of pollution risk, the type of pollution source and the sensitive population at high risk. The smaller scale grids and their spatial codes are used to identify the contribution of various sources of pollution to each sub region (larger grid) and to assess the health risks posed by each source for each sub region. The results of case study show that, for children (sensitive populations, taking school and residential area as major region of activity), the major pollution source is from the abandoned lead-acid battery plant (ALP), traffic emission and agricultural activity. The new models and results of this research present effective spatial information and useful model for quantifying the hazards of source categories and human health a t complex industrial system in the future. Copyright © 2018 Elsevier Ltd. All rights reserved.

Natural and human-induced variability in the composition of fish assemblages in the Northwestern Cuban shelf.

PubMed

González-Sansón, Gaspar; Aguilar, Consuelo; Hernández, Ivet; Cabrera, Yureidy; Suarez-Montes, Noelis; Bretos, Fernando; Guggenheim, David

2009-09-01

The main goal of the study was to obtain field data to build a baseline of fish assemblage composition that can be used comparatively for future analyses of the impact of human actions in the region. A basic network of 68 sampling stations was defined for the entire region (4,050 km2). Fish assemblage species and size composition was estimated using visual census methods at three different spatial scales: a) entire region, b) inside the main reef area and c) along a human impact coastal gradient. Multivariate numerical analyses revealed habitat type as the main factor inducing spatial variability of fish community composition, while the level of human impact appears to play the main role in fish assemblage composition changes along the coast. A trend of decreasing fish size toward the east supports the theory of more severe human impact due to overfishing and higher urban pollution in that direction. This is the first detailed study along the northwest coast of Cuba that focuses on fish community structure and the natural and human-induced variations at different spatial scales for the entire NW shelf. This research also provides input for a more comprehensive understanding of coastal marine fish communities' status in the Gulf of Mexico basin.

Spatial variability of soil magnetic susceptibility in an agricultural field located in Eastern Ukraine

NASA Astrophysics Data System (ADS)

Menshov, Oleksandr; Pereira, Paulo; Kruglov, Oleksandr

2015-04-01

Magnetic susceptibility (MS) have been used to characterize soil properties. It gives an indirect information about heavy metals content and degree of human impacts on soil contamination derived from atmospheric pollution (Girault et al., 2011). This method is inexpensive in relation to chemical analysis and very useful to track soil pollution, since several toxic components deposited on soil surface are rich in particulates produced by oxidation processes (Boyko et al., 2004; Morton-Bernea et al., 2009). Thus, identify the spatial distribution of MS is of major importance, since can give an indirect information of high metals content (Dankoub et al., 2012). This allows also to distinguish the pedogenic and technogenic origin magnetic signal. For example Ukraine chernozems contain fine-grained oxidized magnetite and maghemite of pedogenic origin formed by weathering of the parent material (Jeleńska et al., 2004). However, to a correct understanding of variables distribution, the identification of the most accurate interpolation method is fundamental for a better interpretation of map information (Pereira et al., 2013). The objective of this work is to study the spatial variability of soil MS in an agricultural fields located in the Tcherkascy Tishki area (50.11°N, 36.43 °E, 162 m a.s.l), Ukraine. Soil MS was measured in 77 sampling points in a north facing slope. To estimate the best interpolation method, several interpolation methods were tested, as inverse distance to a weight (IDW) with the power of 1,2,3,4 and 5, Local Polynomial (LP) with the power of 1 and 2, Global Polynomial (GP), radial basis functions - spline with tension (SPT), completely regularized spline (CRS), multiquatratic (MTQ), inverse multiquatratic (IMTQ), and thin plate spline (TPS) - and some geostatistical methods as, ordinary kriging (OK), Simple Kriging (SK) and Universal Kriging (UK), used in previous works (Pereira et al., 2014). On average, the soil MS of the studied plot had 686.05 MS×10-9 m3/kg, and a minimum and a maximum value of 499.33 and 862.27 MS×10-9 m3/kg respectively. The standard deviation was 85.62 and the coefficient of variation 12.48%. This shows that the spatial variability of soil MS was low. The Global Morans I index was of 0.841, a z-score of 7.741 with a p<0.001, indicating that soil MS had a clustered pattern. The variogram results showed that the gaussian model was the the best fitted. The nugget effect was 0.1007. the sill 0.9905 and the nugget/sill ratio of 0.10, which shows that soil MS has a strong spatial dependency. The results of the interpolation tests showed that the errors distribution followed the normal distribution, the average predicted values were similar to the observed and the correlation between these two distributions was high (between 0.85-0.90) in all the cases. The method that predicted better soil MS was LP2 and the less accurate was SK. Soil MS presented high values in the southwestern part and low in the northeast area of the plot. It is clearly observed a increase of soil MS from the top of the slope to the bottom. Acknowledgments RECARE (Preventing and Remediating Degradation of Soils in Europe Through Land Care, FP7-ENV-2013-TWO STAGE), funded by the European Commission; and for the COST action ES1306 (Connecting European connectivity research). References Boyko, T., Scholger, R., Stanjek, H., MAGPROX team (2004) Topsoil magnetic suseptibility mapping as a tool for pollution monitoring: Repetability of in situ measurments. Journal of Applied Geophysics, 55, 249-259. Dankoub, Z., Ayoubi, S., Khademi, H., Sheng-Gao, L. (2012) Spatial distribution of magnetic properties and selected heavy metals in calcareous soils as affected by land use in the Isfahan Region, Central Iran. Pedosphere, 22, 33-47. Girault, F., Poitou, C., Perrier, F., Koirala, B.P., Bhattarai, M. (2011) Soil characterization using patterns of magnetic susceptibility versus effective radimu concentration. Natural Hazards Earth System Science, 11, 2285-2293. Jeleńska, M., Hasso-Agopsowicz, A., Kopcewicz, B., Sukhorada, A., Tyamina, K., Kądziałko-Hofmokl, M., Matviishina, Z. (2004) Magnetic properties of the profiles of polluted and non-polluted soils. A case study from Ukraine. Geophys. J. Int., 159, 104-116. Morton-Bernea, O., Hernandez, E., Martinez-Pichardo, E., Soler-Arechalde, A.M., Santa Cruz, R.L., Gonzalez-Hernandez, G., Beramendi-Orosco, L., Urritia-Fucugaushi, J. (2009) Mexico city topsoils: Heavy metals vs. magnetic susceptibility. Geoderma, 151, 121-125. Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. Modelling the impacts of wildfire on ash thickness in a short-term period, Land Degradation and Development, (In Press), DOI: 10.1002/ldr.2195 Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J., Jordan, A. Burguet, M. (2013) Spatial models for monitoring the spatio-temporal evolution of ashes after fire - a case study of a burnt grassland in Lithuania, Solid Earth, 4, 153-165.

Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia.

PubMed

Thiruchelvam, Loshini; Dass, Sarat C; Zaki, Rafdzah; Yahya, Abqariyah; Asirvadam, Vijanth S

2018-05-07

This study investigated the potential relationship between dengue cases and air quality - as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were -800.66, -796.22, and -790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.

Spatial variability in persistent organic pollutants and polycyclic aromatic hydrocarbons found in beach-stranded pellets along the coast of the state of São Paulo, southeastern Brazil.

PubMed

Taniguchi, Satie; Colabuono, Fernanda I; Dias, Patrick S; Oliveira, Renato; Fisner, Mara; Turra, Alexander; Izar, Gabriel M; Abessa, Denis M S; Saha, Mahua; Hosoda, Junki; Yamashita, Rei; Takada, Hideshige; Lourenço, Rafael A; Magalhães, Caio A; Bícego, Márcia C; Montone, Rosalinda C

2016-05-15

High spatial variability in polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), organochlorine pesticides, such as DDTs, and polybrominated diphenylethers was observed in plastic pellets collected randomly from 41 beaches (15 cities) in 2010 from the coast of state of São Paulo, southeastern Brazil. The highest concentrations ranged, in ng g(-1), from 192 to 13,708, 3.41 to 7554 and <0.11 to 840 for PAHs, PCBs and DDTs, respectively. Similar distribution pattern was presented, with lower concentrations on the relatively less urbanized and industrialized southern coast, and the highest values in the central portion of the coastline, which is affected by both waste disposal and large port and industrial complex. Additional samples were collected in this central area and PCB concentrations, in ngg(-)(1), were much higher in 2012 (1569 to 10,504) than in 2009/2010 (173 to 309) and 2014 (411), which is likely related to leakages of the PCB commercial mixture. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spatial and taxonomic variation in trace element bioaccumulation in two herbivores from a coal combustion waste contaminated stream.

PubMed

Fletcher, Dean E; Lindell, Angela H; Stillings, Garrett K; Mills, Gary L; Blas, Susan A; Vaun McArthur, J

2014-03-01

Dissimilarities in habitat use, feeding habits, life histories, and physiology can result in syntopic aquatic taxa of similar trophic position bioaccumulating trace elements in vastly different patterns. We compared bioaccumulation in a clam, Corbicula fluminea and mayfly nymph Maccaffertium modestum from a coal combustion waste contaminated stream. Collection sites differed in distance to contaminant sources, incision, floodplain activity, and sources of flood event water and organic matter. Contaminants variably accumulated in both sediment and biofilm. Bioaccumulation differed between species and sites with C. fluminea accumulating higher concentrations of Hg, Cs, Sr, Se, As, Be, and Cu, but M. modestum higher Pb and V. Stable isotope analyses suggested both spatial and taxonomic differences in resource use with greater variability and overlap between species in the more physically disturbed site. The complex but essential interactions between organismal biology, divergence in resource use, and bioaccumulation as related to stream habitat requires further studies essential to understand impacts of metal pollution on stream systems. Copyright © 2014 Elsevier Inc. All rights reserved.

A comparative analysis of two highly spatially resolved European atmospheric emission inventories

NASA Astrophysics Data System (ADS)

Ferreira, J.; Guevara, M.; Baldasano, J. M.; Tchepel, O.; Schaap, M.; Miranda, A. I.; Borrego, C.

2013-08-01

A reliable emissions inventory is highly important for air quality modelling applications, especially at regional or local scales, which require high resolutions. Consequently, higher resolution emission inventories have been developed that are suitable for regional air quality modelling. This research performs an inter-comparative analysis of different spatial disaggregation methodologies of atmospheric emission inventories. This study is based on two different European emission inventories with different spatial resolutions: 1) the EMEP (European Monitoring and Evaluation Programme) inventory and 2) an emission inventory developed by the TNO (Netherlands Organisation for Applied Scientific Research). These two emission inventories were converted into three distinct gridded emission datasets as follows: (i) the EMEP emission inventory was disaggregated by area (EMEParea) and (ii) following a more complex methodology (HERMES-DIS - High-Elective Resolution Modelling Emissions System - DISaggregation module) to understand and evaluate the influence of different disaggregation methods; and (iii) the TNO gridded emissions, which are based on different emission data sources and different disaggregation methods. A predefined common grid with a spatial resolution of 12 × 12 km2 was used to compare the three datasets spatially. The inter-comparative analysis was performed by source sector (SNAP - Selected Nomenclature for Air Pollution) with emission totals for selected pollutants. It included the computation of difference maps (to focus on the spatial variability of emission differences) and a linear regression analysis to calculate the coefficients of determination and to quantitatively measure differences. From the spatial analysis, greater differences were found for residential/commercial combustion (SNAP02), solvent use (SNAP06) and road transport (SNAP07). These findings were related to the different spatial disaggregation that was conducted by the TNO and HERMES-DIS for the first two sectors and to the distinct data sources that were used by the TNO and HERMES-DIS for road transport. Regarding the regression analysis, the greatest correlation occurred between the EMEParea and HERMES-DIS because the latter is derived from the first, which does not occur for the TNO emissions. The greatest correlations were encountered for agriculture NH3 emissions, due to the common use of the CORINE Land Cover database for disaggregation. The point source emissions (energy industries, industrial processes, industrial combustion and extraction/distribution of fossil fuels) resulted in the lowest coefficients of determination. The spatial variability of SOx differed among the emissions that were obtained from the different disaggregation methods. In conclusion, HERMES-DIS and TNO are two distinct emission inventories, both very well discretized and detailed, suitable for air quality modelling. However, the different databases and distinct disaggregation methodologies that were used certainly result in different spatial emission patterns. This fact should be considered when applying regional atmospheric chemical transport models. Future work will focus on the evaluation of air quality models performance and sensitivity to these spatial discrepancies in emission inventories. Air quality modelling will benefit from the availability of appropriate resolution, consistent and reliable emission inventories.

Patterns of Spatial Variation of Assemblages Associated with Intertidal Rocky Shores: A Global Perspective

PubMed Central

Cruz-Motta, Juan José; Miloslavich, Patricia; Palomo, Gabriela; Iken, Katrin; Konar, Brenda; Pohle, Gerhard; Trott, Tom; Benedetti-Cecchi, Lisandro; Herrera, César; Hernández, Alejandra; Sardi, Adriana; Bueno, Andrea; Castillo, Julio; Klein, Eduardo; Guerra-Castro, Edlin; Gobin, Judith; Gómez, Diana Isabel; Riosmena-Rodríguez, Rafael; Mead, Angela; Bigatti, Gregorio; Knowlton, Ann; Shirayama, Yoshihisa

2010-01-01

Assemblages associated with intertidal rocky shores were examined for large scale distribution patterns with specific emphasis on identifying latitudinal trends of species richness and taxonomic distinctiveness. Seventy-two sites distributed around the globe were evaluated following the standardized sampling protocol of the Census of Marine Life NaGISA project (www.nagisa.coml.org). There were no clear patterns of standardized estimators of species richness along latitudinal gradients or among Large Marine Ecosystems (LMEs); however, a strong latitudinal gradient in taxonomic composition (i.e., proportion of different taxonomic groups in a given sample) was observed. Environmental variables related to natural influences were strongly related to the distribution patterns of the assemblages on the LME scale, particularly photoperiod, sea surface temperature (SST) and rainfall. In contrast, no environmental variables directly associated with human influences (with the exception of the inorganic pollution index) were related to assemblage patterns among LMEs. Correlations of the natural assemblages with either latitudinal gradients or environmental variables were equally strong suggesting that neither neutral models nor models based solely on environmental variables sufficiently explain spatial variation of these assemblages at a global scale. Despite the data shortcomings in this study (e.g., unbalanced sample distribution), we show the importance of generating biological global databases for the use in large-scale diversity comparisons of rocky intertidal assemblages to stimulate continued sampling and analyses. PMID:21179546

Tracing groundwater recharge in the San Luis Valley, Colorado: Groundwater contamination susceptibility in an agricultural watershed

NASA Astrophysics Data System (ADS)

Patel, Tanya; Hindshaw, Ruth; Singer, Michael

2015-04-01

Water is a vital resource in any agricultural watershed, yet in the arid western United States farming practices threaten the quality and availability of groundwater. This is a pressing concern in the San Luis Valley, southern Colorado, where agriculture comprises 30% of the local economy, and employs over half the valley population. Although 54 % of the water used for irrigation is surface water, farmers do not usually apply this water directly to their fields. Instead, the water is often diverted into pits which recharge the aquifer, and the water is subsequently pumped during the following irrigation season. The Rio Grande Water Conservation District recognises that recharge to the unconfined aquifer has been outpaced by commercial irrigation for at least four decades, resulting in a decline in groundwater levels. Recycled irrigation water, and leakage from unlined canals now represent the greatest recharge contribution to the unconfined aquifer in this region. This makes the shallow groundwater particularly susceptible to agricultural contamination. The purpose of this study is to assess groundwater contamination in the unconfined and upper confined aquifers of the San Luis Valley, which are the most susceptible to contamination due to their close proximity to the surface. Although concentrations of potentially harmful contaminants from agricultural runoff are regularly monitored, the large spatial and temporal fluctuations in values make it difficult to determine long-term trends. We have analysed δ18O, δ2H and major-ion chemistry of 57 groundwater, stream and precipitation samples, collected in June 2014, and interpreted them alongside regional stream flow data and groundwater levels. This will allow us to study the seasonality and locality of groundwater recharge to provide greater insight into the watershed's potential for pollution. A groundwater vulnerability assessment was performed using the model DRASTIC (Depth to water, Recharge, Aquifer media, Soil media, Topography, Influence of the vadose zone and hydraulic Conductivity). Each variable is assigned a weighting and rating, which provides a quantitative assessment of an area's pollution potential. Using this method of investigation, the groundwater vulnerability map produced classifies 5% of the area as having low pollution potential, 34% as having moderate pollution potential, and 61% as having high pollution potential. The groundwater vulnerability map may be used to predict the variation in agricultural contaminant concentrations in the unconfined aquifer. Major ion analyses revealed that nitrate concentrations are highly variable, varying between 0.435 and 949μM/L, and exceed the EPA maximum contaminant level at four sites. The spatial variability in nitrate concentrations, as well as sulphate and phosphate concentrations, is much greater than the differences predicted by the model. This suggests that this variability is not a result of differences in the hydrogeology between sites, but instead may be related to individual farm practices or a result of point sources such as animal waste, septic tanks and sewage release. Understanding the impact of commercial irrigation on groundwater quality and availability is vital for developing effective strategies to stabilise groundwater levels, and protect the farmers and local population that rely on this water.

Implications of different approaches for characterizing ambient air pollutant concentrations within the urban airshed for time-series studies and health benefits analyses.

PubMed

Strickland, Matthew J; Darrow, Lyndsey A; Mulholland, James A; Klein, Mitchel; Flanders, W Dana; Winquist, Andrea; Tolbert, Paige E

2011-05-11

In time-series studies of the health effects of urban air pollutants, decisions must be made about how to characterize pollutant levels within the airshed. Emergency department visits for pediatric asthma exacerbations were collected from Atlanta hospitals. Concentrations of carbon monoxide, nitrogen dioxide, ozone, sulfur dioxide, particulate matter less than 10 microns in diameter (PM10), particulate matter less than 2.5 microns in diameter (PM2.5), and the PM2.5 components elemental carbon, organic carbon, and sulfate were obtained from networks of ambient air quality monitors. For each pollutant we created three different daily metrics. For one metric we used the measurements from a centrally-located monitor; for the second we averaged measurements across the network of monitors; and for the third we estimated the population-weighted average concentration using an isotropic spatial model. Rate ratios for each of the metrics were estimated from time-series models. For pollutants with relatively homogeneous spatial distributions we observed only small differences in the rate ratio across the three metrics. Conversely, for spatially heterogeneous pollutants we observed larger differences in the rate ratios. For a given pollutant, the strength of evidence for an association (i.e., chi-square statistics) tended to be similar across metrics. Given that the chi-square statistics were similar across the metrics, the differences in the rate ratios for the spatially heterogeneous pollutants may seem like a relatively small issue. However, these differences are important for health benefits analyses, where results from epidemiological studies on the health effects of pollutants (per unit change in concentration) are used to predict the health impacts of a reduction in pollutant concentrations. We discuss the relative merits of the different metrics as they pertain to time-series studies and health benefits analyses.

Air quality and social deprivation in four French metropolitan areas – A localized spatiotemporal environmental inequality analysis

PubMed Central

Padilla, Cindy M; Kihal-Talantikite, Wahida; Vieira, Verónica. M; Rosselo, Philippe; LeNir, Geraldine; Zmirou-Navier, Denis; Deguen, Severine

2015-01-01

Several studies have documented that more deprived populations tend to live in areas characterized by higher levels of environmental pollution. Yet, time trends and geographic patterns of this disproportionate distribution of environmental burden remain poorly assessed, especially in Europe. We investigated the spatial and temporal relationship between ambient air nitrogen dioxide (NO2) concentrations and socioeconomic and demographic data in four French Metropolitan Areas (Lille in the north, Lyon in the center, Marseille in the south, and Paris) during two different time periods. The geographical unit used was the census block. The dependent variable was the NO2 annual average concentration (µg/m3) per census block, and the explanatory variables were a neighborhood deprivation index and socioeconomic and demographic data derived from the national census. Generalized additive models were used to account for spatial autocorrelation. We found that the strength and direction of the association between deprivation and NO2 estimates varied between cities. In Paris, census blocks with the higher social categories are exposed to higher mean concentrations of NO2. However, in Lille and Marseille, the most deprived census blocks are the most exposed to NO2. In Lyon, the census blocks in the middle social categories were more likely to have higher concentrations than in the lower social categories. Despite a general reduction in NO2 concentrations over the study period in the four metropolitan areas, we found contrasting results in the temporal trend of environmental inequalities. There is clear evidence of city-specific spatial and temporal environmental inequalities that relate to the historical socioeconomic make-up of the cities and its evolution. Hence, general statements about environmental and social inequalities may not properly characterize situations where people of higher social status find the benefits of living in a specific city outweigh the detriment of higher pollution. PMID:25199972

[Spatial Variability Characteristics of Water Quality and Its Driving Forces in Honghu Lake During High Water-level Period].

PubMed

Li, Kun; Wang, Ling; Li, Zhao-hua; Wang, Xiang-rong; Chen, Hong-bing; Wu, Zhong; Zhu, Peng

2015-04-01

Based on the high-density analysis of 139 monitoring points and samples in water of honghu lake with different degrees of eutrophication during the high water-level period, we could get the figures of spatial variability characteristics of pollution factors, the biomass of aquatic plants and water quality in Honghu Lake using the GIS interpolation methods. The result showed that the concentrations of TN, TP, NH4(+) -N, permanganate index gradually increased from south to north during this period, the trend of water pollution degree in Honghu Lake was the region of inflowing rivers > enclosure culture area > open water area > the lake protection area > region of the Yangtze river into the lake; and the contribution rate of water quality parameters was in the order of TN > TP > permanganate index > NH4(+), -N > DO; under the influence of industrial sewage, agricultural sewage, domestic sewage, bait, aquatic plants and water exchange, 59% of TN, 35.2% of TP, 13.7% of permanganate index, 4.3% of NH4(+)-N exceeded the water quality targets, respectively, accordingly, 66.2% of the water quality also exceeded the water quality target. Nonetheless, DO reached the water quality target due to the influences of monsoon climate and other environment factors. The spatial variation analysis could directly reflect the mutual interaction among human activity, land-use types and environment factors which had an enormous impact on Honghu Lake water environment. In order to ensure that the lake water environment is beneficial for human productions and livings, it is necessary for us to control the discharge of industrial sewage, agricultural sewage and domestic sewage, as well as the expanding area of aquaculture, all the above measures would be significant for gradually resuming the self-purification capacity of water body and finally achieving the ecological sustainable development of Honghu Lake water environment.

Chemical climatology of the southeastern United States, 1999-2013

NASA Astrophysics Data System (ADS)

Hidy, G. M.; Blanchard, C. L.; Baumann, K.; Edgerton, E.; Tanenbaum, S.; Shaw, S.; Knipping, E.; Tombach, I.; Jansen, J.; Walters, J.

2014-11-01

A series of experiments (the Southern Oxidant and Aerosol Study - SOAS) took place in central Alabama in June-July, 2013 as part of the broader Southern Atmosphere Study (SAS). These projects were aimed at studying oxidant photochemistry and formation and impacts of aerosols at a detailed process level in a location where high biogenic organic vapor emissions interact with anthropogenic emissions, and the atmospheric chemistry occurs in a subtropical climate in North America. The majority of the ground-based experiments were located at the Southeastern Aerosol Research and Characterization (SEARCH) Centreville (CTR) site near Brent, Alabama, where extensive, unique aerometric measurements of trace gases and particles and meteorology were made beginning in the early 1990s through 2013. The SEARCH network data permits a characterization of the temporal and spatial context of the SOAS findings. Our earlier analyses of emissions and air quality trends are extended through 2013 to provide a perspective for continued decline in ambient concentrations, and the implications of these changes to regional sulfur oxide, nitrogen-ozone, and carbon chemistry. The narrative supports the SAS program in terms of long-term average chemistry (chemical climatology) and short-term comparisons of early summer average spatial variability across the southeastern US at high temporal (hourly) resolution. The long-term measurements show that the SOAS experiments took place during the second wettest and coolest year in the 2000-2013 period, with lower than average solar radiation. The pollution levels at CTR and other SEARCH sites were the lowest since full measurements began in 1999. Changes in anthropogenic gas and particle emissions between 1999 and 2013 account for the decline in pollutant concentrations at the monitoring sites in the region. The data provide an opportunity to contrast SOAS results with temporally and spatially variable conditions in support of the development of tests for the robustness of SOAS findings.

The impact of anthropogenic emissions and meteorological conditions on the spatial variation of ambient SO2 concentrations: A panel study of 113 Chinese cities.

PubMed

Yang, Xue; Wang, Shaojian; Zhang, Wenzhong; Zhan, Dongsheng; Li, Jiaming

2017-04-15

China has received increased international criticism in recent years in relation to its air pollution levels, both in terms of the transmission of pollutants across international borders and the attendant adverse health effects being witnessed. Whilst existing research has examined the factors influencing ambient air pollutant concentrations, previous studies have failed to adequately explore the determinants of such concentrations from either a source or diffusion perspective. This study addressed both source (specifically, anthropogenic emissions) and diffusion (namely, meteorological conditions) indicators, in order to detect their respective impacts on the spatial variations seen in the distribution of air pollution. Spatial panel data for 113 major cities in China was processed using a range of global regression models-the ordinary least square model, the spatial lag model, and the spatial error model-as well as a local, geographic weighted regression (GWR) model. Results from the study suggest that in 2014, average SO 2 concentrations exceeded China's first-level target. The most polluted cities were found to be predominantly located in northern China, while less polluted cities were located in southern China. Global regression results indicated that precipitation exerts a significant effect on SO 2 reduction (p<0.001) and that a regional increase of 1mm in precipitation can reduce SO 2 concentrations by 0.026μg/m 3 . Both emission and temperature factors were found to aggravate SO 2 concentrations, although no such significant correlation was found in relation to wind speed. GWR results suggest that the association between SO 2 and its factors varied over space. Increased emissions were found to be able to produce more pollution in the northwest than in other parts of the country. Higher wind speeds and temperatures in northwestern areas were shown to reinforce SO 2 pollution, while in southern regions, they had the opposite effect. Further, increased precipitation was found to exert a greater inhibitory effect on SO 2 pollution in the country's northeast than that in other areas. Our findings could provide a detailed reference for formulating regionally specific emission reduction policies in China. Copyright © 2016 Elsevier B.V. All rights reserved.

Uncertainties in historical pollution data from sedimentary records from an Australian urban floodplain lake

NASA Astrophysics Data System (ADS)

Lintern, A.; Leahy, P.; Deletic, A.; Heijnis, H.; Zawadzki, A.; Gadd, P.; McCarthy, D.

2018-05-01

Sediment cores from aquatic environments can provide valuable information about historical pollution levels and sources. However, there is little understanding of the uncertainties associated with these findings. The aim of this study is to fill this knowledge gap by proposing a framework for quantifying the uncertainties in historical heavy metal pollution records reconstructed from sediment cores. This uncertainty framework consists of six sources of uncertainty: uncertainties in (1) metals analysis methods, (2) spatial variability of sediment core heavy metal profiles, (3) sub-sampling intervals, (4) the sediment chronology, (5) the assumption that metal levels in bed sediments reflect the magnitude of metal inputs into the aquatic system, and (6) post-depositional transformation of metals. We apply this uncertainty framework to an urban floodplain lake in South-East Australia (Willsmere Billabong). We find that for this site, uncertainties in historical dated heavy metal profiles can be up to 176%, largely due to uncertainties in the sediment chronology, and in the assumption that the settled heavy metal mass is equivalent to the heavy metal mass entering the aquatic system. As such, we recommend that future studies reconstructing historical pollution records using sediment cores from aquatic systems undertake an investigation of the uncertainties in the reconstructed pollution record, using the uncertainty framework provided in this study. We envisage that quantifying and understanding the uncertainties associated with the reconstructed pollution records will facilitate the practical application of sediment core heavy metal profiles in environmental management projects.

Measuring PM and related air pollutants using low-cost ...

EPA Pesticide Factsheets

Emerging air quality sensors may play a key role in better characterizing levels of air pollution in a variety of settings There are a wide range of low-cost (< $500 US) sensors on the market, but few have been characterized. If accurate, this new generation of inexpensive sensors can potentially allow larger fleets of monitors to be deployed to better study the spatial and temporal variability of pollutants. The small size and light weight of these sensors also allows for the possibility of wearable or drone applications. Sensor networks will very likely play a key role in future estimates of human health impacts of pollutants, in particular particulate matter (PM), and will allow for the better characterization of pollutant sources and source regions.We will present measurements from an assortment of sensors, costing $20-$700, that have been used to measure air pollution in the US, India, and China with a focus on estimating PM concentrations. Their performance has been evaluated in these very different settings with low concentrations seen in the US (up to approximately 20 ug m-3) and much higher concentrations measured in India and China (up to approximately 300 ug m-3). Based on these studies the optimal concentration ranges of these sensors have been determined. Used in conjunction with data from a carbon dioxide sensor, emissions factors were estimated in some of the locations. In addition temperature and humidity sensors can be used to calculate c

Applicability of Fluorescence and Absorbance Spectroscopy to Estimate Organic Pollution in Rivers

PubMed Central

Knapik, Heloise Garcia; Fernandes, Cristovão Vicente Scapulatempo; de Azevedo, Júlio Cesar Rodrigues; do Amaral Porto, Monica Ferreira

2014-01-01

Abstract This article explores the applicability of fluorescence and absorbance spectroscopy for estimating organic pollution in polluted rivers. The relationship between absorbance, fluorescence intensity, dissolved organic carbon, biochemical oxygen demand (BOD), chemical oxygen demand (COD), and other water quality parameters were used to characterize and identify the origin and the spatial variability of the organic pollution in a highly polluted watershed. Analyses were performed for the Iguassu River, located in southern Brazil, with area about 2,700 km2 and ∼3 million inhabitants. Samples were collect at six monitoring sites covering 107 km of the main river. BOD, COD, nitrogen, and phosphorus concentration indicates a high input of sewage to the river. Specific absorbance at 254 and 285 nm (SUVA254 and A285/COD) did not show significant variation between sites monitored, indicating the presence of both dissolved compounds found in domestic effluents and humic and fulvic compounds derived from allochthonous organic matter. Correlations between BOD and tryptophan-like fluorescence peak (peak T2, r=0.7560, and peak T1, r=0.6949) and tyrosine-like fluorescence peak (peak B, r=0.7321) indicated the presence of labile organic matter and thus confirmed the presence of sewage in the river. Results showed that fluorescence and absorbance spectroscopy provide useful information on pollution in rivers from critical watersheds and together are a robust method that is simpler and more rapid than traditional methods employed by regulatory agencies. PMID:25469076

Air pollution, inflammation and preterm birth in Mexico City: study design and methods.

PubMed

O'Neill, Marie S; Osornio-Vargas, Alvaro; Buxton, Miatta A; Sánchez, Brisa N; Rojas-Bracho, Leonora; Castillo-Castrejon, Marisol; Mordhukovich, Irina B; Brown, Daniel G; Vadillo-Ortega, Felipe

2013-03-15

Preterm birth is one of the leading causes of perinatal mortality and is associated with long-term adverse health consequences for surviving infants. Preterm birth rates are rising worldwide, and no effective means for prevention currently exists. Air pollution exposure may be a significant cause of prematurity, but many published studies lack the individual, clinical data needed to elucidate possible biological mechanisms mediating these epidemiological associations. This paper presents the design of a prospective study now underway to evaluate those mechanisms in a cohort of pregnant women residing in Mexico City. We address how air quality may act together with other factors to induce systemic inflammation and influence the duration of pregnancy. Data collection includes: biomarkers relevant to inflammation in cervico-vaginal exudate and peripheral blood, along with full clinical information, pro-inflammatory cytokine gene polymorphisms and air pollution data to evaluate spatial and temporal variability in air pollution exposure. Samples are collected on a monthly basis and participants are followed for the duration of pregnancy. The data will be used to evaluate whether ambient air pollution is associated with preterm birth, controlling for other risk factors. We will evaluate which time windows during pregnancy are most influential in the air pollution and preterm birth association. In addition, the epidemiological study will be complemented with a parallel toxicology invitro study, in which monocytic cells will be exposed to air particle samples to evaluate the expression of biomarkers of inflammation. Copyright © 2012 Elsevier B.V. All rights reserved.

Air pollution, inflammation and preterm birth in Mexico City: Study design and methods

PubMed Central

O’Neill, Marie S.; Osornio-Vargas, Alvaro; Buxton, Miatta A.; Sanchez, Brisa N.; Rojas-Bracho, Leonora; Castillo-Castrejon, Marisol; Mordhukovich, Irina B.; Brown, Daniel G.; Vadillo-Ortega, Felipe

2012-01-01

Preterm birth is one of the leading causes of perinatal mortality and is associated with long-term adverse health consequences for surviving infants. Preterm birth rates are rising worldwide, and no effective means for prevention currently exists. Air pollution exposure may be a significant cause of prematurity, but many published studies lack the individual, clinical data needed to elucidate possible biological mechanisms mediating these epidemiological associations. This paper presents the design of a prospective study now underway to evaluate those mechanisms in a cohort of pregnant women residing in Mexico City. We address how air quality may act together with other factors to induce systemic inflammation and influence the duration of pregnancy. Data collection includes: biomarkers relevant to inflammation in cervico-vaginal exudate and peripheral blood, along with full clinical information, pro-inflammatory cytokine gene polymorphisms and air pollution data to evaluate spatial and temporal variability in air pollution exposure. Samples are collected on a monthly basis and participants are followed for the duration of pregnancy. The data will be used to evaluate whether ambient air pollution is associated with preterm birth, controlling for other risk factors. We will evaluate which time windows during pregnancy are most influential in the air pollution and preterm birth association. In addition, the epidemiological study will be complemented with a parallel toxicology invitro study, in which monocytic cells will be exposed to air particle samples to evaluate the expression of biomarkers of inflammation. PMID:23177781

Oil spill contamination probability in the southeastern Levantine basin.

PubMed

Goldman, Ron; Biton, Eli; Brokovich, Eran; Kark, Salit; Levin, Noam

2015-02-15

Recent gas discoveries in the eastern Mediterranean Sea led to multiple operations with substantial economic interest, and with them there is a risk of oil spills and their potential environmental impacts. To examine the potential spatial distribution of this threat, we created seasonal maps of the probability of oil spill pollution reaching an area in the Israeli coastal and exclusive economic zones, given knowledge of its initial sources. We performed simulations of virtual oil spills using realistic atmospheric and oceanic conditions. The resulting maps show dominance of the alongshore northerly current, which causes the high probability areas to be stretched parallel to the coast, increasing contamination probability downstream of source points. The seasonal westerly wind forcing determines how wide the high probability areas are, and may also restrict these to a small coastal region near source points. Seasonal variability in probability distribution, oil state, and pollution time is also discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Adjoint estimation of ozone climate penalties

NASA Astrophysics Data System (ADS)

Zhao, Shunliu; Pappin, Amanda J.; Morteza Mesbah, S.; Joyce Zhang, J. Y.; MacDonald, Nicole L.; Hakami, Amir

2013-10-01

adjoint of a regional chemical transport model is used to calculate location-specific temperature influences (climate penalties) on two policy-relevant ozone metrics: concentrations in polluted regions (>65 ppb) and short-term mortality in Canada and the U.S. Temperature influences through changes in chemical reaction rates, atmospheric moisture content, and biogenic emissions exhibit significant spatial variability. In particular, high-NOx, polluted regions are prominently distinguished by substantial climate penalties (up to 6.2 ppb/K in major urban areas) as a result of large temperature influences through increased biogenic emissions and nonnegative water vapor sensitivities. Temperature influences on ozone mortality, when integrated across the domain, result in 369 excess deaths/K in Canada and the U.S. over a summer season—an impact comparable to a 5% change in anthropogenic NOx emissions. As such, we suggest that NOx control can be also regarded as a climate change adaptation strategy with regard to ozone air quality.

Modelling of human exposure to air pollution in the urban environment: a GPS-based approach.

PubMed

Dias, Daniela; Tchepel, Oxana

2014-03-01

The main objective of this work was the development of a new modelling tool for quantification of human exposure to traffic-related air pollution within distinct microenvironments by using a novel approach for trajectory analysis of the individuals. For this purpose, mobile phones with Global Positioning System technology have been used to collect daily trajectories of the individuals with higher temporal resolution and a trajectory data mining, and geo-spatial analysis algorithm was developed and implemented within a Geographical Information System to obtain time-activity patterns. These data were combined with air pollutant concentrations estimated for several microenvironments. In addition to outdoor, pollutant concentrations in distinct indoor microenvironments are characterised using a probabilistic approach. An example of the application for PM2.5 is presented and discussed. The results obtained for daily average individual exposure correspond to a mean value of 10.6 and 6.0-16.4 μg m(-3) in terms of 5th-95th percentiles. Analysis of the results shows that the use of point air quality measurements for exposure assessment will not explain the intra- and inter-variability of individuals' exposure levels. The methodology developed and implemented in this work provides time-sequence of the exposure events thus making possible association of the exposure with the individual activities and delivers main statistics on individual's air pollution exposure with high spatio-temporal resolution.

Mining-caused changes to habitat structure affect amphibian and reptile population ecology more than metal pollution.

PubMed

Sasaki, Kiyoshi; Lesbarrères, David; Watson, Glen; Litzgus, Jacqueline

2015-12-01

Emissions from smelting not only contaminate water and soil with metals, but also induce extensive forest dieback and changes in resource availability and microclimate. The relative effects of such co-occurring stressors are often unknown, but this information is imperative in developing targeted restoration strategies. We assessed the role and relative effects of structural alterations of terrestrial habitat and metal pollution caused by century-long smelting operations on amphibian and reptile communities by collecting environmental and time- and area-standardized multivariate abundance data along three spatially replicated impact gradients. Overall, species richness, diversity, and abundance declined progressively with increasing levels of metals (As, Cu, and Ni) and soil temperature (T(s)) and decreasing canopy cover, amount of coarse woody debris (CWD), and relative humidity (RH). The composite habitat variable (which included canopy cover, CWD, T(s), and RH) was more strongly associated with most response metrics than the composite metal variable (As, Cu, and Ni), and canopy cover alone explained 19-74% of the variance. Moreover, species that use terrestrial habitat for specific behaviors (e.g., hibernation, dispersal), especially forest-dependent species, were more severely affected than largely aquatic species. These results suggest that structural alterations of terrestrial habitat and concomitant changes in the resource availability and microclimate have stronger effects than metal pollution per se. Furthermore, much of the variation in response metrics was explained by the joint action of several environmental variables, implying synergistic effects (e.g., exacerbation of metal toxicity by elevated temperatures in sites with reduced canopy cover). We thus argue that the restoration of terrestrial habitat conditions is a key to successful recovery of herpetofauna communities in smelting-altered landscapes.

Urban Air Quality Modelling with AURORA: Prague and Bratislava

NASA Astrophysics Data System (ADS)

Veldeman, N.; Viaene, P.; De Ridder, K.; Peelaerts, W.; Lauwaet, D.; Muhammad, N.; Blyth, L.

2012-04-01

The European Commission, in its strategy to protect the health of the European citizens, states that in order to assess the impact of air pollution on public health, information on long-term exposure to air pollution should be available. Currently, indicators of air quality are often being generated using measured pollutant concentrations. While air quality monitoring stations data provide accurate time series information at specific locations, air quality models have the advantage of being able to assess the spatial variability of air quality (for different resolutions) and predict air quality in the future based on different scenarios. When running such air quality models at a high spatial and temporal resolution, one can simulate the actual situation as closely as possible, allowing for a detailed assessment of the risk of exposure to citizens from different pollutants. AURORA (Air quality modelling in Urban Regions using an Optimal Resolution Approach), a prognostic 3-dimensional Eulerian chemistry-transport model, is designed to simulate urban- to regional-scale atmospheric pollutant concentration and exposure fields. The AURORA model also allows to calculate the impact of changes in land use (e.g. planting of trees) or of emission reduction scenario's on air quality. AURORA is currently being applied within the ESA atmospheric GMES service, PASODOBLE (http://www.myair-eu.org), that delivers information on air quality, greenhouse gases, stratospheric ozone, … At present there are two operational AURORA services within PASODOBLE. Within the "Air quality forecast service" VITO delivers daily air quality forecasts for Belgium at a resolution of 5 km and for the major Belgian cities: Brussels, Ghent, Antwerp, Liege and Charleroi. Furthermore forecast services are provided for Prague, Czech Republic and Bratislava, Slovakia, both at a resolution of 1 km. The "Urban/regional air quality assessment service" provides urban- and regional-scale maps (hourly resolution) for air pollution and human exposure statistics for an entire year. So far we concentrated on Brussels, Belgium and the Rotterdam harbour area, The Netherlands. In this contribution we focus on the operational forecast services. Reference Lefebvre W. et al. (2011) Validation of the MIMOSA-AURORA-IFDM model chain for policy support: Modeling concentrations of elemental carbon in Flanders, Atmospheric Environment 45, 6705-6713

A Comparison of Exposure Metrics for Traffic-Related Air Pollutants: Application to Epidemiology Studies in Detroit, Michigan

PubMed Central

Batterman, Stuart; Burke, Janet; Isakov, Vlad; Lewis, Toby; Mukherjee, Bhramar; Robins, Thomas

2014-01-01

Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding of the key information and metrics needed to assess exposures, as well as the strengths and limitations of alternate exposure metrics. This study develops and evaluates several metrics for characterizing exposure to traffic-related air pollutants for the 218 residential locations of participants in the NEXUS epidemiology study conducted in Detroit (MI, USA). Exposure metrics included proximity to major roads, traffic volume, vehicle mix, traffic density, vehicle exhaust emissions density, and pollutant concentrations predicted by dispersion models. Results presented for each metric include comparisons of exposure distributions, spatial variability, intraclass correlation, concordance and discordance rates, and overall strengths and limitations. While showing some agreement, the simple categorical and proximity classifications (e.g., high diesel/low diesel traffic roads and distance from major roads) do not reflect the range and overlap of exposures seen in the other metrics. Information provided by the traffic density metric, defined as the number of kilometers traveled (VKT) per day within a 300 m buffer around each home, was reasonably consistent with the more sophisticated metrics. Dispersion modeling provided spatially- and temporally-resolved concentrations, along with apportionments that separated concentrations due to traffic emissions and other sources. While several of the exposure metrics showed broad agreement, including traffic density, emissions density and modeled concentrations, these alternatives still produced exposure classifications that differed for a substantial fraction of study participants, e.g., from 20% to 50% of homes, depending on the metric, would be incorrectly classified into “low”, “medium” or “high” traffic exposure classes. These and other results suggest the potential for exposure misclassification and the need for refined and validated exposure metrics. While data and computational demands for dispersion modeling of traffic emissions are non-trivial concerns, once established, dispersion modeling systems can provide exposure information for both on- and near-road environments that would benefit future traffic-related assessments. PMID:25226412

Overview of the Brooklyn traffic real-time ambient pollutant penetration and environmental dispersion (B-TRAPPED) study: theoretical background and model for design of field experiments.

PubMed

Hahn, Intaek; Wiener, Russell W; Richmond-Bryant, Jennifer; Brixey, Laurie A; Henkle, Stacy W

2009-12-01

The Brooklyn traffic real-time ambient pollutant penetration and environmental dispersion (B-TRAPPED) study was a multidisciplinary field research project that investigated the transport, dispersion, and infiltration processes of traffic emission particulate matter (PM) pollutants in a near-highway urban residential area. The urban PM transport, dispersion, and infiltration processes were described mathematically in a theoretical model that was constructed to develop the experimental objectives of the B-TRAPPED study. In the study, simultaneous and continuous time-series PM concentration and meteorological data collected at multiple outdoor and indoor monitoring locations were used to characterize both temporal and spatial patterns of the PM concentration movements within microscale distances (<500 m) from the highway. Objectives of the study included (1) characterizing the temporal and spatial PM concentration fluctuation and distribution patterns in the urban street canyon; (2) investigating the effects of urban structures such as a tall building or an intersection on the transport and dispersion of PM; (3) studying the influence of meteorological variables on the transport, dispersion, and infiltration processes; (4) characterizing the relationships between the building parameters and the infiltration mechanisms; (5) establishing a cause-and-effect relationship between outdoor-released PM and indoor PM concentrations and identifying the dominant mechanisms involved in the infiltration process; (6) evaluating the effectiveness of a shelter-in-place area for protection against outdoor-released PM pollutants; and (7) understanding the predominant airflow and pollutant dispersion patterns within the neighborhood using wind tunnel and CFD simulations. The 10 papers in this first set of papers presenting the results from the B-TRAPPED study address these objectives. This paper describes the theoretical background and models representing the interrelated processes of transport, dispersion, and infiltration. The theoretical solution for the relationship between the time-dependent indoor PM concentration and the initial PM concentration at the outdoor source was obtained. The theoretical models and solutions helped us to identify important parameters in the processes of transport, dispersion, and infiltration. The B-TRAPPED study field experiments were then designed to investigate these parameters in the hope of better understanding urban PM pollutant behaviors.

How much, how long, what, and where: air pollution exposure assessment for epidemiologic studies of respiratory disease.

PubMed

Brauer, Michael

2010-05-01

Epidemiology has played an important role in the understanding of air pollution as a risk factor for respiratory disease and in the evidence base for air quality standards. With the widespread availability of genetic information and increasingly sophisticated measurements of molecular markers of adverse effects, there is a need for more specific and precise assessment of exposure to maximize the potential information to be derived from epidemiologic studies. Here advances in air pollution exposure assessment and their applications to studies of respiratory disease are reviewed, with a focus on recent studies of traffic-related air pollution and asthma. Although continuous measurements of personal exposures for all study subjects for a complete study period might be considered the desired "gold standard" for exposure, this is rarely, if ever, achieved due to feasibility constraints. Given this, exposure is typically estimated using models. Recent applications of geospatial (e.g., land use regression) models to studies of respiratory disease have made possible new study designs focused on spatial variability in exposure within urban areas and have provided new insights into the potential role of traffic-related air pollution (TRAP) as a risk factor for the development of childhood asthma. Substantial uncertainty remains, however, regarding what agent(s) within TRAP might be responsible for the observed associations. Future research will require increasing the specificity of exposure assessment to identify the potential roles of individual air pollution components, to elucidate potential mechanisms, and to facilitate studies of mixtures and gene-air pollution interactions.

Hydrocarbon pollution fixed to combined sewer sediment: a case study in Paris.

PubMed

Rocher, Vincent; Garnaud, Stéphane; Moilleron, Régis; Chebbo, Ghassan

2004-02-01

Over a period of two years (2000-2001), sediment samples were extracted from 40 silt traps (STs) spread through the combined sewer system of Paris. All sediment samples were analysed for physico-chemical parameters (pH, organic matter content, grain size distribution), with total hydrocarbons (THs) and 16 polycyclic aromatic hydrocarbons (PAHs) selected from the priority list of the US-EPA. The two main objectives of the study were (1) to determine the hydrocarbon contamination levels in the sediments of the Paris combined sewer system and (2) to investigate the PAH fingerprints in order to assess their spatial variability and to elucidate the PAH origins. The results show that there is some important inter-site and intra-site variations in hydrocarbon contents. Despite this variability, TH and PAH contamination levels (50th percentile) in the Parisian sewer sediment are estimated at 530 and 18 microg g(-1), respectively. The investigation of the aromatic compound distributions in all of the 40 STs has underlined that there is, at the Paris sewer system scale, a homogeneous PAH background pollution. Moreover, the study of the PAH fingerprints, using specific ratios, suggests the predominance of a pyrolytic origin for those PAHs fixed to the sewer sediment.

Spatial and temporal variations of water quality in Cao-E River of eastern China.

PubMed

Chen, Ding-jiang; Lu, Jun; Yuan, Shao-feng; Jin, Shu-quan; Shen, Ye-na

2006-01-01

Evaluation and analysis of water quality variations were performed with integrated consideration of water quality parameters, hydrological-meteorologic and anthropogenic factors in Cao-E River, Zhejiang Province of China. Cao-E River system has been polluted and the water quality of some reaches are inferior to Grade V according to National Surface Water Quality Standard of China (GB2002). However, mainly polluted indices of each tributary and mainstream are different. Total nitrogen (TN) and total phosphorus (TP) in the water are the main polluted indices for mainstream that varies from 1.52 to 45.85 mg/L and 0.02 to 4.02 mg/L, respectively. TN is the main polluted indices for Sub-watershed I, II, IV and V (0.76 to 18.27 mg/L). BOD5 (0.36 to 289.5 mg/L), CODMn (0.47 to 78.86 mg/L), TN (0.74 to 31.09 mg/L) and TP (0 to 3.75 mg/L) are the main polluted indices for Sub-watershed III. There are tow pollution types along the river including nonpoint source pollution and point source pollution types. Remarkably temporal variations with a few spatial variations occur in nonpoint pollution type reaches (including mainstream, Sub-watershed I and II) that mainly drained by arable field and/or dispersive rural dwelling district, and the maximum pollutant concentration appears in flooding seasons. It implied that the runoff increases the pollutant concentration of the water in the nonpoint pollution type reaches. On the other hand, remarkably spatial variations occur in the point pollution type reaches (include Sub-watershed III, IV and V) and the maximum pollutant concentration appears in urban reaches. The runoff always decreases the pollutant concentration of the river water in the seriously polluted reaches that drained by industrial point sewage. But for the point pollution reaches resulted from centralized town domestic sewage pipeline and from frequent shipping and digging sands, rainfall always increased the concentration of pollutant (TN) in the river water too. Pollution controls were respectively suggested for these tow types according to different pollution causes.

[Spatio-temporal characteristics and source identification of water pollutants in Wenruitang River watershed].

PubMed

Ma, Xiao-xue; Wang, La-chun; Liao, Ling-ling

2015-01-01

Identifying the temp-spatial distribution and sources of water pollutants is of great significance for efficient water quality management pollution control in Wenruitang River watershed, China. A total of twelve water quality parameters, including temperature, pH, dissolved oxygen (DO), total nitrogen (TN), ammonia nitrogen (NH4+ -N), electrical conductivity (EC), turbidity (Turb), nitrite-N (NO2-), nitrate-N(NO3-), phosphate-P(PO4(3-), total organic carbon (TOC) and silicate (SiO3(2-)), were analyzed from September, 2008 to October, 2009. Geographic information system(GIS) and principal component analysis(PCA) were used to determine the spatial distribution and to apportion the sources of pollutants. The results demonstrated that TN, NH4+ -N, PO4(3-) were the main pollutants during flow period, wet period, dry period, respectively, which was mainly caused by urban point sources and agricultural and rural non-point sources. In spatial terms, the order of pollution was tertiary river > secondary river > primary river, while the water quality was worse in city zones than in the suburb and wetland zone regardless of the river classification. In temporal terms, the order of pollution was dry period > wet period > flow period. Population density, land use type and water transfer affected the water quality in Wenruitang River.

Spatial distribution and output characteristics of nonpoint source pollution in the Dongjiang River basin in south China

NASA Astrophysics Data System (ADS)

Rong, Q. Q.; Su, M. R.; Yang, Z. F.; Cai, Y. P.; Yue, W. C.; Dang, Z.

2018-02-01

In this research, the Dongjiang River basin was taken as the study area to analyze the spatial distribution and output characteristics of nonpoint source pollution, based on the export coefficient model. The results showed that the annual total nitrogen and phosphorus (i.e. TN and TP) loads from the Dongjiang River basin were 67916114.6 and 7215279.707 kg, respectively. Residents, forestland and pig were the main contributors for the TN load in the Dongjiang River basin, while residents, forestland and rainfed croplands were the three largest contributors for the TP load. The NPS pollution had a significant spatial variation in this area. The pollution loads overall decreased from the northeast to the southwest part of the basin. Also, the pollution loads from the gentle slope area were larger than those from steep slope areas. Among the ten tributary watersheds in the Dongjiang River basin, the TN and TP loads from the Hanxi River watershed were the largest. On the contrary, the Gongzhuang River watershed contributed least to the total pollution loads of the Dongjiang River basin. For the average pollution load intensities, Hanxi River watershed was still the largest. However, the smallest average TN and TP load intensities were in the Xinfeng River watershed.

Distributed watershed modeling of design storms to identify nonpoint source loading areas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endreny, T.A.; Wood, E.F.

1999-03-01

Watershed areas that generate nonpoint source (NPS) polluted runoff need to be identified prior to the design of basin-wide water quality projects. Current watershed-scale NPS models lack a variable source area (VSA) hydrology routine, and are therefore unable to identify spatially dynamic runoff zones. The TOPLATS model used a watertable-driven VSA hydrology routine to identify runoff zones in a 17.5 km{sup 2} agricultural watershed in central Oklahoma. Runoff areas were identified in a static modeling framework as a function of prestorm watertable depth and also in a dynamic modeling framework by simulating basin response to 2, 10, and 25 yrmore » return period 6 h design storms. Variable source area expansion occurred throughout the duration of each 6 h storm and total runoff area increased with design storm intensity. Basin-average runoff rates of 1 mm h{sup {minus}1} provided little insight into runoff extremes while the spatially distributed analysis identified saturation excess zones with runoff rates equaling effective precipitation. The intersection of agricultural landcover areas with these saturation excess runoff zones targeted the priority potential NPS runoff zones that should be validated with field visits. These intersected areas, labeled as potential NPS runoff zones, were mapped within the watershed to demonstrate spatial analysis options available in TOPLATS for managing complex distributions of watershed runoff. TOPLATS concepts in spatial saturation excess runoff modelling should be incorporated into NPS management models.« less

The Spatial-Temporal Characteristics of Air Pollution in China from 2001–2014

PubMed Central

Bao, Junzhe; Yang, Xiping; Zhao, Zhiyuan; Wang, Zhenkun; Yu, Chuanhua; Li, Xudong

2015-01-01

To provide some useful information about the control of air pollution in China, we studied the spatial-temporal characteristics of air pollution in China from 2001–2014. First, we drew several line charts and histograms of the Air Pollution Index (API) and Air Quality Index (AQI) of 31 capital cities and municipalities to research the distribution across different times and cities; then, we researched the spatial clustering of API and AQI; finally, we examined the shift of the gravity center of API and AQI in different years and months. The API values had a decreasing trend: the high values had a clustering trend in some northern cities, and the low values had a clustering trend in some southern cities. The AQI values were relatively low, from 15:00–17:00 during the day. The gravity center of API had a trend of moving south from 2001–2003, then fluctuated in an unordered pattern and moved north in the winter. The AQI gravity center did not have a regular shift during different months. In conclusion, the government should take action to mitigate air pollution in some typical cities, as well as air pollution during the winter. PMID:26694427

Determination of Spatial Distribution of Air Pollution by Dye Laser Measurement of Differential Absorption of Elastic Backscatter

NASA Technical Reports Server (NTRS)

Ahmed, S. A.; Gergely, J. S.

1973-01-01

This paper presents the results of an analytical study of a lidar system which uses tunable organic dye lasers to accurately determine spatial distribution of molecular air pollutants. Also described will be experimental work to date on simultaneous multiwavelength output dye laser sources for this system. Basically the scheme determines the concentration of air pollutants by measuring the differential absorption of an (at least) two wavelength lidar signal elastically backscattered by the atmosphere. Only relative measurements of the backscattered intensity at each of the two wavelengths, one on and one off the resonance absorption of the pollutant in question, are required. The various parameters of the scheme are examined and the component elements required for a system of this type discussed, with emphasis on the dye laser source. Potential advantages of simultaneous multiwavelength outputs are described. The use of correlation spectroscopy in this context is examined. Comparisons are also made for the use of infrared probing wavelengths and sources instead of dye lasers. Estimates of the sensitivity and accuracy of a practical dye laser system of this type, made for specific pollutants, snow it to have inherent advantages over other schemes for determining pollutant spatial distribution.

Impact of temporal upscaling and chemical transport model horizontal resolution on reducing ozone exposure misclassification

NASA Astrophysics Data System (ADS)

Xu, Yadong; Serre, Marc L.; Reyes, Jeanette M.; Vizuete, William

2017-10-01

We have developed a Bayesian Maximum Entropy (BME) framework that integrates observations from a surface monitoring network and predictions from a Chemical Transport Model (CTM) to create improved exposure estimates that can be resolved into any spatial and temporal resolution. The flexibility of the framework allows for input of data in any choice of time scales and CTM predictions of any spatial resolution with varying associated degrees of estimation error and cost in terms of implementation and computation. This study quantifies the impact on exposure estimation error due to these choices by first comparing estimations errors when BME relied on ozone concentration data either as an hourly average, the daily maximum 8-h average (DM8A), or the daily 24-h average (D24A). Our analysis found that the use of DM8A and D24A data, although less computationally intensive, reduced estimation error more when compared to the use of hourly data. This was primarily due to the poorer CTM model performance in the hourly average predicted ozone. Our second analysis compared spatial variability and estimation errors when BME relied on CTM predictions with a grid cell resolution of 12 × 12 km2 versus a coarser resolution of 36 × 36 km2. Our analysis found that integrating the finer grid resolution CTM predictions not only reduced estimation error, but also increased the spatial variability in daily ozone estimates by 5 times. This improvement was due to the improved spatial gradients and model performance found in the finer resolved CTM simulation. The integration of observational and model predictions that is permitted in a BME framework continues to be a powerful approach for improving exposure estimates of ambient air pollution. The results of this analysis demonstrate the importance of also understanding model performance variability and its implications on exposure error.

Spatial patterning in PM2.5 constituents under an inversion-focused sampling design across an urban area of complex terrain

PubMed Central

Tunno, Brett J; Dalton, Rebecca; Michanowicz, Drew R; Shmool, Jessie L C; Kinnee, Ellen; Tripathy, Sheila; Cambal, Leah; Clougherty, Jane E

2016-01-01

Health effects of fine particulate matter (PM2.5) vary by chemical composition, and composition can help to identify key PM2.5 sources across urban areas. Further, this intra-urban spatial variation in concentrations and composition may vary with meteorological conditions (e.g., mixing height). Accordingly, we hypothesized that spatial sampling during atmospheric inversions would help to better identify localized source effects, and reveal more distinct spatial patterns in key constituents. We designed a 2-year monitoring campaign to capture fine-scale intra-urban variability in PM2.5 composition across Pittsburgh, PA, and compared both spatial patterns and source effects during “frequent inversion” hours vs 24-h weeklong averages. Using spatially distributed programmable monitors, and a geographic information systems (GIS)-based design, we collected PM2.5 samples across 37 sampling locations per year to capture variation in local pollution sources (e.g., proximity to industry, traffic density) and terrain (e.g., elevation). We used inductively coupled plasma mass spectrometry (ICP-MS) to determine elemental composition, and unconstrained factor analysis to identify source suites by sampling scheme and season. We examined spatial patterning in source factors using land use regression (LUR), wherein GIS-based source indicators served to corroborate factor interpretations. Under both summer sampling regimes, and for winter inversion-focused sampling, we identified six source factors, characterized by tracers associated with brake and tire wear, steel-making, soil and road dust, coal, diesel exhaust, and vehicular emissions. For winter 24-h samples, four factors suggested traffic/fuel oil, traffic emissions, coal/industry, and steel-making sources. In LURs, as hypothesized, GIS-based source terms better explained spatial variability in inversion-focused samples, including a greater contribution from roadway, steel, and coal-related sources. Factor analysis produced source-related constituent suites under both sampling designs, though factors were more distinct under inversion-focused sampling. PMID:26507005

Environmental justice implications of industrial hazardous waste generation in India: a national scale analysis

NASA Astrophysics Data System (ADS)

Basu, Pratyusha; Chakraborty, Jayajit

2016-12-01

While rising air and water pollution have become issues of widespread public concern in India, the relationship between spatial distribution of environmental pollution and social disadvantage has received less attention. This lack of attention becomes particularly relevant in the context of industrial pollution, as India continues to pursue industrial development policies without sufficient regard to its adverse social impacts. This letter examines industrial pollution in India from an environmental justice (EJ) perspective by presenting a national scale study of social inequities in the distribution of industrial hazardous waste generation. Our analysis connects district-level data from the 2009 National Inventory of Hazardous Waste Generating Industries with variables representing urbanization, social disadvantage, and socioeconomic status from the 2011 Census of India. Our results indicate that more urbanized and densely populated districts with a higher proportion of socially and economically disadvantaged residents are significantly more likely to generate hazardous waste. The quantity of hazardous waste generated is significantly higher in more urbanized but sparsely populated districts with a higher proportion of economically disadvantaged households, after accounting for other relevant explanatory factors such as literacy and social disadvantage. These findings underscore the growing need to incorporate EJ considerations in future industrial development and waste management in India.

Spatio-temporal distribution of absorbing and non-absorbing aerosols derived from Aura-OMI Aerosol Index over Greece

NASA Astrophysics Data System (ADS)

Kaskaoutis, D. G.; Nastos, P. T.; Kosmopoulos, P. G.; Kambezidis, H. D.; Kharol, S. K.; Badarinath, K. V. S.

2009-04-01

The Aerosol Index (AI) observations derived from the Ozone Monitoring Instrument (OMI) on board the Dutch-Finnish Aura satellite are analyzed over Greece covering the whole period of the OMI available data, from September 2004 to August 2008. The objective of this study was to analyze the spatial, seasonal and inter-annual variability of AI over Greece, detected by OMI during 2004-2008, with an evaluation of potential contributing factors, including precipitation and long-range transport (Sahara dust and European pollution). The AI data cover the whole Greek territory (34o-42oN, 20o-28oE) with a spatial resolution of 0.25o x 0.25o (13 km x 24 km at nadir). The results show significant spatial and temporal variability of the seasonal and monthly mean AI, with higher values at the southern parts and lower values over northern Greece. On the other hand, the AI values do not show significant differences between the western and eastern parts and, therefore, the longitude-averaged AI values can be utilized to reveal the strong south-to-north gradient. This gradient significantly changes from season to season being more intense in spring and summer, while it is minimized in winter. Another significant remark is the dominance of negative AI values over northern Greece in the summer months, indicating the presence of non-UV absorbing aerosols, such as sulfate and sea-salt particles. The great geographical extent of the negative AI values in the summer months is indicative of long-range transport of such aerosols. In contrast, the high positive AI values over south Greece, mainly in spring, clearly reveal the UV-absorbing nature of desert-dust particles affecting the area during Saharan dust events. Synoptically, the spatial distribution in OMI-AI values was related to the Saharan dust events mainly over southern Greece and to the trans-boundary-pollution transport, consisting mainly of sulfate particles, in northern Greece. The annual variation of spatial-averaged AI values shows a predominant spring maximum (0.424±0.329, in April) and a summer minimum due to the negative AI values observed over northern Greece. In the cold period of the year (November to February) the AI values are higher over northern Greece compared to those in south, while in the rest of the year the opposite exists. This study is first of its kind utilizing OMI-AI data and its spatial and temporal distribution over Greece and can be the basis for other studies in the future.

Riparian spiders as sentinels of polychlorinated biphenyl contamination across heterogeneous aquatic ecosystems

USGS Publications Warehouse

Kraus, Johanna M.; Gibson, Polly P.; Walters, David M.; Mills, Marc A.

2017-01-01

Riparian spiders are being used increasingly to track spatial patterns of contaminants in and fluxing from aquatic ecosystems.However, our understanding of the circumstances under which spiders are effective sentinels of aquatic pollution is limited. The present study tests the hypothesis that riparian spiders may be effectively used to track spatial patterns of sediment pollution by polychlorinated biphenyls (PCBs) in aquatic ecosystems with high habitat heterogeneity. The spatial pattern of ΣPCB concentrations in 2 common families of riparian spiders sampled in 2011 to 2013 generally tracked spatial variation in sediment ΣPCBs across all sites within the Manistique River Great Lakes Area of Concern (AOC), a rivermouth ecosystem located on the south shore of the Upper Peninsula, Manistique (MI,USA) that includes harbor, river, backwater, and lake habitats. Sediment ΣPCB concentrations normalized for total organic carbon explained 41% of the variation in lipid-normalized spider ΣPCB concentrations across 11 sites. Furthermore, 2 common riparian spider taxa (Araneidae and Tetragnathidae) were highly correlated (r2> 0.78) and had similar mean ΣPCB concentrations when averaged acrossall years. The results indicate that riparian spiders may be useful sentinels of relative PCB availability to aquatic and riparian food webs in heterogeneous aquatic ecosystems like rivermouths where habitat and contaminant variability may make the use of aquatic taxa lesseffective. Furthermore, the present approach appears robust to heterogeneity in shoreline development and riparian vegetation that support different families of large web-building spiders. Environ Toxicol Chem 2016;9999:1–9. Published 2016 Wiley Periodicals, Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

Spatially resolved chemical imaging of individual atmospheric particles using nanoscale imaging mass spectrometry: insight into particle origin and chemistry

DOE PAGES

Ghosal, Sutapa; Weber, Peter K.; Laskin, Alexander

2014-01-14

Knowledge of the spatially resolved composition of atmospheric particles is essential for differentiating between their surface versus bulk chemistry and understanding particle reactivity and the potential environmental impact. Here, we demonstrate the application of nanometer-scale secondary ion mass spectrometry (CAMECA NanoSIMS 50 ion probe) for 3D chemical imaging of individual atmospheric particles without any sample pre-treatment, such as sectioning of particles. Use of NanoSIMS depth profile analysis enables elemental mapping of particles with nanometer spatial resolution over a broad range of particle sizes. We have used this technique to probe the spatially resolved composition of ambient particles collected during amore » field campaign in Mexico City. Particles collected during this campaign have been extensively characterized in the past using other particle analysis techniques and hence offer a unique opportunity for exploring the utility of depth-resolved chemical imaging in ambient particle research. The particles that we examined in our study include those collected during a pollution episode related to urban waste incineration as well as background particles from the same location before the episode. Particles from the pollution episode show substantial intra-particle compositional variability typical of particles resulting from multiple emission sources. In contrast, the background particles have relatively homogeneous compositions with enhanced presence of nitrogen, oxygen, and chlorine at the particle surface. We also observed the surface enhancement of nitrogen and oxygen species is consistent with the presence of surface nitrates resulting from gas–particle heterogeneous interactions and is indicative of atmospheric ageing of the particles. The results presented here illustrate 3D characterization of ambient particles for insight into their chemical history.« less

Spatially resolved chemical imaging of individual atmospheric particles using nanoscale imaging mass spectrometry: Insighs into particle origin and chemistry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghosal, Sutapa; Weber, Peter K.; Laskin, Alexander

2014-04-21

Knowledge of the spatially-resolved composition of atmospheric particles is essential for differentiating between their surface versus bulk chemistry, understanding particle reactivity and the potential environmental impact. We demonstrate the application of nanometer-scale secondary ion mass spectrometry (Cameca NanoSIMS 50 ion probe) for 3D chemical imaging of individual atmospheric particles without any sample pre-treatment, such as the sectioning of particles. Use of NanoSIMS depth profile analysis enables elemental mapping of particles with nanometer spatial resolution over a broad of range of particle sizes. We have used this technique to probe spatially resolved composition of ambient particles collected during a field campaignmore » in Mexico City. Particles collected during this campaign have been extensively characterized in the past using other particle analysis techniques and hence offer a unique opportunity for exploring the utility of depth resolved chemical imaging in ambient particle research. 1 Particles examined in this study include those collected during a pollution episode related to urban waste incineration as well as background particles from the same location prior to the episode. Particles from the pollution episode show substantial intra-particle compositional variability typical of particles resulting from multiple emission sources. In contrast, the background particles have relatively homogeneous compositions with enhanced presence of nitrogen, oxygen and chlorine at the particle surface. The observed surface enhancement of nitrogen and oxygen species is consistent with the presence of surface nitrates resulting from gas-particle heterogeneous interactions and is indicative of atmospheric ageing of the particles. The results presented here illustrate 3D characterization of ambient particles for insights into their chemical history.« less

Challenges and opportunities to improve understanding on wetland ecosystem and function at the local catchment scale: data fusion, data-model integration, and prediction uncertainty.

NASA Astrophysics Data System (ADS)

Yeo, I. Y.

2016-12-01

Wetlands are valuable landscape features that provide important ecosystem functions and services. The ecosystem processes in wetlands are highly dependent on the hydrology. However, hydroperiod (i.e., change dynamics in inundation extent) is highly variable spatially and temporarily, and extremely difficult to predict owing to the complexity in hydrological processes within wetlands and its interaction with surrounding areas. This study reports the challenges and progress in assessing the catchment scale benefits of wetlands to regulate hydrological regime and water quality improvement in agricultural watershed. A process-based watershed model, Soil and Water Assessment Tool (SWAT) was improved to simulate the cumulative impacts of wetlands on downstream. Newly developed remote sensing products from LiDAR intensity and time series Landsat records, which show the inter-annual changes in fraction inundation, were utilized to describe the change status of inundated areas within forested wetlands, develop spatially varying wetland parameters, and evaluate the predicted inundated areas at the landscape level. We outline the challenges on developing the time series inundation mapping products at a high spatial and temporal resolution and reconciling the catchment scale model with the moderate remote sensing products. We then highlight the importance of integrating spatialized information to model calibration and evaluation to address the issues of equi-finality and prediction uncertainty. This integrated approach was applied to the upper region of Choptank River Watershed, the agricultural watershed in the Coastal Plain of Chesapeake Bay Watershed (in US). In the Mid- Atlantic US, the provision of pollution regulation services provided by wetlands has been emphasized due to declining water quality within the Chesapeake Bay and watersheds, and the preservation and restoration of wetlands has become the top priority to manage nonpoint source water pollution.

Understanding the Patterns and Drivers of Air Pollution on Multiple Time Scales: The Case of Northern China.

PubMed

Liu, Yupeng; Wu, Jianguo; Yu, Deyong; Hao, Ruifang

2018-06-01

China's rapid economic growth during the past three decades has resulted in a number of environmental problems, including the deterioration of air quality. It is necessary to better understand how the spatial pattern of air pollutants varies with time scales and what drive these changes. To address these questions, this study focused on one of the most heavily air-polluted areas in North China. We first quantified the spatial pattern of air pollution, and then systematically examined the relationships of air pollution to several socioeconomic and climatic factors using the constraint line method, correlation analysis, and stepwise regression on decadal, annual, and seasonal scales. Our results indicate that PM 2.5 was the dominant air pollutant in the Beijing-Tianjin-Hebei region, while PM 2.5 and PM 10 were both important pollutants in the Agro-pastoral Transitional Zone (APTZ) region. Our statistical analyses suggest that energy consumption and gross domestic product (GDP) in the industry were the most important factors for air pollution on the decadal scale, but the impacts of climatic factors could also be significant. On the annual and seasonal scales, high wind speed, low relative humidity, and long sunshine duration constrained PM 2.5 accumulation; low wind speed and high relative humidity constrained PM 10 accumulation; and short sunshine duration and high wind speed constrained O 3 accumulation. Our study showed that analyses on multiple temporal scales are not only necessary to determine key drivers of air pollution, but also insightful for understanding the spatial patterns of air pollution, which was important for urban planning and air pollution control.

Understanding the Patterns and Drivers of Air Pollution on Multiple Time Scales: The Case of Northern China

NASA Astrophysics Data System (ADS)

Liu, Yupeng; Wu, Jianguo; Yu, Deyong; Hao, Ruifang

2018-06-01

China's rapid economic growth during the past three decades has resulted in a number of environmental problems, including the deterioration of air quality. It is necessary to better understand how the spatial pattern of air pollutants varies with time scales and what drive these changes. To address these questions, this study focused on one of the most heavily air-polluted areas in North China. We first quantified the spatial pattern of air pollution, and then systematically examined the relationships of air pollution to several socioeconomic and climatic factors using the constraint line method, correlation analysis, and stepwise regression on decadal, annual, and seasonal scales. Our results indicate that PM2.5 was the dominant air pollutant in the Beijing-Tianjin-Hebei region, while PM2.5 and PM10 were both important pollutants in the Agro-pastoral Transitional Zone (APTZ) region. Our statistical analyses suggest that energy consumption and gross domestic product (GDP) in the industry were the most important factors for air pollution on the decadal scale, but the impacts of climatic factors could also be significant. On the annual and seasonal scales, high wind speed, low relative humidity, and long sunshine duration constrained PM2.5 accumulation; low wind speed and high relative humidity constrained PM10 accumulation; and short sunshine duration and high wind speed constrained O3 accumulation. Our study showed that analyses on multiple temporal scales are not only necessary to determine key drivers of air pollution, but also insightful for understanding the spatial patterns of air pollution, which was important for urban planning and air pollution control.

A dynamic urban air pollution population exposure assessment study using model and population density data derived by mobile phone traffic

NASA Astrophysics Data System (ADS)

Gariazzo, Claudio; Pelliccioni, Armando; Bolignano, Andrea

2016-04-01

A dynamic city-wide air pollution exposure assessment study has been carried out for the urban population of Rome, Italy, by using time resolved population distribution maps, derived by mobile phone traffic data, and modelled air pollutants (NO2, O3 and PM2.5) concentrations obtained by an integrated air dispersion modelling system. More than a million of persons were tracked during two months (March and April 2015) for their position within the city and its surroundings areas, with a time resolution of 15 min and mapped over an irregular grid system with a minimum resolution of 0.26 × 0.34 Km2. In addition, demographics information (as gender and age ranges) were available in a separated dataset not connected with the total population one. Such BigData were matched in time and space with air pollution model results and then used to produce hourly and daily resolved cumulative population exposures during the studied period. A significant mobility of population was identified with higher population densities in downtown areas during daytime increasing of up to 1000 people/Km2 with respect to nigh-time one, likely produced by commuters, tourists and working age population. Strong variability (up to ±50% for NO2) of population exposures were detected as an effect of both mobility and time/spatial changing in pollutants concentrations. A comparison with the correspondent stationary approach based on National Census data, allows detecting the inability of latter in estimating the actual variability of population exposure. Significant underestimations of the amount of population exposed to daily PM2.5 WHO guideline was identified for the Census approach. Very small differences (up to a few μg/m3) on exposure were detected for gender and age ranges population classes.

Trans-Pacific transport and evolution of aerosols: evaluation of quasi-global WRF-Chem simulation with multiple observations

NASA Astrophysics Data System (ADS)

Hu, Zhiyuan; Zhao, Chun; Huang, Jianping; Leung, L. Ruby; Qian, Yun; Yu, Hongbin; Huang, Lei; Kalashnikova, Olga V.

2016-05-01

A fully coupled meteorology-chemistry model (WRF-Chem, the Weather Research and Forecasting model coupled with chemistry) has been configured to conduct quasi-global simulation for 5 years (2010-2014) and evaluated with multiple observation data sets for the first time. The evaluation focuses on the simulation over the trans-Pacific transport region using various reanalysis and observational data sets for meteorological fields and aerosol properties. The simulation generally captures the overall spatial and seasonal variability of satellite retrieved aerosol optical depth (AOD) and absorbing AOD (AAOD) over the Pacific that is determined by the outflow of pollutants and dust and the emissions of marine aerosols. The assessment of simulated extinction Ångström exponent (EAE) indicates that the model generally reproduces the variability of aerosol size distributions as seen by satellites. In addition, the vertical profile of aerosol extinction and its seasonality over the Pacific are also well simulated. The difference between the simulation and satellite retrievals can be mainly attributed to model biases in estimating marine aerosol emissions as well as the satellite sampling and retrieval uncertainties. Compared with the surface measurements over the western USA, the model reasonably simulates the observed magnitude and seasonality of dust, sulfate, and nitrate surface concentrations, but significantly underestimates the peak surface concentrations of carbonaceous aerosol likely due to model biases in the spatial and temporal variability of biomass burning emissions and secondary organic aerosol (SOA) production. A sensitivity simulation shows that the trans-Pacific transported dust, sulfate, and nitrate can make significant contribution to surface concentrations over the rural areas of the western USA, while the peaks of carbonaceous aerosol surface concentrations are dominated by the North American emissions. Both the retrievals and simulation show small interannual variability of aerosol characteristics for 2010-2014 averaged over three Pacific sub-regions. The evaluation in this study demonstrates that the WRF-Chem quasi-global simulation can be used for investigating trans-Pacific transport of aerosols and providing reasonable inflow chemical boundaries for the western USA, allowing one to further understand the impact of transported pollutants on the regional air quality and climate with high-resolution nested regional modeling.

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

PubMed Central

Mahara, Gehendra; Wang, Chao; Yang, Kun; Chen, Sipeng; Guo, Jin; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-01-01

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R2 = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R2 = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R2 = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention. PMID:27827946

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models.

PubMed

Mahara, Gehendra; Wang, Chao; Yang, Kun; Chen, Sipeng; Guo, Jin; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-11-04

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran's I statistic and Anselin's local Moran's I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R² = 0.0741, log likelihood = -1819.69, AIC = 3665.38), SLM (R² = 0.0786, log likelihood = -1819.04, AIC = 3665.08) and SEM (R² = 0.0743, log likelihood = -1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide ( p = 0.027), rainfall ( p = 0.036) and sunshine hour ( p = 0.048), while the relative humidity ( p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention.

A pilot study of gaseous pollutants' measurement (NO2, SO2, NH3, HNO3 and O3) in Abidjan, Côte d'Ivoire: contribution to an overview of gaseous pollution in African cities

NASA Astrophysics Data System (ADS)

Bahino, Julien; Yoboué, Véronique; Galy-Lacaux, Corinne; Adon, Marcellin; Akpo, Aristide; Keita, Sékou; Liousse, Cathy; Gardrat, Eric; Chiron, Christelle; Ossohou, Money; Gnamien, Sylvain; Djossou, Julien

2018-04-01

This work is part of the DACCIWA FP7 project (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) in the framework of the Work Package 2 Air Pollution and Health. This study aims to characterize urban air pollution levels through the measurement of NO2, SO2, NH3, HNO3 and O3 in Abidjan, the economic capital of Côte d'Ivoire. Measurements of inorganic gaseous pollutants, i.e. NO2, SO2, NH3, HNO3 and O3 were performed in Abidjan during an intensive campaign within the dry season (15 December 2015 to 16 February 2016), using INDAAF (International Network to study Deposition and Atmospheric chemistry in AFrica) passive samplers exposed in duplicate for 2-week periods. Twenty-one sites were selected in the district of Abidjan to be representative of various anthropogenic and natural sources of air pollution in the city. Results from this intensive campaign show that gas concentrations are strongly linked to surrounding pollution sources and show a high spatial variability. Also, NH3, NO2 and O3 gases were present at relatively higher concentrations at all the sites. NH3 average concentrations varied between 9.1 ± 1.7 ppb at a suburban site and 102.1 ± 9.1 ppb at a domestic fires site. NO2 mean concentration varied from 2.7 ± 0.1 ppb at a suburban site to 25.0 ± 1.7 ppb at an industrial site. Moreover, we measured the highest O3 concentration at the two coastal sites of Gonzagueville and Félix-Houphouët-Boigny International Airport located in the southeast of the city, with average concentrations of 19.1 ± 1.7 and 18.8 ± 3.0 ppb, respectively. The SO2 average concentration never exceeded 7.2 ± 1.2 ppb over all the sites, with 71.5 % of the sampling sites showing concentrations ranging between 0.4 and 1.9 ppb. The HNO3 average concentration ranged between 0.2 and 1.4 ppb. All these results were combined with meteorological parameters to provide the first mapping of gaseous pollutants on the scale of the district of Abidjan using geostatistical analysis (ArcGIS software). Spatial distribution results emphasize the importance of the domestic fires source and the significant impact of the traffic emissions on the scale of the city. In addition, in this work we propose a first overview of gaseous SO2 and NO2 concentrations on the scale of several African cities by comparing literature to our values. The daily SO2 standard of World Health Organization (WHO) is exceeded in most of the cities reported in the overview, with concentrations ranging from 0.2 to 3662 µg m-3. Annual NO2 concentrations ranged from 2 to 175 µg m-3, which are lower than the WHO threshold. As a conclusion, this study constitutes an original database to characterize urban air pollution and a first attempt towards presenting a spatial distribution of the pollution levels at the scale of the metropolis of Abidjan. This work should draw the attention of the African public authorities to the necessity of building an air quality monitoring network in order to (1) to define national standards and to better control the pollutants emissions and (2) to investigate the impact on the health of the growing population in developing African countries.

Estimating the acute health effects of coarse particulate matter accounting for exposure measurement error.

PubMed

Chang, Howard H; Peng, Roger D; Dominici, Francesca

2011-10-01

In air pollution epidemiology, there is a growing interest in estimating the health effects of coarse particulate matter (PM) with aerodynamic diameter between 2.5 and 10 μm. Coarse PM concentrations can exhibit considerable spatial heterogeneity because the particles travel shorter distances and do not remain suspended in the atmosphere for an extended period of time. In this paper, we develop a modeling approach for estimating the short-term effects of air pollution in time series analysis when the ambient concentrations vary spatially within the study region. Specifically, our approach quantifies the error in the exposure variable by characterizing, on any given day, the disagreement in ambient concentrations measured across monitoring stations. This is accomplished by viewing monitor-level measurements as error-prone repeated measurements of the unobserved population average exposure. Inference is carried out in a Bayesian framework to fully account for uncertainty in the estimation of model parameters. Finally, by using different exposure indicators, we investigate the sensitivity of the association between coarse PM and daily hospital admissions based on a recent national multisite time series analysis. Among Medicare enrollees from 59 US counties between the period 1999 and 2005, we find a consistent positive association between coarse PM and same-day admission for cardiovascular diseases.

Climate-based archetypes for the environmental fate assessment of chemicals.

PubMed

Ciuffo, Biagio; Sala, Serenella

2013-11-15

Emissions of chemicals have been on the rise for years, and their impacts are greatly influenced by spatial differentiation. Chemicals are usually emitted locally but their impact can be felt both locally and globally, due to their chemical properties and persistence. The variability of environmental parameters in the emission compartment may affect the chemicals' fate and the exposure at different orders of magnitude. The assessment of the environmental fate of chemicals and the inherent spatial differentiation requires the use of multimedia models at various levels of complexity (from a simple box model to complex computational and high-spatial-resolution models). The objective of these models is to support ecological and human health risk assessment, by reducing the uncertainty of chemical impact assessments. The parameterisation of spatially resolved multimedia models is usually based on scenarios of evaluative environments, or on geographical resolutions related to administrative boundaries (e.g. countries/continents) or landscape areas (e.g. watersheds, eco-regions). The choice of the most appropriate scale and scenario is important from a management perspective, as a balance should be reached between a simplified approach and computationally intensive multimedia models. In this paper, which aims to go beyond the more traditional approach based on scale/resolution (cell, country, and basin), we propose and assess climate-based archetypes for the impact assessment of chemicals released in air. We define the archetypes based on the main drivers of spatial variability, which we systematically identify by adopting global sensitivity analysis techniques. A case study that uses the high resolution multimedia model MAPPE (Multimedia Assessment of Pollutant Pathways in the Environment) is presented. Results of the analysis showed that suitable archetypes should be both climate- and chemical-specific, as different chemicals (or groups of them) have different traits that influence their spatial variability. This hypothesis was tested by comparing the variability of the output of MAPPE for four different climatic zones on four different continents for four different chemicals (which represent different combinations of physical and chemical properties). Results showed the high suitability of climate-based archetypes in assessing the impacts of chemicals released in air. However, further research work is still necessary to test these findings. Copyright © 2013 Elsevier Ltd. All rights reserved.

Modeling the relationship between landscape characteristics and water quality in a typical highly intensive agricultural small watershed, Dongting lake basin, south central China.

PubMed

Li, Hongqing; Liu, Liming; Ji, Xiang

2015-03-01

Understanding the relationship between landscape characteristics and water quality is critically important for estimating pollution potential and reducing pollution risk. Therefore, this study examines the relationship between landscape characteristics and water quality at both spatial and temporal scales. The study took place in the Jinjing River watershed in 2010; seven landscape types and four water quality pollutions were chosen as analysis parameters. Three different buffer areas along the river were drawn to analyze the relationship as a function of spatial scale. The results of a Pearson's correlation coefficient analysis suggest that "source" landscape, namely, tea gardens, residential areas, and paddy lands, have positive effects on water quality parameters, while forests exhibit a negative influence on water quality parameters because they represent a "sink" landscape and the sub-watershed level is identified as a suitable scale. Using the principal component analysis, tea gardens, residential areas, paddy lands, and forests were identified as the main landscape index. A stepwise multiple regression analysis was employed to model the relationship between landscape characteristics and water quality for each season. The results demonstrate that both landscape composition and configuration affect water quality. In summer and winter, the landscape metrics explained approximately 80.7 % of the variance in the water quality variables, which was higher than that for spring and fall (60.3 %). This study can help environmental managers to understand the relationships between landscapes and water quality and provide landscape ecological approaches for water quality control and land use management.

Characterization of Dust Properties at the Source Region During ACE-Asia

NASA Technical Reports Server (NTRS)

Tsay, Si-Chee; Lau, William (Technical Monitor)

2001-01-01

ACE (Aerosol Characterization Experiment)-Asia is designed to study the compelling variability in spatial and temporal scale of both pollution-derived and naturally-occurring aerosols, which often exist in high concentrations over eastern Asia and along the rim of the western Pacific. The phase-I of ACE-Asia was conducted from March-May 2001 in the vicinity of the Gobi desert, east coast of China, Yellow Sea, Korea, and Japan, along the pathway of Kosa (severe events that blanket East Asia with yellow desert dust, peaked in the Spring season). Asian dust typically originates in desert areas far from polluted urban regions. During transport, dust layers can interact with anthropogenic sulfate and soot aerosols from heavily polluted urban areas. Added to the complex effects of clouds and natural marine aerosols, dust particles reaching the marine environment can have drastically different properties than those from the source. Thus, understanding the unique temporal and spatial variations of Asian dust is of special importance in regional-to-global climate issues such as radiative forcing, the hydrological cycle, and primary biological productivity in the mid-Pacific Ocean. During ACE-Asia we have measured continuously aerosol optical/radiative properties, column precipitable water amount, and surface reflectivity over homogeneous areas from surface. The inclusion of flux measurements permits the determination of dust aerosol radiative flux in addition to measurements of loading and optical thickness. At the time of the Terra/MODIS overpass, these ground-based observations can provide valuable data to compare with MODIS retrievals over land. Preliminary results will be presented and discussed their implications in regional climatic effects.

Use of surrogate indicators for the evaluation of potential health risks due to poor urban water quality: A Bayesian Network approach.

PubMed

Wijesiri, Buddhi; Deilami, Kaveh; McGree, James; Goonetilleke, Ashantha

2018-02-01

Urban water pollution poses risks of waterborne infectious diseases. Therefore, in order to improve urban liveability, effective pollution mitigation strategies are required underpinned by predictions generated using water quality models. However, the lack of reliability in current modelling practices detrimentally impacts planning and management decision making. This research study adopted a novel approach in the form of Bayesian Networks to model urban water quality to better investigate the factors that influence risks to human health. The application of Bayesian Networks was found to enhance the integration of quantitative and qualitative spatially distributed data for analysing the influence of environmental and anthropogenic factors using three surrogate indicators of human health risk, namely, turbidity, total nitrogen and fats/oils. Expert knowledge was found to be of critical importance in assessing the interdependent relationships between health risk indicators and influential factors. The spatial variability maps of health risk indicators developed enabled the initial identification of high risk areas in which flooding was found to be the most significant influential factor in relation to human health risk. Surprisingly, population density was found to be less significant in influencing health risk indicators. These high risk areas in turn can be subjected to more in-depth investigations instead of the entire region, saving time and resources. It was evident that decision making in relation to the design of pollution mitigation strategies needs to account for the impact of landscape characteristics on water quality, which can be related to risk to human health. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evolution of the spatiotemporal pattern of PM2.5 concentrations in China - A case study from the Beijing-Tianjin-Hebei region

NASA Astrophysics Data System (ADS)

Yan, Dan; Lei, Yalin; Shi, Yukun; Zhu, Qing; Li, Li; Zhang, Zhien

2018-06-01

Atmospheric haze pollution has become a global concern because of its severe effects on human health and the environment. The Beijing-Tianjin-Hebei urban agglomeration is located in northern China, and its haze is the most serious in China. The high concentration of PM2.5 is the main cause of haze pollution, and thus investigating the temporal and spatial characteristics of PM2.5 is important for understanding the mechanisms underlying PM2.5 pollution and for preventing haze. In this study, the PM2.5 concentration status in 13 cities from the Beijing-Tianjin-Hebei region was statistically analyzed from January 2016 to November 2016, and the spatial variation of PM2.5 was explored via spatial autocorrelation analysis. The research yielded three overall results. (1) The distribution of PM2.5 concentrations in this area varied greatly during the study period. The concentrations increased from late autumn to early winter, and the spatial range expanded from southeast to northwest. In contrast, the PM2.5 concentration decreased rapidly from late winter to early spring, and the spatial range narrowed from northwest to southeast. (2) The spatial dependence degree, by season from high to low, was in the order winter, autumn, spring, summer. Winter (from December to February of the subsequent year) and summer (from June to August) were, respectively, the highest and lowest seasons with regard to the spatial homogeneity of PM2.5 concentrations. (3) The PM2.5 concentration in the Beijing-Tianjin-Hebei region has significant spatial spillovers. Overall, cities far from Bohai Bay, such as Shijiazhuang and Hengshui, demonstrated a high-high concentration of PM2.5 pollution, while coastal cities, such as Chengde and Qinhuangdao, showed a low-low concentration.

Spatial distribution of soil cadmium and its influencing factors in peri-urban farmland: a case study in the Jingyang District, Sichuan, China.

PubMed

Li, Bing; Xiao, Rui; Wang, Changquan; Cao, Linhai; Zhang, Yi; Zheng, Shunqiang; Yang, Lan; Guo, Yong

2017-01-01

Semi-agricultural ecosystems in peri-urban areas are susceptible to contamination. The spatial distribution and influencing factors of such pollution are unclear and poorly constrained in many areas worldwide. Therefore, studying the problems of soil pollution in peri-urban areas is critical for environmental management and agricultural production. In this paper, with cadmium (Cd) as the target pollutant, the spatiotemporal variations of soil cadmium pollution and the relative importance of the affecting factors were analyzed at a peri-urban area from the Jingyang District, Sichuan, China. Statistical results showed that the farmland in the study area could be considered moderately soil Cd-polluted, under the dual influence of natural factors and human activity. In particular, the soil Cd concentration in Tianyuan and Bajiaojing exceeded 0.5 mg kg -1 , for intensive industrial enterprises are distributed in these areas. Correspondingly, the geoaccumulation index also showed that the contamination of Cd in this area was moderately polluted. Moreover, the ecological risk index was 80% in the study area, indicating that the soil Cd pollution potential risk was moderate to high. High geological background values (soil Cd = 0.29 mg kg -1 ), river migration, industrial enterprises, and traffic significantly influenced soil Cd pollution, with natural geological factors playing greater roles. The significant horizontal-spatial effective distances away from Shiting River, Deyang-Aba Highway, and chemical plants were 200, 400, and 100 m, respectively. These results will be useful in guiding farmland cultivation and pollution remediation effectively in the peri-urban areas.

Space time modelling of air quality for environmental-risk maps: A case study in South Portugal

NASA Astrophysics Data System (ADS)

Soares, Amilcar; Pereira, Maria J.

2007-10-01

Since the 1960s, there has been a strong industrial development in the Sines area, on the southern Atlantic coast of Portugal, including the construction of an important industrial harbour and of, mainly, petrochemical and energy-related industries. These industries are, nowadays, responsible for substantial emissions of SO2, NOx, particles, VOCs and part of the ozone polluting the atmosphere. The major industries are spatially concentrated in a restricted area, very close to populated areas and natural resources such as those protected by the European Natura 2000 network. Air quality parameters are measured at the emissions' sources and at a few monitoring stations. Although air quality parameters are measured on an hourly basis, the lack of representativeness in space of these non-homogeneous phenomena makes even their representativeness in time questionable. Hence, in this study, the regional spatial dispersion of contaminants is also evaluated, using diffusive-sampler (Radiello Passive Sampler) campaigns during given periods. Diffusive samplers cover the entire space extensively, but just for a limited period of time. In the first step of this study, a space-time model of pollutants was built, based on a stochastic simulation-direct sequential simulation-with local spatial trend. The spatial dispersion of the contaminants for a given period of time-corresponding to the exposure time of the diffusive samplers-was computed by ordinary kriging. Direct sequential simulation was applied to produce equiprobable spatial maps for each day of that period, using the kriged map as a spatial trend and the daily measurements of pollutants from the monitoring stations as hard data. In the second step, the following environmental risk and costs maps were computed from the set of simulated realizations of pollutants: (i) maps of the contribution of each emission to the pollutant concentration at any spatial location; (ii) costs of badly located monitoring stations.

Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study

NASA Astrophysics Data System (ADS)

Aleksankina, Ksenia; Heal, Mathew R.; Dore, Anthony J.; Van Oijen, Marcel; Reis, Stefan

2018-04-01

Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input-output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ±40 % variation range for the UK emissions of SO2, NOx, and NH3, from which regression coefficients for each input-output combination and each model grid ( > 10 000 across the UK) were calculated. Surface concentrations of SO2, NOx, and NH3 (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO3 and particulate SO42-, NO3-, and NH4+ to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO2, NOx, and NH3 (±4, ±10, and ±20 % respectively). The uncertainties in the surface concentrations of NH3 and NOx and the depositions of NHx and NOy were dominated by the uncertainties in emissions of NH3, and NOx respectively, whilst concentrations of SO2 and deposition of SOy were affected by the uncertainties in both SO2 and NH3 emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH4+, NO3-, SO42-, and HNO3) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs.

North American pollution measurements from geostationary orbit with Tropospheric Emissions: Monitoring of Pollution (TEMPO)

NASA Astrophysics Data System (ADS)

Chance, K.

2017-12-01

TEMPO is the first NASA Earth Venture Instrument. It launches between 2019 and 2021 to measure atmospheric pollution from Mexico City and Cuba to the Canadian oil sands, and from the Atlantic to the Pacific, hourly at high spatial resolution, 10 km2. Geostationary daytime measurements capture the variability in the diurnal cycle of emissions and chemistry at sub-urban scale to improve emission inventories, monitor population exposure, and enable emission-control strategies.TEMPO measures UV/visible Earth reflectance spectra to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, BrO, OClO, IO, aerosols, cloud parameters, and UVB radiation. It tracks aerosol loading. It provides near-real-time air quality products. TEMPO is the North American component of the upcoming the global geostationary constellation for pollution monitoring, together with the European Sentinel-4 and the Korean Geostationary Environmental Monitoring Spectrometer (GEMS).TEMPO science studies include: Intercontinental pollution transport; Solar-induced fluorescence from chlorophyll over land and in the ocean to study tropical dynamics, primary productivity and carbon uptake, to detect red tides, and to study phytoplankton; measurements of stratospheric intrusions that cause air quality exceedances; measurements at peaks in vehicle travel to capture the variability in emissions from mobile sources; measurements of thunderstorm activity, including outflow regions to better quantify lightning NOx and O3 production; cropland measurements to follow the temporal evolution of emissions after fertilizer application and from rain-induced emissions from semi-arid soils; investigating the chemical processing of primary fire emissions and the secondary formation of VOCs and ozone; examining ocean halogen emissions and their impact on the oxidizing capacity of coastal environments; measuring spectra of nighttime lights as markers for human activity, energy conservation, and compliance with outdoor lighting standards intended to reduce light pollution.TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond.

Improving surface-subsurface water budgeting using high resolution satellite imagery applied on a brownfield.

PubMed

Dujardin, J; Batelaan, O; Canters, F; Boel, S; Anibas, C; Bronders, J

2011-01-15

The estimation of surface-subsurface water interactions is complex and highly variable in space and time. It is even more complex when it has to be estimated in urban areas, because of the complex patterns of the land-cover in these areas. In this research a modeling approach with integrated remote sensing analysis has been developed for estimating water fluxes in urban environments. The methodology was developed with the aim to simulate fluxes of contaminants from polluted sites. Groundwater pollution in urban environments is linked to patterns of land use and hence it is essential to characterize the land cover in a detail. An object-oriented classification approach applied on high-resolution satellite data has been adopted. To assign the image objects to one of the land-cover classes a multiple layer perceptron approach was adopted (Kappa of 0.86). Groundwater recharge has been simulated using the spatially distributed WetSpass model and the subsurface water flow using MODFLOW in order to identify and budget water fluxes. The developed methodology is applied to a brownfield case site in Vilvoorde, Brussels (Belgium). The obtained land use map has a strong impact on the groundwater recharge, resulting in a high spatial variability. Simulated groundwater fluxes from brownfield to the receiving River Zenne were independently verified by measurements and simulation of groundwater-surface water interaction based on thermal gradients in the river bed. It is concluded that in order to better quantify total fluxes of contaminants from brownfields in the groundwater, remote sensing imagery can be operationally integrated in a modeling procedure. Copyright © 2010 Elsevier B.V. All rights reserved.

Particulate polycyclic aromatic hydrocarbon spatial variability and aging in Mexico City

NASA Astrophysics Data System (ADS)

Thornhill, D. A.; Herndon, S. C.; Onasch, T. B.; Wood, E. C.; Zavala, M.; Molina, L. T.; Gaffney, J. S.; Marley, N. A.; Marr, L. C.

2007-11-01

As part of the Megacities Initiative: Local and Global Research Observations (MILAGRO) study in the Mexico City Metropolitan Area in March 2006, we measured particulate polycyclic aromatic hydrocarbons (PAHs) and other gaseous species and particulate properties at six locations throughout the city. The measurements were intended to support the following objectives: to describe spatial and temporal patterns in PAH concentrations, to gain insight into sources and transformations of PAHs, and to quantify the relationships between PAHs and other pollutants. Total particulate PAHs at the Instituto Mexicano del Petróleo (T0 supersite) located near downtown averaged 50 ng m-3, and aerosol active surface area averaged 80 mm2 m-3. PAHs were also measured on board the Aerodyne Mobile Laboratory, which visited six sites encompassing a mixture of different land uses and a range of ages of air parcels transported from the city core. Weak intersite correlations suggest that local sources are important and variable and that exposure to PAHs cannot be represented by a single regional-scale value. The relationships between PAHs and other pollutants suggest that a variety of sources and ages of particles are present. Among carbon monoxide, nitrogen oxides (NOx), and carbon dioxide, particulate PAHs are most strongly correlated with NOx. Mexico City's PAH-to-black carbon mass ratio of 0.01 is similar to that found on a freeway loop in the Los Angeles area and approximately 8-30 times higher than that found in other cities. Ratios also indicate that primary combustion particles are rapidly coated by secondary aerosol in Mexico City. If so, the lifetime of PAHs may be prolonged if the coating protects them against photodegradation or heterogeneous reactions.

Spatial analysis and source profiling of beta-agonists and sulfonamides in Langat River basin, Malaysia.

PubMed

Sakai, Nobumitsu; Mohd Yusof, Roslan; Sapar, Marni; Yoneda, Minoru; Ali Mohd, Mustafa

2016-04-01

Beta-agonists and sulfonamides are widely used for treating both humans and livestock for bronchial and cardiac problems, infectious disease and even as growth promoters. There are concerns about their potential environmental impacts, such as producing drug resistance in bacteria. This study focused on their spatial distribution in surface water and the identification of pollution sources in the Langat River basin, which is one of the most urbanized watersheds in Malaysia. Fourteen beta-agonists and 12 sulfonamides were quantitatively analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A geographic information system (GIS) was used to visualize catchment areas of the sampling points, and source profiling was conducted to identify the pollution sources based on a correlation between a daily pollutant load of the detected contaminant and an estimated density of human or livestock population in the catchment areas. As a result, 6 compounds (salbutamol, sulfadiazine, sulfapyridine, sulfamethazine, sulfadimethoxine and sulfamethoxazole) were widely detected in mid catchment areas towards estuary. The source profiling indicated that the pollution sources of salbutamol and sulfamethoxazole were from sewage, while sulfadiazine was from effluents of cattle, goat and sheep farms. Thus, this combination method of quantitative and spatial analysis clarified the spatial distribution of these drugs and assisted for identifying the pollution sources. Copyright © 2016 Elsevier B.V. All rights reserved.

Methodological issues in studies of air pollution and reproductive health.

PubMed

Woodruff, Tracey J; Parker, Jennifer D; Darrow, Lyndsey A; Slama, Rémy; Bell, Michelle L; Choi, Hyunok; Glinianaia, Svetlana; Hoggatt, Katherine J; Karr, Catherine J; Lobdell, Danelle T; Wilhelm, Michelle

2009-04-01

In the past decade there have been an increasing number of scientific studies describing possible effects of air pollution on perinatal health. These papers have mostly focused on commonly monitored air pollutants, primarily ozone (O(3)), particulate matter (PM), sulfur dioxide (SO(2)), carbon monoxide (CO), and nitrogen dioxide (NO(2)), and various indices of perinatal health, including fetal growth, pregnancy duration, and infant mortality. While most published studies have found some marker of air pollution related to some types of perinatal outcomes, variability exists in the nature of the pollutants and outcomes associated. Synthesis of the findings has been difficult for various reasons, including differences in study design and analysis. A workshop was held in September 2007 to discuss methodological differences in the published studies as a basis for understanding differences in study findings and to identify priorities for future research, including novel approaches for existing data. Four broad topic areas were considered: confounding and effect modification, spatial and temporal exposure variations, vulnerable windows of exposure, and multiple pollutants. Here we present a synopsis of the methodological issues and challenges in each area and make recommendations for future study. Two key recommendations include: (1) parallel analyses of existing data sets using a standardized methodological approach to disentangle true differences in associations from methodological differences among studies; and (2) identification of animal studies to inform important mechanistic research gaps. This work is of critical public health importance because of widespread exposure and because perinatal outcomes are important markers of future child and adult health.

The CHRONOS mission: capability for sub-hourly synoptic observations of carbon monoxide and methane to quantify emissions and transport of air pollution

NASA Astrophysics Data System (ADS)

Edwards, David P.; Worden, Helen M.; Neil, Doreen; Francis, Gene; Valle, Tim; Arellano, Avelino F., Jr.

2018-02-01

The CHRONOS space mission concept provides time-resolved abundance for emissions and transport studies of the highly variable and highly uncertain air pollutants carbon monoxide and methane, with sub-hourly revisit rate at fine (˜ 4 km) horizontal spatial resolution across a North American domain. CHRONOS can provide complete synoptic air pollution maps (snapshots) of the continental domain with less than 10 min of observations. This rapid mapping enables visualization of air pollution transport simultaneously across the entire continent and enables a sentinel-like capability for monitoring evolving, or unanticipated, air pollution sources in multiple locations at the same time with high temporal resolution. CHRONOS uses a compact imaging gas filter correlation radiometer for these observations, with heritage from more than 17 years of scientific data and algorithm advances by the science teams for the Measurements of Pollution in the Troposphere (MOPITT) instrument on NASA's Terra spacecraft in low Earth orbit. To achieve continental-scale sub-hourly sampling, the CHRONOS mission would be conducted from geostationary orbit, with the instrument hosted on a communications or meteorological platform. CHRONOS observations would contribute to an integrated observing system for atmospheric composition using surface, suborbital and satellite data with atmospheric chemistry models, as defined by the Committee on Earth Observing Satellites. Addressing the U.S. National Academy's 2007 decadal survey direction to characterize diurnal changes in tropospheric composition, CHRONOS observations would find direct societal applications for air quality management and forecasting to protect public health.

Characterisation of diffuse pollutions from forested watersheds in Japan during storm events - its association with rainfall and watershed features.

PubMed

Zhang, Zhao; Fukushima, Takehiko; Onda, Yuichi; Mizugaki, Shigeru; Gomi, Takashi; Kosugi, Ken'ichirou; Hiramatsu, Shinya; Kitahara, Hikaru; Kuraji, Koichiro; Terajima, Tomomi; Matsushige, Kazuo; Tao, Fulu

2008-02-01

Forest areas have been identified as important sources of nonpoint pollution in Japan. The managers must estimate stormwater quality and quantities from forested watersheds to develop effective management strategies. Therefore, stormwater runoff loads and concentrations of 10 constituents (total suspended solids, dissolved organic carbon, PO(4)-P, dissolved total phosphorus, total phosphorus, NH(4)-N, NO(2)-N, NO(3)-N, dissolved total nitrogen, and total nitrogen) for 72 events across five regions (Aichi, Kochi, Mie, Nagano, and Tokyo) were characterised. Most loads were significantly and positively correlated with stormwater variables (total event rainfall, event duration, and rainfall intensity), but most discharge-weighted event concentrations (DWECs) showed negative correlations with rainfall intensity. Mean water quality concentration during baseflow was correlated significantly with storm concentrations (r=0.41-0.77). Although all pollutant load equations showed high coefficients of determination (R(2)=0.55-0.80), no models predicted well pollutant concentrations, except those for the three N constituents (R(2)=0.59-0.67). Linear regressions to estimate stormwater concentrations and loads were greatly improved by regional grouping. The lower prediction capability of the concentration models for Mie, compared with the other four regions, indicated that other watershed or storm characteristics should be included in the prediction models. Significant differences among regions were found more frequently in concentrations than in loads for all constituents. Since baseflow conditions implied available pollutant sources for stormwater, the similar spatial characteristics of pollutant concentrations between baseflow and stormflow conditions were an important control for stormwater quality.

A simulation study to determine the attenuation and bias in health risk estimates due to exposure measurement error in bi-pollutant models

EPA Science Inventory

To understand the combined health effects of exposure to ambient air pollutant mixtures, it is becoming more common to include multiple pollutants in epidemiologic models. However, the complex spatial and temporal pattern of ambient pollutant concentrations and related exposures ...

Current content of selected pollutants in moss, humus, soil and bark and long-term radial growth of pine trees in the Mezaparks forest in Riga.

PubMed

Pīrāga, Dace; Tabors, Guntis; Nikodemus, Oļģerts; Žīgure, Zane; Brūmelis, Guntis

2017-05-01

The aim of this study was to evaluate the use of various indicators in the assessment of environmental pollution and to determine the response of pine to changes of pollution levels. Mezaparks is a part of Riga that has been subject to various long-term effects of atmospheric pollution and, in particular, historically from a large superphosphate factory. To determine the spatial distribution of pollution, moss, pine bark and soil O and B horizons were used as sorbents in this study, as well as the additional annual increment of pine trees. The current spatial distribution of pollution is best shown by heavy metal accumulation in mosses and the long-term accumulation of P 2 O 5 pollution by the soil O horizon. The methodological problems of using these sorbents were explored in the study. Environmental pollution and its changes could be associated with the tree growth ring annual additional increment of Mezaparks pine forest stands. The additional increment increased after the closing of the Riga superphosphate factory.

Exploring the Spatial Representativeness of NAAQS and Near Roadway Sites Using High-Spatial Resolution Air Pollution Maps Produced by A Mobile Mapping Platform

EPA Science Inventory

In the current study, three Google Street View cars were equipped with the Aclima Environmental Intelligence ™ Platform. The air pollutants of interest, including O3, NO, NO2, CO2, black carbon, and particle number in several size ranges, were measured using a suite of fast...

Urbanization, Trace Metal Pollution, and Malaria Prevalence in the House Sparrow

PubMed Central

Bichet, Coraline; Scheifler, Renaud; Cœurdassier, Michaël; Julliard, Romain; Sorci, Gabriele; Loiseau, Claire

2013-01-01

Anthropogenic pollution poses a threat for the environment and wildlife. Trace metals (TMs) are known to have negative effects on haematological status, oxidative balance, and reproductive success in birds. These pollutants particularly increase in concentration in industrialized, urbanized and intensive agricultural areas. Pollutants can also interfere with the normal functioning of the immune system and, as such, alter the dynamics of host-parasite interactions. Nevertheless, the impact of pollution on infectious diseases has been largely neglected in natural populations of vertebrates. Here, we used a large spatial scale monitoring of 16 house sparrow (Passer domesticus) populations to identify environmental variables likely to explain variation in TMs (lead, cadmium, zinc) concentrations in the feathers. In five of these populations, we also studied the potential link between TMs, prevalence of infection with one species of avian malaria, Plasmodium relictum, and body condition. Our results show that lead concentration is associated with heavily urbanized habitats and that areas with large woodland coverage have higher cadmium and zinc feather concentrations. Our results suggest that lead concentration in the feathers positively correlates with P. relictum prevalence, and that a complex relationship links TM concentrations, infection status, and body condition. This is one of the first studies showing that environmental pollutants are associated with prevalence of an infectious disease in wildlife. The mechanisms underlying this effect are still unknown even though it is tempting to suggest that lead could interfere with the normal functioning of the immune system, as shown in other species. We suggest that more effort should be devoted to elucidate the link between pollution and the dynamics of infectious diseases. PMID:23342022

State of the art molecular markers for fecal pollution source tracking in water.

PubMed

Roslev, Peter; Bukh, Annette S

2011-03-01

Most environmental waters are susceptible to fecal contamination from animal and/or human pollution sources. To attenuate or eliminate such contamination, it is often critical that the pollution sources are rapidly and correctly identified. Fecal pollution source tracking (FST) is a promising research area that aims to identify the origin(s) of fecal pollution in water. This mini-review focuses on the potentials and limitations of library independent molecular markers that are exclusively or strongly associated with fecal pollution from humans and different animals. Fecal-source-associated molecular markers include nucleic acid sequences from prokaryotes and viruses associated with specific biological hosts, but also sequences such as mitochondrial DNA retrieved directly from humans and animals. However, some fecal-source-associated markers may not be absolutely specific for a given source type, and apparent specificity and frequency established in early studies are sometimes compromised by new studies suggesting variation in specificity and abundance on a regional, global and/or temporal scale. It is therefore recommended that FST studies are based on carefully selected arrays of markers, and that identification of human and animal contributions are based on a multi-marker toolkit with several markers for each source category. Furthermore, future FST studies should benefit from increased knowledge regarding sampling strategies and temporal and spatial variability of marker ratios. It will also be important to obtain a better understanding of marker persistence and the quantitative relationship between marker abundance and the relative contribution from individual fecal pollution source types. A combination of enhanced pathogen screening methods, and validated quantitative source tracking techniques could then contribute significantly to future management of environmental water quality including improved microbial risk assessment.

[Spatial distribution prediction of surface soil Pb in a battery contaminated site].

PubMed

Liu, Geng; Niu, Jun-Jie; Zhang, Chao; Zhao, Xin; Guo, Guan-Lin

2014-12-01

In order to enhance the reliability of risk estimation and to improve the accuracy of pollution scope determination in a battery contaminated site with the soil characteristic pollutant Pb, four spatial interpolation models, including Combination Prediction Model (OK(LG) + TIN), kriging model (OK(BC)), Inverse Distance Weighting model (IDW), and Spline model were employed to compare their effects on the spatial distribution and pollution assessment of soil Pb. The results showed that Pb concentration varied significantly and the data was severely skewed. The variation coefficient of the site was higher in the local region. OK(LG) + TIN was found to be more accurate than the other three models in predicting the actual pollution situations of the contaminated site. The prediction accuracy of other models was lower, due to the effect of the principle of different models and datum feature. The interpolation results of OK(BC), IDW and Spline could not reflect the detailed characteristics of seriously contaminated areas, and were not suitable for mapping and spatial distribution prediction of soil Pb in this site. This study gives great contributions and provides useful references for defining the remediation boundary and making remediation decision of contaminated sites.

Microscale spatial distribution and health assessment of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) at nine communities in Xi'an, China.

PubMed

Xu, Hongmei; Ho, Steven Sai Hang; Gao, Meiling; Cao, Junji; Guinot, Benjamin; Ho, Kin Fai; Long, Xin; Wang, Jingzhi; Shen, Zhenxing; Liu, Suixin; Zheng, Chunli; Zhang, Qian

2016-11-01

Spatial variability of polycyclic aromatic hydrocarbons (PAHs) associated with fine particulate matter (PM 2.5 ) was investigated in Xi'an, China, in summer of 2013. Sixteen priority PAHs were quantified in 24-h integrated air samples collected simultaneously at nine urban and suburban communities. The total quantified PAHs mass concentrations ranged from 32.4 to 104.7 ng m -3 , with an average value of 57.1 ± 23.0 ng m -3 . PAHs were observed higher concentrations at suburban communities (average: 86.3 ng m -3 ) than at urban ones (average: 48.8 ng m -3 ) due to a better enforcement of the pollution control policies at the urban scale, and meanwhile the disorganized management of motor vehicles and massive building constructions in the suburbs. Elevated PAH levels were observed in the industrialized regions (west and northwest of Xi'an) from Kriging interpolation analysis. Satellite-based visual interpretations of land use were also applied for the supporting the spatial distribution of PAHs among the communities. The average benzo[a]pyrene-equivalent toxicity (Σ[BaP] eq ) at the nine communities was 6.9 ± 2.2 ng m -3 during the sampling period, showing a generally similar spatial distribution to PAHs levels. On average, the excess inhalation lifetime cancer risk derived from Σ[BaP] eq indicated that eight persons per million of community residents would develop cancer due to PM 2.5 -bound PAHs exposure in Xi'an. The great in-city spatial variability of PAHs confirmed the importance of multiple points sampling to conduct exposure health risk assessment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Remote sensing of exposure to NO2: Satellite versus ground-based measurement in a large urban area

NASA Astrophysics Data System (ADS)

Bechle, Matthew J.; Millet, Dylan B.; Marshall, Julian D.

2013-04-01

Remote sensing may be a useful tool for exploring spatial variability of air pollution exposure within an urban area. To evaluate the extent to which satellite data from the Ozone Monitoring Instrument (OMI) can resolve urban-scale gradients in ground-level nitrogen dioxide (NO2) within a large urban area, we compared estimates of surface NO2 concentrations derived from OMI measurements and US EPA ambient monitoring stations. OMI, aboard NASA's Aura satellite, provides daily afternoon (˜13:30 local time) measurements of NO2 tropospheric column abundance. We used scaling factors (surface-to-column ratios) to relate satellite column measurements to ground-level concentrations. We compared 4138 sets of paired data for 25 monitoring stations in the South Coast Air Basin of California for all of 2005. OMI measurements include more data gaps than the ground monitors (60% versus 5% of available data, respectively), owing to cloud contamination and imposed limits on pixel size. The spatial correlation between OMI columns and corrected in situ measurements is strong (r = 0.93 for annual average data), indicating that the within-urban spatial signature of surface NO2 is well resolved by the satellite sensor. Satellite-based surface estimates employing scaling factors from an urban model provide a reliable measure (annual mean bias: -13%; seasonal mean bias: <1% [spring] to -22% [fall]) of fine-scale surface NO2. We also find that OMI provides good spatial density in the study region (average area [km2] per measurement: 730 for the satellite sensor vs. 1100 for the monitors). Our findings indicate that satellite observations of NO2 from the OMI sensor provide a reliable measure of spatial variability in ground-level NO2 exposure for a large urban area.

Noise, air pollutants and traffic: continuous measurement and correlation at a high-traffic location in New York City.

PubMed

Ross, Zev; Kheirbek, Iyad; Clougherty, Jane E; Ito, Kazuhiko; Matte, Thomas; Markowitz, Steven; Eisl, Holger

2011-11-01

Epidemiological studies have linked both noise and air pollution to common adverse health outcomes such as increased blood pressure and myocardial infarction. In urban settings, noise and air pollution share important sources, notably traffic, and several recent studies have shown spatial correlations between noise and air pollution. The temporal association between these exposures, however, has yet to be thoroughly investigated despite the importance of time series studies in air pollution epidemiology and the potential that correlations between these exposures could at least partly confound statistical associations identified in these studies. An aethelometer, for continuous elemental carbon measurement, was co-located with a continuous noise monitor near a major urban highway in New York City for six days in August 2009. Hourly elemental carbon measurements and hourly data on overall noise levels and low, medium and high frequency noise levels were collected. Hourly average concentrations of fine particles and nitrogen oxides, wind speed and direction and car, truck and bus traffic were obtained from nearby regulatory monitors. Overall temporal patterns, as well as day-night and weekday-weekend patterns, were characterized and compared for all variables. Noise levels were correlated with car, truck, and bus traffic and with air pollutants. We observed strong day-night and weekday-weekend variation in noise and air pollutants and correlations between pollutants varied by noise frequency. Medium and high frequency noise were generally more strongly correlated with traffic and traffic-related pollutants than low frequency noise and the correlation with medium and high frequency noise was generally stronger at night. Correlations with nighttime high frequency noise were particularly high for car traffic (Spearman rho=0.84), nitric oxide (0.73) and nitrogen dioxide (0.83). Wind speed and direction mediated relationships between pollutants and noise. Noise levels are temporally correlated with traffic and combustion pollutants and correlations are modified by the time of day, noise frequency and wind. Our results underscore the potential importance of assessing temporal variation in co-exposures to noise and air pollution in studies of the health effects of these urban pollutants. Copyright © 2011 Elsevier Inc. All rights reserved.

AHP-based spatial analysis of water quality impact assessment due to change in vehicular traffic caused by highway broadening in Sikkim Himalaya

NASA Astrophysics Data System (ADS)

Banerjee, Polash; Ghose, Mrinal Kanti; Pradhan, Ratika

2018-05-01

Spatial analysis of water quality impact assessment of highway projects in mountainous areas remains largely unexplored. A methodology is presented here for Spatial Water Quality Impact Assessment (SWQIA) due to highway-broadening-induced vehicular traffic change in the East district of Sikkim. Pollution load of the highway runoff was estimated using an Average Annual Daily Traffic-Based Empirical model in combination with mass balance model to predict pollution in the rivers within the study area. Spatial interpolation and overlay analysis were used for impact mapping. Analytic Hierarchy Process-Based Water Quality Status Index was used to prepare a composite impact map. Model validation criteria, cross-validation criteria, and spatial explicit sensitivity analysis show that the SWQIA model is robust. The study shows that vehicular traffic is a significant contributor to water pollution in the study area. The model is catering specifically to impact analysis of the concerned project. It can be an aid for decision support system for the project stakeholders. The applicability of SWQIA model needs to be explored and validated in the context of a larger set of water quality parameters and project scenarios at a greater spatial scale.

Interpolating precipitation and its relation to runoff and non-point source pollution.

PubMed

Chang, Chia-Ling; Lo, Shang-Lien; Yu, Shaw-L

2005-01-01

When rainfall spatially varies, complete rainfall data for each region with different rainfall characteristics are very important. Numerous interpolation methods have been developed for estimating unknown spatial characteristics. However, no interpolation method is suitable for all circumstances. In this study, several methods, including the arithmetic average method, the Thiessen Polygons method, the traditional inverse distance method, and the modified inverse distance method, were used to interpolate precipitation. The modified inverse distance method considers not only horizontal distances but also differences between the elevations of the region with no rainfall records and of its surrounding rainfall stations. The results show that when the spatial variation of rainfall is strong, choosing a suitable interpolation method is very important. If the rainfall is uniform, the precipitation estimated using any interpolation method would be quite close to the actual precipitation. When rainfall is heavy in locations with high elevation, the rainfall changes with the elevation. In this situation, the modified inverse distance method is much more effective than any other method discussed herein for estimating the rainfall input for WinVAST to estimate runoff and non-point source pollution (NPSP). When the spatial variation of rainfall is random, regardless of the interpolation method used to yield rainfall input, the estimation errors of runoff and NPSP are large. Moreover, the relationship between the relative error of the predicted runoff and predicted pollutant loading of SS is high. However, the pollutant concentration is affected by both runoff and pollutant export, so the relationship between the relative error of the predicted runoff and the predicted pollutant concentration of SS may be unstable.

Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001-2011.

PubMed

Li, Li; Qian, Jun; Ou, Chun-Quan; Zhou, Ying-Xue; Guo, Cui; Guo, Yuming

2014-07-01

There is an increasing interest in spatial and temporal variation of air pollution and its association with weather conditions. We presented the spatial and temporal variation of Air Pollution Index (API) and examined the associations between API and meteorological factors during 2001-2011 in Guangzhou, China. A Seasonal-Trend Decomposition Procedure Based on Loess (STL) was used to decompose API. Wavelet analyses were performed to examine the relationships between API and several meteorological factors. Air quality has improved since 2005. APIs were highly correlated among five monitoring stations, and there were substantial temporal variations. Timescale-dependent relationships were found between API and a variety of meteorological factors. Temperature, relative humidity, precipitation and wind speed were negatively correlated with API, while diurnal temperature range and atmospheric pressure were positively correlated with API in the annual cycle. Our findings should be taken into account when determining air quality forecasts and pollution control measures. Copyright © 2014 Elsevier Ltd. All rights reserved.

Inversion structure and winter ozone distribution in the Uintah Basin, Utah, U.S.A.

NASA Astrophysics Data System (ADS)

Lyman, Seth; Tran, Trang

2015-12-01

The Uintah Basin in Utah, U.S.A. experiences high concentrations of ozone during some winters due to strong, multi-day temperature inversions that facilitate the buildup of pollution from local sources, including the oil and gas industry. Together, elevation of monitoring sites and proximity to oil and gas wells explain as much as 90% of spatial variability in surface ozone concentrations during inversion episodes (i.e., R2 = 0.90). Inversion conditions start earlier and last longer at lower elevations, at least in part because lower elevations are more insulated from winds aloft that degrade inversion conditions and dilute produced ozone. Surface air transport under inversions is dominated by light, diurnal upslope-downslope flow that limits net transport distances. Thus, different areas of the Basin are relatively isolated from each other, allowing spatial factors like elevation and proximity to sources to strongly influence ozone concentrations at individual sites.

An integrated Bayesian model for estimating the long-term health effects of air pollution by fusing modelled and measured pollution data: A case study of nitrogen dioxide concentrations in Scotland.

PubMed

Huang, Guowen; Lee, Duncan; Scott, Marian

2015-01-01

The long-term health effects of air pollution can be estimated using a spatio-temporal ecological study, where the disease data are counts of hospital admissions from populations in small areal units at yearly intervals. Spatially representative pollution concentrations for each areal unit are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over grid level concentrations from an atmospheric dispersion model. We propose a novel fusion model for estimating spatially aggregated pollution concentrations using both the modelled and monitored data, and relate these concentrations to respiratory disease in a new study in Scotland between 2007 and 2011. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Impact of land-use on water pollution in a rapidly urbanizing catchment in China

NASA Astrophysics Data System (ADS)

Khu, Soon-Thiam; Qin, Huapeng

2010-05-01

Many catchments in developing countries are undergoing fast urbanization which is usually characterized by population increase, economic growth as well as drastic changes of land-use from natural/rural to urban area. During the urbanization process, some catchments experience water quality deterioration due to rapid increase of pollution loads. Nonpoint source pollution resulting from storm water runoff has been recognized as one of the major causes of pollutants in many cities in developing countries. The composition of land-use for a rapidly urbanizing catchment is usually heterogeneous, and this may result in significant spatial variations of storm runoff pollution and increase the difficulties of water quality management in the catchment. The Shiyan Reservoir catchment, a typical rapidly urbanizing area in China, is chosen as the study area, and temporary monitoring sites were set at the outlets of its 6 sub-catchments to synchronously measured rainfall, runoff and water quality during 4 storm events. Three indicators, event pollutant loads per unit area (EPL), event mean concentration (EMC) and pollutant loads transported by the first 50% of runoff volume (FF50), were used to describe the runoff pollution for different pollutants (such as COD, BOD, NH3-N, TN, TP and SS) in each sub-catchment during the storm events; and the correlations between runoff pollution spatial variations and land-use patterns were tested by Spearman's rank correlation analysis. The results indicated that similar spatial variation trends were found for different pollutants (EPL or EMC) in light storm events, which strongly correlate with the proportion of residential land-use; however, they have different trends in heavy storm events, which correlate with the different proportional combination of residential, industrial, agricultural and bare land-use. It is also shown that it is necessary to consider some pervious land-use types in runoff pollution monitoring or management for a rapidly urbanizing area, particularly in heavy storm.

Patterns of fish community composition along a river affected by agricultural and urban disturbance in south-central Chile

DOE Office of Scientific and Technical Information (OSTI.GOV)

Orrego, Rodrigo; Barra, Ricardo; Chiang, Gustavo

2008-03-01

Patterns of fish community composition in a south-central Chile river were investigated along the altitudinal-spatial and environmental gradient and as a function of anthropogenic factors. The spatial pattern of fish communities in different biocoenotic zones of the Chillan River is influenced by both natural factors such a hydrologic features, habitat, and feeding types, and also by water quality variables which can reduce the diversity and abundance of sensitive species. A principal component analysis incorporating both water quality parameters and biomarker responses of representative fish species was used to evaluate the status of fish communities along the spatial gradient of themore » stream. The abundance and diversity of the fish community changed from a low in the upper reaches where the low pollution-tolerant species such as salmonid dominated, to a reduced diversity in the lower reaches of the river where tolerant browser species such as cypriniformes dominated. Even though the spatial pattern of fish community structure is similar to that found for the Chilean Rivers, the structure of these communities is highly influenced by human disturbance, particularly along the lower reaches of the river.« less

Correlation of gravestone decay and air quality 1960-2010

NASA Astrophysics Data System (ADS)

Mooers, H. D.; Carlson, M. J.; Harrison, R. M.; Inkpen, R. J.; Loeffler, S.

2017-03-01

Evaluation of spatial and temporal variability in surface recession of lead-lettered Carrara marble gravestones provides a quantitative measure of acid flux to the stone surfaces and is closely related to local land use and air quality. Correlation of stone decay, land use, and air quality for the period after 1960 when reliable estimates of atmospheric pollution are available is evaluated. Gravestone decay and SO2 measurements are interpolated spatially using deterministic and geostatistical techniques. A general lack of spatial correlation was identified and therefore a land-use-based technique for correlation of stone decay and air quality is employed. Decadally averaged stone decay is highly correlated with land use averaged spatially over an optimum radius of ≈7 km even though air quality, determined by records from the UK monitoring network, is not highly correlated with gravestone decay. The relationships among stone decay, air-quality, and land use is complicated by the relatively low spatial density of both gravestone decay and air quality data and the fact that air quality data is available only as annual averages and therefore seasonal dependence cannot be evaluated. However, acid deposition calculated from gravestone decay suggests that the deposition efficiency of SO2 has increased appreciably since 1980 indicating an increase in the SO2 oxidation process possibly related to reactions with ammonia.

Spatial distribution of diesel transit bus emissions and urban populations: implications of coincidence and scale on exposure.

PubMed

Gouge, Brian; Ries, Francis J; Dowlatabadi, Hadi

2010-09-15

Macroscale emissions modeling approaches have been widely applied in impact assessments of mobile source emissions. However, these approaches poorly characterize the spatial distribution of emissions and have been shown to underestimate emissions of some pollutants. To quantify the implications of these limitations on exposure assessments, CO, NO(X), and HC emissions from diesel transit buses were estimated at 50 m intervals along a bus rapid transit route using a microscale emissions modeling approach. The impacted population around the route was estimated using census, pedestrian count and transit ridership data. Emissions exhibited significant spatial variability. In intervals near major intersections and bus stops, emissions were 1.6-3.0 times higher than average. The coincidence of these emission hot spots and peaks in pedestrian populations resulted in a 20-40% increase in exposure compared to estimates that assumed homogeneous spatial distributions of emissions and/or populations along the route. An additional 19-30% increase in exposure resulted from the underestimate of CO and NO(X) emissions by macroscale modeling approaches. The results of this study indicate that macroscale modeling approaches underestimate exposure due to poor characterization of the influence of vehicle activity on the spatial distribution of emissions and total emissions.

Trace element bias in the use of CO2 vents as analogues for low pH environments: Implications for contamination levels in acidified oceans

NASA Astrophysics Data System (ADS)

Vizzini, S.; Di Leonardo, R.; Costa, V.; Tramati, C. D.; Luzzu, F.; Mazzola, A.

2013-12-01

Research into the effects of ocean acidification on marine ecosystems has increasingly focused on natural CO2 vents, although their intrinsic environmental complexity means observations from these areas may not relate exclusively to pH gradients. In order to assess trace element levels and distribution in the Levante Bay (Vulcano Island, NE Sicily, Italy) and its suitability for studying biological effects of pH decline, Ba, Fe and trace elements (As, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, V and Zn) in sediment were analysed from 7 transects. Where present, Cymodocea nodosa leaves and epiphytes were also analysed. At the spatial scale of the bay, trace element concentrations in sediments and biota showed wide variability, possibly related to both input from fluid emissions and seawater physico-chemical variables (i.e. pH and Eh), which may considerably affect the solubility and bioavailability of potentially harmful trace elements. According to two pollution indices (MSPI: Marine Sediment Pollution Index and SQG-Q: Sediment Quality Guideline Quotient), the bay can be considered to be affected by low contamination with moderate potential for adverse biological effects, especially in the area between about 150 and 350 m from the primary vent, where localized detrimental effects on biota may occur. Generally, biological samples showed concentrations that were comparable with the lower values of seagrass ranges. The overall results of this study support the complex spatial dynamics of trace elements in the CO2 vent studied, which are constrained by both direct input from the vent and/or biogeochemical processes affecting element precipitation at the sediment-seawater interface. Consequently, great caution should be used when relating biological changes along pH gradients to the unifactorial effect of pH only, as interactions with concurrent, multiple stressors, including trace element enrichments, may occur. This finding has implications for the use of CO2 vents as analogues in ocean acidification research. They should be considered more appropriately as analogues for low pH environments with non-negligible trace element contamination which, in a scenario of continuous increase in anthropogenic pollution, may be very common.

Spatial and temporal variability of particulate polycyclic aromatic hydrocarbons in Mexico City

NASA Astrophysics Data System (ADS)

Thornhill, D. A.; de Foy, B.; Herndon, S. C.; Onasch, T. B.; Wood, E. C.; Zavala, M.; Molina, L. T.; Gaffney, J. S.; Marley, N. A.; Marr, L. C.

2008-06-01

As part of the Megacities Initiative: Local and Global Research Observations (MILAGRO) study in the Mexico City Metropolitan Area in March 2006, we measured particulate polycyclic aromatic hydrocarbons (PAHs) and other gaseous species and particulate properties, including light absorbing carbon or effective black carbon (BC), at six locations throughout the city. The measurements were intended to support the following objectives: to describe spatial and temporal patterns in PAH concentrations, to gain insight into sources and transformations of PAHs and BC, and to quantify the relationships between PAHs and other pollutants. Total particulate PAHs at the Instituto Mexicano del Petróleo (T0 supersite) located near downtown averaged 50 ng m-3, and aerosol active surface area averaged 80 mm2 m-3. PAHs were also measured on board the Aerodyne Mobile Laboratory, which visited six sites encompassing a mixture of different land uses and a range of ages of air parcels transported from the city core. A combination of analyses of time series, back trajectories, concentration fields, pollutant ratios, and correlation coefficients supports the concept of T0 as an urban source site, T1 as a receptor site with strong local sources, Pedregal and PEMEX as intermediate sites, Pico Tres Padres as a vertical receptor site, and Santa Ana as a downwind receptor site. Weak intersite correlations suggest that local sources are important and variable and that exposure to PAHs and BC cannot be represented by a single regional-scale value. The relationships between PAHs and other pollutants suggest that a variety of sources and ages of particles are present. Among carbon monoxide, nitrogen oxides (NOx), and carbon dioxide, particulate PAHs are most strongly correlated with NOx. Mexico City's PAH/BC mass ratio of 0.01 is similar to that found on a freeway loop in the Los Angeles area and approximately 8 30 times higher than that found in other cities. Evidence also suggests that primary combustion particles are rapidly coated by secondary aerosol in Mexico City. If so, their optical properties may change, and the lifetime of PAHs may be prolonged if the coating protects them against photodegradation or heterogeneous reactions.

Land use regression modelling of air pollution in high density high rise cities: A case study in Hong Kong.

PubMed

Lee, Martha; Brauer, Michael; Wong, Paulina; Tang, Robert; Tsui, Tsz Him; Choi, Crystal; Cheng, Wei; Lai, Poh-Chin; Tian, Linwei; Thach, Thuan-Quoc; Allen, Ryan; Barratt, Benjamin

2017-08-15

Land use regression (LUR) is a common method of predicting spatial variability of air pollution to estimate exposure. Nitrogen dioxide (NO 2 ), nitric oxide (NO), fine particulate matter (PM 2.5 ), and black carbon (BC) concentrations were measured during two sampling campaigns (April-May and November-January) in Hong Kong (a prototypical high-density high-rise city). Along with 365 potential geospatial predictor variables, these concentrations were used to build two-dimensional land use regression (LUR) models for the territory. Summary statistics for combined measurements over both campaigns were: a) NO 2 (Mean=106μg/m 3 , SD=38.5, N=95), b) NO (M=147μg/m 3 , SD=88.9, N=40), c) PM 2.5 (M=35μg/m 3 , SD=6.3, N=64), and BC (M=10.6μg/m 3 , SD=5.3, N=76). Final LUR models had the following statistics: a) NO 2 (R 2 =0.46, RMSE=28μg/m 3 ) b) NO (R 2 =0.50, RMSE=62μg/m 3 ), c) PM 2.5 (R 2 =0.59; RMSE=4μg/m 3 ), and d) BC (R 2 =0.50, RMSE=4μg/m 3 ). Traditional LUR predictors such as road length, car park density, and land use types were included in most models. The NO 2 prediction surface values were highest in Kowloon and the northern region of Hong Kong Island (downtown Hong Kong). NO showed a similar pattern in the built-up region. Both PM 2.5 and BC predictions exhibited a northwest-southeast gradient, with higher concentrations in the north (close to mainland China). For BC, the port was also an area of elevated predicted concentrations. The results matched with existing literature on spatial variation in concentrations of air pollutants and in relation to important emission sources in Hong Kong. The success of these models suggests LUR is appropriate in high-density, high-rise cities. Copyright © 2017 Elsevier B.V. All rights reserved.

Determinants of black carbon, particle mass and number concentrations in London transport microenvironments

NASA Astrophysics Data System (ADS)

Rivas, Ioar; Kumar, Prashant; Hagen-Zanker, Alex; Andrade, Maria de Fatima; Slovic, Anne Dorothee; Pritchard, John P.; Geurs, Karst T.

2017-07-01

We investigated the determinants of personal exposure concentrations of commuters' to black carbon (BC), ultrafine particle number concentrations (PNC), and particulate matter (PM1, PM2.5 and PM10) in different travel modes. We quantified the contribution of key factors that explain the variation of the previous pollutants in four commuting routes in London, each covered by four transport modes (car, bus, walk and underground). Models were performed for each pollutant, separately to assess the effect of meteorology (wind speed) or ambient concentrations (with either high spatial or temporal resolution). Concentration variations were mainly explained by wind speed or ambient concentrations and to a lesser extent by route and period of the day. In multivariate models with wind speed, the wind speed was the common significant predictor for all the pollutants in the above-ground modes (i.e., car, bus, walk); and the only predictor variable for the PM fractions. Wind speed had the strongest effect on PM during the bus trips, with an increase in 1 m s-1 leading to a decrease in 2.25, 2.90 and 4.98 μg m-3 of PM1, PM2.5 and PM10, respectively. PM2.5 and PM10 concentrations in car trips were better explained by ambient concentrations with high temporal resolution although from a single monitoring station. On the other hand, ambient concentrations with high spatial coverage but lower temporal resolution predicted better the concentrations in bus trips, due to bus routes passing through streets with a high variability of traffic intensity. In the underground models, wind speed was not significant and line and type of windows on the train explained 42% of the variation of PNC and 90% of all PM fractions. Trains in the district line with openable windows had an increase in concentrations of 1 684 cm-3 for PNC and 40.69 μg m-3 for PM2.5 compared with trains that had non-openable windows. The results from this work can be used to target efforts to reduce personal exposures of London commuters.

Real-time management of an urban groundwater well field threatened by pollution.

PubMed

Bauser, Gero; Franssen, Harrie-Jan Hendricks; Kaiser, Hans-Peter; Kuhlmann, Ulrich; Stauffer, Fritz; Kinzelbach, Wolfgang

2010-09-01

We present an optimal real-time control approach for the management of drinking water well fields. The methodology is applied to the Hardhof field in the city of Zurich, Switzerland, which is threatened by diffuse pollution. The risk of attracting pollutants is higher if the pumping rate is increased and can be reduced by increasing artificial recharge (AR) or by adaptive allocation of the AR. The method was first tested in offline simulations with a three-dimensional finite element variably saturated subsurface flow model for the period January 2004-August 2005. The simulations revealed that (1) optimal control results were more effective than the historical control results and (2) the spatial distribution of AR should be different from the historical one. Next, the methodology was extended to a real-time control method based on the Ensemble Kalman Filter method, using 87 online groundwater head measurements, and tested at the site. The real-time control of the well field resulted in a decrease of the electrical conductivity of the water at critical measurement points which indicates a reduced inflow of water originating from contaminated sites. It can be concluded that the simulation and the application confirm the feasibility of the real-time control concept.

The use of GIS and multi-criteria evaluation (MCE) to identify agricultural land management practices which cause surface water pollution in drinking water supply catchments.

PubMed

Grayson, Richard; Kay, Paul; Foulger, Miles

2008-01-01

Diffuse pollution poses a threat to water quality and results in the need for treatment for potable water supplies which can prove costly. Within the Yorkshire region, UK, nitrates, pesticides and water colour present particular treatment problems. Catchment management techniques offer an alternative to 'end of pipe' solutions and allow resources to be targeted to the most polluting areas. This project has attempted to identify such areas using GIS based modelling approaches in catchments where water quality data were available. As no model exists to predict water colour a model was created using an MCE method which is capable of predicting colour concentrations at the catchment scale. CatchIS was used to predict pesticide and nitrate N concentrations and was found to be generally capable of reliably predicting nitrate N loads at the catchment scale. The pesticides results did not match the historic data possibly due to problems with the historic pesticide data and temporal and spatially variability in pesticide usage. The use of these models can be extended to predict water quality problems in catchments where water quality data are unavailable and highlight areas of concern. IWA Publishing 2008.

Simulation of the ocean's spectral radiant thermal source and boundary conditions

NASA Astrophysics Data System (ADS)

Merzlikin, Vladimir; Krass, Maxim; Cheranev, Svyatoslav; Aloric, Aleksandra

2013-05-01

This article considers the analysis of radiant heat transfer for semitransparent natural and polluted seawaters and its physical interpretations. Technogenic or natural pollutions are considered as ensembles of selective scattering, absorbing and emitting particles with complex refractive indices in difference spectral ranges of external radiation. Simulation of spectral radiant thermal sources within short wavelength of solar penetrating radiation for upper oceanic depth was carried out for deep seawater on regions from ˜ 300 to ˜ 600 nm and for subsurface layers (not more ˜ 1 m) - on one ˜ 600 - 1200 nm. Model boundary conditions on exposed oceanic surface are defined by (1) emittance of atmosphere and seawater within long wavelength radiation ˜ 9000 nm, (2) convection, and (3) thermal losses due to evaporation. Spatial and temporal variability of inherent optical properties, temperature distributions of the upper overheated layer of seawater, the appearance of a subsurface temperature maximum and a cool surface skin layer in response to penetrating solar radiation are explained first of all by the effects of volumetric scattering (absorption) and surface cooling of polluted seawater. The suggested analysis can become an important and useful subject of research for oceanographers and climatologists.

Determinants of pollution: what do we really know?

PubMed

Gassebner, Martin; Lamla, Michael J; Sturm, Jan-Egbert

2011-01-01

The recent literature proposes many variables as significant determinants of pollution. This paper gives an overview of this literature and asks which of these factors have an empirically robust impact on water and air pollution. We apply Extreme Bound Analysis (EBA) on a panel of up to 120 countries covering the period 1960–2001. We find supportive evidence of the existence of the environmental Kuznets curve for water pollution. Furthermore, mainly variables capturing the economic structure of a country affect air and water pollution.

Health impact assessment of traffic-related air pollution at the urban project scale: influence of variability and uncertainty.

PubMed

Chart-Asa, Chidsanuphong; Gibson, Jacqueline MacDonald

2015-02-15

This paper develops and then demonstrates a new approach for quantifying health impacts of traffic-related particulate matter air pollution at the urban project scale that includes variability and uncertainty in the analysis. We focus on primary particulate matter having a diameter less than 2.5 μm (PM2.5). The new approach accounts for variability in vehicle emissions due to temperature, road grade, and traffic behavior variability; seasonal variability in concentration-response coefficients; demographic variability at a fine spatial scale; uncertainty in air quality model accuracy; and uncertainty in concentration-response coefficients. We demonstrate the approach for a case study roadway corridor with a population of 16,000, where a new extension of the University of North Carolina (UNC) at Chapel Hill campus is slated for construction. The results indicate that at this case study site, health impact estimates increased by factors of 4-9, depending on the health impact considered, compared to using a conventional health impact assessment approach that overlooks these variability and uncertainty sources. In addition, we demonstrate how the method can be used to assess health disparities. For example, in the case study corridor, our method demonstrates the existence of statistically significant racial disparities in exposure to traffic-related PM2.5 under present-day traffic conditions: the correlation between percent black and annual attributable deaths in each census block is 0.37 (t(114)=4.2, p<0.0001). Overall, our results show that the proposed new campus will cause only a small incremental increase in health risks (annual risk 6×10(-10); lifetime risk 4×10(-8)), compared to if the campus is not built. Nonetheless, the approach we illustrate could be useful for improving the quality of information to support decision-making for other urban development projects. Copyright © 2014 Elsevier B.V. All rights reserved.

Novel Approaches for Estimating Human Exposure to Air Pollutants

EPA Science Inventory

Numerous health studies have used measurements from a few central-site ambient monitors to characterize air pollution exposures. Relying on solely on central-site ambient monitors does not account for the spatial-heterogeneity of ambient air pollution patterns, the temporal varia...

Spatial resolution requirements for traffic-related air pollutant exposure evaluations

EPA Science Inventory

Vehicle emissions represent one of the most important air pollution sources in most urban areas, and elevated concentrations of pollutants found near major roads have been associated with many adverse health impacts. To understand these impacts, exposure estimates should reflect ...

SPATIAL PREDICTION USING COMBINED SOURCES OF DATA

EPA Science Inventory

For improved environmental decision-making, it is important to develop new models for spatial prediction that accurately characterize important spatial and temporal patterns of air pollution. As the U .S. Environmental Protection Agency begins to use spatial prediction in the reg...

A national assessment of the effect of intensive agro-land use practices on nonpoint source pollution using emission scenarios and geo-spatial data.

PubMed

Zhuo, Dong; Liu, Liming; Yu, Huirong; Yuan, Chengcheng

2018-01-01

China's intensive agriculture has led to a broad range of adverse impacts upon ecosystems and thereby caused environmental quality degradation. One of the fundamental problems that face land managers when dealing with agricultural nonpoint source (NPS) pollution is to quantitatively assess the NPS pollution loads from different sources at a national scale. In this study, export scenarios and geo-spatial data were used to calculate the agricultural NPS pollution loads of nutrient, pesticide, plastic film residue, and crop straw burning in China. The results provided the comprehensive and baseline knowledge of agricultural NPS pollution from China's arable farming system in 2014. First, the nitrogen (N) and phosphorus (P) emission loads to water environment were estimated to be 1.44 Tg N and 0.06 Tg P, respectively. East and south China showed the highest load intensities of nutrient release to aquatic system. Second, the amount of pesticide loss to water of seven pesticides that are widely used in China was estimated to be 30.04 tons (active ingredient (ai)). Acetochlor was the major source of pesticide loss to water, contributing 77.65% to the total loss. The environmental impacts of pesticide usage in east and south China were higher than other parts. Third, 19.75% of the plastic film application resided in arable soils. It contributed a lot to soil phthalate ester (PAE) contamination. Fourth, 14.11% of straw produce were burnt in situ, most occurring in May to July (post-winter wheat harvest) in North China Plain and October to November (post-rice harvest days) in southeast China. All the above agricultural NPS pollution loadings were unevenly distributed across China. The spatial correlations between pollution loads at land unit scale were also estimated. Rising labor cost in rural China might be a possible explanation for the general positive correlations of the NPS pollution loads. It also indicated a co-occurred higher NPS pollution loads and a higher human exposure risk in eastern regions. Results from this research might provide full-scale information on the status and spatial variation of various agricultural NPS pollution loads for policy makers to control the NPS pollution in China.

Effect of the pollution level on the functional bacterial groups aiming at degrading bisphenol A and nonylphenol in natural biofilms of an urban river.

PubMed

Cai, Wei; Li, Yi; Wang, Peifang; Niu, Lihua; Zhang, Wenlong; Wang, Chao

2016-08-01

Bisphenol A (BPA) and 4-nonylphenol (NP) are ubiquitous pollutants with estrogenic activity in aquatic environment and have attracted global concern due to their disruption of endocrine systems. This study investigated the spatial distribution characteristics of the bacterial groups involved in the degradation of BPA and NP within biofilms in an urban river using terminal restriction fragment length polymorphism based on 16S rRNA gene sequences. The effects of the pollution level and water parameters on these groups were also assessed. Hierarchical cluster analysis grouped the sampling sites into three clusters reflecting their varying nutrient pollution levels of relatively slight pollution (SP), moderate pollution (MP), and high pollution (HP) based on water quality data and Environmental Quality Standard for Surface Water of China (GB3838-2002). The BPA and NP concentration in river water ranged from 0.8 to 77.5 and 10.2 to 162.9 ng L(-1), respectively. Comamonadaceae, Pseudomonadaceae, Alcaligenaceae, Bacillaceae, Sphingomonadacea, Burkholderiaceae, and Rhizobiaceae were the dominant bacterial taxa involved in BPA and NP degradation, comprising an average of 9.8, 8.1, 7.6, 6.7, 6.2, 4.1, and 2.8 % of total sequences, respectively. The total abundance of these groups showed a slight upward trend and subsequently rapidly decreased with increasing pollution levels. The average proportion of Comamonadaceae in MP river sections was almost 1.5-2 times than that in SP or HP one. The distribution of functional groups was found related to environmental variables, especially pH, conductivity, ammonium nitrogen (NH3-N), and BPA. The abundance of Comamonadaceae and Rhizobiaceae was both closely related to higher values of pH and conductivity as well as lower concentrations of NP and BPA. Alcaligenaceae and Pseudomonadaceae were associated with higher concentrations of TP and CODMn and inversely correlated with DO concentration. This study might provide effective data on bacterial group changes in polluted urban rivers.

Light Pollution Around Tucson, AZ And Its Effect On The Spatial Distribution Of Lesser Long-nosed Bats

NASA Astrophysics Data System (ADS)

Fersch, Alisa; Walker, C.

2012-01-01

Light pollution is a well-known problem for astronomers. It is also gaining attention as an ecological issue. The federally endangered Lesser Long-Nosed Bat (Leptonycteris cursoae) resides for part of the year near Tucson, Arizona. It is possible that this species tends to avoid light. Excess artificial light would therefore interfere with the bats’ flight patterns and foraging habits. In order to test this hypothesis, we quantified night sky brightness with data from the citizen-science campaign GLOBE at Night. Using direct measurements taken with a Sky Quality Meter (SQM), we created a contour map of the artificial night sky brightness around Tucson. When this map is compared to the approximate flight paths of the lesser long-nosed bat, we can see that the bats do appear to be avoiding the brightest area of Tucson. We also used logistic regression to analyze what combination of ecological variables (ecoregion, vegetation cover, landform and light) best describes the observed spatial distribution of lesser long-nosed bats. Of the models that were tested, light alone was not a good predictor of the bat presence or absence. However, light in addition to vegetation and ecoregion was the best model. This information can be useful for making decisions about lighting codes in areas of the city that the bats tend to traverse. The contour map of light pollution in Tucson will be useful for both future astronomy and ecology studies and can also be used for public outreach about light pollution. Fersch was supported by the NOAO/KPNO Research Experiences for Undergraduates (REU) Program which is funded by the National Science Foundation Research Experiences for Undergraduates Program and the Department of Defense ASSURE program through Scientific Program Order No. 13 (AST-0754223) of the Cooperative Agreement No. AST-0132798 between the Association of Universities for Research in Astronomy (AURA) and the NSF.

Spatial variation of urban soil geochemistry in a roadside sports ground in Galway, Ireland.

PubMed

Dao, Ligang; Morrison, Liam; Zhang, Chaosheng

2010-02-01

Characterization of spatial variation of urban soil geochemistry especially heavy metal pollution is essential for a better understanding of pollution sources and potential risks. A total of 294 surface soil samples were collected from a roadside sports ground in Galway, Ireland, and were analysed by ICP-OES for 23 chemical elements (Al, Ca, Ce, Co, Cu, Fe, K, La, Li, Mg, Mn, Na, Ni, P, Pb, S, Sc, Sr, Th, Ti, V, Y and Zn). Strong variations in soil geochemistry were observed and most elements, with the exception of Cu, Pb, P, S and Zn, showed multi-modal features, indicating the existence of mixed populations which proved difficult to separate. To evaluate the pollution level of the study area, the pollution index (PI) values were calculated based on a comparison with the Dutch target and intervention values. None of the concentrations of metal pollutants exceeded their intervention values, indicating the absence of serious contaminated soil, and the ratios to target values were therefore employed to produce the hazard maps. The spatial distribution and hazard maps for Cu, Pb and Zn indicated relatively high levels of pollution along the southern roadside extending almost 30m into the sports ground, revealing the strong influence of pollution from local traffic. However, heavy metal pollution was alleviated along the eastern roadside of the study area by the presence of a belt of shrubs. Therefore, in order to prevent further contamination from traffic emissions, the planting of hedging or erection of low walls should be considered as shields against traffic pollution for roadside parks. The results in this study are useful for management practices in sports and parks in urban areas. Copyright 2009 Elsevier B.V. All rights reserved.

An improved geographically weighted regression model for PM2.5 concentration estimation in large areas

NASA Astrophysics Data System (ADS)

Zhai, Liang; Li, Shuang; Zou, Bin; Sang, Huiyong; Fang, Xin; Xu, Shan

2018-05-01

Considering the spatial non-stationary contributions of environment variables to PM2.5 variations, the geographically weighted regression (GWR) modeling method has been using to estimate PM2.5 concentrations widely. However, most of the GWR models in reported studies so far were established based on the screened predictors through pretreatment correlation analysis, and this process might cause the omissions of factors really driving PM2.5 variations. This study therefore developed a best subsets regression (BSR) enhanced principal component analysis-GWR (PCA-GWR) modeling approach to estimate PM2.5 concentration by fully considering all the potential variables' contributions simultaneously. The performance comparison experiment between PCA-GWR and regular GWR was conducted in the Beijing-Tianjin-Hebei (BTH) region over a one-year-period. Results indicated that the PCA-GWR modeling outperforms the regular GWR modeling with obvious higher model fitting- and cross-validation based adjusted R2 and lower RMSE. Meanwhile, the distribution map of PM2.5 concentration from PCA-GWR modeling also clearly depicts more spatial variation details in contrast to the one from regular GWR modeling. It can be concluded that the BSR enhanced PCA-GWR modeling could be a reliable way for effective air pollution concentration estimation in the coming future by involving all the potential predictor variables' contributions to PM2.5 variations.

Development of improved wildfire smoke exposure estimates for health studies in the western U.S.

NASA Astrophysics Data System (ADS)

Ivey, C.; Holmes, H.; Loria Salazar, S. M.; Pierce, A.; Liu, C.

2016-12-01

Wildfire smoke exposure is a significant health concern in the western U.S. because large wildfires have increased in size and frequency over the past four years due to drought conditions. The transport phenomena in complex terrain and timing of the wildfire emissions make the smoke plumes difficult to simulate using conventional air quality models. Monitoring data can be used to estimate exposure metrics, but in rural areas the monitoring networks are too sparse to calculate wildfire exposure metrics for the entire population in a region. Satellite retrievals provide global, spatiotemporal air quality information and are used to track pollution plumes, estimate human exposures, model emissions, and determine sources (i.e., natural versus anthropogenic) in regulatory applications. Particulate matter (PM) exposures can be estimated using columnar aerosol optical depth (AOD), where satellite AOD retrievals serve as a spatial surrogate to simulate surface PM gradients. These exposure models have been successfully used in health effects studies in the eastern U.S. where complex mountainous terrain and surface reflectance do not limit AOD retrival from satellites. Using results from a chemical transport model (CTM) is another effective method to determine spatial gradients of pollutants. However, the CTM does not adequately capture the temporal and spatial distribution of wildfire smoke plumes. By combining the spatiotemporal pollutant fields from both satellite retrievals and CTM results with ground based pollutant observations the spatial wildfire smoke exposure model can be improved. This work will address the challenge of understanding the spatiotemporal distributions of pollutant concentrations to model human exposures of wildfire smoke in regions with complex terrain, where meteorological conditions as well as emission sources significantly influence the spatial distribution of pollutants. The focus will be on developing models to enhance exposure estimates of elevated PM and ozone concentrations from wildfire smoke plumes in the western U.S.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tian, Hanqin; Chen, Guangsheng; Lu, Chaoqun

Greenhouse gas (GHG)-induced climate change is among the most pressing sustainability challenges facing humanity today, posing serious risks for ecosystem health. Methane (CH 4) and nitrous oxide (N 2O) are the two most important GHGs after carbon dioxide (CO 2), but their regional and global budgets are not well known. In this paper, we applied a process-based coupled biogeochemical model to concurrently estimate the magnitude and spatial and temporal patterns of CH 4 and N 2O fluxes as driven by multiple environmental changes, including climate variability, rising atmospheric CO 2, increasing nitrogen deposition, tropospheric ozone pollution, land use change, andmore » nitrogen fertilizer use.« less

Assimilation of Satellite Data in Regional Air Quality Models

NASA Technical Reports Server (NTRS)

Mcnider, Richard T.; Norris, William B.; Casey, Daniel; Pleim, Jonathan E.; Roselle, Shawn J.; Lapenta, William M.

1997-01-01

In terms of important uncertainty in regional-scale air-pollution models, probably no other aspect ranks any higher than the current ability to specify clouds and soil moisture on the regional scale. Because clouds in models are highly parameterized, the ability of models to predict the correct spatial and radiative characteristics is highly suspect and subject to large error. The poor representation of cloud fields from point measurements at National Weather Services stations and the almost total absence of surface moisture availability observations has made assimilation of these variables difficult to impossible. Yet, the correct inclusion of clouds and surface moisture are of first-order importance in regional-scale photochemistry.

Exploration of health risks related to air pollution and temperature in three Latin American cities

NASA Astrophysics Data System (ADS)

Romero-Lankao, P.; Borbor Cordova, M.; Qin, H.

2013-12-01

We explore whether the health risks related to air pollution and temperature extremes are spatially and socioeconomically differentiated within three Latin American cities: Bogota, Colombia, Mexico City, Mexico, and Santiago, Chile. Based on a theoretical review of three relevant approaches to risk analysis (risk society, environmental justice, and urban vulnerability as impact), we hypothesize that health risks from exposure to air pollution and temperature in these cities do not necessarily depend on socio-economic inequalities. To test this hypothesis, we gathered, validated, and analyzed temperature, air pollution, mortality and socioeconomic vulnerability data from the three study cities. Our results show the association between air pollution levels and socioeconomic vulnerabilities did not always correlate within the study cities. Furthermore, the spatial differences in socioeconomic vulnerabilities within cities do not necessarily correspond with the spatial distribution of health impacts. The present study improves our understanding of the multifaceted nature of health risks and vulnerabilities associated with global environmental change. The findings suggest that health risks from atmospheric conditions and pollutants exist without boundaries or social distinctions, even exhibiting characteristics of a boomerang effect (i.e., affecting rich and poor alike) on a smaller scale such as areas within urban regions. We used human mortality, a severe impact, to measure health risks from air pollution and extreme temperatures. Public health data of better quality (e.g., morbidity, hospital visits) are needed for future research to advance our understanding of the nature of health risks related to climate hazards.

Measuring the impact of pollution on property prices in Madrid: objective versus subjective pollution indicators in spatial models

NASA Astrophysics Data System (ADS)

Mínguez, Román; Montero, José-María; Fernández-Avilés, Gema

2013-04-01

Much work has been done in the context of the hedonic price theory to estimate the impact of air quality on housing prices. Research has employed objective measures of air quality, but only slightly confirms the hedonic theory in the best of cases: the implicit price function relating housing prices to air pollution will, ceteris paribus, be negatively sloped. This paper compares the performance of a spatial Durbin model when using both objective and subjective measures of pollution. On the one hand, we design an Air Pollution Indicator based on measured pollution as the objective measure of pollution. On the other hand, the subjective measure of pollution employed to characterize neighborhoods is the percentage of residents who declare that the neighborhood has serious pollution problems, the percentage being referred to as residents' perception of pollution. For comparison purposes, the empirical part of this research focuses on Madrid (Spain). The study employs a proprietary database containing information about the price and 27 characteristics of 11,796 owner-occupied single family homes. As far as the authors are aware, it is the largest database ever used to analyze the Madrid housing market. The results of the study clearly favor the use of subjective air quality measures.

Investigation of the Nitrogen Dioxide Pollution in Urban Areas using a New Portable ICAD Instrument

NASA Astrophysics Data System (ADS)

Horbanski, Martin; Pöhler, Denis; Adler, Tim; Lampel, Johannes; Kanatschnig, Florian; Oesterle, Tobias; Reh, Miriam; Platt, Ulrich

2016-04-01

Nitrogen oxides (NOx) and especially nitrogen dioxide (NO2), are still among of the most problematic pollutants in urban areas not only in developing, but also in industrialized countries. Despite the measures taken to reduce their emissions, NO2 concentrations in many urban areas exceed the WHO recommended limits of 40 μg/m3 for annual mean and 200 μg/m3 for 1 hour mean. Additionally it is known that the NO2 concentration in urban areas has a strong spatial and temporal variability, due to the large number of NOx emitting point sources (mainly traffic) found in densely populated areas. However, the layout of air monitoring networks in most urban areas, installed to continuously monitor the officially prescribed NO2 limits, does not reflect the high spatial variability because they only conduct measurements at a single or few selected sampling points, mainly on major roads, which are often not representative for the whole urban area. At present these uncertainties about the spatial NO2 distribution constitute severe limitations for the assessment of health risks, for the quality of chemical model calculations, and for developing effective measures to reduce NOx emissions. We developed a new light-weight and portable ICAD (Iterative Cavity Enhanced DOAS) instrument which detects NO2 at a detection limit as low as 0.2 μg/m3 with a high time resolution of seconds. The instrument is based on the Cavity Enhanced (CE-) DOAS technique, which directly identifies and quantifies NO2 by its differential optical absorption. Therefore, it does not suffer from interferences by other trace gas species like O3 or NOy. This is a great advantage over other NO2 instruments (e.g. solid state detectors or chemiluminescence instruments). We present the result of ICAD NO2 measurements, which we recently performed in more than 10 German cities. The ICAD instrument was mounted on mobile platforms like cars and bicycles, measuring the NO2 concentrations along carefully selected tracks. Also several stationary measurements were performed at selected sites. We found that high NO2 concentrations exceeded pollution limits across extensive areas of the cities. Contrary to expectations we found high NO2 concentrations also away from heavily traveled roads e.g. in residential areas and close to kindergardens and schools and even indoors. Thus, the exposure of the populations to NO2 is much higher than expected, which results in higher health risks, particularly for children and elderly people who are risk groups.

New Methods for Personal Exposure Monitoring for Airborne Particles

PubMed Central

Koehler, Kirsten A.; Peters, Thomas

2016-01-01

Airborne particles have been associated with a range of adverse cardiopulmonary outcomes, which has driven its monitoring at stationary, central sites throughout the world. Individual exposures, however, can differ substantially from concentrations measured at central sites due to spatial variability across a region and sources unique to the individual, such as cooking or cleaning in homes, traffic emissions during commutes, and widely varying sources encountered at work. Personal monitoring with small, battery-powered instruments enables the measurement of an individual’s exposure as they go about their daily activities. Personal monitoring can substantially reduce exposure misclassification and improve the power to detect relationships between particulate pollution and adverse health outcomes. By partitioning exposures to known locations and sources, it may be possible to account for variable toxicity of different sources. This review outlines recent advances in the field of personal exposure assessment for particulate pollution. Advances in battery technology have improved the feasibility of 24-hour monitoring, providing the ability to more completely attribute exposures to microenvironment (e.g., work, home, commute). New metrics to evaluate the relationship between particulate matter and health are also being considered, including particle number concentration, particle composition measures, and particle oxidative load. Such metrics provide opportunities to develop more precise associations between airborne particles and health and may provide opportunities for more effective regulations. PMID:26385477

Effects of land-use patterns on in-stream nitrogen in a highly-polluted river basin in Northeast China.

PubMed

Bu, Hongmei; Zhang, Yuan; Meng, Wei; Song, Xianfang

2016-05-15

This study investigated the effects of land-use patterns on nitrogen pollution in the Haicheng River basin in Northeast China during 2010 by conducting statistical and spatial analyses and by analyzing the isotopic composition of nitrate. Correlation and stepwise regressions indicated that land-use types and landscape metrics were correlated well with most river nitrogen variables and significantly predicted them during different sampling seasons. Built-up land use and shape metrics dominated in predicting nitrogen variables over seasons. According to the isotopic compositions of river nitrate in different zones, the nitrogen sources of the river principally originated from synthetic fertilizer, domestic sewage/manure, soil organic matter, and atmospheric deposition. Isotope mixing models indicated that source contributions of river nitrogen significantly varied from forested headwaters to densely populated towns of the river basin. Domestic sewage/manure was a major contributor to river nitrogen with the proportions of 76.4 ± 6.0% and 62.8 ± 2.1% in residence and farmland-residence zones, respectively. This research suggested that regulating built-up land uses and reducing discharges of domestic sewage and industrial wastewater would be effective methods for river nitrogen control. Copyright © 2016 Elsevier B.V. All rights reserved.

Tracing global supply chains to air pollution hotspots

NASA Astrophysics Data System (ADS)

Moran, Daniel; Kanemoto, Keiichiro

2016-09-01

While high-income countries have made significant strides since the 1970s in improving air quality, air pollution continues to rise in many developing countries and the world as a whole. A significant share of the pollution burden in developing countries can be attributed to production for export to consumers in high-income nations. However, it remains a challenge to quantify individual actors’ share of responsibility for pollution, and to involve parties other than primary emitters in cleanup efforts. Here we present a new spatially explicit modeling approach to link SO2, NO x , and PM10 severe emissions hotspots to final consumers via global supply chains. These maps show developed countries reducing their emissions domestically but driving new pollution hotspots in developing countries. This is also the first time a spatially explicit footprint inventory has been established. Linking consumers and supply chains to emissions hotspots creates opportunities for other parties to participate alongside primary emitters and local regulators in pollution abatement efforts.

Identification of a potential toxic hot spot associated with AVS spatial and seasonal variation.

PubMed

Campana, O; Rodríguez, A; Blasco, J

2009-04-01

In risk assessment of aquatic sediments, much attention is paid to the difference between acid-volatile sulfide (AVS) and simultaneously extracted metals (SEMs) as indicators of metal availability. Ten representative sampling sites were selected along the estuary of the Guadalete River. Surficial sediments were sampled in winter and summer to better understand SEM and AVS spatial and seasonal distributions and to establish priority risk areas. Total SEM concentration (SigmaSEM) ranged from 0.3 to 4.7 micromol g(-1). It was not significantly different between seasons, however, it showed a significant difference between sampling stations. AVS concentrations were much more variable, showing significant spatial and temporal variations. The values ranged from 0.8 to 22.4 micromol g(-1). The SEM/AVS ratio was found to be <1 at all except one station located near the mouth of the estuary. The results provided information on a potential pollution source near the mouth of the estuary, probably associated with vessel-related activities carried out in a local harbor area located near the station.

A stochastic-geometric model of soil variation in Pleistocene patterned ground

NASA Astrophysics Data System (ADS)

Lark, Murray; Meerschman, Eef; Van Meirvenne, Marc

2013-04-01

In this paper we examine the spatial variability of soil in parent material with complex spatial structure which arises from complex non-linear geomorphic processes. We show that this variability can be better-modelled by a stochastic-geometric model than by a standard Gaussian random field. The benefits of the new model are seen in the reproduction of features of the target variable which influence processes like water movement and pollutant dispersal. Complex non-linear processes in the soil give rise to properties with non-Gaussian distributions. Even under a transformation to approximate marginal normality, such variables may have a more complex spatial structure than the Gaussian random field model of geostatistics can accommodate. In particular the extent to which extreme values of the variable are connected in spatially coherent regions may be misrepresented. As a result, for example, geostatistical simulation generally fails to reproduce the pathways for preferential flow in an environment where coarse infill of former fluvial channels or coarse alluvium of braided streams creates pathways for rapid movement of water. Multiple point geostatistics has been developed to deal with this problem. Multiple point methods proceed by sampling from a set of training images which can be assumed to reproduce the non-Gaussian behaviour of the target variable. The challenge is to identify appropriate sources of such images. In this paper we consider a mode of soil variation in which the soil varies continuously, exhibiting short-range lateral trends induced by local effects of the factors of soil formation which vary across the region of interest in an unpredictable way. The trends in soil variation are therefore only apparent locally, and the soil variation at regional scale appears random. We propose a stochastic-geometric model for this mode of soil variation called the Continuous Local Trend (CLT) model. We consider a case study of soil formed in relict patterned ground with pronounced lateral textural variations arising from the presence of infilled ice-wedges of Pleistocene origin. We show how knowledge of the pedogenetic processes in this environment, along with some simple descriptive statistics, can be used to select and fit a CLT model for the apparent electrical conductivity (ECa) of the soil. We use the model to simulate realizations of the CLT process, and compare these with realizations of a fitted Gaussian random field. We show how statistics that summarize the spatial coherence of regions with small values of ECa, which are expected to have coarse texture and so larger saturated hydraulic conductivity, are better reproduced by the CLT model than by the Gaussian random field. This suggests that the CLT model could be used to generate an unlimited supply of training images to allow multiple point geostatistical simulation or prediction of this or similar variables.

The spatial structure and temporal synchrony of water quality in stream networks

NASA Astrophysics Data System (ADS)

Abbott, Benjamin; Gruau, Gerard; Zarneske, Jay; Barbe, Lou; Gu, Sen; Kolbe, Tamara; Thomas, Zahra; Jaffrezic, Anne; Moatar, Florentina; Pinay, Gilles

2017-04-01

To feed nine billion people in 2050 while maintaining viable aquatic ecosystems will require an understanding of nutrient pollution dynamics throughout stream networks. Most regulatory frameworks such as the European Water Framework Directive and U.S. Clean Water Act, focus on nutrient concentrations in medium to large rivers. This strategy is appealing because large rivers integrate many small catchments and total nutrient loads drive eutrophication in estuarine and oceanic ecosystems. However, there is growing evidence that to understand and reduce downstream nutrient fluxes we need to look upstream. While headwater streams receive the bulk of nutrients in river networks, the relationship between land cover and nutrient flux often breaks down for small catchments, representing an important ecological unknown since 90% of global stream length occurs in catchments smaller than 15 km2. Though continuous monitoring of thousands of small streams is not feasible, what if we could learn what we needed about where and when to implement monitoring and conservation efforts with periodic sampling of headwater catchments? To address this question we performed repeat synoptic sampling of 56 nested catchments ranging in size from 1 to 370 km2 in western France. Spatial variability in carbon and nutrient concentrations decreased non-linearly as catchment size increased, with thresholds in variance for organic carbon and nutrients occurring between 36 and 68 km2. While it is widely held that temporal variance is higher in smaller streams, we observed consistent temporal variance across spatial scales and the ranking of catchments based on water quality showed strong synchrony in the water chemistry response to seasonal variation and hydrological events. We used these observations to develop two simple management frameworks. The subcatchment leverage concept proposes that mitigation and restoration efforts are more likely to succeed when implemented at spatial scales expressing high variability in the target parameter, which indicates decreased system inertia and demonstrates that alternative system responses are possible. The subcatchment synchrony concept suggests that periodic sampling of headwaters can provide valuable information about pollutant sources and inherent resilience in subcatchments and that if agricultural activity were redistributed based on this assessment of catchment vulnerability to nutrient loading, water quality could be improved while maintaining crop yields.

In-time source tracking of watershed loads of Taihu Lake Basin, China based on spatial relationship modeling.

PubMed

Wang, Ce; Bi, Jun; Zhang, Xu-Xiang; Fang, Qiang; Qi, Yi

2018-05-25

Influent river carrying cumulative watershed load plays a significant role in promoting nuisance algal bloom in river-fed lake. It is most relevant to discern in-stream water quality exceedance and evaluate the spatial relationship between risk location and potential pollution sources. However, no comprehensive studies of source tracking in watershed based on management grid have been conducted for refined water quality management, particularly for plain terrain with complex river network. In this study, field investigations were implemented during 2014 in Taige Canal watershed of Taihu Lake Basin. A Geographical Information System (GIS)-based spatial relationship model was established to characterize the spatial relationships of "point (point-source location and monitoring site)-line (river segment)-plane (catchment)." As a practical exemplification, in-time source tracking was triggered on April 15, 2015 at Huangnianqiao station, where TN and TP concentration violated the water quality standard (TN 4.0 mg/L, TP 0.15 mg/L). Of the target grid cells, 53 and 46 were identified as crucial areas having high pollution intensity for TN and TP pollution, respectively. The estimated non-point source load in each grid cell could be apportioned into different source types based on spatial pollution-related entity objects. We found that the non-point source load derived from rural sewage and livestock and poultry breeding accounted for more than 80% of total TN or TP load than another source type of crop farming. The approach in this study would be of great benefit to local authorities for identifying the serious polluted regions and efficiently making environmental policies to reduce watershed load.

[Spatiotemporal dynamic fuzzy evaluation of wetland environmental pollution risk in Dayang estuary of Liaoning Province, Northeast China based on remote sensing].

PubMed

Sun, Yong-Guang; Zhao, Dong-Zhi; Zhang, Feng-Shou; Wei, Bao-Quan; Chu, Jia-Lan; Su, Xiu

2012-11-01

Based on the aerial image data of Dayang estuary in 2008, and by virtue of Analytic Hierarchy Process (AHP) , remote sensing technology, and GIS spatial analysis, a spatiotemporal evaluation was made on the comprehensive level of wetland environmental pollution risk in Dayang estuary, with the impacts of typical human activities on the dynamic variation of this comprehensive level discussed. From 1958 to 2008, the comprehensive level of the environmental pollution risk in study area presented an increasing trend. Spatially, this comprehensive level declined from land to ocean, and showed a zonal distribution. Tourism development activities unlikely led to the increase of the comprehensive level, while human inhabitation, transportation, and aquaculture would exacerbate the risk of environmental pollution. This study could provide reference for the sea area use planning, ecological function planning, and pollutants control of estuary region.

Plastic pollution in islands of the Atlantic Ocean.

PubMed

Monteiro, Raqueline C P; Ivar do Sul, Juliana A; Costa, Monica F

2018-07-01

Marine plastic pollution is present in all oceans, including remote oceanic islands. Despite the increasing number of articles on plastic pollution in the last years, there is still a lack of studies in islands, that are biodiversity hotspots when compared to the surrounding ocean, and even other recognized highly biodiverse marine environments. Articles published in the peer reviewed literature (N = 20) were analysed according to the presence of macro (>5 mm) and microplastics (<5 mm) on beaches and the marine habitats immediately adjacent to 31 islands of the Atlantic Ocean and Caribbean Sea. The first articles date from the 1980s, but most were published in the 2000s. Articles on macroplastics were predominant in this review (N = 12). Beaches were the most studied environment, possibly due to easy access. The main focus of most articles was the spatial distribution of plastics associated with variables such as position of the beach in relation to wind and currents. Very few studies have analysed plastics colonization by organisms or the identification of persistent organic pollutants (POPs). Islands of the North/South Atlantic and Caribbean Sea were influenced by different sources of macroplastics, being marine-based sources (i.e., fishing activities) predominant in the Atlantic Ocean basin. On the other hand, in the Caribbean Sea, land-based sources were more common. Copyright © 2018 Elsevier Ltd. All rights reserved.

Simulating gas and particulate pollution over the Middle East and the state of Qatar using a 3-D regional air quality modeling system

NASA Astrophysics Data System (ADS)

Fountoukis, Christos; Gladich, Ivan; Ayoub, Mohammed; Kais, Sabre; Ackermann, Luis; Skillern, Adam

2016-04-01

The rapid urbanization, industrialization and economic expansion in the Middle East have led to increased levels of atmospheric pollution with important implications for human health and climate. We applied the online-coupled meteorological and chemical transport Weather Research and Forecasting/Chemistry (WRF-Chem) model over the Middle Eastern domain, to simulate the concentration of gas and aerosols with a special focus over the state of Qatar. WRF-Chem was set to simulate pollutant concentrations along with the meteorology-chemistry interactions through the related direct, indirect and semi-direct feedback mechanisms. A triple-nested domain configuration was used with a high grid resolution (1x1 km2) over the region of Qatar. Model predictions are evaluated against intensive measurements of meteorological parameters (temperature, relative humidity and wind speed) as well as ozone and particulate matter taken from various measurement stations throughout Doha, Qatar during summer 2015. The ability of the model to capture the temporal and spatial variability of the observations is assessed and possible reasons for the model bias are explored through sensitivity tests. Emissions of both fine and coarse mode particles from construction activities in large urban Middle Eastern environments comprise a major pollution source that is unaccounted for in emission inventories used so far in large scale models for this part of the world.

Impact of soil structure heterogeneity on the degradation of organic pollutants at the centimeter scale : 3D Modeling using graph based method

NASA Astrophysics Data System (ADS)

Sinclair Yemini, Francis; Chenu, Claire; Monga, Olivier; Vieuble Gonond, Laure; Juarez, Sabrina; Pihneiro, Marc; otten, Wilfred; Garnier, Patricia

2014-05-01

Contaminant degradation by microorganisms is very variable in soils because of the very heterogeneous spatial relationship of contaminant/degraders. Repacked Soil columns were carried out to study the degradation of 2,4D pesticide labelled with C14 for different scenarios of microorganisms and pesticide initial location. Measurements of global C14-CO2 emission and C14 distribution in the soil column showed that the initial location play a crucial rule on the dissipation of the pollutant. Experiments were simulated using a 3D model able to model microbial degradation and substrate diffusion between aggregates by considering explicitly the 3D structure of soil from CT images. The initial version of the model (Monga et al., 2008) was improved in order to simulate diffusion in samples of large size. Partial differential equations were implemented using freefem++ solver. The model simulates properly the dynamics of 2,4D in the column for the different initial situations. CT images of the same soil but using undisturbed structure instead of repacked aggregates were also carried out. Significant differences of the simulated results were observed between the repacked and the undisturbed soil. The conclusion of our work is that the heterogeneity of the soil structure and location of pollutants and decomposers has a very strong influence on the dissipation of pollutants.

Spatial and temporal trends in the mortality burden of air pollution in China: 2004–2012

PubMed Central

Liu, Miaomiao; Huang, Yining; Ma, Zongwei; Jin, Zhou; Liu, Xingyu; Wang, Haikun; Liu, Yang; Wang, Jinnan; Jantunen, Matti; BiDr, Jun; KinneyDr, Patrick L.

2017-01-01

While recent assessments have quantified the burden of air pollution at the national scale in China, air quality managers would benefit from assessments that disaggregate health impacts over regions and over time. We took advantage of a new 10 × 10 km satellite-based PM2.5 dataset to analyze spatial and temporal trends of air pollution health impacts in China, from 2004 to 2012. Results showed that national PM2.5 related deaths from stroke, ischemic heart disease and lung cancer increased from approximately 800,000 cases in 2004 to over 1.2 million cases in 2012. The health burden exhibited strong spatial variations, with high attributable deaths concentrated in regions including the Beijing–Tianjin Metropolitan Region, Yangtze River Delta, Pearl River Delta, Sichuan Basin, Shandong, Wuhan Metropolitan Region, Changsha–Zhuzhou–Xiangtan, Henan, and Anhui, which have heavy air pollution, high population density, or both. Increasing trends were found in most provinces, but with varied growth rates. While there was some evidence for improving air quality in recent years, this was offset somewhat by the countervailing influences of in–migration together with population growth. We recommend that priority areas for future national air pollution control policies be adjusted to better reflect the spatial hotspots of health burdens. Satellite-based exposure and health impact assessments can be a useful tool for tracking progress on both air quality and population health burden reductions. PMID:27745948

Spatial and temporal trends in the mortality burden of air pollution in China: 2004-2012.

PubMed

Liu, Miaomiao; Huang, Yining; Ma, Zongwei; Jin, Zhou; Liu, Xingyu; Wang, Haikun; Liu, Yang; Wang, Jinnan; Jantunen, Matti; Bi, Jun; Kinney, Patrick L

2017-01-01

While recent assessments have quantified the burden of air pollution at the national scale in China, air quality managers would benefit from assessments that disaggregate health impacts over regions and over time. We took advantage of a new 10×10km satellite-based PM 2.5 dataset to analyze spatial and temporal trends of air pollution health impacts in China, from 2004 to 2012. Results showed that national PM 2.5 related deaths from stroke, ischemic heart disease and lung cancer increased from approximately 800,000 cases in 2004 to over 1.2 million cases in 2012. The health burden exhibited strong spatial variations, with high attributable deaths concentrated in regions including the Beijing-Tianjin Metropolitan Region, Yangtze River Delta, Pearl River Delta, Sichuan Basin, Shandong, Wuhan Metropolitan Region, Changsha-Zhuzhou-Xiangtan, Henan, and Anhui, which have heavy air pollution, high population density, or both. Increasing trends were found in most provinces, but with varied growth rates. While there was some evidence for improving air quality in recent years, this was offset somewhat by the countervailing influences of in-migration together with population growth. We recommend that priority areas for future national air pollution control policies be adjusted to better reflect the spatial hotspots of health burdens. Satellite-based exposure and health impact assessments can be a useful tool for tracking progress on both air quality and population health burden reductions. Copyright © 2016. Published by Elsevier Ltd.

Influence of Air Pollutant Emission Controls on the "Climate Penalty" in the United States

NASA Astrophysics Data System (ADS)

Feng, T.; Couzo, E. A.; Selin, N. E.; Garcia-Menendez, F.; Monier, E.

2016-12-01

Previous work has examined the so-called "climate penalty" (or benefit, where climate change leads to decreased pollutant concentrations) for the U.S. In particular, previous research has identified the role of changes in temperature, precipitation, relative humidity, and biogenic emissions, in altering concentrations of O3 and PM2.5, when emissions of air pollutant precursors are held constant. However, changes in emissions of those precursors can also affect the magnitude of climate penalty/benefit. The effect of changing air pollutant emissions on the climate penalty/benefit has not been systematically studied. Here, we estimate the U.S. climate penalty (for O3 and PM2.5) as a function of four different local (U.S.) non-GHG emissions scenarios using the GEOS-Chem chemical transport model coupled to the MIT Integrated Global System Model linked to the Community Atmosphere Model (IGSM-CAM). Our base case scenario includes global and regional emissions for 2006. We conduct three sensitivity scenarios that adjust U.S. air pollutant precursor (non-GHG) emissions by -50%, +50%, and +100%; global emissions are kept at 2006 levels. This allows us to quantify the avoided climate penalty achieved by non-GHG emissions reductions. To capture inter-annual meteorological variability, our climate penalty calculations use 20-year averages for the present (1991-2010) and future (2091-2110) climate under a no-policy scenario. Consistent with previous work, we find a "climate penalty" for O3 and PM2.5 in U.S. by 2100 across all four scenarios. We also find a climate-related decrease in the concentration of NOx and nitrate, and an increase in black carbon, organic carbon and sulfate. Changes in ammonium are spatially inhomogeneous, with an increase in eastern U.S. and a decrease in middle and western U.S. When air pollutant precursor emissions increase, we find that the O3 "climate penalty" is enhanced. However, the response of the PM2.5 "climate penalty" to changed emissions differs spatially among U.S. regions. It increases with U.S. non-GHG emissions in the East, but decreases with the emissions in the West. We use these results to draw conclusions about whether (and where) U.S. emissions controls could have an additional and previously unquantified benefit in reducing projected climate penalties.

Calibration of low-cost gas sensors for an urban air quality monitoring network

NASA Astrophysics Data System (ADS)

Scott, A.; Kelley, C.; He, C.; Ghugare, P.; Lehman, A.; Benish, S.; Stratton, P.; Dickerson, R. R.; Zuidema, C.; Azdoud, Y.; Ren, X.

2017-12-01

In a warming world, environmental pollution may be exacerbated by anthropogenic activities, such as climate change and the urban heat island effect, as well as natural phenomena such as heat waves. However, monitoring air pollution at federal reference standards (approximately 1 part per billion or ppb for ambient ozone) is cost-prohibitive in heterogeneous urban areas as many expensive devices are required to fully capture a region's geo-spatial variability. Innovation in low-cost sensors provide a potential solution, yet technical challenges remain to overcome possible imprecision in the data. We present the calibrations of ozone and nitrous dioxide from a low-cost air quality monitoring device designed for the Baltimore Open Air Project. The sensors used in this study are commercially available thin film electrochemical sensors from SPEC Sensor, which are amperometric, meaning they generate current proportional to volumetric fraction of gas. The results of sensor calibrations in the laboratory and field are presented.

Estimating the Pollution Risk of Cadmium in Soil Using a Composite Soil Environmental Quality Standard

PubMed Central

Huang, Biao; Zhao, Yongcun

2014-01-01

Estimating standard-exceeding probabilities of toxic metals in soil is crucial for environmental evaluation. Because soil pH and land use types have strong effects on the bioavailability of trace metals in soil, they were taken into account by some environmental protection agencies in making composite soil environmental quality standards (SEQSs) that contain multiple metal thresholds under different pH and land use conditions. This study proposed a method for estimating the standard-exceeding probability map of soil cadmium using a composite SEQS. The spatial variability and uncertainty of soil pH and site-specific land use type were incorporated through simulated realizations by sequential Gaussian simulation. A case study was conducted using a sample data set from a 150 km2 area in Wuhan City and the composite SEQS for cadmium, recently set by the State Environmental Protection Administration of China. The method may be useful for evaluating the pollution risks of trace metals in soil with composite SEQSs. PMID:24672364

GeoMedStat: an integrated spatial surveillance system to track air pollution and associated healthcare events.

PubMed

Faruque, Fazlay S; Li, Hui; Williams, Worth B; Waller, Lance A; Brackin, Bruce T; Zhang, Lei; Grimes, Kim A; Finley, Richard W

2014-12-01

Air pollutants, such as particulate matter with a diameter ≤2.5 microns (PM2.5) and ozone (O3), are known to exacerbate asthma and other respiratory diseases. An integrated surveillance system that tracks such air pollutants and associated disease incidence can assist in risk assessment, healthcare preparedness and public awareness. However, the implementation of such an integrated environmental health surveillance system is a challenge due to the disparate sources of many types of data and the implementation becomes even more complicated for a spatial and real-time system due to lack of standardised technological components and data incompatibility. In addition, accessing and utilising health data that are considered as Protected Health Information (PHI) require maintaining stringent protocols, which have to be supported by the system. This paper aims to illustrate the development of a spatial surveillance system (GeoMedStat) that is capable of tracking daily environmental pollutants along with both daily and historical patient encounter data. It utilises satellite data and the groundmonitor data from the US National Aeronautics and Space Administration (NASA) and the US Environemental Protection Agenecy (EPA), rspectively as inputs estimating air pollutants and is linked to hospital information systems for accessing chief complaints and disease classification codes. The components, developmental methods, functionality of GeoMedStat and its use as a real-time environmental health surveillance system for asthma and other respiratory syndromes in connection with with PM2.5 and ozone are described. It is expected that the framework presented will serve as an example to others developing real-time spatial surveillance systems for pollutants and hospital visits.

Spatial characterization of dissolved trace elements and heavy metals in the upper Han River (China) using multivariate statistical techniques.

PubMed

Li, Siyue; Zhang, Quanfa

2010-04-15

A data matrix (4032 observations), obtained during a 2-year monitoring period (2005-2006) from 42 sites in the upper Han River is subjected to various multivariate statistical techniques including cluster analysis, principal component analysis (PCA), factor analysis (FA), correlation analysis and analysis of variance to determine the spatial characterization of dissolved trace elements and heavy metals. Our results indicate that waters in the upper Han River are primarily polluted by Al, As, Cd, Pb, Sb and Se, and the potential pollutants include Ba, Cr, Hg, Mn and Ni. Spatial distribution of trace metals indicates the polluted sections mainly concentrate in the Danjiang, Danjiangkou Reservoir catchment and Hanzhong Plain, and the most contaminated river is in the Hanzhong Plain. Q-model clustering depends on geographical location of sampling sites and groups the 42 sampling sites into four clusters, i.e., Danjiang, Danjiangkou Reservoir region (lower catchment), upper catchment and one river in headwaters pertaining to water quality. The headwaters, Danjiang and lower catchment, and upper catchment correspond to very high polluted, moderate polluted and relatively low polluted regions, respectively. Additionally, PCA/FA and correlation analysis demonstrates that Al, Cd, Mn, Ni, Fe, Si and Sr are controlled by natural sources, whereas the other metals appear to be primarily controlled by anthropogenic origins though geogenic source contributing to them. 2009 Elsevier B.V. All rights reserved.

Quantification of MTE in surface sediments of Morbihan Coast (South Brittany, France): A preliminary approach for determination of sources and dynamics

NASA Astrophysics Data System (ADS)

Jimenez, Joselyn; Goubert, Evelyne; Labeyrie, Laurent; Coynel, Alexandra; Menier, David

2014-05-01

The Morbihan Coast (South Brittany, France) has an intense coastal activity: farming, industry, urban habitation run-off, yachting and transportation. In the past centuries, tin mining industry was also developed. These different factors may introduce metal trace elements (MTE) into the marine environment at toxic concentration levels. This pollution can particularly affect the oyster production, widely developed in the area. Monitoring MTE in surface sediments at high spatial resolution has been programmed to assess pollutants and their sources in two of the major Morbihan coastal systems concerned with oyster farming, and where available information on MTE impact and sediment quality is limited: the Bay of Quiberon, partly protected from the open ocean by the Quiberon Peninsula and several islands, mostly sandy (coarse to fine, with a significant shelly fraction), with water depths shallower that 25 m, and the Gulf of Morbihan, a shallow depth (less than 5 m, apart from the two paleoriver beds), semi-enclosed, estuarine system with very coarse sand to fine mud, mostly distributed by a strong tidal current system. Fifty two surface sediment samples were collected in April 2013 to characterize the MTE spatial distribution through the salinity and pollution gradients, from the small local rivers and harbor areas to the open marine environments. Analyses cover sedimentological and biogeochemical properties (particulate organic carbon using a LECO-CS-230; MTE using ICP-MS or DMA for Hg). Statistical analyses help to discriminate within the spatial variability the natural (e.g. grain-size effect) and anthropogenic factors. MTE concentrations were also compared to local geochemical background as measured at the bottom of three sediment cores collected in representative sites, for calculating the enrichment index of each MTE and evaluating the degree of sediment contamination. The initial interpretation of the results would indicate a clear distinction between the geochemical gradients linked to natural processes: sediment sources and size fractionation (for example, the relationship between Sr and carbonate concentration in the sand fraction), and gradients linked to polluting factors, in particular in the harbors and protected arias, probably associated with boat maintenance (with Cu, Zn and Sn concentrations exceeding 100 ppm, up to 300 ppm in isolated places). More detailed statistical analyses and implications will be presented at the conference.

Temporal and spatial changes of water quality and management strategies of Dianchi Lake in southwest China

NASA Astrophysics Data System (ADS)

Zhang, T.; Zeng, W. H.; Wang, S. R.; Ni, Z. K.

2013-12-01

Temporal and spatial changes to the water quality of Dianchi Lake in Southwest China were investigated using monthly monitoring data from 2005 to 2012. Based on the analysis of total phosphorus (TP), total nitrogen (TN), and chlorophyll a (Chl a) concentrations, it was determined that, in Caohai Lake, the annual concentrations of these variables ranged from 0.19-1.46, 6.11-16.79, 0.06-0.14 mg L-1, respectively. In addition, the annual concentrations of TP, TN and Chl a in Waihai Lake ranged between 0.13-0.20, 1.82-3.01, 0.04-0.09 mg L-1, respectively. Cluster Analysis (CA) classified the 10 monitoring sites into two groups (group A and group B) based on similarities of water quality characteristics. Our data revealed that the current status of water quality within Caohai Lake was much worse than that of Waihai Lake. Water quality was seriously degraded during the economic boom near the period of the "Eleventh Five-Year Plan" (2005-2010), and gradually improved from 2010 to 2012 because of the "standard emission directive to industry". The main factors that influenced the spatial and temporal changes to water quality were natural factors including lake evolution and regional characteristic as well as human factors such as pollution load into the lake and management strategies that were already adopted. Some activities and regulations were implemented to enhance the lake environment by controlling wastewater emissions and establishing regulations to protect the lakes in the Yunnan Province. However, problems with institutional fragmentation (horizontal and vertical), simple treatment methods, low-intensity investment in pollution control, and lack of meaningful endogenous pollution control strategies were still present in the lake management strategy. To solve these problems, suitable control measures are needed, especially considering the current old-age status of Dianchi Lake. The fundamental improvement of the water quality within Caohai Lake was dependent on the measures taken in the upper reaches of the Caohai Watershed, including further recovery of submerged plants, resource utilization by floating plants and the reinforcement of sediment disposal. Management strategies for endogenous pollution in Waihai Lake were mainly dependent on restocking algae-eating fish and the ecological restoration of macrophytes. In this way, the swamping trend and the ageing process that is occurring in Dianchi Lake can be stunted.

Establishing the common patterns of future tropospheric ozone under diverse climate change scenarios

NASA Astrophysics Data System (ADS)

Jimenez-Guerrero, Pedro; Gómez-Navarro, Juan J.; Jerez, Sonia; Lorente-Plazas, Raquel; Baro, Rocio; Montávez, Juan P.

2013-04-01

The impacts of climate change on air quality may affect long-term air quality planning. However, the policies aimed at improving air quality in the EU directives have not accounted for the variations in the climate. Climate change alone influences future air quality through modifications of gas-phase chemistry, transport, removal, and natural emissions. As such, the aim of this work is to check whether the projected changes in gas-phase air pollution over Europe depends on the scenario driving the regional simulation. For this purpose, two full-transient regional climate change-air quality projections for the first half of the XXI century (1991-2050) have been carried out with MM5+CHIMERE system, including A2 and B2 SRES scenarios. Experiments span the periods 1971-2000, as a reference, and 2071-2100, as future enhanced greenhouse gas and aerosol scenarios (SRES A2 and B2). The atmospheric simulations have a horizontal resolution of 25 km and 23 vertical layers up to 100 mb, and were driven by ECHO-G global climate model outputs. The analysis focuses on the connection between meteorological and air quality variables. Our simulations suggest that the modes of variability for tropospheric ozone and their main precursors hardly change under different SRES scenarios. The effect of changing scenarios has to be sought in the intensity of the changing signal, rather than in the spatial structure of the variation patterns, since the correlation between the spatial patterns of variability in A2 and B2 simulation is r > 0.75 for all gas-phase pollutants included in this study. In both cases, full-transient simulations indicate an enhanced enhanced chemical activity under future scenarios. The causes for tropospheric ozone variations have to be sought in a multiplicity of climate factors, such as increased temperature, different distribution of precipitation patterns across Europe, increased photolysis of primary and secondary pollutants due to lower cloudiness, etc. Nonetheless, according to the results of this work future ozone is conditioned by the dependence of biogenic emissions on the climatological patterns of variability. In this sense, ozone over Europe is mainly driven by the warming-induced increase in biogenic emitting activity (vegetation is kept invariable in the simulations, but estimations of these emissions strongly depends on shortwave radiation and temperature, which are substantially modified in climatic simulations). Moreover, one of the most important drivers for ozone increase is the decrease of cloudiness (related to stronger solar radiation) mostly over southern Europe at the first half of the XXI century. However, given the large uncertainty isoprene sensitivity to climate change and the large uncertainties associated to the cloudiness projections, these results should be carefully considered.