Prediction and analysis of near-road concentrations using a reduced-form emission/dispersion model

PubMed Central

2010-01-01

Background Near-road exposures of traffic-related air pollutants have been receiving increased attention due to evidence linking emissions from high-traffic roadways to adverse health outcomes. To date, most epidemiological and risk analyses have utilized simple but crude exposure indicators, most typically proximity measures, such as the distance between freeways and residences, to represent air quality impacts from traffic. This paper derives and analyzes a simplified microscale simulation model designed to predict short- (hourly) to long-term (annual average) pollutant concentrations near roads. Sensitivity analyses and case studies are used to highlight issues in predicting near-road exposures. Methods Process-based simulation models using a computationally efficient reduced-form response surface structure and a minimum number of inputs integrate the major determinants of air pollution exposures: traffic volume and vehicle emissions, meteorology, and receptor location. We identify the most influential variables and then derive a set of multiplicative submodels that match predictions from "parent" models MOBILE6.2 and CALINE4. The assembled model is applied to two case studies in the Detroit, Michigan area. The first predicts carbon monoxide (CO) concentrations at a monitoring site near a freeway. The second predicts CO and PM2.5 concentrations in a dense receptor grid over a 1 km2 area around the intersection of two major roads. We analyze the spatial and temporal patterns of pollutant concentration predictions. Results Predicted CO concentrations showed reasonable agreement with annual average and 24-hour measurements, e.g., 59% of the 24-hr predictions were within a factor of two of observations in the warmer months when CO emissions are more consistent. The highest concentrations of both CO and PM2.5 were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind speeds) and high emissions (e.g., weekday rush hour). The spatial and temporal variation among predicted concentrations was significant, and resulted in unusual distributional and correlation characteristics, including strong negative correlation for receptors on opposite sides of a road and the highest short-term concentrations on the "upwind" side of the road. Conclusions The case study findings can likely be generalized to many other locations, and they have important implications for epidemiological and other studies. The reduced-form model is intended for exposure assessment, risk assessment, epidemiological, geographical information systems, and other applications. PMID:20579353

Evaluation of groundwater pollution in a mining area using analytical solution: a case study of the Yimin open-pit mine in China.

PubMed

Li, Tianxin; Li, Li; Song, Hongqing; Meng, Linglong; Zhang, Shuli; Huang, Gang

2016-01-01

This study focused on using analytical and numerical models to develop and manage groundwater resources, and predict the effects of management measurements in the groundwater system. Movement of contaminants can be studied based on groundwater flow characteristics. This study can be used for prediction of ion concentration and evaluation of groundwater pollution as the theoretical basis. The Yimin open-pit mine is located in the northern part of the Inner Mongolia Autonomous Region of China. High concentrations of iron and manganese are observed in Yimin open-pit mine because of exploitation and pumping that have increased the concentration of the ions in groundwater. In this study, iron was considered as an index of contamination, and the solute model was calibrated using concentration observations from 14 wells in 2014. The groundwater flow model and analytical solutions were used in this study to forecast pollution concentration and variation trend after calibration. With continuous pumping, contaminants will migrate, and become enriched, towards the wellhead in the flow direction. The concentration of the contaminants and the range of pollution increase with the flow rate increased. The suitable flow rate of single well should be <380 m/day at Yimin open-pit for the standard value of pollution concentration.

Hadoop-Based Distributed System for Online Prediction of Air Pollution Based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Ghaemi, Z.; Farnaghi, M.; Alimohammadi, A.

2015-12-01

The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.

IMPROVE AND APPLY CHEMICAL MECHANISMS FOR DEVELOPING OZONE CONTROL STRATEGIES

EPA Science Inventory

Air quality models that realistically describe the formation of ozone, air toxics, and other pollutants are needed by EPA and state agencies to predict current and future concentrations of these pollutants and develop ways to decrease their concentrations below harmful levels. ...

Potential effects of sulfur pollutants on grape production in New York State

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

Knudson, D.A.; Viessman, S.

1983-01-01

This paper presents the results of a prototype analysis of sulfur pollutants on graph production in New York State. Principal grape production areas for the state are defined and predictions of sulfur dioxide concentrations associated with present and projected sources are computed. Sulfur dioxide concentrations are based on the results of a multi-source dispersion model, whereas concentrations for other pollutants are derived from observations. This information is used in conjunction with results from experiments conducted to identify threshold levels of damage and/or injury to a variety of grape species to pollutants. Determination is then made whether the subject crop ismore » at risk from present and projected concentrations of pollutants.« less

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...

Numerical prediction on the dispersion of pollutant particles

NASA Astrophysics Data System (ADS)

Osman, Kahar; Ali, Zairi; Ubaidullah, S.; Zahid, M. N.

2012-06-01

The increasing concern on air pollution has led people around the world to find more efficient ways to control the problem. Air dispersion modeling is proven to be one of the alternatives that provide economical ways to control the growing threat of air pollution. The objective of this research is to develop a practical numerical algorithm to predict the dispersion of pollutant particles around a specific source of emission. The source selected was a rubber wood manufacturing plant. Gaussian-plume model were used as air dispersion model due to its simplicity and generic application. Results of this study show the concentrations of the pollutant particles on ground level reached approximately 90μg/m3, compared with other software. This value surpasses the limit of 50μg/m3 stipulated by the National Ambient Air Quality Standard (NAAQS) and Recommended Malaysian Guidelines (RMG) set by Environment Department of Malaysia. The results also show high concentration of pollutant particles reading during dru seasons as compared to that of rainy seasons. In general, the developed algorithm is proven to be able to predict particles distribution around emitted source with acceptable accuracy.

Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

NASA Astrophysics Data System (ADS)

Gao, Meng; Yin, Liting; Ning, Jicai

2018-07-01

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations.

Modeling and evaluation of urban pollution events of atmospheric heavy metals from a large Cu-smelter.

PubMed

Chen, Bing; Stein, Ariel F; Castell, Nuria; Gonzalez-Castanedo, Yolanda; Sanchez de la Campa, A M; de la Rosa, J D

2016-01-01

Metal smelting and processing are highly polluting activities that have a strong influence on the levels of heavy metals in air, soil, and crops. We employ an atmospheric transport and dispersion model to predict the pollution levels originated from the second largest Cu-smelter in Europe. The model predicts that the concentrations of copper (Cu), zinc (Zn), and arsenic (As) in an urban area close to the Cu-smelter can reach 170, 70, and 30 ng m−3, respectively. The model captures all the observed urban pollution events, but the magnitude of the elemental concentrations is predicted to be lower than that of the observed values; ~300, ~500, and ~100 ng m−3 for Cu, Zn, and As, respectively. The comparison between model and observations showed an average correlation coefficient of 0.62 ± 0.13. The simulation shows that the transport of heavy metals reaches a peak in the afternoon over the urban area. The under-prediction in the peak is explained by the simulated stronger winds compared with monitoring data. The stronger simulated winds enhance the transport and dispersion of heavy metals to the regional area, diminishing the impact of pollution events in the urban area. This model, driven by high resolution meteorology (2 km in horizontal), predicts the hourly-interval evolutions of atmospheric heavy metal pollutions in the close by urban area of industrial hotspot.

Assessing the pollution risk of a groundwater source field at western Laizhou Bay under seawater intrusion

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

Zeng, Xiankui; Wu, Jichun; Wang, Dong, E-mail: wangdong@nju.edu.cn

Coastal areas have great significance for human living, economy and society development in the world. With the rapid increase of pressures from human activities and climate change, the safety of groundwater resource is under the threat of seawater intrusion in coastal areas. The area of Laizhou Bay is one of the most serious seawater intruded areas in China, since seawater intrusion phenomenon was firstly recognized in the middle of 1970s. This study assessed the pollution risk of a groundwater source filed of western Laizhou Bay area by inferring the probability distribution of groundwater Cl{sup −} concentration. The numerical model ofmore » seawater intrusion process is built by using SEAWAT4. The parameter uncertainty of this model is evaluated by Markov Chain Monte Carlo (MCMC) simulation, and DREAM{sub (ZS)} is used as sampling algorithm. Then, the predictive distribution of Cl{sup -} concentration at groundwater source field is inferred by using the samples of model parameters obtained from MCMC. After that, the pollution risk of groundwater source filed is assessed by the predictive quantiles of Cl{sup -} concentration. The results of model calibration and verification demonstrate that the DREAM{sub (ZS)} based MCMC is efficient and reliable to estimate model parameters under current observation. Under the condition of 95% confidence level, the groundwater source point will not be polluted by seawater intrusion in future five years (2015–2019). In addition, the 2.5% and 97.5% predictive quantiles show that the Cl{sup −} concentration of groundwater source field always vary between 175 mg/l and 200 mg/l. - Highlights: • The parameter uncertainty of seawater intrusion model is evaluated by MCMC. • Groundwater source field won’t be polluted by seawater intrusion in future 5 years. • The pollution risk is assessed by the predictive quantiles of Cl{sup −} concentration.« less

Models for predicting the ratio of particulate pollutant concentrations inside vehicles to roadways

PubMed Central

Hudda, N.; Fruin, S. A.

2013-01-01

Under closed-window driving conditions, the in-vehicle-to-outside (I/O) concentration ratio for traffic-related particulate pollutants ranges from nearly zero to one, and varies up to five-fold across a fleet of vehicles, thus strongly affecting occupant exposures. Concentrations of five particulate pollutants (particle-bound polycyclic aromatic hydrocarbons, black carbon, ultrafine particle number, and fine and coarse particulate mass) were measured simultaneously while systematically varying key influential parameters (i.e., vehicle type, ventilation, and speed). The I/O ratios for these pollutants were primarily determined by vehicle air exchange rate (AER), AER being mostly a function of ventilation setting (recirculation or outside air), vehicle characteristics (e.g., age, interior volume) and driving speed. Small (±0.15) but measurable differences in I/O ratios between pollutants were observed although ratios were highly correlated. This allowed us to build on previous studies of ultrafine particle number I/O ratios to develop predictive models for other particulate pollutants. These models explained over 60% of measured variation, using ventilation setting, driving speed, and easily-obtained vehicle characteristics as predictors. Our results suggest that I/O ratios for different particulate pollutants need not necessarily be measured individually and that exposure to all particulate pollutants may be reduced significantly by simple ventilation choices. PMID:23957386

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.

Air pollution exposure prediction approaches used in air pollution epidemiology studies.

PubMed

Özkaynak, Halûk; Baxter, Lisa K; Dionisio, Kathie L; Burke, Janet

2013-01-01

Epidemiological studies of the health effects of outdoor air pollution have traditionally relied upon surrogates of personal exposures, most commonly ambient concentration measurements from central-site monitors. However, this approach may introduce exposure prediction errors and misclassification of exposures for pollutants that are spatially heterogeneous, such as those associated with traffic emissions (e.g., carbon monoxide, elemental carbon, nitrogen oxides, and particulate matter). We review alternative air quality and human exposure metrics applied in recent air pollution health effect studies discussed during the International Society of Exposure Science 2011 conference in Baltimore, MD. Symposium presenters considered various alternative exposure metrics, including: central site or interpolated monitoring data, regional pollution levels predicted using the national scale Community Multiscale Air Quality model or from measurements combined with local-scale (AERMOD) air quality models, hybrid models that include satellite data, statistically blended modeling and measurement data, concentrations adjusted by home infiltration rates, and population-based human exposure model (Stochastic Human Exposure and Dose Simulation, and Air Pollutants Exposure models) predictions. These alternative exposure metrics were applied in epidemiological applications to health outcomes, including daily mortality and respiratory hospital admissions, daily hospital emergency department visits, daily myocardial infarctions, and daily adverse birth outcomes. This paper summarizes the research projects presented during the symposium, with full details of the work presented in individual papers in this journal issue.

Do causal concentration-response functions exist? A critical review of associational and causal relations between fine particulate matter and mortality.

PubMed

Cox, Louis Anthony Tony

2017-08-01

Concentration-response (C-R) functions relating concentrations of pollutants in ambient air to mortality risks or other adverse health effects provide the basis for many public health risk assessments, benefits estimates for clean air regulations, and recommendations for revisions to existing air quality standards. The assumption that C-R functions relating levels of exposure and levels of response estimated from historical data usefully predict how future changes in concentrations would change risks has seldom been carefully tested. This paper critically reviews literature on C-R functions for fine particulate matter (PM2.5) and mortality risks. We find that most of them describe historical associations rather than valid causal models for predicting effects of interventions that change concentrations. The few papers that explicitly attempt to model causality rely on unverified modeling assumptions, casting doubt on their predictions about effects of interventions. A large literature on modern causal inference algorithms for observational data has been little used in C-R modeling. Applying these methods to publicly available data from Boston and the South Coast Air Quality Management District around Los Angeles shows that C-R functions estimated for one do not hold for the other. Changes in month-specific PM2.5 concentrations from one year to the next do not help to predict corresponding changes in average elderly mortality rates in either location. Thus, the assumption that estimated C-R relations predict effects of pollution-reducing interventions may not be true. Better causal modeling methods are needed to better predict how reducing air pollution would affect public health.

Groundwater pollution by nitrates from livestock wastes.

PubMed Central

Goldberg, V M

1989-01-01

Utilization of wastes from livestock complexes for irrigation involves the danger of groundwater pollution by nitrates. In order to prevent and minimize pollution, it is necessary to apply geological-hydrogeological evidence and concepts to the situation of wastewater irrigation for the purposes of studying natural groundwater protectiveness and predicting changes in groundwater quality as a result of infiltrating wastes. The procedure of protectiveness evaluation and quality prediction is described. With groundwater pollution by nitrate nitrogen, the concentration of ammonium nitrogen noticeably increases. One of the reasons for this change is the process of denitrification due to changes in the hydrogeochemical conditions in a layer. At representative field sites, it is necessary to collect systematic stationary observations of the concentrations of nitrogenous compounds in groundwater and changes in redox conditions and temperature. PMID:2620669

Particulate air pollution and daily mortality in Detroit.

PubMed

Schwartz, J

1991-12-01

Particulate air pollution has been associated with increased mortality during episodes of high pollution concentrations. The relationship at lower concentrations has been more controversial, as has the relative role of particles and sulfur dioxide. Replication has been difficult because suspended particle concentrations are usually measured only every sixth day in the U.S. This study used concurrent measurements of total suspended particulates (TSP) and airport visibility from every sixth day sampling for 10 years to fit a predictive model for TSP. Predicted daily TSP concentrations were then correlated with daily mortality counts in Poisson regression models controlling for season, weather, time trends, overdispersion, and serial correlation. A significant correlation (P less than 0.0001) was found between predicted TSP and daily mortality. This correlation was independent of sulfur dioxide, but not vice versa. The magnitude of the effect was very similar to results recently reported from Steubenville, Ohio (using actual TSP measurements), with each 100 micrograms/m3 increase in TSP resulting in a 6% increase in mortality. Graphical analysis indicated a dose-response relationship with no evidence of a threshold down to concentrations below half of the National Ambient Air Quality Standards for particulate matter.

Risk Assessment and Prediction of Heavy Metal Pollution in Groundwater and River Sediment: A Case Study of a Typical Agricultural Irrigation Area in Northeast China

PubMed Central

Zhong, Shuang; Geng, Hui; Zhang, Fengjun; Liu, Zhaoying; Wang, Tianye; Song, Boyu

2015-01-01

The areas with typical municipal sewage discharge river and irrigation water function were selected as study sites in northeast China. The samples from groundwater and river sediment in this area were collected for the concentrations and forms of heavy metals (Cr(VI), Cd, As, and Pb) analysis. The risk assessment of heavy metal pollution was conducted based on single-factor pollution index (I) and Nemerow pollution index (NI). The results showed that only one groundwater sampling site reached a polluted level of heavy metals. There was a high potential ecological risk of Cd on the N21-2 sampling site in river sediment. The morphological analysis results of heavy metals in sediment showed that the release of heavy metals can be inferred as one of the main pollution sources of groundwater. In addition, the changes in the concentration and migration scope of As were predicted by using the Groundwater Modeling System (GMS). The predicted results showed that As will migrate downstream in the next decade, and the changing trend of As polluted areas was changed with As content districts because of some pump wells downstream to form groundwater depression cone, which made the solute transfer upstream. PMID:26366176

Predicting indoor pollutant concentrations, and applications to air quality management

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

Lorenzetti, David M.

Because most people spend more than 90% of their time indoors, predicting exposure to airborne pollutants requires models that incorporate the effect of buildings. Buildings affect the exposure of their occupants in a number of ways, both by design (for example, filters in ventilation systems remove particles) and incidentally (for example, sorption on walls can reduce peak concentrations, but prolong exposure to semivolatile organic compounds). Furthermore, building materials and occupant activities can generate pollutants. Indoor air quality depends not only on outdoor air quality, but also on the design, maintenance, and use of the building. For example, ''sick building'' symptomsmore » such as respiratory problems and headaches have been related to the presence of air-conditioning systems, to carpeting, to low ventilation rates, and to high occupant density (1). The physical processes of interest apply even in simple structures such as homes. Indoor air quality models simulate the processes, such as ventilation and filtration, that control pollutant concentrations in a building. Section 2 describes the modeling approach, and the important transport processes in buildings. Because advection usually dominates among the transport processes, Sections 3 and 4 describe methods for predicting airflows. The concluding section summarizes the application of these models.« less

Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

NASA Astrophysics Data System (ADS)

Shamsoddini, A.; Aboodi, M. R.; Karami, J.

2017-09-01

Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

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.

Numerical modelling of vehicular pollution dispersion: The application of computational fluid dynamics techniques, a case study

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

Vanderheyden, M.D.; Dajka, S.C.; Sinclair, R.

1997-12-31

Numerical modelling of vehicular emissions using the United States Environmental Protection Agency`s CALINE4 and CAL3QHC dispersion models to predict air quality impacts in the vicinity of roadways is a widely accepted means of evaluating vehicular emissions impacts. The numerical models account for atmospheric dispersion in both open or suburban terrains. When assessing roadways in urban areas with numerous large buildings, however, the models are unable to account for the complex airflows and therefore do not provide satisfactory estimates of pollutant concentrations. Either Wind Tunnel Modelling or Computational Fluid Dynamics (CFD) techniques can be used to assess the impact of vehiclemore » emissions in an urban core. This paper presents a case study where CFD is used to predict worst-case air quality impacts for two development configurations: an existing roadway configuration and a proposed configuration with an elevated pedestrian walkway. In assessing these configurations, worst-case meteorology and traffic conditions are modeled to allow for the prediction of pollutant concentrations due to vehicular emissions on two major streets in Hong Kong. The CFD modelling domain is divided up into thousands of control volumes. Each of these control volumes has a central point called a node where velocities, pollutant concentration and other auxiliary variables are calculated. The region of interest, the pedestrian link and its immediate surroundings, has a denser distribution of nodes in order to give a better resolution of local flow details. Separate CFD modelling runs were undertaken for each development configuration for wind direction increments of 15 degrees. For comparison of the development scenarios, pollutant concentrations (carbon monoxide, nitrogen dioxide and particulate matter) are predicted at up to 99 receptor nodes representing sensitive locations.« less

Predictability Analysis of PM10 Concentrations in Budapest

NASA Astrophysics Data System (ADS)

Ferenczi, Zita

2013-04-01

Climate, weather and air quality may have harmful effects on human health and environment. Over the past few hundred years we had to face the changes in climate in parallel with the changes in air quality. These observed changes in climate, weather and air quality continuously interact with each other: pollutants are changing the climate, thus changing the weather, but climate also has impacts on air quality. The increasing number of extreme weather situations may be a result of climate change, which could create favourable conditions for rising of pollutant concentrations. Air quality in Budapest is determined by domestic and traffic emissions combined with the meteorological conditions. In some cases, the effect of long-range transport could also be essential. While the time variability of the industrial and traffic emissions is not significant, the domestic emissions increase in winter season. In recent years, PM10 episodes have caused the most critical air quality problems in Budapest, especially in winter. In Budapest, an air quality network of 11 stations detects the concentration values of different pollutants hourly. The Hungarian Meteorological Service has developed an air quality prediction model system for the area of Budapest. The system forecasts the concentration of air pollutants (PM10, NO2, SO2 and O3) for two days in advance. In this work we used meteorological parameters and PM10 data detected by the stations of the air quality network, as well as the forecasted PM10 values of the air quality prediction model system. In this work we present the evaluation of PM10 predictions in the last two years and the most important meteorological parameters affecting PM10 concentration. The results of this analysis determine the effect of the meteorological parameters and the emission of aerosol particles on the PM10 concentration values as well as the limits of this prediction system.

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.

Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Gonçalves, Karen dos Santos; Winkler, Mirko S.; Benchimol-Barbosa, Paulo Roberto; de Hoogh, Kees; Artaxo, Paulo Eduardo; de Souza Hacon, Sandra; Schindler, Christian; Künzli, Nino

2018-07-01

Epidemiological studies generally use particulate matter measurements with diameter less 2.5 μm (PM2.5) from monitoring networks. Satellite aerosol optical depth (AOD) data has considerable potential in predicting PM2.5 concentrations, and thus provides an alternative method for producing knowledge regarding the level of pollution and its health impact in areas where no ground PM2.5 measurements are available. This is the case in the Brazilian Amazon rainforest region where forest fires are frequent sources of high pollution. In this study, we applied a non-linear model for predicting PM2.5 concentration from AOD retrievals using interaction terms between average temperature, relative humidity, sine, cosine of date in a period of 365,25 days and the square of the lagged relative residual. Regression performance statistics were tested comparing the goodness of fit and R2 based on results from linear regression and non-linear regression for six different models. The regression results for non-linear prediction showed the best performance, explaining on average 82% of the daily PM2.5 concentrations when considering the whole period studied. In the context of Amazonia, it was the first study predicting PM2.5 concentrations using the latest high-resolution AOD products also in combination with the testing of a non-linear model performance. Our results permitted a reliable prediction considering the AOD-PM2.5 relationship and set the basis for further investigations on air pollution impacts in the complex context of Brazilian Amazon Region.

Model simulation of meteorology and air quality during the summer PUMA intensive measurement campaign in the UK West Midlands conurbation.

PubMed

Baggott, Sarah; Cai, Xiaoming; McGregor, Glenn; Harrison, Roy M

2006-05-01

The Regional Atmospheric Modeling System (RAMS) and Urban Airshed Model (UAM IV) have been implemented for prediction of air pollutant concentrations within the West Midlands conurbation of the United Kingdom. The modelling results for wind speed, direction and temperature are in reasonable agreement with observations for two stations, one in a rural area and the other in an urban area. Predictions of surface temperature are generally good for both stations, but the results suggest that the quality of temperature prediction is sensitive to whether cloud cover is reproduced reliably by the model. Wind direction is captured very well by the model, while wind speed is generally overestimated. The air pollution climate of the UK West Midlands is very different to those for which the UAM model was primarily developed, and the methods used to overcome these limitations are described. The model shows a tendency towards under-prediction of primary pollutant (NOx and CO) concentrations, but with suitable attention to boundary conditions and vertical profiles gives fairly good predictions of ozone concentrations. Hourly updating of chemical concentration boundary conditions yields the best results, with input of vertical profiles desirable. The model seriously underpredicts NO2/NO ratios within the urban area and this appears to relate to inadequate production of peroxy radicals. Overall, the chemical reactivity predicted by the model appears to fall well below that occurring in the atmosphere.

A Unified Spatiotemporal Modeling Approach for Predicting Concentrations of Multiple Air Pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution

PubMed Central

Olives, Casey; Kim, Sun-Young; Sheppard, Lianne; Sampson, Paul D.; Szpiro, Adam A.; Oron, Assaf P.; Lindström, Johan; Vedal, Sverre; Kaufman, Joel D.

2014-01-01

Background: Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time. Objectives: We developed a unified modeling approach for predicting fine particulate matter, nitrogen dioxide, oxides of nitrogen, and black carbon (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999 through early 2012 as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Methods: We obtained monitoring data from regulatory networks and supplemented those data with study-specific measurements collected from MESA Air community locations and participants’ homes. In each region, we applied a spatiotemporal model that included a long-term spatial mean, time trends with spatially varying coefficients, and a spatiotemporal residual. The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression. We estimated time trends from observed time series and used spatial smoothing methods to borrow strength between observations. Results: Prediction accuracy was high for most models, with cross-validation R2 (R2CV) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R2CV ranged from 0.45 to 0.92, and temporally adjusted R2CV ranged from 0.23 to 0.92. Conclusions: This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants. We have generated participant-specific predictions for MESA Air to investigate health effects of long-term air pollution exposures. These successes highlight modeling advances that can be adopted more widely in modern cohort studies. Citation: Keller JP, Olives C, Kim SY, Sheppard L, Sampson PD, Szpiro AA, Oron AP, Lindström J, Vedal S, Kaufman JD. 2015. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution. Environ Health Perspect 123:301–309; http://dx.doi.org/10.1289/ehp.1408145 PMID:25398188

Multivariate space-time modelling of multiple air pollutants and their health effects accounting for exposure uncertainty.

PubMed

Huang, Guowen; Lee, Duncan; Scott, E Marian

2018-03-30

The long-term health effects of air pollution are often estimated using a spatio-temporal ecological areal unit study, but this design leads to the following statistical challenges: (1) how to estimate spatially representative pollution concentrations for each areal unit; (2) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (3) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel 2-stage Bayesian hierarchical model for addressing these 3 challenges, with inference based on Markov chain Monte Carlo simulation. The first stage is a multivariate spatio-temporal fusion model for predicting areal level average concentrations of multiple pollutants from both monitored and modelled pollution data. The second stage is a spatio-temporal model for estimating the health impact of multiple correlated pollutants simultaneously, which accounts for the uncertainty in the estimated pollution concentrations. The novel methodology is motivated by a new study of the impact of both particulate matter and nitrogen dioxide concentrations on respiratory hospital admissions in Scotland between 2007 and 2011, and the results suggest that both pollutants exhibit substantial and independent health effects. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

High-resolution spatiotemporal mapping of PM2.5 concentrations at Mainland China using a combined BME-GWR technique

NASA Astrophysics Data System (ADS)

Xiao, Lu; Lang, Yichao; Christakos, George

2018-01-01

With rapid economic development, industrialization and urbanization, the ambient air PM2.5 has become a major pollutant linked to respiratory, heart and lung diseases. In China, PM2.5 pollution constitutes an extreme environmental and social problem of widespread public concern. In this work we estimate ground-level PM2.5 from satellite-derived aerosol optical depth (AOD), topography data, meteorological data, and pollutant emission using an integrative technique. In particular, Geographically Weighted Regression (GWR) analysis was combined with Bayesian Maximum Entropy (BME) theory to assess the spatiotemporal characteristics of PM2.5 exposure in a large region of China and generate informative PM2.5 space-time predictions (estimates). It was found that, due to its integrative character, the combined BME-GWR method offers certain improvements in the space-time prediction of PM2.5 concentrations over China compared to previous techniques. The combined BME-GWR technique generated realistic maps of space-time PM2.5 distribution, and its performance was superior to that of seven previous studies of satellite-derived PM2.5 concentrations in China in terms of prediction accuracy. The purely spatial GWR model can only be used at a fixed time, whereas the integrative BME-GWR approach accounts for cross space-time dependencies and can predict PM2.5 concentrations in the composite space-time domain. The 10-fold results of BME-GWR modeling (R2 = 0.883, RMSE = 11.39 μg /m3) demonstrated a high level of space-time PM2.5 prediction (estimation) accuracy over China, revealing a definite trend of severe PM2.5 levels from the northern coast toward inland China (Nov 2015-Feb 2016). Future work should focus on the addition of higher resolution AOD data, developing better satellite-based prediction models, and related air pollutants for space-time PM2.5 prediction purposes.

Application of a Three-Layer Photochemical Box Model in an Athens Street Canyon.

PubMed

Proyou, Athena G; Ziomas, Loannis C; Stathopoulos, Antony

1998-05-01

The aim of this paper is to show that a photochemical box model could describe the air pollution diurnal profiles within a typical street canyon in the city of Athens. As sophisticated three-dimensional dispersion models are computationally expensive and they cannot serve to simulate pollution levels in the scale of an urban street canyon, a suitably modified three-layer photochemical box model was applied. A street canyon of Athens with heavy traffic was chosen to apply the aforementioned model. The model was used to calculate pollutant concentrations during two days with meteorological conditions favoring pollutant accumulation. Road traffic emissions were calculated based on existing traffic load measurements. Meteorological data, as well as various pollutant concentrations, in order to compare with the model results, were provided by available measurements. The calculated concentrations were found to be in good agreement with measured concentration levels and show that, when traffic load and traffic composition data are available, this model can be used to predict pollution episodes. It is noteworthy that high concentrations persisted, even after additional traffic restriction measures were taken on the second day because of the high pollution levels.

GAS-PHASE MASS TRANSFER MODEL FOR PREDICTING VOLATILE ORGANIC COMPOUND (VOC) EMISSION RATES FROM INDOOR POLLUTANT SOURCES

EPA Science Inventory

Analysis of the impact of sources on indoor pollutant concentrations and occupant exposure to indoor pollutants requires knowledge of the emission rates from the sources. Emission rates are often determined by chamber testing and the data from the chamber test are fitted to an em...

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.

Developing a smartphone software package for predicting atmospheric pollutant concentrations at mobile locations.

PubMed

Larkin, Andrew; Williams, David E; Kile, Molly L; Baird, William M

2015-06-01

There is considerable evidence that exposure to air pollution is harmful to health. In the U.S., ambient air quality is monitored by Federal and State agencies for regulatory purposes. There are limited options, however, for people to access this data in real-time which hinders an individual's ability to manage their own risks. This paper describes a new software package that models environmental concentrations of fine particulate matter (PM 2.5 ), coarse particulate matter (PM 10 ), and ozone concentrations for the state of Oregon and calculates personal health risks at the smartphone's current location. Predicted air pollution risk levels can be displayed on mobile devices as interactive maps and graphs color-coded to coincide with EPA air quality index (AQI) categories. Users have the option of setting air quality warning levels via color-coded bars and were notified whenever warning levels were exceeded by predicted levels within 10 km. We validated the software using data from participants as well as from simulations which showed that the application was capable of identifying spatial and temporal air quality trends. This unique application provides a potential low-cost technology for reducing personal exposure to air pollution which can improve quality of life particularly for people with health conditions, such as asthma, that make them more susceptible to these hazards.

Indoor air quality of low and middle income urban households in Durban, South Africa.

PubMed

Jafta, Nkosana; Barregard, Lars; Jeena, Prakash M; Naidoo, Rajen N

2017-07-01

Elevated levels of indoor air pollutants may cause cardiopulmonary disease such as lower respiratory infection, chronic obstructive lung disease and lung cancer, but the association with tuberculosis (TB) is unclear. So far the risk estimates of TB infection or/and disease due to indoor air pollution (IAP) exposure are based on self-reported exposures rather than direct measurements of IAP, and these exposures have not been validated. The aim of this paper was to characterize and develop predictive models for concentrations of three air pollutants (PM 10 , NO 2 and SO 2 ) in homes of children participating in a childhood TB study. Children younger than 15 years living within the eThekwini Municipality in South Africa were recruited for a childhood TB case control study. The homes of these children (n=246) were assessed using a walkthrough checklist, and in 114 of them monitoring of three indoor pollutants was also performed (sampling period: 24h for PM 10 , and 2-3 weeks for NO 2 and SO 2 ). Linear regression models were used to predict PM 10 and NO 2 concentrations from household characteristics, and these models were validated using leave out one cross validation (LOOCV). SO 2 concentrations were not modeled as concentrations were very low. Mean indoor concentrations of PM 10 (n=105) , NO 2 (n=82) and SO 2 (n=82) were 64μg/m 3 (range 6.6-241); 19μg/m 3 (range 4.5-55) and 0.6μg/m 3 (range 0.005-3.4) respectively with the distributions for all three pollutants being skewed to the right. Spearman correlations showed weak positive correlations between the three pollutants. The largest contributors to the PM 10 predictive model were type of housing structure (formal or informal), number of smokers in the household, and type of primary fuel used in the household. The NO 2 predictive model was influenced mostly by the primary fuel type and by distance from the major roadway. The coefficients of determination (R 2 ) for the models were 0.41 for PM 10 and 0.31 for NO 2 . Spearman correlations were significant between measured vs. predicted PM 10 and NO 2 with coefficients of 0.66 and 0.55 respectively. Indoor PM 10 levels were relatively high in these households. Both PM 10 and NO 2 can be modeled with a reasonable validity and these predictive models can decrease the necessary number of direct measurements that are expensive and time consuming. Copyright © 2017 Elsevier Inc. All rights reserved.

GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

USGS Publications Warehouse

Yang, X.; Jin, W.

2010-01-01

Nonpoint source pollution is the leading cause of the U.S.'s water quality problems. One important component of nonpoint source pollution control is an understanding of what and how watershed-scale conditions influence ambient water quality. This paper investigated the use of spatial regression to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration in the Cedar River Watershed, Iowa. An Arc Hydro geodatabase was constructed to organize various datasets on the watershed. Spatial regression models were developed to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration and predict NO3NO2-N concentration at unmonitored locations. Unlike the traditional ordinary least square (OLS) method, the spatial regression method incorporates the potential spatial correlation among the observations in its coefficient estimation. Study results show that NO3NO2-N observations in the Cedar River Watershed are spatially correlated, and by ignoring the spatial correlation, the OLS method tends to over-estimate the impacts of watershed characteristics on stream NO3NO2-N concentration. In conjunction with kriging, the spatial regression method not only makes better stream NO3NO2-N concentration predictions than the OLS method, but also gives estimates of the uncertainty of the predictions, which provides useful information for optimizing the design of stream monitoring network. It is a promising tool for better managing and controlling nonpoint source pollution. ?? 2010 Elsevier Ltd.

Characterization and Scaling of Black Carbon Aerosol Concentration with City Population Based on In-Situ Measurements and Analysis

NASA Astrophysics Data System (ADS)

Paredes-Miranda, G.; Arnott, W. P.; Moosmuller, H.

2010-12-01

The global trend toward urbanization and the resulting increase in city population has directed attention toward air pollution in megacities. A closely related question of importance for urban planning and attainment of air quality standards is how pollutant concentrations scale with city population. In this study, we use measurements of light absorption and light scattering coefficients as proxies for primary (i.e., black carbon; BC) and total (i.e., particulate matter; PM) pollutant concentration, to start addressing the following questions: What patterns and generalizations are emerging from our expanding data sets on urban air pollution? How does the per-capita air pollution vary with economic, geographic, and meteorological conditions of an urban area? Does air pollution provide an upper limit on city size? Diurnal analysis of black carbon concentration measurements in suburban Mexico City, Mexico, Las Vegas, NV, USA, and Reno, NV, USA for similar seasons suggests that commonly emitted primary air pollutant concentrations scale approximately as the square root of the urban population N, consistent with a simple 2-d box model. The measured absorption coefficient Babs is approximately proportional to the BC concentration (primary pollution) and thus scales with the square root of population (N). Since secondary pollutants form through photochemical reactions involving primary pollutants, they scale also with square root of N. Therefore the scattering coefficient Bsca, a proxy for PM concentration is also expected to scale with square root of N. Here we present light absorption and scattering measurements and data on meteorological conditions and compare the population scaling of these pollutant measurements with predictions from the simple 2-d box model. We find that these basin cities are connected by the square root of N dependence. Data from other cities will be discussed as time permits.

[Research on Kalman interpolation prediction model based on micro-region PM2.5 concentration].

PubMed

Wang, Wei; Zheng, Bin; Chen, Binlin; An, Yaoming; Jiang, Xiaoming; Li, Zhangyong

2018-02-01

In recent years, the pollution problem of particulate matter, especially PM2.5, is becoming more and more serious, which has attracted many people's attention from all over the world. In this paper, a Kalman prediction model combined with cubic spline interpolation is proposed, which is applied to predict the concentration of PM2.5 in the micro-regional environment of campus, and to realize interpolation simulation diagram of concentration of PM2.5 and simulate the spatial distribution of PM2.5. The experiment data are based on the environmental information monitoring system which has been set up by our laboratory. And the predicted and actual values of PM2.5 concentration data have been checked by the way of Wilcoxon signed-rank test. We find that the value of bilateral progressive significance probability was 0.527, which is much greater than the significant level α = 0.05. The mean absolute error (MEA) of Kalman prediction model was 1.8 μg/m 3 , the average relative error (MER) was 6%, and the correlation coefficient R was 0.87. Thus, the Kalman prediction model has a better effect on the prediction of concentration of PM2.5 than those of the back propagation (BP) prediction and support vector machine (SVM) prediction. In addition, with the combination of Kalman prediction model and the spline interpolation method, the spatial distribution and local pollution characteristics of PM2.5 can be simulated.

Study of atmospheric diffusion using LANDSAT

NASA Technical Reports Server (NTRS)

Torsani, J. A.; Viswanadham, Y.

1982-01-01

The parameters of diffusion patterns of atmospheric pollutants under different conditions were investigated for use in the Gaussian model for calculation of pollution concentration. Value for the divergence pattern of concentration distribution along the Y axis were determined using LANDSAT images. Multispectral scanner images of a point source plume having known characteristics, wind and temperature data, and cloud cover and solar elevation data provided by LANDSAT, were analyzed using the 1-100 system for image analysis. These measured values are compared with pollution transport as predicted by the Pasquill-Gifford, Juelich, and Hoegstroem atmospheric models.

MLP based models to predict PM10, O3 concentrations, in Sines industrial area

NASA Astrophysics Data System (ADS)

Durao, R.; Pereira, M. J.

2012-04-01

Sines is an important Portuguese industrial area located southwest cost of Portugal with important nearby protected natural areas. The main economical activities are related with this industrial area, the deep-water port, petrochemical and thermo-electric industry. Nevertheless, tourism is also an important economic activity especially in summer time with potential to grow. The aim of this study is to develop prediction models of pollutant concentration categories (e.g. low concentration and high concentration) in order to provide early warnings to the competent authorities who are responsible for the air quality management. The knowledge in advanced of pollutant high concentrations occurrence will allow the implementation of mitigation actions and the release of precautionary alerts to population. The regional air quality monitoring network consists in three monitoring stations where a set of pollutants' concentrations are registered on a continuous basis. From this set stands out the tropospheric ozone (O3) and particulate matter (PM10) due to the high concentrations occurring in the region and their adverse effects on human health. Moreover, the major industrial plants of the region monitor SO2, NO2 and particles emitted flows at the principal chimneys (point sources), also on a continuous basis,. Therefore Artificial neuronal networks (ANN) were the applied methodology to predict next day pollutant concentrations; due to the ANNs structure they have the ability to capture the non-linear relationships between predictor variables. Hence the first step of this study was to apply multivariate exploratory techniques to select the best predictor variables. The classification trees methodology (CART) was revealed to be the most appropriate in this case.. Results shown that pollutants atmospheric concentrations are mainly dependent on industrial emissions and a complex combination of meteorological factors and the time of the year. In the second step, the Multi-layer perceptron (MLP) have shown to be able to learn the existent complex relationships using different combination of meteorological and emissions variables. Furthermore, MLP models identified what are the meteorological conditions that most affect O3 and PM10 concentrations in the region, namely wind speed and direction, boundary layer height, temperature, sunshine duration, relative humidity and the weather type. The developed MLP models showed good predictive success with model performances between 0.66 and 0.87, indicating a reasonable accuracy for models development and generalization capability. These performance values are obtained using cross entropy error functions. This error functions are only available for classification problems and ensure that the network outputs are true class membership probabilities, which is known to enhance the performance of classification neural networks.

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.

Validation and optimization of SST k-ω turbulence model for pollutant dispersion within a building array

NASA Astrophysics Data System (ADS)

Yu, Hesheng; Thé, Jesse

2016-11-01

The prediction of the dispersion of air pollutants in urban areas is of great importance to public health, homeland security, and environmental protection. Computational Fluid Dynamics (CFD) emerges as an effective tool for pollutant dispersion modelling. This paper reports and quantitatively validates the shear stress transport (SST) k-ω turbulence closure model and its transitional variant for pollutant dispersion under complex urban environment for the first time. Sensitivity analysis is performed to establish recommendation for the proper use of turbulence models in urban settings. The current SST k-ω simulation is validated rigorously by extensive experimental data using hit rate for velocity components, and the "factor of two" of observations (FAC2) and fractional bias (FB) for concentration field. The simulation results show that current SST k-ω model can predict flow field nicely with an overall hit rate of 0.870, and concentration dispersion with FAC2 = 0.721 and FB = 0.045. The flow simulation of the current SST k-ω model is slightly inferior to that of a detached eddy simulation (DES), but better than that of standard k-ε model. However, the current study is the best among these three model approaches, when validated against measurements of pollutant dispersion in the atmosphere. This work aims to provide recommendation for proper use of CFD to predict pollutant dispersion in urban environment.

Air Pollutants from Jeddah Desalination—Power Plant (KSA)

NASA Astrophysics Data System (ADS)

Al-Seroury, F. A.; Mayhoub, A. B.

2011-10-01

Ground level concentrations due to emissions from the Jeddah dual—purpose plant (sea water desalination and electric power production) have been estimated using the standard Gaussian plume model (GPM). The main types of pollutants emitted from the plant are: Hydro-carbons HC, carbon monoxide CO, Nitrogen oxides NOx and sulfur dioxide SO2. Thermal stability classes for Jeddah city are estimated for the months of the year (2007). It was found that the dominant stability class for the city is the moderately unstable class B (according to pasquill classification). The results of stability classes' evaluation together with the meteorological wind—data are used to predict the ground level concentration (glc) of the pollutants against the downwind distance from the plant location. The month and day of each calculated value of the pollutant concentration during the year (2007) have been specified. The maximum (glc) and their positions on the ground for each pollutant are found.

Prediction of hourly PM2.5 using a space-time support vector regression model

NASA Astrophysics Data System (ADS)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

Persistent Organic Pollutants in Norwegian Men from 1979 to 2007: Intraindividual Changes, Age–Period–Cohort Effects, and Model Predictions

PubMed Central

Breivik, Knut; Fuskevåg, Ole-Martin; Nieboer, Evert; Odland, Jon Øyvind; Sandanger, Torkjel Manning

2013-01-01

Background: Longitudinal monitoring studies of persistent organic pollutants (POPs) in human populations are important to better understand changes with time and age, and for future predictions. Objectives: We sought to describe serum POP time trends on an individual level, investigate age–period–cohort effects, and compare predicted polychlorinated biphenyl (PCB) concentrations to measured values. Methods: Serum was sampled in 1979, 1986, 1994, 2001, and 2007 from a cohort of 53 men in Northern Norway and analyzed for 41 POPs. Time period, age, and birth cohort effects were assessed by graphical analyses and mixed-effect models. We derived the predicted concentrations of four PCBs for each sampling year using the CoZMoMAN model. Results: The median decreases in summed serum POP concentrations (lipid-adjusted) in 1986, 1994, 2001, and 2007 relative to 1979 were –22%, –52%, –54%, and –68%, respectively. We observed substantial declines in all POP groups with the exception of chlordanes. Time period (reflected by sampling year) was the strongest descriptor of changes in PCB-153 concentrations. Predicted PCB-153 concentrations were consistent with measured concentrations in the study population. Conclusions: Our results suggest substantial intraindividual declines in serum concentrations of legacy POPs from 1979 to 2007 in men from Northern Norway. These changes are consistent with reduced environmental exposure during these 30 years and highlight the relation between historic emissions and POP concentrations measured in humans. Observed data and interpretations are supported by estimates from the CoZMoMAN emission-based model. A longitudinal decrease in concentrations with age was evident for all birth cohorts. Overall, our findings support the relevance of age–period–cohort effects to human biomonitoring of environmental contaminants. Citation: Nøst TH, Breivik K, Fuskevåg OM, Nieboer E, Odland JØ, Sandanger TM. 2013. Persistent organic pollutants in Norwegian men from 1979 to 2007: intraindividual changes, age–period–cohort effects, and model predictions. Environ Health Perspect 121:1292–1298; http://dx.doi.org/10.1289/ehp.1206317 PMID:24007675

Fine-scale estimation of carbon monoxide and fine particulate matter concentrations in proximity to a road intersection by using wavelet neural network with genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Zhanyong; Lu, Feng; He, Hong-di; Lu, Qing-Chang; Wang, Dongsheng; Peng, Zhong-Ren

2015-03-01

At road intersections, vehicles frequently stop with idling engines during the red-light period and speed up rapidly in the green-light period, which generates higher velocity fluctuation and thus higher emission rates. Additionally, the frequent changes of wind direction further add the highly variable dispersion of pollutants at the street scale. It is, therefore, very difficult to estimate the distribution of pollutant concentrations using conventional deterministic causal models. For this reason, a hybrid model combining wavelet neural network and genetic algorithm (GA-WNN) is proposed for predicting 5-min series of carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations in proximity to an intersection. The proposed model is examined based on the measured data under two situations. As the measured pollutant concentrations are found to be dependent on the distance to the intersection, the model is evaluated in three locations respectively, i.e. 110 m, 330 m and 500 m. Due to the different variation of pollutant concentrations on varied time, the model is also evaluated in peak and off-peak traffic time periods separately. Additionally, the proposed model, together with the back-propagation neural network (BPNN), is examined with the measured data in these situations. The proposed model is found to perform better in predictability and precision for both CO and PM2.5 than BPNN does, implying that the hybrid model can be an effective tool to improve the accuracy of estimating pollutants' distribution pattern at intersections. The outputs of these findings demonstrate the potential of the proposed model to be applicable to forecast the distribution pattern of air pollution in real-time in proximity to road intersection.

Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models.

PubMed

Garcia, J M; Teodoro, F; Cerdeira, R; Coelho, L M R; Kumar, Prashant; Carvalho, M G

2016-09-01

A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means.

Diffusion from a line source

NASA Technical Reports Server (NTRS)

Burns, R. E.

1973-01-01

The problem with predicting pollutant diffusion from a line source of arbitrary geometry is treated. The concentration at the line source may be arbitrarily varied with time. Special attention is given to the meteorological inputs which act as boundary conditions for the problem, and a mixing layer of arbitrary depth is assumed. Numerical application of the derived theory indicates the combinations of meteorological parameters that may be expected to result in high pollution concentrations.

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.

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach.

PubMed

Nascimento, L F C; Rizol, P M S R; Peneluppi, A P

2014-08-29

Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach.

PubMed

Nascimento, L F C; Rizol, P M S R; Peneluppi, A P

2014-11-01

Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.

Developing a smartphone software package for predicting atmospheric pollutant concentrations at mobile locations

PubMed Central

Larkin, Andrew; Williams, David E.; Kile, Molly L.; Baird, William M.

2014-01-01

Background There is considerable evidence that exposure to air pollution is harmful to health. In the U.S., ambient air quality is monitored by Federal and State agencies for regulatory purposes. There are limited options, however, for people to access this data in real-time which hinders an individual's ability to manage their own risks. This paper describes a new software package that models environmental concentrations of fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone concentrations for the state of Oregon and calculates personal health risks at the smartphone's current location. Predicted air pollution risk levels can be displayed on mobile devices as interactive maps and graphs color-coded to coincide with EPA air quality index (AQI) categories. Users have the option of setting air quality warning levels via color-coded bars and were notified whenever warning levels were exceeded by predicted levels within 10 km. We validated the software using data from participants as well as from simulations which showed that the application was capable of identifying spatial and temporal air quality trends. This unique application provides a potential low-cost technology for reducing personal exposure to air pollution which can improve quality of life particularly for people with health conditions, such as asthma, that make them more susceptible to these hazards. PMID:26146409

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.

Modeling Air Pollution Exposure Metrics for the Diabetes and Environment Panel Study (DEPS)

EPA Science Inventory

Air pollution health studies of fine particulate matter (PM) often use outdoor concentrations as exposure surrogates. To improve exposure assessments, we developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metric...

THE EFFECTS OF ROADSIDE STRUCTURES ON THE TRANSPORT AND DISPERSION OF ULTRAFINE PARTICLES FROM HIGHWAYS.

EPA Science Inventory

Understanding the transport and dispersion of pollutants from traffic sources, particularly within 300 meters of a roadway is important both for urban planning and for air quality assessments. Predicting pollutant concentration patterns in complex environments depends on accurat...

Specification and prediction of nickel mobilization using artificial intelligence methods

NASA Astrophysics Data System (ADS)

Gholami, Raoof; Ziaii, Mansour; Ardejani, Faramarz Doulati; Maleki, Shahoo

2011-12-01

Groundwater and soil pollution from pyrite oxidation, acid mine drainage generation, and release and transport of toxic metals are common environmental problems associated with the mining industry. Nickel is one toxic metal considered to be a key pollutant in some mining setting; to date, its formation mechanism has not yet been fully evaluated. The goals of this study are 1) to describe the process of nickel mobilization in waste dumps by introducing a novel conceptual model, and 2) to predict nickel concentration using two algorithms, namely the support vector machine (SVM) and the general regression neural network (GRNN). The results obtained from this study have shown that considerable amount of nickel concentration can be arrived into the water flow system during the oxidation of pyrite and subsequent Acid Drainage (AMD) generation. It was concluded that pyrite, water, and oxygen are the most important factors for nickel pollution generation while pH condition, SO4, HCO3, TDS, EC, Mg, Fe, Zn, and Cu are measured quantities playing significant role in nickel mobilization. SVM and GRNN have predicted nickel concentration with a high degree of accuracy. Hence, SVM and GRNN can be considered as appropriate tools for environmental risk assessment.

Community air pollution in Canada: a review and predictions for the 1980s.

PubMed Central

Bates, D. V.

1979-01-01

The main trends in Canadian air pollution since the national program of surveillance began are reviewed in this paper. In common with the United States, significant improvements in sulfur dioxide and particulate pollution have been recorded in a number of cities after the institution of control measures. However, some areas with a concentration of certain industries still have considerable particulate pollution. Since emission of nitrogen dioxide is increasing in the United States, the consequent photochemical pollution in southern Ontario will probably continue to increase. Nitrogen dioxide concentrations in the air are elevated in some western Canadian cities, presumably because of the presence of plants that burn natural gas to generate electricity and increasing pollution from automobiles. There is increasing concern about community air pollution in cities with large metal-fabricating plants, and community exposure to asbestos fibres is likely to be an important concern in the 1980s. PMID:445269

Variation of surface ozone in Campo Grande, Brazil: meteorological effect analysis and prediction.

PubMed

Pires, J C M; Souza, A; Pavão, H G; Martins, F G

2014-09-01

The effect of meteorological variables on surface ozone (O3) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O3 concentration, relative humidity and solar radiation. The threshold model that considers two O3 regimes was the one that correctly describes the effect of important meteorological variables in O3 behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O3 regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O3 concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O3 behaviour, being useful to define policy strategies for human health protection regarding air pollution.

Evaluation of Observation-Fused Regional Air Quality Model Results for Population Air Pollution Exposure Estimation

PubMed Central

Chen, Gang; Li, Jingyi; Ying, Qi; Sherman, Seth; Perkins, Neil; Rajeshwari, Sundaram; Mendola, Pauline

2014-01-01

In this study, Community Multiscale Air Quality (CMAQ) model was applied to predict ambient gaseous and particulate concentrations during 2001 to 2010 in 15 hospital referral regions (HRRs) using a 36-km horizontal resolution domain. An inverse distance weighting based method was applied to produce exposure estimates based on observation-fused regional pollutant concentration fields using the differences between observations and predictions at grid cells where air quality monitors were located. Although the raw CMAQ model is capable of producing satisfying results for O3 and PM2.5 based on EPA guidelines, using the observation data fusing technique to correct CMAQ predictions leads to significant improvement of model performance for all gaseous and particulate pollutants. Regional average concentrations were calculated using five different methods: 1) inverse distance weighting of observation data alone, 2) raw CMAQ results, 3) observation-fused CMAQ results, 4) population-averaged raw CMAQ results and 5) population-averaged fused CMAQ results. It shows that while O3 (as well as NOx) monitoring networks in the HRR regions are dense enough to provide consistent regional average exposure estimation based on monitoring data alone, PM2.5 observation sites (as well as monitors for CO, SO2, PM10 and PM2.5 components) are usually sparse and the difference between the average concentrations estimated by the inverse distance interpolated observations, raw CMAQ and fused CMAQ results can be significantly different. Population-weighted average should be used to account spatial variation in pollutant concentration and population density. Using raw CMAQ results or observations alone might lead to significant biases in health outcome analyses. PMID:24747248

Classifying environmental pollutants: Part 3. External validation of the classification system.

PubMed

Verhaar, H J; Solbé, J; Speksnijder, J; van Leeuwen, C J; Hermens, J L

2000-04-01

In order to validate a classification system for the prediction of the toxic effect concentrations of organic environmental pollutants to fish, all available fish acute toxicity data were retrieved from the ECETOC database, a database of quality-evaluated aquatic toxicity measurements created and maintained by the European Centre for the Ecotoxicology and Toxicology of Chemicals. The individual chemicals for which these data were available were classified according to the rulebase under consideration and predictions of effect concentrations or ranges of possible effect concentrations were generated. These predictions were compared to the actual toxicity data retrieved from the database. The results of this comparison show that generally, the classification system provides adequate predictions of either the aquatic toxicity (class 1) or the possible range of toxicity (other classes) of organic compounds. A slight underestimation of effect concentrations occurs for some highly water soluble, reactive chemicals with low log K(ow) values. On the other end of the scale, some compounds that are classified as belonging to a relatively toxic class appear to belong to the so-called baseline toxicity compounds. For some of these, additional classification rules are proposed. Furthermore, some groups of compounds cannot be classified, although they should be amenable to predictions. For these compounds additional research as to class membership and associated prediction rules is proposed.

A model to investigate the influence of suspended sediment on the mass transport of a pollutant in open channel flow. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Chapman, R. S.

1977-01-01

An explicit two-dimensional finite difference model, designed to investigate the influence of suspended sediment on the pollutant transport process, is presented. Specific attention is directed toward examining the role of suspended sediment in: (1) the turbulent vertical transport mechanism in a stratified flow, and (2) pollutant uptake due to sorption. Results presented indicate that suspended sediment plays a major role in the pollutant transport process, and subsequently, any meaningful attempt to model the fate of a pollutant in an alluvial channel must account for the presence of a suspended sediment concentration profile. Similarly, the vertical and longitudinal pollutant concentration distributions provided by the model may be utilized to improve upon the predictive capacities of existing water quality models.

High resolution tempo-spatial ozone prediction with SVM and LSTM

NASA Astrophysics Data System (ADS)

Gao, D.; Zhang, Y.; Qu, Z.; Sadighi, K.; Coffey, E.; LIU, Q.; Hannigan, M.; Henze, D. K.; Dick, R.; Shang, L.; Lv, Q.

2017-12-01

To investigate and predict the exposure of ozone and other pollutants in urban areas, we utilize data from various infrastructures including EPA, NOAA and RIITS from government of Los Angeles and construct statistical models to conduct ozone concentration prediction in Los Angeles areas at finer spatial and temporal granularity. Our work involves cyber data such as traffic, roads and population data as features for prediction. Two statistical models, Support Vector Machine (SVM) and Long Short-term Memory (LSTM, deep learning method) are used for prediction. . Our experiments show that kernelized SVM gains better prediction performance when taking traffic counts, road density and population density as features, with a prediction RMSE of 7.99 ppb for all-time ozone and 6.92 ppb for peak-value ozone. With simulated NOx from Chemical Transport Model(CTM) as features, SVM generates even better prediction performance, with a prediction RMSE of 6.69ppb. We also build LSTM, which has shown great advantages at dealing with temporal sequences, to predict ozone concentration by treating ozone concentration as spatial-temporal sequences. Trained by ozone concentration measurements from the 13 EPA stations in LA area, the model achieves 4.45 ppb RMSE. Besides, we build a variant of this model which adds spatial dynamics into the model in the form of transition matrix that reveals new knowledge on pollutant transition. The forgetting gate of the trained LSTM is consistent with the delay effect of ozone concentration and the trained transition matrix shows spatial consistency with the common direction of winds in LA area.

Evaluation of accuracy of linear regression models in predicting urban stormwater discharge characteristics.

PubMed

Madarang, Krish J; Kang, Joo-Hyon

2014-06-01

Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

A changing climate: impacts on human exposures to O3 using an integrated modeling methodology

EPA Science Inventory

Predicting the impacts of changing climate on human exposure to air pollution requires future scenarios that account for changes in ambient pollutant concentrations, population sizes and distributions, and housing stocks. An integrated methodology to model changes in human exposu...

Characterization of air pollutant concentrations, fleet emission factors, and dispersion near a North Carolina interstate freeway across two seasons

NASA Astrophysics Data System (ADS)

Saha, Provat K.; Khlystov, Andrey; Snyder, Michelle G.; Grieshop, Andrew P.

2018-03-01

We present field measurement data and modeling of multiple traffic-related air pollutants during two seasons at a site adjoining Interstate 40, near Durham, North Carolina. We analyze spatial-temporal and seasonal trends and fleet-average pollutant emission factors and use our data to evaluate a line source dispersion model. Month-long measurement campaigns were performed in summer 2015 and winter 2016. Data were collected at a fixed near-road site located within 10 m from the highway edge, an upwind background site and, under favorable meteorological conditions, along downwind perpendicular transects. Measurements included the size distribution, chemical composition, and volatility of submicron particles, black carbon (BC), nitrogen oxides (NOx), meteorological conditions and traffic activity data. Results show strong seasonal and diurnal differences in spatial distribution of traffic sourced pollutants. A strong signature of vehicle emissions was observed within 100-150 m from the highway edge with significantly higher concentrations during morning. Substantially higher concentrations and less-sharp near-road gradients were observed in winter for many species. Season-specific fleet-average fuel-based emission factors for NO, NOx, BC, and particle number (PN) were derived based on up- and down-wind roadside measurements. The campaign-average NOx and PN emission factors were 20% and 300% higher in winter than summer, respectively. These results suggest that the combined effect of higher emissions and their slower downwind dispersion in winter dictate the observed higher downwind concentrations and wider highway influence zone in winter for several species. Finally, measurements of traffic data, emission factors, and pollutant concentrations were integrated to evaluate a line source dispersion model (R-LINE). The dispersion model captured the general trends in the spatial and temporal patterns in near-road concentrations. However, there was a tendency for the model to under-predict concentrations near the road in the mornings and over-predict concentrations in the evenings.

National-scale exposure prediction for long-term concentrations of particulate matter and nitrogen dioxide in South Korea.

PubMed

Kim, Sun-Young; Song, Insang

2017-07-01

The limited spatial coverage of the air pollution data available from regulatory air quality monitoring networks hampers national-scale epidemiological studies of air pollution. The present study aimed to develop a national-scale exposure prediction model for estimating annual average concentrations of PM 10 and NO 2 at residences in South Korea using regulatory monitoring data for 2010. Using hourly measurements of PM 10 and NO 2 at 277 regulatory monitoring sites, we calculated the annual average concentrations at each site. We also computed 322 geographic variables in order to represent plausible local and regional pollution sources. Using these data, we developed universal kriging models, including three summary predictors estimated by partial least squares (PLS). The model performance was evaluated with fivefold cross-validation. In sensitivity analyses, we compared our approach with two alternative approaches, which added regional interactions and replaced the PLS predictors with up to ten selected variables. Finally, we predicted the annual average concentrations of PM 10 and NO 2 at 83,463 centroids of residential census output areas in South Korea to investigate the population exposure to these pollutants and to compare the exposure levels between monitored and unmonitored areas. The means of the annual average concentrations of PM 10 and NO 2 for 2010, across regulatory monitoring sites in South Korea, were 51.63 μg/m3 (SD = 8.58) and 25.64 ppb (11.05), respectively. The universal kriging exposure prediction models yielded cross-validated R 2 s of 0.45 and 0.82 for PM 10 and NO 2 , respectively. Compared to our model, the two alternative approaches gave consistent or worse performances. Population exposure levels in unmonitored areas were lower than in monitored areas. This is the first study that focused on developing a national-scale point wise exposure prediction approach in South Korea, which will allow national exposure assessments and epidemiological research to answer policy-related questions and to draw comparisons among different countries. Copyright © 2017 Elsevier Ltd. All rights reserved.

Regression trees modeling and forecasting of PM10 air pollution in urban areas

NASA Astrophysics Data System (ADS)

Stoimenova, M.; Voynikova, D.; Ivanov, A.; Gocheva-Ilieva, S.; Iliev, I.

2017-10-01

Fine particulate matter (PM10) air pollution is a serious problem affecting the health of the population in many Bulgarian cities. As an example, the object of this study is the pollution with PM10 of the town of Pleven, Northern Bulgaria. The measured concentrations of this air pollutant for this city consistently exceeded the permissible limits set by European and national legislation. Based on data for the last 6 years (2011-2016), the analysis shows that this applies both to the daily limit of 50 micrograms per cubic meter and the allowable number of daily concentration exceedances to 35 per year. Also, the average annual concentration of PM10 exceeded the prescribed norm of no more than 40 micrograms per cubic meter. The aim of this work is to build high performance mathematical models for effective prediction and forecasting the level of PM10 pollution. The study was conducted with the powerful flexible data mining technique Classification and Regression Trees (CART). The values of PM10 were fitted with respect to meteorological data such as maximum and minimum air temperature, relative humidity, wind speed and direction and others, as well as with time and autoregressive variables. As a result the obtained CART models demonstrate high predictive ability and fit the actual data with up to 80%. The best models were applied for forecasting the level pollution for 3 to 7 days ahead. An interpretation of the modeling results is presented.

Soil TPH Concentration Estimation Using Vegetation Indices in an Oil Polluted Area of Eastern China

PubMed Central

Zhu, Linhai; Zhao, Xuechun; Lai, Liming; Wang, Jianjian; Jiang, Lianhe; Ding, Jinzhi; Liu, Nanxi; Yu, Yunjiang; Li, Junsheng; Xiao, Nengwen; Zheng, Yuanrun; Rimmington, Glyn M.

2013-01-01

Assessing oil pollution using traditional field-based methods over large areas is difficult and expensive. Remote sensing technologies with good spatial and temporal coverage might provide an alternative for monitoring oil pollution by recording the spectral signals of plants growing in polluted soils. Total petroleum hydrocarbon concentrations of soils and the hyperspectral canopy reflectance were measured in wetlands dominated by reeds (Phragmites australis) around oil wells that have been producing oil for approximately 10 years in the Yellow River Delta, eastern China to evaluate the potential of vegetation indices and red edge parameters to estimate soil oil pollution. The detrimental effect of oil pollution on reed communities was confirmed by the evidence that the aboveground biomass decreased from 1076.5 g m−2 to 5.3 g m−2 with increasing total petroleum hydrocarbon concentrations ranging from 9.45 mg kg−1 to 652 mg kg−1. The modified chlorophyll absorption ratio index (MCARI) best estimated soil TPH concentration among 20 vegetation indices. The linear model involving MCARI had the highest coefficient of determination (R 2 = 0.73) and accuracy of prediction (RMSE = 104.2 mg kg−1). For other vegetation indices and red edge parameters, the R2 and RMSE values ranged from 0.64 to 0.71 and from 120.2 mg kg−1 to 106.8 mg kg−1 respectively. The traditional broadband normalized difference vegetation index (NDVI), one of the broadband multispectral vegetation indices (BMVIs), produced a prediction (R 2 = 0.70 and RMSE = 110.1 mg kg−1) similar to that of MCARI. These results corroborated the potential of remote sensing for assessing soil oil pollution in large areas. Traditional BMVIs are still of great value in monitoring soil oil pollution when hyperspectral data are unavailable. PMID:23342066

The role of meteorological conditions and pollution control strategies in reducing air pollution in Beijing during APEC 2014 and Victory Parade 2015

NASA Astrophysics Data System (ADS)

Liang, Pengfei; Zhu, Tong; Fang, Yanhua; Li, Yingruo; Han, Yiqun; Wu, Yusheng; Hu, Min; Wang, Junxia

2017-11-01

To control severe air pollution in China, comprehensive pollution control strategies have been implemented throughout the country in recent years. To evaluate the effectiveness of these strategies, the influence of meteorological conditions on levels of air pollution needs to be determined. Using the intensive air pollution control strategies implemented during the Asia-Pacific Economic Cooperation Forum in 2014 (APEC 2014) and the 2015 China Victory Day Parade (Victory Parade 2015) as examples, we estimated the role of meteorological conditions and pollution control strategies in reducing air pollution levels in Beijing. Atmospheric particulate matter of aerodynamic diameter ≤ 2.5 µm (PM2.5) samples were collected and gaseous pollutants (SO2, NO, NOx, and O3) were measured online at a site in Peking University (PKU). To determine the influence of meteorological conditions on the levels of air pollution, we first compared the air pollutant concentrations during days with stable meteorological conditions. However, there were few days with stable meteorological conditions during the Victory Parade. As such, we were unable to estimate the level of emission reduction efforts during this period. Finally, a generalized linear regression model (GLM) based only on meteorological parameters was built to predict air pollutant concentrations, which could explain more than 70 % of the variation in air pollutant concentration levels, after incorporating the nonlinear relationships between certain meteorological parameters and the concentrations of air pollutants. Evaluation of the GLM performance revealed that the GLM, even based only on meteorological parameters, could be satisfactory to estimate the contribution of meteorological conditions in reducing air pollution and, hence, the contribution of control strategies in reducing air pollution. Using the GLM, we found that the meteorological conditions and pollution control strategies contributed 30 and 28 % to the reduction of the PM2.5 concentration during APEC and 38 and 25 % during the Victory Parade, respectively, based on the assumption that the concentrations of air pollutants are only determined by meteorological conditions and emission intensities. We also estimated the contribution of meteorological conditions and control strategies in reducing the concentrations of gaseous pollutants and PM2.5 components with the GLMs, revealing the effective control of anthropogenic emissions.

AMMONIA EMISSIONS AND THEIR IMPLICATIONS ON FINE PARTICULATE MATTER FORMATION IN NORTH CAROLINA

EPA Science Inventory

Ammonia (NH₃) is an important atmospheric pollutant that plays a key role in several air pollution problems. The accuracy of NH₃ emissions can have a large effect on air quality model (AQM) predictions of aerosol sulfate, nitrate, and ammonium concentration...

Combining Regional- and Local-Scale Air Quality Models with Exposure Models for Use in Environmental Health Studies

EPA Science Inventory

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including: air pollution concentrations; human activity patterns, such as the amount of time spent outdoors vs. indoors, commuting, wal...

Developing a Method for Resolving NOx Emission Inventory Biases Using Discrete Kalman Filter Inversion, Direct Sensitivities, and Satellite-Based Columns

EPA Science Inventory

An inverse method was developed to integrate satellite observations of atmospheric pollutant column concentrations and direct sensitivities predicted by a regional air quality model in order to discern biases in the emissions of the pollutant precursors.

Modeling population exposures to outdoor sources of hazardous air pollutants.

PubMed

Ozkaynak, Halûk; Palma, Ted; Touma, Jawad S; Thurman, James

2008-01-01

Accurate assessment of human exposures is an important part of environmental health effects research. However, most air pollution epidemiology studies rely upon imperfect surrogates of personal exposures, such as information based on available central-site outdoor concentration monitoring or modeling data. In this paper, we examine the limitations of using outdoor concentration predictions instead of modeled personal exposures for over 30 gaseous and particulate hazardous air pollutants (HAPs) in the US. The analysis uses the results from an air quality dispersion model (the ASPEN or Assessment System for Population Exposure Nationwide model) and an inhalation exposure model (the HAPEM or Hazardous Air Pollutant Exposure Model, Version 5), applied by the US. Environmental protection Agency during the 1999 National Air Toxic Assessment (NATA) in the US. Our results show that the total predicted chronic exposure concentrations of outdoor HAPs from all sources are lower than the modeled ambient concentrations by about 20% on average for most gaseous HAPs and by about 60% on average for most particulate HAPs (mainly, due to the exclusion of indoor sources from our modeling analysis and lower infiltration of particles indoors). On the other hand, the HAPEM/ASPEN concentration ratio averages for onroad mobile source exposures were found to be greater than 1 (around 1.20) for most mobile-source related HAPs (e.g. 1, 3-butadiene, acetaldehyde, benzene, formaldehyde) reflecting the importance of near-roadway and commuting environments on personal exposures to HAPs. The distribution of the ratios of personal to ambient concentrations was found to be skewed for a number of the VOCs and reactive HAPs associated with major source emissions, indicating the importance of personal mobility factors. We conclude that the increase in personal exposures from the corresponding predicted ambient levels tends to occur near locations where there are either major emission sources of HAPs or when individuals are exposed to either on- or nonroad sources of HAPs during their daily activities. These findings underscore the importance of applying exposure-modeling methods, which incorporate information on time-activity, commuting, and exposure factors data, for the purposes of assigning exposures in air pollution health studies.

Integrating a street-canyon model with a regional Gaussian dispersion model for improved characterisation of near-road air pollution

NASA Astrophysics Data System (ADS)

Fallah-Shorshani, Masoud; Shekarrizfard, Maryam; Hatzopoulou, Marianne

2017-03-01

The development and use of dispersion models that simulate traffic-related air pollution in urban areas has risen significantly in support of air pollution exposure research. In order to accurately estimate population exposure, it is important to generate concentration surfaces that take into account near-road concentrations as well as the transport of pollutants throughout an urban region. In this paper, an integrated modelling chain was developed to simulate ambient Nitrogen Dioxide (NO2) in a dense urban neighbourhood while taking into account traffic emissions, the regional background, and the transport of pollutants within the urban canopy. For this purpose, we developed a hybrid configuration including 1) a street canyon model, which simulates pollutant transfer along streets and intersections, taking into account the geometry of buildings and other obstacles, and 2) a Gaussian puff model, which resolves the transport of contaminants at the top of the urban canopy and accounts for regional meteorology. Each dispersion model was validated against measured concentrations and compared against the hybrid configuration. Our results demonstrate that the hybrid approach significantly improves the output of each model on its own. An underestimation appears clearly for the Gaussian model and street-canyon model compared to observed data. This is due to ignoring the building effect by the Gaussian model and undermining the contribution of other roads by the canyon model. The hybrid approach reduced the RMSE (of observed vs. predicted concentrations) by 16%-25% compared to each model on its own, and increased FAC2 (fraction of predictions within a factor of two of the observations) by 10%-34%.

Facultative Stabilization Pond: Measuring Biological Oxygen Demand using Mathematical Approaches

NASA Astrophysics Data System (ADS)

Wira S, Ihsan; Sunarsih, Sunarsih

2018-02-01

Pollution is a man-made phenomenon. Some pollutants which discharged directly to the environment could create serious pollution problems. Untreated wastewater will cause contamination and even pollution on the water body. Biological Oxygen Demand (BOD) is the amount of oxygen required for the oxidation by bacteria. The higher the BOD concentration, the greater the organic matter would be. The purpose of this study was to predict the value of BOD contained in wastewater. Mathematical modeling methods were chosen in this study to depict and predict the BOD values contained in facultative wastewater stabilization ponds. Measurements of sampling data were carried out to validate the model. The results of this study indicated that a mathematical approach can be applied to predict the BOD contained in the facultative wastewater stabilization ponds. The model was validated using Absolute Means Error with 10% tolerance limit, and AME for model was 7.38% (< 10%), so the model is valid. Furthermore, a mathematical approach can also be applied to illustrate and predict the contents of wastewater.

How uncertain is model-based prediction of copper loads in stormwater runoff?

PubMed

Lindblom, E; Ahlman, S; Mikkelsen, P S

2007-01-01

In this paper, we conduct a systematic analysis of the uncertainty related with estimating the total load of pollution (copper) from a separate stormwater drainage system, conditioned on a specific combination of input data, a dynamic conceptual pollutant accumulation-washout model and measurements (runoff volumes and pollutant masses). We use the generalized likelihood uncertainty estimation (GLUE) methodology and generate posterior parameter distributions that result in model outputs encompassing a significant number of the highly variable measurements. Given the applied pollution accumulation-washout model and a total of 57 measurements during one month, the total predicted copper masses can be predicted within a range of +/-50% of the median value. The message is that this relatively large uncertainty should be acknowledged in connection with posting statements about micropollutant loads as estimated from dynamic models, even when calibrated with on-site concentration data.

Dynamic modelling of solids in a full-scale activated sludge plant preceded by CEPT as a preliminary step for micropollutant removal modelling.

PubMed

Baalbaki, Zeina; Torfs, Elena; Maere, Thomas; Yargeau, Viviane; Vanrolleghem, Peter A

2017-04-01

The presence of micropollutants in the environment has triggered research on quantifying and predicting their fate in wastewater treatment plants (WWTPs). Since the removal of micropollutants is highly related to conventional pollutant removal and affected by hydraulics, aeration, biomass composition and solids concentration, the fate of these conventional pollutants and characteristics must be well predicted before tackling models to predict the fate of micropollutants. In light of this, the current paper presents the dynamic modelling of conventional pollutants undergoing activated sludge treatment using a limited set of additional daily composite data besides the routine data collected at a WWTP over one year. Results showed that as a basis for modelling, the removal of micropollutants, the Bürger-Diehl settler model was found to capture the actual effluent total suspended solids (TSS) concentrations more efficiently than the Takács model by explicitly modelling the overflow boundary. Results also demonstrated that particular attention must be given to characterizing incoming TSS to obtain a representative solids balance in the presence of a chemically enhanced primary treatment, which is key to predict the fate of micropollutants.

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.

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.

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.

Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England.

PubMed

Pannullo, Francesca; Lee, Duncan; Neal, Lucy; Dalvi, Mohit; Agnew, Paul; O'Connor, Fiona M; Mukhopadhyay, Sabyasachi; Sahu, Sujit; Sarran, Christophe

2017-03-27

Estimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future. We conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO 2 ); ozone (O 3 ); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM 2.5 ), and less than 10 micrometers (PM 10 ); and sulphur dioxide (SO 2 ). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP). NO 2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO 2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO 2 emissions and concentrations. NO 2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time.

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.

Local and regional interactions between air quality and climate in New Delhi- A sector based analysis

NASA Astrophysics Data System (ADS)

Marrapu, Pallavi

Deteriorating air quality is one of the major problems faced worldwide and in particular in Asia. The world's most polluted megacities are located in Asia highlighting the urgent need for efforts to improve the air quality. New Delhi (India), one of the world's most polluted cities, was the host of the Common Wealth Games during the period of 4-14 October 2010. This high profile event provided a good opportunity to accelerate efforts to improve air quality. Computational advances now allow air quality forecast models to fully couple the meteorology with chemical constituents within a unified modeling system that allows two-way interactions. The WRF-Chem model is used to simulate air quality in New Delhi. The thesis focuses on evaluating air quality and meteorology feedbacks. Four nested domains ranging from South Asia, Northern India, NCR Delhi and Delhi city at 45km, 15km, 5km and 1.67km resolution for a period of 20 day (26th Sep--15th Oct, 2010) are used in the study. The predicted mean surface concentrations of various pollutants show similar spatial distributions with peak values in the middle of the domain reflecting the traffic and population patterns in the city. Along with these activities, construction dust and industrial emissions contribute to high levels of criteria pollutants. The study evaluates the WRF-Chem capabilities using a new emission inventory developed over Delhi at a fine resolution of 1.67km and evaluating the results with observational data from 11 monitoring sties placed at various Game venues. The contribution of emission sectors including transportation, power, industry, and domestic to pollutant concentrations at targeted regions are studied and the results show that transportation and domestic sector are the major contributors to the pollution levels in Delhi, followed by industry. Apart from these sectors, emissions outside of Delhi contribute 20-50% to surface concentrations depending on the species. This indicates that pollution control efforts should take a regional perspective. Air quality projections in Delhi for 2030 are investigated. The Greenhouse Gas and Air Pollution I nteractions and Synergies (GAINS) model is used to generate a 2030 future emission scenario for Delhi using projections of air quality control measures and energy demands. Net reductions in CO concentrations by 50%, and increases of 140% and 40% in BC and NOx concentrations, respectively, are predicted. The net changes in concentration are associated with increases in transport and industry sectors. The domestic sector still has a significant contribution to air pollutant levels. The air quality levels show a profound effect under this scenario on the environment and human health. The increase in pollution from 2010 to 2030 is predicted to cause an increase in surface temperature by ˜0.65K. These increasing pollution levels also show effects on the radiative forcing. The high aerosols loading i.e. BC, PM2.5 and PM10 levels show strong influence on the short and longwave fluxes causing strong surface dimming and strong atmosphere heating due to BC. These results indicate transport and domestic sectors should be targeted for air quality and climate mitigations.

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.

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.

Stochastic modeling for river pollution of Sungai Perlis

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

Yunus, Nurul Izzaty Mohd.; Rahman, Haliza Abd.; Bahar, Arifah

2015-02-03

River pollution has been recognized as a contributor to a wide range of health problems and disorders in human. It can pose health dangers to humans who come into contact with it, either directly or indirectly. Therefore, it is most important to measure the concentration of Biochemical Oxygen Demand (BOD) as a water quality parameter since the parameter has long been the basic means for determining the degree of water pollution in rivers. In this study, BOD is used as a parameter to estimate the water quality at Sungai Perlis. It has been observed that Sungai Perlis is polluted duemore » to lack of management and improper use of resources. Therefore, it is of importance to model the Sungai Perlis water quality in order to describe and predict the water quality systems. The BOD concentration secondary data set is used which was extracted from the Drainage and Irrigation Department Perlis State website. The first order differential equation from Streeter – Phelps model was utilized as a deterministic model. Then, the model was developed into a stochastic model. Results from this study shows that the stochastic model is more adequate to describe and predict the BOD concentration and the water quality systems in Sungai Perlis by having smaller value of mean squared error (MSE)« less

Ambient air pollution, lung function and airway responsiveness in children with asthma

PubMed Central

Ierodiakonou, Despo; Zanobetti, Antonella; Coull, Brent A.; Melly, Steve; Postma, Dirkje S.; Boezen, H. Marike; Vonk, Judith M.; Williams, Paul V.; Shapiro, Gail G.; McKone, Edward F.; Hallstrand, Teal S.; Koenig, Jane Q.; Schildcrout, Jonathan S.; Lumley, Thomas; Fuhlbrigge, Anne N.; Koutrakis, Petros; Schwartz, Joel; Weiss, Scott T.; Gold, Diane R

2016-01-01

Background Although ambient air pollution has been linked to reduced lung function in healthy children, longitudinal analyses of pollution effects in asthma are lacking. Objective To investigate pollution effects in a longitudinal asthma study and effect modification by controller medications. Methods We examined associations of lung function and methacholine responsiveness (PC20) with ozone, carbon monoxide (CO), nitrogen dioxide (NO2) and sulfur dioxide (SO2) levels in 1,003 asthmatic children participating in a 4-year clinical trial. We further investigated whether budesonide and nedocromil modified pollution effects. Daily pollutant concentrations were linked to zip/postal code of residence. Linear mixed models tested associations of within-subject pollutant concentrations with FEV1 and FVC %predicted, FEV1/FVC and PC20, adjusting for seasonality and confounders. Results Same-day and 1-week average CO levels were negatively associated with post-bronchodilator %predicted FEV1 (change(95%CI) per IQR: −0.33(−0.49, −0.16), −0.41(−0.62, −0.21), respectively) and FVC (−0.19(−0.25, −0.07), −0.25(−0.43, −0.07)). Longer-term four-month averages of CO were negatively associated with prebronchodilator %predicted FEV1 and FVC (−0.36(−0.62, −0.10), −0.21(−0.42, −0.01)). Four-month averaged CO and ozone levels were negatively associated with FEV1/FVC (p<0.05). Increased four-month average NO2 levels were associated with reduced post-bronchodilator FEV1 and FVC %predicted. Long-term exposures to SO2 were associated with reduced PC20 (%change(95%CI) per IQR:-6(-11,-1.5)). Treatment augmented the negative short-term CO effect on PC20. Conclusions Air pollution adversely influences lung function and PC20 in asthmatic children. Treatment with controller medications may not protect but worsens the CO effects on PC20. This clinical trial design evaluates modification of pollution effects by treatment without confounding by indication. PMID:26187234

DYNAMIC EVALUATION OF REGIONAL AIR QUALITY MODELS: ASSESSING CHANGES TO O 3 STEMMING FROM CHANGES IN EMISSIONS AND METEOROLOGY

EPA Science Inventory

Regional-scale air quality models are used to estimate the response of air pollutants to potential emission control strategies as part of the decision-making process. Traditionally, the model predicted pollutant concentrations are evaluated for the “base case” to assess a model’s...

USING VISUAL PLUMES PREDICTIONS TO MODULATE COMBINED SEWER OVERFLOW (CSO) RATES

EPA Science Inventory

High concentrations of pathogens and toxic residues in creeks and rivers can pose risks to human health and ecological systems. Combined Sewer Overflows (CSOs) discharging into these watercourses often contribute significantly to elevating pollutant concentrations during wet weat...

Comparison of Calibration Techniques for Low-Cost Air Quality Monitoring

NASA Astrophysics Data System (ADS)

Malings, C.; Ramachandran, S.; Tanzer, R.; Kumar, S. P. N.; Hauryliuk, A.; Zimmerman, N.; Presto, A. A.

2017-12-01

Assessing the intra-city spatial distribution and temporal variability of air quality can be facilitated by a dense network of monitoring stations. However, the cost of implementing such a network can be prohibitive if high-quality but high-cost monitoring systems are used. To this end, the Real-time Affordable Multi-Pollutant (RAMP) sensor package has been developed at the Center for Atmospheric Particle Studies of Carnegie Mellon University, in collaboration with SenSevere LLC. This self-contained unit can measure up to five gases out of CO, SO2, NO, NO2, O3, VOCs, and CO2, along with temperature and relative humidity. Responses of individual gas sensors can vary greatly even when exposed to the same ambient conditions. Those of VOC sensors in particular were observed to vary by a factor-of-8, which suggests that each sensor requires its own calibration model. To this end, we apply and compare two different calibration methods to data collected by RAMP sensors collocated with a reference monitor station. The first method, random forest (RF) modeling, is a rule-based method which maps sensor responses to pollutant concentrations by implementing a trained sequence of decision rules. RF modeling has previously been used for other RAMP gas sensors by the group, and has produced precise calibrated measurements. However, RF models can only predict pollutant concentrations within the range observed in the training data collected during the collocation period. The second method, Gaussian process (GP) modeling, is a probabilistic Bayesian technique whereby broad prior estimates of pollutant concentrations are updated using sensor responses to generate more refined posterior predictions, as well as allowing predictions beyond the range of the training data. The accuracy and precision of these techniques are assessed and compared on VOC data collected during the summer of 2017 in Pittsburgh, PA. By combining pollutant data gathered by each RAMP sensor and applying appropriate calibration techniques, the potentially noisy or biased responses of individual sensors can be mapped to pollutant concentration values which are comparable to those of reference instruments.

Determinations of the combined effect of toxic substances in predictions of atmospheric pollution (in Russian)

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

Gusey, M.I.; Gil'denskiol'd, R.S.; Baikov, B.K.

There have recently been several investigations of the combined effect of several pollutants present simultaneously in the atmosphere. As a rule the combined effect of toxic substances in the atmosphere at the levels of liminal and subliminal concentrations are in accordance with the principle of simple summation. There is a definite gap between theory and practice in the establishment of standards for atmospheric pollutants. 17 references, 1 table.

Potential ecological risk assessment and prediction of soil heavy metal pollution around coal gangue dump

NASA Astrophysics Data System (ADS)

Jiang, X.; Lu, W. X.; Yang, Q. C.; Yang, Z. P.

2014-03-01

Aim of the present study is to evaluate the potential ecological risk and predict the trend of soil heavy metal pollution around a~coal gangue dump in Jilin Province (Northeast China). The concentrations of Cd, Pb, Cu, Cr and Zn were monitored by inductively coupled plasma mass spectrometry (ICP-MS). The potential ecological risk index method developed by Hakanson (1980) was employed to assess the potential risk of heavy metal pollution. The potential ecological risk in an order of E(Cd) > E(Pb) > E(Cu) > E(Cr) > E(Zn) have been obtained, which showed that Cd was the most important factor led to risk. Based on the Cd pollution history, the cumulative acceleration and cumulative rate of Cd were estimated, and the fixed number of years exceeding standard prediction model was established, which was used to predict the pollution trend of Cd under the accelerated accumulation mode and the uniform mode. Pearson correlation analysis and correspondence analysis are employed to identify the sources of heavy metal, and the relationship between sampling points and variables. These findings provide some useful insights for making appropriate management strategies to prevent and decrease heavy metal pollution around coal gangue dump in Yangcaogou coal mine and other similar areas elsewhere.

Uncertainty characterization and quantification in air pollution models. Application to the ADMS-Urban model.

NASA Astrophysics Data System (ADS)

Debry, E.; Malherbe, L.; Schillinger, C.; Bessagnet, B.; Rouil, L.

2009-04-01

Evaluation of human exposure to atmospheric pollution usually requires the knowledge of pollutants concentrations in ambient air. In the framework of PAISA project, which studies the influence of socio-economical status on relationships between air pollution and short term health effects, the concentrations of gas and particle pollutants are computed over Strasbourg with the ADMS-Urban model. As for any modeling result, simulated concentrations come with uncertainties which have to be characterized and quantified. There are several sources of uncertainties related to input data and parameters, i.e. fields used to execute the model like meteorological fields, boundary conditions and emissions, related to the model formulation because of incomplete or inaccurate treatment of dynamical and chemical processes, and inherent to the stochastic behavior of atmosphere and human activities [1]. Our aim is here to assess the uncertainties of the simulated concentrations with respect to input data and model parameters. In this scope the first step consisted in bringing out the input data and model parameters that contribute most effectively to space and time variability of predicted concentrations. Concentrations of several pollutants were simulated for two months in winter 2004 and two months in summer 2004 over five areas of Strasbourg. The sensitivity analysis shows the dominating influence of boundary conditions and emissions. Among model parameters, the roughness and Monin-Obukhov lengths appear to have non neglectable local effects. Dry deposition is also an important dynamic process. The second step of the characterization and quantification of uncertainties consists in attributing a probability distribution to each input data and model parameter and in propagating the joint distribution of all data and parameters into the model so as to associate a probability distribution to the modeled concentrations. Several analytical and numerical methods exist to perform an uncertainty analysis. We chose the Monte Carlo method which has already been applied to atmospheric dispersion models [2, 3, 4]. The main advantage of this method is to be insensitive to the number of perturbed parameters but its drawbacks are its computation cost and its slow convergence. In order to speed up this one we used the method of antithetic variable which takes adavantage of the symmetry of probability laws. The air quality model simulations were carried out by the Association for study and watching of Atmospheric Pollution in Alsace (ASPA). The output concentrations distributions can then be updated with a Bayesian method. This work is part of an INERIS Research project also aiming at assessing the uncertainty of the CHIMERE dispersion model used in the Prev'Air forecasting platform (www.prevair.org) in order to deliver more accurate predictions. (1) Rao, K.S. Uncertainty Analysis in Atmospheric Dispersion Modeling, Pure and Applied Geophysics, 2005, 162, 1893-1917. (2) Beekmann, M. and Derognat, C. Monte Carlo uncertainty analysis of a regional-scale transport chemistry model constrained by measurements from the Atmospheric Pollution Over the PAris Area (ESQUIF) campaign, Journal of Geophysical Research, 2003, 108, 8559-8576. (3) Hanna, S.R. and Lu, Z. and Frey, H.C. and Wheeler, N. and Vukovich, J. and Arunachalam, S. and Fernau, M. and Hansen, D.A. Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain, Atmospheric Environment, 2001, 35, 891-903. (4) Romanowicz, R. and Higson, H. and Teasdale, I. Bayesian uncertainty estimation methodology applied to air pollution modelling, Environmetrics, 2000, 11, 351-371.

Predicting ecological effects of pollutants: A role for marine mesocosms

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

Ward, T.J.

1994-12-31

The major uncertainty in predicting the ecological effects of a pollutant is the relationship between dose and the ecological response. Mesocosms may be used to simulate population-level biological processes and to estimate the nature and shape of dose-related responses to pollutants, for use in predictive evaluations of pollutant impacts. To ensure that responses observed in mesocosm tests are representative it is necessary to confirm that the simulated processes operate at rates similar to those found in the field. Pilot experiments were conducted in small marine mesocosms simulating major processes in two local habitat types: unvegetated sand and sand colonized bymore » the brown macroalga Sargassum. The results showed that for a range of variates (such as the % of egg-bearing harpacticoid copepods, or the chlorophyll a concentration in surface sediments) the mean values for measurements in the tanks over a 9 week period did not consistently converge or diverge from those in the field. Also, for a number of the variates, a modelled decrease of more than about 60% in the mean could be detected with greater than 80% statistical power. This indicates that the effects of a pollutant could be detected with acceptable power. Use of a combination of such variates based on different functional or taxonomic groups for pollutant effects testing could greatly decrease uncertainty about the predicted effects of pollutants discharged to these habitats.« less

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.

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China.

PubMed

Ji, Xiaoliang; Shang, Xu; Dahlgren, Randy A; Zhang, Minghua

2017-07-01

Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the rural-suburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2 ), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2 , and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

Effects of air pollution on thermal structure and dispersion in an urban planetary boundary layer

NASA Technical Reports Server (NTRS)

Viskanta, R.; Johnson, R. O.; Bergstrom, R. W.

1977-01-01

The short-term effects of urbanization and air pollution on the transport processes in the urban planetary boundary layer (PBL) are studied. The investigation makes use of an unsteady two-dimensional transport model which has been developed by Viskanta et al., (1976). The model predicts pollutant concentrations and temperature in the PBL. The potential effects of urbanization and air pollution on the thermal structure in the urban PBL are considered, taking into account the results of numerical simulations modeling the St. Louis, Missouri metropolitan area.

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

Voynikova, D. S., E-mail: desi-sl2000@yahoo.com; Gocheva-Ilieva, S. G., E-mail: snegocheva@yahoo.com; Ivanov, A. V., E-mail: aivanov-99@yahoo.com

Numerous time series methods are used in environmental sciences allowing the detailed investigation of air pollution processes. The goal of this study is to present the empirical analysis of various aspects of stochastic modeling and in particular the ARIMA/SARIMA methods. The subject of investigation is air pollution in the town of Kardzhali, Bulgaria with 2 problematic pollutants – sulfur dioxide (SO2) and particulate matter (PM10). Various SARIMA Transfer Function models are built taking into account meteorological factors, data transformations and the use of different horizons selected to predict future levels of concentrations of the pollutants.

Development of a modeling approach to estimate indoor-to-outdoor sulfur ratios and predict indoor PM2.5 and black carbon concentrations for Eastern Massachusetts households

PubMed Central

Tang, Chia Hsi; Garshick, Eric; Grady, Stephanie; Coull, Brent; Schwartz, Joel; Koutrakis, Petros

2018-01-01

The effects of indoor air pollution on human health have drawn increasing attention among the scientific community as individuals spend most of their time indoors. However, indoor air sampling is labor-intensive and costly, which limits the ability to study the adverse health effects related to indoor air pollutants. To overcome this challenge, many researchers have attempted to predict indoor exposures based on outdoor pollutant concentrations, home characteristics, and weather parameters. Typically, these models require knowledge of the infiltration factor, which indicates the fraction of ambient particles that penetrates indoors. For estimating indoor fine particulate matter (PM2.5) exposure, a common approach is to use the indoor-to-outdoor sulfur ratio (Sindoor/Soutdoor) as a proxy of the infiltration factor. The objective of this study was to develop a robust model that estimates Sindoor/Soutdoor for individual households that can be incorporated into models to predict indoor PM2.5 and black carbon (BC) concentrations. Overall, our model adequately estimated Sindoor/Soutdoor with an out-of-sample by home-season R2 of 0.89. Estimated Sindoor/Soutdoor reflected behaviors that influence particle infiltration, including window opening, use of forced air heating, and air purifier. Sulfur ratio-adjusted models predicted indoor PM2.5 and BC with high precision, with out-of-sample R2 values of 0.79 and 0.76, respectively. PMID:29064481

Estimation of Cadmium uptake by tobacco plants from laboratory leaching tests.

PubMed

Marković, Jelena P; Jović, Mihajlo D; Smičiklas, Ivana D; Šljivić-Ivanović, Marija Z; Smiljanić, Slavko N; Onjia, Antonije E; Popović, Aleksandar R

2018-03-21

The objective of the present study was to determine the impact of cadmium (Cd) concentration in the soil on its uptake by tobacco plants, and to compare the ability of diverse extraction procedures for determining Cd bioavailability and predicting soil-to-plant transfer and Cd plant concentrations. The pseudo-total digestion procedure, modified Tessier sequential extraction and six standard single-extraction tests for estimation of metal mobility and bioavailability were used for the leaching of Cd from a native soil, as well as samples artificially contaminated over a wide range of Cd concentrations. The results of various leaching tests were compared between each other, as well as with the amounts of Cd taken up by tobacco plants in pot experiments. In the native soil sample, most of the Cd was found in fractions not readily available under natural conditions, but with increasing pollution level, Cd amounts in readily available forms increased. With increasing concentrations of Cd in the soil, the quantity of pollutant taken up in tobacco also increased, while the transfer factor (TF) decreased. Linear and non-linear empirical models were developed for predicting the uptake of Cd by tobacco plants based on the results of selected leaching tests. The non-linear equations for ISO 14870 (diethylenetriaminepentaacetic acid extraction - DTPA), ISO/TS 21268-2 (CaCl 2 leaching procedure), US EPA 1311 (toxicity characteristic leaching procedure - TCLP) single step extractions, and the sum of the first two fractions of the sequential extraction, exhibited the best correlation with the experimentally determined concentrations of Cd in plants over the entire range of pollutant concentrations. This approach can improve and facilitate the assessment of human exposure to Cd by tobacco smoking, but may also have wider applicability in predicting soil-to-plant transfer.

Mercury concentrations in China's coastal waters and implications for fish consumption by vulnerable populations.

PubMed

Tong, Yindong; Wang, Mengzhu; Bu, Xiaoge; Guo, Xin; Lin, Yan; Lin, Huiming; Li, Jing; Zhang, Wei; Wang, Xuejun

2017-12-01

We assessed mercury (Hg) pollution in China's coastal waters, including the Bohai Sea, the Yellow Sea, the East China Sea and the South China Sea, based on a nationwide dataset from 301 sampling sites. A methylmercury (MeHg) intake model for humans based on the marine food chain and human fish consumption was established to determine the linkage between water pollutants and the pollutant intake by humans. The predicted MeHg concentration in fish from the Bohai Sea was the highest among the four seas included in the study. The MeHg intake through dietary ingestion was dominant for the fish and was considerably higher than the MeHg intake through water respiration. The predicted MeHg concentrations in human blood in the coastal regions of China ranged from 1.37 to 2.77 μg/L for pregnant woman and from 0.43 to 1.00 μg/L for infants, respectively, based on different diet sources. The carnivorous fish consumption advisory for pregnant women was estimated to be 288-654 g per week to maintain MeHg concentrations in human blood at levels below the threshold level (4.4 μg/L established by the US Environmental Protection Agency). With a 50% increase in Hg concentrations in water in the Bohai Sea, the bioaccumulated MeHg concentration (4.5 μg/L) in the fish consumers will be higher than the threshold level. This study demonstrates the importance in controlling Hg pollution in China's coastal waters. An official recommendation guideline for the fish consumption rate and its sources will be necessary for vulnerable populations in China. Copyright © 2017 Elsevier Ltd. All rights reserved.

Daily Estimation of High Resolution PM2.5 Concentrations over BTH area by Fusing MODIS AOD and Ground Observations

NASA Astrophysics Data System (ADS)

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

2017-04-01

The satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) is often used to predict ground-level fine particulate matter (PM2.5) concentrations. The associated estimation accuracy is always reduced by AOD missing values and by insufficiently accounting for the spatio-temporal PM2.5 variations. This study aims to estimate PM2.5 concentrations at a high resolution with enhanced accuracy by fusing MODIS AOD and ground observations in the polluted and populated Beijing-Tianjin-Hebei (BTH) area of China in 2014 and 2015. A Bayesian-based statistical downscaler was employed to model the spatio-temporally varied AOD-PM2.5 relationships. We resampled a 3 km MODIS AOD product to a 4 km resolution in a Lambert conic conformal projection, to assist comparison and fusion with CMAQ predictions. A two-step method was used to fill the missing AOD values to obtain a full AOD dataset with complete spatial coverage. The downscaler has a relatively good performance in the fitting procedure (R2 = 0.75) and in the cross validation procedure (with two evaluation methods, R2 = 0.58 by random method and R2 = 0.47 by city-specific method). The number of missing AOD values was serious and related to elevated PM2.5 concentrations. The gap-filled AOD values corresponded well with our understanding of PM2.5 pollution conditions in BTH. The prediction accuracy of PM2.5 concentrations were improved in terms of their annual and seasonal mean. As a result of its fine spatio-temporal resolution and complete spatial coverage, the daily PM2.5 estimation dataset could provide extensive and insightful benefits to related studies in the BTH area. This may include understanding the formation processes of regional PM2.5 pollution episodes, evaluating daily human exposure, and establishing pollution controlling measures.

Spatiotemporal Characterization of Ambient PM2.5 Concentrations in Shandong Province (China).

PubMed

Yang, Yong; Christakos, George

2015-11-17

China experiences severe particulate matter (PM) pollution problems closely linked to its rapid economic growth. Advancing the understanding and characterization of spatiotemporal air pollution distribution is an area where improved quantitative methods are of great benefit to risk assessment and environmental policy. This work uses the Bayesian maximum entropy (BME) method to assess the space-time variability of PM2.5 concentrations and predict their distribution in the Shandong province, China. Daily PM2.5 concentrations obtained at air quality monitoring sites during 2014 were used. On the basis of the space-time PM2.5 distributions generated by BME, we performed three kinds of querying analysis to reveal the main distribution features. The results showed that the entire region of interest is seriously polluted (BME maps identified heavy pollution clusters during 2014). Quantitative characterization of pollution severity included both pollution level and duration. The number of days during which regional PM2.5 exceeded 75, 115, 150, and 250 μg m(-3) varied: 43-253, 13-128, 4-66, and 0-15 days, respectively. The PM2.5 pattern exhibited an increasing trend from east to west, with the western part of Shandong being a heavily polluted area (PM2.5 exceeded 150 μg m(-3) during long time periods). Pollution was much more serious during winter than during other seasons. Site indicators of PM2.5 pollution intensity and space-time variation were used to assess regional uncertainties and risks with their interpretation depending on the pollutant threshold. The observed PM2.5 concentrations exceeding a specified threshold increased almost linearly with increasing threshold value, whereas the relative probability of excess pollution decreased sharply with increasing threshold.

Spatial Interpolation of Fine Particulate Matter Concentrations Using the Shortest Wind-Field Path Distance

PubMed Central

Li, Longxiang; Gong, Jianhua; Zhou, Jieping

2014-01-01

Effective assessments of air-pollution exposure depend on the ability to accurately predict pollutant concentrations at unmonitored locations, which can be achieved through spatial interpolation. However, most interpolation approaches currently in use are based on the Euclidean distance, which cannot account for the complex nonlinear features displayed by air-pollution distributions in the wind-field. In this study, an interpolation method based on the shortest path distance is developed to characterize the impact of complex urban wind-field on the distribution of the particulate matter concentration. In this method, the wind-field is incorporated by first interpolating the observed wind-field from a meteorological-station network, then using this continuous wind-field to construct a cost surface based on Gaussian dispersion model and calculating the shortest wind-field path distances between locations, and finally replacing the Euclidean distances typically used in Inverse Distance Weighting (IDW) with the shortest wind-field path distances. This proposed methodology is used to generate daily and hourly estimation surfaces for the particulate matter concentration in the urban area of Beijing in May 2013. This study demonstrates that wind-fields can be incorporated into an interpolation framework using the shortest wind-field path distance, which leads to a remarkable improvement in both the prediction accuracy and the visual reproduction of the wind-flow effect, both of which are of great importance for the assessment of the effects of pollutants on human health. PMID:24798197

Spatial interpolation of fine particulate matter concentrations using the shortest wind-field path distance.

PubMed

Li, Longxiang; Gong, Jianhua; Zhou, Jieping

2014-01-01

Effective assessments of air-pollution exposure depend on the ability to accurately predict pollutant concentrations at unmonitored locations, which can be achieved through spatial interpolation. However, most interpolation approaches currently in use are based on the Euclidean distance, which cannot account for the complex nonlinear features displayed by air-pollution distributions in the wind-field. In this study, an interpolation method based on the shortest path distance is developed to characterize the impact of complex urban wind-field on the distribution of the particulate matter concentration. In this method, the wind-field is incorporated by first interpolating the observed wind-field from a meteorological-station network, then using this continuous wind-field to construct a cost surface based on Gaussian dispersion model and calculating the shortest wind-field path distances between locations, and finally replacing the Euclidean distances typically used in Inverse Distance Weighting (IDW) with the shortest wind-field path distances. This proposed methodology is used to generate daily and hourly estimation surfaces for the particulate matter concentration in the urban area of Beijing in May 2013. This study demonstrates that wind-fields can be incorporated into an interpolation framework using the shortest wind-field path distance, which leads to a remarkable improvement in both the prediction accuracy and the visual reproduction of the wind-flow effect, both of which are of great importance for the assessment of the effects of pollutants on human health.

Modeling indoor air pollution of outdoor origin in homes of SAPALDIA subjects in Switzerland.

PubMed

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

2015-09-01

Given the shrinking spatial contrasts in outdoor air pollution in Switzerland and the trends toward tightly insulated buildings, the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) needs to understand to what extent outdoor air pollution remains a determinant for residential indoor exposure. The objectives of this paper are to identify determining factors for indoor air pollution concentrations of particulate matter (PM), ultrafine particles in the size range from 15 to 300nm, black smoke measured as light absorbance of PM (PMabsorbance) and nitrogen dioxide (NO2) and to develop predictive indoor models for SAPALDIA. Multivariable regression models were developed based on indoor and outdoor measurements among homes of selected SAPALDIA participants in three urban (Basel, Geneva, Lugano) and one rural region (Wald ZH) in Switzerland, various home characteristics and reported indoor sources such as cooking. Outdoor levels of air pollutants were important predictors for indoor air pollutants, except for the coarse particle fraction. The fractions of outdoor concentrations infiltrating indoors were between 30% and 66%, the highest one was observed for PMabsorbance. A modifying effect of open windows was found for NO2 and the ultrafine particle number concentration. Cooking was associated with increased particle and NO2 levels. This study shows that outdoor air pollution remains an important determinant of residential indoor air pollution in Switzerland. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

PubMed

Ding, Weifu; Zhang, Jiangshe; Leung, Yee

2016-10-01

In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained based on sparse response back-propagation in which only a small number of neurons respond to the specified stimulus simultaneously and provide a high convergence rate for the trained network, in addition to low energy consumption and greater generalization. Our method is evaluated on Hong Kong air monitoring station data and corresponding meteorological variables for which five air quality parameters were gathered at four monitoring stations in Hong Kong over 4 years (2012-2015). Our results show that our training method has more advantages in terms of the precision of the prediction, effectiveness, and generalization of traditional linear regression algorithms when compared with a feedforward artificial neural network trained using traditional back-propagation.

COMPARISONS OF SPATIAL PATTERNS OF WET DEPOSITION TO MODEL PREDICTIONS

EPA Science Inventory

The Community Multiscale Air Quality model, (CMAQ), is a "one-atmosphere" model, in that it uses a consistent set of chemical reactions and physical principles to predict concentrations of primary pollutants, photochemical smog, and fine aerosols, as well as wet and dry depositi...

[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.

Spatial and temporal microbial pollution patterns in a tropical estuary during high and low river flow conditions.

PubMed

Wiegner, T N; Edens, C J; Abaya, L M; Carlson, K M; Lyon-Colbert, A; Molloy, S L

2017-01-30

Spatial and temporal patterns of coastal microbial pollution are not well documented. Our study examined these patterns through measurements of fecal indicator bacteria (FIB), nutrients, and physiochemical parameters in Hilo Bay, Hawai'i, during high and low river flow. >40% of samples tested positive for the human-associated Bacteroides marker, with highest percentages near rivers. Other FIB were also higher near rivers, but only Clostridium perfringens concentrations were related to discharge. During storms, FIB concentrations were three times to an order of magnitude higher, and increased with decreasing salinity and water temperature, and increasing turbidity. These relationships and high spatial resolution data for these parameters were used to create Enterococcus spp. and C. perfringens maps that predicted exceedances with 64% and 95% accuracy, respectively. Mapping microbial pollution patterns and predicting exceedances is a valuable tool that can improve water quality monitoring and aid in visualizing FIB hotspots for management actions. Copyright © 2016 Elsevier Ltd. All rights reserved.

An assessment of air pollutant exposure methods in Mexico City, Mexico.

PubMed

Rivera-González, Luis O; Zhang, Zhenzhen; Sánchez, Brisa N; Zhang, Kai; Brown, Daniel G; Rojas-Bracho, Leonora; Osornio-Vargas, Alvaro; Vadillo-Ortega, Felipe; O'Neill, Marie S

2015-05-01

Geostatistical interpolation methods to estimate individual exposure to outdoor air pollutants can be used in pregnancy cohorts where personal exposure data are not collected. Our objectives were to a) develop four assessment methods (citywide average (CWA); nearest monitor (NM); inverse distance weighting (IDW); and ordinary Kriging (OK)), and b) compare daily metrics and cross-validations of interpolation models. We obtained 2008 hourly data from Mexico City's outdoor air monitoring network for PM10, PM2.5, O3, CO, NO2, and SO2 and constructed daily exposure metrics for 1,000 simulated individual locations across five populated geographic zones. Descriptive statistics from all methods were calculated for dry and wet seasons, and by zone. We also evaluated IDW and OK methods' ability to predict measured concentrations at monitors using cross validation and a coefficient of variation (COV). All methods were performed using SAS 9.3, except ordinary Kriging which was modeled using R's gstat package. Overall, mean concentrations and standard deviations were similar among the different methods for each pollutant. Correlations between methods were generally high (r=0.77 to 0.99). However, ranges of estimated concentrations determined by NM, IDW, and OK were wider than the ranges for CWA. Root mean square errors for OK were consistently equal to or lower than for the IDW method. OK standard errors varied considerably between pollutants and the computed COVs ranged from 0.46 (least error) for SO2 and PM10 to 3.91 (most error) for PM2.5. OK predicted concentrations measured at the monitors better than IDW and NM. Given the similarity in results for the exposure methods, OK is preferred because this method alone provides predicted standard errors which can be incorporated in statistical models. The daily estimated exposures calculated using these different exposure methods provide flexibility to evaluate multiple windows of exposure during pregnancy, not just trimester or pregnancy-long exposures. Many studies evaluating associations between outdoor air pollution and adverse pregnancy outcomes rely on outdoor air pollution monitoring data linked to information gathered from large birth registries, and often lack residence location information needed to estimate individual exposure. This study simulated 1,000 residential locations to evaluate four air pollution exposure assessment methods, and describes possible exposure misclassification from using spatial averaging versus geostatistical interpolation models. An implication of this work is that policies to reduce air pollution and exposure among pregnant women based on epidemiologic literature should take into account possible error in estimates of effect when spatial averages alone are evaluated.

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.

Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information.

PubMed

Pan, Long; Yao, Enjian; Yang, Yang

2016-12-01

With the rapid development of urbanization and motorization in China, traffic-related air pollution has become a major component of air pollution which constantly jeopardizes public health. This study proposes an integrated framework for estimating the concentration of traffic-related air pollution with real-time traffic and basic meteorological information and also for further evaluating the impact of traffic-related air pollution. First, based on the vehicle emission factor models sensitive to traffic status, traffic emissions are calculated according to the real-time link-based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on differences in meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Subsequently, with basic meteorological data, Gaussian dispersion model and puff integration model are applied respectively to estimate the concentration of traffic-related air pollution. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic-related air pollution on public health. Results show that there is a certain correlation between traffic indicators (i.e., traffic speed and traffic intensity) of the affecting area and traffic-related CO concentration of the target grid, which indicates the methods to determine the affecting areas are reliable. Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic-related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area. Copyright © 2016 Elsevier Ltd. All rights reserved.

Simulation of Tracer Concentration Data in the Brush Creek Drainage Flow Using an Integrated Puff Model.

NASA Astrophysics Data System (ADS)

Rao, K. Shankar; Eckman, Richard M.; Hosker, Rayford P., Jr.

1989-07-01

During the 1984 ASCOT field study in Brush Creek Valley, two perfluorocarbon tracers were released into the nocturnal drainage flow at two different heights. The resulting surface concentrations were sampled at 90 sites, and vertical concentration profiles at 11 sites. These detailed tracer measurements provide a valuable dataset for developing and testing models of pollutant transport and dispersion in valleys.In this paper, we present the results of Gaussian puff model simulations of the tracer releases in Brush Creek Valley. The model was modified to account for the restricted lateral dispersion in the valley, and for the gross elevation differences between the release site and the receptors. The variable wind fields needed to transport the puffs were obtained by interpolation between wind profiles measured using tethered balloons at five along-valley sites. Direct turbulence measurements were used to estimate diffusion. Subsidence in the valley flow was included for elevated releases.Two test simulations-covering different nights, tracers, and release heights-were performed. The predicted hourly concentrations were compared with observations at 51 ground-level locations. At most sites, the predicted and observed concentrations agree within a factor of 2 to 6. For the elevated release simulation, the observed mean concentration is 40 pL/L, the predicted mean is 21 pL/L, the correlation coefficient between the observed and predicted concentrations is 0.24, and the index of agreement is 0.46. For the surface release simulation, the observed mean is 85 pL/L, and the predicted mean is 73 pL/L. The correlation coefficient is 0.23, and the index of agreement is 0.42. The results suggest that this modified puff model can be used as a practical tool for simulating pollutant transport and dispersion in deep valleys.

PREDICTING POPULATION EXPOSURES TO PM: THE IMPORTANCE OF MICROENVIRONMENTAL CONCENTRATIONS AND HUMAN ACTIVITIES

EPA Science Inventory

The Stochastic Human Exposure and Dose Simulation (SHEDS) models being developed by the US EPA/NERL use a probabilistic approach to predict population exposures to pollutants. The SHEDS model for particulate matter (SHEDS-PM) estimates the population distribution of PM exposure...

Setting priorities for research on pollution reduction functions of agricultural buffers.

PubMed

Dosskey, Michael G

2002-11-01

The success of buffer installation initiatives and programs to reduce nonpoint source pollution of streams on agricultural lands will depend the ability of local planners to locate and design buffers for specific circumstances with substantial and predictable results. Current predictive capabilities are inadequate, and major sources of uncertainty remain. An assessment of these uncertainties cautions that there is greater risk of overestimating buffer impact than underestimating it. Priorities for future research are proposed that will lead more quickly to major advances in predictive capabilities. Highest priority is given for work on the surface runoff filtration function, which is almost universally important to the amount of pollution reduction expected from buffer installation and for which there remain major sources of uncertainty for predicting level of impact. Foremost uncertainties surround the extent and consequences of runoff flow concentration and pollutant accumulation. Other buffer functions, including filtration of groundwater nitrate and stabilization of channel erosion sources of sediments, may be important in some regions. However, uncertainty surrounds our ability to identify and quantify the extent of site conditions where buffer installation can substantially reduce stream pollution in these ways. Deficiencies in predictive models reflect gaps in experimental information as well as technology to account for spatial heterogeneity of pollutant sources, pathways, and buffer capabilities across watersheds. Since completion of a comprehensive watershed-scale buffer model is probably far off, immediate needs call for simpler techniques to gage the probable impacts of buffer installation at local scales.

Reducing mortality risk by targeting specific air pollution sources: Suva, Fiji.

PubMed

Isley, C F; Nelson, P F; Taylor, M P; Stelcer, E; Atanacio, A J; Cohen, D D; Mani, F S; Maata, M

2018-01-15

Health implications of air pollution vary dependent upon pollutant sources. This work determines the value, in terms of reduced mortality, of reducing ambient particulate matter (PM 2.5 : effective aerodynamic diameter 2.5μm or less) concentration due to different emission sources. Suva, a Pacific Island city with substantial input from combustion sources, is used as a case-study. Elemental concentration was determined, by ion beam analysis, for PM 2.5 samples from Suva, spanning one year. Sources of PM 2.5 have been quantified by positive matrix factorisation. A review of recent literature has been carried out to delineate the mortality risk associated with these sources. Risk factors have then been applied for Suva, to calculate the possible mortality reduction that may be achieved through reduction in pollutant levels. Higher risk ratios for black carbon and sulphur resulted in mortality predictions for PM 2.5 from fossil fuel combustion, road vehicle emissions and waste burning that surpass predictions for these sources based on health risk of PM 2.5 mass alone. Predicted mortality for Suva from fossil fuel smoke exceeds the national toll from road accidents in Fiji. The greatest benefit for Suva, in terms of reduced mortality, is likely to be accomplished by reducing emissions from fossil fuel combustion (diesel), vehicles and waste burning. Copyright © 2017. Published by Elsevier B.V.

A diagnostic model for studying daytime urban air quality trends

NASA Technical Reports Server (NTRS)

Brewer, D. A.; Remsberg, E. E.; Woodbury, G. E.

1981-01-01

A single cell Eulerian photochemical air quality simulation model was developed and validated for selected days of the 1976 St. Louis Regional Air Pollution Study (RAPS) data sets; parameterizations of variables in the model and validation studies using the model are discussed. Good agreement was obtained between measured and modeled concentrations of NO, CO, and NO2 for all days simulated. The maximum concentration of O3 was also predicted well. Predicted species concentrations were relatively insensitive to small variations in CO and NOx emissions and to the concentrations of species which are entrained as the mixed layer rises.

Predicting changes of glass optical properties in polluted atmospheric environment by a neural network model

NASA Astrophysics Data System (ADS)

Verney-Carron, A.; Dutot, A. L.; Lombardo, T.; Chabas, A.

2012-07-01

Soiling results from the deposition of pollutants on materials. On glass, it leads to an alteration of its intrinsic optical properties. The nature and intensity of this phenomenon mirrors the pollution of an environment. This paper proposes a new statistical model in order to predict the evolution of haze (H) (i.e. diffuse/direct transmitted light ratio) as a function of time and major pollutant concentrations in the atmosphere (SO2, NO2, and PM10 (Particulate Matter < 10 μm)). The model was parameterized by using a large set of data collected in European cities (especially, Paris and its suburbs, Athens, Krakow, Prague, and Rome) during field exposure campaigns (French, European, and international programs). This statistical model, called NEUROPT-Glass, comes from an artificial neural network with two hidden layers and uses a non-linear parametric regression named Multilayer Perceptron (MLP). The results display a high determination coefficient (R2 = 0.88) between the measured and the predicted hazes and minimizes the dispersion of data compared to existing multilinear dose-response functions. Therefore, this model can be used with a great confidence in order to predict the soiling of glass as a function of time in world cities with different levels of pollution or to assess the effect of pollution reduction policies on glass soiling problems in urban environments.

Non-destructive pollution exposure assessment in the European hedgehog (Erinaceus europaeus): II. Hair and spines as indicators of endogenous metal and As concentrations.

PubMed

D'Havé, Helga; Scheirs, Jan; Mubiana, Valentine Kayawe; Verhagen, Ron; Blust, Ronny; De Coen, Wim

2006-08-01

The role of hair and spines of the European hedgehog as non-destructive monitoring tools of metal (Ag, Al, Cd, Co, Cr, Cu, Fe, Ni, Pb, Zn) and As pollution in terrestrial ecosystems was investigated. Our results showed that mean pollution levels of a random sample of hedgehogs in Flanders are low to moderate. Yet, individual hedgehogs may be at risk for metal toxicity. Tissue distribution analyses (hair, spines, liver, kidney, muscle and fat tissue) indicated that metals and As may reach considerable concentrations in external tissues, such as hair and spines. Positive relationships were observed between concentrations in hair and those in liver, kidney and muscle for Al, Co, Cr, Cu, and Pb (0.43 < r < 0.85). Spine concentrations were positively related to liver, kidney and muscle concentrations for Cd, Co, Cr, Cu and Pb (0.37 < r < 0.62). Hair Ag, As, Fe and Zn and spine Ag, Al, As and Fe were related to metal concentrations in one or two of the investigated internal tissues (0.31 < r < 0.45). The regression models presented here may be used to predict metal and As concentrations in internal tissues of hedgehogs when concentrations in hair or spines are available. The present study demonstrated the possibility of using hair and spines for non-destructive monitoring of metal and As pollution in hedgehogs.

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.

A novel approach combining self-organizing map and parallel factor analysis for monitoring water quality of watersheds under non-point source pollution

PubMed Central

Zhang, Yixiang; Liang, Xinqiang; Wang, Zhibo; Xu, Lixian

2015-01-01

High content of organic matter in the downstream of watersheds underscored the severity of non-point source (NPS) pollution. The major objectives of this study were to characterize and quantify dissolved organic matter (DOM) in watersheds affected by NPS pollution, and to apply self-organizing map (SOM) and parallel factor analysis (PARAFAC) to assess fluorescence properties as proxy indicators for NPS pollution and labor-intensive routine water quality indicators. Water from upstreams and downstreams was sampled to measure dissolved organic carbon (DOC) concentrations and excitation-emission matrix (EEM). Five fluorescence components were modeled with PARAFAC. The regression analysis between PARAFAC intensities (Fmax) and raw EEM measurements indicated that several raw fluorescence measurements at target excitation-emission wavelength region could provide similar DOM information to massive EEM measurements combined with PARAFAC. Regression analysis between DOC concentration and raw EEM measurements suggested that some regions in raw EEM could be used as surrogates for labor-intensive routine indicators. SOM can be used to visualize the occurrence of pollution. Relationship between DOC concentration and PARAFAC components analyzed with SOM suggested that PARAFAC component 2 might be the major part of bulk DOC and could be recognized as a proxy indicator to predict the DOC concentration. PMID:26526140

Validation of a novel air toxic risk model with air monitoring.

PubMed

Pratt, Gregory C; Dymond, Mary; Ellickson, Kristie; Thé, Jesse

2012-01-01

Three modeling systems were used to estimate human health risks from air pollution: two versions of MNRiskS (for Minnesota Risk Screening), and the USEPA National Air Toxics Assessment (NATA). MNRiskS is a unique cumulative risk modeling system used to assess risks from multiple air toxics, sources, and pathways on a local to a state-wide scale. In addition, ambient outdoor air monitoring data were available for estimation of risks and comparison with the modeled estimates of air concentrations. Highest air concentrations and estimated risks were generally found in the Minneapolis-St. Paul metropolitan area and lowest risks in undeveloped rural areas. Emissions from mobile and area (nonpoint) sources created greater estimated risks than emissions from point sources. Highest cancer risks were via ingestion pathway exposures to dioxins and related compounds. Diesel particles, acrolein, and formaldehyde created the highest estimated inhalation health impacts. Model-estimated air concentrations were generally highest for NATA and lowest for the AERMOD version of MNRiskS. This validation study showed reasonable agreement between available measurements and model predictions, although results varied among pollutants, and predictions were often lower than measurements. The results increased confidence in identifying pollutants, pathways, geographic areas, sources, and receptors of potential concern, and thus provide a basis for informing pollution reduction strategies and focusing efforts on specific pollutants (diesel particles, acrolein, and formaldehyde), geographic areas (urban centers), and source categories (nonpoint sources). The results heighten concerns about risks from food chain exposures to dioxins and PAHs. Risk estimates were sensitive to variations in methodologies for treating emissions, dispersion, deposition, exposure, and toxicity. © 2011 Society for Risk Analysis.

Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on field measurements in urban Beijing

NASA Astrophysics Data System (ADS)

Ren, Jingye; Zhang, Fang; Wang, Yuying; Collins, Don; Fan, Xinxin; Jin, Xiaoai; Xu, Weiqi; Sun, Yele; Cribb, Maureen; Li, Zhanqing

2018-05-01

Understanding the impacts of aerosol chemical composition and mixing state on cloud condensation nuclei (CCN) activity in polluted areas is crucial for accurately predicting CCN number concentrations (NCCN). In this study, we predict NCCN under five assumed schemes of aerosol chemical composition and mixing state based on field measurements in Beijing during the winter of 2016. Our results show that the best closure is achieved with the assumption of size dependent chemical composition for which sulfate, nitrate, secondary organic aerosols, and aged black carbon are internally mixed with each other but externally mixed with primary organic aerosol and fresh black carbon (external-internal size-resolved, abbreviated as EI-SR scheme). The resulting ratios of predicted-to-measured NCCN (RCCN_p/m) were 0.90 - 0.98 under both clean and polluted conditions. Assumption of an internal mixture and bulk chemical composition (INT-BK scheme) shows good closure with RCCN_p/m of 1.0 -1.16 under clean conditions, implying that it is adequate for CCN prediction in continental clean regions. On polluted days, assuming the aerosol is internally mixed and has a chemical composition that is size dependent (INT-SR scheme) achieves better closure than the INT-BK scheme due to the heterogeneity and variation in particle composition at different sizes. The improved closure achieved using the EI-SR and INT-SR assumptions highlight the importance of measuring size-resolved chemical composition for CCN predictions in polluted regions. NCCN is significantly underestimated (with RCCN_p/m of 0.66 - 0.75) when using the schemes of external mixtures with bulk (EXT-BK scheme) or size-resolved composition (EXT-SR scheme), implying that primary particles experience rapid aging and physical mixing processes in urban Beijing. However, our results show that the aerosol mixing state plays a minor role in CCN prediction when the κorg exceeds 0.1.

Air pollution dispersion models for human exposure predictions in London.

PubMed

Beevers, Sean D; Kitwiroon, Nutthida; Williams, Martin L; Kelly, Frank J; Ross Anderson, H; Carslaw, David C

2013-01-01

The London household survey has shown that people travel and are exposed to air pollutants differently. This argues for human exposure to be based upon space-time-activity data and spatio-temporal air quality predictions. For the latter, we have demonstrated the role that dispersion models can play by using two complimentary models, KCLurban, which gives source apportionment information, and Community Multi-scale Air Quality Model (CMAQ)-urban, which predicts hourly air quality. The KCLurban model is in close agreement with observations of NO(X), NO(2) and particulate matter (PM)(10/2.5), having a small normalised mean bias (-6% to 4%) and a large Index of Agreement (0.71-0.88). The temporal trends of NO(X) from the CMAQ-urban model are also in reasonable agreement with observations. Spatially, NO(2) predictions show that within 10's of metres of major roads, concentrations can range from approximately 10-20 p.p.b. up to 70 p.p.b. and that for PM(10/2.5) central London roadside concentrations are approximately double the suburban background concentrations. Exposure to different PM sources is important and we predict that brake wear-related PM(10) concentrations are approximately eight times greater near major roads than at suburban background locations. Temporally, we have shown that average NO(X) concentrations close to roads can range by a factor of approximately six between the early morning minimum and morning rush hour maximum periods. These results present strong arguments for the hybrid exposure model under development at King's and, in future, for in-building models and a model for the London Underground.

Identifying the impacts of climate on the regional transport of haze pollution and inter-cities correspondence within the Yangtze River Delta.

PubMed

Xiao, Hang; Huang, Zhongwen; Zhang, Jingjing; Zhang, Huiling; Chen, Jinsheng; Zhang, Han; Tong, Lei

2017-09-01

Regional haze pollution has become an important environmental issue in the Yangtze River Delta (YRD) region. Regional transport and inter-influence of PM 2.5 among cities occurs frequently as a result of the subtropical monsoon climate. Backward trajectory statistics indicated that a north wind prevailed from October to March, while a southeast wind predominated from May to September. The temporal relationships of carbon and nitrogen isotopes among cities were dependent on the prevailing wind direction. Regional PM 2.5 pollution was confirmed in the YRD region by means of significant correlations and similar cyclical characteristics of PM 2.5 among Lin'an, Ningbo, Nanjing and Shanghai. Granger causality tests of the time series of PM 2.5 values indicate that the regional transport of haze pollutants is governed by prevailing wind direction, as the PM 2.5 concentrations from upwind area cities generally influence that of the downwind cities. Furthermore, stronger correlation coefficients were identified according to monsoon pathways. To clarify the impacts of the monsoon climate, a vector autoregressive (VAR) model was introduced. Variance decomposition in the VAR model also indicated that the upwind area cities contributed significantly to PM 2.5 in the downwind area cities. Finally, we attempted to predict daily PM 2.5 concentrations in each city based on the VAR model using data from all cities and obtained fairly reasonable predictions. These indicate that statistical methods of the Granger causality test and VAR model have the potential to evaluate inter-influence and the relative contribution of PM 2.5 among cities, and to predict PM 2.5 concentrations as well. Copyright © 2017 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.

An effective chaos-geometric computational approach to analysis and prediction of evolutionary dynamics of the environmental systems: Atmospheric pollution dynamics

NASA Astrophysics Data System (ADS)

Buyadzhi, V. V.; Glushkov, A. V.; Khetselius, O. Yu; Bunyakova, Yu Ya; Florko, T. A.; Agayar, E. V.; Solyanikova, E. P.

2017-10-01

The present paper concerns the results of computational studying dynamics of the atmospheric pollutants (dioxide of nitrogen, sulphur etc) concentrations in an atmosphere of the industrial cities (Odessa) by using the dynamical systems and chaos theory methods. A chaotic behaviour in the nitrogen dioxide and sulphurous anhydride concentration time series at several sites of the Odessa city is numerically investigated. As usually, to reconstruct the corresponding attractor, the time delay and embedding dimension are needed. The former is determined by the methods of autocorrelation function and average mutual information, and the latter is calculated by means of a correlation dimension method and algorithm of false nearest neighbours. Further, the Lyapunov’s exponents spectrum, Kaplan-Yorke dimension and Kolmogorov entropy are computed. It has been found an existence of a low-D chaos in the time series of the atmospheric pollutants concentrations.

Associations of ozone and PM2.5 concentrations with Parkinson’s disease among participants in the Agricultural Health Study

PubMed Central

Bowman, Christal; Davis, J. Allen; Hoppin, Jane A.; Blair, Aaron; Chen, Honglei; Patel, Molini M.; Sandler, Dale P.; Tanner, Caroline M.; Vinikoor-Imler, Lisa; Ward, Mary H.; Luben, Thomas J.; Kamel, Freya

2015-01-01

Objective This study describes associations of ozone and fine particulate matter with Parkinson’s disease observed among farmers in North Carolina and Iowa. Methods We used logistic regression to determine the associations of these pollutants with self-reported, doctor-diagnosed Parkinson’s disease. Daily predicted pollutant concentrations were used to derive surrogates of long-term exposure and link them to study participants’ geocoded addresses. Results We observed positive associations of Parkinson’s disease with ozone (OR=1.39; 95% CI: 0.98, 1.98) and fine particulate matter (OR=1.34; 95% CI: 0.93, 1.93) in North Carolina but not in Iowa. Conclusion The plausibility of an effect of ambient concentrations of these pollutants on Parkinson’s disease risk is supported by experimental data demonstrating damage to dopaminergic neurons at relevant concentrations. Additional studies are needed to address uncertainties related to confounding and to examine temporal aspects of the associations we observed. PMID:25951420

Potential ecological risk assessment and prediction of soil heavy-metal pollution around coal gangue dump

NASA Astrophysics Data System (ADS)

Jiang, X.; Lu, W. X.; Zhao, H. Q.; Yang, Q. C.; Yang, Z. P.

2014-06-01

The aim of the present study is to evaluate the potential ecological risk and trend of soil heavy-metal pollution around a coal gangue dump in Jilin Province (Northeast China). The concentrations of Cd, Pb, Cu, Cr and Zn were monitored by inductively coupled plasma mass spectrometry (ICP-MS). The potential ecological risk index method developed by Hakanson (1980) was employed to assess the potential risk of heavy-metal pollution. The potential ecological risk in the order of ER(Cd) > ER(Pb) > ER(Cu) > ER(Cr) > ER(Zn) have been obtained, which showed that Cd was the most important factor leading to risk. Based on the Cd pollution history, the cumulative acceleration and cumulative rate of Cd were estimated, then the fixed number of years exceeding the standard prediction model was established, which was used to predict the pollution trend of Cd under the accelerated accumulation mode and the uniform mode. Pearson correlation analysis and correspondence analysis are employed to identify the sources of heavy metals and the relationship between sampling points and variables. These findings provided some useful insights for making appropriate management strategies to prevent or decrease heavy-metal pollution around a coal gangue dump in the Yangcaogou coal mine and other similar areas elsewhere.

Rapid identification of soil cadmium pollution risk at regional scale based on visible and near-infrared spectroscopy.

PubMed

Chen, Tao; Chang, Qingrui; Clevers, J G P W; Kooistra, L

2015-11-01

Soil heavy metal pollution due to long-term sewage irrigation is a serious environmental problem in many irrigation areas in northern China. Quickly identifying its pollution status is an important basis for remediation. Visible-near-infrared reflectance spectroscopy (VNIRS) provides a useful tool. In a case study, 76 soil samples were collected and their reflectance spectra were used to estimate cadmium (Cd) concentration by partial least squares regression (PLSR) and back propagation neural network (BPNN). To reduce noise, six pre-treatments were compared, in which orthogonal signal correction (OSC) was first used in soil Cd estimation. Spectral analysis and geostatistics were combined to identify Cd pollution hotspots. Results showed that Cd was accumulated in topsoil at the study area. OSC can effectively remove irrelevant information to improve prediction accuracy. More accurate estimation was achieved by applying a BPNN. Soil Cd pollution hotspots could be identified by interpolating the predicted values obtained from spectral estimates. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mitigation of severe urban haze pollution by a precision air pollution control approach.

PubMed

Yu, Shaocai; Li, Pengfei; Wang, Liqiang; Wu, Yujie; Wang, Si; Liu, Kai; Zhu, Tong; Zhang, Yuanhang; Hu, Min; Zeng, Liming; Zhang, Xiaoye; Cao, Junji; Alapaty, Kiran; Wong, David C; Pleim, Jon; Mathur, Rohit; Rosenfeld, Daniel; Seinfeld, John H

2018-05-25

Severe and persistent haze pollution involving fine particulate matter (PM 2.5 ) concentrations reaching unprecedentedly high levels across many cities in China poses a serious threat to human health. Although mandatory temporary cessation of most urban and surrounding emission sources is an effective, but costly, short-term measure to abate air pollution, development of long-term crisis response measures remains a challenge, especially for curbing severe urban haze events on a regular basis. Here we introduce and evaluate a novel precision air pollution control approach (PAPCA) to mitigate severe urban haze events. The approach involves combining predictions of high PM 2.5 concentrations, with a hybrid trajectory-receptor model and a comprehensive 3-D atmospheric model, to pinpoint the origins of emissions leading to such events and to optimize emission controls. Results of the PAPCA application to five severe haze episodes in major urban areas in China suggest that this strategy has the potential to significantly mitigate severe urban haze by decreasing PM 2.5 peak concentrations by more than 60% from above 300 μg m -3 to below 100 μg m -3 , while requiring ~30% to 70% less emission controls as compared to complete emission reductions. The PAPCA strategy has the potential to tackle effectively severe urban haze pollution events with economic efficiency.

Development of model for prediction of Leachate Pollution Index (LPI) in absence of leachate parameters.

PubMed

Lothe, Anjali G; Sinha, Alok

2017-05-01

Leachate pollution index (LPI) is an environmental index which quantifies the pollution potential of leachate generated in landfill site. Calculation of Leachate pollution index (LPI) is based on concentration of 18 parameters present in leachate. However, in case of non-availability of all 18 parameters evaluation of actual values of LPI becomes difficult. In this study, a model has been developed to predict the actual values of LPI in case of partial availability of parameters. This model generates eleven equations that helps in determination of upper and lower limit of LPI. The geometric mean of these two values results in LPI value. Application of this model to three landfill site results in LPI value with an error of ±20% for ∑ i n w i ⩾0.6. Copyright © 2016 Elsevier Ltd. All rights reserved.

*Combining regional - and local-scale air quality models with exposure models for use in environmental health studies - Title changed from Linking air quality and exposure models for use in environmental health studies

EPA Science Inventory

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including air pollution concentrations; human activity patterns, such as the amount of time spent outdoors versus indoors, commuting, w...

Airborne measurements of air pollution chemistry and transport. 1: Initial survey of major air basins in California

NASA Technical Reports Server (NTRS)

Gloria, H. R.; Pitts, J. N., Jr.; Behar, J. V.; Bradburn, G. A.; Reinisch, R. F.; Zafonte, L.

1972-01-01

An instrumented aircraft has been used to study photochemical air pollution in the State of California. Simultaneous measurements of the most important chemical constituents (ozone, total oxidant, hydrocarbons, and nitrogen oxides, as well as several meteorological variables) were made. State-of-the-art measurement techniques and sampling procedures are discussed. Data from flights over the South Coast Air Basin, the San Francisco Bay Area, the San Joaquin Valley, the Santa Clara and Salinas Valleys, and the Pacific Ocean within 200 miles of the California coast are presented. Pollutants were found to be concentrated in distant layers up to at least 18,000 feet. In many of these layers, the pollutant concentrations were much higher than at ground level. These findings bring into serious question the validity of the present practice of depending solely on data from ground-based monitoring stations for predictive models.

International Standards on stability of digital prints

NASA Astrophysics Data System (ADS)

Adelstein, Peter Z.

2010-06-01

The International Standards Organization (ISO) is a worldwide recognized standardizing body which has responsibility for standards on permanence of digital prints. This paper is an update on the progress made to date by ISO in writing test methods in this area. Three technologies are involved, namely ink jet, dye diffusion thermal transfer (dye-sublimation) and electrophotography. Two types of test methods are possible, namely comparative tests and predictive tests. To date a comparative test on water fastness has been published and final balloting is underway on a comparative test on humidity fastness. Predictive tests are being finalized on thermal stability and pollution susceptibility. The test method on thermal stability is intended to predict the print life during normal aging. One of the testing concerns is that some prints do not show significant image change in practical testing times. The test method on pollution susceptibility only deals with ozone and assumes that the reciprocity law applies. This law assumes that a long time under a low pollutant concentration is equivalent to a short time under the high concentration used in the test procedure. Longer term studies include a predictive test for light stability and the preparation of a material specification. The latter requires a decision about the proper colour target to be used and what constitutes an unacceptable colour change. Moreover, a specification which gives a predictive life is very dependent upon the conditions the print encounters and will only apply to specific levels of temperature, ozone and light.

Assessment of air quality benefits from national air pollution control policies in China. Part II: Evaluation of air quality predictions and air quality benefits assessment

NASA Astrophysics Data System (ADS)

Wang, Litao; Jang, Carey; Zhang, Yang; Wang, Kai; Zhang, Qiang; Streets, David; Fu, Joshua; Lei, Yu; Schreifels, Jeremy; He, Kebin; Hao, Jiming; Lam, Yun-Fat; Lin, Jerry; Meskhidze, Nicholas; Voorhees, Scott; Evarts, Dale; Phillips, Sharon

2010-09-01

Following the meteorological evaluation in Part I, this Part II paper presents the statistical evaluation of air quality predictions by the U.S. Environmental Protection Agency (U.S. EPA)'s Community Multi-Scale Air Quality (Models-3/CMAQ) model for the four simulated months in the base year 2005. The surface predictions were evaluated using the Air Pollution Index (API) data published by the China Ministry of Environmental Protection (MEP) for 31 capital cities and daily fine particulate matter (PM 2.5, particles with aerodiameter less than or equal to 2.5 μm) observations of an individual site in Tsinghua University (THU). To overcome the shortage in surface observations, satellite data are used to assess the column predictions including tropospheric nitrogen dioxide (NO 2) column abundance and aerosol optical depth (AOD). The result shows that CMAQ gives reasonably good predictions for the air quality. The air quality improvement that would result from the targeted sulfur dioxide (SO 2) and nitrogen oxides (NO x) emission controls in China were assessed for the objective year 2010. The results show that the emission controls can lead to significant air quality benefits. SO 2 concentrations in highly polluted areas of East China in 2010 are estimated to be decreased by 30-60% compared to the levels in the 2010 Business-As-Usual (BAU) case. The annual PM 2.5 can also decline by 3-15 μg m -3 (4-25%) due to the lower SO 2 and sulfate concentrations. If similar controls are implemented for NO x emissions, NO x concentrations are estimated to decrease by 30-60% as compared with the 2010 BAU scenario. The annual mean PM 2.5 concentrations will also decline by 2-14 μg m -3 (3-12%). In addition, the number of ozone (O 3) non-attainment areas in the northern China is projected to be much lower, with the maximum 1-h average O 3 concentrations in the summer reduced by 8-30 ppb.

Numerical simulation on pollutant dispersion from vehicle exhaust in street configurations.

PubMed

Yassin, Mohamed F; Kellnerová, R; Janour, Z

2009-09-01

The impact of the street configurations on pollutants dispersion from vehicles exhausts within urban canyons was numerically investigated using a computational fluid dynamics (CFD) model. Three-dimensional flow and dispersion of gaseous pollutants were modeled using standard kappa - epsilon turbulence model, which was numerically solved based on Reynolds-averaged Navier-Stokes equations by the commercial CFD code FLUENT. The concentration fields in the urban canyons were examined in three cases of street configurations: (1) a regular-shaped intersection, (2) a T-shaped intersection and (3) a Skew-shaped crossing intersection. Vehicle emissions were simulated as double line sources along the street. The numerical model was validated against wind tunnel results in order to optimize the turbulence model. Numerical predictions agreed reasonably well with wind tunnel results. The results obtained indicate that the mean horizontal velocity was very small in the center near the lower region of street canyon. The lowest turbulent kinetic energy was found at the separation and reattachment points associated with the corner of the down part of the upwind and downwind buildings in the street canyon. The pollutant concentration at the upwind side in the regular-shaped street intersection was higher than that in the T-shaped and Skew-shaped street intersections. Moreover, the results reveal that the street intersections are important factors to predict the flow patterns and pollutant dispersion in street canyon.

Moss bags as sentinels for human safety in mercury-polluted groundwaters.

PubMed

Cesa, Mattia; Nimis, Pier Luigi; Buora, Clara; Lorenzonetto, Alberta; Pozzobon, Alessandro; Raris, Marina; Rosa, Maria; Salvadori, Michela

2014-05-01

An equation to estimate Hg concentrations of <4 μg/L in groundwaters of a polluted area in NE Italy was set out by using transplants of the aquatic moss Rhynchostegium riparioides as trace element bioaccumulators. The equation is derived from a previous mathematical model which was implemented under laboratory conditions. The work aimed at (1) checking the compliance of the uptake kinetics with the model, (2) improving/adapting the model for groundwater monitoring, (3) comparing the performances of two populations of moss collected from different sites, and (4) assessing the environmental impact of Hg contamination on a small river. The main factors affecting Hg uptake in the field were-as expected-water concentration and time of exposure, even though the uptake kinetics in the field were slightly different from those which were previously observed in the lab, since the redox environmental conditions influence the solubility of cationic Fe, which is a negative competitor of Hg(2+). The equation was improved by including the variable 'dissolved oxygen concentration'. A numerical parameter depending on the moss collection site was also provided, since the differences in uptake efficiency were observed between the two populations tested. Predicted Hg concentrations well fitted the values measured in situ (approximately ±50%), while a notable underestimation was observed when the equation was used to predict Hg concentration in a neighbouring river (-96%), probably due to the organic pollution which hampers metal uptake by mosses.

Groundwater Pollution Source Identification using Linked ANN-Optimization Model

NASA Astrophysics Data System (ADS)

Ayaz, Md; Srivastava, Rajesh; Jain, Ashu

2014-05-01

Groundwater is the principal source of drinking water in several parts of the world. Contamination of groundwater has become a serious health and environmental problem today. Human activities including industrial and agricultural activities are generally responsible for this contamination. Identification of groundwater pollution source is a major step in groundwater pollution remediation. Complete knowledge of pollution source in terms of its source characteristics is essential to adopt an effective remediation strategy. Groundwater pollution source is said to be identified completely when the source characteristics - location, strength and release period - are known. Identification of unknown groundwater pollution source is an ill-posed inverse problem. It becomes more difficult for real field conditions, when the lag time between the first reading at observation well and the time at which the source becomes active is not known. We developed a linked ANN-Optimization model for complete identification of an unknown groundwater pollution source. The model comprises two parts- an optimization model and an ANN model. Decision variables of linked ANN-Optimization model contain source location and release period of pollution source. An objective function is formulated using the spatial and temporal data of observed and simulated concentrations, and then minimized to identify the pollution source parameters. In the formulation of the objective function, we require the lag time which is not known. An ANN model with one hidden layer is trained using Levenberg-Marquardt algorithm to find the lag time. Different combinations of source locations and release periods are used as inputs and lag time is obtained as the output. Performance of the proposed model is evaluated for two and three dimensional case with error-free and erroneous data. Erroneous data was generated by adding uniformly distributed random error (error level 0-10%) to the analytically computed concentration values. The main advantage of the proposed model is that it requires only upper half of the breakthrough curve and is capable of predicting source parameters when the lag time is not known. Linking of ANN model with proposed optimization model reduces the dimensionality of the decision variables of the optimization model by one and hence complexity of optimization model is reduced. The results show that our proposed linked ANN-Optimization model is able to predict the source parameters for the error-free data accurately. The proposed model was run several times to obtain the mean, standard deviation and interval estimate of the predicted parameters for observations with random measurement errors. It was observed that mean values as predicted by the model were quite close to the exact values. An increasing trend was observed in the standard deviation of the predicted values with increasing level of measurement error. The model appears to be robust and may be efficiently utilized to solve the inverse pollution source identification problem.

Screening for potentially hazardous PRTR chemicals in the Lake Biwa-Yodo River basin of Japan using a one-box multimedia model.

PubMed

Ariyadasa, B H A K T; Kondo, Akira; Inoue, Yoshio

2015-02-01

A system is needed to predict the behavior, fate, and occurrence of environmental pollutants for effective environmental monitoring. Available monitoring data and computational modeling were used to develop a one-box multimedia model based on the mass balance of the emitted chemicals. Eight physiochemical phenomena in the atmosphere, soil, water, and sediment were considered in this model. This study was carried out in the Lake Biwa-Yodo River basin which provides multiple land uses and also the natural water resource for nearly 13 million of population in the region. Annual emissions for 214 nonmetallic compounds were calculated using the chemical emission data on Japanese pollutant release and transfer registry and used for executing the model simulations for 1997, 2002, and 2008 as input data. The calculated chemical concentrations by the model for all the environmental media were analyzed to determine trends in concentration over this study span. The majority of the chemicals decreased in concentration over time. Among the 214 nonmetallic chemical pollutants, 36 chemicals did not decrease in concentration and were in the top 10 % for concentration on average. Of these 36 pollutants, 7 occur in all 4 environmental media and pose a potential health risk at humans in the Lake Biwa-Yodo River basin.

EFFECTS OF USING THE CB05 VERSUS THE CB4 CHEMICAL MECHANISMS ON MODEL PREDICTIONS

EPA Science Inventory

The Carbon Bond 4 (CB4) chemical mechanism has been widely used for many years in box and air quality models to predict the effect of atmospheric chemistry on pollutant concentrations. Because of the importance of this mechanism and the length of time since its original developm...

EFFECTS OF USING THE CB05 VERSUS THE CB4 CHEMICAL MECHANISM ON MODEL PREDICTIONS

EPA Science Inventory

The Carbon Bond 4 (CB4) chemical mechanism has been widely used for many years in box and air quality models to predict the effect of atmospheric chemistry on pollutant concentrations. Because of the importance of this mechanism and the length of time since its original developm...

CONTENDING WITH SPACE-TIME INTERACTION IN THE SPATIAL PREDICTION OF POLLUTION: VANCOUVER'S HOURLY AMBIENT PM 10 FIELD

EPA Science Inventory

In this article we describe an approach for predicting average hourly concentrations of ambient PM10 in Vancouver. We know our solution also applies to hourly ozone fields and believe it may be quite generally applicable. We use a hierarchal Bayesian approach. At the primary ...

DETERMINING PARTICLE EMISSION SOURCE STRENGTHS FOR COMMON RESIDENTIAL INDOOR SOURCES USING REAL-TIME MEASUREMENTS AND PIECEWISE-CONTINUOUS SOLUTIONS TO THE MASS BALANCE EQUATION

EPA Science Inventory

A variety of common activities in the home, such as smoking and cooking, generate indoor particle concentrations. Mathematical indoor air quality models permit predictions of indoor pollutant concentrations in homes, provided that parameter values such as source strengths and ...

Impacts of the mixing state and chemical composition on the cloud condensation nuclei (CCN) activity in Beijing during winter, 2016

NASA Astrophysics Data System (ADS)

Ren, J.; Zhang, F.

2017-12-01

Abstract.Understanding aerosol chemical composition and mixing state on CCN activity in polluted urban area is crucial to determine NCCN accurately and thus to quantify aerosol indirect effects. Aerosol hrgroscopicity, size-resolved cloud condensation nuclei (CCN) concentration and chemical composition are measured under polluted and background conditions in Beijing based on the Air Pollution and Human Health (APHH) field campaign in winter 2016. The CCN number concentration (NCCN) is predicted by using κ-Köhler theory from the PNSD and five simplified of the mixing state and chemical composition. The assumption of EIS (sulfate, nitrate and SOA internally mixed, and POA and BC externally mixed with size-resolved chemical composition) shows the best closure to predict NCCN with the ratio of predicted to measured NCCN of 0.96-1.12 both in POL and BG conditions. Under BG conditions, IB (internal mixture with bulk chemical composition) scheme achieves the best CCN closure during any periods of a day. In polluted days, EIS and IS (internal mixture with size-resolved chemical composition) scheme may achieve better closure than IB scheme due to the heterogeneity in particles composition across different size. ES (external mixture with size-resolved chemical composition) and EB (external mixture with bulk chemical composition) scheme markedly underestimate the NCCN with the ratio of predicted to measured NCCN of 0.6-0.8. In addition, we note that assumptions of size-resolved composition (IS or ES) show very limited promotes by comparing with the assumptions of bulk composition (IB or EB), furthermore, the prediction becomes worse by using size-resolved assumption in clean days. The predicted NCCN during eve-rush periods shows the most sensitivity to the five different assumptions, with ratios of the predicted and measured NCCN ranging from 0.5 to 1.4, reflecting great impacts from evening traffic and cooking sources. The result from the sensitivity examination of predict NCCN to particles mixing state and organic volume fractions with the aging of organic particles suggests that the mixing state of particles plays a minor role when the κorg exceeds 0.1. Our study could provide new dataset to evaluate the CCN parameterization in models in those heavily polluted regions with large fraction of POA and BC.

Microenvironmental characteristics important for personal exposures to aldehydes in Sacramento, CA, and Milwaukee, WI

NASA Astrophysics Data System (ADS)

Raymer, J. H.; Akland, G.; Johnson, T. R.; Long, T.; Michael, L.; Cauble, L.; McCombs, M.

Oxygenated additives in gasoline are designed to decrease the ozone-forming hydrocarbons and total air toxics, yet they can increase the emissions of aldehydes and thus increase human exposure to these toxic compounds. This paper describes a study conducted to characterize targeted aldehydes in microenvironments in Sacramento, CA, and Milwaukee, WI, and to improve our understanding of the impact of the urban environment on human exposure to air toxics. Data were obtained from microenvironmental concentration measurements, integrated, 24-h personal measurements, indoor and outdoor pollutant monitors at the participants' residences, from ambient pollutant monitors at fixed-site locations in each city, and from real-time diaries and questionnaires completed by the technicians and participants. As part of this study, a model to predict personal exposures based on individual time/activity data was developed for comparison to measured concentrations. Predicted concentrations were generally within 25% of the measured concentrations. The microenvironments that people encounter daily provide for widely varying exposures to aldehydes. The activities that occur in those microenvironments can modulate the aldehyde concentrations dramatically, especially for environments such as "indoor at home." By considering personal activity, location (microenvironment), duration in the microenvironment, and a knowledge of the general concentrations of aldehydes in the various microenvironments, a simple model can do a reasonably good job of predicting the time-averaged personal exposures to aldehydes, even in the absence of monitoring data. Although concentrations of aldehydes measured indoors at the participants' homes tracked well with personal exposure, there were instances where personal exposures and indoor concentrations differed significantly. Key to the ability to predict exposure based on time/activity data is the quality and completeness of the microenvironmental characterizations for the chemicals of interest. Consistent with many earlier studies, personal exposures are difficult to predict using data from regional outdoor monitors.

Early prenatal exposure to air pollution and its associations with birth defects in a state-wide birth cohort from North Carolina.

PubMed

Vinikoor-Imler, Lisa C; Davis, J Allen; Meyer, Robert E; Luben, Thomas J

2013-10-01

Few studies have examined the potential relationship between air pollution and birth defects. The objective of this study was to investigate whether maternal exposure to particulate matter (PM2.5 ) and ozone (O3 ) during pregnancy is associated with birth defects among women living throughout North Carolina. Information on maternal and infant characteristics was obtained from North Carolina birth certificates and health service data (2003-2005) and linked with information on birth defects from the North Carolina Birth Defects Monitoring Program. The 24-hr PM2.5 and O3 concentrations were estimated using a hierarchical Bayesian model of air pollution generated by combining modeled air pollution predictions from the U.S. Environmental Protection Agency's Community Multi-Scale Air Quality model with air monitor data from the Environmental Protection Agency's Air Quality System. Maternal residence was geocoded and assigned pollutant concentrations averaged over weeks 3 to 8 of gestation. Binomial regression was performed and adjusted for potential confounders. No association was observed between either PM2.5 or O3 concentrations and most birth defects. Positive effect estimates were observed between air pollution and microtia/anotia and lower limb deficiency defects, but the 95% confidence intervals were wide and included the null. Overall, this study suggested a possible relationship between air pollution concentration during early pregnancy and certain birth defects (e.g., microtia/anotia, lower limb deficiency defects), although this study did not have the power to detect such an association. The risk for most birth defects does not appear to be affected by ambient air pollution. Copyright © 2013 Wiley Periodicals, Inc.

Using a chemistry transport model to account for the spatial variability of exposure concentrations in epidemiologic air pollution studies.

PubMed

Valari, Myrto; Menut, Laurent; Chatignoux, Edouard

2011-02-01

Environmental epidemiology and more specifically time-series analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM2.5 (particulate matter [PM] < or = 10 microm in aerodynamic diameter) were captured and parametrized. Exposures predicted with this model were used in a time-series study of the short-term effect of air pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants.

Fuzzy rule based estimation of agricultural diffuse pollution concentration in streams.

PubMed

Singh, Raj Mohan

2008-04-01

Outflow from the agricultural fields carries diffuse pollutants like nutrients, pesticides, herbicides etc. and transports the pollutants into the nearby streams. It is a matter of serious concern for water managers and environmental researchers. The application of chemicals in the agricultural fields, and transport of these chemicals into streams are uncertain that cause complexity in reliable stream quality predictions. The chemical characteristics of applied chemical, percentage of area under the chemical application etc. are some of the main inputs that cause pollution concentration as output in streams. Each of these inputs and outputs may contain measurement errors. Fuzzy rule based model based on fuzzy sets suits to address uncertainties in inputs by incorporating overlapping membership functions for each of inputs even for limited data availability situations. In this study, the property of fuzzy sets to address the uncertainty in input-output relationship is utilized to obtain the estimate of concentrations of a herbicide, atrazine, in a stream. The data of White river basin, a part of the Mississippi river system, is used for developing the fuzzy rule based models. The performance of the developed methodology is found encouraging.

Long-term ambient air pollution and lung function impairment in Chinese children from a high air pollution range area: The Seven Northeastern Cities (SNEC) study

NASA Astrophysics Data System (ADS)

Zeng, Xiao-Wen; Vivian, Elaina; Mohammed, Kahee A.; Jakhar, Shailja; Vaughn, Michael; Huang, Jin; Zelicoff, Alan; Xaverius, Pamela; Bai, Zhipeng; Lin, Shao; Hao, Yuan-Tao; Paul, Gunther; Morawska, Lidia; Wang, Si-Quan; Qian, Zhengmin; Dong, Guang-Hui

2016-08-01

Epidemiological studies have reported inconsistent and inconclusive associations between long-term exposure to ambient air pollution and lung function in children from Europe and America, where air pollution levels were typically low. The aim of the present study is to examine the relationship between air pollutants and lung function in children selected from heavily industrialized and polluted cities in northeastern China. During 2012, 6740 boys and girls aged 7-14 years were recruited in 24 districts of seven northeastern cities. Portable electronic spirometers were used to measure lung function. Four-year average concentrations of particulate matter with an aerodynamic diameter ≤10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) were measured at monitoring stations in the 24 districts. Two-staged regression models were used in the data analysis, controlling for covariates. Overall, for all subjects, the increased odds of lung function impairment associated with exposure to air pollutants, ranged from 5% (adjusted odds ratio [aOR] = 1.05; 95% confidence interval [CI] = 1.01, 1.10) for FVC < 85% predicted per 46.3 μg/m3 for O3 to 81% (aOR = 1.81; 95%CI = 1.44, 2.28) for FEV1 < 85% predicted per 30.6 μg/m3 for PM10. The linear regression models consistently showed a negative relationship between all air pollutants and lung function measures across subjects. There were significant interaction terms indicating gender differences for lung function impairment and pulmonary function from exposure to some pollutants (P < 0.10). In conclusion, long term exposure to high concentrations of ambient air pollution is associated with decreased pulmonary function and lung function impairment, and females appear to be more susceptible than males.

Predicting Air Quality at First Ingress into Vehicles Visiting the International Space Station.

PubMed

Romoser, Amelia A; Scully, Robert R; Limero, Thomas F; De Vera, Vanessa; Cheng, Patti F; Hand, Jennifer J; James, John T; Ryder, Valerie E

2017-02-01

NASA regularly performs ground-based offgas tests (OGTs), which allow prediction of accumulated volatile pollutant concentrations at first entry on orbit, on whole modules and vehicles scheduled to connect to the International Space Station (ISS). These data guide crew safety operations and allow for estimation of ISS air revitalization systems impact from additional pollutant load. Since volatiles released from vehicle, module, and payload materials can affect crew health and performance, prediction of first ingress air quality is important. To assess whether toxicological risk is typically over or underpredicted, OGT and first ingress samples from 10 vehicles and modules were compared. Samples were analyzed by gas chromatography and gas chromatography-mass spectrometry. The rate of pollutant accumulation was extrapolated over time. Ratios of analytical values and Spacecraft Maximum Allowable Concentrations were used to predict total toxicity values (T-values) at first entry. Results were also compared by compound. Frequently overpredicted was 2-butanone (9/10), whereas propanal (6/10) and ethanol (8/10) were typically underpredicted, but T-values were not substantially affected. Ingress sample collection delay (estimated by octafluoropropane introduced from ISS atmosphere) and T-value prediction accuracy correlated well (R2 = 0.9008), highlighting the importance of immediate air sample collection and accounting for ISS air dilution. Importantly, T-value predictions were conservative 70% of the time. Results also suggest that T-values can be normalized to octafluoropropane levels to adjust for ISS air dilution at first ingress. Finally, OGT and ingress sampling has allowed small leaks in vehicle fluid systems to be recognized and addressed.Romoser AA, Scully RR, Limero TF, De Vera V, Cheng PF, Hand JJ, James JT, Ryder VE. Predicting air quality at first ingress into vehicles visiting the International Space Station. Aerosp Med Hum Perform. 2017; 88(2):104-113.

Evaluation of the Community Multiscale Air Quality Model for Simulating Winter Ozone Formation in the Uinta Basin with Intensive Oil and Gas Production

NASA Astrophysics Data System (ADS)

Matichuk, R.; Tonnesen, G.; Luecken, D.; Roselle, S. J.; Napelenok, S. L.; Baker, K. R.; Gilliam, R. C.; Misenis, C.; Murphy, B.; Schwede, D. B.

2015-12-01

The western United States is an important source of domestic energy resources. One of the primary environmental impacts associated with oil and natural gas production is related to air emission releases of a number of air pollutants. Some of these pollutants are important precursors to the formation of ground-level ozone. To better understand ozone impacts and other air quality issues, photochemical air quality models are used to simulate the changes in pollutant concentrations in the atmosphere on local, regional, and national spatial scales. These models are important for air quality management because they assist in identifying source contributions to air quality problems and designing effective strategies to reduce harmful air pollutants. The success of predicting oil and natural gas air quality impacts depends on the accuracy of the input information, including emissions inventories, meteorological information, and boundary conditions. The treatment of chemical and physical processes within these models is equally important. However, given the limited amount of data collected for oil and natural gas production emissions in the past and the complex terrain and meteorological conditions in western states, the ability of these models to accurately predict pollution concentrations from these sources is uncertain. Therefore, this presentation will focus on understanding the Community Multiscale Air Quality (CMAQ) model's ability to predict air quality impacts associated with oil and natural gas production and its sensitivity to input uncertainties. The results will focus on winter ozone issues in the Uinta Basin, Utah and identify the factors contributing to model performance issues. The results of this study will help support future air quality model development, policy and regulatory decisions for the oil and gas sector.

Support vector machine-an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?

PubMed

Liu, Mei; Lu, Jun

2014-09-01

Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.

Phenology of a Vegetation Barrier and Resulting Impacts on Near-Highway Particle Number and Black Carbon Concentrations on a School Campus.

PubMed

Fuller, Christina H; Carter, David R; Hayat, Matthew J; Baldauf, Richard; Watts Hull, Rebecca

2017-02-08

Traffic-related air pollution is a persistent concern especially in urban areas where populations live in close proximity to roadways. Innovative solutions are needed to minimize human exposure and the installation of vegetative barriers shows potential as a method to reduce near-road concentrations. This study investigates the impact of an existing stand of deciduous and evergreen trees on near-road total particle number (PNC) and black carbon (BC) concentrations across three seasons. Measurements were taken during spring, fall and winter on the campus of a middle school in the Atlanta (GA, USA) area at distances of 10 m and 50 m from a major interstate highway. We identified consistent decreases in BC concentrations, but not for PNC, with increased distance from the highway. In multivariable models, hour of day, downwind conditions, distance to highway, temperature and relative humidity significantly predicted pollutant concentrations. The magnitude of effect of these variables differed by season, however, we were not able to show a definitive impact of the vegetative barrier on near-road concentrations. More detailed studies are necessary to further examine the specific configurations and scenarios that may produce pollutant and exposure reductions.

Phenology of a Vegetation Barrier and Resulting Impacts on Near-Highway Particle Number and Black Carbon Concentrations on a School Campus

PubMed Central

Fuller, Christina H.; Carter, David R.; Hayat, Matthew J.; Baldauf, Richard; Watts Hull, Rebecca

2017-01-01

Traffic-related air pollution is a persistent concern especially in urban areas where populations live in close proximity to roadways. Innovative solutions are needed to minimize human exposure and the installation of vegetative barriers shows potential as a method to reduce near-road concentrations. This study investigates the impact of an existing stand of deciduous and evergreen trees on near-road total particle number (PNC) and black carbon (BC) concentrations across three seasons. Measurements were taken during spring, fall and winter on the campus of a middle school in the Atlanta (GA, USA) area at distances of 10 m and 50 m from a major interstate highway. We identified consistent decreases in BC concentrations, but not for PNC, with increased distance from the highway. In multivariable models, hour of day, downwind conditions, distance to highway, temperature and relative humidity significantly predicted pollutant concentrations. The magnitude of effect of these variables differed by season, however, we were not able to show a definitive impact of the vegetative barrier on near-road concentrations. More detailed studies are necessary to further examine the specific configurations and scenarios that may produce pollutant and exposure reductions. PMID:28208726

Prediction of health effects of cross-border atmospheric pollutants using an aerosol forecast model.

PubMed

Onishi, Kazunari; Sekiyama, Tsuyoshi Thomas; Nojima, Masanori; Kurosaki, Yasunori; Fujitani, Yusuke; Otani, Shinji; Maki, Takashi; Shinoda, Masato; Kurozawa, Youichi; Yamagata, Zentaro

2018-08-01

Health effects of cross-border air pollutants and Asian dust are of significant concern in Japan. Currently, models predicting the arrival of aerosols have not investigated the association between arrival predictions and health effects. We investigated the association between subjective health symptoms and unreleased aerosol data from the Model of Aerosol Species in the Global Atmosphere (MASINGAR) acquired from the Japan Meteorological Agency, with the objective of ascertaining if these data could be applied to predicting health effects. Subjective symptom scores were collected via self-administered questionnaires and, along with modeled surface aerosol concentration data, were used to conduct a risk evaluation using generalized estimating equations between October and November 2011. Altogether, 29 individuals provided 1670 responses. Spearman's correlation coefficients were determined for the relationship between the proportion of the participants reporting the maximum score of two or more for each symptom and the surface concentrations for each considered aerosol species calculated using MASINGAR; the coefficients showed significant intermediate correlations between surface sulfate aerosol concentration and respiratory, throat, and fever symptoms (R = 0.557, 0.454, and 0.470, respectively; p < 0.01). In the general estimation equation (logit link) analyses, a significant linear association of surface sulfate aerosol concentration, with an endpoint determined by reported respiratory symptom scores of two or more, was observed (P trend = 0.001, odds ratio [OR] of the highest quartile [Q4] vs. the lowest [Q1] = 5.31, 95% CI = 2.18 to 12.96), with adjustment for potential confounding. The surface sulfate aerosol concentration was also associated with throat and fever symptoms. In conclusion, our findings suggest that modeled data are potentially useful for predicting health risks of cross-border aerosol arrivals. Copyright © 2018 Elsevier Ltd. All rights reserved.

Comparative assessment of GIS-based methods and metrics for estimating long-term exposures to air pollution

NASA Astrophysics Data System (ADS)

Gulliver, John; de Hoogh, Kees; Fecht, Daniela; Vienneau, Danielle; Briggs, David

2011-12-01

The development of geographical information system techniques has opened up a wide array of methods for air pollution exposure assessment. The extent to which these provide reliable estimates of air pollution concentrations is nevertheless not clearly established. Nor is it clear which methods or metrics should be preferred in epidemiological studies. This paper compares the performance of ten different methods and metrics in terms of their ability to predict mean annual PM 10 concentrations across 52 monitoring sites in London, UK. Metrics analysed include indicators (distance to nearest road, traffic volume on nearest road, heavy duty vehicle (HDV) volume on nearest road, road density within 150 m, traffic volume within 150 m and HDV volume within 150 m) and four modelling approaches: based on the nearest monitoring site, kriging, dispersion modelling and land use regression (LUR). Measures were computed in a GIS, and resulting metrics calibrated and validated against monitoring data using a form of grouped jack-knife analysis. The results show that PM 10 concentrations across London show little spatial variation. As a consequence, most methods can predict the average without serious bias. Few of the approaches, however, show good correlations with monitored PM 10 concentrations, and most predict no better than a simple classification based on site type. Only land use regression reaches acceptable levels of correlation ( R2 = 0.47), though this can be improved by also including information on site type. This might therefore be taken as a recommended approach in many studies, though care is needed in developing meaningful land use regression models, and like any method they need to be validated against local data before their application as part of epidemiological studies.

Impact of climate change on runoff pollution in urban environments

NASA Astrophysics Data System (ADS)

Coutu, S.; Kramer, S.; Barry, D. A.; Roudier, P.

2012-12-01

Runoff from urban environments is generally contaminated. These contaminants mostly originate from road traffic and building envelopes. Facade envelopes generate lead, zinc and even biocides, which are used for facade protection. Road traffic produces particles from tires and brakes. The transport of these pollutants to the environment is controlled by rainfall. The interval, duration and intensity of rainfall events are important as the dynamics of the pollutants are often modeled with non-linear buildup/washoff functions. Buildup occurs during dry weather when pollution accumulates, and is subsequently washed-off at the time of the following rainfall, contaminating surface runoff. Climate predictions include modified rainfall distributions, with changes in both number and intensity of events, even if the expected annual rainfall varies little. Consequently, pollutant concentrations in urban runoff driven by buildup/washoff processes will be affected by these changes in rainfall distributions. We investigated to what extent modifications in future rainfall distributions will impact the concentrations of pollutants present in urban surface runoff. The study used the example of Lausanne, Switzerland (temperate climate zone). Three emission scenarios (time horizon 2090), multiple combinations of RCM/GCM and modifications in rain event frequency were used to simulate future rainfall distributions with various characteristics. Simulated rainfall events were used as inputs for four pairs of buildup/washoff models, in order to compare future pollution concentrations in surface runoff. In this way, uncertainty in model structure was also investigated. Future concentrations were estimated to be between ±40% of today's concentrations depending on the season and, importantly, on the choice of the RCM/GCM model. Overall, however, the dominant factor was the uncertainty inherent in buildup/washoff models, which dominated over the uncertainty in future rainfall distributions. Consequently, the choice of a proper buildup/washoff model, with calibrated site-specific coefficients, is a major factor in modeling future runoff concentrations from contaminated urban surfaces.

[Explore the spatial and temporal patterns of water pollution in the Yincungang canal of the Lake Taihu basin, China].

PubMed

Yang, Xiao-Ying; Luo, Xing-Zhang; Zheng, Zheng; Fang, Shu-Bo

2012-09-01

Two high-density snap-shot samplings were conducted along the Yincungang canal, one important tributary of the Lake Tai, in April (low flow period) and June (high flow period) of 2010. Geostatistical analysis based on the river network distance was used to analyze the spatial and temporal patterns of the pollutant concentrations along the canal with an emphasis on chemical oxygen demand (COD) and total nitrogen (TN). Study results have indicated: (1) COD and TN concentrations display distinctly different spatial and temporal patterns between the low and high flow periods. COD concentration in June is lower than that in April, while TN concentration has the contrary trend. (2) COD load is relatively constant during the period between the two monitoring periods. The spatial correlation structure of COD is exponential for both April and June, and the change of COD concentration is mainly influenced by hydrological conditions. (3) Nitrogen load from agriculture increased significantly during the period between the two monitoring periods. Large amount of chaotic fertilizing by individual farmers has led to the loss of the spatial correlation among the observed TN concentrations. Hence, changes of TN concentration in June are under the dual influence of agricultural fertilizing and hydrological conditions. In the view of the complex hydrological conditions and serious water pollution in the Lake Taihu region, geostatistical analysis is potentially a useful tool for studying the characteristics of pollutant distribution and making predictions in the region.

PM2.5 in the Yangtze River Delta, China: Chemical compositions, seasonal variations, and regional pollution events.

PubMed

Ming, Lili; Jin, Ling; Li, Jun; Fu, Pingqing; Yang, Wenyi; Liu, Di; Zhang, Gan; Wang, Zifa; Li, Xiangdong

2017-04-01

Fine particle (PM 2.5 ) samples were collected simultaneously at three urban sites (Shanghai, Nanjing, and Hangzhou) and one rural site near Ningbo in the Yangtze River Delta (YRD) region, China, on a weekly basis from September 2013 to August 2014. In addition, high-frequency daily sampling was conducted in Shanghai and Nanjing for one month during each season. Severe regional PM 2.5 pollution episodes were frequently observed in the YRD, with annual mean concentrations of 94.6 ± 55.9, 97.8 ± 40.5, 134 ± 54.3, and 94.0 ± 57.6 μg m -3 in Shanghai, Nanjing, Hangzhou, and Ningbo, respectively. The concentrations of PM 2.5 and ambient trace metals at the four sites showed clear seasonal trends, with higher concentrations in winter and lower concentrations in summer. In Shanghai, similar seasonal patterns were found for organic carbon (OC), elemental carbon (EC), and water-soluble inorganic ions (K + , NH 4 + , Cl - , NO 3 - , and SO 4 2- ). Air mass backward trajectory and potential source contribution function (PSCF) analyses implied that areas of central and northern China contributed significantly to the concentration and chemical compositions of PM 2.5 in Shanghai during winter. Three heavy pollution events in Shanghai were observed during autumn and winter. The modelling results of the Nested Air Quality Prediction Modeling System (NAQPMS) showed the sources and transport of PM 2.5 in the YRD during the three pollution processes. The contribution of secondary species (SOC, NH 4 + , NO 3 - , and SO 4 2- ) in pollution event (PE) periods was much higher than in BPE (before pollution event) and APE (after pollution event) periods, suggesting the importance of secondary aerosol formation during the three pollution events. Furthermore, the bioavailability of Cu, and Zn in the wintertime PM 2.5 samples from Shanghai was much higher during the pollution days than during the non-pollution days. Copyright © 2017 Elsevier Ltd. All rights reserved.

Detection and estimation trends linked to air quality and mortality on French Riviera over the 1990-2005 period to develop a prediction model of an aggregate risk index

NASA Astrophysics Data System (ADS)

Sicard, P.; Mangin, A.; Hebel, P.; Lesne, O.; Malléa, P.

2009-04-01

There is a profound relation between human health and well being from the one side and air pollution levels from the other. Air quality in South of France and more specifically in Nice, is known to be bad, especially in summer. The main objectives are to establish correlations between air pollution, exposure of people and reactivity of these people to this aggression, to validate a risk index built from air quality and pollen data in the area of Nice and to construct a prediction model of this sanitary index. The spatial extent of the experiment will be mainly the territory of "Alpes Maritimes". All the tasks are performed in collaboration with the "Heath-Environment Network" of the "Centre Hospitalier Universitaire" of Nice. The development of an adequate tool for observation (health index and/or indices per pathology) to understand impacts of pollution levels in an area is of utmost importance. These indexes should take into account the possible adverse effects associated with the coexistence of all the pollutants and environmental parameters. This tool must be able to inform the citizens about the levels of pollution in an adequate and understandable way but also to be used by relevant authorities to take a series of predetermined measures to protect the health of the population. This paper describes the first step to construct a prediction model of this sanitary index with a confidence interval 99% (and 95%): detection and estimation trends observed in concentrations of pollutants, emissions and mortality over the 1990-2005 period in the "Alpes Maritimes" area. The non-parametric Mann-Kendall test has been developed for detecting and estimating monotonic trends in the time series and applied in our study at annual values of pollutants air concentrations. An important objective of many environmental monitoring programs is to detect changes or trends in pollution levels over time. Over the period 1990-2005, concerning the emissions of the main pollutants, we obtained significant decreasing trends. Between 1994 and 2005, from the SO2 concentrations, decreasing trends of 1.2 %.year-1 (urban stations) and of 5.4 %.year-1 (traffic stations) were calculated. Over the same period, we obtained a decreasing trend of 1.3 %.year-1 for the NO2 concentrations (urban stations) and of 3.1 %.year-1 for the traffic stations. In addition, a decreasing trend of 0.5 %.year-1 was calculated for the suburban stations over the 1998-2005 period. Globally, the concentration of the major pollutants showed a clear downward trend and those main reductions have reflected the reduction policy of the emissions over twenty years. By considering the ozone mean values in urban areas over the 1997-2005 period, an increasing of 3.0 %.year-1 was obtained with annual averages and 3.9 %.year-1 with median values. Over the 1990-2005 period, we obtained significant decreasing trends concerning the "ischemic heart diseases" (- 1.20 %.year-1) and "asthma" (- 4.03 %.year-1) categories. No significant sex-related difference was identified for these groups. An annual change rate of + 0.31 %.year-1 for the "airway diseases" and of + 2.50%.year-1 for the "unknown causes" were identified. For these categories, we noted a sex-related difference. In fact, we obtained for males a decreasing trend contrary to females.

Modeling the plant uptake of organic chemicals, including the soil-air-plant pathway.

PubMed

Collins, Chris D; Finnegan, Eilis

2010-02-01

The soil-air-plant pathway is potentially important in the vegetative accumulation of organic pollutants from contaminated soils. While a number of qualitative frameworks exist for the prediction of plant accumulation of organic chemicals by this pathway, there are few quantitative models that incorporate this pathway. The aim of the present study was to produce a model that included this pathway and could quantify its contribution to the total plant contamination for a range of organic pollutants. A new model was developed from three submodels for the processes controlling plant contamination via this pathway: aerial deposition, soil volatilization, and systemic translocation. Using the combined model, the soil-air-plant pathway was predicted to account for a significant proportion of the total shoot contamination for those compounds with log K(OA) > 9 and log K(AW) < -3. For those pollutants with log K(OA) < 9 and log K(AW) > -3 there was a higher deposition of pollutant via the soil-air-plant pathway than for those chemicals with log K(OA) > 9 and log K(AW) < -3, but this was an insignificant proportion of the total shoot contamination because of the higher mobility of these compounds via the soil-root-shoot pathway. The incorporation of the soil-air-plant pathway into the plant uptake model did not significantly improve the prediction of the contamination of vegetation from polluted soils when compared across a range of studies. This was a result of the high variability between the experimental studies where the bioconcentration factors varied by 2 orders of magnitude at an equivalent log K(OA). One potential reason for this is the background air concentration of the pollutants under study. It was found background air concentrations would dominate those from soil volatilization in many situations unless there was a soil hot spot of contamination, i.e., >100 mg kg(-1).

The influence of roadside solid and vegetation barriers on near-road air quality

NASA Astrophysics Data System (ADS)

Ghasemian, Masoud; Amini, Seyedmorteza; Princevac, Marko

2017-12-01

The current study evaluates the influence of roadside solid and vegetation barriers on the near-road air quality. Reynolds Averaged Navier-Stokes (RANS) technique coupled with the k - ε realizable turbulence model is utilized to investigate the flow pattern and pollutant concentration. A scalar transport equation is solved for a tracer gas to represent the roadway pollutant emissions. In addition, a broad range of turbulent Schmidt numbers are tested to calibrate the scalar transport equation. Three main scenarios including flat terrain, solid barrier, and vegetative barrier are studied. To validate numerical methodology, predicted pollutant concentration is compared with published wind tunnel data. Results show that the solid barrier induces an updraft motion and lofts the vehicle emission plume. Therefore, the ground-level pollutant concentration decreases compared to the flat terrain. For the vegetation barrier, different sub-scenarios with different vegetation densities ranging from approximately flat terrain to nearly solid barrier are examined. Dense canopies act in a similar manner as a solid barrier and mitigate the pollutant concentration through vertical mixing. On the other hand, the high porosity vegetation barriers reduce the wind speed and lead to a higher pollutant concentration. As the vegetation density increases, i.e. the barrier porosity decreases, the recirculation zone behind the canopy becomes larger and moves toward the canopy. The dense plant canopy with LAD = 3.33m-2m3 can improve the near-road air quality by 10% and high porosity canopy with LAD = 1m-2m3 deteriorates near-road air quality by 15%. The results of this study can be implemented as green infrastructure design strategies by urban planners and forestry organizations.

The Impact of Air Pollution, Including Asian Sand Dust, on Respiratory Symptoms and Health-related Quality of Life in Outpatients With Chronic Respiratory Disease in Korea: A Panel Study.

PubMed

Nakao, Motoyuki; Ishihara, Yoko; Kim, Cheol-Hong; Hyun, In-Gyu

2018-05-01

Air pollution is a growing concern in Korea because of transboundary air pollution from mainland China. A panel study was conducted to clarify the effects of air pollution on respiratory symptoms and health-related quality of life (HR-QoL) in outpatients with and without chronic obstructive pulmonary disease (COPD) in Korea. Patients filled out a questionnaire including self-reported HR-QoL in February and were followed up in May and July. The study was conducted from 2013 to 2015, with different participants each year. Air quality parameters were applied in a generalized estimating equation as independent variables to predict factors affecting HR-QoL. Lower physical fitness scores were associated with Asian sand dust events. Daily activity scores were worse when there were high concentrations of particulate matter (PM) less than 10 μm in diameter (PM 10 ). Lower social functioning scores were associated with high PM less than 2.5 μm in diameter and nitrogen dioxide (NO 2 ) concentrations. High NO 2 concentrations also showed a significant association with mental health scores. Weather-related cough was prevalent when PM 10 , NO 2 , or ozone (O 3 ) concentrations were high, regardless of COPD severity. High PM 10 concentrations were associated with worsened wheezing, particularly in COPD patients. The results suggest that PM, NO 2 , and O 3 cause respiratory symptoms leading to HR-QoL deterioration. While some adverse effects of air pollution appeared to occur regardless of COPD, others occurred more often and more intensely in COPD patients. The public sector, therefore, needs to consider tailoring air pollution countermeasures to people with different conditions to minimize adverse health effects.

The use of LIDAR to characterize aircraft exhaust plumes

DOT National Transportation Integrated Search

2003-06-22

Aircraft emissions are a growing concern for the FAA, airports, and the community. U.S. : and international air quality models were previously unable to accurately predict initial : plume dispersion and the resulting pollutant concentrations because ...

Neighborhood-Scale Spatial Models of Diesel Exhaust Concentration Profile Using 1-Nitropyrene and Other Nitroarenes

PubMed Central

Schulte, Jill K.; Fox, Julie R.; Oron, Assaf P.; Larson, Timothy V.; Simpson, Christopher D.; Paulsen, Michael; Beaudet, Nancy; Kaufman, Joel D.; Magzamen, Sheryl

2016-01-01

With emerging evidence that diesel exhaust exposure poses distinct risks to human health, the need for fine-scale models of diesel exhaust pollutants is growing. We modeled the spatial distribution of several nitrated polycyclic aromatic hydrocarbons (NPAHs) to identify fine-scale gradients in diesel exhaust pollution in two Seattle, WA neighborhoods. Our modeling approach fused land-use regression, meteorological dispersion modeling, and pollutant monitoring from both fixed and mobile platforms. We applied these modeling techniques to concentrations of 1-nitropyrene (1-NP), a highly specific diesel exhaust marker, at the neighborhood scale. We developed models of two additional nitroarenes present in secondary organic aerosol: 2-nitro-pyrene and 2-nitrofluoranthene. Summer predictors of 1-NP, including distance to railroad, truck emissions, and mobile black carbon measurements, showed a greater specificity to diesel sources than predictors of other NPAHs. Winter sampling results did not yield stable models, likely due to regional mixing of pollutants in turbulent weather conditions. The model of summer 1-NP had an R2 of 0.87 and cross-validated R2 of 0.73. The synthesis of high-density sampling and hybrid modeling was successful in predicting diesel exhaust pollution at a very fine scale and identifying clear gradients in NPAH concentrations within urban neighborhoods. PMID:26501773

Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution.

PubMed

Hu, Bifeng; Chen, Songchao; Hu, Jie; Xia, Fang; Xu, Junfeng; Li, Yan; Shi, Zhou

2017-01-01

Rapid heavy metal soil surveys at large scale with high sampling density could not be conducted with traditional laboratory physical and chemical analyses because of the high cost, low efficiency and heavy workload involved. This study explored a rapid approach to assess heavy metals contamination in 301 farmland soils from Fuyang in Zhejiang Province, in the southern Yangtze River Delta, China, using portable proximal soil sensors. Portable X-ray fluorescence spectroscopy (PXRF) was used to determine soil heavy metals total concentrations while soil pH was predicted by portable visible-near infrared spectroscopy (PVNIR). Zn, Cu and Pb were successfully predicted by PXRF (R2 >0.90 and RPD >2.50) while As and Ni were predicted with less accuracy (R2 <0.75 and RPD <1.40). The pH values were well predicted by PVNIR. Classification of heavy metals contamination grades in farmland soils was conducted based on previous results; the Kappa coefficient was 0.87, which showed that the combination of PXRF and PVNIR was an effective and rapid method to determine the degree of pollution with soil heavy metals. This study provides a new approach to assess soil heavy metals pollution; this method will facilitate large-scale surveys of soil heavy metal pollution.

Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution

PubMed Central

Hu, Bifeng; Chen, Songchao; Hu, Jie; Xia, Fang; Xu, Junfeng; Li, Yan; Shi, Zhou

2017-01-01

Rapid heavy metal soil surveys at large scale with high sampling density could not be conducted with traditional laboratory physical and chemical analyses because of the high cost, low efficiency and heavy workload involved. This study explored a rapid approach to assess heavy metals contamination in 301 farmland soils from Fuyang in Zhejiang Province, in the southern Yangtze River Delta, China, using portable proximal soil sensors. Portable X-ray fluorescence spectroscopy (PXRF) was used to determine soil heavy metals total concentrations while soil pH was predicted by portable visible-near infrared spectroscopy (PVNIR). Zn, Cu and Pb were successfully predicted by PXRF (R2 >0.90 and RPD >2.50) while As and Ni were predicted with less accuracy (R2 <0.75 and RPD <1.40). The pH values were well predicted by PVNIR. Classification of heavy metals contamination grades in farmland soils was conducted based on previous results; the Kappa coefficient was 0.87, which showed that the combination of PXRF and PVNIR was an effective and rapid method to determine the degree of pollution with soil heavy metals. This study provides a new approach to assess soil heavy metals pollution; this method will facilitate large-scale surveys of soil heavy metal pollution. PMID:28234944

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

NASA Astrophysics Data System (ADS)

Hu, Jianlin; Li, Xun; Huang, Lin; Ying, Qi; Zhang, Qiang; Zhao, Bin; Wang, Shuxiao; Zhang, Hongliang

2017-11-01

Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are -0.11 and 0.24, respectively, which are better than the MFB (-0.25 to -0.16) and MFE (0.26-0.31) of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06-0.19 and MNE of 0.16-0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1h. The study demonstrates that ensemble predictions from combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories, and the results are publicly available for future health effect studies.

Prediction of agriculture derived groundwater nitrate distribution in North China Plain with GIS-based BPNN

NASA Astrophysics Data System (ADS)

Wang, M. X.; Liu, G. D.; Wu, W. L.; Bao, Y. H.; Liu, W. N.

2006-07-01

In recent years, nitrate contamination of groundwater has become a growing concern for people in rural areas in North China Plain (NCP) where groundwater is used as drinking water. The objective of this study was to simulate agriculture derived groundwater nitrate pollution patterns with artificial neural network (ANN), which has been proved to be an effective tool for prediction in many branches of hydrology when data are not sufficient to understand the physical process of the systems but relative accurate predictions is needed. In our study, a back propagation neural network (BPNN) was developed to simulate spatial distribution of NO3-N concentrations in groundwater with land use information and site-specific hydrogeological properties in Huantai County, a typical agriculture dominated region of NCP. Geographic information system (GIS) tools were used in preparing and processing input-output vectors data for the BPNN. The circular buffer zones centered on the sampling wells were designated so as to consider the nitrate contamination of groundwater due to neighboring field. The result showed that the GIS-based BPNN simulated groundwater NO3-N concentration efficiently and captured the general trend of groundwater nitrate pollution patterns. The optimal result was obtained with a learning rate of 0.02, a 4-7-1 architecture and a buffer zone radius of 400 m. Nitrogen budget combined with GIS-based BPNN can serve as a cost-effective tool for prediction and management of groundwater nitrate pollution in an agriculture dominated regions in North China Plain.

A multi-approach monitoring of particulate matter, metals and PAHs in an urban street canyon.

PubMed

De Nicola, Flavia; Murena, Fabio; Costagliola, M Antonietta; Alfani, Anna; Baldantoni, Daniela; Prati, M Vittoria; Sessa, Ludovica; Spagnuolo, Valeria; Giordano, Simonetta

2013-07-01

For the first time until now, the results from a prediction model (Atmospheric Dispersion Modelling System (ADMS)-Road) of pollutant dispersion in a street canyon were compared to the results obtained from biomonitors. In particular, the instrumental monitoring of particulate matter (PM10) and the biomonitoring of 14 polycyclic aromatic hydrocarbons (PAHs) and 11 metals by Quercus ilex leaves and Hypnum cupressiforme moss bags, acting as long- and short-term accumulators, respectively, were carried out. For both PAHs and metals, similar bioaccumulation trends were observed, with higher concentrations in biomonitors exposed at the leeward canyon side, affected by primary air vortex. The major pollutant accumulation at the leeward side was also predicted by the ADMS-Road model, on the basis of the prevailing wind direction that determines different exposure of the street canyon sides to pollutants emitted by vehicular traffic. A clear vertical (3, 6 and 9 m) distribution gradient of pollutants was not observed, so that both the model and biomonitoring results suggested that local air turbulences in the street canyon could contribute to uniform pollutant distribution at different heights.

A review of AirQ Models and their applications for forecasting the air pollution health outcomes.

PubMed

Oliveri Conti, Gea; Heibati, Behzad; Kloog, Itai; Fiore, Maria; Ferrante, Margherita

2017-03-01

Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects.

Study of meteorological aspects and urban concentration of SO2 in atmospheric environment of La Plata, Argentina.

PubMed

Ratto, Gustavo; Videla, Fabián; Almandos, J Reyna; Maronna, Ricardo; Schinca, Daniel

2006-10-01

This article presents and discusses SO(2) (ppbv) concentration measurements combined with meteorological data (mainly wind speed and direction) for a five-year campaign (1996 to 2000), in a site near an oil refinery plant close to the city of La Plata and surroundings (aprox. 740.000 inh.), considered one of the six most affected cities by air pollution in the country. Since there is no monitoring network in the area, the obtained results should be considered as medium term accumulated data that enables to determine trends by analyzing together gas concentrations and meteorological parameters. Preliminary characterization of the behaviour of the predominant winds of the region in relation with potential atmospheric gas pollutants from seasonal wind roses is possible to carry out from the data. These results are complemented with monthly averaged SO(2) measurements. In particular, for year 2000, pollutant roses were determined which enable predictions about contamination emission sources. As a general result we can state that there is a clear increase in annual SO(2) concentration and that the selected site should be considered as a key site for future survey monitoring network deployment. Annual SO(2) average concentration and prevailing seasonal winds determined in this work, together with the potential health impact of SO(2) reveals the need for a comprehensive and systematic study involving particulate matter an other basic pollutant gases.

CFD Investigation of Pollutant Emission in Can-Type Combustor Firing Natural Gas, LNG and Syngas

NASA Astrophysics Data System (ADS)

Hasini, H.; Fadhil, SSA; Mat Zian, N.; Om, NI

2016-03-01

CFD investigation of flow, combustion process and pollutant emission using natural gas, liquefied natural gas and syngas of different composition is carried out. The combustor is a can-type combustor commonly used in thermal power plant gas turbine. The investigation emphasis on the comparison of pollutant emission such in particular CO2, and NOx between different fuels. The numerical calculation for basic flow and combustion process is done using the framework of ANSYS Fluent with appropriate model assumptions. Prediction of pollutant species concentration at combustor exit shows significant reduction of CO2 and NOx for syngas combustion compared to conventional natural gas and LNG combustion.

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.

Exposure-Relevant Ozone Chemistry in Occupied Spaces

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

Coleman, Beverly Kaye

2009-04-01

Ozone, an ambient pollutant, is transformed into other airborne pollutants in the indoor environment. In this dissertation, the type and amount of byproducts that result from ozone reactions with common indoor surfaces, surface residues, and vapors were determined, pollutant concentrations were related to occupant exposure, and frameworks were developed to predict byproduct concentrations under various indoor conditions. In Chapter 2, an analysis is presented of secondary organic aerosol formation from the reaction of ozone with gas-phase, terpene-containing consumer products in small chamber experiments under conditions relevant for residential and commercial buildings. The full particle size distribution was continuously monitored, andmore » ultrafine and fine particle concentrations were in the range of 10 to>300 mu g m -3. Particle nucleation and growth dynamics were characterized.Chapter 3 presents an investigation of ozone reactions with aircraft cabin surfaces including carpet, seat fabric, plastics, and laundered and worn clothing fabric. Small chamber experiments were used to determine ozone deposition velocities, ozone reaction probabilities, byproduct emission rates, and byproduct yields for each surface category. The most commonly detected byproducts included C1?C10 saturated aldehydes and skin oil oxidation products. For all materials, emission rates were higher with ozone than without. Experimental results were used to predict byproduct exposure in the cabin and compare to other environments. Byproduct levels are predicted to be similar to ozone levels in the cabin, which have been found to be tens to low hundreds of ppb in the absence of an ozone converter. In Chapter 4, a model is presented that predicts ozone uptake by and byproduct emission from residual chemicals on surfaces. The effects of input parameters (residue surface concentration, ozone concentration, reactivity of the residue and the surface, near-surface airflow conditions, and byproduct yield) were explored. In Chapter 5, the reaction of ozone with permethrin, a residual insecticide used in aircraft cabins, to form phosgene is investigated. A derivatization technique was developed to detect phosgene at low levels, and chamber experiments were conducted with permethrin-coated cabin materials. It was determined that phosgene formation, if it occurs in the aircraft cabin, is not likely to exceed the relevant, health-based phosgene exposure guidelines.« less

Estimation of daily PM10 concentrations in Italy (2006-2012) using finely resolved satellite data, land use variables and meteorology.

PubMed

Stafoggia, Massimo; Schwartz, Joel; Badaloni, Chiara; Bellander, Tom; Alessandrini, Ester; Cattani, Giorgio; De' Donato, Francesca; Gaeta, Alessandra; Leone, Gianluca; Lyapustin, Alexei; Sorek-Hamer, Meytar; de Hoogh, Kees; Di, Qian; Forastiere, Francesco; Kloog, Itai

2017-02-01

Health effects of air pollution, especially particulate matter (PM), have been widely investigated. However, most of the studies rely on few monitors located in urban areas for short-term assessments, or land use/dispersion modelling for long-term evaluations, again mostly in cities. Recently, the availability of finely resolved satellite data provides an opportunity to estimate daily concentrations of air pollutants over wide spatio-temporal domains. Italy lacks a robust and validated high resolution spatio-temporally resolved model of particulate matter. The complex topography and the air mixture from both natural and anthropogenic sources are great challenges difficult to be addressed. We combined finely resolved data on Aerosol Optical Depth (AOD) from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, ground-level PM 10 measurements, land-use variables and meteorological parameters into a four-stage mixed model framework to derive estimates of daily PM 10 concentrations at 1-km2 grid over Italy, for the years 2006-2012. We checked performance of our models by applying 10-fold cross-validation (CV) for each year. Our models displayed good fitting, with mean CV-R2=0.65 and little bias (average slope of predicted VS observed PM 10 =0.99). Out-of-sample predictions were more accurate in Northern Italy (Po valley) and large conurbations (e.g. Rome), for background monitoring stations, and in the winter season. Resulting concentration maps showed highest average PM 10 levels in specific areas (Po river valley, main industrial and metropolitan areas) with decreasing trends over time. Our daily predictions of PM 10 concentrations across the whole Italy will allow, for the first time, estimation of long-term and short-term effects of air pollution nationwide, even in areas lacking monitoring data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of toxic metals concentration using artificial intelligence techniques

NASA Astrophysics Data System (ADS)

Gholami, R.; Kamkar-Rouhani, A.; Doulati Ardejani, F.; Maleki, Sh.

2011-12-01

Groundwater and soil pollution are noted to be the worst environmental problem related to the mining industry because of the pyrite oxidation, and hence acid mine drainage generation, release and transport of the toxic metals. The aim of this paper is to predict the concentration of Ni and Fe using a robust algorithm named support vector machine (SVM). Comparison of the obtained results of SVM with those of the back-propagation neural network (BPNN) indicates that the SVM can be regarded as a proper algorithm for the prediction of toxic metals concentration due to its relative high correlation coefficient and the associated running time. As a matter of fact, the SVM method has provided a better prediction of the toxic metals Fe and Ni and resulted the running time faster compared with that of the BPNN.

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

A meta-analysis and statistical modelling of nitrates in groundwater at the African scale

NASA Astrophysics Data System (ADS)

Ouedraogo, Issoufou; Vanclooster, Marnik

2016-06-01

Contamination of groundwater with nitrate poses a major health risk to millions of people around Africa. Assessing the space-time distribution of this contamination, as well as understanding the factors that explain this contamination, is important for managing sustainable drinking water at the regional scale. This study aims to assess the variables that contribute to nitrate pollution in groundwater at the African scale by statistical modelling. We compiled a literature database of nitrate concentration in groundwater (around 250 studies) and combined it with digital maps of physical attributes such as soil, geology, climate, hydrogeology, and anthropogenic data for statistical model development. The maximum, medium, and minimum observed nitrate concentrations were analysed. In total, 13 explanatory variables were screened to explain observed nitrate pollution in groundwater. For the mean nitrate concentration, four variables are retained in the statistical explanatory model: (1) depth to groundwater (shallow groundwater, typically < 50 m); (2) recharge rate; (3) aquifer type; and (4) population density. The first three variables represent intrinsic vulnerability of groundwater systems to pollution, while the latter variable is a proxy for anthropogenic pollution pressure. The model explains 65 % of the variation of mean nitrate contamination in groundwater at the African scale. Using the same proxy information, we could develop a statistical model for the maximum nitrate concentrations that explains 42 % of the nitrate variation. For the maximum concentrations, other environmental attributes such as soil type, slope, rainfall, climate class, and region type improve the prediction of maximum nitrate concentrations at the African scale. As to minimal nitrate concentrations, in the absence of normal distribution assumptions of the data set, we do not develop a statistical model for these data. The data-based statistical model presented here represents an important step towards developing tools that will allow us to accurately predict nitrate distribution at the African scale and thus may support groundwater monitoring and water management that aims to protect groundwater systems. Yet they should be further refined and validated when more detailed and harmonized data become available and/or combined with more conceptual descriptions of the fate of nutrients in the hydrosystem.

Analytical solutions to compartmental indoor air quality models with application to environmental tobacco smoke concentrations measured in a house.

PubMed

Ott, Wayne R; Klepeis, Neil E; Switzer, Paul

2003-08-01

This paper derives the analytical solutions to multi-compartment indoor air quality models for predicting indoor air pollutant concentrations in the home and evaluates the solutions using experimental measurements in the rooms of a single-story residence. The model uses Laplace transform methods to solve the mass balance equations for two interconnected compartments, obtaining analytical solutions that can be applied without a computer. Environmental tobacco smoke (ETS) sources such as the cigarette typically emit pollutants for relatively short times (7-11 min) and are represented mathematically by a "rectangular" source emission time function, or approximated by a short-duration source called an "impulse" time function. Other time-varying indoor sources also can be represented by Laplace transforms. The two-compartment model is more complicated than the single-compartment model and has more parameters, including the cigarette or combustion source emission rate as a function of time, room volumes, compartmental air change rates, and interzonal air flow factors expressed as dimensionless ratios. This paper provides analytical solutions for the impulse, step (Heaviside), and rectangular source emission time functions. It evaluates the indoor model in an unoccupied two-bedroom home using cigars and cigarettes as sources with continuous measurements of carbon monoxide (CO), respirable suspended particles (RSP), and particulate polycyclic aromatic hydrocarbons (PPAH). Fine particle mass concentrations (RSP or PM3.5) are measured using real-time monitors. In our experiments, simultaneous measurements of concentrations at three heights in a bedroom confirm an important assumption of the model-spatial uniformity of mixing. The parameter values of the two-compartment model were obtained using a "grid search" optimization method, and the predicted solutions agreed well with the measured concentration time series in the rooms of the home. The door and window positions in each room had considerable effect on the pollutant concentrations observed in the home. Because of the small volumes and low air change rates of most homes, indoor pollutant concentrations from smoking activity in a home can be very high and can persist at measurable levels indoors for many hours.

Numerical simulation of trace element transport on subsurface environment pollution in coal mine spoil.

PubMed

Qiang, Xue; Bing, Liang; Hui-yun, Wang; Lei, Liu

2006-01-01

An understanding of the dynamic behavior of trace elements leaching from coal mine spoil is important in predicting the groundwater quality. The relationship between trace element concentrations and leaching times, pH values of the media is studied. Column leaching tests conducted in the laboratory showed that there was a close correlation between pH value and trace element concentrations. The longer the leaching time, the higher the trace element concentrations. Different trace elements are differently affected by pH values of leaching media. A numerical model for water flow and trace element transport has been developed based on analyzing the characteristics of migration and transformation of trace elements leached from coal mine spoil. Solutions to the coupled model are accomplished by Eulerian-Lagrangian localized adjoint method. Numerical simulation shows that rainfall intensity determined maximum leaching depth. As rainfall intensity is 3.6ml/s, the outflow concentrations indicate a breakthrough of trace elements beyond the column base, with peak concentration at 90cm depth. And the subsurface pollution range has a trend of increase with time. The model simulations are compared to experimental results of trace element concentrations, with reasonable agreement between them. The analysis and modeling of trace elements suggested that the infiltration of rainwater through the mine spoil might lead to potential groundwater pollution. It provides theoretical evidence for quantitative assessment soil-water quality of trace element transport on environment pollution.

Spatio-temporal variational characteristics analysis of heavy metals pollution in water of the typical northern rivers, China

NASA Astrophysics Data System (ADS)

Lu, Hongwei; Yu, Sen

2018-04-01

The rapid urbanization and industrialization in developing countries have increased pollution by heavy metals, which is a concern for human health and the environment. In this study, according to the data obtained from the monitoring stations in the Songhua River basin, the multivariate statistical analysis methods are applied to the hydrological data of the Songhua River basin in order to examine the relation between human activities and the spatio-temporal change of heavy metals (Pb and Cu) in water. By comparing the concentrations at different flow periods, the minimum Pb concentrations are found to have occurred most frequently in low flow periods while the maximum values mostly appeared in average flow periods. Moreover, the minimum Cu concentration in the water frequently occurred in high flow periods. The results show there are low Pb and Cu concentrations in upstream and downstream sections and high concentrations in mid-stream sections, and high concentrations are most frequently measured in the sections of Ashihe' downstream and estuary. Moreover, we have predicted the future (during 2018-2025) trend of the change for the heavy metals pollution in the rivers. The results demonstrated intense human activities are the most important factor causing jump features of typical heavy metal pollution in the different periods for different sections of this study area. The research would provide decision-making and planning for the Songhua River basin during the period of China's 13th Five-Year Plan.

Final report: the use of LIDAR to characterize aircraft initial plume characteristics

DOT National Transportation Integrated Search

2004-02-28

Aircraft emissions are a growing concern for the FAA, airports, and the community. U.S. : and international air quality models were previously unable to accurately predict initial : plume dispersion and the resulting pollutant concentrations because ...

Updating Sea Spray Aerosol Emissions in the Community Multiscale Air Quality Model

NASA Astrophysics Data System (ADS)

Gantt, B.; Bash, J. O.; Kelly, J.

2014-12-01

Sea spray aerosols (SSA) impact the particle mass concentration and gas-particle partitioning in coastal environments, with implications for human and ecosystem health. In this study, the Community Multiscale Air Quality (CMAQ) model is updated to enhance fine mode SSA emissions, include sea surface temperature (SST) dependency, and revise surf zone emissions. Based on evaluation with several regional and national observational datasets in the continental U.S., the updated emissions generally improve surface concentrations predictions of primary aerosols composed of sea-salt and secondary aerosols affected by sea-salt chemistry in coastal and near-coastal sites. Specifically, the updated emissions lead to better predictions of the magnitude and coastal-to-inland gradient of sodium, chloride, and nitrate concentrations at Bay Regional Atmospheric Chemistry Experiment (BRACE) sites near Tampa, FL. Including SST-dependency to the SSA emission parameterization leads to increased sodium concentrations in the southeast U.S. and decreased concentrations along the Pacific coast and northeastern U.S., bringing predictions into closer agreement with observations at most Interagency Monitoring of Protected Visual Environments (IMPROVE) and Chemical Speciation Network (CSN) sites. Model comparison with California Research at the Nexus of Air Quality and Climate Change (CalNex) observations will also be discussed, with particular focus on the South Coast Air Basin where clean marine air mixes with anthropogenic pollution in a complex environment. These SSA emission updates enable more realistic simulation of chemical processes in coastal environments, both in clean marine air masses and mixtures of clean marine and polluted conditions.

Potential risks to human respiratory health from "acid fog": evidence from experimental studies of volunteers.

PubMed

Hackney, J D; Linn, W S; Avol, E L

1985-11-01

Observations of high acidity (pH as low as 1.7) in fogwater collected in polluted areas have provoked concern for public health. Effects of exposure to acidic pollutants have not been studied under foggy conditions; thus there is no directly relevant information from which to estimate the health risk. Indirectly relevant information is available from numerous studies of volunteers exposed to "acid fog precursors" under controlled conditions at less than 100% relative humidity. The effect of fog in modifying responses to inhaled acidic pollutants is difficult to predict: depending on circumstances, fog droplets might either increase or decrease the effective dose of pollutants to the lower respiratory tract. Fog inhalation per se may have unfavorable effects in some individuals. Sulfur dioxide is known to exacerbate airway constriction in exercising asthmatics, at exposure concentrations attainable in ambient air. Nitrogen dioxide has shown little untoward respiratory effect at ambient concentrations in most studies, although it has been suggested to increase bronchial reactivity. Sulfuric acid aerosol has shown no clear effects at concentrations within the ambient range. At somewhat higher levels, increased bronchial reactivity and change in mucociliary clearance have been suggested. Almost no information is available concerning nitric acid.

Potential risks to human respiratory health from "acid fog": evidence from experimental studies of volunteers.

PubMed Central

Hackney, J D; Linn, W S; Avol, E L

1985-01-01

Observations of high acidity (pH as low as 1.7) in fogwater collected in polluted areas have provoked concern for public health. Effects of exposure to acidic pollutants have not been studied under foggy conditions; thus there is no directly relevant information from which to estimate the health risk. Indirectly relevant information is available from numerous studies of volunteers exposed to "acid fog precursors" under controlled conditions at less than 100% relative humidity. The effect of fog in modifying responses to inhaled acidic pollutants is difficult to predict: depending on circumstances, fog droplets might either increase or decrease the effective dose of pollutants to the lower respiratory tract. Fog inhalation per se may have unfavorable effects in some individuals. Sulfur dioxide is known to exacerbate airway constriction in exercising asthmatics, at exposure concentrations attainable in ambient air. Nitrogen dioxide has shown little untoward respiratory effect at ambient concentrations in most studies, although it has been suggested to increase bronchial reactivity. Sulfuric acid aerosol has shown no clear effects at concentrations within the ambient range. At somewhat higher levels, increased bronchial reactivity and change in mucociliary clearance have been suggested. Almost no information is available concerning nitric acid. PMID:3000761

Degradation of organic pollutants in/on snow and ice by singlet molecular oxygen (¹O₂*) and an organic triplet excited state.

PubMed

Bower, Jonathan P; Anastasio, Cort

2014-04-01

Singlet molecular oxygen (¹O₂*) can be a significant sink for a variety of electron-rich pollutants in surface waters and atmospheric drops. We recently found that ¹O₂* concentrations are enhanced by up to a factor of 10(4) on illuminated ice compared to in the equivalent liquid solution, suggesting that ¹O₂* could be an important oxidant for pollutants in snow. To examine this, here we study the degradation of three model organic pollutants: furfuryl alcohol (to represent furans), tryptophan (for aromatic amino acids), and bisphenol A (for phenols). Each compound was studied in illuminated aqueous solution and ice containing Rose Bengal (RB, a sensitizer for ¹O₂*) and sodium chloride (to adjust the concentration of total solutes). The RB-mediated loss of each organic compound is enhanced on illuminated ice compared to in solution, by factors of 6400 for furfuryl alcohol, 8300 for tryptophan, and 50 for bisphenol A for ice containing 0.065 mM total solutes. Rates of loss of furfuryl alcohol and tryptophan decrease at a higher total solute concentration, in qualitative agreement with predictions from freezing-point depression. In contrast, the loss of bisphenol A on ice is independent of total solute concentration. Relative to liquid tests, the enhanced loss of tryptophan on ice during control experiments made with deoxygenated solutions and solutions in D₂O show that the triplet excited state of Rose Bengal may also contribute to loss of pollutants on ice.

A changing climate: impacts on human exposures to O3 using ...

EPA Pesticide Factsheets

Predicting the impacts of changing climate on human exposure to air pollution requires future scenarios that account for changes in ambient pollutant concentrations, population sizes and distributions, and housing stocks. An integrated methodology to model changes in human exposures due to these impacts was developed by linking climate, air quality, land-use, and human exposure models. This methodology was then applied to characterize changes in predicted human exposures to O3 under multiple future scenarios. Regional climate projections for the U.S. were developed by downscaling global circulation model (GCM) scenarios for three of the Intergovernmental Panel on Climate Change’s (IPCC’s) Representative Concentration Pathways (RCPs) using the Weather Research and Forecasting (WRF) model. The regional climate results were in turn used to generate air quality (concentration) projections using the Community Multiscale Air Quality (CMAQ) model. For each of the climate change scenarios, future U.S. census-tract level population distributions from the Integrated Climate and Land Use Scenarios (ICLUS) model for four future scenarios based on the IPCC’s Special Report on Emissions Scenarios (SRES) storylines were used. These climate, air quality, and population projections were used as inputs to EPA’s Air Pollutants Exposure (APEX) model for 12 U.S. cities. Probability density functions show changes in the population distribution of 8 h maximum daily O3 exposur

N-fixation in legumes--An assessment of the potential threat posed by ozone pollution.

PubMed

Hewitt, D K L; Mills, G; Hayes, F; Norris, D; Coyle, M; Wilkinson, S; Davies, W

2016-01-01

The growth, development and functioning of legumes are often significantly affected by exposure to tropospheric ozone (O3) pollution. However, surprisingly little is known about how leguminous Nitrogen (N) fixation responds to ozone, with a scarcity of studies addressing this question in detail. In the last decade, ozone impacts on N-fixation in soybean, cowpea, mung bean, peanut and clover have been shown for concentrations which are now commonly recorded in ambient air or are likely to occur in the near future. We provide a synthesis of the existing literature addressing this issue, and also explore the effects that may occur on an agroecosystem scale by predicting reductions in Trifolium (clovers) root nodule biomass in United Kingdom (UK) pasture based on ozone concentration data for a "high" (2006) and "average" ozone year (2008). Median 8% and 5% reductions in clover root nodule biomass in pasture across the UK were predicted for 2006 and 2008 respectively. Seasonal exposure to elevated ozone, or short-term acute concentrations >100 ppb, are sufficient to reduce N-fixation and/or impact nodulation, in a range of globally-important legumes. However, an increasing global burden of CO2, the use of artificial fertiliser, and reactive N-pollution may partially mitigate impacts of ozone on N-fixation. Copyright © 2015 Elsevier Ltd. All rights reserved.

The influence of vegetation on the horizontal and vertical distribution of pollutants in a street canyon.

PubMed

Salmond, J A; Williams, D E; Laing, G; Kingham, S; Dirks, K; Longley, I; Henshaw, G S

2013-01-15

Space constraints in cities mean that there are only limited opportunities for increasing tree density within existing urban fabric and it is unclear whether the net effect of increased vegetation in street canyons is beneficial or detrimental to urban air quality at local scales. This paper presents data from a field study undertaken in Auckland, New Zealand designed to determine the local impact of a deciduous tree canopy on the distribution of the oxides of nitrogen within a street canyon. The results showed that the presence of leaves on the trees had a marked impact on the transport of pollutants and led to a net accumulation of pollutants in the canyon below the tree tops. The incidence and magnitude of temporally localised spikes in pollutant concentration were reduced within the tree canopy itself. A significant difference in pollutant concentrations with height was not observed when leaves were absent. Analysis of the trends in concentration associated with different wind directions showed a smaller difference between windward and leeward sides when leaves were on the trees. A small relative increase in concentrations on the leeward side was observed during leaf-on relative to leaf-off conditions as predicted by previous modelling studies. However the expected reduction in concentrations on the windward side was not observed. The results suggest that the presence of leaves on the trees reduces the upwards transport of fresh vehicle emissions, increases the storage of pollutants within the canopy space and reduces the penetration of clean air downwards from aloft. Differences observed between NO and NO(2) concentrations could not be accounted for by dispersion processes alone, suggesting that there may also be some changes in the chemistry of the atmosphere associated with the presence of leaves on the trees. Copyright © 2012 Elsevier B.V. All rights reserved.

[Uncertainty analysis of ecological risk assessment caused by heavy-metals deposition from MSWI emission].

PubMed

Liao, Zhi-Heng; Sun, Jia-Ren; Wu, Dui; Fan, Shao-Jia; Ren, Ming-Zhong; Lü, Jia-Yang

2014-06-01

The CALPUFF model was applied to simulate the ground-level atmospheric concentrations of Pb and Cd from municipal solid waste incineration (MSWI) plants, and the soil concentration model was used to estimate soil concentration increments after atmospheric deposition based on Monte Carlo simulation, then ecological risk assessment was conducted by the potential ecological risk index method. The results showed that the largest atmospheric concentrations of Pb and Cd were 5.59 x 109-3) microg x m(-3) and 5.57 x 10(-4) microg x m(-3), respectively, while the maxima of soil concentration incremental medium of Pb and Cd were 2.26 mg x kg(-1) and 0.21 mg x kg(-1), respectively; High risk areas were located next to the incinerators, Cd contributed the most to the ecological risk, and Pb was basically free of pollution risk; Higher ecological hazard level was predicted at the most polluted point in urban areas with a 55.30% probability, while in rural areas, the most polluted point was assessed to moderate ecological hazard level with a 72.92% probability. In addition, sensitivity analysis of calculation parameters in the soil concentration model was conducted, which showed the simulated results of urban and rural area were most sensitive to soil mix depth and dry deposition rate, respectively.

TESTS OF INDOOR AIR QUALITY SINKS

EPA Science Inventory

Experiments were conducted in a room-size test chamber to determine the sink effects of selected materials on indoor air concentrations of p-dichlorobenzene (PDCB). hese effects might alter pollutant behavior from that predicted using similar indoor air quality models, by reducin...

Combined effects of pharmaceuticals, personal care products, biocides and organic contaminants on the growth of Skeletonema pseudocostatum.

PubMed

Petersen, Karina; Heiaas, Harald Hasle; Tollefsen, Knut Erik

2014-05-01

Organisms in the environment are exposed to a number of pollutants from different compound groups. In addition to the classic pollutants like the polychlorinated biphenyls, polyaromatic hydrocarbons (PAHs), alkylphenols, biocides, etc. other compound groups of concern are constantly emerging. Pharmaceuticals and personal care products (PPCPs) can be expected to co-occur with other organic contaminants like biocides, PAHs and alkylphenols in areas affected by wastewater, industrial effluents and intensive recreational activity. In this study, representatives from these four different compound groups were tested individually and in mixtures in a growth inhibition assay with the marine algae Skeletonema pseudocostatum (formerly Skeletonema costatum) to determine whether the combined effects could be predicted by models for additive effects; the concentration addition (CA) and independent action (IA) prediction model. The eleven tested compounds reduced the growth of S. pseudocostatum in the microplate test in a concentration-dependent manner. The order of toxicity of these chemicals were irgarol>fluoxetine>diuron>benzo(a)pyrene>thioguanine>triclosan>propranolol>benzophenone 3>cetrimonium bromide>4-tert-octylphenol>endosulfan. Several binary mixtures and a mixture of eight compounds from the four different compound groups were tested. All tested mixtures were additive as model deviation ratios, the deviation between experimental and predicted effect concentrations, were within a factor of 2 from one or both prediction models (e.g. CA and IA). Interestingly, a concentration dependent shift from IA to CA, potentially due to activation of similar toxicity pathways at higher concentrations, was observed for the mixture of eight compounds. The combined effects of the multi-compound mixture were clearly additive and it should therefore be expected that PPCPs, biocides, PAHs and alkylphenols will collectively contribute to the risk in areas contaminated by such complex mixtures. Copyright © 2014 Elsevier B.V. All rights reserved.

Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses.

PubMed

Leng, Xiang'zi; Wang, Jinhua; Ji, Haibo; Wang, Qin'geng; Li, Huiming; Qian, Xin; Li, Fengying; Yang, Meng

2017-08-01

Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters. About 38% and 77% of PM 2.5 concentrations in Xianlin and Pukou, respectively, were beyond the Chinese National Ambient Air Quality Standard limit of 75 μg/m 3 . Nearly all elements had higher concentrations in industrial areas, and in winter among the four seasons. Anthropogenic elements such as Pb, Zn, Cd and Cu showed larger percentages in the fine fraction (ø≤2.5 μm), whereas the crustal elements including Al, Ba, Fe, Ni, Sr and Ti showed larger percentages in the coarse fraction (ø > 2.5 μm). SVM showed a higher training correlation coefficient (R), and lower mean absolute error (MAE) as well as lower root mean square error (RMSE), than MLR and BP-ANN for most metals. All the three methods showed better prediction results for Ni, Al, V, Cd and As, whereas relatively poor for Cr and Fe. The daily airborne metal concentrations in 2015 were then predicted by the fully trained SVM models and the results showed the heaviest pollution of airborne heavy metals occurred in December and January, whereas the lightest pollution occurred in June and July. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data

NASA Astrophysics Data System (ADS)

Baxter, Lisa K.; Clougherty, Jane E.; Paciorek, Christopher J.; Wright, Rosalind J.; Levy, Jonathan I.

Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system, and property assessment data) and questionnaire responses to predict residential indoor concentrations of traffic-related air pollutants for lower socioeconomic status (SES) urban households. As part of a prospective birth cohort study in urban Boston, we collected indoor and outdoor 3-4 day samples of nitrogen dioxide (NO 2) and fine particulate matter (PM 2.5) in 43 low SES residences across multiple seasons from 2003 to 2005. Elemental carbon (EC) concentrations were determined via reflectance analysis. Multiple traffic indicators were derived using Massachusetts Highway Department data and traffic counts collected outside sampling homes. Home characteristics and occupant behaviors were collected via a standardized questionnaire. Additional housing information was collected through property tax records, and ambient concentrations were collected from a centrally located ambient monitor. The contributions of ambient concentrations, local traffic and indoor sources to indoor concentrations were quantified with regression analyses. PM 2.5 was influenced less by local traffic but had significant indoor sources, while EC was associated with traffic and NO 2 with both traffic and indoor sources. Comparing models based on covariate selection using p-values or a Bayesian approach yielded similar results, with traffic density within a 50 m buffer of a home and distance from a truck route as important contributors to indoor levels of NO 2 and EC, respectively. The Bayesian approach also highlighted the uncertanity in the models. We conclude that by utilizing public databases and focused questionnaire data we can identify important predictors of indoor concentrations for multiple air pollutants in a high-risk population.

Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian; Wang, Xie-Kang; Yan, Sui-Hang; Lam, Joseph C

2004-09-01

The forecasting of air pollutant trends has received much attention in recent years. It is an important and popular topic in environmental science, as concerns have been raised about the health impacts caused by unacceptable ambient air pollutant levels. Of greatest concern are metropolitan cities like Hong Kong. In Hong Kong, respirable suspended particulates (RSP), nitrogen oxides (NOx), and nitrogen dioxide (NO2) are major air pollutants due to the dominant usage of diesel fuel by commercial vehicles and buses. Hence, the study of the influence and the trends relating to these pollutants is extremely significant to the public health and the image of the city. The use of neural network techniques to predict trends relating to air pollutants is regarded as a reliable and cost-effective method for the task of prediction. The works reported here involve developing an improved neural network model that combines both the principal component analysis technique and the radial basis function network and forecasts pollutant tendencies based on a recorded database. Compared with general neural network models, the proposed model features a more simple network architecture, a faster training speed, and a more satisfactory prediction performance. The improved model was evaluated with hourly time series of RSP, NOx and NO2 concentrations monitored at the Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000 and proved to be effective. The model developed is a potential tool for forecasting air quality parameters and is superior to traditional neural network methods.

Modeling the effect of succimer (DMSA; dimercaptosuccinic acid) chelation therapy in patients poisoned by lead.

PubMed

van Eijkeren, Jan C H; Olie, J Daniël N; Bradberry, Sally M; Vale, J Allister; de Vries, Irma; Clewell, Harvey J; Meulenbelt, Jan; Hunault, Claudine C

2017-02-01

Kinetic models could assist clinicians potentially in managing cases of lead poisoning. Several models exist that can simulate lead kinetics but none of them can predict the effect of chelation in lead poisoning. Our aim was to devise a model to predict the effect of succimer (dimercaptosuccinic acid; DMSA) chelation therapy on blood lead concentrations. We integrated a two-compartment kinetic succimer model into an existing PBPK lead model and produced a Chelation Lead Therapy (CLT) model. The accuracy of the model's predictions was assessed by simulating clinical observations in patients poisoned by lead and treated with succimer. The CLT model calculates blood lead concentrations as the sum of the background exposure and the acute or chronic lead poisoning. The latter was due either to ingestion of traditional remedies or occupational exposure to lead-polluted ambient air. The exposure duration was known. The blood lead concentrations predicted by the CLT model were compared to the measured blood lead concentrations. Pre-chelation blood lead concentrations ranged between 99 and 150 μg/dL. The model was able to simulate accurately the blood lead concentrations during and after succimer treatment. The pattern of urine lead excretion was successfully predicted in some patients, while poorly predicted in others. Our model is able to predict blood lead concentrations after succimer therapy, at least, in situations where the duration of lead exposure is known.

Short-term effects of air pollution, markers of endothelial activation, and coagulation to predict major adverse cardiovascular events in patients with acute coronary syndrome: insights from AIRACOS study.

PubMed

Dominguez-Rodriguez, Alberto; Abreu-Gonzalez, Pedro; Rodríguez, Sergio; Avanzas, Pablo; Juarez-Prera, Ruben A

2017-07-01

The aim of this study was to determine whether markers of inflammation and coagulation are associated with short-term particulate matter exposure and predict major adverse cardiovascular events at 360 d in patients with acute coronary syndrome (ACS). We included 307 consecutive patients, and assessed the average concentrations of data on atmospheric pollution in ambient air and meteorological variables from 1 d up to 7 d prior to admission. In patients with ACS, the markers of endothelial activation and coagulation, but not black carbon exposure, are associated with major adverse cardiovascular events at one-year follow-up.

Particulate matter pollution in the coal-producing regions of the Appalachian Mountains: Integrated ground-based measurements and satellite analysis.

PubMed

Aneja, Viney P; Pillai, Priya R; Isherwood, Aaron; Morgan, Peter; Aneja, Saurabh P

2017-04-01

This study integrates the relationship between measured surface concentrations of particulate matter 10 μm or less in diameter (PM 10 ), satellite-derived aerosol optical depth (AOD), and meteorology in Roda, Virginia, during 2008. A multiple regression model was developed to predict the concentrations of particles 2.5 μm or less in diameter (PM 2.5 ) at an additional location in the Appalachia region, Bristol, TN. The model was developed by combining AOD retrievals from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor on board the EOS Terra and Aqua Satellites with the surface meteorological observations. The multiple regression model predicted PM 2.5 (r 2 = 0.62), and the two-variable (AOD-PM 2.5 ) model predicted PM 2.5 (r 2 = 0.4). The developed model was validated using particulate matter recordings and meteorology observations from another location in the Appalachia region, Hazard, Kentucky. The model was extrapolated to the Roda, VA, sampling site to predict PM 2.5 mass concentrations. We used 10 km x 10 km resolution MODIS 550 nm AOD to predict ground level PM 2.5 . For the relevant period in 2008, in Roda, VA, the predicted PM 2.5 mass concentration is 9.11 ± 5.16 μg m -3 (mean ± 1SD). This is the first study that couples ground-based Particulate Matter measurements with satellite retrievals to predict surface air pollution at Roda, Virginia. Roda is representative of the Appalachian communities that are commonly located in narrow valleys, or "hollows," where homes are placed directly along the roads in a region of active mountaintop mining operations. Our study suggests that proximity to heavy coal truck traffic subjects these communities to chronic exposure to coal dust and leads us to conclude that there is an urgent need for new regulations to address the primary sources of this particulate matter.

Meteorological influence on predicting surface SO2 concentration from satellite remote sensing in Shanghai, China.

PubMed

Xue, Dan; Yin, Jingyuan

2014-05-01

In this study, we explored the potential applications of the Ozone Monitoring Instrument (OMI) satellite sensor in air pollution research. The OMI planetary boundary layer sulfur dioxide (SO2_PBL) column density and daily average surface SO2 concentration of Shanghai from 2004 to 2012 were analyzed. After several consecutive years of increase, the surface SO2 concentration finally declined in 2007. It was higher in winter than in other seasons. The coefficient between daily average surface SO2 concentration and SO2_PBL was only 0.316. But SO2_PBL was found to be a highly significant predictor of the surface SO2 concentration using the simple regression model. Five meteorological factors were considered in this study, among them, temperature, dew point, relative humidity, and wind speed were negatively correlated with surface SO2 concentration, while pressure was positively correlated. Furthermore, it was found that dew point was a more effective predictor than temperature. When these meteorological factors were used in multiple regression, the determination coefficient reached 0.379. The relationship of the surface SO2 concentration and meteorological factors was seasonally dependent. In summer and autumn, the regression model performed better than in spring and winter. The surface SO2 concentration predicting method proposed in this study can be easily adapted for other regions, especially most useful for those having no operational air pollution forecasting services or having sparse ground monitoring networks.

Forecasting ozone concentrations in the east of Croatia using nonparametric Neural Network Models

NASA Astrophysics Data System (ADS)

Kovač-Andrić, Elvira; Sheta, Alaa; Faris, Hossam; Gajdošik, Martina Šrajer

2016-07-01

Ozone is one of the most significant secondary pollutants with numerous negative effects on human health and environment including plants and vegetation. Therefore, more effort is made recently by governments and associations to predict ozone concentrations which could help in establishing better plans and regulation for environment protection. In this study, we use two Artificial Neural Network based approaches (MPL and RBF) to develop, for the first time, accurate ozone prediction models, one for urban and another one for rural area in the eastern part of Croatia. The evaluation of actual against the predicted ozone concentrations revealed that MLP and RBF models are very competitive for the training and testing data in the case of Kopački Rit area whereas in the case of Osijek city, MLP shows better evaluation results with 9% improvement in the correlation coefficient. Furthermore, subsequent feature selection process has improved the prediction power of RBF network.

Acid–base chemical reaction model for nucleation rates in the polluted atmospheric boundary layer

PubMed Central

Chen, Modi; Titcombe, Mari; Jiang, Jingkun; Jen, Coty; Kuang, Chongai; Fischer, Marc L.; Eisele, Fred L.; Siepmann, J. Ilja; Hanson, David R.; Zhao, Jun; McMurry, Peter H.

2012-01-01

Climate models show that particles formed by nucleation can affect cloud cover and, therefore, the earth's radiation budget. Measurements worldwide show that nucleation rates in the atmospheric boundary layer are positively correlated with concentrations of sulfuric acid vapor. However, current nucleation theories do not correctly predict either the observed nucleation rates or their functional dependence on sulfuric acid concentrations. This paper develops an alternative approach for modeling nucleation rates, based on a sequence of acid–base reactions. The model uses empirical estimates of sulfuric acid evaporation rates obtained from new measurements of neutral molecular clusters. The model predicts that nucleation rates equal the sulfuric acid vapor collision rate times a prefactor that is less than unity and that depends on the concentrations of basic gaseous compounds and preexisting particles. Predicted nucleation rates and their dependence on sulfuric acid vapor concentrations are in reasonable agreement with measurements from Mexico City and Atlanta. PMID:23091030

Acid-base chemical reaction model for nucleation rates in the polluted atmospheric boundary layer.

PubMed

Chen, Modi; Titcombe, Mari; Jiang, Jingkun; Jen, Coty; Kuang, Chongai; Fischer, Marc L; Eisele, Fred L; Siepmann, J Ilja; Hanson, David R; Zhao, Jun; McMurry, Peter H

2012-11-13

Climate models show that particles formed by nucleation can affect cloud cover and, therefore, the earth's radiation budget. Measurements worldwide show that nucleation rates in the atmospheric boundary layer are positively correlated with concentrations of sulfuric acid vapor. However, current nucleation theories do not correctly predict either the observed nucleation rates or their functional dependence on sulfuric acid concentrations. This paper develops an alternative approach for modeling nucleation rates, based on a sequence of acid-base reactions. The model uses empirical estimates of sulfuric acid evaporation rates obtained from new measurements of neutral molecular clusters. The model predicts that nucleation rates equal the sulfuric acid vapor collision rate times a prefactor that is less than unity and that depends on the concentrations of basic gaseous compounds and preexisting particles. Predicted nucleation rates and their dependence on sulfuric acid vapor concentrations are in reasonable agreement with measurements from Mexico City and Atlanta.

Impact of Diurnal Variations of Precursors on the Prediction of Ozone

NASA Astrophysics Data System (ADS)

Hamer, P. D.; Bowman, K. W.; Henze, D. K.; Singh, K.

2009-12-01

Using a photochemical box model and its adjoint, constructed using the Kinetic Pre-Processor, we investigate the impacts of changing observational capacity, observation frequency and quality upon the ability to both understand and predict the nature of peak ozone events within a variety of polluted environments. The model consists of a chemical mechanism based on the Master Chemical Mechanism utilising 171 chemical species and 524 chemical reactions interacting with emissions, dry deposition and mixing schemes. The model was run under a variety of conditions designed to simulate a range of summertime polluted environments spanning a range of NOx and volatile organic compound regimes (VOCs). Using the forward model we were able to generate simulated atmospheric conditions representative of a particular polluted environment, which could in turn be used to generate a set of pseudo observations of key photochemical constituents. The model was then run under somewhat less polluted conditions to generate a background and then perturbed back towards the polluted trajectory using sequential data assimilation and the pseudo observations. Using a combination of the adjoint sensitivity analysis and the sequential data assimilation described here we assess the optimal time of observation and the diversity of observed chemical species required to provide acceptable forecast estimates of ozone concentrations. As the photochemical regime changes depending on NOx and VOC concentrations different observing strategies become favourable. The impact of using remote sensing based observations of the free tropospheric photochemical state are investigated to demonstrate the advantage of gaining knowledge of atmospheric trace gases away from the immediate photochemical environment.

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.

Pollution distribution of heavy metals in surface soil at an informal electronic-waste recycling site.

PubMed

Fujimori, Takashi; Takigami, Hidetaka

2014-02-01

We studied distribution of heavy metals [lead (Pb), copper (Cu) and zinc (Zn)] in surface soil at an electronic-waste (e-waste) recycling workshop near Metro Manila in the Philippines to evaluate the pollution size (spot size, small area or the entire workshop), as well as to assess heavy metal transport into the surrounding soil environment. On-site length-of-stride-scale (~70 cm) measurements were performed at each surface soil point using field-portable X-ray fluorescence (FP-XRF). The surface soil at the e-waste recycling workshop was polluted with Cu, Zn and Pb, which were distributed discretely in surface soil. The site was divided into five areas based on the distance from an entrance gate (y-axis) of the e-waste recycling workshop. The three heavy metals showed similar concentration gradients in the y-axis direction. Zn, Pb and Cu concentrations were estimated to decrease to half of their maximum concentrations at ~3, 7 and 7 m from the pollution spot, respectively, inside the informal e-waste recycling workshop. Distance from an entrance may play an important role in heavy metal transport at the soil surface. Using on-site FP-XRF, we evaluated the metal ratio to characterise pollution features of the solid surface. Variability analysis of heavy metals revealed vanishing surficial autocorrelation over metre ranges. Also, the possibility of concentration prediction at unmeasured points using geostatistical kriging was evaluated, and heavy metals had a relative "small" pollution scales and remained inside the original workshop compared with toxic organohalogen compounds. Thus, exposure to heavy metals may directly influence the health of e-waste workers at the original site rather than the surrounding habitat and environmental media.

Vegetation fires and air pollution in Vietnam.

PubMed

Le, Thanh Ha; Thanh Nguyen, Thi Nhat; Lasko, Kristofer; Ilavajhala, Shriram; Vadrevu, Krishna Prasad; Justice, Chris

2014-12-01

Forest fires are a significant source of air pollution in Asia. In this study, we integrate satellite remote sensing data and ground-based measurements to infer fire-air pollution relationships in selected regions of Vietnam. We first characterized the active fires and burnt areas at a regional scale from MODIS satellite data. We then used satellite-derived active fire data to correlate the resulting atmospheric pollution. Further, we analyzed the relationship between satellite atmospheric variables and ground-based air pollutant parameters. Our results show peak fire activity during March in Vietnam, with hotspots in the Northwest and Central Highlands. Active fires were significantly correlated with UV Aerosol Index (UVAI), aerosol extinction absorption optical depth (AAOD), and Carbon Monoxide. The use of satellite aerosol optical thickness improved the prediction of Particulate Matter (PM) concentration significantly. Copyright © 2014 Elsevier Ltd. All rights reserved.

Assessment and prediction of urban air pollution caused by motor transport exhaust gases using computer simulation methods

NASA Astrophysics Data System (ADS)

Boyarshinov, Michael G.; Vaismana, Yakov I.

2016-10-01

The following methods were used in order to identify the pollution fields of urban air caused by the motor transport exhaust gases: the mathematical model, which enables to consider the influence of the main factors that determine pollution fields formation in the complex spatial domain; the authoring software designed for computational modeling of the gas flow, generated by numerous mobile point sources; the results of computing experiments on pollutant spread analysis and evolution of their concentration fields. The computational model of exhaust gas distribution and dispersion in a spatial domain, which includes urban buildings, structures and main traffic arteries, takes into account a stochastic character of cars apparition on the borders of the examined territory and uses a Poisson process. The model also considers the traffic lights switching and permits to define the fields of velocity, pressure and temperature of the discharge gases in urban air. The verification of mathematical model and software used confirmed their satisfactory fit to the in-situ measurements data and the possibility to use the obtained computing results for assessment and prediction of urban air pollution caused by motor transport exhaust gases.

Source tagging modeling study of heavy haze episodes under complex regional transport processes over Wuhan megacity, Central China.

PubMed

Lu, Miaomiao; Tang, Xiao; Wang, Zifa; Gbaguidi, Alex; Liang, Shengwen; Hu, Ke; Wu, Lin; Wu, Huangjian; Huang, Zhen; Shen, Longjiao

2017-12-01

Wuhan as a megacity of Central China was suffering from severe particulate matter pollution according to previous observation studies, however, the mechanism behind the pollution formation especially the impact of regional chemical transport is still unclear. This study, carried out on the Nested Air Quality Prediction Modeling System (NAQPMS) coupled with an on-line source-tagging module, explores different roles regional transport had in two strong haze episodes over Wuhan in October 2014 and quantitatively assesses the contributions from local and regional sources to PM 2.5 concentration. Validation of predictions based on observations shows modeling system good skills in reproducing key meteorological and chemical features. The first short-time haze episode occurred on 12 October under strong northerly winds, with a hourly PM 2.5 peak of 180 μg m -3 , and was found to be caused primarily by the long-range transport from the northern regions, which contributed 60.6% of the episode's PM 2.5 concentration (versus a total of 32.7% from sources in and near Wuhan). The second episode lasted from the 15-20 October under stable regional large-scale synoptic conditions and weak winds, and had an hourly PM 2.5 peak of 231.0 μg m -3 . In this episode, both the long-distance transport from far regions and short-range transport from the Wuhan-cluster were the primary causes of the haze episode and account for 24.8% and 29.2% of the PM 2.5 concentration respectively. Therefore, regional transport acts as a crucial driver of haze pollution over Wuhan through not only long-range transfer of pollutants, but also short-range aerosol movement under specific meteorological conditions. The present findings highlight the important role of regional transport in urban haze formation and indicate that the joint control of multi city-clusters are needed to reduce the particulate pollution level in Wuhan. Copyright © 2017 Elsevier Ltd. All rights reserved.

Simulations of Sulfate-Nitrate-Ammonium (SNA) aerosols during the extreme haze events over Northern China in October 2014

NASA Astrophysics Data System (ADS)

Chen, D.; Liu, Z.; Fast, J. D.; Ban, J.

2017-12-01

Extreme haze events have occurred frequently over China in recent years. Although many studies have investigated the formation mechanisms associated with PM2.5 for heavily polluted regions in China based on observational data, adequately predicting peak PM2.5 concentrations is still challenging for regional air quality models. In this study, we evaluate the performance of one configuration of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) and use the model to investigate the sensitivity of heterogeneous reactions on simulated peak sulfate, nitrate, and ammonium concentrations in the vicinity of Beijing during four extreme haze episodes in October 2014 over the North China Plain. The highest observed PM2.5 concentration of 469 μg m-3 occurred in Beijing. Comparisons with observations show that the model reproduced the temporal variability in PM2.5 with the highest PM2.5 values on polluted days (defined as days in which observed PM2.5 is greater than 75 μg m-3), but predictions of sulfate, nitrate, and ammonium were too low on days with the highest observed concentrations. Observational data indicate that the sulfur/nitric oxidation rates are strongly correlated with relative humidity during periods of peak PM2.5; however, the model failed to reproduce the highest PM2.5 concentrations due to missing heterogeneous/aqueous reactions. As the parameterizations of those heterogeneous reactions are not well established yet, estimates of SO2-to-H2SO4 and NO2/NO3-to-HNO3 reaction rates that depend on relative humidity were applied which improved the simulation of sulfate, nitrate, and ammonium enhancement on polluted days in terms of both concentrations and partitioning among those species. Sensitivity simulations showed that the extremely high heterogeneous reaction rates and also higher emission rates than those reported in the emission inventory were likely important factors contributing to those peak PM2.5 concentrations.

Application of data mining to the analysis of meteorological data for air quality prediction: A case study in Shenyang

NASA Astrophysics Data System (ADS)

Zhao, Chang; Song, Guojun

2017-08-01

Air pollution is one of the important reasons for restricting the current economic development. PM2.5 which is a vital factor in the measurement of air pollution is defined as a kind of suspended particulate matter with its equivalent diameter less than 25μm, which may enter the alveoli and therefore make a great impact on the human body. Meteorological factors are also one of the main factors affecting the production of PM2.5, therefore, it is essential to establish the model between meteorological factors and PM2.5 for the prediction. Data mining is a promising approach to model PM2.5 change, Shenyang which is one of the most important industrial city in Northeast China with severe air pollutions is set as the case city. Meteorological data (wind direction, wind speed, temperature, humidity, rainfall, etc.) from 2013 to 2015 and PM2.5 concentration data are used for this prediction. As to the requirements of the World Health Organization (WHO), three data mining models, whereby the predictions of PM2.5 are directly generated by the meteorological data. After assessment, the random forest model is appeared to offer better prediction performance than the other two. At last, the accuracy of the generated models are analysed.

Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data

NASA Astrophysics Data System (ADS)

Ni, X. Y.; Huang, H.; Du, W. P.

2017-02-01

The PM2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM2.5. In this paper, we attempted to realize the relevance analysis and short-term prediction of PM2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO2, SO2, PM10) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO2, PM10, and the daily number of microblog entries with key words 'Beijing; Air pollution' show high mathematical correlation with PM2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM2.5 and it also helps to broaden the application of big data and the multi-source data mining methods.

Application of Weather Research and Forecasting Model with Chemistry (WRF/Chem) over northern China: Sensitivity study, comparative evaluation, and policy implications

NASA Astrophysics Data System (ADS)

Wang, Litao; Zhang, Yang; Wang, Kai; Zheng, Bo; Zhang, Qiang; Wei, Wei

2016-01-01

An extremely severe and persistent haze event occurred over the middle and eastern China in January 2013, with the record-breaking high concentrations of fine particulate matter (PM2.5). In this study, an online-coupled meteorology-air quality model, the Weather Research and Forecasting Model with Chemistry (WRF/Chem), is applied to simulate this pollution episode over East Asia and northern China at 36- and 12-km grid resolutions. A number of simulations are conducted to examine the sensitivities of the model predictions to various physical schemes. The results show that all simulations give similar predictions for temperature, wind speed, wind direction, and humidity, but large variations exist in the prediction for precipitation. The concentrations of PM2.5, particulate matter with aerodynamic diameter of 10 μm or less (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2) are overpredicted partially due to the lack of wet scavenging by the chemistry-aerosol option with the 1999 version of the Statewide Air Pollution Research Center (SAPRC-99) mechanism with the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Volatility Basis Set (VBS) for secondary organic aerosol formation. The optimal set of configurations with the best performance is the simulation with the Gorddard shortwave and RRTM longwave radiation schemes, the Purdue Lin microphysics scheme, the Kain-Fritsch cumulus scheme, and a nudging coefficient of 1 × 10-5 for water vapor mixing ratio. The emission sensitivity simulations show that the PM2.5 concentrations are most sensitive to nitrogen oxide (NOx) and SO2 emissions in northern China, but to NOx and ammonia (NH3) emissions in southern China. 30% NOx emission reductions may result in an increase in PM2.5 concentrations in northern China because of the NH3-rich and volatile organic compound (VOC) limited conditions over this area. VOC emission reductions will lead to a decrease in PM2.5 concentrations in eastern China. However, 30% reductions in the emissions of SO2, NOx, NH3, and VOC, individually or collectively, are insufficient to effectively mitigate the severe pollution over northern China. More aggressive emission controls, which needs to be identified in further studies, are needed in this area to reach the objective of 25% PM2.5 concentration reduction in 2017 proposed in the Action Plan for Air Pollution Prevention and Control by the State Council in 2013.

ENHANCED AIR POLLUTION EPIDEMIOLOGY USING A SOURCE-ORIENTED CHEMICAL TRANSPORT MODEL

EPA Science Inventory

Air quality model predictions describing source-oriented PM component concentrations in multiple size cuts will provide new inputs to examine the effects of acute and chronic PM exposure on mortality and morbidity. Associations between adverse health effects and PM sources/com...

COMPARISON OF SPATIAL PATTERNS OF POLLUTANT DISTRIBUTION WITH CMAQ PREDICTIONS

EPA Science Inventory

To evaluate the Models-3/Community Multiscale Air Quality (CMAQ) modeling system in reproducing the spatial patterns of aerosol concentrations over the country on timescales of months and years, the spatial patterns of model output are compared with those derived from observation...

PREDICTING ENVIRONMENTAL CONCENTRATIONS OF AIRBORNE POLLUTANTS IN TERRESTRIAL RECEPTORS: THE CASE OF MERCURY

EPA Science Inventory

The question of mercury in the environment has rapidly become one of the high-profile environmental issues in several countries and internationally. There are environmental and human health concerns associated with elevated levels of mercury linked to industrial mercury emissions...

Prediction of Particle Concentration using Traffic Emission Model

NASA Astrophysics Data System (ADS)

He, Hong-di; Lu, Jane Wei-zhen

2010-05-01

Vehicle emission is regarded as one of major sources of air pollution in urban area. Much attention has been addressed on it especially at traffic intersection. At intersection, vehicles frequently stop with idling engine during the red time and speed-up rapidly in the green time, which result in a high velocity fluctuation and produce extra pollutants to the surrounding air. To deeply understand such process, a semi-empirical model for predicting the changing effect of traffic flow patterns on particulate concentrations is proposed. The performance of the model is evaluated using the correlation coefficient and other parameters. From the results, the correlation coefficients in morning and afternoon data were found to be 0.86 an 0.73 respectively, which implies that the semi-empirical model for morning and afternoon data are 86% and 73% error free. Due to less affected by possible factors such as traffic volume and movement of pedestrian, the dispersion of the particulate matter in the morning is smaller and then contributes to higher performance than that in the afternoon.

The evaluation of rainfall influence on combined sewer overflows characteristics: the Berlin case study.

PubMed

Sandoval, S; Torres, A; Pawlowsky-Reusing, E; Riechel, M; Caradot, N

2013-01-01

The present study aims to explore the relationship between rainfall variables and water quality/quantity characteristics of combined sewer overflows (CSOs), by the use of multivariate statistical methods and online measurements at a principal CSO outlet in Berlin (Germany). Canonical correlation results showed that the maximum and average rainfall intensities are the most influential variables to describe CSO water quantity and pollutant loads whereas the duration of the rainfall event and the rain depth seem to be the most influential variables to describe CSO pollutant concentrations. The analysis of partial least squares (PLS) regression models confirms the findings of the canonical correlation and highlights three main influences of rainfall on CSO characteristics: (i) CSO water quantity characteristics are mainly influenced by the maximal rainfall intensities, (ii) CSO pollutant concentrations were found to be mostly associated with duration of the rainfall and (iii) pollutant loads seemed to be principally influenced by dry weather duration before the rainfall event. The prediction quality of PLS models is rather low (R² < 0.6) but results can be useful to explore qualitatively the influence of rainfall on CSO characteristics.

A theoretical framework for the episodic-urban air quality management plan ( e-UAQMP)

NASA Astrophysics Data System (ADS)

Gokhale, Sharad; Khare, Mukesh

The present research proposes the local urban air quality management plan which combines two different modelling approaches (hybrid model) and possesses an improved predictive ability including the 'probabilistic exceedances over norms' and their 'frequency of occurrences' and so termed, herein, as episodic-urban air quality management plan ( e-UAQMP). The e-UAQMP deals with the consequences of 'extreme' concentrations of pollutant, mainly occurring at urban 'hotspots' e.g. traffic junctions, intersections and signalized roadways and are also influenced by complexities of traffic generated 'wake' effects. The e-UAQMP (based on probabilistic approach), also acts as an efficient preventive measure to predict the 'probability of exceedances' so as to prepare a successful policy responses in relation to the protection of urban environment as well as disseminating information to its sensitive 'receptors'. The e-UAQMP may be tailored to the requirements of the local area for the policy implementation programmes. The importance of such policy-making framework in the context of current air pollution 'episodes' in urban environments is discussed. The hybrid model that is based on both deterministic and stochastic based approaches predicting the 'average' as well as 'extreme' concentration distribution of air pollutants together in form of probability has been used at two air quality control regions (AQCRs) in the Delhi city, India, in formulating and executing the e-UAQMP— first, the income tax office (ITO), one of the busiest signalized traffic intersection and second, the Sirifort, one of the busiest signalized roadways.

Sensitivity of local air quality to the interplay between small- and large-scale circulations: a large-eddy simulation study

NASA Astrophysics Data System (ADS)

Wolf-Grosse, Tobias; Esau, Igor; Reuder, Joachim

2017-06-01

Street-level urban air pollution is a challenging concern for modern urban societies. Pollution dispersion models assume that the concentrations decrease monotonically with raising wind speed. This convenient assumption breaks down when applied to flows with local recirculations such as those found in topographically complex coastal areas. This study looks at a practically important and sufficiently common case of air pollution in a coastal valley city. Here, the observed concentrations are determined by the interaction between large-scale topographically forced and local-scale breeze-like recirculations. Analysis of a long observational dataset in Bergen, Norway, revealed that the most extreme cases of recurring wintertime air pollution episodes were accompanied by increased large-scale wind speeds above the valley. Contrary to the theoretical assumption and intuitive expectations, the maximum NO2 concentrations were not found for the lowest 10 m ERA-Interim wind speeds but in situations with wind speeds of 3 m s-1. To explain this phenomenon, we investigated empirical relationships between the large-scale forcing and the local wind and air quality parameters. We conducted 16 large-eddy simulation (LES) experiments with the Parallelised Large-Eddy Simulation Model (PALM) for atmospheric and oceanic flows. The LES accounted for the realistic relief and coastal configuration as well as for the large-scale forcing and local surface condition heterogeneity in Bergen. They revealed that emerging local breeze-like circulations strongly enhance the urban ventilation and dispersion of the air pollutants in situations with weak large-scale winds. Slightly stronger large-scale winds, however, can counteract these local recirculations, leading to enhanced surface air stagnation. Furthermore, this study looks at the concrete impact of the relative configuration of warmer water bodies in the city and the major transport corridor. We found that a relatively small local water body acted as a barrier for the horizontal transport of air pollutants from the largest street in the valley and along the valley bottom, transporting them vertically instead and hence diluting them. We found that the stable stratification accumulates the street-level pollution from the transport corridor in shallow air pockets near the surface. The polluted air pockets are transported by the local recirculations to other less polluted areas with only slow dilution. This combination of relatively long distance and complex transport paths together with weak dispersion is not sufficiently resolved in classical air pollution models. The findings have important implications for the air quality predictions over urban areas. Any prediction not resolving these, or similar local dynamic features, might not be able to correctly simulate the dispersion of pollutants in cities.

Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China 2004-2013

NASA Technical Reports Server (NTRS)

Ma, Zongwei; Hu, Xuefei; Sayer, Andrew M.; Levy, Robert; Zhang, Qiang; Xue, Yingang; Tong, Shilu; Bi, Jun; Huang, Lei; Liu, Yang

2016-01-01

Three decades of rapid economic development is causing severe and widespread PM2.5(particulate matter (is) less than 2.5 ) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data. We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1 deg resolution using the most recent satellite data and evaluated model performance with available ground observations. We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model predicted PM2.5 concentrations from 2004 to early 2014 using ground observations. The overall model cross-validation R(sup 2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM(sub 2.5) concentrations with little bias at the monthly (R(sup 2) = 0.73), regression slope = 0.91) and seasonal (R(sup 2) = 0.79), regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 micro-g/cu cm between 2004 and 2007 and a decrease of 0.46 micro-g/cu cm between 2008 and 2013. Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.

A modeling study of the nonlinear response of fine particles to air pollutant emissions in the Beijing-Tianjin-Hebei region

NASA Astrophysics Data System (ADS)

Zhao, Bin; Wu, Wenjing; Wang, Shuxiao; Xing, Jia; Chang, Xing; Liou, Kuo-Nan; Jiang, Jonathan H.; Gu, Yu; Jang, Carey; Fu, Joshua S.; Zhu, Yun; Wang, Jiandong; Lin, Yan; Hao, Jiming

2017-10-01

The Beijing-Tianjin-Hebei (BTH) region has been suffering from the most severe fine-particle (PM2. 5) pollution in China, which causes serious health damage and economic loss. Quantifying the source contributions to PM2. 5 concentrations has been a challenging task because of the complicated nonlinear relationships between PM2. 5 concentrations and emissions of multiple pollutants from multiple spatial regions and economic sectors. In this study, we use the extended response surface modeling (ERSM) technique to investigate the nonlinear response of PM2. 5 concentrations to emissions of multiple pollutants from different regions and sectors over the BTH region, based on over 1000 simulations by a chemical transport model (CTM). The ERSM-predicted PM2. 5 concentrations agree well with independent CTM simulations, with correlation coefficients larger than 0.99 and mean normalized errors less than 1 %. Using the ERSM technique, we find that, among all air pollutants, primary inorganic PM2. 5 makes the largest contribution (24-36 %) to PM2. 5 concentrations. The contribution of primary inorganic PM2. 5 emissions is especially high in heavily polluted winter and is dominated by the industry as well as residential and commercial sectors, which should be prioritized in PM2. 5 control strategies. The total contributions of all precursors (nitrogen oxides, NOx; sulfur dioxides, SO2; ammonia, NH3; non-methane volatile organic compounds, NMVOCs; intermediate-volatility organic compounds, IVOCs; primary organic aerosol, POA) to PM2. 5 concentrations range between 31 and 48 %. Among these precursors, PM2. 5 concentrations are primarily sensitive to the emissions of NH3, NMVOC + IVOC, and POA. The sensitivities increase substantially for NH3 and NOx and decrease slightly for POA and NMVOC + IVOC with the increase in the emission reduction ratio, which illustrates the nonlinear relationships between precursor emissions and PM2. 5 concentrations. The contributions of primary inorganic PM2. 5 emissions to PM2. 5 concentrations are dominated by local emission sources, which account for over 75 % of the total primary inorganic PM2. 5 contributions. For precursors, however, emissions from other regions could play similar roles to local emission sources in the summer and over the northern part of BTH. The source contribution features for various types of heavy-pollution episodes are distinctly different from each other and from the monthly mean results, illustrating that control strategies should be differentiated based on the major contributing sources during different types of episodes.

Assessing the impact of race, social factors and air pollution on birth outcomes: a population-based study.

PubMed

Gray, Simone C; Edwards, Sharon E; Schultz, Bradley D; Miranda, Marie Lynn

2014-01-29

Both air pollution exposure and socioeconomic status (SES) are important indicators of children's health. Using highly resolved modeled predictive surfaces, we examine the joint effects of air pollution exposure and measures of SES in a population level analysis of pregnancy outcomes in North Carolina (NC). Daily measurements of particulate matter <2.5 μm in aerodynamic diameter (PM2.5) and ozone (O3) were calculated through a spatial hierarchical Bayesian model which produces census-tract level point predictions. Using multilevel models and NC birth data from 2002-2006, we examine the association between pregnancy averaged PM2.5 and O3, individual and area-based SES indicators, and birth outcomes. Maternal race and education, and neighborhood household income were associated with adverse birth outcomes. Predicted concentrations of PM2.5 and O3 were also associated with an additional effect on reductions in birth weight and increased risks of being born low birth weight and small for gestational age. This paper builds on and complements previous work on the relationship between pregnancy outcomes and air pollution exposure by using 1) highly resolved air pollution exposure data; 2) a five-year population level sample of pregnancies; and 3) including personal and areal level measures of social determinants of pregnancy outcomes. Results show a stable and negative association between air pollution exposure and adverse birth outcomes. Additionally, the more socially disadvantaged populations are at a greater risk; controlling for both SES and environmental stressors provides a better understanding of the contributing factors to poor children's health outcomes.

The photochemical pollution episode of 5-16 July 1983 in North-West England

NASA Astrophysics Data System (ADS)

Colbeck, I.; Harrison, Roy M.

Ground level ozone, NO x and specific C 2-C 6 hydrocarbon measurements from a rural site in N-W England during a photochemical pollution episode are presented. Maximum hourly ozone concentrations exceeded 80 ppb for ten consecutive days with a maximum of 156 ppb. Mid-morning ozone concentrations were found to be indicative of the amount of ozone from continental sources. The air mass trajectories, total NMHC and alkane : alkene ratios all indicate that in the early to middle stages of the episode the air had been exposed to recent precursor emissions relative to more aged air before and after this period. The measurements are compared with the predictions of recent theoretical models of ozone formation over England.

Comparison of in situ DGT measurement with ex situ methods for predicting cadmium bioavailability in soils with combined pollution to biotas.

PubMed

Wang, Peifang; Liu, Cui; Yao, Yu; Wang, Chao; Wang, Teng; Yuan, Ye; Hou, Jun

2017-05-01

To assess the capabilities of the different techniques in predicting Cadmium (Cd) bioavailability in Cd-contaminated soils with the addition of Zn, one in situ technique (diffusive gradients in thin films; DGT) was compared with soil solution concentration and four widely used single-step extraction methods (acetic acid, EDTA, sodium acetate and CaCl 2 ). Wheat and maize were selected as tested species. The results demonstrated that single Cd-polluted soils inhibited the growth of wheat and maize significantly compared with control plants; the shoot and root biomasses of the plants both dropped significantly (P < 0.05). The addition of Zn exhibited a strong antagonism to the physiological toxicity induced by Cd. The Pearson correlation coefficient presented positive correlations (P < 0.01, R > 0.9) between Cd concentrations in two plants and Cd bioavailability indicated by each method in soils. Consequently, the results indicated that the DGT technique could be regarded as a good predictor of Cd bioavailability to plants, comparable to soil solution concentration and the four single-step extraction methods. Because the DGT technique can offer in situ data, it is expected to be widely used in more areas.

Analysis and predictive models of stormwater runoff volumes, loads, and pollutant concentrations from watersheds in the Twin Cities metropolitan area, Minnesota, USA.

PubMed

Brezonik, Patrick L; Stadelmann, Teresa H

2002-04-01

Urban nonpoint source pollution is a significant contributor to water quality degradation. Watershed planners need to be able to estimate nonpoint source loads to lakes and streams if they are to plan effective management strategies. To meet this need for the twin cities metropolitan area, a large database of urban and suburban runoff data was compiled. Stormwater runoff loads and concentrations of 10 common constituents (six N and P forms, TSS, VSS, COD, Pb) were characterized, and effects of season and land use were analyzed. Relationships between runoff variables and storm and watershed characteristics were examined. The best regression equation to predict runoff volume for rain events was based on rainfall amount, drainage area, and percent impervious area (R2 = 0.78). Median event-mean concentrations (EMCs) tended to be higher in snowmelt runoff than in rainfall runoff, and significant seasonal differences were found in yields (kg/ha) and EMCs for most constituents. Simple correlations between explanatory variables and stormwater loads and EMCs were weak. Rainfall amount and intensity and drainage area were the most important variables in multiple linear regression models to predict event loads, but uncertainty was high in models developed with the pooled data set. The most accurate models for EMCs generally were found when sites were grouped according to common land use and size.

Land Use Regression Models for Alkylbenzenes in a Middle Eastern Megacity: Tehran Study of Exposure Prediction for Environmental Health Research (Tehran SEPEHR).

PubMed

Amini, Heresh; Schindler, Christian; Hosseini, Vahid; Yunesian, Masud; Künzli, Nino

2017-08-01

Land use regression (LUR) has not been applied thus far to ambient alkylbenzenes in highly polluted megacities. We advanced LUR models for benzene, toluene, ethylbenzene, p-xylene, m-xylene, o-xylene (BTEX), and total BTEX using measurement based estimates of annual means at 179 sites in Tehran megacity, Iran. Overall, 520 predictors were evaluated, such as The Weather Research and Forecasting Model meteorology predictions, emission inventory, and several new others. The final models with R 2 values ranging from 0.64 for p-xylene to 0.70 for benzene were mainly driven by traffic-related variables but the proximity to sewage treatment plants was present in all models indicating a major local source of alkylbenzenes not used in any previous study. We further found that large buffers are needed to explain annual mean concentrations of alkylbenzenes in complex situations of a megacity. About 83% of Tehran's surface had benzene concentrations above air quality standard of 5 μg/m 3 set by European Union and Iranian Government. Toluene was the predominant alkylbenzene, and the most polluted area was the city center. Our analyses on differences between wealthier and poorer areas also showed somewhat higher concentrations for the latter. This is the largest LUR study to predict all BTEX species in a megacity.

Evaluation of emission control strategies to reduce ozone pollution in the Paso del Norte region using a photochemical air quality modeling system

NASA Astrophysics Data System (ADS)

Valenzuela, Victor Hugo

Air pollution emissions control strategies to reduce ozone precursor pollutants are analyzed by applying a photochemical modeling system. Simulations of air quality conditions during an ozone episode which occurred in June, 2006 are undertaken by increasing or reducing area source emissions in Ciudad Juarez, Chihuahua, Mexico. Two air pollutants are primary drivers in the formation of tropospheric ozone. Oxides of nitrogen (NOx) and volatile organic compounds (VOC) undergo multiple chemical reactions under favorable meteorological conditions to form ozone, which is a secondary pollutant that irritates respiratory systems in sensitive individuals especially the elderly and young children. The U.S. Environmental Protection Agency established National Ambient Air Quality Standards (NAAQS) to limit ambient air pollutants such as ozone by establishing an 8-hour average concentration of 0.075 ppm as the threshold at which a violation of the standard occurs. Ozone forms primarily due reactions in the troposphere of NOx and VOC emissions generated primarily by anthropogenic sources in urban regions. Data from emissions inventories indicate area sources account for ˜15 of NOx and ˜45% of regional VOC emissions. Area sources include gasoline stations, automotive paint bodyshops and nonroad mobile sources. Multiplicity of air pollution emissions sources provides an opportunity to investigate and potentially implement air quality improvement strategies to reduce emissions which contribute to elevated ozone concentrations. A baseline modeling scenario was established using the CAMx photochemical air quality model from which a series of sensitivity analyses for evaluating air quality control strategies were conducted. Modifications to area source emissions were made by varying NOx and / or VOC emissions in the areas of particular interest. Model performance was assessed for each sensitivity analysis. Normalized bias (NB) and normalized error (NE) were used to identify variability of the PREDICTED to OBSERVED ozone concentrations of both BASELINE model and simulations with modified emissions assessed by the sensitivity analysis. All simulations were found to vary within acceptable ranges of these two criteria variables. Simulation results indicate ozone formation in the PdN region is VOC-limited. Under VOC-limited conditions, modifications to NOx emissions do not produce a marked increase or decrease in ozone concentrations. Modifications to VOC emissions generated the highest variability in ozone concentrations. Increasing VOC emissions by 75% produced results which minimized model bias and error when comparing PREDICTED and OBSERVED ozone concentrations. Increasing VOC emissions by 75% either alone or in combination with a 75% increase in NOx emissions generated PREDICTED ozone concentrations very near to OBSERVED ozone. By evaluating the changes in ambient ozone concentrations through photochemical modeling, air quality planners may identify the most efficient or effective VOC emissions control strategies for area sources. Among the strategies to achieve emissions reductions are installation of gasoline vapor recovery systems, replacing high-pressure low-volume surface coating paint spray guns with high-volume low-pressure spray paint guns, requiring emissions control booths for surface coating operations as well as undertaking solvent management practices, requiring the sale of low VOC paint solvents in the surface-coating industry, and requiring low-VOC solvents in the dry cleaning industry. Other strategies to reduce VOC emissions include initiating Eco-Driving strategies to reduce fuel consumption from mobile sources and minimize vehicle idling at the international ports of entry by reducing bridge wait times. This dissertation depicts a tool for evaluating impacts of emissions on regional air quality by addressing the highly unresolved fugitive emissions in the Paso del Norte region. It provides a protocol for decision makers to assess the effects of various emission control strategies in the region. Impacts of specific source categories such as the international ports of entry, gasoline stations, paint body shops, truck stops, and military installations on the regional air quality can be easily and systematically addressed in a timely manner in the future.

Predicting temporal changes in total iron concentrations in groundwaters flowing from abandoned deep mines: a first approximation

NASA Astrophysics Data System (ADS)

Younger, Paul L.

2000-06-01

Discharges of contaminated groundwater from abandoned deep mines are a major environmental problem in many parts of the world. While process-based models of pollutant generation have been successfully developed for certain surface mines and waste rock piles of relatively simple geometry and limited areal extent, such models are not readily applicable to large systems of laterally extensive, interconnected, abandoned deep mines. As a first approximation for such systems, hydrological and lithological factors, which can reasonably be expected to influence pollutant release, have been assessed by empirically assessing data from 81 abandoned deep coal mine discharges in the UK. These data demonstrate that after flooding of a deep mine is complete and groundwater begins to migrate from the mine voids into surface waters or adjoining aquifers, flushing of the mine voids by fresh recharge results in a gradual improvement in the quality of groundwater (principally manifested as decreasing Fe concentrations and stabilisation of pH around 7). Alternative representations of the flushing process have been examined. While elegant analytical solutions of the advection-dispersion equation can be made to mimic the changes in iron concentration, parameterisation is tendentious in practice. Scrutiny of the UK data suggest that to a first approximation, the duration of the main period of flushing can be predicted to endure around four times as long as the foregoing process of mine flooding. Short- and long-term iron concentrations (i.e. at the start of the main period of flushing and after its completion, respectively) can be estimated from the sulphur content of the worked strata. If strata composition data are unavailable, some indication of pollution potential can be obtained from considerations of the proximity of worked strata to marine beds (which typically have high pyrite contents). The long-term concentrations of iron in a particular discharge can also be approximated on the basis of the proximity of the discharge location to the outcrop of the most closely associated coal seam (MCACS) and, thus, to zones of possible ongoing pyrite oxidation. The practical application of these simple predictive techniques is facilitated by means of a flowchart.

Simulations of Sulfate-Nitrate-Ammonium (SNA) aerosols during the extreme haze events over Northern China in 2014

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

Chen, Dan; Liu, Zhiquan; Fast, Jerome D.

Extreme haze events have occurred frequently over China in recent years. Although many studies have investigated the formation mechanisms associated with PM2.5 for heavily polluted regions in China based on observational data, adequately predicting peak PM2.5 concentrations is still challenging for regional air quality models. In this study, we evaluate the performance of one configuration of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) and use the model to investigate the sensitivity of heterogeneous reactions on simulated peak sulfate, nitrate, and ammonium concentrations in the vicinity of Beijing during four extreme haze episodes in October 2014 over themore » North China Plain. The highest observed PM2.5 concentration of 469 μg m-3 occurred in Beijing. Comparisons with observations show that the model reproduced the temporal variability in PM2.5 with the highest PM2.5 values on polluted days (defined as days in which observed PM2.5 is greater than 75 μg m-3), but predictions of sulfate, nitrate, and ammonium were too low on days with the highest observed concentrations. Observational data indicate that the sulfur/nitric oxidation rates are strongly correlated with relative humidity during periods of peak PM2.5; however, the model failed to reproduce the highest PM2.5 concentrations due to missing heterogeneous reactions. As the parameterizations of those reactions is not well established yet, estimates of SO2-to-H2SO4 and NO2/NO3-to-HNO3 reaction rates that depend on relative humidity were applied which improved the simulation of sulfate, nitrate, and ammonium enhancement on polluted days in terms of both concentrations and partitioning among those species. Sensitivity simulations showed that the extremely high heterogeneous reaction rates and also higher emission rates than those reported in the emission inventory« less

Investigation of local meteorological events and their relationship with ozone and aerosols during an ESCOMPTE photochemical episode

NASA Astrophysics Data System (ADS)

Augustin, P.; Delbarre, H.; Lohou, F.; Campistron, B.; Puygrenier, V.; Cachier, H.; Lombardo, T.

2006-11-01

The international ESCOMPTE campaign, which took place in summer 2001 in the most highly polluted French region, was devoted to validate air pollution prediction models. Surface and remote sensing instruments (Lidar, Radar and Sodar) were deployed over the Marseille area, along the Mediterranean coast, in order to investigate the fine structure of the sea-breeze circulation and its relationship with the pollutant concentrations. The geographical situation of the Marseille region combines a complex coastline and relief which both lead to a peculiar behaviour of the sea-breeze circulation. Several local sea breezes, perpendicular to the nearest coastline, settled in during the morning. In the afternoons, when the thermal gradient between the continental and marine surface grows up, a southerly or a westerly sea breeze may dominate. Their respective importance is then a function of time, space and altitude. Furthermore, an oscillation of the westerly sea breeze with a period of about 3 h is also highlighted. We show that these dynamical characteristics have profound influences on the atmospheric boundary-layer (ABL) development and on pollutant concentrations. In fact, the direction and intensity of the sea-breeze determine the route and the transit time of the stable marine air flow over the continental surface. Thus, the ABL depth may exhibit several collapses correlated with the westerly sea-breeze pulsation. The ozone and aerosol concentrations are also related to the dynamical features. In the suburbs and parts of the city under pulsed sea breezes, a higher ABL depth and higher ozone concentrations are observed. In the city centre, this relationship between pulsed sea-breeze intensity and ozone concentration is different, emphasising the importance of the transit time and also the build-up of pollutants in the marine air mass along the route. Finally, the variations of aerosol concentration are also described according to the breeze direction.

Pollutant dispersion in a large indoor space: Part 1 -- Scaled experiments using a water-filled model with occupants and furniture.

PubMed

Thatcher, T L; Wilson, D J; Wood, E E; Craig, M J; Sextro, R G

2004-08-01

Scale modeling is a useful tool for analyzing complex indoor spaces. Scale model experiments can reduce experimental costs, improve control of flow and temperature conditions, and provide a practical method for pretesting full-scale system modifications. However, changes in physical scale and working fluid (air or water) can complicate interpretation of the equivalent effects in the full-scale structure. This paper presents a detailed scaling analysis of a water tank experiment designed to model a large indoor space, and experimental results obtained with this model to assess the influence of furniture and people in the pollutant concentration field at breathing height. Theoretical calculations are derived for predicting the effects from losses of molecular diffusion, small scale eddies, turbulent kinetic energy, and turbulent mass diffusivity in a scale model, even without Reynolds number matching. Pollutant dispersion experiments were performed in a water-filled 30:1 scale model of a large room, using uranine dye injected continuously from a small point source. Pollutant concentrations were measured in a plane, using laser-induced fluorescence techniques, for three interior configurations: unobstructed, table-like obstructions, and table-like and figure-like obstructions. Concentrations within the measurement plane varied by more than an order of magnitude, even after the concentration field was fully developed. Objects in the model interior had a significant effect on both the concentration field and fluctuation intensity in the measurement plane. PRACTICAL IMPLICATION: This scale model study demonstrates both the utility of scale models for investigating dispersion in indoor environments and the significant impact of turbulence created by furnishings and people on pollutant transport from floor level sources. In a room with no furniture or occupants, the average concentration can vary by about a factor of 3 across the room. Adding furniture and occupants can increase this spatial variation by another factor of 3.

Assessment and prediction of short term hospital admissions: the case of Athens, Greece

NASA Astrophysics Data System (ADS)

Kassomenos, P.; Papaloukas, C.; Petrakis, M.; Karakitsios, S.

The contribution of air pollution on hospital admissions due to respiratory and heart diseases is a major issue in the health-environmental perspective. In the present study, an attempt was made to run down the relationships between air pollution levels and meteorological indexes, and corresponding hospital admissions in Athens, Greece. The available data referred to a period of eight years (1992-2000) including the daily number of hospital admissions due to respiratory and heart diseases, hourly mean concentrations of CO, NO 2, SO 2, O 3 and particulates in several monitoring stations, as well as, meteorological data (temperature, relative humidity, wind speed/direction). The relations among the above data were studied through widely used statistical techniques (multivariate stepwise analyses) and Artificial Neural Networks (ANNs). Both techniques revealed that elevated particulate concentrations are the dominant parameter related to hospital admissions (an increase of 10 μg m -3 leads to an increase of 10.2% in the number of admissions), followed by O 3 and the rest of the pollutants (CO, NO 2 and SO 2). Meteorological parameters also play a decisive role in the formation of air pollutant levels affecting public health. Consequently, increased/decreased daily hospital admissions are related to specific types of meteorological conditions that favor/do not favor the accumulation of pollutants in an urban complex. In general, the role of meteorological factors seems to be underestimated by stepwise analyses, while ANNs attribute to them a more important role. Comparison of the two models revealed that ANN adaptation in complicate environmental issues presents improved modeling results compared to a regression technique. Furthermore, the ANN technique provides a reliable model for the prediction of the daily hospital admissions based on air quality data and meteorological indices, undoubtedly useful for regulatory purposes.

A pilot study using scripted ventilation conditions to identify key factors affecting indoor pollutant concentration and air exchange rate in a residence.

PubMed

Johnson, Ted; Myers, Jeffrey; Kelly, Thomas; Wisbith, Anthony; Ollison, Will

2004-01-01

A pilot study was conducted using an occupied, single-family test house in Columbus, OH, to determine whether a script-based protocol could be used to obtain data useful in identifying the key factors affecting air-exchange rate (AER) and the relationship between indoor and outdoor concentrations of selected traffic-related air pollutants. The test script called for hourly changes to elements of the test house considered likely to influence air flow and AER, including the position (open or closed) of each window and door and the operation (on/off) of the furnace, air conditioner, and ceiling fans. The script was implemented over a 3-day period (January 30-February 1, 2002) during which technicians collected hourly-average data for AER, indoor, and outdoor air concentrations for six pollutants (benzene, formaldehyde (HCHO), polycyclic aromatic hydrocarbons (PAH), carbon monoxide (CO), nitric oxide (NO), and nitrogen oxides (NO(x))), and selected meteorological variables. Consistent with expectations, AER tended to increase with the number of open exterior windows and doors. The 39 AER values measured during the study when all exterior doors and windows were closed varied from 0.36 to 2.29 h(-1) with a geometric mean (GM) of 0.77 h(-1) and a geometric standard deviation (GSD) of 1.435. The 27 AER values measured when at least one exterior door or window was opened varied from 0.50 to 15.8 h(-1) with a GM of 1.98 h(-1) and a GSD of 1.902. AER was also affected by temperature and wind speed, most noticeably when exterior windows and doors were closed. Results of a series of stepwise linear regression analyses suggest that (1) outdoor pollutant concentration and (2) indoor pollutant concentration during the preceding hour were the "variables of choice" for predicting indoor pollutant concentration in the test house under the conditions of this study. Depending on the pollutant and ventilation conditions, one or more of the following variables produced a small, but significant increase in the explained variance (R(2)-value) of the regression equations: AER, number and location of apertures, wind speed, air-conditioning operation, indoor temperature, outdoor temperature, and relative humidity. The indoor concentrations of CO, PAH, NO, and NO(x) were highly correlated with the corresponding outdoor concentrations. The indoor benzene concentrations showed only moderate correlation with outdoor benzene levels, possibly due to a weak indoor source. Indoor formaldehyde concentrations always exceeded outdoor levels, and the correlation between indoor and outdoor concentrations was not statistically significant, indicating the presence of a strong indoor source.

Location identification for indoor instantaneous point contaminant source by probability-based inverse Computational Fluid Dynamics modeling.

PubMed

Liu, X; Zhai, Z

2008-02-01

Indoor pollutions jeopardize human health and welfare and may even cause serious morbidity and mortality under extreme conditions. To effectively control and improve indoor environment quality requires immediate interpretation of pollutant sensor readings and accurate identification of indoor pollution history and source characteristics (e.g. source location and release time). This procedure is complicated by non-uniform and dynamic contaminant indoor dispersion behaviors as well as diverse sensor network distributions. This paper introduces a probability concept based inverse modeling method that is able to identify the source location for an instantaneous point source placed in an enclosed environment with known source release time. The study presents the mathematical models that address three different sensing scenarios: sensors without concentration readings, sensors with spatial concentration readings, and sensors with temporal concentration readings. The paper demonstrates the inverse modeling method and algorithm with two case studies: air pollution in an office space and in an aircraft cabin. The predictions were successfully verified against the forward simulation settings, indicating good capability of the method in finding indoor pollutant sources. The research lays a solid ground for further study of the method for more complicated indoor contamination problems. The method developed can help track indoor contaminant source location with limited sensor outputs. This will ensure an effective and prompt execution of building control strategies and thus achieve a healthy and safe indoor environment. The method can also assist the design of optimal sensor networks.

Modeling indoor particulate exposures in inner city school classrooms

PubMed Central

Gaffin, Jonathan M.; Petty, Carter R.; Hauptman, Marissa; Kang, Choong-Min; Wolfson, Jack M.; Awad, Yara Abu; Di, Qian; Lai, Peggy S.; Sheehan, William J.; Baxi, Sachin; Coull, Brent A.; Schwartz, Joel D.; Gold, Diane R.; Koutrakis, Petros; Phipatanakul, Wanda

2016-01-01

Outdoor air pollution penetrates buildings and contributes to total indoor exposures. We investigated the relationship of indoor to outdoor particulate matter in inner-city school classrooms. The School Inner City Asthma Study investigates the effect of classroom-based environmental exposures on students with asthma in the northeast United States. Mixed-effects linear models were used to determine the relationships between indoor PM2.5 and BC and their corresponding outdoor concentrations, and to develop a model for predicting exposures to these pollutants. The indoor-outdoor sulfur ratio was used as an infiltration factor of outdoor fine particles. Weeklong concentrations of PM2.5 and BC in 199 samples from 136 classrooms (30 school buildings) were compared to those measured at a central monitoring site averaged over the same timeframe. Mixed effects regression models found significant random intercept and slope effects, which indicate that: 1) there are important PM2.5 sources in classrooms; 2) the penetration of outdoor PM2.5 particles varies by school, and 3) the site-specific outside PM2.5 levels (inferred by the models) differ from those observed at the central monitor site. Similar results were found for BC except for lack of indoor sources. The fitted predictions from the sulfur-adjusted models were moderately predictive of observed indoor pollutant levels (Out of sample correlations: PM2.5: r2 = 0.68, BC; r2 = 0.61). Our results suggest that PM2.5 has important classroom sources, which vary by school. Furthermore, using these mixed effects models, classroom exposures can be accurately predicted for dates when central site measures are available but indoor measures are not available. PMID:27599884

Predicting Airborne Particle Levels Aboard Washington State School Buses

PubMed Central

Adar, Sara D.; Davey, Mark; Sullivan, James R.; Compher, Michael; Szpiro, Adam; Liu, L.-J. Sally

2008-01-01

School buses contribute substantially to childhood air pollution exposures yet they are rarely quantified in epidemiology studies. This paper characterizes fine particulate matter (PM2.5) aboard school buses as part of a larger study examining the respiratory health impacts of emission-reducing retrofits. To assess onboard concentrations, continuous PM2.5 data were collected during 85 trips aboard 43 school buses during normal driving routines, and aboard hybrid lead vehicles traveling in front of the monitored buses during 46 trips. Ordinary and partial least square regression models for PM2.5 onboard buses were created with and without control for roadway concentrations, which were also modeled. Predictors examined included ambient PM2.5 levels, ambient weather, and bus and route characteristics. Concentrations aboard school buses (21 μg/m3) were four and two-times higher than ambient and roadway levels, respectively. Differences in PM2.5 levels between the buses and lead vehicles indicated an average of 7 μg/m3 originating from the bus's own emission sources. While roadway concentrations were dominated by ambient PM2.5, bus concentrations were influenced by bus age, diesel oxidative catalysts, and roadway concentrations. Cross validation confirmed the roadway models but the bus models were less robust. These results confirm that children are exposed to air pollution from the bus and other roadway traffic while riding school buses. In-cabin air pollution is higher than roadway concentrations and is likely influenced by bus characteristics. PMID:18985175

Predicting airborne particle levels aboard Washington State school buses

NASA Astrophysics Data System (ADS)

Adar, Sara D.; Davey, Mark; Sullivan, James R.; Compher, Michael; Szpiro, Adam; Sally Liu, L.-J.

School buses contribute substantially to childhood air pollution exposures yet they are rarely quantified in epidemiology studies. This paper characterizes fine particulate matter (PM 2.5) aboard school buses as part of a larger study examining the respiratory health impacts of emission reducing retrofits. To assess onboard concentrations, continuous PM 2.5 data were collected during 85 trips aboard 43 school buses during normal driving routines, and aboard hybrid lead vehicles traveling in front of the monitored buses during 46 trips. Ordinary and partial least squares regression models for PM 2.5 onboard buses were created with and without control for roadway concentrations, which were also modeled. Predictors examined included ambient PM 2.5 levels, ambient weather, and bus and route characteristics. Average concentrations aboard school buses (21 μg m -3) were four and two-times higher than ambient and roadway levels, respectively. Differences in PM 2.5 levels between the buses and lead vehicles indicated an average of 7 μg m -3 originating from the bus's own emission sources. While roadway concentrations were dominated by ambient PM 2.5, bus concentrations were influenced by bus age, diesel oxidative catalysts, and roadway concentrations. Cross-validation confirmed the roadway models but the bus models were less robust. These results confirm that children are exposed to air pollution from the bus and other roadway traffic while riding school buses. In-cabin air pollution is higher than roadway concentrations and is likely influenced by bus characteristics.

Source apportionment of trace metals in surface waters of a polluted stream using multivariate statistical analyses.

PubMed

Pekey, Hakan; Karakaş, Duran; Bakoğlu, Mithat

2004-11-01

Surface water samples were collected from ten previously selected sites of the polluted Dil Deresi stream, during two field surveys, December 2001 and April 2002. All samples were analyzed using ICP-AES, and the concentrations of trace metals (Al, As, Ba, Cd, Co, Cr, Cu, Fe, Pb, Sn and Zn) were determined. The results were compared with national and international water quality guidelines, as well as literature values reported for similar rivers. Factor analysis (FA) and a factor analysis-multiple regression (FA-MR) model were used for source apportionment and estimation of contributions from identified sources to the concentration of each parameter. By a varimax rotated factor analysis, four source types were identified as the paint industry; sewage, crustal and road traffic runoff for trace metals, explaining about 83% of the total variance. FA-MR results showed that predicted concentrations were calculated with uncertainties lower than 15%.

Simultaneous statistical bias correction of multiplePM2.5 species from a regional photochemical grid model

EPA Science Inventory

In recent years environmental epidemiologists have begun utilizing regionalscale air quality computer models to predict ambient air pollution concentrations in health studies instead of or in addition to monitoring data from central sites. The advantages of using such models i...

Associations between summertime ambient pollutants and respiratory morbidity in New York City: Comparison of results using ambient concentrations versus predicted exposures

EPA Science Inventory

Epidemiological analyses of air quality often estimate human exposure from ambient monitoring data, potentially leading to exposure misclassification and subsequent bias in estimated health risks. To investigate this, we conducted a case-crossover study of summertime ambient ozon...

Dynamic Evaluation of a Regional Air Quality Model: Assessing the Emissions-Induced Weekly Ozone Cycle

EPA Science Inventory

Air quality models are used to predict changes in pollutant concentrations resulting from envisioned emission control policies. Recognizing the need to assess the credibility of air quality models in a policy-relevant context, we perform a dynamic evaluation of the community Mult...

The Extent and Prediction of Heavy Metal Pollution in Soils of Shahrood and Damghan, Iran.

PubMed

Sakizadeh, Mohamad; Mirzaei, Rouhollah; Ghorbani, Hadi

2015-12-01

The levels of 12 heavy metals (Ag, Ba, Be, Cd, Co, Cr, Cu, Ni, Pb, Tl, V, Zn) were considered in 229 soil samples in Semnan Province, Iran. To discriminate between natural and anthropogenic inputs of heavy metals, factor analysis was used. Seven factors accounting for 90.5 % of the total variance were extracted. The mining and agricultural activities along with geogenic sources have been attributed as the main causes of the levels of heavy metals in the study area. The partial least squares regression was utilized to predict the level of soil pollution index (SPI) considering the concentrations of 12 heavy metals. The eigenvectors from the first three PLS represented more than 98 % of the overall variance. The correlation coefficient between the observed and predicted SPI was 0.99 indicating the high efficiency of this method. The resultant coefficient of determination for three PLS components was 0.984 confirming the predictive ability of this method.

Feasibility Studies on Pipeline Disposal of Concentrated Copper Tailings Slurry for Waste Minimization

NASA Astrophysics Data System (ADS)

Senapati, Pradipta Kumar; Mishra, Barada Kanta

2017-06-01

The conventional lean phase copper tailings slurry disposal systems create pollution all around the disposal area through seepage and flooding of waste slurry water. In order to reduce water consumption and minimize pollution, the pipeline disposal of these waste slurries at high solids concentrations may be considered as a viable option. The paper presents the rheological and pipeline flow characteristics of copper tailings samples in the solids concentration range of 65-72 % by weight. The tailings slurry indicated non-Newtonian behaviour at these solids concentrations and the rheological data were best fitted by Bingham plastic model. The influence of solids concentration on yield stress and plastic viscosity for the copper tailings samples were discussed. Using a high concentration test loop, pipeline experiments were conducted in a 50 mm nominal bore (NB) pipe by varying the pipe flow velocity from 1.5 to 3.5 m/s. A non-Newtonian Bingham plastic pressure drop model predicted the experimental data reasonably well for the concentrated tailings slurry. The pressure drop model was used for higher size pipes and the operating conditions for pipeline disposal of concentrated copper tailings slurry in a 200 mm NB pipe with respect to specific power consumption were discussed.

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

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.

Assessing groundwater vulnerability in the Kinshasa region, DR Congo, using a calibrated DRASTIC model

NASA Astrophysics Data System (ADS)

Mfumu Kihumba, Antoine; Vanclooster, Marnik; Ndembo Longo, Jean

2017-02-01

This study assessed the vulnerability of groundwater against pollution in the Kinshasa region, DR Congo, as a support of a groundwater protection program. The parametric vulnerability model (DRASTIC) was modified and calibrated to predict the intrinsic vulnerability as well as the groundwater pollution risk. The method uses groundwater body specific parameters for the calibration of the factor ratings and weightings of the original DRASTIC model. These groundwater specific parameters are inferred from the statistical relation between the original DRASTIC model and observed nitrate pollution for a specific period. In addition, site-specific land use parameters are integrated into the method. The method is fully embedded in a Geographic Information System (GIS). Following these modifications, the correlation coefficient between groundwater pollution risk and observed nitrate concentrations for the 2013-2014 survey improved from r = 0.42, for the original DRASTIC model, to r = 0.61 for the calibrated model. As a way to validate this pollution risk map, observed nitrate concentrations from another survey (2008) are compared to pollution risk indices showing a good degree of coincidence with r = 0.51. The study shows that a calibration of a vulnerability model is recommended when vulnerability maps are used for groundwater resource management and land use planning at the regional scale and that it is adapted to a specific area.

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.

Bioaccumulation of organic pollutants in Indo-Pacific humpback dolphin: A review on current knowledge and future prospects.

PubMed

Sanganyado, Edmond; Rajput, Imran Rashid; Liu, Wenhua

2018-06-01

Indo-Pacific humpback dolphin (Sousa chinensis) are chronically exposed to organic pollutants since they inhabit shallow coastal waters that are often impacted by anthropogenic activities. The aim of this review was to evaluate existing knowledge on the occurrence of organic pollutants in Indo-Pacific humpback dolphins, identify knowledge gaps, and offer recommendations for future research directions. We discussed the trends in the bioaccumulation of organic pollutants in Indo-Pacific humpback dolphins focusing on sources, physicochemical properties, and usage patterns. Furthermore, we examined factors that influence bioaccumulation such as gender, age, dietary intake and tissue-specific distribution. Studies on bioaccumulation in Indo-Pacific humpback dolphin remain scarce, despite high concentrations above 13,000 ng/g lw we previously detected for PFOS, ∑PBDE and chlorinated paraffins. The maximum concentration of organochlorines detected was 157,000 ng/g wt. Furthermore, variations in bioaccumulation were shown to be caused by factors such as usage patterns and physicochemical properties of the pollutant. However, restrictions in sampling inhibit investigations on exposure pathway and toxicity of organic pollutants in Indo-Pacific humpback dolphin. We proposed the use of biopsy sampling, predictive bioaccumulation and toxicity modeling, and monitoring other emerging contaminants such as microplastics and pharmaceuticals for future health risk assessment on this critically endangered marine mammal species. Copyright © 2018 Elsevier Ltd. All rights reserved.

Use of mobile and passive badge air monitoring data for NOX and ozone air pollution spatial exposure prediction models.

PubMed

Xu, Wei; Riley, Erin A; Austin, Elena; Sasakura, Miyoko; Schaal, Lanae; Gould, Timothy R; Hartin, Kris; Simpson, Christopher D; Sampson, Paul D; Yost, Michael G; Larson, Timothy V; Xiu, Guangli; Vedal, Sverre

2017-03-01

Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012. Performance of exposure prediction models for long-term nitrogen oxides (NO X ) and ozone (O 3 ) concentrations were compared using a state-of-the-art approach for model development based on land use regression (LUR) and geostatistical smoothing. Model performance was evaluated using leave-one-out cross-validation (LOOCV). Models performed well using the mobile peak traffic monitoring data for both NO X and O 3 , with LOOCV R 2 s of 0.70 and 0.71, respectively, in the summer, and 0.90 and 0.58, respectively, in the winter. Models using 2-week passive samples for NO X had LOOCV R 2 s of 0.60 and 0.65 in the summer and winter months, respectively. The passive badge sampling data were not adequate for developing models for O 3 . Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NO X and O 3 and are a better source of data for these models than 2-week passive badge data.

Wavelength dependent light absorption as a cost effective, real-time surrogate for ambient concentrations of polycyclic aromatic hydrocarbons

NASA Astrophysics Data System (ADS)

Brown, Richard J. C.; Butterfield, David M.; Goddard, Sharon L.; Hussain, Delwar; Quincey, Paul G.; Fuller, Gary W.

2016-02-01

Many monitoring stations used to assess ambient air concentrations of pollutants regulated by European air quality directives suffer from being expensive to establish and operate, and from their location being based on the results of macro-scale modelling exercises rather than measurement assessments in candidate locations. To address these issues for the monitoring of polycyclic aromatic hydrocarbons (PAHs), this study has used data from a combination of the ultraviolet and infrared channels of aethalometers (referred to as UV BC), operated as part of the UK Black Carbon Network, as a surrogate measurement. This has established a relationship between concentrations of the PAH regulated in Europe, benzo[a]pyrene (B[a]P), and the UV BC signal at locations where these measurements have been made together from 2008 to 2014. This relationship was observed to be non-linear. Relationships for individual site types were used to predict measured concentrations with, on average, 1.5% accuracy across all annual averages, and with only 1 in 36 of the predicted annual averages deviating from the measured annual average by more than the B[a]P data quality objective for uncertainty of 50% (at -65%, with the range excluding this value between + 38% and -37%). These relationships were then used to predict B[a]P concentrations at stations where UV BC measurement are made, but PAH measurements are not. This process produced results which reflected expectations based on knowledge of the pollution climate at these stations gained from the measurements of other air quality networks, or from nearby stations. The influence of domestic solid fuel heating was clear using this approach which highlighted Strabane in Northern Ireland as a station likely to be in excess of the air quality directive target value for B[a]P.

Probabilistic forecasting for extreme NO2 pollution episodes.

PubMed

Aznarte, José L

2017-10-01

In this study, we investigate the convenience of quantile regression to predict extreme concentrations of NO 2 . Contrarily to the usual point-forecasting, where a single value is forecast for each horizon, probabilistic forecasting through quantile regression allows for the prediction of the full probability distribution, which in turn allows to build models specifically fit for the tails of this distribution. Using data from the city of Madrid, including NO 2 concentrations as well as meteorological measures, we build models that predict extreme NO 2 concentrations, outperforming point-forecasting alternatives, and we prove that the predictions are accurate, reliable and sharp. Besides, we study the relative importance of the independent variables involved, and show how the important variables for the median quantile are different than those important for the upper quantiles. Furthermore, we present a method to compute the probability of exceedance of thresholds, which is a simple and comprehensible manner to present probabilistic forecasts maximizing their usefulness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Environmental research program. 1995 Annual report

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

Brown, N.J.

1996-06-01

The objective of the Environmental Research Program is to enhance the understanding of, and mitigate the effects of pollutants on health, ecological systems, global and regional climate, and air quality. The program is multidisciplinary and includes fundamental research and development in efficient and environmentally benign combustion, pollutant abatement and destruction, and novel methods of detection and analysis of criteria and noncriteria pollutants. This diverse group conducts investigations in combustion, atmospheric and marine processes, flue-gas chemistry, and ecological systems. Combustion chemistry research emphasizes modeling at microscopic and macroscopic scales. At the microscopic scale, functional sensitivity analysis is used to explore themore » nature of the potential-to-dynamics relationships for reacting systems. Rate coefficients are estimated using quantum dynamics and path integral approaches. At the macroscopic level, combustion processes are modelled using chemical mechanisms at the appropriate level of detail dictated by the requirements of predicting particular aspects of combustion behavior. Parallel computing has facilitated the efforts to use detailed chemistry in models of turbulent reacting flow to predict minor species concentrations.« less

Optimal Design of Air Quality Monitoring Network and its Application in an Oil Refinery Plant: An Approach to Keep Health Status of Workers.

PubMed

ZoroufchiBenis, Khaled; Fatehifar, Esmaeil; Ahmadi, Javad; Rouhi, Alireza

2015-01-01

Industrial air pollution is a growing challenge to humane health, especially in developing countries, where there is no systematic monitoring of air pollution. Given the importance of the availability of valid information on population exposure to air pollutants, it is important to design an optimal Air Quality Monitoring Network (AQMN) for assessing population exposure to air pollution and predicting the magnitude of the health risks to the population. A multi-pollutant method (implemented as a MATLAB program) was explored for configur-ing an AQMN to detect the highest level of pollution around an oil refinery plant. The method ranks potential monitoring sites (grids) according to their ability to represent the ambient concentration. The term of cluster of contiguous grids that exceed a threshold value was used to calculate the Station Dosage. Selection of the best configuration of AQMN was done based on the ratio of a sta-tion's dosage to the total dosage in the network. Six monitoring stations were needed to detect the pollutants concentrations around the study area for estimating the level and distribution of exposure in the population with total network efficiency of about 99%. An analysis of the design procedure showed that wind regimes have greatest effect on the location of monitoring stations. The optimal AQMN enables authorities to implement an effective program of air quality management for protecting human health.

Optimal Design of Air Quality Monitoring Network and its Application in an Oil Refinery Plant: An Approach to Keep Health Status of Workers

PubMed Central

ZoroufchiBenis, Khaled; Fatehifar, Esmaeil; Ahmadi, Javad; Rouhi, Alireza

2015-01-01

Background: Industrial air pollution is a growing challenge to humane health, especially in developing countries, where there is no systematic monitoring of air pollution. Given the importance of the availability of valid information on population exposure to air pollutants, it is important to design an optimal Air Quality Monitoring Network (AQMN) for assessing population exposure to air pollution and predicting the magnitude of the health risks to the population. Methods: A multi-pollutant method (implemented as a MATLAB program) was explored for configur­ing an AQMN to detect the highest level of pollution around an oil refinery plant. The method ranks potential monitoring sites (grids) according to their ability to represent the ambient concentration. The term of cluster of contiguous grids that exceed a threshold value was used to calculate the Station Dosage. Selection of the best configuration of AQMN was done based on the ratio of a sta­tion’s dosage to the total dosage in the network. Results: Six monitoring stations were needed to detect the pollutants concentrations around the study area for estimating the level and distribution of exposure in the population with total network efficiency of about 99%. An analysis of the design procedure showed that wind regimes have greatest effect on the location of monitoring stations. Conclusion: The optimal AQMN enables authorities to implement an effective program of air quality management for protecting human health. PMID:26933646

Numerical simulation of hydrodynamic and water quality effects of shoreline changes in Bohai Bay

NASA Astrophysics Data System (ADS)

Jia, Han; Shen, Yongming; Su, Meirong; Yu, Chunxue

2018-02-01

This study uses the HD and Ecolab modules of MIKE to simulate the hydrodynamic and water quality and predict the influence of shoreline changes in Bohai Bay, China. The study shows that shoreline changes weaken the residual current and generate a counter-clockwise circulation south of Huanghua Port, thereby resulting in weak water exchange capacity and low pollutant-diffusing capacity. Shoreline changes reduce the area of Bohai Bay, resulting in a smaller tidal prism and further weakening the water exchange capacity. This situation is not conducive to the diffusion of pollutants, and therefore may lead to increased water pollution in the bay. Shoreline changes hinder the spread of runoff, weaken the dilution effect of the river on seawater, and block the spread of coastal residual current, thereby resulting in increased salinity near the reclamation area. Shoreline changes lead to an increase in PO4-P concentration and decrease in DIN concentration. The value of N/P near the project decreases, thereby weakening the phosphorus-limited effect.

Toxic effects of Hydrilla verticillata exposed to toluene, ethylbenzene and xylene and safety assessment for protecting aquatic macrophytes.

PubMed

Yan, Sha; Zhou, Qixing

2011-10-01

Little information is available about the toxicity of toluene, ethylbenzene and xylene acting on macrophytes, and their toxicity data are rarely used in regulation and criteria decisions. The results extended the knowledge on toxic effects of toluene, ethylbenzene and xylene on aquatic plants. The responses of Hydrilla verticillata to these pollutants were investigated. Chlorophyll levels, lipid peroxidation, and antioxidant enzymes (superoxide dismutase and guaiacol peroxidase) showed diverse responses at different concentrations of toluene, ethylbenzene and xylene. The linear regression analyses were performed respectively, suggesting the concentrations of toluene, ethylbenzene and xylene expected to protect aquatic macrophytes were 7.30 mg L⁻¹, 1.15 mg L⁻¹ and 2.36 mg L⁻¹, respectively. This study emphasized that aquatic plants are also sensitive to organic pollutants as fishes and zooplanktons, indicating that macrophytes could be helpful in predicting the toxicity of these pollutants and should be considered in regulation and criteria decisions for aquatic environment protection. Copyright © 2011 Elsevier Ltd. All rights reserved.

Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China

PubMed Central

Liu, Dong-jun; Li, Li

2015-01-01

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field. PMID:26110332

Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China.

PubMed

Liu, Dong-jun; Li, Li

2015-06-23

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field.

Actinobacteria consortium as an efficient biotechnological tool for mixed polluted soil reclamation: Experimental factorial design for bioremediation process optimization.

PubMed

Aparicio, Juan Daniel; Raimondo, Enzo Emanuel; Gil, Raúl Andrés; Benimeli, Claudia Susana; Polti, Marta Alejandra

2018-01-15

The objective of the present work was to establish optimal biological and physicochemical parameters in order to remove simultaneously lindane and Cr(VI) at high and/or low pollutants concentrations from the soil by an actinobacteria consortium formed by Streptomyces sp. M7, MC1, A5, and Amycolatopsis tucumanensis AB0. Also, the final aim was to treat real soils contaminated with Cr(VI) and/or lindane from the Northwest of Argentina employing the optimal biological and physicochemical conditions. In this sense, after determining the optimal inoculum concentration (2gkg -1 ), an experimental design model with four factors (temperature, moisture, initial concentration of Cr(VI) and lindane) was employed for predicting the system behavior during bioremediation process. According to response optimizer, the optimal moisture level was 30% for all bioremediation processes. However, the optimal temperature was different for each situation: for low initial concentrations of both pollutants, the optimal temperature was 25°C; for low initial concentrations of Cr(VI) and high initial concentrations of lindane, the optimal temperature was 30°C; and for high initial concentrations of Cr(VI), the optimal temperature was 35°C. In order to confirm the model adequacy and the validity of the optimization procedure, experiments were performed in six real contaminated soils samples. The defined actinobacteria consortium reduced the contaminants concentrations in five of the six samples, by working at laboratory scale and employing the optimal conditions obtained through the factorial design. Copyright © 2017 Elsevier B.V. All rights reserved.

Limits to Cloud Susceptibility

NASA Technical Reports Server (NTRS)

Coakley, James A., Jr.

2002-01-01

1-kilometer AVHRR observations of ship tracks in low-level clouds off the west coast of the U S. were used to determine limits for the degree to which clouds might be altered by increases in anthropogenic aerosols. Hundreds of tracks were analyzed to determine whether the changes in droplet radii, visible optical depths, and cloud top altitudes that result from the influx of particles from underlying ships were consistent with expectations based on simple models for the indirect effect of aerosols. The models predict substantial increases in sunlight reflected by polluted clouds due to the increases in droplet numbers and cloud liquid water that result from the elevated particle concentrations. Contrary to the model predictions, the analysis of ship tracks revealed a 15-20% reduction in liquid water for the polluted clouds. Studies performed with a large-eddy cloud simulation model suggested that the shortfall in cloud liquid water found in the satellite observations might be attributed to the restriction that the 1-kilometer pixels be completely covered by either polluted or unpolluted cloud. The simulation model revealed that a substantial fraction of the indirect effect is caused by a horizontal redistribution of cloud water in the polluted clouds. Cloud-free gaps in polluted clouds fill in with cloud water while the cloud-free gaps in the surrounding unpolluted clouds remain cloud-free. By limiting the analysis to only overcast pixels, the current study failed to account for the gap-filling predicted by the simulation model. This finding and an analysis of the spatial variability of marine stratus suggest new ways to analyze ship tracks to determine the limit to which particle pollution will alter the amount of sunlight reflected by clouds.

Investigating the association between birth weight and complementary air pollution metrics: a cohort study.

PubMed

Laurent, Olivier; Wu, Jun; Li, Lianfa; Chung, Judith; Bartell, Scott

2013-02-17

Exposure to air pollution is frequently associated with reductions in birth weight but results of available studies vary widely, possibly in part because of differences in air pollution metrics. Further insight is needed to identify the air pollution metrics most strongly and consistently associated with birth weight. We used a hospital-based obstetric database of more than 70,000 births to study the relationships between air pollution and the risk of low birth weight (LBW, <2,500 g), as well as birth weight as a continuous variable, in term-born infants. Complementary metrics capturing different aspects of air pollution were used (measurements from ambient monitoring stations, predictions from land use regression models and from a Gaussian dispersion model, traffic density, and proximity to roads). Associations between air pollution metrics and birth outcomes were investigated using generalized additive models, adjusting for maternal age, parity, race/ethnicity, insurance status, poverty, gestational age and sex of the infants. Increased risks of LBW were associated with ambient O(3) concentrations as measured by monitoring stations, as well as traffic density and proximity to major roadways. LBW was not significantly associated with other air pollution metrics, except that a decreased risk was associated with ambient NO(2) concentrations as measured by monitoring stations. When birth weight was analyzed as a continuous variable, small increases in mean birth weight were associated with most air pollution metrics (<40 g per inter-quartile range in air pollution metrics). No such increase was observed for traffic density or proximity to major roadways, and a significant decrease in mean birth weight was associated with ambient O3 concentrations. We found contrasting results according to the different air pollution metrics examined. Unmeasured confounders and/or measurement errors might have produced spurious positive associations between birth weight and some air pollution metrics. Despite this, ambient O(3) was associated with a decrement in mean birth weight and significant increases in the risk of LBW were associated with traffic density, proximity to roads and ambient O(3). This suggests that in our study population, these air pollution metrics are more likely related to increased risks of LBW than the other metrics we studied. Further studies are necessary to assess the consistency of such patterns across populations.

Investigating the association between birth weight and complementary air pollution metrics: a cohort study

PubMed Central

2013-01-01

Background Exposure to air pollution is frequently associated with reductions in birth weight but results of available studies vary widely, possibly in part because of differences in air pollution metrics. Further insight is needed to identify the air pollution metrics most strongly and consistently associated with birth weight. Methods We used a hospital-based obstetric database of more than 70,000 births to study the relationships between air pollution and the risk of low birth weight (LBW, <2,500 g), as well as birth weight as a continuous variable, in term-born infants. Complementary metrics capturing different aspects of air pollution were used (measurements from ambient monitoring stations, predictions from land use regression models and from a Gaussian dispersion model, traffic density, and proximity to roads). Associations between air pollution metrics and birth outcomes were investigated using generalized additive models, adjusting for maternal age, parity, race/ethnicity, insurance status, poverty, gestational age and sex of the infants. Results Increased risks of LBW were associated with ambient O3 concentrations as measured by monitoring stations, as well as traffic density and proximity to major roadways. LBW was not significantly associated with other air pollution metrics, except that a decreased risk was associated with ambient NO2 concentrations as measured by monitoring stations. When birth weight was analyzed as a continuous variable, small increases in mean birth weight were associated with most air pollution metrics (<40 g per inter-quartile range in air pollution metrics). No such increase was observed for traffic density or proximity to major roadways, and a significant decrease in mean birth weight was associated with ambient O3 concentrations. Conclusions We found contrasting results according to the different air pollution metrics examined. Unmeasured confounders and/or measurement errors might have produced spurious positive associations between birth weight and some air pollution metrics. Despite this, ambient O3 was associated with a decrement in mean birth weight and significant increases in the risk of LBW were associated with traffic density, proximity to roads and ambient O3. This suggests that in our study population, these air pollution metrics are more likely related to increased risks of LBW than the other metrics we studied. Further studies are necessary to assess the consistency of such patterns across populations. PMID:23413962

Statistical Analysis of the Impacts of Regional Transportation on the Air Quality in Beijing

NASA Astrophysics Data System (ADS)

Huang, Zhongwen; Zhang, Huiling; Tong, Lei; Xiao, Hang

2016-04-01

From October to December 2015, Beijing-Tianjin-Hebei (BTH) region had experienced several severe haze events. In order to assess the effects of the regional transportation on the air quality in Beijing, the air monitoring data (PM2.5, SO2, NO2 and CO) from that period published by Chinese National Environmental Monitoring Center (CNEMC) was collected and analyzed with various statistical models. The cities within BTH area were clustered into three groups according to the geographical conditions, while the air pollutant concentrations of cities within a group sharing similar variation trends. The Granger causality test results indicate that significant causal relationships exist between the air pollutant data of Beijing and its surrounding cities (Baoding, Chengde, Tianjin and Zhangjiakou) for the reference period. Then, linear regression models were constructed to capture the interdependency among the multiple time series. It shows that the observed air pollutant concentrations in Beijing were well consistent with the model-fitted results. More importantly, further analysis suggests that the air pollutants in Beijing were strongly affected by regional transportation, as the local sources only contributed 17.88%, 27.12%, 14.63% and 31.36% of PM2.5, SO2, NO2 and CO concentrations, respectively. And the major foreign source for Beijing was from Southwest (Baoding) direction, account for more than 42% of all these air pollutants. Thus, by combining various statistical models, it may not only be able to quickly predict the air qualities of any cities on a regional scale, but also to evaluate the local and regional source contributions for a particular city. Key words: regional transportation, air pollution, Granger causality test, statistical models

A mechanistic modeling system for estimating large scale emissions and transport of pollen and co-allergens

PubMed Central

Efstathiou, Christos; Isukapalli, Sastry

2011-01-01

Allergic airway diseases represent a complex health problem which can be exacerbated by the synergistic action of pollen particles and air pollutants such as ozone. Understanding human exposures to aeroallergens requires accurate estimates of the spatial distribution of airborne pollen levels as well as of various air pollutants at different times. However, currently there are no established methods for estimating allergenic pollen emissions and concentrations over large geographic areas such as the United States. A mechanistic modeling system for describing pollen emissions and transport over extensive domains has been developed by adapting components of existing regional scale air quality models and vegetation databases. First, components of the Biogenic Emissions Inventory System (BEIS) were adapted to predict pollen emission patterns. Subsequently, the transport module of the Community Multiscale Air Quality (CMAQ) modeling system was modified to incorporate description of pollen transport. The combined model, CMAQ-pollen, allows for simultaneous prediction of multiple air pollutants and pollen levels in a single model simulation, and uses consistent assumptions related to the transport of multiple chemicals and pollen species. Application case studies for evaluating the combined modeling system included the simulation of birch and ragweed pollen levels for the year 2002, during their corresponding peak pollination periods (April for birch and September for ragweed). The model simulations were driven by previously evaluated meteorological model outputs and emissions inventories for the eastern United States for the simulation period. A semi-quantitative evaluation of CMAQ-pollen was performed using tree and ragweed pollen counts in Newark, NJ for the same time periods. The peak birch pollen concentrations were predicted to occur within two days of the peak measurements, while the temporal patterns closely followed the measured profiles of overall tree pollen. For the case of ragweed pollen, the model was able to capture the patterns observed during September 2002, but did not predict an early peak; this can be associated with a wider species pollination window and inadequate spatial information in current land cover databases. An additional sensitivity simulation was performed to comparatively evaluate the dispersion patterns predicted by CMAQ-pollen with those predicted by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which is used extensively in aerobiological studies. The CMAQ estimated concentration plumes matched the equivalent pollen scenario modeled with HYSPLIT. The novel pollen modeling approach presented here allows simultaneous estimation of multiple airborne allergens and other air pollutants, and is being developed as a central component of an integrated population exposure modeling system, the Modeling Environment for Total Risk studies (MENTOR) for multiple, co-occurring contaminants that include aeroallergens and irritants. PMID:21516207

A mechanistic modeling system for estimating large-scale emissions and transport of pollen and co-allergens

NASA Astrophysics Data System (ADS)

Efstathiou, Christos; Isukapalli, Sastry; Georgopoulos, Panos

2011-04-01

Allergic airway diseases represent a complex health problem which can be exacerbated by the synergistic action of pollen particles and air pollutants such as ozone. Understanding human exposures to aeroallergens requires accurate estimates of the spatial distribution of airborne pollen levels as well as of various air pollutants at different times. However, currently there are no established methods for estimating allergenic pollen emissions and concentrations over large geographic areas such as the United States. A mechanistic modeling system for describing pollen emissions and transport over extensive domains has been developed by adapting components of existing regional scale air quality models and vegetation databases. First, components of the Biogenic Emissions Inventory System (BEIS) were adapted to predict pollen emission patterns. Subsequently, the transport module of the Community Multiscale Air Quality (CMAQ) modeling system was modified to incorporate description of pollen transport. The combined model, CMAQ-pollen, allows for simultaneous prediction of multiple air pollutants and pollen levels in a single model simulation, and uses consistent assumptions related to the transport of multiple chemicals and pollen species. Application case studies for evaluating the combined modeling system included the simulation of birch and ragweed pollen levels for the year 2002, during their corresponding peak pollination periods (April for birch and September for ragweed). The model simulations were driven by previously evaluated meteorological model outputs and emissions inventories for the eastern United States for the simulation period. A semi-quantitative evaluation of CMAQ-pollen was performed using tree and ragweed pollen counts in Newark, NJ for the same time periods. The peak birch pollen concentrations were predicted to occur within two days of the peak measurements, while the temporal patterns closely followed the measured profiles of overall tree pollen. For the case of ragweed pollen, the model was able to capture the patterns observed during September 2002, but did not predict an early peak; this can be associated with a wider species pollination window and inadequate spatial information in current land cover databases. An additional sensitivity simulation was performed to comparatively evaluate the dispersion patterns predicted by CMAQ-pollen with those predicted by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which is used extensively in aerobiological studies. The CMAQ estimated concentration plumes matched the equivalent pollen scenario modeled with HYSPLIT. The novel pollen modeling approach presented here allows simultaneous estimation of multiple airborne allergens and other air pollutants, and is being developed as a central component of an integrated population exposure modeling system, the Modeling Environment for Total Risk studies (MENTOR) for multiple, co-occurring contaminants that include aeroallergens and irritants.

Klang River water quality modelling using music

NASA Astrophysics Data System (ADS)

Zahari, Nazirul Mubin; Zawawi, Mohd Hafiz; Muda, Zakaria Che; Sidek, Lariyah Mohd; Fauzi, Nurfazila Mohd; Othman, Mohd Edzham Fareez; Ahmad, Zulkepply

2017-09-01

Water is an essential resource that sustains life on earth; changes in the natural quality and distribution of water have ecological impacts that can sometimes be devastating. Recently, Malaysia is facing many environmental issues regarding water pollution. The main causes of river pollution are rapid urbanization, arising from the development of residential, commercial, industrial sites, infrastructural facilities and others. The purpose of the study was to predict the water quality of the Connaught Bridge Power Station (CBPS), Klang River. Besides that, affects to the low tide and high tide and. to forecast the pollutant concentrations of the Biochemical Oxygen Demand (BOD) and Total Suspended Solid (TSS) for existing land use of the catchment area through water quality modeling (by using the MUSIC software). Besides that, to identifying an integrated urban stormwater treatment system (Best Management Practice or BMPs) to achieve optimal performance in improving the water quality of the catchment using the MUSIC software in catchment areas having tropical climates. Result from MUSIC Model such as BOD5 at station 1 can be reduce the concentration from Class IV to become Class III. Whereas, for TSS concentration from Class III to become Class II at the station 1. The model predicted a mean TSS reduction of 0.17%, TP reduction of 0.14%, TN reduction of 0.48% and BOD5 reduction of 0.31% for Station 1 Thus, from the result after purposed BMPs the water quality is safe to use because basically water quality monitoring is important due to threat such as activities are harmful to aquatic organisms and public health.

The statistical evaluation and comparison of ADMS-Urban model for the prediction of nitrogen dioxide with air quality monitoring network.

PubMed

Dėdelė, Audrius; Miškinytė, Auksė

2015-09-01

In many countries, road traffic is one of the main sources of air pollution associated with adverse effects on human health and environment. Nitrogen dioxide (NO2) is considered to be a measure of traffic-related air pollution, with concentrations tending to be higher near highways, along busy roads, and in the city centers, and the exceedances are mainly observed at measurement stations located close to traffic. In order to assess the air quality in the city and the air pollution impact on public health, air quality models are used. However, firstly, before the model can be used for these purposes, it is important to evaluate the accuracy of the dispersion modelling as one of the most widely used method. The monitoring and dispersion modelling are two components of air quality monitoring system (AQMS), in which statistical comparison was made in this research. The evaluation of the Atmospheric Dispersion Modelling System (ADMS-Urban) was made by comparing monthly modelled NO2 concentrations with the data of continuous air quality monitoring stations in Kaunas city. The statistical measures of model performance were calculated for annual and monthly concentrations of NO2 for each monitoring station site. The spatial analysis was made using geographic information systems (GIS). The calculation of statistical parameters indicated a good ADMS-Urban model performance for the prediction of NO2. The results of this study showed that the agreement of modelled values and observations was better for traffic monitoring stations compared to the background and residential stations.

Leachate pollution index as an effective tool in determining the phytotoxicity of municipal solid waste leachate.

PubMed

Arunbabu, V; Indu, K S; Ramasamy, E V

2017-10-01

Phytoremediation is a promising option for the treatment of municipal solid waste leachate. Combining the leachate pollution index with the phytotoxicity data will be useful in predicting the suitable concentration of leachate for the phytoremediation applications. Understanding the tolerant mechanisms of plants to leachate stress will further help to select the appropriate dose. The aim of the study was to investigate the effect of different concentrations of leachate on germination, growth, chlorophyll content and antioxidant enzyme activities in the plant Vigna unguiculata. The crude leachate has an LPI value of 31.99 with high concentration of organic matter, ammonia and dissolved solids. The results of the phytotoxicity study suggest that at lower concentrations the leachate enhanced the germination and promoted plant growth. Up to 5% concentration (v/v) of the leachate which had a LPI value of 11.84 the growth promotion was observed in V. unguiculata. This was made possible by the controlled modulation of reactive oxygen species through the enhanced antioxidant enzyme activities. However at higher concentration, the pollutants in leachate disrupt the enzyme activities and leads to the peroxidation of membrane lipids and significantly affected the plant growth. The study suggest that phytotoxic effects in plants are directly related to the LPI value and leachate with LPI values less than 10 are likely to promote plant growth and LPI values greater than 10 are likely to exert detrimental effect on the plant. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using NASA Satellite Aerosol Optical Depth to Enhance PM2.5 Concentration Datasets for Use in Human Health and Epidemiology Studies

NASA Astrophysics Data System (ADS)

Huff, A. K.; Weber, S.; Braggio, J.; Talbot, T.; Hall, E.

2012-12-01

Fine particulate matter (PM2.5) is a criterion air pollutant, and its adverse impacts on human health are well established. Traditionally, studies that analyze the health effects of human exposure to PM2.5 use concentration measurements from ground-based monitors and predicted PM2.5 concentrations from air quality models, such as the U.S. EPA's Community Multi-scale Air Quality (CMAQ) model. There are shortcomings associated with these datasets, however. Monitors are not distributed uniformly across the U.S., which causes spatially inhomogeneous measurements of pollutant concentrations. There are often temporal variations as well, since not all monitors make daily measurements. Air quality model output, while spatially and temporally uniform, represents predictions of PM2.5 concentrations, not actual measurements. This study is exploring the potential of combining Aerosol Optical Depth (AOD) data from the MODIS instrument on NASA's Terra and Aqua satellites with PM2.5 monitor data and CMAQ predictions to create PM2.5 datasets that more accurately reflect the spatial and temporal variations in ambient PM2.5 concentrations on the metropolitan scale, with the overall goal of enhancing capabilities for environmental public health decision-making. AOD data provide regional information about particulate concentrations that can fill in the spatial and temporal gaps in the national PM2.5 monitor network. Furthermore, AOD is a measurement, so it reflects actual concentrations of particulates in the atmosphere, in contrast to PM2.5 predictions from air quality models. Results will be presented from the Battelle/U.S. EPA statistical Hierarchical Bayesian Model (HBM), which was used to combine three PM2.5 concentration datasets: monitor measurements, AOD data, and CMAQ model predictions. The study is focusing on the Baltimore, MD and New York City, NY metropolitan regions for the period 2004-2006. For each region, combined monitor/AOD/CMAQ PM2.5 datasets generated by the HBM are being correlated with data on inpatient hospitalizations and emergency room visits for seven respiratory and cardiovascular diseases using statistical case-crossover analyses. Preliminary results will be discussed regarding the potential for the addition of AOD data to increase the correlation between PM2.5 concentrations and health outcomes. Environmental public health tracking programs associated with the Maryland Department of Health and Mental Hygiene, the New York State Department of Health, the CDC, and the U.S. EPA have expressed interest in using the results of this study to enhance their existing environmental health surveillance activities.

Air pollution and respiratory health among diabetic and non-diabetic subjects in Pune, India-results from the Wellcome Trust Genetic Study.

PubMed

Khafaie, Morteza Abdullatif; Salvi, Sundeep Santosh; Yajnik, Chittaranjan Sakerlal; Ojha, Ajay; Khafaie, Behzad; Gore, Sharad Damodar

2017-06-01

Diabetics may be more vulnerable to the harmful effects of ambient air pollutants than healthy individuals. But, the risk factors that lead to susceptibility to air pollution in diabetics have not yet been identified. We examined the effect of exposure to ambient PM 10 on chronic symptoms and the pulmonary function tests (PFT) in diabetic and non-diabetic subjects. Also, to investigate possible determinants of susceptibility, we recruited 400 type 2 diabetic and 465 healthy subjects who were investigated for chronic respiratory symptoms (CRSs) and then underwent measurement of forced vital capacity (FVC) and forced expiratory volume 1 (FEV1) according to standard protocol. Percent predicted FEV1 and FVC (FEV1% and FVC%, respectively) for each subject were calculated. Particulate matter (PM 10 ) concentrations at residence place of subjects were estimated using AERMOD dispersion model. The association between PM 10 and CRSs was explored using logistic regression. We also used linear regression models controlling for potential confounders to study the association between chronic exposure to PM 10 and FEV1% and FVC%. Prevalence of current wheezing, allergy symptom, chest tightness, FEV1/FVC <70%, and physician-diagnosed asthma and COPD was significantly higher among diabetic subjects than non-diabetics. There was no significant difference between percent predicted value of PFT among diabetic and non-diabetic subjects (P < 0.05). We estimated that 1 SD increase in PM 10 concentration was associated with a greater risk of having dyspnea by 1.50-fold (95% CI, 1.12-2.01). Higher exposure to PM 10 concentration was also significantly associated with lower FVC%. The size of effect for 1 SD μg/m 3 (=98.38) increase in PM 10 concentration was 3.71% (95% CI, 0.48-4.99) decrease in FVC%. In addition, we indicated that strength of these associations was higher in overweight, smoker, and aged persons. We demonstrated a possible contribution of air pollution to reduced lung function independent of diabetes status. This study suggests that decline in exposure may significantly reduce disease manifestation as dyspnea and impaired lung function. We conduct that higher BMI, smoking, and older age were associated with higher levels of air pollution effects.

Effect of water quality sampling time and frequency on storm load predictions of a prominent regression-based model

USDA-ARS?s Scientific Manuscript database

High frequency in situ measurements of nitrate can greatly reduce the uncertainty in nitrate flux estimates. Water quality databases maintained by various federal and state agencies often consist of pollutant concentration data obtained from periodic grab samples collected from gauged reaches of a s...

Monitoring of event-based mobilization of hydrophobic pollutants in rivers: calibration of turbidity as a proxy for particle facilitated transport in field and laboratory.

PubMed

Rügner, Hermann; Schwientek, Marc; Egner, Marius; Grathwohl, Peter

2014-08-15

Transport of many pollutants in rivers is coupled to mobilization of suspended particles which typically occurs during floods. Since the amount of total suspended solids (TSS) in rivers can be monitored by turbidity measurements this may be used as a proxy for the total concentration of particle associated pollutants such as PAHs, PCBs, etc. and several heavy metals. Online turbidity measurements (e.g. by optical backscattering sensors) would then also allow for an assessment of particle and pollutant flux dynamics if once calibrated against TSS and total pollutant concentrations for a given catchment. In this study, distinct flood and thus turbidity events were sampled at high temporal resolution in three contrasting sub-catchments of the River Neckar in Southwest Germany (Ammer, Goldersbach, Steinlach) as well as in the River Neckar itself and investigated for the total amount of PAHs and TSS in water; turbidity (NTU) and grain size distributions of suspended solids were determined as well. Laboratory experiments were performed with natural river bed sediments from different locations (Ammer) to investigate PAH concentrations, TSS and turbidity during sedimentation of suspended particles under controlled conditions (yielding smaller and smaller suspended particles and TSS with time). Laboratory and field results agreed very well and showed that turbidity and TSS were linearly correlated over an extended turbidity range up to 2000 NTU for the field samples and up to 8000 NTU in lab experiments. This also holds for total PAH concentrations which can be reasonably well predicted based on turbidity measurements and TSS vs. PAHs relationships - even for high turbidity values observed during flood events (>2000 NTU). Total PAH concentrations on suspended solids were independent of grain size of suspended particles. This implies that for the rivers investigated the sorption capacity of particles did not change significantly during the observed events. Copyright © 2014. Published by Elsevier B.V.

EPA Office of Water (OW): 2002 SPARROW Total NP (Catchments)

EPA Pesticide Factsheets

SPARROW (SPAtially Referenced Regressions On Watershed attributes) is a watershed modeling tool with output that allows the user to interpret water quality monitoring data at the regional and sub-regional scale. The model relates in-stream water-quality measurements to spatially referenced characteristics of watersheds, including pollutant sources and environmental factors that affect rates of pollutant delivery to streams from the land and aquatic, in-stream processing . The core of the model consists of a nonlinear regression equation describing the non-conservative transport of contaminants from point and non-point (or ??diffuse??) sources on land to rivers and through the stream and river network. SPARROW estimates contaminant concentrations, loads (or ??mass,?? which is the product of concentration and streamflow), and yields in streams (mass of nitrogen and of phosphorus entering a stream per acre of land). It empirically estimates the origin and fate of contaminants in streams and receiving bodies, and quantifies uncertainties in model predictions. The model predictions are illustrated through detailed maps that provide information about contaminant loadings and source contributions at multiple scales for specific stream reaches, basins, or other geographic areas.

Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China

PubMed Central

Yao, Hong; Zhuang, Wei; Qian, Yu; Xia, Bisheng; Yang, Yang; Qian, Xin

2016-01-01

Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R2 = 0.86–0.93 for 72 data sets collected in the industrial river and R2 = 0.60–0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals’ concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density. PMID:27028017

Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China.

PubMed

Yao, Hong; Zhuang, Wei; Qian, Yu; Xia, Bisheng; Yang, Yang; Qian, Xin

2016-01-01

Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R(2) = 0.86-0.93 for 72 data sets collected in the industrial river and R(2) = 0.60-0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals' concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density.

Near-roadway monitoring of vehicle emissions as a function of mode of operation for light-duty vehicles.

PubMed

Wen, Dongqi; Zhai, Wenjuan; Xiang, Sheng; Hu, Zhice; Wei, Tongchuan; Noll, Kenneth E

2017-11-01

Determination of the effect of vehicle emissions on air quality near roadways is important because vehicles are a major source of air pollution. A near-roadway monitoring program was undertaken in Chicago between August 4 and October 30, 2014, to measure ultrafine particles, carbon dioxide, carbon monoxide, traffic volume and speed, and wind direction and speed. The objective of this study was to develop a method to relate short-term changes in traffic mode of operation to air quality near roadways using data averaged over 5-min intervals to provide a better understanding of the processes controlling air pollution concentrations near roadways. Three different types of data analysis are provided to demonstrate the type of results that can be obtained from a near-roadway sampling program based on 5-min measurements: (1) development of vehicle emission factors (EFs) for ultrafine particles as a function of vehicle mode of operation, (2) comparison of measured and modeled CO 2 concentrations, and (3) application of dispersion models to determine concentrations near roadways. EFs for ultrafine particles are developed that are a function of traffic volume and mode of operation (free flow and congestion) for light-duty vehicles (LDVs) under real-world conditions. Two air quality models-CALINE4 (California Line Source Dispersion Model, version 4) and AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model)-are used to predict the ultrafine particulate concentrations near roadways for comparison with measured concentrations. When using CALINE4 to predict air quality levels in the mixing cell, changes in surface roughness and stability class have no effect on the predicted concentrations. However, when using AERMOD to predict air quality in the mixing cell, changes in surface roughness have a significant impact on the predicted concentrations. The paper provides emission factors (EFs) that are a function of traffic volume and mode of operation (free flow and congestion) for LDVs under real-world conditions. The good agreement between monitoring and modeling results indicates that high-resolution, simultaneous measurements of air quality and meteorological and traffic conditions can be used to determine real-world, fleet-wide vehicle EFs as a function of vehicle mode of operation under actual driving conditions.

Impact of geocoding methods on associations between long-term exposure to urban air pollution and lung function.

PubMed

Jacquemin, Bénédicte; Lepeule, Johanna; Boudier, Anne; Arnould, Caroline; Benmerad, Meriem; Chappaz, Claire; Ferran, Joane; Kauffmann, Francine; Morelli, Xavier; Pin, Isabelle; Pison, Christophe; Rios, Isabelle; Temam, Sofia; Künzli, Nino; Slama, Rémy; Siroux, Valérie

2013-09-01

Errors in address geocodes may affect estimates of the effects of air pollution on health. We investigated the impact of four geocoding techniques on the association between urban air pollution estimated with a fine-scale (10 m × 10 m) dispersion model and lung function in adults. We measured forced expiratory volume in 1 sec (FEV1) and forced vital capacity (FVC) in 354 adult residents of Grenoble, France, who were participants in two well-characterized studies, the Epidemiological Study on the Genetics and Environment on Asthma (EGEA) and the European Community Respiratory Health Survey (ECRHS). Home addresses were geocoded using individual building matching as the reference approach and three spatial interpolation approaches. We used a dispersion model to estimate mean PM10 and nitrogen dioxide concentrations at each participant's address during the 12 months preceding their lung function measurements. Associations between exposures and lung function parameters were adjusted for individual confounders and same-day exposure to air pollutants. The geocoding techniques were compared with regard to geographical distances between coordinates, exposure estimates, and associations between the estimated exposures and health effects. Median distances between coordinates estimated using the building matching and the three interpolation techniques were 26.4, 27.9, and 35.6 m. Compared with exposure estimates based on building matching, PM10 concentrations based on the three interpolation techniques tended to be overestimated. When building matching was used to estimate exposures, a one-interquartile range increase in PM10 (3.0 μg/m3) was associated with a 3.72-point decrease in FVC% predicted (95% CI: -0.56, -6.88) and a 3.86-point decrease in FEV1% predicted (95% CI: -0.14, -3.24). The magnitude of associations decreased when other geocoding approaches were used [e.g., for FVC% predicted -2.81 (95% CI: -0.26, -5.35) using NavTEQ, or 2.08 (95% CI -4.63, 0.47, p = 0.11) using Google Maps]. Our findings suggest that the choice of geocoding technique may influence estimated health effects when air pollution exposures are estimated using a fine-scale exposure model.

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.

Biologically plausible particulate air pollution mortality concentration-response functions.

PubMed Central

Roberts, Steven

2004-01-01

In this article I introduce an alternative method for estimating particulate air pollution mortality concentration-response functions. This method constrains the particulate air pollution mortality concentration-response function to be biologically plausible--that is, a non-decreasing function of the particulate air pollution concentration. Using time-series data from Cook County, Illinois, the proposed method yields more meaningful particulate air pollution mortality concentration-response function estimates with an increase in statistical accuracy. PMID:14998745

The Effects of Urban Form on Ambient Air Pollution and Public Health Risk: A Case Study in Raleigh, North Carolina

PubMed Central

Rodriguez, Daniel A.; Huegy, Joseph; Gibson, Jacqueline MacDonald

2014-01-01

Since motor vehicles are a major air pollution source, urban designs that decrease private automobile use could improve air quality and decrease air pollution health risks. Yet, the relationships among urban form, air quality, and health are complex and not fully understood. To explore these relationships, we model the effects of three alternative development scenarios on annual average fine particulate matter (PM2.5) concentrations in ambient air and associated health risks from PM2.5 exposure in North Carolina’s Raleigh-Durham-Chapel Hill area. We integrate transportation demand, land-use regression, and health risk assessment models to predict air quality and health impacts for three development scenarios: current conditions, compact development, and sprawling development. Compact development slightly decreases (−0.2%) point estimates of regional annual average PM2.5 concentrations, while sprawling development slightly increases (+1%) concentrations. However, point estimates of health impacts are in opposite directions: compact development increases (+39%) and sprawling development decreases (−33%) PM2.5-attributable mortality. Further, compactness increases local variation in PM2.5 concentrations and increases the severity of local air pollution hotspots. Hence, this research suggests that while compact development may improve air quality from a regional perspective, it may also increase the concentration of PM2.5 in local hotspots and increase population exposure to PM2.5. Health effects may be magnified if compact neighborhoods and PM2.5 hotspots are spatially co-located. We conclude that compactness alone is an insufficient means of reducing the public health impacts of transportation emissions in automobile-dependent regions. Rather, additional measures are needed to decrease automobile dependence and the health risks of transportation emissions. PMID:25490890

Exploring ozone pollution in Chengdu, southwestern China: A case study from radical chemistry to O3-VOC-NOx sensitivity.

PubMed

Tan, Zhaofeng; Lu, Keding; Jiang, Meiqing; Su, Rong; Dong, Huabin; Zeng, Limin; Xie, Shaodong; Tan, Qinwen; Zhang, Yuanhang

2018-09-15

We present the in-situ measurements in Chengdu, a major city in south west of China, in September 2016. The concentrations of ozone and its precursor were measured at four sites. Although the campaign was conducted in early autumn, up to 100 ppbv (parts per billion by volume) daily maximum ozone was often observed at all sites. The observed ozone concentrations showed good agreement at all sites, which implied that ozone pollution is a regional issue in Chengdu. To better understand the ozone formation in Chengdu, an observation based model is used in this study to calculate the RO x radical concentrations (RO x  = OH + HO 2  + RO 2 ) and ozone production rate (P(O 3 )). The model predicts OH daily maximum is in the range of 4-8 × 10 6  molecules cm -3 , and HO 2 and RO 2 are in the range of 3-6 × 10 8  molecules cm -3 . The modelled radical concentrations show a distinct difference between ozone pollution and attainment period. The relative incremental reactivity (RIR) results demonstrate that anthropogenic VOCs reduction is the most efficient way to mitigate ozone pollution at all sites, of which alkenes dominate >50% of the ozone production. Empirical kinetic modelling approach shows that three out of four sites are under the VOC-limited regime, while Pengzhou is in a transition regime due to the local petrochemical industry. The ozone budget analysis showed that the local ozone production driven by the photochemical process is important to the accumulation of ozone concentrations. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparison of land use regression models for NO2 based on routine and campaign monitoring data from an urban area of Japan.

PubMed

Kashima, Saori; Yorifuji, Takashi; Sawada, Norie; Nakaya, Tomoki; Eboshida, Akira

2018-08-01

Typically, land use regression (LUR) models have been developed using campaign monitoring data rather than routine monitoring data. However, the latter have advantages such as low cost and long-term coverage. Based on the idea that LUR models representing regional differences in air pollution and regional road structures are optimal, the objective of this study was to evaluate the validity of LUR models for nitrogen dioxide (NO 2 ) based on routine and campaign monitoring data obtained from an urban area. We selected the city of Suita in Osaka (Japan). We built a model based on routine monitoring data obtained from all sites (routine-LUR-All), and a model based on campaign monitoring data (campaign-LUR) within the city. Models based on routine monitoring data obtained from background sites (routine-LUR-BS) and based on data obtained from roadside sites (routine-LUR-RS) were also built. The routine LUR models were based on monitoring networks across two prefectures (i.e., Osaka and Hyogo prefectures). We calculated the predictability of the each model. We then compared the predicted NO 2 concentrations from each model with measured annual average NO 2 concentrations from evaluation sites. The routine-LUR-All and routine-LUR-BS models both predicted NO 2 concentrations well: adjusted R 2 =0.68 and 0.76, respectively, and root mean square error=3.4 and 2.1ppb, respectively. The predictions from the routine-LUR-All model were highly correlated with the measured NO 2 concentrations at evaluation sites. Although the predicted NO 2 concentrations from each model were correlated, the LUR models based on routine networks, and particularly those based on all monitoring sites, provided better visual representations of the local road conditions in the city. The present study demonstrated that LUR models based on routine data could estimate local traffic-related air pollution in an urban area. The importance and usefulness of data from routine monitoring networks should be acknowledged. Copyright © 2018 Elsevier B.V. All rights reserved.

Development of a distance-to-roadway proximity metric to compare near-road pollutant levels to a central site monitor

NASA Astrophysics Data System (ADS)

Barzyk, Timothy M.; George, Barbara Jane; Vette, Alan F.; Williams, Ronald W.; Croghan, Carry W.; Stevens, Carvin D.

The primary objective of the Detroit Exposure and Aerosol Research Study (DEARS) was to compare air pollutant concentrations measured at various neighborhoods, or exposure monitoring areas (EMAs), throughout a major metropolitan area to levels measured at a central site or community monitor. One of the EMAs was located near a busy freeway (annual average daily traffic (AADT) of ˜130,000) so that impacts of mobile sources could be examined. Air pollution concentrations from the roadway-proximate sites were compared to the central site monitor. The volatile organic compounds (VOCs) selected (benzene, toluene, ethylbenzene, m,p- and o-xylene, 1,3 butadiene, 1,3,5-trimethylbenzene and 4-ethyltoluene) are typically associated with mobile sources. Gradients were also evident that demonstrated the amplification of pollutant levels near the roadway compared to the community monitor. A novel distance-to-roadway proximity metric was developed to plot the measurements and model these gradients. Effective distance represents the actual distance an air parcel travels from the middle of a roadway to a site and varies as a function of wind direction, whereas perpendicular distance is a fixed distance oriented normal to the roadway. Perpendicular distance is often used as a proxy for exposures to traffic emissions in epidemiological studies. Elevated concentrations of all the compounds were found for both a summer and winter season. Effective distance was found to be a statistically significant ( p < 0.05) univariate predictor for concentrations of toluene, ethylbenzene, m,p-xylene and o-xylene for summer 2005. For each of these pollutants, effective distance yielded lower p-values than the corresponding perpendicular distance models, and model fit improved. Results demonstrate that this near-road EMA had elevated levels of traffic-related VOCs compared to the community monitor, and that effective distance was a more accurate predictor of the degree to which they were elevated as a function of distance. Effective distance produced a range of distance-to-roadway values for a single site based on wind direction, thus increasing the number and range of values that could be used to plot and predict relative differences in pollutant concentrations between two sites.

Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5

NASA Astrophysics Data System (ADS)

Ausati, Shadi; Amanollahi, Jamil

2016-10-01

Since Sanandaj is considered one of polluted cities of Iran, prediction of any type of pollution especially prediction of suspended particles of PM2.5, which are the cause of many diseases, could contribute to health of society by timely announcements and prior to increase of PM2.5. In order to predict PM2.5 concentration in the Sanandaj air the hybrid models consisting of an ensemble empirical mode decomposition and general regression neural network (EEMD-GRNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), principal component regression (PCR), and linear model such as multiple liner regression (MLR) model were used. In these models the data of suspended particles of PM2.5 were the dependent variable and the data related to air quality including PM2.5, PM10, SO2, NO2, CO, O3 and meteorological data including average minimum temperature (Min T), average maximum temperature (Max T), average atmospheric pressure (AP), daily total precipitation (TP), daily relative humidity level of the air (RH) and daily wind speed (WS) for the year 2014 in Sanandaj were the independent variables. Among the used models, EEMD-GRNN model with values of R2 = 0.90, root mean square error (RMSE) = 4.9218 and mean absolute error (MAE) = 3.4644 in the training phase and with values of R2 = 0.79, RMSE = 5.0324 and MAE = 3.2565 in the testing phase, exhibited the best function in predicting this phenomenon. It can be concluded that hybrid models have accurate results to predict PM2.5 concentration compared with linear model.

Modeling indoor air pollution from cookstove emissions in developing countries using a Monte Carlo single-box model

NASA Astrophysics Data System (ADS)

Johnson, Michael; Lam, Nick; Brant, Simone; Gray, Christen; Pennise, David

2011-06-01

A simple Monte Carlo single-box model is presented as a first approach toward examining the relationship between emissions of pollutants from fuel/cookstove combinations and the resulting indoor air pollution (IAP) concentrations. The model combines stove emission rates with expected distributions of kitchen volumes and air exchange rates in the developing country context to produce a distribution of IAP concentration estimates. The resulting distribution can be used to predict the likelihood that IAP concentrations will meet air quality guidelines, including those recommended by the World Health Organization (WHO) for fine particulate matter (PM2.5) and carbon monoxide (CO). The model can also be used in reverse to estimate the probability that specific emission factors will result in meeting air quality guidelines. The modeled distributions of indoor PM2.5 concentration estimated that only 4% of homes using fuelwood in a rocket-style cookstove, even under idealized conditions, would meet the WHO Interim-1 annual PM2.5 guideline of 35 μg m-3. According to the model, the PM2.5 emissions that would be required for even 50% of homes to meet this guideline (0.055 g MJ-delivered-1) are lower than those for an advanced gasifier fan stove, while emissions levels similar to liquefied petroleum gas (0.018 g MJ-delivered-1) would be required for 90% of homes to meet the guideline. Although the predicted distribution of PM concentrations (median = 1320 μg m-3) from inputs for traditional wood stoves was within the range of reported values for India (108-3522 μg m-3), the model likely overestimates IAP concentrations. Direct comparison with simultaneously measured emissions rates and indoor concentrations of CO indicated the model overestimated IAP concentrations resulting from charcoal and kerosene emissions in Kenyan kitchens by 3 and 8 times respectively, although it underestimated the CO concentrations resulting from wood-burning cookstoves in India by approximately one half. The potential overestimation of IAP concentrations is thought to stem from the model's assumption that all stove emissions enter the room and are completely mixed. Future versions of the model may be improved by incorporating these factors into the model, as well as more comprehensive and representative data on stove emissions performance, daily cooking energy requirements, and kitchen characteristics.

Hydroxyl and Hydroperoxy Radical Chemistry during the MCMA-2006 Field Campaign: Measurement and Model Comparison

NASA Astrophysics Data System (ADS)

Dusanter, S.; Vimal, D.; Stevens, P. S.; Volkamer, R.; Molina, L. T.

2007-12-01

The Mexico City Metropolitan Area (MCMA) field campaign, held in March 2006, was a unique opportunity to collect data in one of the most polluted megacities in the world. Such environments exhibit a complex oxidation chemistry involving a strong coupling between odd hydrogen radicals (HOX=OH+HO2) and nitrogen oxides species (NOX=NO+NO2). High levels of volatile organic compounds (VOCs) and NOX control the HOX budget and lead to elevated tropospheric ozone formation. The HOX-NOX coupling can be investigated by comparing measured and model-predicted HOx concentrations. Atmospheric HOX concentrations were measured by the Indiana University laser-induced fluorescence instrument and data were collected at the Instituto Mexicano del Petroleo between 14 and 31 March. Measured hydroxyl radical (OH) concentrations are comparable to that measured in less polluted urban environments and suggest that the OH concentrations are highly buffered under high NOX conditions. In contrast, hydroperoxy radical (HO2) concentrations are more sensitive to the NOX levels and are highly variable between different urban sites. Enhanced levels of OH and HO2 radicals were observed on several days between 9h30-11h00 AM and suggest an additional HOX source for the morning hours and/or a fast HOX cycling under the high NOX conditions of the MCMA. A preliminary investigation of the HOX chemistry occurring in the MCMA urban atmosphere was performed using a photochemical box model based on the Regional Atmospheric Chemistry Mechanism (RACM). Model comparisons will be presented and the agreement between measured and predicted HOX concentrations will be discussed.

Retrieve Aerosol Concentration Based On Surface Model and Distribution of Concentration of PM2.5 ——A Case Study of Beijing

NASA Astrophysics Data System (ADS)

Cui, H.

2017-12-01

As China's economy continues to grow, urbanization continues to advance, along with growth in all areas to pollutant emissions in the air industry, air quality also continued to deteriorate. Aerosol concentrations as a measure of air quality of the most important part of are more and more people's attention. Traditional monitoring stations measuring aerosol concentration method is accurate, but time-consuming and can't be done simultaneously measure a large area, can only rely on data from several monitoring sites to predict the concentration of the panorama. Remote Sensing Technology retrieves aerosol concentrations being by virtue of their efficient, fast advantages gradually into sight. In this paper, by the method of surface model to start with the physical processes of atmospheric transport, innovative aerosol concentration coefficient proposed to replace the traditional aerosol concentrations, pushed to a set of retrieval of aerosol concentration coefficient method, enabling fast and efficient Get accurate air pollution target area. At the same paper also monitoring data for PM2.5 in Beijing were analyzed from different angles, from the perspective of the data summarized in Beijing PM2.5 concentration of time, space, geographical distribution and concentration of PM2.5 and explored the relationship between aerosol concentration coefficient and concentration of PM2.5.

Relation between different metal pollution criteria in sediments and its contribution on assessing toxicity.

PubMed

Alves, Cristina M; Ferreira, Carlos M H; Soares, Helena M V M

2018-05-14

Several tools have been developed and applied to evaluate the metal pollution status of sediments and predict their potential ecological risk assessment. To date, a comprehensive relationship between the information given by these sediment tools for predicting metal bioavailability and the effective toxicity observed is lacking. In this work, the possible inter-correlations between the data outcoming from using several qualitative evaluation tools of the sediment contamination (contamination factor, CF, the enrichment factor, EF, or the geoaccumulation index, Igeo), metal speciation on sediments (evaluated by the modified BCR sequential extraction procedure) and free metal concentrations in pore waters were studied. It was also our aim to evaluate if these assessment tools could be used for predicting the pore waters toxicity data as toxicity proxy. Principal component analysis and cluster analysis revealed that two quality indices used (CF and EF) were highly correlatable with the more labile fractions from BCR sediment speciation. However, neither of these parameters did correlate with the toxicity of pore waters measured by the chronic toxicity (72 h) in Pseudokirchneriella subcapitata. In contrast, the toxic effects of the given total metal load in sediments were better evaluated by using an additive metal approach using pore water free metal concentrations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Analysis of carbon monoxide (CO) with Delhi Finite Line Source (DFLS) in MT Haryono Street, Medan City

NASA Astrophysics Data System (ADS)

Turmuzi, M.; Suryati, I.; Mashaly, E. T.; Batubara, F.

2018-02-01

One source to decrease urban air ambient quality is transportation sector. Important pollutants are released by gas emissions from vehicles are carbon monoxide (CO), hydrocarbons (HC), nitrogen dioxide (NO2), particulate matter and others. The presence of CO pollutants in the ambient air can be predicted by modeling air quality. This study aims to estimate CO concentration resulting from transportation activities using Delhi Finite Line Source (DFLS) model, comparing CO prediction using a DFLS model with CO observation in the field, and determine the suitability of the DFLS model application on the MT Haryono street in Medan City. Research was conducted for 3 days at two sample points with frequency twice daily. Based on research results, the range of CO concentration from observation between 22.903 μg/m3 - 27.484 μg/m3. CO observation is still below the ambient air quality standard. According to the DFLS calculations, the range of CO concentration between 1.499 μg/m3- 2.051 μg/m3. The calculation index of agreement (IOA) validation test obtained value of d = 0.22. The DFLS model is not suitable to be applied on MT Haryono street because many factors affected such as wind direction and wind velocity, ambient air temperature and humidity

[Implementation results of emission standards of air pollutants for thermal power plants: a numerical simulation].

PubMed

Wang, Zhan-Shan; Pan, Li-Bo

2014-03-01

The emission inventory of air pollutants from the thermal power plants in the year of 2010 was set up. Based on the inventory, the air quality of the prediction scenarios by implementation of both 2003-version emission standard and the new emission standard were simulated using Models-3/CMAQ. The concentrations of NO2, SO2, and PM2.5, and the deposition of nitrogen and sulfur in the year of 2015 and 2020 were predicted to investigate the regional air quality improvement by the new emission standard. The results showed that the new emission standard could effectively improve the air quality in China. Compared with the implementation results of the 2003-version emission standard, by 2015 and 2020, the area with NO2 concentration higher than the emission standard would be reduced by 53.9% and 55.2%, the area with SO2 concentration higher than the emission standard would be reduced by 40.0%, the area with nitrogen deposition higher than 1.0 t x km(-2) would be reduced by 75.4% and 77.9%, and the area with sulfur deposition higher than 1.6 t x km(-2) would be reduced by 37.1% and 34.3%, respectively.

Measurement and prediction of indoor air quality using a breathing thermal manikin.

PubMed

Melikov, A; Kaczmarczyk, J

2007-02-01

The analyses performed in this paper reveal that a breathing thermal manikin with realistic simulation of respiration including breathing cycle, pulmonary ventilation rate, frequency and breathing mode, gas concentration, humidity and temperature of exhaled air and human body shape and surface temperature is sensitive enough to perform reliable measurement of characteristics of air as inhaled by occupants. The temperature, humidity, and pollution concentration in the inhaled air can be measured accurately with a thermal manikin without breathing simulation if they are measured at the upper lip at a distance of <0.01 m from the face. Body surface temperature, shape and posture as well as clothing insulation have impact on the measured inhaled air parameters. Proper simulation of breathing, especially of exhalation, is needed for studying the transport of exhaled air between occupants. A method for predicting air acceptability based on inhaled air parameters and known exposure-response relationships established in experiments with human subjects is suggested. Recommendations for optimal simulation of human breathing by means of a breathing thermal manikin when studying pollution concentration, temperature and humidity of the inhaled air as well as the transport of exhaled air (which may carry infectious agents) between occupants are outlined. In order to compare results obtained with breathing thermal manikins, their nose and mouth geometry should be standardized.

Untreated runoff quality from roof and road surfaces in a low intensity rainfall climate.

PubMed

Charters, Frances J; Cochrane, Thomas A; O'Sullivan, Aisling D

2016-04-15

Sediment and heavy metals in stormwater runoff are key pollutants of urban waterways, and their presence in stormwater is driven by climatic factors such as rainfall intensity. This study describes the total suspended solids (TSS) and heavy metal concentrations found in runoff from four different urban surfaces within a residential/institutional catchment, in a climate where rainfall is typically of low intensity (<5.1mm·h(-1)). The results were compared to untreated runoff quality from a compilation of international studies. The road runoff had the highest TSS concentrations, while copper and galvanized roof runoff had the highest copper and zinc concentrations, respectively. Pollutant concentrations were found to be significantly different between surfaces; quantification and prediction of pollutant contributions from urban surfaces should thus take account of the different surface materials, instead of being aggregated into more generalized categories such as land use. The TSS and heavy metal concentrations were found to be at the low to medium end of ranges observed internationally, except for total copper and zinc concentrations generated by dissolution of copper and galvanized roofing material respectively; these concentrations were at least as high as those reported internationally. TSS wash-off from the roofs was seen to be a source-limited process, where all available TSS is washed off during the rain event despite the low intensity rainfall, whereas both road TSS and heavy metals wash-off from roof and road surfaces appeared to all be transport-limited and therefore some carryover of pollutants occurs between rain events. A first flush effect was seen from most surfaces for TSS, but not for heavy metals. This study demonstrates that in low intensity rainfall climates, quantification of untreated runoff quality from key individual surface types in a catchment are needed to enable development of targeted and appropriately sized stormwater treatment systems. Copyright © 2016 Elsevier B.V. All rights reserved.

A model for dispersion from area sources in convective turbulence. [for air pollution

NASA Technical Reports Server (NTRS)

Crane, G.; Panofsky, H. A.; Zeman, O.

1977-01-01

Four independent estimates of the vertical distribution of the eddy coefficient for dispersion of a passive contaminant from an extensive area source in a convective layer have been presented. The estimates were based on the following methods: (1) a second-order closure prediction, (2) field data of pollutant concentrations over Los Angeles, (3) lab measurements of particle dispersion, and (4) assumption of equality between momentum and mass transfer coefficients in the free convective limit. It is suggested that K-values estimated both from second-order closure theory and from Los Angeles measurements are systematically underestimated.

Has reducing fine particulate matter and ozone caused reduced mortality rates in the United States?

PubMed

Cox, Louis Anthony Tony; Popken, Douglas A

2015-03-01

Between 2000 and 2010, air pollutant levels in counties throughout the United States changed significantly, with fine particulate matter (PM2.5) declining over 30% in some counties and ozone (O3) exhibiting large variations from year to year. This history provides an opportunity to compare county-level changes in average annual ambient pollutant levels to corresponding changes in all-cause (AC) and cardiovascular disease (CVD) mortality rates over the course of a decade. Past studies have demonstrated associations and subsequently either interpreted associations causally or relied on subjective judgments to infer causation. This article applies more quantitative methods to assess causality. This article examines data from these "natural experiments" of changing pollutant levels for 483 counties in the 15 most populated US states using quantitative methods for causal hypothesis testing, such as conditional independence and Granger causality tests. We assessed whether changes in historical pollution levels helped to predict and explain changes in CVD and AC mortality rates. A causal relation between pollutant concentrations and AC or CVD mortality rates cannot be inferred from these historical data, although a statistical association between them is well supported. There were no significant positive associations between changes in PM2.5 or O3 levels and corresponding changes in disease mortality rates between 2000 and 2010, nor for shorter time intervals of 1 to 3 years. These findings suggest that predicted substantial human longevity benefits resulting from reducing PM2.5 and O3 may not occur or may be smaller than previously estimated. Our results highlight the potential for heterogeneity in air pollution health effects across regions, and the high potential value of accountability research comparing model-based predictions of health benefits from reducing air pollutants to historical records of what actually occurred. Copyright © 2015 Elsevier Inc. All rights reserved.

Optimum Installation of Sorptive Building Materials Using Contribution Ratio of Pollution Source for Improvement of Indoor Air Quality.

PubMed

Park, Seonghyun; Seo, Janghoo

2016-04-01

Reinforcing the insulation and airtightness of buildings and the use of building materials containing new chemical substances have caused indoor air quality problems. Use of sorptive building materials along with removal of pollutants, constant ventilation, bake-out, etc. are gaining attention in Korea and Japan as methods for improving such indoor air quality problems. On the other hand, sorptive building materials are considered a passive method of reducing the concentration of pollutants, and their application should be reviewed in the early stages. Thus, in this research, activated carbon was prepared as a sorptive building material. Then, computational fluid dynamics (CFD) was conducted, and a method for optimal installation of sorptive building materials was derived according to the indoor environment using the contribution ratio of pollution source (CRP) index. The results show that a method for optimal installation of sorptive building materials can be derived by predicting the contribution ratio of pollutant sources according to the CRP index.

Prediction of corridor effect from the launching of the satellite power system. [air pollutant concentration into narrow band of latitude

NASA Technical Reports Server (NTRS)

Borucki, W. J.; Whitten, R. C.; Woodward, H. T.; Capone, L. A.; Riegel, C. A.

1982-01-01

A diagnostic model is developed to define the parameters which control the corridor effect of contaminants deposited in a narrow latitudinal band of the earth's atmosphere by numerous launches of the STS and heavy lift launch vehicles for construction of satellite solar power systems. Identified factors included the pollution injection rate, the ambient background levels of the pollutant species, and the transport properties related to the dilution rate of the chemicals. If the chemical life of the pollutant was shorter or the same length of time as the transport time, alterations in the chemical production and loss rates were found to be parameters necessarily added to the model. A comparison with NASA Ames Research Center two-dimensional model results indicate that the corridor effect was possile with operations above 60 km in the case of H2O, H2, and NO production.

Sensitivity of two dispersion models (AERMOD and ISCST3) to input parameters for a rural ground-level area source.

PubMed

Faulkner, William B; Shaw, Bryan W; Grosch, Tom

2008-10-01

As of December 2006, the American Meteorological Society/U.S. Environmental Protection Agency (EPA) Regulatory Model with Plume Rise Model Enhancements (AERMOD-PRIME; hereafter AERMOD) replaced the Industrial Source Complex Short Term Version 3 (ISCST3) as the EPA-preferred regulatory model. The change from ISCST3 to AERMOD will affect Prevention of Significant Deterioration (PSD) increment consumption as well as permit compliance in states where regulatory agencies limit property line concentrations using modeling analysis. Because of differences in model formulation and the treatment of terrain features, one cannot predict a priori whether ISCST3 or AERMOD will predict higher or lower pollutant concentrations downwind of a source. The objectives of this paper were to determine the sensitivity of AERMOD to various inputs and compare the highest downwind concentrations from a ground-level area source (GLAS) predicted by AERMOD to those predicted by ISCST3. Concentrations predicted using ISCST3 were sensitive to changes in wind speed, temperature, solar radiation (as it affects stability class), and mixing heights below 160 m. Surface roughness also affected downwind concentrations predicted by ISCST3. AERMOD was sensitive to changes in albedo, surface roughness, wind speed, temperature, and cloud cover. Bowen ratio did not affect the results from AERMOD. These results demonstrate AERMOD's sensitivity to small changes in wind speed and surface roughness. When AERMOD is used to determine property line concentrations, small changes in these variables may affect the distance within which concentration limits are exceeded by several hundred meters.

STEMS-Air: a simple GIS-based air pollution dispersion model for city-wide exposure assessment.

PubMed

Gulliver, John; Briggs, David

2011-05-15

Current methods of air pollution modelling do not readily meet the needs of air pollution mapping for short-term (i.e. daily) exposure studies. The main limiting factor is that for those few models that couple with a GIS there are insufficient tools for directly mapping air pollution both at high spatial resolution and over large areas (e.g. city wide). A simple GIS-based air pollution model (STEMS-Air) has been developed for PM(10) to meet these needs with the option to choose different exposure averaging periods (e.g. daily and annual). STEMS-Air uses the grid-based FOCALSUM function in ArcGIS in conjunction with a fine grid of emission sources and basic information on meteorology to implement a simple Gaussian plume model of air pollution dispersion. STEMS-Air was developed and validated in London, UK, using data on concentrations of PM(10) from routinely available monitoring data. Results from the validation study show that STEMS-Air performs well in predicting both daily (at four sites) and annual (at 30 sites) concentrations of PM(10). For daily modelling, STEMS-Air achieved r(2) values in the range 0.19-0.43 (p<0.001) based solely on traffic-related emissions and r(2) values in the range 0.41-0.63 (p<0.001) when adding information on 'background' levels of PM(10). For annual modelling of PM(10), the model returned r(2) in the range 0.67-0.77 (P<0.001) when compared with monitored concentrations. The model can thus be used for rapid production of daily or annual city-wide air pollution maps either as a screening process in urban air quality planning and management, or as the basis for health risk assessment and epidemiological studies. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

Impacts of compact growth and electric vehicles on future air quality and urban exposures may be mixed.

PubMed

Yu, Haofei; Stuart, Amy L

2017-01-15

'Smart' growth and electric vehicles are potential solutions to the negative impacts of worldwide urbanization on air pollution and health. However, the effects of planning strategies on distinct types of pollutants, and on human exposures, remain understudied. The goal of this work was to investigate the potential impacts of alternative urban designs for the area around Tampa, Florida USA, on emissions, ambient concentrations, and exposures to oxides of nitrogen (NO x ), 1,3-butadiene, and benzene. We studied three potential future scenarios: sprawling growth, compact growth, and 100% vehicle fleet electrification with compact growth. We projected emissions in the seven-county region to 2050 based on One Bay regional visioning plan data. We estimated pollutant concentrations in the county that contains Tampa using the CALPUFF dispersion model. We applied residential population projections to forecast acute (highest hour) and chronic (annual average) exposure. The compact scenario was projected to result in lower regional emissions of all pollutants than sprawl, with differences of -18%, -3%, and -14% for NO x , butadiene, and benzene, respectively. Within Hillsborough County, the compact form also had lower emissions, concentrations, and exposures than sprawl for NO x (-16%/-5% for acute/chronic exposures, respectively), but higher exposures for butadiene (+41%/+30%) and benzene (+21%/+9%). The addition of complete vehicle fleet electrification to the compact scenario mitigated these in-county increases for the latter pollutants, lowering predicted exposures to butadiene (-25%/-39%) and benzene (-5%/-19%), but also resulted in higher exposures to NO x (+81%/+30%) due to increased demand on power plants. These results suggest that compact forms may have mixed impacts on exposures and health. 'Smart' urban designs should consider multiple pollutants and the diverse mix of pollutant sources. Cleaner power generation will also likely be needed to support aggressive adoption of electric vehicles. Copyright © 2016 Elsevier B.V. All rights reserved.

Entomopathogenic nematode food webs in an ancient, mining pollution gradient in Spain.

PubMed

Campos-Herrera, Raquel; Rodríguez Martín, José Antonio; Escuer, Miguel; García-González, María Teresa; Duncan, Larry W; Gutiérrez, Carmen

2016-12-01

Mining activities pollute the environment with by-products that cause unpredictable impacts in surrounding areas. Cartagena-La Unión mine (Southeastern-Spain) was active for >2500years. Despite its closure in 1991, high concentrations of metals and waste residues remain in this area. A previous study using nematodes suggested that high lead content diminished soil biodiversity. However, the effects of mine pollution on specific ecosystem services remain unknown. Entomopathogenic nematodes (EPN) play a major role in the biocontrol of insect pests. Because EPNs are widespread throughout the world, we speculated that EPNs would be present in the mined areas, but at increased incidence with distance from the pollution focus. We predicted that the natural enemies of nematodes would follow a similar spatial pattern. We used qPCR techniques to measure abundance of five EPN species, five nematophagous fungi species, two bacterial ectoparasites of EPNs and one group of free-living nematodes that compete for the insect-cadaver. The study comprised 193 soil samples taken from mining sites, natural areas and agricultural fields. The highest concentrations of iron and zinc were detected in the mined area as was previously described for lead, cadmium and nickel. Molecular tools detected very low numbers of EPNs in samples found to be negative by insect-baiting, demonstrating the importance of the approach. EPNs were detected at low numbers in 13% of the localities, without relationship to heavy-metal concentrations. Only Acrobeloides-group nematodes were inversely related to the pollution gradient. Factors associated with agricultural areas explained 98.35% of the biotic variability, including EPN association with agricultural areas. Our study suggests that EPNs have adapted to polluted habitats that might support arthropod hosts. By contrast, the relationship between abundance of Acrobeloides-group and heavy-metal levels, revealed these taxa as especially well suited bio-indicators of soil mining pollution. Copyright © 2016 Elsevier B.V. All rights reserved.

Development of mathematical techniques for the assimilation of remote sensing data into atmospheric models

NASA Technical Reports Server (NTRS)

Seinfeld, J. H. (Principal Investigator)

1982-01-01

The problem of the assimilation of remote sensing data into mathematical models of atmospheric pollutant species was investigated. The data assimilation problem is posed in terms of the matching of spatially integrated species burden measurements to the predicted three-dimensional concentration fields from atmospheric diffusion models. General conditions were derived for the reconstructability of atmospheric concentration distributions from data typical of remote sensing applications, and a computational algorithm (filter) for the processing of remote sensing data was developed.

Development of mathematical techniques for the assimilation of remote sensing data into atmospheric models

NASA Technical Reports Server (NTRS)

Seinfeld, J. H. (Principal Investigator)

1982-01-01

The problem of the assimilation of remote sensing data into mathematical models of atmospheric pollutant species was investigated. The problem is posed in terms of the matching of spatially integrated species burden measurements to the predicted three dimensional concentration fields from atmospheric diffusion models. General conditions are derived for the "reconstructability' of atmospheric concentration distributions from data typical of remote sensing applications, and a computational algorithm (filter) for the processing of remote sensing data is developed.

Regional severe particle pollution and its association with synoptic weather patterns in the Yangtze River Delta region, China

NASA Astrophysics Data System (ADS)

Shu, Lei; Xie, Min; Gao, Da; Wang, Tijian; Fang, Dexian; Liu, Qian; Huang, Anning; Peng, Liwen

2017-11-01

Regional air pollution is significantly associated with dominant weather systems. In this study, the relationship between the particle pollution over the Yangtze River Delta (YRD) region and weather patterns is investigated. First, the pollution characteristics of particles in the YRD are studied using in situ monitoring data (PM2.5 and PM10) in 16 cities and Terra/MODIS AOD (aerosol optical depth) products collected from December 2013 to November 2014. The results show that the regional mean value of AOD is high in the YRD, with an annual mean value of 0.71±0.57. The annual mean particle concentrations in the cities of Jiangsu Province all exceed the national air quality standard. The pollution level is higher in inland areas, and the highest concentrations of PM2.5 and PM10 are 79 and 130 µg m-3, respectively, in Nanjing. The PM2.5 : PM10 ratios are typically high, thus indicating that PM2.5 is the overwhelmingly dominant particle pollutant in the YRD. The wintertime peak of particle concentrations is tightly linked to the increased emissions during the heating season as well as adverse meteorological conditions. Second, based on NCEP (National Center for Environmental Prediction) reanalysis data, synoptic weather classification is conducted and five typical synoptic patterns are objectively identified. Finally, the synthetic analysis of meteorological fields and backward trajectories are applied to further clarify how these patterns impact particle concentrations. It is demonstrated that air pollution is more or less influenced by high-pressure systems. The relative position of the YRD to the anti-cyclonic circulation exerts significant effects on the air quality of the YRD. The YRD is largely influenced by polluted air masses from the northern and the southern inland areas when it is located at the rear of the East Asian major trough. The significant downward motion of air masses results in stable weather conditions, thereby hindering the diffusion of air pollutants. Thus, this pattern is quite favorable for the accumulation of pollutants in the YRD, resulting in higher regional mean PM10 (116.5 ± 66.9 µg m-3), PM2.5 (75.9 ± 49.9 µg m-3), and AOD (0.74) values. Moreover, this pattern is also responsible for the occurrence of most large-scale regional PM2.5 (70.4 %) and PM10 (78.3 %) pollution episodes. High wind speed and clean marine air masses may also play important roles in the mitigation of pollution in the YRD. Especially when the clean marine air masses account for a large proportion of all trajectories (i.e., when the YRD is affected by the cyclonic system or oceanic circulation), the air in the YRD has a lesser chance of being polluted. The observed correlation between weather patterns and particle pollution can provide valuable insight into making decisions about pollution control and mitigation strategies.

Application of an integrated Weather Research and Forecasting (WRF)/CALPUFF modeling tool for source apportionment of atmospheric pollutants for air quality management: A case study in the urban area of Benxi, China.

PubMed

Wu, Hao; Zhang, Yan; Yu, Qi; Ma, Weichun

2018-04-01

In this study, the authors endeavored to develop an effective framework for improving local urban air quality on meso-micro scales in cities in China that are experiencing rapid urbanization. Within this framework, the integrated Weather Research and Forecasting (WRF)/CALPUFF modeling system was applied to simulate the concentration distributions of typical pollutants (particulate matter with an aerodynamic diameter <10 μm [PM 10 ], sulfur dioxide [SO 2 ], and nitrogen oxides [NO x ]) in the urban area of Benxi. Statistical analyses were performed to verify the credibility of this simulation, including the meteorological fields and concentration fields. The sources were then categorized using two different classification methods (the district-based and type-based methods), and the contributions to the pollutant concentrations from each source category were computed to provide a basis for appropriate control measures. The statistical indexes showed that CALMET had sufficient ability to predict the meteorological conditions, such as the wind fields and temperatures, which provided meteorological data for the subsequent CALPUFF run. The simulated concentrations from CALPUFF showed considerable agreement with the observed values but were generally underestimated. The spatial-temporal concentration pattern revealed that the maximum concentrations tended to appear in the urban centers and during the winter. In terms of their contributions to pollutant concentrations, the districts of Xihu, Pingshan, and Mingshan all affected the urban air quality to different degrees. According to the type-based classification, which categorized the pollution sources as belonging to the Bengang Group, large point sources, small point sources, and area sources, the source apportionment showed that the Bengang Group, the large point sources, and the area sources had considerable impacts on urban air quality. Finally, combined with the industrial characteristics, detailed control measures were proposed with which local policy makers could improve the urban air quality in Benxi. In summary, the results of this study showed that this framework has credibility for effectively improving urban air quality, based on the source apportionment of atmospheric pollutants. The authors endeavored to build up an effective framework based on the integrated WRF/CALPUFF to improve the air quality in many cities on meso-micro scales in China. Via this framework, the integrated modeling tool is accurately used to study the characteristics of meteorological fields, concentration fields, and source apportionments of pollutants in target area. The impacts of classified sources on air quality together with the industrial characteristics can provide more effective control measures for improving air quality. Through the case study, the technical framework developed in this study, particularly the source apportionment, could provide important data and technical support for policy makers to assess air pollution on the scale of a city in China or even the world.

Burden of Mortality and Disease Attributable to Multiple Air Pollutants in Warsaw, Poland

PubMed Central

Kałuszko, Andrzej; Nahorski, Zbigniew

2017-01-01

Air pollution is a significant public health issue all over the world, especially in urban areas where a large number of inhabitants are affected. In this study, we quantify the health burden due to local air pollution for Warsaw, Poland. The health impact of the main air pollutants, PM, NOX, SO2, CO, C6H6, BaP and heavy metals is considered. The annual mean concentrations are predicted with the CALPUFF air quality modeling system using the year 2012 emission and meteorological data. The emission field comprises point, mobile and area sources. The exposure to these pollutants was estimated using population data with a spatial resolution of 0.5 × 0.5 km2. Changes in mortality and in disability-adjusted life-years (DALYs) were estimated with relative risk functions obtained from literature. It has been predicted that local emissions cause approximately 1600 attributable deaths and 29,000 DALYs per year. About 80% of the health burden was due to exposure to fine particulate matter (PM2.5). Mobile and area sources contributed 46% and 52% of total DALYs, respectively. When the inflow from outside was included, the burden nearly doubled to 51,000 DALYs. These results indicate that local decisions can potentially reduce associated negative health effects, but a national-level policy is required for reducing the strong environmental impact of PM emissions. PMID:29117145

Quantifying the health impacts of air pollution under a changing climate-a review of approaches and methodology.

PubMed

Sujaritpong, Sarunya; Dear, Keith; Cope, Martin; Walsh, Sean; Kjellstrom, Tord

2014-03-01

Climate change has been predicted to affect future air quality, with inevitable consequences for health. Quantifying the health effects of air pollution under a changing climate is crucial to provide evidence for actions to safeguard future populations. In this paper, we review published methods for quantifying health impacts to identify optimal approaches and ways in which existing challenges facing this line of research can be addressed. Most studies have employed a simplified methodology, while only a few have reported sensitivity analyses to assess sources of uncertainty. The limited investigations that do exist suggest that examining the health risk estimates should particularly take into account the uncertainty associated with future air pollution emissions scenarios, concentration-response functions, and future population growth and age structures. Knowledge gaps identified for future research include future health impacts from extreme air pollution events, interactions between temperature and air pollution effects on public health under a changing climate, and how population adaptation and behavioural changes in a warmer climate may modify exposure to air pollution and health consequences.

Pollution of HCHs, DDTs and PCBs in tidal flat of Hangzhou Bay 2009-2013

NASA Astrophysics Data System (ADS)

Zhao, Peng; Gong, Wenjie; Mao, Guohua; Li, Jige; Xu, Fenfen; Shi, Jiawei

2016-05-01

The concentration and distribution of three persistent organic pollutants (hexachlorocyclohexanes (HCHs), dichlorodiphenyltrichloroethanes (DDTs) and polychlorinated biphenyls (PCBs)) was assessed in tidal flat sediments collected from the south bank of Hangzhou Bay, China from 2009 to 2013. Gas chromatography coupled to triple quadrupole mass spectrometry (GC-MS/MS) was used for analysis, based on United States Environmental Protection Agency methods EPA8080A, EPA8081B, and EPA3550B. The results showed that the levels of HCHs, DDTs and PCBs decreased in the order of DDTs < HCHs < PCBs, and their mass fractions ranged from 0.29-32.91, 0.09-13.19 and 0.16-4.10 μg/kg (dry mass), respectively. The levels of HCHs, DDTs and PCBs decreased slowly from 2009 to 2013, with considerably greater concentrations in winter than in spring and summer. In this study area, the concentrations of DDTs and HCHs decreased gradually towards the mouth of Hangzhou Bay, while the concentrations of PCBs were related to changes in the local economy. In addition, the sources of HCHs and DDTs were identified as atmospheric precipitation and historical residues. Finally, we predicted that PCBs pollution primarily originated from Aroclor 1254(Lot A4), which might root in the illegal demolition and stacking of abandoned paint, transformer or electronic equipment in the south bank of Hangzhou Bay.

High variation subarctic topsoil pollutant concentration prediction using neural network residual kriging

NASA Astrophysics Data System (ADS)

Sergeev, A. P.; Tarasov, D. A.; Buevich, A. G.; Subbotina, I. E.; Shichkin, A. V.; Sergeeva, M. V.; Lvova, O. A.

2017-06-01

The work deals with the application of neural networks residual kriging (NNRK) to the spatial prediction of the abnormally distributed soil pollutant (Cr). It is known that combination of geostatistical interpolation approaches (kriging) and neural networks leads to significantly better prediction accuracy and productivity. Generalized regression neural networks and multilayer perceptrons are classes of neural networks widely used for the continuous function mapping. Each network has its own pros and cons; however both demonstrated fast training and good mapping possibilities. In the work, we examined and compared two combined techniques: generalized regression neural network residual kriging (GRNNRK) and multilayer perceptron residual kriging (MLPRK). The case study is based on the real data sets on surface contamination by chromium at a particular location of the subarctic Novy Urengoy, Russia, obtained during the previously conducted screening. The proposed models have been built, implemented and validated using ArcGIS and MATLAB environments. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. MLRPK showed the best predictive accuracy comparing to the geostatistical approach (kriging) and even to GRNNRK.

Forecasting air quality time series using deep learning.

PubMed

Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

2018-04-13

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.

Linking In-Vehicle Ultrafine Particle Exposures to On-Road Concentrations

PubMed Central

Hudda, Neelakshi; Eckel, Sandrah P.; Knibbs, Luke D.; Sioutas, Constantinos; Delfino, Ralph J.; Fruin, Scott A.

2013-01-01

For traffic-related pollutants like ultrafine particles (UFP, Dp < 100 nm), a significant fraction of overall exposure occurs within or close to the transit microenvironment. Therefore, understanding exposure to these pollutants in such microenvironments is crucial to accurately assessing overall UFP exposure. The aim of this study was to develop models for predicting in-cabin UFP concentrations if roadway concentrations are known, taking into account vehicle characteristics, ventilation settings, driving conditions and air exchange rates (AER). Particle concentrations and AER were measured in 43 and 73 vehicles, respectively, under various ventilation settings and driving speeds. Multiple linear regression (MLR) and generalized estimating equation (GEE) regression models were used to identify and quantify the factors that determine inside-to-outside (I/O) UFP ratios and AERs across a full range of vehicle types and ages. AER was the most significant determinant of UFP I/O ratios, and was strongly influenced by ventilation setting (recirculation or outside air intake). Inclusion of ventilation fan speed, vehicle age or mileage, and driving speed explained greater than 79% of the variability in measured UFP I/O ratios. PMID:23888122

An assessment of air pollution and its attributable mortality in Ulaanbaatar, Mongolia.

PubMed

Allen, Ryan W; Gombojav, Enkhjargal; Barkhasragchaa, Baldorj; Byambaa, Tsogtbaatar; Lkhasuren, Oyuntogos; Amram, Ofer; Takaro, Tim K; Janes, Craig R

2013-03-01

Epidemiologic studies have consistently reported associations between outdoor fine particulate matter (PM 2.5 ) air pollution and adverse health effects. Although Asia bears the majority of the public health burden from air pollution, few epidemiologic studies have been conducted outside of North America and Europe due in part to challenges in population exposure assessment. We assessed the feasibility of two current exposure assessment techniques, land use regression (LUR) modeling and mobile monitoring, and estimated the mortality attributable to air pollution in Ulaanbaatar, Mongolia. We developed LUR models for predicting wintertime spatial patterns of NO 2 and SO 2 based on 2-week passive Ogawa measurements at 37 locations and freely available geographic predictors. The models explained 74% and 78% of the variance in NO 2 and SO 2 , respectively. Land cover characteristics derived from satellite images were useful predictors of both pollutants. Mobile PM 2.5 monitoring with an integrating nephelometer also showed promise, capturing substantial spatial variation in PM 2.5 concentrations. The spatial patterns in SO 2 and PM, seasonal and diurnal patterns in PM 2.5 , and high wintertime PM 2.5 /PM 10 ratios were consistent with a major impact from coal and wood combustion in the city's low-income traditional housing (ger) areas. The annual average concentration of PM 2.5 measured at a centrally located government monitoring site was 75 μg/m 3 or more than seven times the World Health Organization's PM 2.5 air quality guideline, driven by a wintertime average concentration of 148 μg/m 3 . PM 2.5 concentrations measured in a traditional housing area were higher, with a wintertime mean PM 2.5 concentration of 250 μg/m 3 . We conservatively estimated that 29% (95% CI, 12-43%) of cardiopulmonary deaths and 40% (95% CI, 17-56%) of lung cancer deaths in the city are attributable to outdoor air pollution. These deaths correspond to nearly 10% of the city's total mortality, with estimates ranging to more than 13% of mortality under less conservative model assumptions. LUR models and mobile monitoring can be successfully implemented in developing country cities, thus cost-effectively improving exposure assessment for epidemiology and risk assessment. Air pollution represents a major threat to public health in Ulaanbaatar, Mongolia, and reducing home heating emissions in traditional housing areas should be the primary focus of air pollution control efforts.

Effect of dissolved oxygen concentration (microaerobic and aerobic) on selective enrichment culture for bioaugmentation of acidic industrial wastewater.

PubMed

Quan, Ying; Han, Hui; Zheng, Shaokui

2012-09-01

The successful application of bioaugmentation is largely dependent on the selective enrichment of culture with regards to pH, temperature, salt, or specific toxic organic pollutants. In this study, we investigated the effect of dissolved oxygen (DO) concentrations (aerobic, >2 mg L(-1); microaerobic, <1 mg L(-1)) on yeast enrichment culture for bioaugmentation of acidic industrial wastewater (pH 3.9-4.7). Clone library analyses revealed that the yeast community shifted in response to different DO levels, and that Candida humilis and Candida pseudolambica were individually dominant in the aerobic and microaerobic enrichment cultures. This would significantly influence the isolation results, and further hinder bioaugmentation due to differences in DO environments during the enrichment and application periods. However, differences in the selective enrichment culture cannot be predicted based on differences in pollutant removal performance. Thus, DO concentrations (aerobic/microaerobic) should be considered a secondary selective pressure to achieve successful bioaugmentation. Copyright © 2012 Elsevier Ltd. All rights reserved.

Optimization of Malachite Green Removal from Water by TiO₂ Nanoparticles under UV Irradiation.

PubMed

Ma, Yongmei; Ni, Maofei; Li, Siyue

2018-06-13

TiO₂ nanoparticles with surface porosity were prepared by a simple and efficient method and presented for the removal of malachite green (MG), a representative organic pollutant, from aqueous solution. Photocatalytic degradation experiments were systematically conducted to investigate the influence of TiO₂ dosage, pH value, and initial concentrations of MG. The kinetics of the reaction were monitored via UV spectroscopy and the kinetic process can be well predicted by the pseudo first-order model. The rate constants of the reaction kinetics were found to decrease as the initial MG concentration increased; increased via elevated pH value at a certain amount of TiO₂ dosage. The maximum efficiency of photocatalytic degradation was obtained when the TiO₂ dosage, pH value and initial concentrations of MG were 0.6 g/L, 8 and 10 −5 mol/L (M), respectively. Results from this study provide a novel optimization and an efficient strategy for water pollutant treatment.

Modeling of wastewater quality in an urban area during festival and rainy days.

PubMed

Obaid, H A; Shahid, S; Basim, K N; Chelliapan, S

2015-01-01

Water pollution during festival periods is a major problem in all festival cities across the world. Reliable prediction of water pollution is essential in festival cities for sewer and wastewater management in order to ensure public health and a clean environment. This article aims to model the biological oxygen demand (BOD(5)), and total suspended solids (TSS) parameters in wastewater in the sewer networks of Karbala city center during festival and rainy days using structural equation modeling and multiple linear regression analysis methods. For this purpose, 34 years (1980-2014) of rainfall, temperature and sewer flow data during festival periods in the study area were collected, processed, and employed. The results show that the TSS concentration increases by 26-46 mg/l while BOD(5) concentration rises by 9-19 mg/l for an increase of rainfall by 1 mm during festival periods. It was also found that BOD(5) concentration rises by 4-17 mg/l for each increase of 10,000 population.

Investigations of VOCs in and around buildings close to service stations

NASA Astrophysics Data System (ADS)

Hicklin, William; Farrugia, Pierre S.; Sinagra, Emmanuel

2018-01-01

Gas service stations are one of the major sources of volatile organic compounds in urban environments. Their emissions are expected not only to affect the ambient air quality but also that in any nearby buildings. This is particularly the case in Malta where most service stations have been built within residential zones. For this reason, it is important to understand the dispersion of volatile organic compounds (VOCs) from service stations and their infiltration into nearby residences. Two models were considered; one to predict the dispersion of VOCs in the outdoor environment in the vicinity of the service station and another one to predict the filtration of the compounds indoors. The two models can be used in tandem to predict the concentration of indoor VOCs that originate from a service station in the vicinity. Outdoor and indoor concentrations of VOCs around a service station located in a street canyon were measured, and the results used to validate the models. Predictions made using the models were found to be in general agreement with the measured concentrations of the pollutants.

Study of indoor radon distribution using measurements and CFD modeling.

PubMed

Chauhan, Neetika; Chauhan, R P; Joshi, M; Agarwal, T K; Aggarwal, Praveen; Sahoo, B K

2014-10-01

Measurement and/or prediction of indoor radon ((222)Rn) concentration are important due to the impact of radon on indoor air quality and consequent inhalation hazard. In recent times, computational fluid dynamics (CFD) based modeling has become the cost effective replacement of experimental methods for the prediction and visualization of indoor pollutant distribution. The aim of this study is to implement CFD based modeling for studying indoor radon gas distribution. This study focuses on comparison of experimentally measured and CFD modeling predicted spatial distribution of radon concentration for a model test room. The key inputs for simulation viz. radon exhalation rate and ventilation rate were measured as a part of this study. Validation experiments were performed by measuring radon concentration at different locations of test room using active (continuous radon monitor) and passive (pin-hole dosimeters) techniques. Modeling predictions have been found to be reasonably matching with the measurement results. The validated model can be used to understand and study factors affecting indoor radon distribution for more realistic indoor environment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Statistical analysis of PM₁₀ concentrations at different locations in Malaysia.

PubMed

Sansuddin, Nurulilyana; Ramli, Nor Azam; Yahaya, Ahmad Shukri; Yusof, Noor Faizah Fitri Md; Ghazali, Nurul Adyani; Madhoun, Wesam Ahmed Al

2011-09-01

Malaysia has experienced several haze events since the 1980s as a consequence of the transboundary movement of air pollutants emitted from forest fires and open burning activities. Hazy episodes can result from local activities and be categorized as "localized haze". General probability distributions (i.e., gamma and log-normal) were chosen to analyze the PM(10) concentrations data at two different types of locations in Malaysia: industrial (Johor Bahru and Nilai) and residential (Kota Kinabalu and Kuantan). These areas were chosen based on their frequently high PM(10) concentration readings. The best models representing the areas were chosen based on their performance indicator values. The best distributions provided the probability of exceedances and the return period between the actual and predicted concentrations based on the threshold limit given by the Malaysian Ambient Air Quality Guidelines (24-h average of 150 μg/m(3)) for PM(10) concentrations. The short-term prediction for PM(10) exceedances in 14 days was obtained using the autoregressive model.

Removal of emerging pharmaceutical contaminants by adsorption in a fixed-bed column: A review.

PubMed

Ahmed, M J; Hameed, B H

2018-03-01

Pharmaceutical pollutants substantially affect the environment; thus, their treatments have been the focus of many studies. In this article, the fixed-bed adsorption of pharmaceuticals on various adsorbents was reviewed. The experimental breakthrough curves of these pollutants under various flow rates, inlet concentrations, and bed heights were examined. Fixed-bed data in terms of saturation uptakes, breakthrough time, and the length of the mass transfer zone were included. The three most popular breakthrough models, namely, Adams-Bohart, Thomas, and Yoon-Nelson, were also reviewed for the correlation of breakthrough curve data along with the evaluation of model parameters. Compared with the Adams-Bohart model, the Thomas and Yoon-Nelson more effectively predicted the breakthrough data for the studied pollutants. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Impacts of Mercury Pollution Controls on Atmospheric Mercury Concentration and Occupational Mercury Exposure in a Hospital.

PubMed

Li, Ping; Yang, Yan; Xiong, Wuyan

2015-12-01

Mercury (Hg) and Hg-containing products are used in a wide range of settings in hospitals. Hg pollution control measures were carried out in the pediatric ward of a hospital to decrease the possibility of Hg pollution occurring and to decrease occupational Hg exposure. Total gaseous Hg (TGM) concentrations in the pediatric ward and hair and urine Hg concentrations for the pediatric staff were determined before and after the Hg pollution control measures had been implemented. A questionnaire survey performed indicated that the pediatric staff had little understanding of Hg pollution and that appropriate disposal techniques were not always used after Hg leakage. TGM concentrations in the pediatric ward and urine Hg (UHg) concentrations for the pediatric staff were 25.7 and 22.2% lower, respectively, after the Hg pollution control measures had been implemented than before, which indicated that the control measures were effective. However, TGM concentrations in the pediatric ward remained significantly higher than background concentrations and UHg concentrations for the pediatric staff were remained significantly higher than the concentrations in control group, indicating continued existence of certain Hg pollution.

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.

Prediction of traveltime and longitudinal dispersion in rivers and streams

USGS Publications Warehouse

Jobson, Harvey E.

1996-01-01

The possibility of a contaminant being accidentally or intentionally spilled upstream from a water supply is a constant concern to those diverting and using water from streams and rivers. Although many excellent models are available to estimate traveltime and dispersion, none can be used with confidence before calibration and verification to the particular river reach in question. Therefore, the availability of reliable input information is usually the weakest link in the chain of events needed to predict the rate of movement, dilution, and mixing of contaminants in rivers and streams. Measured tracer-response curves produced from the injection of a known quantity of soluble tracer provide an efficient method of obtaining the necessary data. The purpose of this report is to use previously presented concepts along with extensive data collected on time of travel and dispersion to provide guidance to water-resources managers and planners in responding to spills. This is done by providing methods to estimate (1) the rate of movement of a contaminant through a river reach, (2) the rate of attenuation of the peak concentration of a conservative contaminant with time, and (3) the length of time required for the contaminant plume to pass a point in the river. Although the accuracy of the predictions can be greatly increased by performing time-oftravel studies on the river reach in question, the emphasis of this report is on providing methods for making estimates where few data are available. Results from rivers of all sizes can be combined by defining the unit concentration as that concentration of a conservative pollutant that would result from injecting a unit of mass into a unit of flow. Unit-peak concentrations are compiled for more than 60 different rivers representing a wide range of sizes, slopes, and geomorphic types. Analyses of these data indicate that the unitpeak concentration is well correlated with the time required for a pollutant cloud to reach a specific point in the river. The variance among different rivers is, of course, larger than for a specific river reach. Other river characteristics that were compiled and included in the correlation included the drainage area, the reach slope, the mean annual discharge, and the discharge at the time of the measurement. The most significant other variable in the correlation was the ratio of the river discharge to mean annual discharge. The prediction of the traveltime is more difficult than the prediction of unit-peak concentration; but the logarithm of stream velocity can be assumed to be linearly correlated with the logarithm of discharge. More than 980 subreaches for about 90 different rivers were analyzed and prediction equations were developed based on the drainage area, the reach slope, the mean annual discharge, and the discharge at the time of the measurement. The highest probable velocity, which will result in the highest concentration, is usually of concern after an accidental spill. Therefore, an envelope curve for which more than 99 percent of the velocities were smaller was developed to address this concern. The time of arrival of the leading edge of the pollutant indicates when a problem will first exist and defines the overall shape of the tracer-response function. The traveltime of the leading edge is generally about 89 percent of the traveltime to the peak concentration. The area under a tracer-response function (a known value when unit concentrations are used) can be closely approximated as the area under a triangle with a height of the peak concentration and a base extending from the leading edge to a point where the concentration has reduced to 1C percent of the peak. Knowing the time of the leading edge and the peak, the peak concentration, and the time when the response function has reduced to 10 percent of its peak value allows the complete response function to be sketched with fair accuracy. Four example applications are included to illustrate how the prediction equations developed in this report can be used either to calibrate a mathematical model or to make predictions directly.

Protecting Astronaut Health at First Entry into Vehicles Visiting the international Space Station: Insights from Whole-Module Offgas Testing

NASA Technical Reports Server (NTRS)

Meyers, Valerie

2014-01-01

NASA has accumulated considerable experience in offgas testing of whole modules prior to their docking with the International Space Station (ISS). Since 1998, the Space Toxicology Office has performed offgas testing of the Lab module, both MPLM modules, US Airlock, Node 1, Node 2, Node 3, ATV1, HTV1, and three commercial vehicles. The goal of these tests is twofold: first, to protect the crew from adverse health effects of accumulated volatile pollutants when they first enter the module on orbit, and secondly, to determine the additional pollutant load that the ISS air revitalization systems must handle. In order to predict the amount of accumulated pollutants, the module is sealed for at least 1/5th the worst-case time interval that could occur between the last clean air purge and final hatch closure on the ground and the crew's first entry on orbit. This time can range from a few days to a few months. Typically, triplicate samples are taken at pre-planned times throughout the test. Samples are then analyzed by gas chromatography and mass spectrometry, and the rate of accumulation of pollutants is then extrapolated over time. The analytical values are indexed against 7-day spacecraft maximum allowable concentrations (SMACs) to provide a prediction of the total toxicity value (T-value) at the time of first entry. This T-value and the toxicological effects of specific pollutants that contribute most to the overall toxicity are then used to guide first entry operations. Finally, results are compared to first entry samples collected on orbit to determine the predictive ability of the ground-based offgas test.

Investigating Electrostatic Precipitator Design Parameters for Efficient Control of Particulate Matter in Thermal Power Plant: A Case Study

NASA Astrophysics Data System (ADS)

Rai, P.; Gautam, N.; Chandra, H.

2018-06-01

This work deals with the analysis and modification of operational parameters for meeting the emission standards, set by Central Pollution Control Board (CPCB)/State Pollution Control Board (SPCB) from time to time of electrostatic precipitator (ESP). The analysis is carried out by using standard chemical analysis supplemented by the relevant data collected from Korba East Phase (Ph)-III thermal power plant, under Chhattisgarh State Electricity Board (CSEB) operating at Korba, Chhattisgarh. Chemical analysis is used to predict the emission level for different parameters of ESP. The results reveal that for a constant outlet PM concentration and fly ash percentage, the total collection area decreases with the increase in migration velocity. For constant migration velocity and outlet PM concentration, the total collection area increases with the increase in the fly ash percent. For constant migration velocity and outlet e PM concentration, the total collection area increases with the ash content in the coal. i.e. from minimum ash to maximum ash. As far as the efficiency is concerned, it increases with the fly ash percent, ash content and the inlet dust concentration but decreases with the outlet PM concentration at constant migration velocity, fly ash and ash content.

Investigating Electrostatic Precipitator Design Parameters for Efficient Control of Particulate Matter in Thermal Power Plant: A Case Study

NASA Astrophysics Data System (ADS)

Rai, P.; Gautam, N.; Chandra, H.

2018-02-01

This work deals with the analysis and modification of operational parameters for meeting the emission standards, set by Central Pollution Control Board (CPCB)/State Pollution Control Board (SPCB) from time to time of electrostatic precipitator (ESP). The analysis is carried out by using standard chemical analysis supplemented by the relevant data collected from Korba East Phase (Ph)-III thermal power plant, under Chhattisgarh State Electricity Board (CSEB) operating at Korba, Chhattisgarh. Chemical analysis is used to predict the emission level for different parameters of ESP. The results reveal that for a constant outlet PM concentration and fly ash percentage, the total collection area decreases with the increase in migration velocity. For constant migration velocity and outlet PM concentration, the total collection area increases with the increase in the fly ash percent. For constant migration velocity and outlet e PM concentration, the total collection area increases with the ash content in the coal. i.e. from minimum ash to maximum ash. As far as the efficiency is concerned, it increases with the fly ash percent, ash content and the inlet dust concentration but decreases with the outlet PM concentration at constant migration velocity, fly ash and ash content.

Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research.

PubMed

Hanigan, Ivan C; Williamson, Grant J; Knibbs, Luke D; Horsley, Joshua; Rolfe, Margaret I; Cope, Martin; Barnett, Adrian G; Cowie, Christine T; Heyworth, Jane S; Serre, Marc L; Jalaludin, Bin; Morgan, Geoffrey G

2017-11-07

Exposure to traffic related nitrogen dioxide (NO 2 ) air pollution is associated with adverse health outcomes. Average pollutant concentrations for fixed monitoring sites are often used to estimate exposures for health studies, however these can be imprecise due to difficulty and cost of spatial modeling at the resolution of neighborhoods (e.g., a scale of tens of meters) rather than at a coarse scale (around several kilometers). The objective of this study was to derive improved estimates of neighborhood NO 2 concentrations by blending measurements with modeled predictions in Sydney, Australia (a low pollution environment). We implemented the Bayesian maximum entropy approach to blend data with uncertainty defined using informative priors. We compiled NO 2 data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighborhood annual average NO 2 . The spatial model produced a posterior probability density function of estimated annual average concentrations that spanned an order of magnitude from 3 to 35 ppb. Validation using independent data showed improvement, with root mean squared error improvement of 6% compared with the land use regression model and 16% over the chemical transport model. These estimates will be used in studies of health effects and should minimize misclassification bias.

A shoreline fumigation model with wind shear

NASA Astrophysics Data System (ADS)

Zhibian, Li; Zengquan, Yao

A fumigation model has been developed for a plume discharged from an elevated stack in a shoreline environment by introducing different wind directions above and within thermal internal boundary laye:r (TIBL) into a dispersion model. When a continuous point source release occurs above the TIBL pollutants will disperse in the marine stable flow, until the plume intersects the TIBL surface. The fumigation in ithe TIBL is interpreted as occurring from an area source on the imaginary surface of the TIBL. It is assumed that the wind direction varies with height above and below L( x) = Ax2, the height of the TIBL at the distance x. The change of wind direction above and within the TIBL causes the pollutants to change their direction of transport and leads to development of a curved ground level concentration (glc) axis; a decreasing glc along the centreline of the fumigation and a widening pollutant distribution in the transverse direction. Predicted concentration distributions using the wind shear model are compared with observations from an SF 6 tracer experiment near Hangzhou Bay in May-June of 1987. The comparison and an evaluation of the model performance show that the new model is not only more theoretically acceptable than those based on empirical coefficients but also provides concentration distributions which agree well with. SF 6 tracer experiments.

Characterising urban zinc generation to identify surface pollutant hotspots in a low intensity rainfall climate.

PubMed

Charters, F J; Cochrane, T A; O'Sullivan, A D

2017-09-01

Characterising stormwater runoff quality provides useful insights into the dynamics of pollutant generation and wash off rates. These can be used to prioritise stormwater management strategies. This study examined the effects of a low intensity rainfall climate on zinc contributions from different impermeable urban surface types. First flush (FF) and steady state samples were collected from seven different surfaces for characterisation, and the data were also used to calibrate an event-based pollutant load model to predict individual 'hotspot' surfaces across the catchment. Unpainted galvanised roofs generated very high concentrations of zinc, primarily in the more biologically available dissolved form. An older, unpainted galvanised roof had FF concentrations averaging 32,338 μg/L, while the new unpainted roof averaged 4,782 μg/L. Roads and carparks also had elevated zinc, but FF concentrations averaged only 822-1,584 μg/L. Modelling and mapping expected zinc loads from individual impermeable surfaces across the catchment identified specific commercial roof surfaces to be targeted for zinc management. The results validate a policy strategy to replace old galvanised roof materials and avoid unpainted galvanised roofing in future urban development for better urban water quality outcomes. In the interim, readily-implemented treatment options are required to help mitigate chronic zinc impacts on receiving waterways.

Simulation of polycyclic aromatic hydrocarbons transport in multimedia

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

Chen, L.; Chu, C.J.

1999-07-01

Many studies have indicated that the threat from toxic air pollutants such as VOCs comes not through inhalation by humans while the pollutants are in a gaseous state but through absorption when the pollutants are in a solid state such as in an aerosol or particulate form. Pollutants such as Polycyclic Aromatic Hydrocarbons (PAHs) usually exist in a semi-volatile state. To assess the risk of the PAHs, one needs to estimate the dose of the pollutants to which a human would be exposed through various pathways. In this study, the authors modified a Spatial Multimedia Compartmental Model (SMCM) originally developedmore » by UCLA Professor Cohen to predict the PAHs distribution among multimedia such as air, water, soil and sediment in the Taipei metropolitan area. Three PAHs were considered in this study. They are Benzo(a)pyrene, Pyrene and Chrysene. When PAHs are emitted into atmosphere, physical and chemical mechanisms may redistribute the PAHs among multimedia. Five cases of PAHs distribution in multimedia were simulated: (1) PAHs distribution in a dry condition, (2) PAHs distribution when there are different dry deposition velocities, (3) PAHs distribution under a single rainfall event, (4) PAHs distribution when there are different soil properties, (5) PAHs distribution under a random rainfall case. The simulation results are concluded: (1) In the dry case, the PAHs accumulate mostly in soil and air compartments, (2) Different dry depositing velocities will affect the PAHs distribution among compartments. (3) Different soil properties affect the PAHs concentration in the soil and sediment compartments, (4) The soil PAHs concentrations usually increase for those PAHs with a high solid/gas ratio. (5) The random rainfall only affects the PAHs concentration in the soil.« less

Air pollution and acute respiratory infections among children 0-4 years of age: an 18-year time-series study.

PubMed

Darrow, Lyndsey A; Klein, Mitchel; Flanders, W Dana; Mulholland, James A; Tolbert, Paige E; Strickland, Matthew J

2014-11-15

Upper and lower respiratory infections are common in early childhood and may be exacerbated by air pollution. We investigated short-term changes in ambient air pollutant concentrations, including speciated particulate matter less than 2.5 μm in diameter (PM2.5), in relation to emergency department (ED) visits for respiratory infections in young children. Daily counts of ED visits for bronchitis and bronchiolitis (n = 80,399), pneumonia (n = 63,359), and upper respiratory infection (URI) (n = 359,246) among children 0-4 years of age were collected from hospitals in the Atlanta, Georgia, area for the period 1993-2010. Daily pollutant measurements were combined across monitoring stations using population weighting. In Poisson generalized linear models, 3-day moving average concentrations of ozone, nitrogen dioxide, and the organic carbon fraction of particulate matter less than 2.5 μm in diameter (PM2.5) were associated with ED visits for pneumonia and URI. Ozone associations were strongest and were observed at low (cold-season) concentrations; a 1-interquartile range increase predicted a 4% increase (95% confidence interval: 2%, 6%) in visits for URI and an 8% increase (95% confidence interval: 4%, 13%) in visits for pneumonia. Rate ratios tended to be higher in the 1- to 4-year age group compared with infants. Results suggest that primary traffic pollutants, ozone, and the organic carbon fraction of PM2.5 exacerbate upper and lower respiratory infections in early life, and that the carbon fraction of PM2.5 is a particularly harmful component of the ambient particulate matter mixture. © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Health Impacts and Economic Costs of Air Pollution in the Metropolitan Area of Skopje.

PubMed

Martinez, Gerardo Sanchez; Spadaro, Joseph V; Chapizanis, Dimitris; Kendrovski, Vladimir; Kochubovski, Mihail; Mudu, Pierpaolo

2018-03-29

Urban outdoor air pollution, especially particulate matter, remains a major environmental health problem in Skopje, the capital of the former Yugoslav Republic of Macedonia. Despite the documented high levels of pollution in the city, the published evidence on its health impacts is as yet scarce. we obtained, cleaned, and validated Particulate Matter (PM) concentration data from five air quality monitoring stations in the Skopje metropolitan area, applied relevant concentration-response functions, and evaluated health impacts against two theoretical policy scenarios. We then calculated the burden of disease attributable to PM and calculated the societal cost due to attributable mortality. In 2012, long-term exposure to PM 2.5 (49.2 μg/m³) caused an estimated 1199 premature deaths (CI95% 821-1519). The social cost of the predicted premature mortality in 2012 due to air pollution was estimated at between 570 and 1470 million euros. Moreover, PM 2.5 was also estimated to be responsible for 547 hospital admissions (CI95% 104-977) from cardiovascular diseases, and 937 admissions (CI95% 937-1869) for respiratory disease that year. Reducing PM 2.5 levels to the EU limit (25 μg/m³) could have averted an estimated 45% of PM-attributable mortality, while achieving the WHO Air Quality Guidelines (10 μg/m³) could have averted an estimated 77% of PM-attributable mortality. Both scenarios would also attain significant reductions in attributable respiratory and cardiovascular hospital admissions. Besides its health impacts in terms of increased premature mortality and hospitalizations, air pollution entails significant economic costs to the population of Skopje. Reductions in PM 2.5 concentrations could provide substantial health and economic gains to the city.

Air Quality Response Modeling for Decision Support | Science ...

EPA Pesticide Factsheets

Air quality management relies on photochemical models to predict the responses of pollutant concentrations to changes in emissions. Such modeling is especially important for secondary pollutants such as ozone and fine particulate matter which vary nonlinearly with changes in emissions. Numerous techniques for probing pollutant-emission relationships within photochemical models have been developed and deployed for a variety of decision support applications. However, atmospheric response modeling remains complicated by the challenge of validating sensitivity results against observable data. This manuscript reviews the state of the science of atmospheric response modeling as well as efforts to characterize the accuracy and uncertainty of sensitivity results. The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being use

Assessing Contamination Potential of Nitrate-N in Groundwater of Lanyang Plain

NASA Astrophysics Data System (ADS)

Liang, Ching-Ping; Tu, Yu-Lin; Lin, Chien-Wen; Jang, Cheng-Shin

2013-04-01

Nitrate-N pollution is often relevant to agricultural activities such as the fertilization of crops. Significant increases in the nitrate-N pollution of groundwater are found in natural recharging zones of Taiwan. The increasing nitrate-N contamination seriously threatens public drinking water supply and human health. Constructing a correct map of aquifer contamination potential is an effective and feasible way to protect groundwater for quality assessment and management. Therefore, in this study, we use DRASTIC model with the help of geographic information system (GIS) to assess and predict the contamination potential of nitrate-N in the aquifer of Lanyang Plain, Taiwan. Seven factors of hydrogeology and hydrology, which includes seven parameters - Depth to groundwater, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity, are considered to carry out this assessment. The validity of the presented model is established by comparing the results with the measured nitrate concentration in wells within the study area. Adjusting factor weightings via the discriminant analysis is performed to improve the assessment and prediction. The analyzed results can provide residents with suggestive strategies against nitrate-N pollution in agricultural regions and government administrators with explicit information of Nitrate-N pollution extents when plans of water resources are considered.

Concentration and risk of pharmaceuticals in freshwater systems are related to the population density and the livestock units in Iberian Rivers.

PubMed

Osorio, Victoria; Larrañaga, Aitor; Aceña, Jaume; Pérez, Sandra; Barceló, Damià

2016-01-01

Considerable amounts of pharmaceuticals are used in human and veterinary medicine, which are not efficiently removed during wastewater and slurries treatment and subsequently entering continuously into freshwater systems. The intrinsic biological activity of these non-regulated pollutants turns their presence in the aquatic environment into an ecological matter of concern. We present the first quantitative study relating the presence of pharmaceuticals and their predicted ecotoxicological effects with human population and livestock units. Four representative Iberian River basins (Spain) were studied: Llobregat, Ebro, Júcar and Guadalquivir. The levels of pharmaceuticals were determined in surface water and sediment samples collected from 77 locations along their stream networks. Predicted total toxic units to algae, Daphnia and fish were estimated for pharmaceuticals detected in surface waters. The use of chemometrics enabled the study of pharmaceuticals for: their spatial distribution along the rivers in two consecutive years; their potential ecotoxicological risk to aquatic organisms; and the relationships among their occurrence and predicted ecotoxicity with human population and animal farming pressure. The Llobregat and the Ebro River basins were characterized as the most polluted and at highest ecotoxicological risk, followed by Júcar and Guadalquivir. No significant acute risks of pharmaceuticals to aquatic organisms were observed. However potential chronic ecotoxicological effects on algae could be expected at two hot spots of pharmaceuticals pollution identified in the Llobregat and Ebro basins. Analgesics/antiinflammatories, antibiotics and diuretics were the most relevant therapeutic groups across the four river basins. Among them, hydrochlorothiazide and gemfibrozil, as well as azithromycin and ibuprofen were widely spread and concentrated pharmaceuticals in surface waters and sediments, respectively. Regarding their predicted ecotoxicity, sertraline, gemfibrozil and loratidine were identified as the more concerning compounds. Significantly positive relationships were found among levels of pharmaceuticals and toxic units and population density and livestock units in both surface water and sediment matrices. Copyright © 2015. Published by Elsevier B.V.

Analysis of local scale tree-atmosphere interaction on pollutant concentration in idealized street canyons and application to a real urban junction

NASA Astrophysics Data System (ADS)

Buccolieri, Riccardo; Salim, Salim Mohamed; Leo, Laura Sandra; Di Sabatino, Silvana; Chan, Andrew; Ielpo, Pierina; de Gennaro, Gianluigi; Gromke, Christof

2011-03-01

This paper first discusses the aerodynamic effects of trees on local scale flow and pollutant concentration in idealized street canyon configurations by means of laboratory experiments and Computational Fluid Dynamics (CFD). These analyses are then used as a reference modelling study for the extension a the neighbourhood scale by investigating a real urban junction of a medium size city in southern Italy. A comparison with previous investigations shows that street-level concentrations crucially depend on the wind direction and street canyon aspect ratio W/H (with W and H the width and the height of buildings, respectively) rather than on tree crown porosity and stand density. It is usually assumed in the literature that larger concentrations are associated with perpendicular approaching wind. In this study, we demonstrate that while for tree-free street canyons under inclined wind directions the larger the aspect ratio the lower the street-level concentration, in presence of trees the expected reduction of street-level concentration with aspect ratio is less pronounced. Observations made for the idealized street canyons are re-interpreted in real case scenario focusing on the neighbourhood scale in proximity of a complex urban junction formed by street canyons of similar aspect ratios as those investigated in the laboratory. The aim is to show the combined influence of building morphology and vegetation on flow and dispersion and to assess the effect of vegetation on local concentration levels. To this aim, CFD simulations for two typical winter/spring days show that trees contribute to alter the local flow and act to trap pollutants. This preliminary study indicates that failing to account for the presence of vegetation, as typically practiced in most operational dispersion models, would result in non-negligible errors in the predictions.

To what extent can biogenic SOA be controlled?

PubMed

Carlton, Annmarie G; Pinder, Robert W; Bhave, Prakash V; Pouliot, George A

2010-05-01

The implicit assumption that biogenic secondary organic aerosol (SOA) is natural and can not be controlled hinders effective air quality management. Anthropogenic pollution facilitates transformation of naturally emitted volatile organic compounds (VOCs) to the particle phase, enhancing the ambient concentrations of biogenic secondary organic aerosol (SOA). It is therefore conceivable that some portion of ambient biogenic SOA can be removed by controlling emissions of anthropogenic pollutants. Direct measurement of the controllable fraction of biogenic SOA is not possible, but can be estimated through 3-dimensional photochemical air quality modeling. To examine this in detail, 22 CMAQ model simulations were conducted over the continental U.S. (August 15 to September 4, 2003). The relative contributions of five emitted pollution classes (i.e., NO(x), NH(3), SO(x), reactive non methane carbon (RNMC) and primary carbonaceous particulate matter (PCM)) on biogenic SOA were estimated by removing anthropogenic emissions of these pollutants, one at a time and all together. Model results demonstrate a strong influence of anthropogenic emissions on predicted biogenic SOA concentrations, suggesting more than 50% of biogenic SOA in the eastern U.S. can be controlled. Because biogenic SOA is substantially enhanced by controllable emissions, classification of SOA as biogenic or anthropogenic based solely on VOC origin is not sufficient to describe the controllable fraction.

Source tagging modeling study of regional contributions to acid rain in summer over Liaoning Province, Northeastern China.

PubMed

Gbaguidi, Alex E; Wang, Zifa; Wang, Wei; Yang, Ting; Chen, Huan-Sheng

2018-04-01

Strong acid rain was recently observed over Northeastern China, particularly in summer in Liaoning Province where alkaline dust largely neutralized acids in the past. This seems to be related to the regional transboundary pollution and poses new challenges in acid rain control scheme in China. In order to delve into the regional transport impact, and quantify its potential contributions to such an "eruption" of acid rain over Liaoning, this paper employs an online source tagging model in coupling with the Nested Air Quality Prediction Modeling System (NAQPMS). Validation of predictions shows the model capability in reproducing key meteorological and chemical features. Acid concentration over Liaoning is more pronounced in August (average of 0.087 mg/m 3 ) with strong pollutant import from regional sources against significant depletion of basic species. Seasonal mean contributions from regional sources are assessed at both lower and upper boundary layers to elucidate the main pathways of the impact of regional sources on acid concentration over Liaoning. At the upper layer (1.2 km), regional sources contribute to acid concentration over Liaoning by 67%, mainly from Shandong (16%), Hebei (13%), Tianjin (11%) and Korean Peninsula (9%). Identified main city-receptors in Liaoning are Dandong, Dalian, Chaohu, Yingkou, Liaoyang, Jinfu, Shengyang, Panjin, Tieling, Benxi, Anshan and Fushun. At lower layer (120 m) where Liaoning local contribution is dominant (58%), regional sources account for 39% in acid concentration. However, inter-municipal acid exchanges are prominent at this layer and many cities in Liaoning are revealed as important sources of local acid production. Seasonal acid contribution average within 1.2 km-120 m attains 55%, suggesting dominance of vertical pollutant transport from regional sources towards lower boundary layer in Liaoning. As direct environmental implication, this study provides policy makers with a perspective of regulating the regional transboundary environmental impact assessment in China with application to acid rain control. Copyright © 2018 Elsevier Ltd. All rights reserved.

Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality

NASA Astrophysics Data System (ADS)

Taylan, Osman

2017-02-01

High ozone concentration is an important cause of air pollution mainly due to its role in the greenhouse gas emission. Ozone is produced by photochemical processes which contain nitrogen oxides and volatile organic compounds in the lower atmospheric level. Therefore, monitoring and controlling the quality of air in the urban environment is very important due to the public health care. However, air quality prediction is a highly complex and non-linear process; usually several attributes have to be considered. Artificial intelligent (AI) techniques can be employed to monitor and evaluate the ozone concentration level. The aim of this study is to develop an Adaptive Neuro-Fuzzy inference approach (ANFIS) to determine the influence of peripheral factors on air quality and pollution which is an arising problem due to ozone level in Jeddah city. The concentration of ozone level was considered as a factor to predict the Air Quality (AQ) under the atmospheric conditions. Using Air Quality Standards of Saudi Arabia, ozone concentration level was modelled by employing certain factors such as; nitrogen oxide (NOx), atmospheric pressure, temperature, and relative humidity. Hence, an ANFIS model was developed to observe the ozone concentration level and the model performance was assessed by testing data obtained from the monitoring stations established by the General Authority of Meteorology and Environment Protection of Kingdom of Saudi Arabia. The outcomes of ANFIS model were re-assessed by fuzzy quality charts using quality specification and control limits based on US-EPA air quality standards. The results of present study show that the ANFIS model is a comprehensive approach for the estimation and assessment of ozone level and is a reliable approach to produce more genuine outcomes.

Antibiotics and antibiotic resistance genes in global lakes: A review and meta-analysis.

PubMed

Yang, Yuyi; Song, Wenjuan; Lin, Hui; Wang, Weibo; Du, Linna; Xing, Wei

2018-04-10

Lakes are an important source of freshwater, containing nearly 90% of the liquid surface fresh water worldwide. Long retention times in lakes mean pollutants from discharges slowly circulate around the lakes and may lead to high ecological risk for ecosystem and human health. In recent decades, antibiotics and antibiotic resistance genes (ARGs) have been regarded as emerging pollutants. The occurrence and distribution of antibiotics and ARGs in global freshwater lakes are summarized to show the pollution level of antibiotics and ARGs and to identify some of the potential risks to ecosystem and human health. Fifty-seven antibiotics were reported at least once in the studied lakes. Our meta-analysis shows that sulfamethoxazole, sulfamerazine, sulfameter, tetracycline, oxytetracycline, erythromycin, and roxithromycin were found at high concentrations in both lake water and lake sediment. There is no significant difference in the concentration of sulfonamides in lake water from China and that from other countries worldwide; however, there was a significant difference in quinolones. Erythromycin had the lowest predicted hazardous concentration for 5% of the species (HC 5 ) and the highest ecological risk in lakes. There was no significant difference in the concentration of sulfonamide resistance genes (sul1 and sul2) in lake water and river water. There is surprisingly limited research on the role of aquatic biota in propagation of ARGs in freshwater lakes. As an environment that is susceptible to cumulative build-up of pollutants, lakes provide an important environment to study the fate of antibiotics and transport of ARGs with a broad range of niches including bacterial community, aquatic plants and animals. Copyright © 2018 Elsevier Ltd. All rights reserved.

Sources identification of antibiotic pollution combining land use information and multivariate statistics.

PubMed

Li, Jia; Zhang, Haibo; Chen, Yongshan; Luo, Yongming; Zhang, Hua

2016-07-01

To quantify the extent of antibiotic contamination and to identity the dominant pollutant sources in the Tiaoxi River Watershed, surface water samples were collected at eight locations and analyzed for four tetracyclines and three sulfonamides using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The observed maximum concentrations of tetracycline (623 ng L(-1)), oxytetracycline (19,810 ng L(-1)), and sulfamethoxazole (112 ng L(-1)) exceeded their corresponding Predicted No Effect Concentration (PNEC) values. In particular, high concentrations of antibiotics were observed in wet summer with heavy rainfall. The maximum concentrations of antibiotics appeared in the vicinity of intensive aquaculture areas. High-resolution land use data were used for identifying diffuse source of antibiotic pollution in the watershed. Significant correlations between tetracycline and developed (r = 0.93), tetracycline and barren (r = 0.87), oxytetracycline and barren (r = 0.82), and sulfadiazine and agricultural facilities (r = 0.71) were observed. In addition, the density of aquaculture significantly correlated with doxycycline (r = 0.74) and oxytetracycline (r = 0.76), while the density of livestock significantly correlated with sulfadiazine (r = 0.71). Principle Component Analysis (PCA) indicated that doxycycline, tetracycline, oxytetracycline, and sulfamethoxazole were from aquaculture and domestic sources, whereas sulfadiazine and sulfamethazine were from livestock wastewater. Flood or drainage from aquaculture ponds was identified as a major source of antibiotics in the Tiaoxi watershed. A hot-spot map was created based on results of land use analysis and multi-variable statistics, which provided an effective management tool of sources identification in watersheds with multiple diffuse sources of antibiotic pollution.

On The Development of One-way Nesting of Air-pollution Model Smog Into Numerical Weather Prediction Model Eta

NASA Astrophysics Data System (ADS)

Halenka, T.; Bednar, J.; Brechler, J.

The spatial distribution of air pollution on the regional scale (Bohemian region) is simulated by means of Charles University puff model SMOG. The results are used for the assessment of the concentration fields of ozone, nitrogen oxides and other ozone precursors. Current improved version of the model covers up to 16 groups of basic compounds and it is based on trajectory computation and puff interaction both by means of Gaussian diffusion mixing and chemical reactions of basic species. Gener- ally, the method used for trajectory computation is valuable mainly for episodes sim- ulation, nevertheless, climatological study can be solved as well by means of average wind rose. For the study being presented huge database of real emission sources was incorporated with all kind of sources included. Some problem with the background values of concentrations was removed. The model SMOG has been nested into the forecast model ETA to obtain appropriate meteorological data input. We can estimate air pollution characteristics both for episodes analysis and the prediction of future air quality conditions. Necessary prognostic variables from the numerical weather pre- diction model are taken for the region of the central Bohemia, where the original puff model was tested. We used mainly 850 hPa wind field for computation of prognos- tic trajectories, the influence of surface temperature as a parameter of photochemistry reactions as well as the effect of cloudness has been tested.

Effects of olive tree branches burning emissions on PM2.5 concentrations

NASA Astrophysics Data System (ADS)

Papadakis, G. Z.; Megaritis, A. G.; Pandis, S. N.

2015-07-01

An olive tree branches burning emission inventory for Greece is developed based on recently measured emission factors and the spatial distribution of olive trees. A three-dimensional chemical transport model (CTM), PMCAMx, is used to estimate the corresponding impact on PM2.5 concentrations during a typical winter period. Assuming that burning of olive tree branches takes place only during days with low wind speed and without precipitation, the contribution of olive tree branches burning emissions on PM2.5 levels is more significant during the most polluted days. Increases of hourly PM2.5 exceeding 50% and locally reaching up to 150% in Crete are predicted during the most polluted periods. On a monthly-average basis, the corresponding emissions are predicted to increase PM2.5 levels up to 1.5 μg m-3 (20%) in Crete and Peloponnese, where the largest fraction of olive trees is located, and by 0.4 μg m-3 (5%) on average over Greece. OA and EC levels increase by 20% and 13% respectively on average over Greece, and up to 70% in Crete. The magnitude of the effect is quite sensitive to burning practices. Assuming that burning of olive tree branches takes place during all days results in a smaller effect of burning on PM2.5 levels (9% increase instead of 20%). These results suggest that this type of agricultural waste burning is a major source of particulate pollution in the Mediterranean countries where this practice is prevalent during winter.

A simple semi-empirical technique for apportioning the impact of roadways on air quality in an urban neighbourhood

NASA Astrophysics Data System (ADS)

Elangasinghe, M. A.; Dirks, K. N.; Singhal, N.; Costello, S. B.; Longley, I.; Salmond, J. A.

2014-02-01

Air pollution from the transport sector has a marked effect on human health, so isolating the pollutant contribution from a roadway is important in understanding its impact on the local neighbourhood. This paper proposes a novel technique based on a semi-empirical air pollution model to quantify the impact from a roadway on the air quality of a local neighbourhood using ambient records of a single air pollution monitor. We demonstrate the proposed technique using a case study, in which we quantify the contribution from a major highway with respect to the local background concentration in Auckland, New Zealand. Comparing the diurnal variation of the model-separated background contribution with real measurements from a site upwind of the highway shows that the model estimates are reliable. Amongst all of the pollutants considered, the best estimations of the background were achieved for nitrogen oxides. Although the multi-pronged approach worked well for predominantly vehicle-related pollutants, it could not be used effectively to isolate emissions of PM10 due to the complex and less predictable influence of natural sources (such as marine aerosols). The proposed approach is useful in situations where ambient records from an upwind background station are not available (as required by other techniques) and is potentially transferable to situations such as intersections and arterial roads. Applying this technique to longer time series could help to understand the changes in pollutant concentrations from the road and background sources for different emission scenarios, for different years or seasons. Modelling results also show the potential of such a hybrid semi-empirical models to contribute to our understanding of the physical parameters determining air quality and to validate emissions inventory data.

Predicting nitrogen flux along a vertical canopy gradient in a mixed conifer forest stand of the San Bernardino Mountains in California

Treesearch

Michael J. Arbaugh; Andrzej Bytnerowicz; Mark E. Fenn

1998-01-01

A 3-year study of nitrogenous (N) air pollution deposition to ponderosa pine (Pinus ponderosa Dougl. ex. Laws.) seedlings along a mature tree vertical canopy gradient was conducted in the mixed conifer forest of the San Bernardino Mountains of southern California. Concentrations of nitric acid vapor (HNO3), particulate nitrate...

Performance and evaluation of a coupled prognostic model TAPM over a mountainous complex terrain industrial area

NASA Astrophysics Data System (ADS)

Matthaios, Vasileios N.; Triantafyllou, Athanasios G.; Albanis, Triantafyllos A.; Sakkas, Vasileios; Garas, Stelios

2018-05-01

Atmospheric modeling is considered an important tool with several applications such as prediction of air pollution levels, air quality management, and environmental impact assessment studies. Therefore, evaluation studies must be continuously made, in order to improve the accuracy and the approaches of the air quality models. In the present work, an attempt is made to examine the air pollution model (TAPM) efficiency in simulating the surface meteorology, as well as the SO2 concentrations in a mountainous complex terrain industrial area. Three configurations under different circumstances, firstly with default datasets, secondly with data assimilation, and thirdly with updated land use, ran in order to investigate the surface meteorology for a 3-year period (2009-2011) and one configuration applied to predict SO2 concentration levels for the year of 2011.The modeled hourly averaged meteorological and SO2 concentration values were statistically compared with those from five monitoring stations across the domain to evaluate the model's performance. Statistical measures showed that the surface temperature and relative humidity are predicted well in all three simulations, with index of agreement (IOA) higher than 0.94 and 0.70 correspondingly, in all monitoring sites, while an overprediction of extreme low temperature values is noted, with mountain altitudes to have an important role. However, the results also showed that the model's performance is related to the configuration regarding the wind. TAPM default dataset predicted better the wind variables in the center of the simulation than in the boundaries, while improvement in the boundary horizontal winds implied the performance of TAPM with updated land use. TAPM assimilation predicted the wind variables fairly good in the whole domain with IOA higher than 0.83 for the wind speed and higher than 0.85 for the horizontal wind components. Finally, the SO2 concentrations were assessed by the model with IOA varied from 0.37 to 0.57, mostly dependent on the grid/monitoring station of the simulated domain. The present study can be used, with relevant adaptations, as a user guideline for future conducting simulations in mountainous complex terrain.

Detection of heavy metal Cd in polluted fresh leafy vegetables by laser-induced breakdown spectroscopy.

PubMed

Yao, Mingyin; Yang, Hui; Huang, Lin; Chen, Tianbing; Rao, Gangfu; Liu, Muhua

2017-05-10

In seeking a novel method with the ability of green analysis in monitoring toxic heavy metals residue in fresh leafy vegetables, laser-induced breakdown spectroscopy (LIBS) was applied to prove its capability in performing this work. The spectra of fresh vegetable samples polluted in the lab were collected by optimized LIBS experimental setup, and the reference concentrations of cadmium (Cd) from samples were obtained by conventional atomic absorption spectroscopy after wet digestion. The direct calibration employing intensity of single Cd line and Cd concentration exposed the weakness of this calibration method. Furthermore, the accuracy of linear calibration can be improved a little by triple Cd lines as characteristic variables, especially after the spectra were pretreated. However, it is not enough in predicting Cd in samples. Therefore, partial least-squares regression (PLSR) was utilized to enhance the robustness of quantitative analysis. The results of the PLSR model showed that the prediction accuracy of the Cd target can meet the requirement of determination in food safety. This investigation presented that LIBS is a promising and emerging method in analyzing toxic compositions in agricultural products, especially combined with suitable chemometrics.

Modelling cadmium contamination in paddy soils under long-term remediation measures: Model development and stochastic simulations.

PubMed

Peng, Chi; Wang, Meie; Chen, Weiping

2016-09-01

A pollutant accumulation model (PAM) based on the mass balance theory was developed to simulate long-term changes of heavy metal concentrations in soil. When combined with Monte Carlo simulation, the model can predict the probability distributions of heavy metals in a soil-water-plant system with fluctuating environmental parameters and inputs from multiple pathways. The model was used for evaluating different remediation measures to deal with Cd contamination of paddy soils in Youxian county (Hunan province), China, under five scenarios, namely the default scenario (A), not returning paddy straw to the soil (B), reducing the deposition of Cd (C), liming (D), and integrating several remediation measures (E). The model predicted that the Cd contents of soil can lowered significantly by (B) and those of the plants by (D). However, in the long run, (D) will increase soil Cd. The concentrations of Cd in both soils and rice grains can be effectively reduced by (E), although it will take decades of effort. The history of Cd pollution and the major causes of Cd accumulation in soil were studied by means of sensitivity analysis and retrospective simulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Prediction of BOD, COD, and total nitrogen concentrations in a typical urban river using a fluorescence excitation-emission matrix with PARAFAC and UV absorption indices.

PubMed

Hur, Jin; Cho, Jinwoo

2012-01-01

The development of a real-time monitoring tool for the estimation of water quality is essential for efficient management of river pollution in urban areas. The Gap River in Korea is a typical urban river, which is affected by the effluent of a wastewater treatment plant (WWTP) and various anthropogenic activities. In this study, fluorescence excitation-emission matrices (EEM) with parallel factor analysis (PARAFAC) and UV absorption values at 220 nm and 254 nm were applied to evaluate the estimation capabilities for biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total nitrogen (TN) concentrations of the river samples. Three components were successfully identified by the PARAFAC modeling from the fluorescence EEM data, in which each fluorophore group represents microbial humic-like (C1), terrestrial humic-like organic substances (C2), and protein-like organic substances (C3), and UV absorption indices (UV(220) and UV(254)), and the score values of the three PARAFAC components were selected as the estimation parameters for the nitrogen and the organic pollution of the river samples. Among the selected indices, UV(220), C3 and C1 exhibited the highest correlation coefficients with BOD, COD, and TN concentrations, respectively. Multiple regression analysis using UV(220) and C3 demonstrated the enhancement of the prediction capability for TN.

Natural lead concentrations in pristine boreal forest soils and past pollution trends: A reference for critical load models

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

Bindler, R.; Braennvall, M.L.; Renberg, I.

1999-10-01

Knowledge of natural, prepollution concentrations of heavy metals in forest soils and temporal trends of soil pollution are essential for understanding present-day pollution and for establishing realistic goals for reductions of atmospheric pollution deposition. Soils not exposed to deposition of atmospheric pollution no longer exist and, for example, present lead (Pb) pollution conditions in northern European soils are a consequence of nearly 4,000 years of atmospheric pollution. The authors use analyses of Pb concentrations and stable Pb isotopes ({sup 206}Pb/{sup 207}Pb ratios) of ombrotrophic peat and forest soils from southern Sweden and a model for Pb cycling in forest soilsmore » to derive an estimate for the prepollution concentration of Pb in the mor layer of boreal forest soils and to back-calculate Pb concentrations for the last 5,500 years. While the present-day concentrations of the mor layer are typically 40--100 {micro}g g{sup {minus}1} (0.25--1.0 g m{sup {minus}2}), Pb concentrations of pristine forest mor layers in Sweden were quite low, {le}0.1 {micro}g g{sup {minus}1} ({le}1 mg m{sup {minus}2}). Large-scale atmospheric pollution from the Greek and Roman cultures increased Pb concentrations to about 1 {micro}g g{sup {minus}1}. Lead (Pb) concentrations increased to about 4 {micro}g g{sup {minus}1} following the increase of metal production and atmospheric pollution in Medieval Europe.« less

[Epidemiological studies of air pollution and health effects in areas near roadways with heavy traffic in Tokyo].

PubMed

Ono, M; Murakami, M; Nitta, H; Nakai, S; Maeda, K

1990-05-01

Recent concern regarding health effects of air pollution in Japan has concentrated mainly on traffic-induced air pollution and its health effects in large cities. In Japan, where many people in large cities have been living near major roadways, the increase of automobile exhaust due to heavy traffic congestion will predictably cause a greater impact on people living near major roadways. We surveyed the characterization of residential suspended particulate matter (SPM) and nitrogen dioxide (NO2) concentrations along the major roadways in Tokyo, along with a health survey on the respiratory conditions of residents living in the same area, to examine the relationships between indoor pollutant levels, prevalence of respiratory symptoms and distance from roadways. The environmental monitoring was conducted in five phases. Using a newly developed SPM sampler and NO2 filter badge, continuous 4 day (96 hours) measurements were conducted in two hundred residential homes for four weeks. NO2 was measured in the living room, kitchen and outside of each home, while SPM was monitored in the living room. Health information was collected in October 1987 using ATS-DLD self-administered questionnaires. Of the 1,093 homes investigated, responses from 805 homes were received. The following results were obtained. SPM and NO2 concentrations showed large variations. Indoor pollution levels mostly depended on indoor sources, i.e. cigarette smoking and unventilated space heaters, and the effects of those indoor sources were influenced by the building structure with respect to air tightness. An association between increase in pollutant levels and the distance from the roadway was observed. However its effect is small compared to indoor source effects. The prevalence rate of respiratory symptoms was higher in those areas nearest roadways with heavy traffic both in children and adults. These results suggest the presence of a relationship between automobile exhaust and health effects.

Source Contributions to Premature Mortality Due to Ambient Particulate Matter in China

NASA Astrophysics Data System (ADS)

Hu, J.; Huang, L.; Ying, Q.; Zhang, H.; Shi, Z.

2016-12-01

Outdoor air pollution is linked to various health effects. Globally it is estimated that ambient air pollution caused 3.3 million premature deaths in 2010. The health risk occurs predominantly in developing countries, particularly in Asia. China has been suffering serious air pollution in recent decades. The annual concentrations of ambient PM2.5 are more than five times higher than the WHO guideline value in many populous Chinese cities. Sustained exposure to high PM2.5 concentrations greatly threatens public health in this country. Recognizing the severity of the air pollution situation, the Chinese government has set a target in 2013 to reduce PM2.5 level by up to 25% in major metropolitan areas by 2017. It is urgently needed for China to assess premature mortality caused by outdoor air pollution, identify source contributions of the premature mortality, and evaluate responses of the premature mortality to air quality improvement, in order to design effective control plans and set priority for air pollution controls to better protect public health. In this study, we determined the spatial distribution of excess mortality (ΔMort) due to adult (> 30 years old) ischemic heart disease (IHD), cerebrovascular disease (CEV), chronic obstructive pulmonary disease (COPD) and lung cancer (LC) at 36-km horizontal resolution for 2013 from the predicted annual-average surface PM2.5 concentrations using an updated source-oriented Community Multiscale Air Quality (CMAQ) model along with an ensemble of four regional and global emission inventories. Observation data fusing was applied to provide additional correction of the biases in the PM2.5 concentration field from the ensemble. Source contributions to ΔMort were determined based on total ΔMort and fractional source contributions to PM2.5 mass concentrations. We estimated that ΔMort due to COPD, LC, IHD and CEV are 0.329, 0.148, 0.239 and 0.953 million in China, respectively, leading to a total ΔMort of 1.669 million. Industries and residential sources were the two leading sources to ΔMort, contributing to 0.508 (30.5%) and 0.366 (21.9%) mp, respectively. Secondary ammonium ion from agriculture sources, secondary organic aerosol and aerosols from power generation sources were responsible for ΔMort of 0.204, 0.179 and 0.172 mp, respectively.

Effects of climate change on aerosol concentrations in Europe

NASA Astrophysics Data System (ADS)

Megaritis, Athanasios G.; Fountoukis, Christos; Pandis, Spyros N.

2013-04-01

High concentrations of particulate matter less than 2.5 μm in size (PM2.5), ozone and other major constituents of air pollution, have adverse effects on human health, visibility and ecosystems (Seinfeld and Pandis, 2006), and are strongly influenced by meteorology. Emissions control policy is currently made assuming that climate will remain constant in the future. However, climate change over the next decades is expected to be significant (IPCC, 2007) and may impact local and regional air quality. Determining the sensitivity of the concentrations of air pollutants to climate change is an important step toward estimating future air quality. In this study we applied PMCAMx (Fountoukis et al., 2011), a three dimensional chemical transport model, over Europe, in order to quantify the individual effects of various meteorological parameters on fine particulate matter (PM2.5) concentrations. A suite of perturbations in various meteorological factors, such as temperature, wind speed, absolute humidity and precipitation were imposed separately on base case conditions to determine the sensitivities of PM2.5 concentrations and composition to these parameters. Different simulation periods (summer, autumn 2008 and winter 2009) are used to examine also the seasonal dependence of the air quality - climate interactions. The results of these sensitivity simulations suggest that there is an important link between changes in meteorology and PM2.5 levels. We quantify through separate sensitivity simulations the processes which are mainly responsible for the final predicted changes in PM2.5 concentration and composition. The predicted PM2.5 response to those meteorology perturbations was found to be quite variable in space and time. These results suggest that, the changes in concentrations caused by changes in climate should be taken into account in long-term air quality planning. References Fountoukis C., Racherla P. N., Denier van der Gon H. A. C., Polymeneas P., Charalampidis P. E., Pilinis C., Wiedensohler A., Dall'Osto M., O'Dowd C., and S. N. Pandis: Evaluation of a three-dimensional chemical transport model (PMCAMx) in the European domain during the EUCAARI May 2008 campaign, Atmos. Chem. Phys., 11, 10331-10347, 2011. Intergovernmental Panel on Climate Change (IPCC), Fourth Assessment Report: Summary for Policymakers, 2007. Seinfeld, J. H., and Pandis, S. N.: Atmospheric chemistry and physics: From air pollution to climate change, 2nd ed.; John Wiley and Sons, Hoboken, NJ, 2006.

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

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.

A simulation of air pollution model parameter estimation using data from a ground-based LIDAR remote sensor

NASA Technical Reports Server (NTRS)

Kibler, J. F.; Suttles, J. T.

1977-01-01

One way to obtain estimates of the unknown parameters in a pollution dispersion model is to compare the model predictions with remotely sensed air quality data. A ground-based LIDAR sensor provides relative pollution concentration measurements as a function of space and time. The measured sensor data are compared with the dispersion model output through a numerical estimation procedure to yield parameter estimates which best fit the data. This overall process is tested in a computer simulation to study the effects of various measurement strategies. Such a simulation is useful prior to a field measurement exercise to maximize the information content in the collected data. Parametric studies of simulated data matched to a Gaussian plume dispersion model indicate the trade offs available between estimation accuracy and data acquisition strategy.

Urban local air quality management framework for non-attainment areas in Indian cities.

PubMed

Gulia, Sunil; Nagendra, S M Shiva; Barnes, Jo; Khare, Mukesh

2018-04-01

Increasing urban air pollution level in Indian cities is one of the major concerns for policy makers due to its impact on public health. The growth in population and increase in associated motorised road transport demand is one of the major causes of increasing air pollution in most urban areas along with other sources e.g., road dust, construction dust, biomass burning etc. The present study documents the development of an urban local air quality management (ULAQM) framework at urban hotspots (non-attainment area) and a pathway for the flow of information from goal setting to policy making. The ULAQM also includes assessment and management of air pollution episodic conditions at these hotspots, which currently available city/regional-scale air quality management plans do not address. The prediction of extreme pollutant concentrations using a hybrid model differentiates the ULAQM from other existing air quality management plans. The developed ULAQM framework has been applied and validated at one of the busiest traffic intersections in Delhi and Chennai cities. Various scenarios have been tested targeting the effective reductions in elevated levels of NO x and PM 2.5 concentrations. The results indicate that a developed ULAQM framework is capable of providing an evidence-based graded action to reduce ambient pollution levels within the specified standard level at pre-identified locations. The ULAQM framework methodology is generalised and therefore can be applied to other non-attainment areas of the country. Copyright © 2017 Elsevier B.V. All rights reserved.

The accumulation of heavy metals in agricultural land and the associated potential ecological risks in Shenzhen, China.

PubMed

Wu, Jiansheng; Song, Jing; Li, Weifeng; Zheng, Maokun

2016-01-01

Accumulation of heavy metals in agricultural land and their ecological risks are key issues in soil security studies. This study investigated the concentrations of six heavy metals--copper (Cu), zinc (Zn), lead (Pb), nickel (Ni), and chromium (Cr) in Shenzhen's agricultural lands and examined the potential hazards and possible sources of these metals. Eighty-two samples from agricultural topsoil were collected. Potential ecological risk index was used to calculate the potential risk of heavy metals. Principal component analysis (PCA) was applied to explore pollution sources of the metals. Finally, Kriging was used to predict the spatial distribution of the metals' potential ecological risks. The concentrations of the heavy metals were higher than their background values. Most of them presented little potential ecological risk, except for the heavy metal cadmium (Cd). Four districts (Longgang, Longhua, Pingshan, and Dapeng) exhibited some degree of potential risk, which tended to have more industries and road networks. Three major sources of heavy metals included geochemical processes, industrial pollutants, and traffic pollution. The heavy metal Cd was the main contributor to the pollution in agricultural land during the study period. It also poses the potential hazard for the future. High potential risk is closely related to industrial pollution and transportation. Since the 1980s, the sources of heavy metals have evolved from parent rock weathering, erosion, degradation of organics, and mineralization to human disturbances resulting in chemical changes in the soil.

[Analysis of heavy-metal-mediated disease and development of a novel remediation system based on fieldwork and experimental research].

PubMed

Yajima, Ichiro; Zou, Cunchao; Li, Xiang; Nakano, Chizuru; Omata, Yasuhiro; Kumasaka, Mayuko Y

2015-01-01

Heavy-metal pollution occurs in various environments, including water, air and soil, and has serious effects on human health. Since heavy-metal pollution in drinking water causes various diseases including skin cancer, it has become a global problem worldwide. However, there is limited information on the mechanism of development of heavy-metal-mediated disease. We performed both fieldwork and experimental studies to elucidate the levels of heavy-metal pollution and mechanisms of development of heavy-metal-related disease and to develop a novel remediation system. Our fieldwork in Bangladesh, Vietnam and Malaysia demonstrated that drinking well water in these countries was polluted with high concentrations of several heavy metals including arsenic, barium, iron and manganese. Our experimental studies based on the data from our fieldwork demonstrated that these heavy metals caused skin cancer and hearing loss. Further experimental studies resulted in the development of a novel remediation system with which toxic heavy metals were absorbed from polluted drinking water. Implementation of both fieldwork and experimental studies is important for prediction, prevention and therapy of heavy-metal-mediated diseases.

The development of an ecological approach to manage the pollution risk from highway runoff.

PubMed

Crabtree, B; Dempsey, P; Johnson, I; Whitehead, M

2009-01-01

In the UK, the Highways Agency is responsible for operating, maintaining and improving the strategic road network in England. One focus of the Highways Agency's ongoing research into the nature and impact of highway runoff is aimed at ensuring that the Highways Agency will meet the requirements of the EU Water Framework Directive. A research programme, undertaken in partnership with the Environment Agency, is in progress to develop a better understanding of pollutants in highway runoff and their ecological impact. The paper presents the outcome of a study to: (1) monitor pollutants in highway runoff under different climate and traffic conditions; (2) develop standards to assess potential ecological risks from soluble pollutants in highway runoff; and (3) develop a model to predict pollutant concentrations in highway runoff. The model has been embedded in a design tool incorporating risk assessment procedures and receiving water standards for soluble and insoluble pollutants--the latter has been developed elsewhere in another project within the research programme. The design tool will be used to support improved guidance on where, and to what level, treatment of runoff is required for highway designers to manage the risk of ecological impact from highway runoff.

Evaluation of NO2 predictions by the plume volume molar ratio method (PVMRM) and ozone limiting method (OLM) in AERMOD using new field observations.

PubMed

Hendrick, Elizabeth M; Tino, Vincent R; Hanna, Steven R; Egan, Bruce A

2013-07-01

The U.S. Environmental Protection Agency (EPA) plume volume molar ratio method (PVMRM) and the ozone limiting method (OLM) are in the AERMOD model to predict the 1-hr average NO2/NO(x) concentration ratio. These ratios are multiplied by the AERMOD predicted NO(x) concentration to predict the 1-hr average NO2 concentration. This paper first briefly reviews PVMRM and OLM and points out some scientific parameterizations that could be improved (such as specification of relative dispersion coefficients) and then discusses an evaluation of the PVMRM and OLM methods as implemented in AERMOD using a new data set. While AERMOD has undergone many model evaluation studies in its default mode, PVMRM and OLM are nondefault options, and to date only three NO2 field data sets have been used in their evaluations. Here AERMOD/PVMRM and AERMOD/OLM codes are evaluated with a new data set from a northern Alaskan village with a small power plant. Hourly pollutant concentrations (NO, NO2, ozone) as well as meteorological variables were measured at a single monitor 500 m from the plant. Power plant operating parameters and emissions were calculated based on hourly operator logs. Hourly observations covering 1 yr were considered, but the evaluations only used hours when the wind was in a 60 degrees sector including the monitor and when concentrations were above a threshold. PVMRM is found to have little bias in predictions of the C(NO2)/C(NO(x)) ratio, which mostly ranged from 0.2 to 0.4 at this site. OLM overpredicted the ratio. AERMOD overpredicts the maximum NO(x) concentration but has an underprediction bias for lower concentrations. AERMOD/PVMRM overpredicts the maximum C(NO2) by about 50%, while AERMOD/OLM overpredicts by a factor of 2. For 381 hours evaluated, there is a relative mean bias in C(NO2) predictions of near zero for AERMOD/PVMRM, while the relative mean bias reflects a factor of 2 overprediction for AERMOD/OLM. This study was initiated because the new stringent 1-hr NO2 NAAQS has prompted modelers to more widely use the PVMRM and OLM methods for conversion of NO(x) to NO2 in the AERMOD regulatory model. To date these methods have been evaluated with a limited number of data sets. This study identified a new data set of ambient pollutant and meteorological monitoring near an isolated power plant in Wainwright, Alaska. To supplement the existing evaluations, this new data were used to evaluate PVMRM and OLM. This new data set has been and will be made available to other scientists for future investigations.

Sensitivity tests of two-dimensional model predictions of corridor effects. [concentration of spacecraft produced air pollutants into narrow latitude ranges

NASA Technical Reports Server (NTRS)

Borucki, W. J.; Whitten, R. C.; Capone, L. A.; Riegel, C. A.

1981-01-01

Future aerospace-vehicle systems, such as supersonic transport fleets, the Space Shuttle (SS), and the Heavy-Lift Launch Vehicle (HLLV) system will inject substantial amounts of pollutants into the stratosphere. It is, therefore, pertinent to ask whether the operation of these systems will lead to deleterious effects in the atmosphere. The current investigation is concerned with the development of criteria to assess the likelihood of a detectable corridor effect being caused by the long-term deposition of pollutants at a single latitude. The sources are assumed to operate continuously and at a uniform rate for periods of many years. It is found that transport by meridional winds and by eddy processes acts to diminish the corridor effect by advecting the pollutants out of the region of injection and by mixing them with the ambient air. Attention is given to the altitude for which a detectable corridor effect can be expected for the hypothetical launching of 400 HLLV's per year for 10 years.

An instantaneous spatiotemporal model to predict a bicyclist's Black Carbon exposure based on mobile noise measurements

NASA Astrophysics Data System (ADS)

Dekoninck, Luc; Botteldooren, Dick; Int Panis, Luc

2013-11-01

Several studies have shown that a significant amount of daily air pollution exposure, in particular Black Carbon (BC), is inhaled during trips. Assessing this contribution to exposure remains difficult because on the one hand local air pollution maps lack spatio-temporal resolution, at the other hand direct measurement of particulate matter concentration remains expensive. This paper proposes to use in-traffic noise measurements in combination with geographical and meteorological information for predicting BC exposure during commuting trips. Mobile noise measurements are cheaper and easier to perform than mobile air pollution measurements and can easily be used in participatory sensing campaigns. The uniqueness of the proposed model lies in the choice of noise indicators that goes beyond the traditional overall A-weighted noise level used in previous work. Noise and BC exposures are both related to the traffic intensity but also to traffic speed and traffic dynamics. Inspired by theoretical knowledge on the emission of noise and BC, the low frequency engine related noise and the difference between high frequency and low frequency noise that indicates the traffic speed, are introduced in the model. In addition, it is shown that splitting BC in a local and a background component significantly improves the model. The coefficients of the proposed model are extracted from 200 commuter bicycle trips. The predicted average exposure over a single trip correlates with measurements with a Pearson coefficient of 0.78 using only four parameters: the low frequency noise level, wind speed, the difference between high and low frequency noise and a street canyon index expressing local air pollution dispersion properties.

Investigation on the reaction of phenolic pollutions to mono-rhamnolipid micelles using MEUF.

PubMed

Liu, Zhifeng; Yu, Mingda; Zeng, Guangming; Li, Min; Zhang, Jiachao; Zhong, Hua; Liu, Yang; Shao, Binbin; Li, Zhigang; Wang, Zhiquan; Liu, Guansheng; Yang, Xin

2017-01-01

Micellar-enhanced ultrafiltration (MEUF) processes of resorcinol, phenol, and 1-Naphthol with rhamnolipid as an anionic biosurfactant were investigated using polysulfone membrane. The effects of retentate/permeate concentration of phenolic pollutants (C R /C P ), distribution coefficient of phenolic pollutions (D), concentration ratios of phenolic pollutions (α P ) and rhamnolipids (α R ) and adsorption capacity of the membrane (N m ) were studied by operating pressure, pH condition, feed surfactant, and phenolic pollution concentrations. Results showed that C R (with pH) increased and ranked in the following order: resorcinol > phenol > 1-Naphthol, which is same with C R (with pressure), C R (with surfactant), C R /C P (with pollution), α, P and D, while C P (with pH), C P (with pressure), and C P (with surfactant) ranked in the reverse order. The operating pressure increased the solubility of phenolic from 0 to 0.1 MPa and then decreased slowly above 0.1 MPa. The concentration ratio of rhamnolipid was nearly at 2.0 and that of phenolic pollution was slightly above 1.0. D of phenolic pollutants reached the maximum at phenolic pollution concentration of 0.1 mM and the feed rhamnolipid concentration at 1 CMC. Moreover, zeta potential in feed stream and retentate stream and membrane adsorption of phenolic pollutions were firstly investigated in this article; the magnitudes of zeta potential with the feed stream of three phenolic pollutions were nearly the same and slightly lower than those with the retentate stream. The adsorption capacity of the membrane (N m ) was calculated and compared to the former research, which showed that rhamnolipid significantly decreases the membrane adsorption of phenolic pollutions at a relatively lower concentration. It was implied that rhamnolipid can be substituted for chemical surfactants.

Impact of climate change on mercury concentrations and deposition in the eastern United States.

PubMed

Megaritis, Athanasios G; Murphy, Benjamin N; Racherla, Pavan N; Adams, Peter J; Pandis, Spyros N

2014-07-15

The global-regional climate-air pollution modeling system (GRE-CAPS) was applied over the eastern United States to study the impact of climate change on the concentration and deposition of atmospheric mercury. Summer and winter periods (300 days for each) were simulated, and the present-day model predictions (2000s) were compared to the future ones (2050s) assuming constant emissions. Climate change affects Hg(2+) concentrations in both periods. On average, atmospheric Hg(2+) levels are predicted to increase in the future by 3% in summer and 5% in winter respectively due to enhanced oxidation of Hg(0) under higher temperatures. The predicted concentration change of Hg(2+) was found to vary significantly in space due to regional-scale changes in precipitation, ranging from -30% to 30% during summer and -20% to 40% during winter. Particulate mercury, Hg(p) has a similar spatial response to climate change as Hg(2+), while Hg(0) levels are not predicted to change significantly. In both periods, the response of mercury deposition to climate change varies spatially with an average predicted increase of 6% during summer and 4% during winter. During summer, deposition increases are predicted mostly in the western parts of the domain while mercury deposition is predicted to decrease in the Northeast and also in many areas in the Midwest and Southeast. During winter mercury deposition is predicted to change from -30% to 50% mainly due to the changes in rainfall and the corresponding changes in wet deposition. Copyright © 2014 Elsevier B.V. All rights reserved.

Fractal Analysis of Air Pollutant Concentrations

NASA Astrophysics Data System (ADS)

Cortina-Januchs, M. G.; Barrón-Adame, J. M.; Vega-Corona, A.; Andina, D.

2010-05-01

Air pollution poses significant threats to human health and the environment throughout the developed and developing countries. This work focuses on fractal analysis of pollutant concentration in Salamanca, Mexico. The city of Salamanca has been catalogued as one of the most polluted cities in Mexico. The main causes of pollution in this city are fixed emission sources, such as chemical industry and electricity generation. Sulphur Dioxide (SO2) and Particulate Matter less than 10 micrometer in diameter (PM10) are the most important pollutants in this region. Air pollutant concentrations were investigated by applying the box counting method in time series obtained of the Automatic Environmental Monitoring Network (AEMN). One year of time series of hourly average concentrations were analyzed in order to characterize the temporal structures of SO2 and PM10.

An Integrated Approach to Economic and Environmental Aspects of Air Pollution and Climate Interactions

NASA Astrophysics Data System (ADS)

Sarofim, M. C.

2007-12-01

Emissions of greenhouses gases and conventional pollutants are closely linked through shared generation processes and thus policies directed toward long-lived greenhouse gases affect emissions of conventional pollutants and, similarly, policies directed toward conventional pollutants affect emissions of greenhouse gases. Some conventional pollutants such as aerosols also have direct radiative effects. NOx and VOCs are ozone precursors, another substance with both radiative and health impacts, and these ozone precursors also interact with the chemistry of the hydroxyl radical which is the major methane sink. Realistic scenarios of future emissions and concentrations must therefore account for both air pollution and greenhouse gas policies and how they interact economically as well as atmospherically, including the regional pattern of emissions and regulation. We have modified a 16 region computable general equilibrium economic model (the MIT Emissions Prediction and Policy Analysis model) by including elasticities of substitution for ozone precursors and aerosols in order to examine these interactions between climate policy and air pollution policy on a global scale. Urban emissions are distributed based on population density, and aged using a reduced form urban model before release into an atmospheric chemistry/climate model (the earth systems component of the MIT Integrated Global Systems Model). This integrated approach enables examination of the direct impacts of air pollution on climate, the ancillary and complementary interactions between air pollution and climate policies, and the impact of different population distribution algorithms or urban emission aging schemes on global scale properties. This modeling exercise shows that while ozone levels are reduced due to NOx and VOC reductions, these reductions lead to an increase in methane concentrations that eliminates the temperature effects of the ozone reductions. However, black carbon reductions do have significant direct effects on global mean temperatures, as do ancillary reductions of greenhouse gases due to the pollution constraints imposed in the economic model. Finally, we show that the economic benefits of coordinating air pollution and climate policies rather than separate implementation are on the order of 20% of the total policy cost.

Modeling the Transport and Fate of Fecal Pollution and Nutrients of Miyun Reservoir

NASA Astrophysics Data System (ADS)

Liu, L.; Fu, X.; Wang, G.

2009-12-01

Miyun Reservoir, a mountain valley reservoir, is located 100 km northeast of Beijing City. Besides the functions of flood control, irrigation and fishery for Beijing area, Miyun Reservoir is the main drinking water storage for Beijing city. The water quality is therefore of great importance. Recently, the concentration of fecal pollution and nutrients in the reservoir are constantly rising to arrest the attention of Beijing municipality. Fecal pollution from sewage is a significant public health concern due to the known presence of human viruses and parasites in these discharges. To investigate the transport and fate of the fecal pollution and nutrients at Miyun reservoir and the health risks associated with drinking and fishery, the reservoir and two tributaries, Chaohe river and Baihe river discharging into it are being examined for bacterial, nutrients and other routine pollution. To understand the relative importance of different processes influencing pollution transport and inactivation, a finite-element model of surf-zone hydrodynamics (coupled with models for temperature, fecal pollution, nutrients and other routine contaminants) is used. The developed models are being verified by the observed water quality data including water temperature, conductivities and dissolved oxygen from the reservoir and its tributaries. Different factors impacting the inactivation of fecal pollution and the transport of nutrients such as water temperature, sedimentation, sunlight insolation are evaluated for Miyun reservoir by a sensitivity analysis analogized from the previous research of Lake Michigan (figure 1, indicating that solar insolation dominates the inactivation of E. Coli, an indicator of fecal pollution, Liu et al. 2006). The calibrated modeling system can be used to temporally and spatially simulate and predict the variation of the concentration of fecal pollution and nutrients of Miyun reservoir. Therefore this research can provide a forecasting tool for the administrative agencies and policy makers to make correct decisions for the water utilization of Minyun reservoir once some emergency events occur. Key words: Fecal pollution, Modeling, Transport, Inactivation Figure 1: Relative contributions of settling and solar insolation to the overall inactivation of E. coli at the Mt. Baldy Beach (Liu et al. 2006)

Characterizing the Indoor-Outdoor Relationship of Fine Particulate Matter in Non-Heating Season for Urban Residences in Beijing

PubMed Central

Huang, Lihui; Pu, Zhongnan; Li, Mu; Sundell, Jan

2015-01-01

Objective Ambient fine particulate matter (PM2.5) pollution is currently a major public health concern in Chinese urban areas. However, PM2.5 exposure primarily occurs indoors. Given such, we conducted this study to characterize the indoor-outdoor relationship of PM2.5 mass concentrations for urban residences in Beijing. Methods In this study, 24-h real-time indoor and ambient PM2.5 mass concentrations were concurrently collected for 41 urban residences in the non-heating season. The diurnal variation of pollutant concentrations was characterized. Pearson correlation analysis was used to examine the correlation between indoor and ambient PM2.5 mass concentrations. Regression analysis with ordinary least square was employed to characterize the influences of a variety of factors on PM2.5 mass concentration. Results Hourly ambient PM2.5 mass concentrations were 3–280 μg/m3 with a median of 58 μg/m3, and hourly indoor counterpart were 4–193 μg/m3 with a median of 34 μg/m3. The median indoor/ambient ratio of PM2.5 mass concentration was 0.62. The diurnal variation of residential indoor and ambient PM2.5 mass concentrations tracked with each other well. Strong correlation was found between indoor and ambient PM2.5 mass concentrations on the community basis (coefficients: r≥0.90, p<0.0001), and the ambient data explained ≥84% variance of the indoor data. Regression analysis suggested that the variables, such as traffic conditions, indoor smoking activities, indoor cleaning activities, indoor plants and number of occupants, had significant influences on the indoor PM2.5 mass concentrations. Conclusions PM2.5 of ambient origin made dominant contribution to residential indoor PM2.5 exposure in the non-heating season under the high ambient fine particle pollution condition. Nonetheless, the large inter-residence variability of infiltration factor of ambient PM2.5 raised the concern of exposure misclassification when using ambient PM2.5 mass concentrations as exposure surrogates. PM2.5 of indoor origin still had minor influence on indoor PM2.5 mass concentrations, particularly at 11:00–13:00 and 22:00–0:00. The predictive models suggested that particles from traffic emission, secondary aerosols, particles from indoor smoking, resuspended particles due to indoor cleaning and particles related to indoor plants contributed to indoor PM2.5 mass concentrations in this study. Real-time ventilation measurements and improvement of questionnaire design to involve more variables subject to built environment were recommended to enhance the performance of the predictive models. PMID:26397734

Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF-Chem CO tracer model

NASA Astrophysics Data System (ADS)

Saide, Pablo E.; Carmichael, Gregory R.; Spak, Scott N.; Gallardo, Laura; Osses, Axel E.; Mena-Carrasco, Marcelo A.; Pagowski, Mariusz

2011-05-01

This study presents a system to predict high pollution events that develop in connection with enhanced subsidence due to coastal lows, particularly in winter over Santiago de Chile. An accurate forecast of these episodes is of interest since the local government is entitled by law to take actions in advance to prevent public exposure to PM10 concentrations in excess of 150 μg m -3 (24 h running averages). The forecasting system is based on accurately simulating carbon monoxide (CO) as a PM10/PM2.5 surrogate, since during episodes and within the city there is a high correlation (over 0.95) among these pollutants. Thus, by accurately forecasting CO, which behaves closely to a tracer on this scale, a PM estimate can be made without involving aerosol-chemistry modeling. Nevertheless, the very stable nocturnal conditions over steep topography associated with maxima in concentrations are hard to represent in models. Here we propose a forecast system based on the WRF-Chem model with optimum settings, determined through extensive testing, that best describe both meteorological and air quality available measurements. Some of the important configurations choices involve the boundary layer (PBL) scheme, model grid resolution (both vertical and horizontal), meteorological initial and boundary conditions and spatial and temporal distribution of the emissions. A forecast for the 2008 winter is performed showing that this forecasting system is able to perform similarly to the authority decision for PM10 and better than persistence when forecasting PM10 and PM2.5 high pollution episodes. Problems regarding false alarm predictions could be related to different uncertainties in the model such as day to day emission variability, inability of the model to completely resolve the complex topography and inaccuracy in meteorological initial and boundary conditions. Finally, according to our simulations, emissions from previous days dominate episode concentrations, which highlights the need for 48 h forecasts that can be achieved by the system presented here. This is in fact the largest advantage of the proposed system.

[Evaluation and source analysis of the mercury pollution in soils and vegetables around a large-scale zinc smelting plant].

PubMed

Liu, Fang; Wang, Shu-Xiao; Wu, Qing-Ru; Lin, Hai

2013-02-01

The farming soil and vegetable samples around a large-scale zinc smelter were collected for mercury content analyses, and the single pollution index method with relevant regulations was used to evaluate the pollution status of sampled soils and vegetables. The results indicated that the surface soil and vegetables were polluted with mercury to different extent. Of the soil samples, 78% exceeded the national standard. The mercury concentration in the most severely contaminated area was 29 times higher than the background concentration, reaching the severe pollution degree. The mercury concentration in all vegetable samples exceeded the standard of non-pollution vegetables. Mercury concentration, in the most severely polluted vegetables were 64.5 times of the standard, and averagely the mercury concentration in the vegetable samples was 25.4 times of the standard. For 85% of the vegetable samples, the mercury concentration, of leaves were significantly higher than that of roots, which implies that the mercury in leaves mainly came from the atmosphere. The mercury concentrations in vegetable roots were significantly correlated with that in soils, indicating the mercury in roots was mainly from soil. The mercury emissions from the zinc smelter have obvious impacts on the surrounding soils and vegetables. Key words:zinc smelting; mercury pollution; soil; vegetable; mercury content

Source apportionment of ambient non-methane hydrocarbons in Hong Kong: application of a principal component analysis/absolute principal component scores (PCA/APCS) receptor model.

PubMed

Guo, H; Wang, T; Louie, P K K

2004-06-01

Receptor-oriented source apportionment models are often used to identify sources of ambient air pollutants and to estimate source contributions to air pollutant concentrations. In this study, a PCA/APCS model was applied to the data on non-methane hydrocarbons (NMHCs) measured from January to December 2001 at two sampling sites: Tsuen Wan (TW) and Central & Western (CW) Toxic Air Pollutants Monitoring Stations in Hong Kong. This multivariate method enables the identification of major air pollution sources along with the quantitative apportionment of each source to pollutant species. The PCA analysis identified four major pollution sources at TW site and five major sources at CW site. The extracted pollution sources included vehicular internal engine combustion with unburned fuel emissions, use of solvent particularly paints, liquefied petroleum gas (LPG) or natural gas leakage, and industrial, commercial and domestic sources such as solvents, decoration, fuel combustion, chemical factories and power plants. The results of APCS receptor model indicated that 39% and 48% of the total NMHCs mass concentrations measured at CW and TW were originated from vehicle emissions, respectively. 32% and 36.4% of the total NMHCs were emitted from the use of solvent and 11% and 19.4% were apportioned to the LPG or natural gas leakage, respectively. 5.2% and 9% of the total NMHCs mass concentrations were attributed to other industrial, commercial and domestic sources, respectively. It was also found that vehicle emissions and LPG or natural gas leakage were the main sources of C(3)-C(5) alkanes and C(3)-C(5) alkenes while aromatics were predominantly released from paints. Comparison of source contributions to ambient NMHCs at the two sites indicated that the contribution of LPG or natural gas at CW site was almost twice that at TW site. High correlation coefficients (R(2) > 0.8) between the measured and predicted values suggested that the PCA/APCS model was applicable for estimation of sources of NMHCs in ambient air.

Model elucidating the sources and formation mechanisms of severe haze pollution over Northeast mega-city cluster in China.

PubMed

Yang, Ting; Gbaguidi, Alex; Yan, Pingzhong; Zhang, Wending; Zhu, Lili; Yao, Xuefeng; Wang, Zifa; Chen, Hui

2017-11-01

Recent studies on regional haze pollution over China come up in general with strong variability of main causes of heavy polluted episodes, in linkage with local specificities, sources and pollution characteristics. This paper therefore aims at elucidating the main specific sources and formation mechanisms of observed strong haze pollution episodes over 1-15 November 2015 in Northeast region considered as one of biggest megacity clusters in China. The Northeast China mega-city cluster, including Heilong Jiang, Jilin and Liaoning provinces, is adjacent to Russia in the north, Mongolian at the west, North Korea at east, and representing key geographical location in the regional and transnational air pollution issues in China due to the presence of heavy industries and intense economic activities. The present study, based on air quality monitoring, remote sensing satellite data and sensitivity experiments carried on the Nested Air Quality Prediction Modeling System (NAQPMS), quantitatively assesses the impact of meteorological conditions and potential contributions from regional chemical transport, intensive energy combustion, illegal emission and biomass burning emissions to PM 2.5 concentration variation. The results indicate strong inversion occurrence at lower atmosphere with weak near-surface wind speed and high relative humidity, leading to PM 2.5 concentration increase of about 30-50%. Intensive energy combustion (plausibly for heating activities) and illegal emission also significantly enhance the overall PM 2.5 accumulation by 100-200 μg m -3 (60-70% increase), against 75-100 μg m -3 from the biomass burning under the northeast-southwest transport pathway, corresponding to a contribution of 10-20% to PM 2.5 concentration increase. Obviously, stagnant meteorological conditions, energy combustion, illegal emission and biomass burning are main drivers of strong haze formation and spatial distribution over Northeast China megacity cluster. In clear, much effort on emission abatement at both local and regional scales is still an urgent imperative to overcome current critical haze pollution. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Allocating emissions to 4 km and 1 km horizontal spatial resolutions and its impact on simulated NOx and O3 in Houston, TX

NASA Astrophysics Data System (ADS)

Pan, Shuai; Choi, Yunsoo; Roy, Anirban; Jeon, Wonbae

2017-09-01

A WRF-SMOKE-CMAQ air quality modeling system was used to investigate the impact of horizontal spatial resolution on simulated nitrogen oxides (NOx) and ozone (O3) in the Greater Houston area (a non-attainment area for O3). We employed an approach recommended by the United States Environmental Protection Agency to allocate county-based emissions to model grid cells in 1 km and 4 km horizontal grid resolutions. The CMAQ Integrated Process Rate analyses showed a substantial difference in emissions contributions between 1 and 4 km grids but similar NOx and O3 concentrations over urban and industrial locations. For example, the peak NOx emissions at an industrial and urban site differed by a factor of 20 for the 1 km and 8 for the 4 km grid, but simulated NOx concentrations changed only by a factor of 1.2 in both cases. Hence, due to the interplay of the atmospheric processes, we cannot expect a similar level of reduction of the gas-phase air pollutants as the reduction of emissions. Both simulations reproduced the variability of NASA P-3B aircraft measurements of NOy and O3 in the lower atmosphere (from 90 m to 4.5 km). Both simulations provided similar reasonable predictions at surface, while 1 km case depicted more detailed features of emissions and concentrations in heavily polluted areas, such as highways, airports, and industrial regions, which are useful in understanding the major causes of O3 pollution in such regions, and to quantify transport of O3 to populated communities in urban areas. The Integrated Reaction Rate analyses indicated a distinctive difference of chemistry processes between the model surface layer and upper layers, implying that correcting the meteorological conditions at the surface may not help to enhance the O3 predictions. The model-observation O3 bias in our studies (e.g., large over-prediction during the nighttime or along Gulf of Mexico coastline), were due to uncertainties in meteorology, chemistry or other processes. Horizontal grid resolution is unlikely the major contributor to these biases.

Mobile Air Monitoring: Measuring Change in Air Quality in the City of Hamilton, 2005-2010

ERIC Educational Resources Information Center

Adams, Matthew D.; DeLuca, Patrick F.; Corr, Denis; Kanaroglou, Pavlos S.

2012-01-01

This paper examines the change in air pollutant concentrations between 2005 and 2010 occurring in the City of Hamilton, Ontario, Canada. After analysis of stationary air pollutant concentration data, we analyze mobile air pollutant concentration data. Air pollutants included in the analysis are CO, PM[subscript 2.5], SO[subscript 2], NO,…

Contribution of ship emissions to the concentration and deposition of air pollutants in Europe

NASA Astrophysics Data System (ADS)

Aksoyoglu, S.; Prévôt, A. S. H.; Baltensperger, U.

2015-11-01

Emissions from the marine transport sector are one of the least regulated anthropogenic emission sources and contribute significantly to air pollution. Although strict limits were introduced recently for the maximum sulfur content in marine fuels in the SECAs (sulfur emission control areas) and in the EU ports, sulfur emissions outside the SECAs and emissions of other components in all European maritime areas have continued to increase in the last two decades. We have used the air quality model CAMx with and without ship emissions for the year 2006 to determine the effects of international shipping on the annual as well as seasonal concentrations of ozone, primary and secondary components of PM2.5 and the dry and wet deposition of nitrogen and sulfur compounds in Europe. Our results suggest that emissions from international shipping affect the air quality in northern and southern Europe differently and their contributions to the air concentrations vary seasonally. The largest changes in pollutant concentrations due to ship emissions were predicted for summer. Increased concentrations of the primary particle mass were found only along the shipping routes whereas concentrations of the secondary pollutants were affected over a larger area. Concentrations of particulate sulfate increased due to ship emissions in the Mediterranean (up to 60 %), in the English Channel and the North Sea (30-35 %) while increases in particulate nitrate levels were found especially in the north, around the Benelux area (20 %) where there were high NH3 land-based emissions. Our model results showed that not only the atmospheric concentrations of pollutants are affected by ship emissions, but also depositions of nitrogen and sulfur compounds increase significantly along the shipping routes. NOx emissions from the ships especially in the English Channel and the North Sea, cause a decrease in the dry deposition of reduced nitrogen at source regions by moving it from the gas-phase to the particle phase which then contributes to an increase in the wet deposition at coastal areas with higher precipitation. In the western Mediterranean region on the other hand, model results show an increase in the deposition of oxidized nitrogen (mostly HNO3) due to the ship traffic. Dry deposition of SO2 seems to be significant along the shipping routes whereas sulfate wet deposition occurs mainly along the Scandinavian and Adriatic coasts. The results presented in this paper suggest that evolution of NOx emissions from ships and land-based NH3 emissions will play a significant role in the future European air quality.

Detection of critical PM2.5 emission sources and their contributions to a heavy haze episode in Beijing, China, using an adjoint model

NASA Astrophysics Data System (ADS)

Zhai, Shixian; An, Xingqin; Zhao, Tianliang; Sun, Zhaobin; Wang, Wei; Hou, Qing; Guo, Zengyuan; Wang, Chao

2018-05-01

Air pollution sources and their regional transport are important issues for air quality control. The Global-Regional Assimilation and Prediction System coupled with the China Meteorological Administration Unified Atmospheric Chemistry Environment (GRAPES-CUACE) aerosol adjoint model was applied to detect the sensitive primary emission sources of a haze episode in Beijing occurring between 19 and 21 November 2012. The high PM2.5 concentration peaks occurring at 05:00 and 23:00 LT (GMT+8) over Beijing on 21 November 2012 were set as the cost functions for the aerosol adjoint model. The critical emission regions of the first PM2.5 concentration peak were tracked to the west and south of Beijing, with 2 to 3 days of cumulative transport of air pollutants to Beijing. The critical emission regions of the second peak were mainly located to the south of Beijing, where southeasterly moist air transport led to the hygroscopic growth of particles and pollutant convergence in front of the Taihang Mountains during the daytime on 21 November. The temporal variations in the sensitivity coefficients for the two PM2.5 concentration peaks revealed that the response time of the onset of Beijing haze pollution from the local primary emissions is approximately 1-2 h and that from the surrounding primary emissions it is approximately 7-12 h. The upstream Hebei province has the largest impact on the two PM2.5 concentration peaks, and the contribution of emissions from Hebei province to the first PM2.5 concentration peak (43.6 %) is greater than that to the second PM2.5 concentration peak (41.5 %). The second most influential province for the 05:00 LT PM2.5 concentration peak is Beijing (31.2 %), followed by Shanxi (9.8 %), Tianjin (9.8 %), and Shandong (5.7 %). The second most influential province for the 23:00 LT PM2.5 concentration peak is Beijing (35.7 %), followed by Shanxi (8.1 %), Shandong (8.0 %), and Tianjin (6.7 %). The adjoint model results were compared with the forward sensitivity simulations of the Models-3/CMAQ system. The two modeling approaches are highly comparable in their assessments of atmospheric pollution control schemes for critical emission regions, but the adjoint method has higher computational efficiency than the forward sensitivity method. The results also imply that critical regional emission reduction could be more efficient than individual peak emission control for improving regional PM2.5 air quality.

Fractional kalman filter to estimate the concentration of air pollution

NASA Astrophysics Data System (ADS)

Vita Oktaviana, Yessy; Apriliani, Erna; Khusnul Arif, Didik

2018-04-01

Air pollution problem gives important effect in quality environment and quality of human’s life. Air pollution can be caused by nature sources or human activities. Pollutant for example Ozone, a harmful gas formed by NOx and volatile organic compounds (VOCs) emitted from various sources. The air pollution problem can be modeled by TAPM-CTM (The Air Pollution Model with Chemical Transport Model). The model shows concentration of pollutant in the air. Therefore, it is important to estimate concentration of air pollutant. Estimation method can be used for forecast pollutant concentration in future and keep stability of air quality. In this research, an algorithm is developed, based on Fractional Kalman Filter to solve the model of air pollution’s problem. The model will be discretized first and then it will be estimated by the method. The result shows that estimation of Fractional Kalman Filter has better accuracy than estimation of Kalman Filter. The accuracy was tested by applying RMSE (Root Mean Square Error).

Active-passive measurements and CFD based modelling for indoor radon dispersion study.

PubMed

Chauhan, Neetika; Chauhan, R P

2015-06-01

Computational fluid dynamics (CFD) play a significant role in indoor pollutant dispersion study. Radon is an indoor pollutant which is radioactive and inert gas in nature. The concentration level and spatial distribution of radon may be affected by the dwelling's ventilation conditions. Present work focus at the study of indoor radon gas distribution via measurement and CFD modeling in naturally ventilated living room. The need of the study is the prediction of activity level and to study the effect of natural ventilation on indoor radon. Two measurement techniques (Passive measurement using pin-hole dosimeters and active measurement using continuous radon monitor (SRM)) were used for the validation purpose of CFD results. The CFD simulation results were compared with the measurement results at 15 points, 3 XY planes at different heights along with the volumetric average concentration. The simulation results found to be comparable with the measurement results. The future scope of these CFD codes is to study the effect of varying inflow rate of air on the radon concentration level and dispersion pattern. Copyright © 2015 Elsevier Ltd. All rights reserved.

Electrocoagulation efficiency of the tannery effluent treatment using aluminium electrodes.

PubMed

Espinoza-Quiñones, Fernando R; Fornari, Marilda M T; Módenes, Aparecido N; Palácio, Soraya M; Trigueros, Daniela E G; Borba, Fernando H; Kroumov, Alexander D

2009-01-01

An electro-coagulation laboratory scale system using aluminium plates electrodes was studied for the removal of organic and inorganic pollutants as a by-product from leather finishing industrial process. A fractional factorial 2(3) experimental design was applied in order to obtain optimal values of the system state variables. The electro-coagulation (EC) process efficiency was based on the chemical oxygen demand (COD), turbidity, total suspended solid, total fixed solid, total volatile solid, and chemical element concentration values. Analysis of variance (ANOVA) for final pH, total fixed solid (TFS), turbidity and Ca concentration have confirmed the predicted models by the experimental design within a 95% confidence level. The reactor working conditions close to real effluent pH (7.6) and electrolysis time in the range 30-45 min were enough to achieve the cost effective reduction factors of organic and inorganic pollutants' concentrations. An appreciable improvement in COD removal efficiency was obtained for electro-coagulation treatment. Finally, the technical-economical analysis results have clearly shown that the electro-coagulation method is very promising for industrial application.

Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study

PubMed Central

2012-01-01

Background Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments. Methods National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM2.5 and NO2) and a chemical transport model (for O3). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions. Results Calibration of the national PM2.5 surface using annual spatiotemporal interpolation predicted historical PM2.5 measurement data best (R2 = 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO2 (R2 = 0.38) and O3 (R2 = 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM2.5, NO2 and O3 exposures of 11.3 μg/m3 (SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM2.5, NO2 and O3 exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls. Conclusions We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments. For submission to: Environmental Health PMID:22475580

Local cartography of persistent organic pollutants (PCDD/F, PCB) concentrations in soils of three French departments. How to define background concentrations?

NASA Astrophysics Data System (ADS)

Clozel, Blandine

2017-04-01

As part of the Regional Health Plan for the Rhône-Alpes area (France), a cartography of soil contamination by persistent organic pollutants (dioxins/furans (PCDD/PCDF) and polychlorinated biphenyls (PCB)) was undertaken in order to define the background concentrations of soils located away from point source pollution. In the natural environment, PCDD/PCDF and PCB comes from air pollution and accumulate in the upper part of the soils. To define the background concentration of persistent organic pollutants from diffuse atmospheric origin in soils, sampling was carried out within the first 5 centimeters of soils that have been very little anthropized and untilled for more than 15 years. In such soils mixing and dilution of the pollutants is very limited. 170 samples were collected following a systematic plan of grid type (mesh of 8 x 8 km) in an area of 14 000km2, avoiding soil of high altitude and from urban area. Beyond their total concentration, the ratio of the congeners of PCBs (7 indicators and 12 dioxin-like) and of the 17 dioxins/furans was also used for interpretation. As expected, the concentrations in pollutants are globally lower in the rural zones than in the more industrialized ones. However, the pollutants are relatively enriched in valleys, confirming that the meteorological conditions and the local topography play a significant role in the repartition of the diffuse atmospheric pollution. For the vast majority of samples, even some of those presenting the highest total concentration, the ratio of the various congeners argues for an ancient origin of the contamination. All studies at the French or European level of the atmospheric concentration of organic pollutants indicate a progressive decrease in emissions of these contaminants for about 20 years. However, the soils have been receptors since a long time and such pollutants have accumulated. The congeners ratio give evolved signature of pollution indicating, on one hand, it is mainly due to past activities but, on the other hand, indicate that it will persist because of its high stability. These results show the importance of knowing the spatial distribution of the concentrations of PCDD/PCDF and PCB and taking into account the signature of their congeners when defining the reference value of background concentration which are applied to distinguish a recent point source pollution

Changes in Escherichia coli to Cryptosporidium ratios for various fecal pollution sources and drinking water intakes.

PubMed

Lalancette, Cindy; Papineau, Isabelle; Payment, Pierre; Dorner, Sarah; Servais, Pierre; Barbeau, Benoit; Di Giovanni, George D; Prévost, Michèle

2014-05-15

Assessing the presence of human pathogenic Cryptosporidium oocysts in surface water remains a significant water treatment and public health challenge. Most drinking water suppliers rely on fecal indicators, such as the well-established Escherichia coli (E. coli), to avoid costly Cryptosporidium assays. However, the use of E. coli has significant limitations in predicting the concentration, the removal and the transport of Cryptosporidium. This study presents a meta-analysis of E. coli to Cryptosporidium concentration paired ratios to compare their complex relationships in eight municipal wastewater sources, five agricultural fecal pollution sources and at 13 drinking water intakes (DWI) to a risk threshold based on US Environmental Protection Agency (USEPA) regulations. Ratios lower than the USEPA risk threshold suggested higher concentrations of oocysts in relation to E. coli concentrations, revealing an underestimed risk for Cryptosporidium based on E. coli measurements. In raw sewage (RS), high ratios proved E. coli (or fecal coliforms) concentrations were a conservative indicator of Cryptosporidium concentrations, which was also typically true for secondary treated wastewater (TWW). Removals of fecal indicator bacteria (FIB) and parasites were quantified in WWTPs and their differences are put forward as a plausible explanation of the sporadic ratio shift. Ratios measured from agricultural runoff surface water were typically lower than the USEPA risk threshold and within the range of risk misinterpretation. Indeed, heavy precipitation events in the agricultural watershed led to high oocyst concentrations but not to E. coli or enterococci concentrations. More importantly, ratios established in variously impacted DWI from 13 Canadian drinking water plants were found to be related to dominant fecal pollution sources, namely municipal sewage. In most cases, when DWIs were mainly influenced by municipal sewage, E. coli or fecal coliforms concentrations agreed with Cryptosporidium concentrations as estimated by the meta-analysis, but when DWIs were influenced by agricultural runoff or wildlife, there was a poor relationship. Average recovery values were available for 6 out of 22 Cryptosporidium concentration data sets and concomitant analysis demonstrated no changes in trends, with and without correction. Nevertheless, recovery assays performed along with every oocyst count would have enhanced the precision of this work. Based on our findings, the use of annual averages of E. coli concentrations as a surrogate for Cryptosporidium concentrations can result in an inaccurate estimate of the Cryptosporidium risk for agriculture impacted drinking water intakes or for intakes with more distant wastewater sources. Studies of upstream fecal pollution sources are recommended for drinking water suppliers to improve their interpretation of source water quality data. Copyright © 2014 Elsevier Ltd. All rights reserved.

Applications of MODIS satellite data and products for monitoring air quality in the state of Texas

NASA Astrophysics Data System (ADS)

Hutchison, Keith D.

The Center for Space Research (CSR), in conjunction with the Monitoring Operations Division (MOD) of the Texas Commission on Environmental Quality (TCEQ), is evaluating the use of remotely sensed satellite data to assist in monitoring and predicting air quality in Texas. The challenges of meeting air quality standards established by the US Environmental Protection Agency (US EPA) are impacted by the transport of pollution into Texas that originates from outside our borders and are cumulative with those generated by local sources. In an attempt to quantify the concentrations of all pollution sources, MOD has installed ground-based monitoring stations in rural regions along the Texas geographic boundaries including the Gulf coast, as well as urban regions that are the predominant sources of domestic pollution. However, analysis of time-lapse GOES satellite imagery at MOD, clearly demonstrates the shortcomings of using only ground-based observations for monitoring air quality across Texas. These shortcomings include the vastness of State borders, that can only be monitored with a large number of ground-based sensors, and gradients in pollution concentration that depend upon the location of the point source, the meteorology governing its transport to Texas, and its diffusion across the region. With the launch of NASA's MODerate resolution Imaging Spectroradiometer (MODIS), the transport of aerosol-borne pollutants can now be monitored over land and ocean surfaces. Thus, CSR and MOD personnel have applied MODIS data to several classes of pollution that routinely impact Texas air quality. Results demonstrate MODIS data and products can detect and track the migration of pollutants. This paper presents one case study in which continental haze from the northeast moved into the region and subsequently required health advisories to be issued for 150 counties in Texas. It is concluded that MODIS provides the basis for developing advanced data products that will, when used in conjunction with ground-based observations, create a cost-effective and accurate pollution monitoring system for the entire state of Texas.

Preliminary investigation of uncombusted auto fuel vapor dispersion within a residential garage microenvironment

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

Lansari, A.; Streicher, J.J.; Huber, A.H.

1993-01-01

Evaporative emissions from vehicles in an attached garage may represent a significant source of indoor pollution and human exposure. A pilot field study was undertaken to investigate potential in-house dispersion of evaporative emissions of uncombusted fuels from a vehicle parked inside an attached garage. In a set of experiments using sulfur hexafluoride tracer gas, the multizonal mass balance model, CONTAM88, was used to predict interzonal air flow rates and SF6 concentration distributions within the garage and house. Several experiments were included to evaluate the effect of meteorology and mechanical mixing mechanisms on the dispersion of automobile fuel vapor. Measurements indicatedmore » that approximately three percent of the garage maximum concentration was measured in a room adjacent to the garage. The model successfully predicted garage concentrations under well mixed conditions, but underpredicted the measured concentrations within various rooms of the house, in which mixing was incomplete. Multizonal mass balance models such as CONTAM88 may be useful in approximating contaminant concentrations at various locations within the house.« less

Validation of an antibody-based biosensor for rapid quantification of 2,4,6-trinitrotoluene (TNT) contamination in ground water and river water.

PubMed

Bromage, Erin S; Vadas, George G; Harvey, Ellen; Unger, Michael A; Kaattari, Stephen L

2007-10-15

Nitroaromatics are common pollutants of soil and groundwater at military installations because of their manufacture, storage, and use at these sites. Long-term monitoring of these pollutants comprise a significant percentage of restoration costs. Further, remediation activities often have to be delayed, while the samples are processed via traditional chemical assessment protocols. Here we describe a rapid (<5 min), cost-effective, accurate method using a KinExA Inline Biosensor for monitoring of 2,4,6-trinitrotoluene (TNT) in field water samples. The biosensor, which is based on KinExA technology, accurately estimated the concentration of TNT in double-blind comparisons with similar accuracy to traditional high-performance liquid chromatography(HPLC). In the assessment of field samples, the biosensor accurately predicted the concentration of TNT over the range of 1-30,000 microg/L when compared to either HPLC or quantitative gas chromatography-mass spectrometry (GC-MS). Various pre-assessment techniques were explored to examine whether field samples could be assessed untreated, without the removal of particulates or the use of solvents. In most cases, the KinExA Inline Biosensor gave a uniform assessment of TNT concentration independent of pretreatment method. This indicates that this sensor possesses significant promise for rapid, on-site assessment of TNT pollution in environmental water samples.

Pulmonary Function and Incidence of Selected Respiratory Diseases Depending on the Exposure to Ambient PM10.

PubMed

Badyda, Artur; Gayer, Anna; Czechowski, Piotr Oskar; Majewski, Grzegorz; Dąbrowiecki, Piotr

2016-11-22

It is essential in pulmonary disease research to take into account traffic-related air pollutant exposure among urban inhabitants. In our study, 4985 people were examined for spirometric parameters in the presented research which was conducted in the years 2008-2012. The research group was divided into urban and rural residents. Traffic density, traffic structure and velocity, as well as concentrations of selected air pollutants (CO, NO₂ and PM 10 ) were measured at selected areas. Among people who live in the city, lower percentages of predicted values of spirometric parameters were noticed in comparison to residents of rural areas. Taking into account that the difference in the five-year mean concentration of PM 10 in the considered city and rural areas was over 17 μg/m³, each increase of PM 10 by 10 μg/m³ is associated with the decline in FEV₁ (forced expiratory volume during the first second of expiration) by 1.68%. These findings demonstrate that traffic-related air pollutants may have a significant influence on the decline of pulmonary function and the growing rate of respiratory diseases.

Pulmonary Function and Incidence of Selected Respiratory Diseases Depending on the Exposure to Ambient PM10

PubMed Central

Badyda, Artur; Gayer, Anna; Czechowski, Piotr Oskar; Majewski, Grzegorz; Dąbrowiecki, Piotr

2016-01-01

It is essential in pulmonary disease research to take into account traffic-related air pollutant exposure among urban inhabitants. In our study, 4985 people were examined for spirometric parameters in the presented research which was conducted in the years 2008–2012. The research group was divided into urban and rural residents. Traffic density, traffic structure and velocity, as well as concentrations of selected air pollutants (CO, NO2 and PM10) were measured at selected areas. Among people who live in the city, lower percentages of predicted values of spirometric parameters were noticed in comparison to residents of rural areas. Taking into account that the difference in the five-year mean concentration of PM10 in the considered city and rural areas was over 17 μg/m3, each increase of PM10 by 10 μg/m3 is associated with the decline in FEV1 (forced expiratory volume during the first second of expiration) by 1.68%. These findings demonstrate that traffic-related air pollutants may have a significant influence on the decline of pulmonary function and the growing rate of respiratory diseases. PMID:27879677

Dynamic behavior of semivolatile organic compounds in indoor air

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

Loy, Michael David Van

1998-12-09

Exposures to a wide range of air pollutants are often dominated by those occurring in buildings because of three factors: 1) most people spend a large fraction of their time indoors, 2) many pollutants have strong indoor sources, and 3) the dilution volume in buildings is generally several orders of magnitude smaller than that of an urban airshed. Semivolatile organic compounds (SVOCS) are emitted by numerous indoor sources, including tobacco combustion, cooking, carpets, paints, resins, and glues, so indoor gasphase concentrations of these compounds are likely to be elevated relative to ambient levels. The rates of uptake and release ofmore » reversibly sorbing SVOCS by indoor materials directly affect both peak concentrations and persistence of the pollutants indoors after source elimination. Thus, accurate predictions of SVOC dynamics in indoor air require an understanding of contaminant sorption on surface materials such as carpet and wallboard. The dynamic behaviors of gas-phase nicotine and phenanthrene were investigated in a 20 ms stainless steel chamber containing carpet and painted wallboard. Each compound was studied independently, first in the empty chamber, then with each sorbent individually, and finally with both sorbents in the chamber.« less

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.

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.

Linking North American Summer Ozone Pollution Episodes to Subseasonal Atmospheric Variability

NASA Astrophysics Data System (ADS)

White, E. C.; Watt-Meyer, O.; Kushner, P. J.; Jones, D. B. A.

2017-12-01

Ozone concentrations in the planetary boundary layer (PBL) are positively correlated with surface air temperature due to shared influences including incident solar radiation and PBL stagnancy, as well as the temperature-sensitive emission of ozone precursor compounds. While previous studies have linked heat waves in North America to modes of subseasonal atmospheric variability, such analyses have not been applied to summertime ozone pollution episodes. This study investigates a possible link between subseasonal atmospheric variability in reanalysis data and summertime ozone pollution episodes identified in almost thirty years of in-situ measurements from the Air Quality System (AQS) network in the United States. AQS stations are grouped into regions likely to experience simultaneous extreme ozone concentrations using statistical clustering methods. Composite meteorological patterns are calculated for ozone episodes in each of these regions. The same analysis is applied to heat waves identified in AQS temperature records for comparison. Local meteorological features during typical ozone episodes include extreme temperatures and reduced cloud cover related to anomalous synoptic-scale anticyclonic circulation aloft. These anticyclonic anomalies are typically embedded in wave trains extending from the North Pacific to North Atlantic. Spectral analysis of these wave trains reveals that low-frequency standing waves play a prominent role. These long-lived circulation patterns may provide a means to increase air quality prediction lead-times and to estimate the frequency of ozone pollution episodes under climate change.

Analysis of photochemical pollution in summer and winter using a photochemical box model in the center of Tokyo, Japan.

PubMed

Huang, H; Akustu, Y; Arai, M; Tamura, M

2001-07-01

In order to give an effective and rapid analysis of the photochemical pollution and information for emission control strategies, a photochemical box model (PBM) was applied to one moderate summer episode, 11 July 1996, and one typical winter episode, 3 December 1996, in the center of Tokyo, Japan. The box model gave a good prediction of the photochemical pollution with minimal investment. As expected, the peak ozone in summer is higher than in winter. The NOx concentrations in winter are higher than those in summer. In summer, NO and NO2 have one peak in the morning. In winter, NO and NO2 show two peaks during the day. Three model runs including no reactions, a zero ozone boundary condition and dark reactions were conducted to understand the photochemical processes. The effects of emission reduction on the formation of the photochemical pollution in the center of Tokyo have been studied. The results show that the reduction of NMHC emission can decrease the ozone, however, the reduction of NOx emission can increase the ozone. It can be concluded that if the NOx emission are reduced, the reduction of NMHC should be more emphasized in order to decrease the ozone concentration in the center of Tokyo, Japan, especially the reduction of the NMHC from stationary source emission.

Implementation of a WRF-CMAQ Air Quality Modeling System in Bogotá, Colombia

NASA Astrophysics Data System (ADS)

Nedbor-Gross, R.; Henderson, B. H.; Pachon, J. E.; Davis, J. R.; Baublitz, C. B.; Rincón, A.

2014-12-01

Due to a continuous economic growth Bogotá, Colombia has experienced air pollution issues in recent years. The local environmental authority has implemented several strategies to curb air pollution that have resulted in the decrease of PM10 concentrations since 2010. However, more activities are necessary in order to meet international air quality standards in the city. The University of Florida Air Quality and Climate group is collaborating with the Universidad de La Salle to prioritize regulatory strategies for Bogotá using air pollution simulations. To simulate pollution, we developed a modeling platform that combines the Weather Research and Forecasting Model (WRF), local emissions, and the Community Multi-scale Air Quality model (CMAQ). This platform is the first of its kind to be implemented in the megacity of Bogota, Colombia. The presentation will discuss development and evaluation of the air quality modeling system, highlight initial results characterizing photochemical conditions in Bogotá, and characterize air pollution under proposed regulatory strategies. The WRF model has been configured and applied to Bogotá, which resides in a tropical climate with complex mountainous topography. Developing the configuration included incorporation of local topography and land-use data, a physics sensitivity analysis, review, and systematic evaluation. The threshold, however, was set based on synthesis of model performance under less mountainous conditions. We will evaluate the impact that differences in autocorrelation contribute to the non-ideal performance. Air pollution predictions are currently under way. CMAQ has been configured with WRF meteorology, global boundary conditions from GEOS-Chem, and a locally produced emission inventory. Preliminary results from simulations show promising performance of CMAQ in Bogota. Anticipated results include a systematic performance evaluation of ozone and PM10, characterization of photochemical sensitivity, and air quality predictions under proposed regulatory scenarios.

The relationship between particulate pollution levels in Australian cities, meteorology, and landscape fire activity detected from MODIS hotspots.

PubMed

Price, Owen F; Williamson, Grant J; Henderson, Sarah B; Johnston, Fay; Bowman, David M J S

2012-01-01

Smoke from bushfires is an emerging issue for fire managers because of increasing evidence for its public health effects. Development of forecasting models to predict future pollution levels based on the relationship between bushfire activity and current pollution levels would be a useful management tool. As a first step, we use daily thermal anomalies detected by the MODIS Active Fire Product (referred to as "hotspots"), pollution concentrations, and meteorological data for the years 2002 to 2008, to examine the statistical relationship between fire activity in the landscapes and pollution levels around Perth and Sydney, two large Australian cities. Resultant models were statistically significant, but differed in their goodness of fit and the distance at which the strength of the relationship was strongest. For Sydney, a univariate model for hotspot activity within 100 km explained 24% of variation in pollution levels, and the best model including atmospheric variables explained 56% of variation. For Perth, the best radius was 400 km, explaining only 7% of variation, while the model including atmospheric variables explained 31% of the variation. Pollution was higher when the atmosphere was more stable and in the presence of on-shore winds, whereas there was no effect of wind blowing from the fires toward the pollution monitors. Our analysis shows there is a good prospect for developing region-specific forecasting tools combining hotspot fire activity with meteorological data.

The study of azaarene behavior over atmosphere of subtropical city(Keelung)

NASA Astrophysics Data System (ADS)

Liu, Chih Yun

2017-04-01

In this study, we collected the Total Suspended Particulates (TSP) from July 2014 to February 2016 in the subtropical city (Keelung), and researched azaarene behavior over atmosphere. Polycyclic Aromatic Compounds (PAHs) are ubiquitous pollutants in the environment; they have known carcinogens and/or mutagens, mainly produce from incomplete combustion. Azaarenes are polycyclic aromatic hydrocarbon derivative compounds in which a carbon atom in one of the aromatic rings is substituted by a nitrogen atom. Organism exposure to azaarenes occurs through inhalation of polluted air and by ingestion of food and/or water containing combustion products and accumulate in the body. Total azaarene concentration (16 individual compound concentration of the aggregate) is between 0.92 to 3.76 μg/m3, results showed that the concentration of azaarenes have significant seasonal variation, they have higher concentration in the cold month. In molecular weight, the highest proportion is the molecular weight equal to 143(ΣMQ) and then the molecular weight equal to 179(BAP), ΣMQ would rise from 30% 40% to 40% 50% during the cold month and warm months. Compared to ring number, 2-rings are biggest part, the smallest is 4-rings, its ratio has slight variation, but primary species is 2-rings. Emissions from transportation, local housing heating, factories burning fossil fuels and dust from Mainland south air mass are pollutant, their sources and climate conditions can affect concentration and composition of compound. There are highly significant correlation between 3-rings and 4-rings, which suggests that there are similar source strengths and transport mechanisms for these compounds. Correlation between concentration of azaarenes and ambient temperature is negative moderation, with concentration of atmospheric suspended particles is positive moderate correlation. Finally, we establish the relationship between the three parameters to predict concentration of azaarenes over atmosphere of subtropical regions. Key words: azaarenes, atmospheric suspended particles, subtropical city, multiple regression analysis.

Joint space-time geostatistical model for air quality surveillance

NASA Astrophysics Data System (ADS)

Russo, A.; Soares, A.; Pereira, M. J.

2009-04-01

Air pollution and peoples' generalized concern about air quality are, nowadays, considered to be a global problem. Although the introduction of rigid air pollution regulations has reduced pollution from industry and power stations, the growing number of cars on the road poses a new pollution problem. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, a generalized and growing interest on air quality issues led to research intensification and publication of several articles with quite different levels of scientific depth. As most natural phenomena, air quality can be seen as a space-time process, where space-time relationships have usually quite different characteristics and levels of uncertainty. As a result, the simultaneous integration of space and time is not an easy task to perform. This problem is overcome by a variety of methodologies. The use of stochastic models and neural networks to characterize space-time dispersion of air quality is becoming a common practice. The main objective of this work is to produce an air quality model which allows forecasting critical concentration episodes of a certain pollutant by means of a hybrid approach, based on the combined use of neural network models and stochastic simulations. A stochastic simulation of the spatial component with a space-time trend model is proposed to characterize critical situations, taking into account data from the past and a space-time trend from the recent past. To identify near future critical episodes, predicted values from neural networks are used at each monitoring station. In this paper, we describe the design of a hybrid forecasting tool for ambient NO2 concentrations in Lisbon, Portugal.

Pollutant Plume Dispersion over Hypothetical Urban Areas based on Wind Tunnel Measurements

NASA Astrophysics Data System (ADS)

Mo, Ziwei; Liu, Chun-Ho

2017-04-01

Gaussian plume model is commonly adopted for pollutant concentration prediction in the atmospheric boundary layer (ABL). However, it has a number of limitations being applied to pollutant dispersion over complex land-surface morphology. In this study, the friction factor (f), as a measure of aerodynamic resistance induced by rough surfaces in the engineering community, was proposed to parameterize the vertical dispersion coefficient (σz) in the Gaussian model. A series of wind tunnel experiments were carried out to verify the mathematical hypothesis and to characterize plume dispersion as a function of surface roughness as well. Hypothetical urban areas, which were assembled in the form of idealized street canyons of different aspect (building-height-to-street-width) ratios (AR = 1/2, 1/4, 1/8 and 1/12), were fabricated by aligning identical square aluminum bars at different separation apart in cross flows. Pollutant emitted from a ground-level line source into the turbulent boundary layer (TBL) was simulated using water vapour generated by ultrasonic atomizer. The humidity and the velocity (mean and fluctuating components) were measured, respectively, by humidity sensors and hot-wire anemometry (HWA) with X-wire probes in streamwise and vertical directions. Wind tunnel results showed that the pollutant concentration exhibits the conventional Gaussian distribution, suggesting the feasibility of using water vapour as a passive scalar in wind tunnel experiments. The friction factor increased with decreasing aspect ratios (widening the building separation). It was peaked at AR = 1/8 and decreased thereafter. Besides, a positive correlation between σz/xn (x is the distance from the pollutant source) and f1/4 (correlation coefficient r2 = 0.61) was observed, formulating the basic parameterization of plume dispersion over urban areas.

Characterization of exposure in epidemiological studies on air pollution from biodegradable wastes: Misclassification and comparison of exposure assessment strategies.

PubMed

Cantuaria, Manuella Lech; Suh, Helen; Løfstrøm, Per; Blanes-Vidal, Victoria

2016-11-01

The assignment of exposure is one of the main challenges faced by environmental epidemiologists. However, misclassification of exposures has not been explored in population epidemiological studies on air pollution from biodegradable wastes. The objective of this study was to investigate the use of different approaches for assessing exposure to air pollution from biodegradable wastes by analyzing (1) the misclassification of exposure that is committed by using these surrogates, (2) the existence of differential misclassification (3) the effects that misclassification may have on health effect estimates and the interpretation of epidemiological results, and (4) the ability of the exposure measures to predict health outcomes using 10-fold cross validation. Four different exposure assessment approaches were studied: ammonia concentrations at the residence (Metric I), distance to the closest source (Metric II), number of sources within certain distances from the residence (Metric IIIa,b) and location in a specific region (Metric IV). Exposure-response models based on Metric I provided the highest predictive ability (72.3%) and goodness-of-fit, followed by IV, III and II. When compared to Metric I, Metric IV yielded the best results for exposure misclassification analysis and interpretation of health effect estimates, followed by Metric IIIb, IIIa and II. The study showed that modelled NH 3 concentrations provide more accurate estimations of true exposure than distances-based surrogates, and that distance-based surrogates (especially those based on distance to the closest point source) are imprecise methods to identify exposed populations, although they may be useful for initial studies. Copyright © 2016 Elsevier GmbH. All rights reserved.

IASI Satellite Observation and Forecast of Pollutants

NASA Astrophysics Data System (ADS)

Clerbaux, C.; Boynard, A.; George, M.; Hadji-Lazaro, J.; Safieddine, S.; Viatte, C.; Clarisse, L.; Pierre-Francois, C.; Hurtmans, D.; van Damme, M.; Wespes, C.; Whitburn, S.

2017-12-01

The IASI family of instruments has been sounding the atmosphere since 2006 onboard the Metop satellite series. Using the radiance data recorded in the thermal infrared spectral range, concentrations for atmospheric pollutants such as carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2) and ammonia (NH3) can be derived. IASI CO and O3 fields are assimilated in regional and global models in order to predict air quality over Europe. Enhanced levels of pollutants are detected in near-real time, and can be followed at city, country and continent levels. This talk will present the findings for an extended time period (2008-2017), and will review the IASI capability to observe exceptional events both at the local and regional scales, as well as seasonal variations due other dynamic patterns (monsoon, ENSO, …). Progresses and current limitations to derive long term trends will also be discussed.

Persistence analysis of extreme CO, NO2 and O3 concentrations in ambient air of Delhi

NASA Astrophysics Data System (ADS)

Chelani, Asha B.

2012-05-01

Persistence analysis of air pollutant concentration and corresponding exceedance time series is carried out to examine for temporal evolution. For this purpose, air pollutant concentrations, namely, CO, NO2 and O3 observed during 2000-2009 at a traffic site in Delhi are analyzed using detrended fluctuation analysis. Two types of extreme values are analyzed; exceeded concentrations to a threshold provided by national pollution controlling agency and time interval between two exceedances. The time series of three pollutants is observed to possess persistence property whereas the extreme value time series of only primary pollutant concentrations is found to be persistent. Two time scaling regions are observed to be significant in extreme time series of CO and NO2, mainly attributed to implementation of CNG in vehicles. The presence of persistence in three pollutant concentration time series is linked to the property of self-organized criticality. The observed persistence in the time interval between two exceeded levels is a matter of concern as persistent high concentrations can trigger health problems.

A novel method to construct an air quality index based on air pollution profiles.

PubMed

Thach, Thuan-Quoc; Tsang, Hilda; Cao, Peihua; Ho, Lai-Ming

2018-01-01

Air quality indices based on the maximum of sub-indices of pollutants are easy to produce and help quantify the degree of air pollution. However, they discount the additive effects of multiple pollutants and are only sensitive to changes in highest sub-index. We propose a simple and concise method to construct an air quality index that takes into account additive effects of multiple pollutants and evaluate the extent to which this index predicts health effects. We obtained concentrations of four criteria pollutants: particulate matter with aerodynamic diameter ≤ 10μm (PM 10 ), sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ) and ozone (O 3 ) and daily admissions to Hong Kong hospitals for cardiovascular and respiratory diseases for all ages and those 65 years or older for years 2001-2012. We derived sub-indices of the four criteria pollutants, calculated by normalizing pollutant concentrations to their respective short-term WHO Air Quality Guidelines (WHO AQG). We aggregated the sub-indices using the root-mean-power function with an optimal power to form an overall air quality index. The optimal power was determined by minimizing the sum of over- and under-estimated days. We then assessed associations between the pollution bands of the index and cardiovascular and respiratory admissions using a time-stratified case-crossover design adjusted for ambient temperature, relative humidity and influenza epidemics. Further, we conducted case-crossover analyses using the Hong Kong air quality data with the respective standards and classification of pollution bands of the China Air Quality Index (AQI), the United Kingdom Daily AQI (DAQI), and the United States Environmental Protection Agency (USEPA) AQI. The mean concentrations of PM 10 and SO 2 based on maximum 3-h mean exceeded the WHO AQG by 37% and 50%, respectively. We identified the combined condition of observed high-pollution days as either at least one pollutant > 1.5×WHO AQG or at least two pollutants > 1.0×WHO AQG to characterize the typical pollution profiles over the study period, which resulted in the optimal power=3.0. The distribution of days in different pollution bands of the index was: 5.8% for "Low" (0-50), 37.6% for "Moderate" (51-100), 31.1% for "High" (101-150), 14.7% for "Very High" (151-200), and 10.8% for "Serious" (201+). For cardiovascular and respiratory admissions, there were significant associations with the pollution bands of the index for all ages and those 65 years or older. The trends of increasing pollution bands in relation to increasing excess risks of cardiovascular and respiratory admissions were significant for the proposed index, the China AQI, the UK DAQI and the USEPA AQI (P value for test for linear trend < 0.0001), suggesting a dose-response relation. We have developed a simple and concise method to construct an air quality index that accounts for multiple pollutants to quantify air quality conditions for Hong Kong. Further developments are needed in order to support the extension of the method to other settings. Copyright © 2017 Elsevier GmbH. All rights reserved.

The Characteristics of Air Pollutants during Two Distinct Episodes of Fireworks Burning in a Valley City of North China

PubMed Central

Song, Yang; Wan, Xiaoming; Bai, Shuoxin; Guo, Dong; Ren, Ci; Zeng, Yu; Li, Yirui; Li, Xuewen

2017-01-01

Background The elevation and dissipation of pollutants after the ignition of fireworks in different functional areas of a valley city were investigated. Methods The Air Quality Index (AQI) as well as inter-day and intra-day concentrations of various air pollutants (PM10, PM2.5, SO2, NO2, CO, O3) were measured during two episodes that took place during Chinese New Year festivities. Results For the special terrain of Jinan, the mean concentrations of pollutants increased sharply within 2–4 h of the firework displays, and concentrations were 4–6 times higher than the usual levels. It took 2–3 d for the pollutants to dissipate to background levels. Compared to Preliminary Eve (more fireworks are ignited on New Year’s Eve, but the amounts of other human activities are also lesser), the primary pollutants PM2.5, PM10, and CO reached higher concentrations on New Year’s Eve, and the highest concentrations of these pollutants were detected in living quarters. All areas suffered from serious pollution problems on New Year’s Eve (rural = urban for PM10, but rural > urban for PM2.5). However, SO2 and NO2 levels were 20%–60% lower in living quarters and industrial areas compared to the levels in these same areas on Preliminary Eve. In contrast to the other pollutants, O3 concentrations fell instead of rising with the firework displays. Conclusion Interactions between firework displays and other human activities caused different change trends of pollutants. PM2.5 and PM10 were the main pollutants, and the rural living quarter had some of the highest pollution levels. PMID:28045925

The Characteristics of Air Pollutants during Two Distinct Episodes of Fireworks Burning in a Valley City of North China.

PubMed

Song, Yang; Wan, Xiaoming; Bai, Shuoxin; Guo, Dong; Ren, Ci; Zeng, Yu; Li, Yirui; Li, Xuewen

2017-01-01

The elevation and dissipation of pollutants after the ignition of fireworks in different functional areas of a valley city were investigated. The Air Quality Index (AQI) as well as inter-day and intra-day concentrations of various air pollutants (PM10, PM2.5, SO2, NO2, CO, O3) were measured during two episodes that took place during Chinese New Year festivities. For the special terrain of Jinan, the mean concentrations of pollutants increased sharply within 2-4 h of the firework displays, and concentrations were 4-6 times higher than the usual levels. It took 2-3 d for the pollutants to dissipate to background levels. Compared to Preliminary Eve (more fireworks are ignited on New Year's Eve, but the amounts of other human activities are also lesser), the primary pollutants PM2.5, PM10, and CO reached higher concentrations on New Year's Eve, and the highest concentrations of these pollutants were detected in living quarters. All areas suffered from serious pollution problems on New Year's Eve (rural = urban for PM10, but rural > urban for PM2.5). However, SO2 and NO2 levels were 20%-60% lower in living quarters and industrial areas compared to the levels in these same areas on Preliminary Eve. In contrast to the other pollutants, O3 concentrations fell instead of rising with the firework displays. Interactions between firework displays and other human activities caused different change trends of pollutants. PM2.5 and PM10 were the main pollutants, and the rural living quarter had some of the highest pollution levels.

On the Pollutant Plume Dispersion in the Urban Canopy Layer over 2D Idealized Street Canyons: A Large-Eddy Simulation Approach

NASA Astrophysics Data System (ADS)

Wong, Colman C. C.; Liu, Chun-Ho

2010-05-01

Anthropogenic emissions are the major sources of air pollutants in urban areas. To improve the air quality in dense and mega cities, a simple but reliable prediction method is necessary. In the last five decades, the Gaussian pollutant plume model has been widely used for the estimation of air pollutant distribution in the atmospheric boundary layer (ABL) in an operational manner. Whereas, it was originally designed for rural areas with rather open and flat terrain. The recirculating flows below the urban canopy layer substantially modify the near-ground urban wind environment and so does the pollutant distribution. Though the plume height and dispersion are often adjusted empirically, the accuracy of applying the Gaussian pollutant plume model in urban areas, of which the bottom of the flow domain consists of numerous inhomogeneous buildings, is unclear. To elucidate the flow and pollutant transport, as well as to demystify the uncertainty of employing the Gaussian pollutant plume model over urban roughness, this study was performed to examine how the Gaussian-shape pollutant plume in the urban canopy layer is modified by the idealized two-dimensional (2D) street canyons at the bottom of the ABL. The specific objective is to develop a parameterization so that the geometric effects of urban morphology on the operational pollutant plume dispersion models could be taken into account. Because atmospheric turbulence is the major means of pollutant removal from street canyons to the ABL, the large-eddy simulation (LES) was adopted to calculate explicitly the flows and pollutant transport in the urban canopy layer. The subgrid-scale (SGS) turbulent kinetic energy (TKE) conservation was used to model the SGS processes in the incompressible, isothermal conditions. The computational domain consists of 12 identical idealized street canyons of unity aspect ratio which were placed evenly in the streamwise direction. Periodic boundary conditions (BCs) for the flow were applied in the horizontal and the spanwise directions. The prevalent wind was driven by a background pressure gradient in the roughness sublayer only, no background force was prescribed inside the street canyons. While the periodic BC of pollutant was used in the spanwise direction, zero pollutant and an open BC were applied, respectively, at the inflow and outflow of the streamwise extent to avoid pollutant being reflected back into the computational domain. The ground of the first street canyon was assigned as the pollutant source on which a BC of constant pollutant concentration was prescribed. The LES results showed that, in the neutrally stratified ABL, the pollutant distribution in the urban canopy layer resembled the Gaussian plume shape in general even recirculating flows were observed in the street canyons. The roof-level horizontal profile of pollutant concentration in the streamwise direction showed that the sharp drop on the leeward side of each street canyon was likely caused by the air and pollutant entrainments. On the windward side of each street canyon, a mild increase in pollutant concentration was observed that did not follow the Gaussian plume closely. Those deviations extended to a certain height over the roof level of the street canyons. It in turn suggests that the Gaussian pollutant plume model should be applied with caution in the urban canopy layer in the vicinity over urban roughness. To further analyze the effects of urban roughness on the plume dispersion in detail, a few LES calculations with different aspect ratios are currently being undertaken so as to compare with the current LES results.

Association between Long-Term Exposure to Particulate Matter Air Pollution and Mortality in a South Korean National Cohort: Comparison across Different Exposure Assessment Approaches

PubMed Central

Kim, Sun-Young; Kim, Ho

2017-01-01

Increasing numbers of cohort studies have reported that long-term exposure to ambient particulate matter is associated with mortality. However, there has been little evidence from Asian countries. We aimed to explore the association between long-term exposure to particulate matter with a diameter ≤10 µm (PM10) and mortality in South Korea, using a nationwide population-based cohort and an improved exposure assessment (EA) incorporating time-varying concentrations and residential addresses (EA1). We also compared the association across different EA approaches. We used information from 275,337 people who underwent health screening from 2002 to 2006 and who had follow-up data for 12 years in the National Health Insurance Service-National Sample Cohort. Individual exposures were computed as 5-year averages using predicted residential district-specific annual-average PM10 concentrations for 2002–2006. We estimated hazard ratios (HRs) of non-accidental and five cause-specific mortalities per 10 µg/m3 increase in PM10 using the Cox proportional hazards model. Then, we compared the association of EA1 with three other approaches based on time-varying concentrations and/or addresses: predictions in each year and addresses at baseline (EA2); predictions at baseline and addresses in each year (EA3); and predictions and addresses at baseline (EA4). We found a marginal association between long-term PM10 and non-accidental mortality. The HRs of five cause-specific mortalities were mostly higher than that of non-accidental mortality, but statistically insignificant. In the comparison between EA approaches, the HRs of EA1 were similar to those of EA2 but higher than EA3 and EA4. Our findings confirmed the association between long-term exposure to PM10 and mortality based on a population-representative cohort in South Korea, and suggested the importance of assessing individual exposure incorporating air pollution changes over time. PMID:28946613

Metal pollution (Cd, Pb, Zn, and As) in agricultural soils and soybean, Glycine max, in southern China.

PubMed

Zhao, Yunyun; Fang, Xiaolong; Mu, Yinghui; Cheng, Yanbo; Ma, Qibin; Nian, Hai; Yang, Cunyi

2014-04-01

Crops produced on metal-polluted agricultural soils may lead to chronic toxicity to humans via the food chain. To assess metal pollution in agricultural soils and soybean in southern China, 30 soybean grain samples and 17 soybean-field soil samples were collected from 17 sites in southern China, and metal concentrations of samples were analyzed by graphite furnace atomic absorption spectrophotometer. The integrated pollution index was used to evaluate if the samples were contaminated by Cd, Pb, Zn and As. Results showed that Cd concentration of 12 samples, Pb concentration of 2 samples, Zn concentration of 2 samples, and As concentrations of 2 samples were above the maximum permissible levels in soils. The integrated pollution index indicated that 11 of 17 soil samples were polluted by metals. Metal concentrations in soybean grain samples ranged from 0.11 to 0.91 mg kg(-1) for Cd; 0.34 to 2.83 mg kg(-1) for Pb; 42 to 88 mg kg(-1) for Zn; and 0.26 to 5.07 mg kg(-1) for As, which means all 30 soybean grain samples were polluted by Pb, Pb/Cd, Cd/Pb/As or Pb/As. Taken together, our study provides evidence that metal pollution is an important concern in agricultural soils and soybeans in southern China.

Predicting As, Cd, Cu, Pb and Zn levels in grasses (Agrostis sp. and Poa sp.) and stinging nettle (Urtica dioica) applying soil-plant transfer models.

PubMed

Boshoff, Magdalena; De Jonge, Maarten; Scheifler, Renaud; Bervoets, Lieven

2014-09-15

The aim of this study was to derive regression-based soil-plant models to predict and compare metal(loid) (i.e. As, Cd, Cu, Pb and Zn) concentrations in plants (grass Agrostis sp./Poa sp. and nettle Urtica dioica L.) among sites with a wide range of metal pollution and a wide variation in soil properties. Regression models were based on the pseudo total (aqua-regia) and exchangeable (0.01 M CaCl2) soil metal concentrations. Plant metal concentrations were best explained by the pseudo total soil metal concentrations in combination with soil properties. The most important soil property that influenced U. dioica metal concentrations was the clay content, while for grass organic matter (OM) and pH affected the As (OM) and Cu and Zn (pH). In this study multiple linear regression models proved functional in predicting metal accumulation in plants on a regional scale. With the proposed models based on the pseudo total metal concentration, the percentage of variation explained for the metals As, Cd, Cu, Pb and Zn were 0.56%, 0.47%, 0.59%, 0.61%, 0.30% in nettle and 0.46%, 0.38%, 0.27%, 0.50%, 0.28% in grass. Copyright © 2014 Elsevier B.V. All rights reserved.

Threat of plastic pollution to seabirds is global, pervasive, and increasing.

PubMed

Wilcox, Chris; Van Sebille, Erik; Hardesty, Britta Denise

2015-09-22

Plastic pollution in the ocean is a global concern; concentrations reach 580,000 pieces per km(2) and production is increasing exponentially. Although a large number of empirical studies provide emerging evidence of impacts to wildlife, there has been little systematic assessment of risk. We performed a spatial risk analysis using predicted debris distributions and ranges for 186 seabird species to model debris exposure. We adjusted the model using published data on plastic ingestion by seabirds. Eighty of 135 (59%) species with studies reported in the literature between 1962 and 2012 had ingested plastic, and, within those studies, on average 29% of individuals had plastic in their gut. Standardizing the data for time and species, we estimate the ingestion rate would reach 90% of individuals if these studies were conducted today. Using these results from the literature, we tuned our risk model and were able to capture 71% of the variation in plastic ingestion based on a model including exposure, time, study method, and body size. We used this tuned model to predict risk across seabird species at the global scale. The highest area of expected impact occurs at the Southern Ocean boundary in the Tasman Sea between Australia and New Zealand, which contrasts with previous work identifying this area as having low anthropogenic pressures and concentrations of marine debris. We predict that plastics ingestion is increasing in seabirds, that it will reach 99% of all species by 2050, and that effective waste management can reduce this threat.

Threat of plastic pollution to seabirds is global, pervasive, and increasing

PubMed Central

Wilcox, Chris; Van Sebille, Erik; Hardesty, Britta Denise

2015-01-01

Plastic pollution in the ocean is a global concern; concentrations reach 580,000 pieces per km2 and production is increasing exponentially. Although a large number of empirical studies provide emerging evidence of impacts to wildlife, there has been little systematic assessment of risk. We performed a spatial risk analysis using predicted debris distributions and ranges for 186 seabird species to model debris exposure. We adjusted the model using published data on plastic ingestion by seabirds. Eighty of 135 (59%) species with studies reported in the literature between 1962 and 2012 had ingested plastic, and, within those studies, on average 29% of individuals had plastic in their gut. Standardizing the data for time and species, we estimate the ingestion rate would reach 90% of individuals if these studies were conducted today. Using these results from the literature, we tuned our risk model and were able to capture 71% of the variation in plastic ingestion based on a model including exposure, time, study method, and body size. We used this tuned model to predict risk across seabird species at the global scale. The highest area of expected impact occurs at the Southern Ocean boundary in the Tasman Sea between Australia and New Zealand, which contrasts with previous work identifying this area as having low anthropogenic pressures and concentrations of marine debris. We predict that plastics ingestion is increasing in seabirds, that it will reach 99% of all species by 2050, and that effective waste management can reduce this threat. PMID:26324886

In-home air pollution is linked to respiratory morbidity in former smokers with chronic obstructive pulmonary disease.

PubMed

Hansel, Nadia N; McCormack, Meredith C; Belli, Andrew J; Matsui, Elizabeth C; Peng, Roger D; Aloe, Charles; Paulin, Laura; Williams, D'Ann L; Diette, Gregory B; Breysse, Patrick N

2013-05-15

The effect of indoor air pollutants on respiratory morbidity among patients with chronic obstructive pulmonary disease (COPD) in developed countries is uncertain. The first longitudinal study to investigate the independent effects of indoor particulate matter (PM) and nitrogen dioxide (NO(2)) concentrations on COPD morbidity in a periurban community. Former smokers with COPD were recruited and indoor air was monitored over a 1-week period in the participant's bedroom and main living area at baseline, 3 months, and 6 months. At each visit, participants completed spirometry and questionnaires assessing respiratory symptoms. Exacerbations were assessed by questionnaires administered at clinic visits and monthly telephone calls. Participants (n = 84) had moderate or severe COPD with a mean FEV1 of 48.6% predicted. The mean (± SD) indoor PM(2.5) and NO(2) concentrations were 11.4 ± 13.3 µg/m(3) and 10.8 ± 10.6 ppb in the bedroom, and 12.2 ± 12.2 µg/m(3) and 12.2 ± 11.8 ppb in the main living area. Increases in PM(2.5) concentrations in the main living area were associated with increases in respiratory symptoms, rescue medication use, and risk of severe COPD exacerbations. Increases in NO(2) concentrations in the main living area were independently associated with worse dyspnea. Increases in bedroom NO(2) concentrations were associated with increases in nocturnal symptoms and risk of severe COPD exacerbations. Indoor pollutant exposure, including PM(2.5) and NO(2), was associated with increased respiratory symptoms and risk of COPD exacerbation. Future investigations should include intervention studies that optimize indoor air quality as a novel therapeutic approach to improving COPD health outcomes.

A hybrid modeling with data assimilation to evaluate human exposure level

NASA Astrophysics Data System (ADS)

Koo, Y. S.; Cheong, H. K.; Choi, D.; Kim, A. L.; Yun, H. Y.

2015-12-01

Exposure models are designed to better represent human contact with PM (Particulate Matter) and other air pollutants such as CO, SO2, O3, and NO2. The exposure concentrations of the air pollutants to human are determined by global and regional long range transport of global and regional scales from Europe and China as well as local emissions from urban and road vehicle sources. To assess the exposure level in detail, the multiple scale influence from background to local sources should be considered. A hybrid air quality modeling methodology combing a grid-based chemical transport model with a local plume dispersion model was used to provide spatially and temporally resolved air quality concentration for human exposure levels in Korea. In the hybrid modeling approach, concentrations from a grid-based chemical transport model and a local plume dispersion model are added to provide contributions from photochemical interactions, long-range (regional) transport and local-scale dispersion. The CAMx (Comprehensive Air quality Model with Extensions was used for the background concentrations from anthropogenic and natural emissions in East Asia including Korea while the road dispersion by vehicle emission was calculated by CALPUFF model. The total exposure level of the pollutants was finally assessed by summing the background and road contributions. In the hybrid modeling, the data assimilation method based on the optimal interpolation was applied to overcome the discrepancies between the model predicted concentrations and observations. The air quality data from the air quality monitoring stations in Korea. The spatial resolution of the hybrid model was 50m for the Seoul Metropolitan Ares. This example clearly demonstrates that the exposure level could be estimated to the fine scale for the exposure assessment by using the hybrid modeling approach with data assimilation.

Ambient ammonia exposures in an agricultural community and pediatric asthma morbidity

PubMed Central

Loftus, Christine; Yost, Michael; Sampson, Paul; Torres, Elizabeth; Arias, Griselda; Vasquez, Victoria Breckwich; Hartin, Kris; Armstrong, Jenna; Tchong-French, Maria; Vedal, Sverre; Bhatti, Parveen; Karr, Catherine

2015-01-01

Background Large-scale animal feeding operations compromise regional air quality in the rural United States through emission of pollutants such as ammonia gas. Exposure to airborne pollution from animal feeding operations may cause pediatric asthma exacerbations in surrounding communities. Objectives To describe spatial and temporal patterns in ambient ammonia concentrations in an agricultural region, and to investigate associations between short-term fluctuations in ammonia and subsequent changes in respiratory health in children with asthma. Methods For 13 months in the Yakima Valley of Washington State, 14 monitors sampled ammonia in outdoor air for 24-hour periods every 6 days. School-age children with asthma (n=51) were followed for two health outcomes: biweekly reports of asthma symptoms and quick relief medication usage, and daily measurements of forced expiratory volume in one second (FEV1). We assessed associations between each outcome and ammonia using generalized estimating equations. Results 24-hour ammonia concentrations varied from 0.2 to 238.1 μg/m3 during the study period and displayed a strong correlation with proximity to animal feeding operations. FEV1% was 3.8% lower (95% CI: 0.2, 7.3) per interquartile increase in one-day lagged ammonia concentration and 3.0% lower (95% CI: 0.5, 5.8) for two-day lagged concentration. We observed no associations between self-reported asthma symptoms or medication usage and estimated ammonia exposure. Conclusions Ammonia concentrations were elevated in this community and strongly predicted by proximity to animal feeding operations. Ammonia's association with acute lung function decrements in children with asthma in the surrounding community may be causal or, alternatively, ammonia may be a marker for other pollutants from animal feeding operations associated with respiratory effects. PMID:26352250

A class of non-linear exposure-response models suitable for health impact assessment applicable to large cohort studies of ambient air pollution.

PubMed

Nasari, Masoud M; Szyszkowicz, Mieczysław; Chen, Hong; Crouse, Daniel; Turner, Michelle C; Jerrett, Michael; Pope, C Arden; Hubbell, Bryan; Fann, Neal; Cohen, Aaron; Gapstur, Susan M; Diver, W Ryan; Stieb, David; Forouzanfar, Mohammad H; Kim, Sun-Young; Olives, Casey; Krewski, Daniel; Burnett, Richard T

2016-01-01

The effectiveness of regulatory actions designed to improve air quality is often assessed by predicting changes in public health resulting from their implementation. Risk of premature mortality from long-term exposure to ambient air pollution is the single most important contributor to such assessments and is estimated from observational studies generally assuming a log-linear, no-threshold association between ambient concentrations and death. There has been only limited assessment of this assumption in part because of a lack of methods to estimate the shape of the exposure-response function in very large study populations. In this paper, we propose a new class of variable coefficient risk functions capable of capturing a variety of potentially non-linear associations which are suitable for health impact assessment. We construct the class by defining transformations of concentration as the product of either a linear or log-linear function of concentration multiplied by a logistic weighting function. These risk functions can be estimated using hazard regression survival models with currently available computer software and can accommodate large population-based cohorts which are increasingly being used for this purpose. We illustrate our modeling approach with two large cohort studies of long-term concentrations of ambient air pollution and mortality: the American Cancer Society Cancer Prevention Study II (CPS II) cohort and the Canadian Census Health and Environment Cohort (CanCHEC). We then estimate the number of deaths attributable to changes in fine particulate matter concentrations over the 2000 to 2010 time period in both Canada and the USA using both linear and non-linear hazard function models.

Polybrominated diphenyl ethers in residential and agricultural soils from an electronic waste polluted region in South China: distribution, compositional profile, and sources.

PubMed

Zhang, Shaohui; Xu, Xijin; Wu, Yousheng; Ge, Jingjing; Li, Weiqiu; Huo, Xia

2014-05-01

A detailed investigation was conducted to understand the concentration, distribution, profile and possible source of polybrominated diphenyl ethers (PBDEs) in residential and agricultural soils from Guiyu, Shantou, China, one of the largest electronic waste (e-waste) recycling and dismantling areas in the world. Ten PBDEs were analyzed in 46 surface soil samples in terms of individual and total concentrations, together with soil organic matter concentrations. Much higher concentrations of the total PBDEs were predicted in the residential areas (more than 2000 ng g(-1)), exhibiting a clear urban source, while in the agricultural areas, concentrations were lower than 1500 ng g(-1). PBDE-209 was the most dominant congener among the study sites, indicating the prevalence of commercial deca-PBDE. However signature congeners from commercial octa-PBDE were also found. The total PBDE concentrations were significantly correlated with each individual PBDE. Principal component analysis indicated that PBDEs were mainly distributed in three groups according to the number of bromine atoms on the phenyl rings, and potential source. This study showed that the informal e-waste recycling has already introduced PBDEs into surrounding areas as pollutant which thus warrants an urgent investigation into the transport of PBDEs in the soil-plant system of agricultural areas. Copyright © 2013 Elsevier Ltd. All rights reserved.

Stochastic backscatter modelling for the prediction of pollutant removal from an urban street canyon: A large-eddy simulation

NASA Astrophysics Data System (ADS)

O'Neill, J. J.; Cai, X.-M.; Kinnersley, R.

2016-10-01

The large-eddy simulation (LES) approach has recently exhibited its appealing capability of capturing turbulent processes inside street canyons and the urban boundary layer aloft, and its potential for deriving the bulk parameters adopted in low-cost operational urban dispersion models. However, the thin roof-level shear layer may be under-resolved in most LES set-ups and thus sophisticated subgrid-scale (SGS) parameterisations may be required. In this paper, we consider the important case of pollutant removal from an urban street canyon of unit aspect ratio (i.e. building height equal to street width) with the external flow perpendicular to the street. We show that by employing a stochastic SGS model that explicitly accounts for backscatter (energy transfer from unresolved to resolved scales), the pollutant removal process is better simulated compared with the use of a simpler (fully dissipative) but widely-used SGS model. The backscatter induces additional mixing within the shear layer which acts to increase the rate of pollutant removal from the street canyon, giving better agreement with a recent wind-tunnel experiment. The exchange velocity, an important parameter in many operational models that determines the mass transfer between the urban canopy and the external flow, is predicted to be around 15% larger with the backscatter SGS model; consequently, the steady-state mean pollutant concentration within the street canyon is around 15% lower. A database of exchange velocities for various other urban configurations could be generated and used as improved input for operational street canyon models.

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.)

Characterization of a large biogenic secondary organic aerosol event from eastern Canadian forests

NASA Astrophysics Data System (ADS)

Slowik, J. G.; Stroud, C.; Bottenheim, J. W.; Brickell, P. C.; Chang, R. Y.-W.; Liggio, J.; Makar, P. A.; Martin, R. V.; Moran, M. D.; Shantz, N. C.; Sjostedt, S. J.; van Donkelaar, A.; Vlasenko, A.; Wiebe, H. A.; Xia, A. G.; Zhang, J.; Leaitch, W. R.; Abbatt, J. P. D.

2010-03-01

Measurements of aerosol composition, volatile organic compounds, and CO are used to determine biogenic secondary organic aerosol (SOA) concentrations at a rural site 70 km north of Toronto. These biogenic SOA levels are many times higher than past observations and occur during a period of increasing temperatures and outflow from Northern Ontario and Quebec forests in early summer. A regional chemical transport model approximately predicts the event timing and accurately predicts the aerosol loading, identifying the precursors as monoterpene emissions from the coniferous forest. The agreement between the measured and modeled biogenic aerosol concentrations contrasts with model underpredictions for polluted regions. Correlations of the oxygenated organic aerosol mass with tracers such as CO support a secondary aerosol source and distinguish biogenic, pollution, and biomass burning periods during the field campaign. Using the Master Chemical Mechanism, it is shown that the levels of CO observed during the biogenic event are consistent with a photochemical source arising from monoterpene oxidation. The biogenic aerosol mass correlates with satellite measurements of regional aerosol optical depth, indicating that the event extends across the eastern Canadian forest. This regional event correlates with increased temperatures, indicating that temperature-dependent forest emissions can significantly affect climate through enhanced direct optical scattering and higher cloud condensation nuclei numbers.

Development of an on-line source-tagged model for sulfate, nitrate and ammonium: A modeling study for highly polluted periods in Shanghai, China.

PubMed

Wu, Jian-Bin; Wang, Zifa; Wang, Qian; Li, Jie; Xu, Jianming; Chen, HuanSheng; Ge, Baozhu; Zhou, Guangqiang; Chang, Luyu

2017-02-01

An on-line source-tagged model coupled with an air quality model (Nested Air Quality Prediction Model System, NAQPMS) was applied to estimate source contributions of primary and secondary sulfate, nitrate and ammonium (SNA) during a representative winter period in Shanghai. This source-tagged model system could simultaneously track spatial and temporal sources of SNA, which were apportioned to their respective primary precursors in a simulation run. The results indicate that in the study period, local emissions in Shanghai accounted for over 20% of SNA contributions and that Jiangsu and Shandong were the two major non-local sources. In particular, non-local emissions had higher contributions during recorded pollution periods. This suggests that the transportation of pollutants plays a key role in air pollution in Shanghai. The temporal contributions show that the emissions from the "current day" (emission contribution from the current day during which the model was simulating) contributed 60%-70% of the sulfate and ammonium concentrations but only 10%-20% of the nitrate concentration, while the previous days' contributions increased during the recorded pollution periods. Emissions that were released within three days contributed over 85% averagely for SNA in January 2013. To evaluate the source-tagged model system, the results were compared by sensitivity analysis (emission perturbation of -30%) and backward trajectory analysis. The consistency of the comparison results indicated that the source-tagged model system can track sources of SNA with reasonable accuracy. Copyright © 2016 Elsevier Ltd. All rights reserved.

Reduction of air pollutant concentrations in an indoor ice-skating rink

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

Lee, K.; Yanagisawa, Yukio; Spengler, J.D.

1994-01-01

High carbon monoxide and nitrogen dioxide concentrations were measured in an indoor ice-skating rink with fuel-powered ice-resurfacing equipment. In 22% to 33% of the measurements over 90-min segments, CO concentrations exceeded 20 [mu]L/L as a 90-min average in the absence of rink ventilation. Average NO[sub 2] concentrations over 14 h were higher than 600 nL/L. Reduction of air pollutant concentrations in the ice-skating rink is necessary to prevent air-pollutant-exposure-related health incidents. Various methods for reducing air pollutants in an ice-skating rink were evaluated by simultaneously measuring CO and NO[sub 2] concentrations. Single pollution reduction attempts, such as extension of themore » exhaust pipe, reduction in the number of resurfacer operations, or use of an air recirculation system, did not significantly reduce air pollutant concentrations in the rink. Full operation of the mechanical ventilation system combined with reduced resurfacer operation was required to keep the air pollutant levels in the skating rink below the recommended guidelines. This investigation showed that management of clean air quality in an ice-skating rink is practically difficult as long as fuel-powered resurfacing equipment is used. 16 refs., 3 figs., 5 tabs.« less

Fate of SO2 and Particulate SO4 Based on Airborne Measurements in the Oil Sands Region of Alberta, Canada

NASA Astrophysics Data System (ADS)

Hayden, K. L.; Li, S. M.; McLaren, R.; Liu, P.; O'brien, J.; Gordon, M.; Darlington, A.; Liggio, J.; Mittermeier, R. L.; Staebler, R. M.; Makar, P.; Stroud, C.; Akingunola, A.; Leithead, A.; Moussa, S.

2016-12-01

An intensive airborne measurement campaign was undertaken in August to September 2013 to support the objectives of the Joint Oil Sands Monitoring (JOSM) program. The overarching objectives of the study were to characterize air pollutants being emitted, to determine the extent of atmospheric transport and chemical transformation, to support air quality model prediction capabilities, and to compare measurements with satellite column retrievals. Sulphur dioxide (SO2) and particulate sulphate (p-SO4) were among the pollutants studied. SO2 is emitted from elevated stacks within the oil sands facilities and undergoes atmospheric transformation into p-SO4. Deposition of these species from the atmosphere to the surface can lead to impacts on ecosystems downwind of the facilities. The processes of emission, transformation, transport, and deposition of SO2 and p-SO4 were investigated in detail using data collected during aircraft flights that were designed to study pollution transformation. The aircraft was flown at increasing distances downwind of the oil sands facilities, sampling the same plume at different times as it was transported away from the sources. Flight tracks were perpendicular to the wind direction at multiple altitudes to create virtual flight screens that encompassed the entire plume. Fluxes across each of the virtual screens were determined using the wind speed vector normal to the screen and the pollutant concentrations; the flux integration across the two-dimensional plume transect on the screen yielded the pollutant transfer rates at that particular screen location. Transformation of SO2 to p-SO4 between screens was determined based on OH radical levels estimated using concurrently measured concentrations of a suite of hydrocarbons. Based on mass balance between screens using the transfer rates, SO2 oxidation rates and p-SO4 formation rates, the deposition rates of both species are estimated along the plume transport path downwind of the oil sands operations. These observation-derived estimates are compared to corresponding predicted results from a nested air-quality model (GEM-MACH) operating for the same time period.

40 CFR 423.15 - New source performance standards (NSPS).

Code of Federal Regulations, 2014 CFR

2014-07-01

... performance standards: (a) The pH of all discharges, except once through cooling water, shall be within the... the concentration listed in the following table: Pollutant or pollutant property NSPS effluent... cleaning wastes times the concentration listed in the following table: Pollutant or pollutant property NSPS...

40 CFR 423.15 - New source performance standards (NSPS).

Code of Federal Regulations, 2013 CFR

2013-07-01

... performance standards: (a) The pH of all discharges, except once through cooling water, shall be within the... the concentration listed in the following table: Pollutant or pollutant property NSPS effluent... cleaning wastes times the concentration listed in the following table: Pollutant or pollutant property NSPS...

40 CFR 423.15 - New source performance standards (NSPS).

Code of Federal Regulations, 2012 CFR

2012-07-01

... performance standards: (a) The pH of all discharges, except once through cooling water, shall be within the... the concentration listed in the following table: Pollutant or pollutant property NSPS effluent... cleaning wastes times the concentration listed in the following table: Pollutant or pollutant property NSPS...

Distribution of arsenic and copper in sediment pore water: an ecological risk assessment case study for offshore drilling waste discharges.

PubMed

Sadiq, Rehan; Husain, Tahir; Veitch, Brian; Bose, Neil

2003-12-01

Due to the hydrophobic nature of synthetic based fluids (SBFs), drilling cuttings are not very dispersive in the water column and settle down close to the disposal site. Arsenic and copper are two important toxic heavy metals, among others, found in the drilling waste. In this article, the concentrations of heavy metals are determined using a steady state "aquivalence-based" fate model in a probabilistic mode. Monte Carlo simulations are employed to determine pore water concentrations. A hypothetical case study is used to determine the water quality impacts for two discharge options: 4% and 10% attached SBFs, which correspond to the best available technology option and the current discharge practice in the U.S. offshore. The exposure concentration (CE) is a predicted environmental concentration, which is adjusted for exposure probability and bioavailable fraction of heavy metals. The response of the ecosystem (RE) is defined by developing an empirical distribution function of predicted no-effect concentration. The pollutants' pore water concentrations within the radius of 750 m are estimated and cumulative distributions of risk quotient (RQ=CE/RE) are developed to determine the probability of RQ greater than 1.

Horizontal Advection and Mixing of Pollutants in the Urban Atmospheric Environment

NASA Astrophysics Data System (ADS)

Magnusson, S. P.; Entekhabi, D.; Britter, R.; Norford, L.; Fernando, H. J.

2013-12-01

Although urban air quality and its impacts on the public health have long been studied, the increasing urbanization is raising concerns on how to better control and mitigate these health impacts. A necessary element in predicting exposure levels is fundamental understanding of flow and dispersion in urban canyons. The complex topology of building structures and roads requires the resolution of turbulence phenomena within urban canyons. The use of dense and low porosity construction material can lead to rapid heating in response to direct solar exposure due to large thermal mass. Hence thermal and buoyancy effects may be as important as mechanically-forced or shear-induced flows. In this study, the transport of pollutants within the urban environment, as well as the thermal and advection effects, are investigated. The focus is on the horizontal transport or the advection effects within the urban environment. With increased urbanization and larger and more spread cities, concern about how the upstream air quality situation can affect downstream areas. The study also examines the release and the dispersion of hazardous material. Due to the variety and complexity of urban areas around the world, the urban environment is simplified into adjacent two-dimensional urban street canyons. Pollutants are released inside each canyon. Computational Fluid Dynamics (CFD) simulations are applied to evaluate and quantify the flow rate out of each canyon and also the exchange of pollutants between the canyons. Imagine a row of ten adjacent urban street canyons of aspect ratio 1 with horizontal flow perpendicular to it as shown in the attached figure. C is the concentration of pollutants. The first digit indicates in what canyon the pollutant is released and the second digit indicates the location of that pollutant. For example, C3,4 is the concentration of pollutant released inside canyon 3 measured in canyon 4. The same amount of pollution is released inside the ten street canyons. Some amount of the released material in each canyon is transported to its downstream canyons. For example if the most downstream canyon (number 10) is considered, pollutants released inside its upstream canyons are transported to it. For the neutral case (density of air and pollutants is the same), preliminary simulations show that the pollution concentration in the tenth canyon increases by 50% due to its nine upstream canyons. Also in the tenth canyon C9,10/C10,10 is equal to 10%. More simulations are being performed for canyons of various aspect ratios and density differences between the air and the pollutants. Accidental release of hazardous materials or chemical attacks can lead to necessary evacuation of people from cities. Knowing the spread of pollutants and particles within the urban environment can be crucial for engineers working on evacuation plans for cities. The ten adjacent canyons. Material is released inside each canyon.

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.

A database and tool for boundary conditions for regional air quality modeling: description and evaluation

NASA Astrophysics Data System (ADS)

Henderson, B. H.; Akhtar, F.; Pye, H. O. T.; Napelenok, S. L.; Hutzell, W. T.

2014-02-01

Transported air pollutants receive increasing attention as regulations tighten and global concentrations increase. The need to represent international transport in regional air quality assessments requires improved representation of boundary concentrations. Currently available observations are too sparse vertically to provide boundary information, particularly for ozone precursors, but global simulations can be used to generate spatially and temporally varying lateral boundary conditions (LBC). This study presents a public database of global simulations designed and evaluated for use as LBC for air quality models (AQMs). The database covers the contiguous United States (CONUS) for the years 2001-2010 and contains hourly varying concentrations of ozone, aerosols, and their precursors. The database is complemented by a tool for configuring the global results as inputs to regional scale models (e.g., Community Multiscale Air Quality or Comprehensive Air quality Model with extensions). This study also presents an example application based on the CONUS domain, which is evaluated against satellite retrieved ozone and carbon monoxide vertical profiles. The results show performance is largely within uncertainty estimates for ozone from the Ozone Monitoring Instrument and carbon monoxide from the Measurements Of Pollution In The Troposphere (MOPITT), but there were some notable biases compared with Tropospheric Emission Spectrometer (TES) ozone. Compared with TES, our ozone predictions are high-biased in the upper troposphere, particularly in the south during January. This publication documents the global simulation database, the tool for conversion to LBC, and the evaluation of concentrations on the boundaries. This documentation is intended to support applications that require representation of long-range transport of air pollutants.

Healthy neighborhoods: walkability and air pollution.

PubMed

Marshall, Julian D; Brauer, Michael; Frank, Lawrence D

2009-11-01

The built environment may influence health in part through the promotion of physical activity and exposure to pollution. To date, no studies have explored interactions between neighborhood walkability and air pollution exposure. We estimated concentrations of nitric oxide (NO), a marker for direct vehicle emissions), and ozone (O(3)) and a neighborhood walkability score, for 49,702 (89% of total) postal codes in Vancouver, British Columbia, Canada. NO concentrations were estimated from a land-use regression model, O(3) was estimated from ambient monitoring data; walkability was calculated based on geographic attributes such as land-use mix, street connectivity, and residential density. All three attributes exhibit an urban-rural gradient, with high walkability and NO concentrations, and low O(3) concentrations, near the city center. Lower-income areas tend to have higher NO concentrations and walkability and lower O(3) concentrations. Higher-income areas tend to have lower pollution (NO and O(3)). "Sweet-spot" neighborhoods (low pollution, high walkability) are generally located near but not at the city center and are almost exclusively higher income. Increased concentration of activities in urban settings yields both health costs and benefits. Our research identifies neighborhoods that do especially well (and especially poorly) for walkability and air pollution exposure. Work is needed to ensure that the poor do not bear an undue burden of urban air pollution and that neighborhoods designed for walking, bicycling, or mass transit do not adversely affect resident's exposure to air pollution. Analyses presented here could be replicated in other cities and tracked over time to better understand interactions among neighborhood walkability, air pollution exposure, and income level.

Long term assessment of air quality from a background station on the Malaysian Peninsula.

PubMed

Latif, Mohd Talib; Dominick, Doreena; Ahamad, Fatimah; Khan, Md Firoz; Juneng, Liew; Hamzah, Firdaus Mohamad; Nadzir, Mohd Shahrul Mohd

2014-06-01

Rural background stations provide insight into seasonal variations in pollutant concentrations and allow for comparisons to be made with stations closer to anthropogenic emissions. In Malaysia, the designated background station is located in Jerantut, Pahang. A fifteen-year data set focusing on ten major air pollutants and four meteorological variables from this station were analysed. Diurnal, monthly and yearly pollutant concentrations were derived from hourly continuous monitoring data. Statistical methods employed included principal component regression (PCR) and sensitivity analysis. Although only one of the yearly concentrations of the pollutants studied exceeded national and World Health Organisation (WHO) guideline standards, namely PM10, seven of the pollutants (NO, NO2, NOx, O3, PM10, THC and CH4) showed a positive upward trend over the 15-year period. High concentrations of PM10 were recorded during severe haze episodes in this region. Whilst, monthly concentrations of most air pollutants, such as: PM10, O3, NOx, NO2, CO and NmHC were recorded at higher concentrations between June and September, during the southwest monsoon. Such results correspond with the mid-range transport of pollutants from more urbanised and industrial areas. Diurnal patterns, rationed between major air pollutants and sensitivity analysis, indicate the influence of local traffic emissions on air quality at the Jerantut background station. Although the pollutant concentrations have not shown a rapid increase, an alternative background station will need to be assigned within the next decade if development projects in the surrounding area are not halted. Copyright © 2014 Elsevier B.V. All rights reserved.

Air quality modeling for effective environmental management in the mining region.

PubMed

Asif, Zunaira; Chen, Zhi; Han, Yi

2018-04-18

Air quality in the mining sector is a serious environmental concern and associated with many health issues. The air quality management in mining region has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanism. A modeling approach called mining air dispersion model (MADM) is developed to predict air pollutants concentration in the mining region while considering the deposition effect. The model is taken into account through the planet's boundary conditions and assuming that the eddy diffusivity depends on the downwind distance. The developed MADM is applied to a mining site in Canada. The model provides values as the predicted concentrations of PM 10 , PM 2.5 , TSP, NO 2 and six heavy metals (As, Pb, Hg, Cd, Zn, Cr) at various receptor locations. The model shows that neutral stability conditions are dominant for the study site. The maximum mixing height is achieved (1280 m) during the evening of summer, and minimum mixing height (380 m) is attained during the evening of winter. The dust fall (PM coarse) deposition flux is maximum during February and March with the deposition velocity of 4.67 cm/s. The results are evaluated with the monitoring field values, revealing a good agreement for the target air pollutants with R-squared ranging from 0.72 to 0.96 for PM 2.5 ; 0.71 to 0.82 for PM 10 and from 0.71 to 0.89 for NO 2 . The analyses illustrate that presented algorithm in this model can be used to assess air quality for the mining site in a systematic way. The comparison of MADM and CALPUFF modeling values are made for four different pollutants (PM 2.5 , PM 10 , TSP, and NO 2 ) under three different atmospheric stability classes (stable, neutral and unstable). Further, MADM results are statistically tested against CALPUFF for the air pollutants and model performance is found satisfactory.

Experiments with data assimilation in comprehensive air quality models: Impacts on model predictions and observation requirements (Invited)

NASA Astrophysics Data System (ADS)

Mathur, R.

2009-12-01

Emerging regional scale atmospheric simulation models must address the increasing complexity arising from new model applications that treat multi-pollutant interactions. Sophisticated air quality modeling systems are needed to develop effective abatement strategies that focus on simultaneously controlling multiple criteria pollutants as well as use in providing short term air quality forecasts. In recent years the applications of such models is continuously being extended to address atmospheric pollution phenomenon from local to hemispheric spatial scales over time scales ranging from episodic to annual. The need to represent interactions between physical and chemical atmospheric processes occurring at these disparate spatial and temporal scales requires the use of observation data beyond traditional in-situ networks so that the model simulations can be reasonably constrained. Preliminary applications of assimilation of remote sensing and aloft observations within a comprehensive regional scale atmospheric chemistry-transport modeling system will be presented: (1) A methodology is developed to assimilate MODIS aerosol optical depths in the model to represent the impacts long-range transport associated with the summer 2004 Alaskan fires on surface-level regional fine particulate matter (PM2.5) concentrations across the Eastern U.S. The episodic impact of this pollution transport event on PM2.5 concentrations over the eastern U.S. during mid-July 2004, is quantified through the complementary use of the model with remotely-sensed, aloft, and surface measurements; (2) Simple nudging experiments with limited aloft measurements are performed to identify uncertainties in model representations of physical processes and assess the potential use of such measurements in improving the predictive capability of atmospheric chemistry-transport models. The results from these early applications will be discussed in context of uncertainties in the model and in the remote sensing data and needs for defining a future optimum observing strategy.

A modeling investigation of the impact of street and building configurations on personal air pollutant exposure in isolated deep urban canyons.

PubMed

Ng, Wai-Yin; Chau, Chi-Kwan

2014-01-15

This study evaluated the effectiveness of different configurations for two building design elements, namely building permeability and setback, proposed for mitigating air pollutant exposure problems in isolated deep canyons by using an indirect exposure approach. The indirect approach predicted the exposures of three different population subgroups (i.e. pedestrians, shop vendors and residents) by multiplying the pollutant concentrations with the duration of exposure within a specific micro-environment. In this study, the pollutant concentrations for different configurations were predicted using a computational fluid dynamics model. The model was constructed based on the Reynolds-Averaged Navier-Stokes (RANS) equations with the standard k-ε turbulence model. Fifty-one canyon configurations with aspect ratios of 2, 4, 6 and different building permeability values (ratio of building spacing to the building façade length) or different types of building setback (recess of a high building from the road) were examined. The findings indicated that personal exposures of shop vendors were extremely high if they were present inside a canyon without any setback or separation between buildings and when the prevailing wind was perpendicular to the canyon axis. Building separation and building setbacks were effective in reducing personal air exposures in canyons with perpendicular wind, although their effectiveness varied with different configurations. Increasing the permeability value from 0 to 10% significantly lowered the personal exposures on the different population subgroups. Likewise, the personal exposures could also be reduced by the introduction of building setbacks despite their effects being strongly influenced by the aspect ratio of a canyon. Equivalent findings were observed if the reduction in the total development floor area (the total floor area permitted to be developed within a particular site area) was also considered. These findings were employed to formulate a hierarchy decision making model to guide the planning of deep canyons in high density urban cities. © 2013 Elsevier B.V. All rights reserved.

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

NASA Astrophysics Data System (ADS)

Brokamp, Cole; Jandarov, Roman; Rao, M. B.; LeMasters, Grace; Ryan, Patrick

2017-02-01

Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental components were measured at 24 sampling stations from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Over 50 different predictors associated with transportation, physical features, community socioeconomic characteristics, greenspace, land cover, and emission point sources were used to construct LUR and LURF models. Cross validation was used to quantify and compare model performance. LURF and LUR models were created for aluminum (Al), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), vanadium (V), zinc (Zn), and total PM2.5 in the CCAAPS study area. LURF utilized a more diverse and greater number of predictors than LUR and LURF models for Al, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all showed a decrease in fractional predictive error of at least 5% compared to their LUR models. LURF models for Al, Cu, Fe, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all had a cross validated fractional predictive error less than 30%. Furthermore, LUR models showed a differential exposure assessment bias and had a higher prediction error variance. Random forest and other machine learning methods may provide more accurate exposure assessment.

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches.

PubMed

Brokamp, Cole; Jandarov, Roman; Rao, M B; LeMasters, Grace; Ryan, Patrick

2017-02-01

Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental components were measured at 24 sampling stations from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Over 50 different predictors associated with transportation, physical features, community socioeconomic characteristics, greenspace, land cover, and emission point sources were used to construct LUR and LURF models. Cross validation was used to quantify and compare model performance. LURF and LUR models were created for aluminum (Al), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), vanadium (V), zinc (Zn), and total PM2.5 in the CCAAPS study area. LURF utilized a more diverse and greater number of predictors than LUR and LURF models for Al, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all showed a decrease in fractional predictive error of at least 5% compared to their LUR models. LURF models for Al, Cu, Fe, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all had a cross validated fractional predictive error less than 30%. Furthermore, LUR models showed a differential exposure assessment bias and had a higher prediction error variance. Random forest and other machine learning methods may provide more accurate exposure assessment.

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

PubMed Central

Brokamp, Cole; Jandarov, Roman; Rao, M.B.; LeMasters, Grace; Ryan, Patrick

2017-01-01

Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental components were measured at 24 sampling stations from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Over 50 different predictors associated with transportation, physical features, community socioeconomic characteristics, greenspace, land cover, and emission point sources were used to construct LUR and LURF models. Cross validation was used to quantify and compare model performance. LURF and LUR models were created for aluminum (Al), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), vanadium (V), zinc (Zn), and total PM2.5 in the CCAAPS study area. LURF utilized a more diverse and greater number of predictors than LUR and LURF models for Al, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all showed a decrease in fractional predictive error of at least 5% compared to their LUR models. LURF models for Al, Cu, Fe, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all had a cross validated fractional predictive error less than 30%. Furthermore, LUR models showed a differential exposure assessment bias and had a higher prediction error variance. Random forest and other machine learning methods may provide more accurate exposure assessment. PMID:28959135

Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon.

PubMed

Ghaedi, M; Hosaininia, R; Ghaedi, A M; Vafaei, A; Taghizadeh, F

2014-10-15

In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope(SEM), Brunauer-Emmett-Teller(BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55m(2)/g) and low pore size (<22.46Å) and average particle size lower than 48.8Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02g adsorbent mass, 10mgL(-1) initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R(2)) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way. Copyright © 2014 Elsevier B.V. All rights reserved.

Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon

NASA Astrophysics Data System (ADS)

Ghaedi, M.; Hosaininia, R.; Ghaedi, A. M.; Vafaei, A.; Taghizadeh, F.

2014-10-01

In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope (SEM), Brunauer-Emmett-Teller (BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55 m2/g) and low pore size (<22.46 Å) and average particle size lower than 48.8 Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02 g adsorbent mass, 10 mg L-1 initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30 min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R2) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way.

Quantifying uncertainty on sediment loads using bootstrap confidence intervals

NASA Astrophysics Data System (ADS)

Slaets, Johanna I. F.; Piepho, Hans-Peter; Schmitter, Petra; Hilger, Thomas; Cadisch, Georg

2017-01-01

Load estimates are more informative than constituent concentrations alone, as they allow quantification of on- and off-site impacts of environmental processes concerning pollutants, nutrients and sediment, such as soil fertility loss, reservoir sedimentation and irrigation channel siltation. While statistical models used to predict constituent concentrations have been developed considerably over the last few years, measures of uncertainty on constituent loads are rarely reported. Loads are the product of two predictions, constituent concentration and discharge, integrated over a time period, which does not make it straightforward to produce a standard error or a confidence interval. In this paper, a linear mixed model is used to estimate sediment concentrations. A bootstrap method is then developed that accounts for the uncertainty in the concentration and discharge predictions, allowing temporal correlation in the constituent data, and can be used when data transformations are required. The method was tested for a small watershed in Northwest Vietnam for the period 2010-2011. The results showed that confidence intervals were asymmetric, with the highest uncertainty in the upper limit, and that a load of 6262 Mg year-1 had a 95 % confidence interval of (4331, 12 267) in 2010 and a load of 5543 Mg an interval of (3593, 8975) in 2011. Additionally, the approach demonstrated that direct estimates from the data were biased downwards compared to bootstrap median estimates. These results imply that constituent loads predicted from regression-type water quality models could frequently be underestimating sediment yields and their environmental impact.

Characteristics and determinants of ambient fungal spores in Hualien, Taiwan

NASA Astrophysics Data System (ADS)

Ho, Hsiao-Man; Rao, Carol Y.; Hsu, Hsiao-Hsien; Chiu, Yueh-Hsiu; Liu, Chi-Ming; Chao, H. Jasmine

Characteristics and determinants of ambient aeroallergens are of much concern in recent years because of the apparent health impacts of allergens. Yet relatively little is known about the complex behaviors of ambient aeroallergens. To address this issue, we monitored ambient fungal spores in Hualien, Taiwan from 1993-1996 to examine the compositions and temporal variations of fungi, and to evaluate possible determinants. We used a Burkard seven-day volumetric spore trap to collect daily fungal spores. Air pollutants, meteorological factors, and Asian dust events were included in the statistical analyses to predict fungal levels. We found that the most dominant fungal categories were ascospores, followed by Cladosporium and Aspergillus/Penicillium. The majority of the fungal categories had significant diurnal and seasonal variations. Total fungi, Cladosporium, Ganoderma, Arthrinium/Papularia, Cercospora, Periconia, Alternaria, Botrytis, and PM 10 had significantly higher concentrations ( p<0.05) during the period affected by Asian dust events. In multiple regression models, we found that temperature was consistently and positively associated with fungal concentrations. Other factors correlated with fungal concentrations included ozone, particulate matters with an aerodynamic diameter less than 10 μm (PM 10), relative humidity, rainfall, atmospheric pressure, total hydrocarbons, carbon monoxide, nitrogen dioxide, and sulfur dioxide. Most of the fungal categories had higher levels in 1994 than in 1995-96, probably due to urbanization of the study area. In this study, we demonstrated complicated interrelationships between fungi and air pollution/meteorological factors. In addition, long-range transport of air pollutants contributed significantly to local aeroallergen levels. Future studies should examine the health impacts of aeroallergens, as well as the synergistic/antagonistic effects of weather, and local and global-scale air pollutions.

Key factors affecting urban runoff pollution under cold climatic conditions

NASA Astrophysics Data System (ADS)

Valtanen, Marjo; Sillanpää, Nora; Setälä, Heikki

2015-10-01

Urban runoff contains various pollutants and has the potential of deteriorating the quality of aquatic ecosystems. In this study our objective is to shed light on the factors that control the runoff water quality in urbanized catchments. The effects of runoff event characteristics, land use type and catchment imperviousness on event mass loads (EML) and event mean concentrations (EMC) were studied during warm and cold periods in three study catchments (6.1, 6.5 and 12.6 ha in size) in the city of Lahti, Finland. Runoff and rainfall were measured continuously for two years at each catchment. Runoff samples were taken for total nutrients (tot-P and tot-N), total suspended solids (TSS), heavy metals (Zn, Cr, Al, Co, Ni, Cu, Pb, Mn) and total organic carbon (TOC). Stepwise multiple linear regression analysis (SMLR) was used to identify general relationships between the following variables: event water quality, runoff event characteristics and catchment characteristics. In general, the studied variables explained 50-90% of the EMLs but only 30-60% of the EMCs, with runoff duration having an important role in most of the SMLR models. Mean runoff intensity or peak flow was also often included in the runoff quality models. Yet, the importance (being the first, second or third best) and role (negative or positive impact) of the explanatory variables varied between the cold and warm period. Land use type often explained cold period concentrations, but imperviousness alone explained EMCs weakly. As for EMLs, the influence of imperviousness and/or land use was season and pollutant dependent. The study suggests that pollutant loads can be - throughout the year - adequately predicted by runoff characteristics given that seasonal differences are taken into account. Although pollutant concentrations were sensitive to variation in seasonal and catchment conditions as well, the accurate estimation of EMCs would require a more complete set of explanatory factors than used in this study.

Multi-phase distribution and comprehensive ecological risk assessment of heavy metal pollutants in a river affected by acid mine drainage.

PubMed

Liao, Jianbo; Ru, Xuan; Xie, Binbin; Zhang, Wanhui; Wu, Haizhen; Wu, Chaofei; Wei, Chaohai

2017-07-01

To date, there is a lack of a comprehensive research on heavy metals detection and ecological risk assessment in river water, sediments, pore water (PW) and suspended solids (SS). Here, the concentrations of heavy metals, including Cu, Zn, Mn, Cd, Pb and As, and their distribution between the four phases was studied. Samples for analysis were taken from twelve sites of the Hengshi River, Guangdong Province, China, during the rainy and dry seasons. A new comprehensive ecological risk index (CERI) based on considering metal contents, pollution indices, toxicity coefficients and water categories is offered for prediction of potential risk on aquatic organisms. The results of comprehensive analysis showed that the highest concentrations of Cu, Zn and Mn of 6.42, 87.17 and 98.74mg/L, respectively, in PW were comparable with those in water, while concentrations of Cd, Pb and As of 609.5, 2757 and 96.38μg/L, respectively, were 2-5 times higher. The sum of the exchangeable and carbonate fractions of target metals in sediments followed the order of Cd > Mn > Zn > Pb > Cu > As. The distribution of heavy metals in phases followed the order of sediment > SS > water > PW, having the sum content in water and PW lower than 2% of total. The elevated ecological risk for a single metal and the phase were 34,585 for Cd and 1160 for water, respectively, implied Cd as a priority pollutant in the considered area. According to the CERI, the maximum risk value of 769.3 was smaller than 1160 in water, but higher than those in other phases. Out of considering the water categories and contribution coefficients, the CERI was proved to be more reliable for assessing the pollution of rivers with heavy metals. These results imply that the CERI has a potential of adequate assessment of multi-phase composite metals pollution. Copyright © 2017 Elsevier Inc. All rights reserved.

Evaluation of chemical simulations from EMEP4ASIA

NASA Astrophysics Data System (ADS)

Pommier, M.; Gauss, M.; Fagerli, H.; Benedictow, A.; Nyiri, A.; Valdebenito, Á.; Wind, P.

2016-12-01

The EMEP/MSC-W chemistry transport model (CTM) has been used for decades to simulate concentrations of surface air pollutants over Europe and to calculate source-receptor relationships between European countries. Within the framework of the operational air pollution forecasts for East Asia, being offered by the EU project PANDA, this study aims to evaluate the EMEP/MSC-W CTM in simulating high pollution events over Asian cities. This work is the first attempt to use this CTM with a fine horizontal resolution (0.1°×0.1°) over Asia and to simulate the pollution over urban regions. The main part of the work has been to focus on the evaluation of the EMEP/MSC-W CTM with measurements from different platforms (satellite, ground-based, in situ) and to identify the biases or the errors in the simulation. This evaluation is important in order to establish the capabilities of the model to identify air pollution sources. Regional distributions and temporal variation of main pollutants are thus discussed. For example, the daily variation in Ox is well captured while the NOx is under-predicted and the O3 is overestimated, especially in winter. The CTM performs also very well on day-to-day variation in PM25 or on the regional distribution in CO total column as over Beijing.

Pollutant concentrations and pollution loads in stormwater runoff from different land uses in Chongqing.

PubMed

Wang, Shumin; He, Qiang; Ai, Hainan; Wang, Zhentao; Zhang, Qianqian

2013-03-01

To investigate the distribution of pollutant concentrations and pollution loads in stormwater runoff in Chongqing, six typical land use types were selected and studied from August 2009 to September 2011. Statistical analysis on the distribution of pollutant concentrations in all water samples shows that pollutant concentrations fluctuate greatly in rainfall-runoff, and the concentrations of the same pollutant also vary greatly in different rainfall events. In addition, it indicates that the event mean concentrations (EMCs) of total suspended solids (TSS) and chemical oxygen demand (COD) from urban traffic roads (UTR) are significantly higher than those from residential roads (RR), commercial areas (CA), concrete roofs (CR), tile roofs (TRoof), and campus catchment areas (CCA); and the EMCs of total phosphorus (TP) and NH3-N from UTR and CA are 2.35-5 and 3 times of the class-II standard values specified in the Environmental Quality Standards for Surface Water (GB 3838-2002). The EMCs of Fe, Pb and Cd are also much higher than the class-III standard values. The analysis of pollution load producing coefficients (PLPC) reveals that the main pollution source of TSS, COD and TP is UTR. The analysis of correlations between rainfall factors and EMCs/PLPC indicates that rainfall duration is correlated with EMCs/PLPC of TSS for TRoof and TP for UTR, while rainfall intensity is correlated with EMCs/PLPC of TP for both CR and CCA. The results of this study provide a reference for better management of non-point source pollution in urban regions.

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.

40 CFR 50.14 - Treatment of air quality monitoring data influenced by exceptional events.

Code of Federal Regulations, 2010 CFR

2010-07-01

... specific air pollution concentration at a particular air quality monitoring location. (2) Demonstration to... exceptional event caused a specific air pollution concentration in excess of one or more national ambient air... specific air pollution concentration in excess of one or more national ambient air quality standards at a...

40 CFR 60.2975 - What equations must I use?

Code of Federal Regulations, 2011 CFR

2011-07-01

... § 60.2975 What equations must I use? (a) Percent oxygen. Adjust all pollutant concentrations to 7 percent oxygen using equation 1 of this section. ER16DE05.000 Where: Cadj = pollutant concentration adjusted to 7 percent oxygen Cmeas = pollutant concentration measured on a dry basis (20.9-7) = 20.9...

Marine molluscs in environmental monitoring. III. Trace metals and organic pollutants in animal tissue and sediments

NASA Astrophysics Data System (ADS)

Feldstein, Tamar; Kashman, Yoel; Abelson, Avigdor; Fishelson, Lev; Mokady, Ofer; Bresler, Vladimir; Erel, Yigal

2003-10-01

Concentrations of trace elements and organic pollutants were determined in marine sediments and molluscs from the Mediterranean and Red Sea coasts of Israel. Two bivalve species (Donax trunculus, Pteria aegyptia), two gastropod species (Patella caerulea, Cellana rota) and sediments were sampled at polluted and relatively clean, reference, sites. Along the Mediterranean coast of Israel, sediments and molluscs from Haifa Bay stations were enriched with both organic and trace element contaminants. In the Red Sea, differences between the polluted and reference sites were less pronounced. Bio-concentration factors indicate a significant concentration of Zn, As, Cd, Sn and Pb in animal tissue relative to the concentrations of these elements in the sediments. In contrast, Ce, La and U were not concentrated in molluscs. The trace element results indicate a saturation of the detoxification mechanisms in molluscs from polluted sites. The concentrations of organic pollutants at the same sites are at the lower range of values recorded in other studies. However, synergistic effects between these compounds and between them and metals can lead to acute toxicity.

Numerical prediction of pollutant dispersion and transport in an atmospheric boundary layer

NASA Astrophysics Data System (ADS)

Zeoli, Stéphanie; Bricteux, Laurent; Mech. Eng. Dpt. Team

2014-11-01

The ability to accurately predict concentration levels of air pollutant released from point sources is required in order to determine their environmental impact. A wall modeled large-eddy simulation (WMLES) of the ABL is performed using the OpenFoam based solver SOWFA (Churchfield and Lee, NREL). It uses Boussinesq approximation for buoyancy effects and takes into account Coriolis forces. A synthetic eddy method is proposed to properly model turbulence inlet velocity boundary conditions. This method will be compared with the standard pressure gradient forcing. WMLES are usually performed using a standard Smagorinsky model or its dynamic version. It is proposed here to investigate a subgrid scale (SGS) model with a better spectral behavior. To this end, a regularized variational multiscale (RVMs) model (Jeanmart and Winckelmans, 2007) is implemented together with standard wall function in order to preserve the dynamics of the large scales within the Ekman layer. The influence of the improved SGS model on the wind simulation and scalar transport will be discussed based on turbulence diagnostics.

A joint prevention and control mechanism for air pollution in the Beijing-Tianjin-Hebei region in china based on long-term and massive data mining of pollutant concentration

NASA Astrophysics Data System (ADS)

Wang, Hongbo; Zhao, Laijun

2018-02-01

China's Beijing-Tianjin-Hebei (BTH) region suffers from the country's worst air pollution. The problem has caused widespread concern both at home and abroad. Based on long-term and massive data mining of PM2.5 and PM10 concentration, we found that these pollutants showed similar variations in four seasons, but the most severe pollution was in winter. Through cluster analysis of the winter daily average concentration (DAC) of the two pollutants, we defined regions with similar variations in pollutant concentrations in winter. For the most polluted cities in BTH, the relationship between correlation coefficients for winter DAC and the distance between cities revealed that PM2.5 has regional, large-scale characteristics, with concentrated outbreaks, whereas PM10 has local, small-scale characteristics, with outbreaks at multiple locations. By selecting the key cities with the strongest linear relationship between the pollutant's DAC of each city and the daily individual air quality index values of the BTH region and through cluster analysis on the correlations between the pollutant DACs of the key cities, we defined regional divisions suitable for Joint Prevention and Control of Atmospheric Pollution (JPCAP) program to control PM2.5 and PM10. Comprehensively considering the degree of influence of regional atmospheric pollution control (RAPC) on air quality in BTH, as well as the elasticity and urgency of RAPC, we defined the control grades of the JPCAP regions. We found both the regions and corresponding control grades were consistent for PM2.5 and PM10. The thinking and methods of atmospheric pollution control we proposed will have broad significance for implementation of RAPC in other regions around the world.

Measurement of NOx and CO Fluxes from a Tall Tower in Beijing.

NASA Astrophysics Data System (ADS)

Squires, F. A.; Drysdale, W. S.; Hamilton, J.; Lee, J. D.; Vaughan, A. R.; Wild, O.; Mullinger, N.; Nemitz, E.; Metzger, S.; Zhang, Q.

2017-12-01

China's air quality problems are well publicised; in 2010, 1.2 million premature deaths were attributed to outdoor air pollution in China. One of the major air quality issues is high concentrations of nitrogen oxides (NOx). China is the largest NOx emitter, contributing an estimated 18 % to global NOx emissions. Beijing itself is reported to have NO2 concentrations 42 % higher than the annual national standard. Given the high levels of pollution, increased focus has been placed on improving emissions estimates which are typically developed using a `bottom-up' approach where emissions are predicted from their sources. Emission inventories in China have large uncertainties and are rapidly changing with time in response to economic development, environmental regulation and new technologies. In fact, China is the largest contributor to the uncertainty in the source and the magnitude of air pollutants in air quality models. Recent studies have shown a discrepancy between NOx inventories and measured NOx emissions for UK cities, highlighting the limitations of bottom-up emissions inventories and the importance of accurate measurement data to improve the estimates. 5 Hz measurements of NOx and CO concentration were made as part of the Air Pollutants in Beijing (AIRPOLL-Beijing) project during two field campaigns in Nov-Dec 2016 and May-June 2017. Sampling took place from an inlet co-located with a sonic anemometer at 102 m on a meteorological tower in central Beijing. Analysis of the covariance between vertical wind speed and concentration enabled the calculation of emission flux, with an estimated footprint of between 2 - 5 km from the tower (which typically included some major ring roads and expressways). Fluxes were quantified using the continuous wavelet transformation (CWT) method, which enabled one minute resolved fluxes to be calculated. These data were compared to existing emissions estimates from the Multi-resolution Emission Inventory for China (MEIC). It is anticipated that this work will be used to evaluate the accuracy of emissions inventories for Beijing and to develop improved emissions estimates.

Organochlorine pesticides and polychlorinated biphenyls in air, grass and yak butter from Namco in the central Tibetan Plateau.

PubMed

Wang, Chuanfei; Wang, Xiaoping; Yuan, Xiaohua; Ren, Jiao; Gong, Ping

2015-06-01

Limited studies on bioaccumulation of persistent organic pollutants (POPs) along terrestrial food chains were conducted. The food chain air-grass-yak (butter) in the pasture region of Namco in the central Tibetan Plateau (TP) was chosen for study. The air, grass and butter POPs in the TP were at the lower end of the concentrations generally found around the globe. HCB was the main pollutant in air and butter. Besides HCB, β-HCH and p,p'-DDE were the other major compounds in butter. Along the food chain, DDTs and high molecular weight PCB-138, 153 and 180 had higher Biological Concentration Factor values. The air-butter transfer factors of POPs were derived and demonstrated the practical advantage in predicting the atmospheric OCPs and PCBs to the TP. This study sheds light on the transfer and accumulation of POPs along the terrestrial food chain of the TP. Copyright © 2015 Elsevier Ltd. All rights reserved.

The impact of ambient air pollution on the human blood metabolome.

PubMed

Vlaanderen, J J; Janssen, N A; Hoek, G; Keski-Rahkonen, P; Barupal, D K; Cassee, F R; Gosens, I; Strak, M; Steenhof, M; Lan, Q; Brunekreef, B; Scalbert, A; Vermeulen, R C H

2017-07-01

Biological perturbations caused by air pollution might be reflected in the compounds present in blood originating from air pollutants and endogenous metabolites influenced by air pollution (defined here as part of the blood metabolome). We aimed to assess the perturbation of the blood metabolome in response to short term exposure to air pollution. We exposed 31 healthy volunteers to ambient air pollution for 5h. We measured exposure to particulate matter, particle number concentrations, absorbance, elemental/organic carbon, trace metals, secondary inorganic components, endotoxin content, gaseous pollutants, and particulate matter oxidative potential. We collected blood from the participants 2h before and 2 and 18h after exposure. We employed untargeted metabolite profiling to monitor 3873 metabolic features in 493 blood samples from these volunteers. We assessed lung function using spirometry and six acute phase proteins in peripheral blood. We assessed the association of the metabolic features with the measured air pollutants and with health markers that we previously observed to be associated with air pollution in this study. We observed 89 robust associations between air pollutants and metabolic features two hours after exposure and 118 robust associations 18h after exposure. Some of the metabolic features that were associated with air pollutants were also associated with acute health effects, especially changes in forced expiratory volume in 1s. We successfully identified tyrosine, guanosine, and hypoxanthine among the associated features. Bioinformatics approach Mummichog predicted enriched pathway activity in eight pathways, among which tyrosine metabolism. This study demonstrates for the first time the application of untargeted metabolite profiling to assess the impact of air pollution on the blood metabolome. Copyright © 2017 Elsevier Inc. All rights reserved.

Modeling long-term effects attributed to nitrogen dioxide (NO2) and sulfur dioxide (SO2) exposure on asthma morbidity in a nationwide cohort in Israel.

PubMed

Greenberg, N; Carel, R S; Derazne, E; Tiktinsky, A; Tzur, D; Portnov, B A

2017-01-01

Studies have provided extensive documentation that acutely elevated environmental exposures contribute to chronic health problems. However, only attention has been paid to the effects of modificate of exposure assessment methods in environmental health investigations, leading to uncertainty and gaps in our understanding of exposure- and dose-response relationships. The goal of the present study was to evaluate whether average or peak concentration exerts a greater influence on asthma outcome, and which of the exposure models may better explain various physiological responses generated by nitrogen dioxide (NO 2 ) or sulfur dioxide (SO 2 ) air pollutants. The effects of annual NO 2 and SO 2 exposures on asthma prevalence were determined in 137,040 17-year-old males in Israel, who underwent standard health examinations before induction to military service during 1999-2008. Three alternative models of cumulative exposure were used: arithmetic mean level (AM), average peak concentration (APC), and total number of air pollution exposure episodes (NEP). Air pollution data for NO 2 and SO 2 levels were linked to the residence of each subject and asthma prevalence was predicted using bivariate logistic regression. There was significant increased risk for asthma occurrence attributed to NO 2 exposure in all models with the highest correlations demonstrated using the APC model. Data suggested that exposure-response is better correlated with NO 2 peak concentration than with average exposure concentration in subjects with asthma. For SO 2 , there was a weaker but still significant exposure response association in all models. These differences may be related to differences in physiological responses including effects on different regions of the airways following exposure to these pollutants. NO 2 , which is poorly soluble in water, penetrates deep into the bronchial tree, producing asthmatic manifestations such as inflammation and increased mucus production as a result of high gaseous concentrations in the lung parenchyma. In contrast, SO 2 , which is highly water soluble, exerts its effects rapidly in the upper airways, leading to similar limited correlations at all levels of exposure with fewer asthmatic manifestations observed. These data indicate that differing exposure assessment methods may be needed to capture specific disease consequences associated with these air pollutants.

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.

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.

Influence of trans-boundary air pollution from China on multi-day high PM10 episodes in Seoul, Korea

NASA Astrophysics Data System (ADS)

Oh, H. R.; Ho, C. H.; Kim, J.; Chen, D.; Lee, S.; Choi, Y. S.; Chang, L. S.; Song, C. K.

2014-12-01

Air quality problems have become a serious global issue as it causes over 3 million deaths per year all over the world. With generations of massive air pollutants in China, the effects of trans-boundary transports of air pollutants on human health have become a serious international concern in East Asia. However, only a limited number of studies are available for providing scientific evidences for quantifying the sources and transports of air pollutants over major countries in East Asia. Here, it is shown that particulate matters originated from China played major role in the occurrence of multi-day (≥ 4 days) severe air pollution episodes in Seoul, Korea, in which the concentration of particulate matter of diameters ≤ 10 μm exceeds 100 μg m-3. Observations show that these multi-day severe air quality episodes occur when a strong high-pressure system resides over the eastern China - Korea region. Such a weather condition confines air pollutants within the atmospheric boundary layer and spread them by slow westerlies within the boundary layer from China into the neighboring countries. Understanding such dynamical processes is a key for advancing the predictability of trans-boundary air pollutants and their health impacts in East Asia as well as developing international measures to improve air quality for the region.

Impact of Geocoding Methods on Associations between Long-term Exposure to Urban Air Pollution and Lung Function

PubMed Central

Jacquemin, Bénédicte; Lepeule, Johanna; Boudier, Anne; Arnould, Caroline; Benmerad, Meriem; Chappaz, Claire; Ferran, Joane; Kauffmann, Francine; Morelli, Xavier; Pin, Isabelle; Pison, Christophe; Rios, Isabelle; Temam, Sofia; Künzli, Nino; Slama, Rémy

2013-01-01

Background: Errors in address geocodes may affect estimates of the effects of air pollution on health. Objective: We investigated the impact of four geocoding techniques on the association between urban air pollution estimated with a fine-scale (10 m × 10 m) dispersion model and lung function in adults. Methods: We measured forced expiratory volume in 1 sec (FEV1) and forced vital capacity (FVC) in 354 adult residents of Grenoble, France, who were participants in two well-characterized studies, the Epidemiological Study on the Genetics and Environment on Asthma (EGEA) and the European Community Respiratory Health Survey (ECRHS). Home addresses were geocoded using individual building matching as the reference approach and three spatial interpolation approaches. We used a dispersion model to estimate mean PM10 and nitrogen dioxide concentrations at each participant’s address during the 12 months preceding their lung function measurements. Associations between exposures and lung function parameters were adjusted for individual confounders and same-day exposure to air pollutants. The geocoding techniques were compared with regard to geographical distances between coordinates, exposure estimates, and associations between the estimated exposures and health effects. Results: Median distances between coordinates estimated using the building matching and the three interpolation techniques were 26.4, 27.9, and 35.6 m. Compared with exposure estimates based on building matching, PM10 concentrations based on the three interpolation techniques tended to be overestimated. When building matching was used to estimate exposures, a one-interquartile range increase in PM10 (3.0 μg/m3) was associated with a 3.72-point decrease in FVC% predicted (95% CI: –0.56, –6.88) and a 3.86-point decrease in FEV1% predicted (95% CI: –0.14, –3.24). The magnitude of associations decreased when other geocoding approaches were used [e.g., for FVC% predicted –2.81 (95% CI: –0.26, –5.35) using NavTEQ, or 2.08 (95% CI –4.63, 0.47, p = 0.11) using Google Maps]. Conclusions: Our findings suggest that the choice of geocoding technique may influence estimated health effects when air pollution exposures are estimated using a fine-scale exposure model. Citation: Jacquemin B, Lepeule J, Boudier A, Arnould C, Benmerad M, Chappaz C, Ferran J, Kauffmann F, Morelli X, Pin I, Pison C, Rios I, Temam S, Künzli N, Slama R, Siroux V. 2013. Impact of geocoding methods on associations between long-term exposure to urban air pollution and lung function. Environ Health Perspect 121:1054–1060; http://dx.doi.org/10.1289/ehp.1206016 PMID:23823697

Relations of exhaled nitric oxide and FEV1 to personal endotoxin exposure in schoolchildren with asthma.

PubMed

Delfino, Ralph J; Staimer, Norbert; Tjoa, Thomas; Gillen, Daniel L

2015-12-01

Asthma prevalence and acute exacerbations have been associated with endotoxin exposure. However, there are limited data on relations between acute asthma outcomes in children and personal exposure to endotoxin or whether this relation is modified by personal air pollution exposures. We made repeated measurements of the fractional concentration of exhaled NO (FeNO), forced expiratory volume in 1 s (FEV1) and personal endotoxin exposures in patients with persistent asthma aged 9-18 years, each of whom was followed for 10 consecutive days in Riverside and Whittier, California. Endotoxin was measured in PM2.5, and simultaneously we measured personal exposure to air pollutants: NO2 and PM2.5 mass, elemental carbon and organic carbon. Endotoxin exposure-response relations and interactions between endotoxin and air pollutants were analysed with mixed models controlling for personal temperature, humidity and the 10-day period. Neither percent-predicted FEV1 nor FeNO was associated with personal endotoxin overall; however, endotoxin was associated with FEV1 among patients with average percent-predicted FEV1<80%. When NO2 was above its median, FeNO increased by 2.2% (95% CI -0.8% to 5.2%) for an interquartile increase in personal endotoxin, whereas FeNO was lower by -1.8% (95% CI -4% to 0.5%) when NO2 was≤its median. However, this is out of 12 interaction tests between personal endotoxin and a binary air pollutant for each outcome (FEV1 and FeNO), and there were no interactions with any continuous-scaled pollutant. Personal endotoxin exposure was not associated with acute daily changes in FeNO or FEV1 in a cohort panel of schoolchildren with asthma, except for decreased FEV1 among patients with more severe asthma (percent-predicted FEV1<80%). There was limited evidence of effect modification of endotoxin by personal exposure to air pollution. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Making Air Pollution Visible: A Tool for Promoting Environmental Health Literacy.

PubMed

Cleary, Ekaterina Galkina; Patton, Allison P; Wu, Hsin-Ching; Xie, Alan; Stubblefield, Joseph; Mass, William; Grinstein, Georges; Koch-Weser, Susan; Brugge, Doug; Wong, Carolyn

2017-04-12

Digital maps are instrumental in conveying information about environmental hazards geographically. For laypersons, computer-based maps can serve as tools to promote environmental health literacy about invisible traffic-related air pollution and ultrafine particles. Concentrations of these pollutants are higher near major roadways and increasingly linked to adverse health effects. Interactive computer maps provide visualizations that can allow users to build mental models of the spatial distribution of ultrafine particles in a community and learn about the risk of exposure in a geographic context. The objective of this work was to develop a new software tool appropriate for educating members of the Boston Chinatown community (Boston, MA, USA) about the nature and potential health risks of traffic-related air pollution. The tool, the Interactive Map of Chinatown Traffic Pollution ("Air Pollution Map" hereafter), is a prototype that can be adapted for the purpose of educating community members across a range of socioeconomic contexts. We built the educational visualization tool on the open source Weave software platform. We designed the tool as the centerpiece of a multimodal and intergenerational educational intervention about the health risk of traffic-related air pollution. We used a previously published fine resolution (20 m) hourly land-use regression model of ultrafine particles as the algorithm for predicting pollution levels and applied it to one neighborhood, Boston Chinatown. In designing the map, we consulted community experts to help customize the user interface to communication styles prevalent in the target community. The product is a map that displays ultrafine particulate concentrations averaged across census blocks using a color gradation from white to dark red. The interactive features allow users to explore and learn how changing meteorological conditions and traffic volume influence ultrafine particle concentrations. Users can also select from multiple map layers, such as a street map or satellite view. The map legends and labels are available in both Chinese and English, and are thus accessible to immigrants and residents with proficiency in either language. The map can be either Web or desktop based. The Air Pollution Map incorporates relevant language and landmarks to make complex scientific information about ultrafine particles accessible to members of the Boston Chinatown community. In future work, we will test the map in an educational intervention that features intergenerational colearning and the use of supplementary multimedia presentations. ©Ekaterina Galkina Cleary, Allison P Patton, Hsin-Ching Wu, Alan Xie, Joseph Stubblefield, William Mass, Georges Grinstein, Susan Koch-Weser, Doug Brugge, Carolyn Wong. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 12.04.2017.

Chemotactic selection of pollutant degrading soil bacteria

DOEpatents

Hazen, Terry C.

1994-01-01

A method for identifying soil microbial strains which may be bacterial degraders of pollutants comprising the steps of placing a concentration of a pollutant in a substantially closed container, placing the container in a sample of soil for a period of time ranging from one minute to several hours, retrieving the container, collecting the contents of the container, and microscopically determining the identity of the bacteria present. Different concentrations of the pollutant can be used to determine which bacteria respond to each concentration. The method can be used for characterizing a polluted site or for looking for naturally occurring biological degraders of the pollutant. Then bacteria identified as degraders of the pollutant and as chemotactically attracted to the pollutant are used to inoculate contaminated soil. To enhance the effect of the bacteria on the pollutant, nutrients are cyclicly provided to the bacteria then withheld to alternately build up the size of the bacterial colony or community and then allow it to degrade the pollutant.

Chemotactic selection of pollutant degrading soil bacteria

DOEpatents

Hazen, T.C.

1991-03-04

A method is described for identifying soil microbial strains which may be bacterial degraders of pollutants. This method includes: Placing a concentration of a pollutant in a substantially closed container; placing the container in a sample of soil for a period of time ranging from one minute to several hours; retrieving the container and collecting its contents; microscopically determining the identity of the bacteria present. Different concentrations of the pollutant can be used to determine which bacteria respond to each concentration. The method can be used for characterizing a polluted site or for looking for naturally occurring biological degraders of the pollutant. Then bacteria identified as degraders of the pollutant and as chemotactically attracted to the pollutant are used to innoculate contaminated soil. To enhance the effect of the bacteria on the pollutant, nutrients are cyclicly provided to the bacteria then withheld to alternately build up the size of the bacterial colony or community and then allow it to degrade the pollutant.

MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China.

PubMed

Liang, Fengchao; Xiao, Qingyang; Wang, Yujie; Lyapustin, Alexei; Li, Guoxing; Gu, Dongfeng; Pan, Xiaochuan; Liu, Yang

2018-03-01

Satellite-driven statistical models have been proven to be able to provide spatially resolved PM 2.5 estimates worldwide. The North China Plain has been suffering from severe PM 2.5 pollution in recent years. An accurate assessment of the spatiotemporal characteristics of PM 2.5 levels in this region is crucial to design effective air pollution control policy. Our objective is to estimate daily PM 2.5 concentrations at 1km spatial resolution from 2004 to 2014 in Beijing and its surrounding areas using the Multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD). A high-performance three-stage model was developed with AOD, meteorological, demographic and land use variables as predictors, which includes a custom-designed PM 2.5 gap-filling method. The 11-year average annual coverage increased from 177days to 279days and annual PM 2.5 prediction error decreased from 14.1μg/m 3 to 8.3μg/m 3 after gap-filling techniques were applied. Results show that the 11-year overall mean of predicted PM 2.5 was 67.1μg/m 3 in our study domain. The cross-validation R 2 value of our model is 0.82 in 2013 and 0.79 in 2014. In addition, the models predicted historical PM 2.5 concentrations with relatively high accuracy at the seasonal and annual levels (R 2 ranged from 0.78 to 0.86). Our long-term PM 2.5 prediction filled the gaps left by ground monitors, which would be beneficial to PM 2.5 related epidemiological studies in Beijing. Copyright © 2017 Elsevier B.V. All rights reserved.

Forecasting long-range atmospheric transport episodes of polychlorinated biphenyls using FLEXPART

NASA Astrophysics Data System (ADS)

Halse, Anne Karine; Eckhardt, Sabine; Schlabach, Martin; Stohl, Andreas; Breivik, Knut

2013-06-01

The analysis of concentrations of persistent organic pollutants (POPs) in ambient air is costly and can only be done for a limited number of samples. It is thus beneficial to maximize the information content of the samples analyzed via a targeted observation strategy. Using polychlorinated biphenyls (PCBs) as an example, a forecasting system to predict and evaluate long-range atmospheric transport (LRAT) episodes of POPs at a remote site in southern Norway has been developed. The system uses the Lagrangian particle transport model FLEXPART, and can be used for triggering extra ("targeted") sampling when LRAT episodes are predicted to occur. The system was evaluated by comparing targeted samples collected over 12-25 h during individual LRAT episodes with monitoring samples regularly collected over one day per week throughout a year. Measured concentrations in all targeted samples were above the 75th percentile of the concentrations obtained from the regular monitoring program and included the highest measured values of all samples. This clearly demonstrates the success of the targeted sampling strategy.

Prediction of the P-leaching potential of arable soils in areas with high livestock densities*

PubMed Central

Werner, Wilfried; Trimborn, Manfred; Pihl, Uwe

2006-01-01

Due to long-term positive P-balances many surface soils in areas with high livestock density in Germany are oversupplied with available P, creating a potential for vertical P losses by leaching. In extensive studies to characterize the endangering of ground water to P pollution by chemical soil parameters it is shown that the available P content and the P concentration of the soil solution in the deeper soil layers, as indicators of the P-leaching potential, cannot be satisfactorily predicted from the available P content of the topsoils. The P equilibrium concentration in the soil solution directly above ground water table or the pipe drainage system highly depends on the relative saturation of the P-sorption capacity in this layer. A saturation index of <20% normally corresponds with P equilibrium concentrations of <0.2 mg P/L. Phytoremediation may reduce the P leaching potential of P-enriched soils only over a very long period. PMID:16773724

Roles of Meteorology in Changes of Air Pollutants Concentrations in China from 2010 to 2015

NASA Astrophysics Data System (ADS)

Wang, P.; Kota, S. H.; Hu, J.; Ying, Q.; Zhang, H.

2017-12-01

Tremendous efforts have been made to control the severe air pollution in China in recent years. However, no significant improvement was observed according to annual fine particulate matter (PM2.5) concentrations and the concentrations in severe air pollution events in winter. This is partially due to the role of meteorology, which affects the emission, transport, transformation, and deposition of air pollutants. In this study, simulation of air pollutants over China was conducted for six years from 2010 to 2015 with constant anthropogenic emissions to verify the changes of air pollutants due to meteorology changes only. Model performance was validated by comparing with meteorological observations and air pollutants measures from various sources. Four different regions/cities were selected to understand the changes in wind, mixing layer height, temperature, and relative humanity at different seasons. The changes in concentrations of pollutants including PM2.5 and its chemical components and ozone were analyzed and associated with meteorological changes. This study would provide information for designing effective control strategies in different areas with the consideration of meteorological and climate changes.

Establishment and application of the estimation model for pollutant concentrfation in agriculture drain

NASA Astrophysics Data System (ADS)

Li, Qiangkun; Hu, Yawei; Jia, Qian; Song, Changji

2018-02-01

It is the key point of quantitative research on agricultural non-point source pollution load, the estimation of pollutant concentration in agricultural drain. In the guidance of uncertainty theory, the synthesis of fertilization and irrigation is used as an impulse input to the farmland, meanwhile, the pollutant concentration in agricultural drain is looked as the response process corresponding to the impulse input. The migration and transformation of pollutant in soil is expressed by Inverse Gaussian Probability Density Function. The law of pollutants migration and transformation in soil at crop different growth periods is reflected by adjusting parameters of Inverse Gaussian Distribution. Based on above, the estimation model for pollutant concentration in agricultural drain at field scale was constructed. Taking the of Qing Tong Xia Irrigation District in Ningxia as an example, the concentration of nitrate nitrogen and total phosphorus in agricultural drain was simulated by this model. The results show that the simulated results accorded with measured data approximately and Nash-Sutcliffe coefficients were 0.972 and 0.964, respectively.

Availability of polycyclic aromatic hydrocarbons to earthworms (Eisenia andrei, Oligochaeta) in field-polluted soils and soil-sediment mixtures.

PubMed

Jager, Tjalling; Baerselman, Rob; Dijkman, Ellen; de Groot, Arthur C; Hogendoorn, Elbert A; de Jong, Ad; Kruitbosch, Jantien A W; Peijnenburg, Willie J G M

2003-04-01

The bioavailability of polycyclic aromatic hydrocarbons (PAHs) for earthworms (Eisenia andrei) was experimentally determined in seven field-polluted soils and 15 soil-sediment mixtures. The pore-water concentration of most PAHs was higher than predicted. However, most of the compound was associated with dissolved organic carbon (DOC) and not directly available for uptake by earthworms. The apparent sorption could be reasonably predicted on the basis of interactions with DOC; however, the biota-soil accumulation factors (BSAFs) for earthworms were up to two orders of magnitude lower than predicted by equilibrium partitioning. The large variability between sites was not fully explained by differences in sorption. Experimental results indicate that the pool of freely dissolved PAHs in the pore water became partially depleted because of uptake by the earthworms and that bioaccumulation is thus also influenced by the kinetics of PAH desorption and mass transport. A pilot study with Lumbricus rubellus showed that steady-state body residues were well correlated to E. andrei. Current results show that depositing dredge spoil on land may lead to increased bioavailability of the lower-molecular-weight PAHs. However, risk assessment can conservatively rely on equilibrium partitioning, but accurate prediction requires quantification of the kinetics of bioavailability.

Assessment and prediction of air quality using fuzzy logic and autoregressive models

NASA Astrophysics Data System (ADS)

Carbajal-Hernández, José Juan; Sánchez-Fernández, Luis P.; Carrasco-Ochoa, Jesús A.; Martínez-Trinidad, José Fco.

2012-12-01

In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.

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.

Characteristics of Atmospheric Pollutants Distribution and Removal Effect of Rainfall on Atmospheric Pollutants in Mining Cities

NASA Astrophysics Data System (ADS)

Wen-feng, Tang; You-biao, Hu

2018-05-01

This paper studies the characteristics of atmospheric pollutant (SO2, NO2, PM2.5 and PM10) and the effects of rainfall on the removal of atmospheric pollutants. The results show atmospheric pollutants concentration vary in different seasons and functional area: atmospheric pollutants concentration in summer and autumn is lower than that in winter and spring; the concentration of SO2 and NO2 in coal-chemical industry areas and light industrial areas is higher, the concentration difference of PM2.5 and PM10 in different functional areas is very small, the removal efficiency of rainfall on atmospheric pollutant is gradually improved with the increasing of daily rainfall, rainfall intensity and rainfall duration, the ability of rainfall to remove pollutants tends to be stable after daily rainfall and rainfall intensity exceeds 30mm and 20mm/h respectively, the effect of rainfall on the removal of PM2.5 was slightly worse than the effect of rainfall on other atmospheric pollutants, the rainfall duration should be 60min, 60min and 80min respectively when the effect of rainfall on NO2, PM10 and SO2 tends to be stable.

The Relationship between Particulate Pollution Levels in Australian Cities, Meteorology, and Landscape Fire Activity Detected from MODIS Hotspots

PubMed Central

Price, Owen F.; Williamson, Grant J.; Henderson, Sarah B.; Johnston, Fay; Bowman, David M. J. S.

2012-01-01

Smoke from bushfires is an emerging issue for fire managers because of increasing evidence for its public health effects. Development of forecasting models to predict future pollution levels based on the relationship between bushfire activity and current pollution levels would be a useful management tool. As a first step, we use daily thermal anomalies detected by the MODIS Active Fire Product (referred to as “hotspots”), pollution concentrations, and meteorological data for the years 2002 to 2008, to examine the statistical relationship between fire activity in the landscapes and pollution levels around Perth and Sydney, two large Australian cities. Resultant models were statistically significant, but differed in their goodness of fit and the distance at which the strength of the relationship was strongest. For Sydney, a univariate model for hotspot activity within 100 km explained 24% of variation in pollution levels, and the best model including atmospheric variables explained 56% of variation. For Perth, the best radius was 400 km, explaining only 7% of variation, while the model including atmospheric variables explained 31% of the variation. Pollution was higher when the atmosphere was more stable and in the presence of on-shore winds, whereas there was no effect of wind blowing from the fires toward the pollution monitors. Our analysis shows there is a good prospect for developing region-specific forecasting tools combining hotspot fire activity with meteorological data. PMID:23071788

Bayesian Maximum Entropy Integration of Ozone Observations and Model Predictions: A National Application.

PubMed

Xu, Yadong; Serre, Marc L; Reyes, Jeanette; Vizuete, William

2016-04-19

To improve ozone exposure estimates for ambient concentrations at a national scale, we introduce our novel Regionalized Air Quality Model Performance (RAMP) approach to integrate chemical transport model (CTM) predictions with the available ozone observations using the Bayesian Maximum Entropy (BME) framework. The framework models the nonlinear and nonhomoscedastic relation between air pollution observations and CTM predictions and for the first time accounts for variability in CTM model performance. A validation analysis using only noncollocated data outside of a validation radius rv was performed and the R(2) between observations and re-estimated values for two daily metrics, the daily maximum 8-h average (DM8A) and the daily 24-h average (D24A) ozone concentrations, were obtained with the OBS scenario using ozone observations only in contrast with the RAMP and a Constant Air Quality Model Performance (CAMP) scenarios. We show that, by accounting for the spatial and temporal variability in model performance, our novel RAMP approach is able to extract more information in terms of R(2) increase percentage, with over 12 times for the DM8A and over 3.5 times for the D24A ozone concentrations, from CTM predictions than the CAMP approach assuming that model performance does not change across space and time.

40 CFR 463.34 - New source performance standards.

Code of Federal Regulations, 2013 CFR

2013-07-01

... pollutant concentrations: Subpart C [Finishing water] Concentration used to calculate NSPS Pollutant or pollutant property Maximum for any 1 day (mg/l) Maximum for monthly average (mg/l) TSS 130 37 pH (1) (1) 1...

Assessment of regional air quality by a concentration-dependent Pollution Permeation Index

PubMed Central

Liang, Chun-Sheng; Liu, Huan; He, Ke-Bin; Ma, Yong-Liang

2016-01-01

Although air quality monitoring networks have been greatly improved, interpreting their expanding data in both simple and efficient ways remains challenging. Therefore, needed are new analytical methods. We developed such a method based on the comparison of pollutant concentrations between target and circum areas (circum comparison for short), and tested its applications by assessing the air pollution in Jing-Jin-Ji, Yangtze River Delta, Pearl River Delta and Cheng-Yu, China during 2015. We found the circum comparison can instantly judge whether a city is a pollution permeation donor or a pollution permeation receptor by a Pollution Permeation Index (PPI). Furthermore, a PPI-related estimated concentration (original concentration plus halved average concentration difference) can be used to identify some overestimations and underestimations. Besides, it can help explain pollution process (e.g., Beijing’s PM2.5 maybe largely promoted by non-local SO2) though not aiming at it. Moreover, it is applicable to any region, easy-to-handle, and able to boost more new analytical methods. These advantages, despite its disadvantages in considering the whole process jointly influenced by complex physical and chemical factors, demonstrate that the PPI based circum comparison can be efficiently used in assessing air pollution by yielding instructive results, without the absolute need for complex operations. PMID:27731344

Effect of Ventilation Strategies on Residential Ozone Levels

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

Walker, Iain S.; Sherman, Max H.

Elevated outdoor ozone levels are associated with adverse health effects. Because people spend the vast majority of their time indoors, reduction in indoor levels of ozone of outdoor origin would lower population exposures and might also lead to a reduction in ozone-associated adverse health effects. In most buildings, indoor ozone levels are diminished with respect to outdoor levels to an extent that depends on surface reactions and on the degree to which ozone penetrates the building envelope. Ozone enters buildings from outdoors together with the airflows that are driven by natural and mechanical means, including deliberate ventilation used to reducemore » concentrations of indoor-generated pollutants. When assessing the effect of deliberate ventilation on occupant health one should consider not only the positive effects on removing pollutants of indoor origin but also the possibility that enhanced ventilation might increase indoor levels of pollutants originating outdoors. This study considers how changes in residential ventilation that are designed to comply with ASHRAE Standard 62.2 might influence indoor levels of ozone. Simulation results show that the building envelope can contribute significantly to filtration of ozone. Consequently, the use of exhaust ventilation systems is predicted to produce lower indoor ozone concentrations than would occur with balanced ventilation systems operating at the same air-­exchange rate. We also investigated a strategy for reducing exposure to ozone that would deliberately reduce ventilation rates during times of high outdoor ozone concentration while still meeting daily average ventilation requirements.« less