A Hybrid Approach for Estimating Total Deposition in the ...

EPA Pesticide Factsheets

Atmospheric deposition of nitrogen and sulfur causes many deleterious effects on ecosystems including acidification and excess eutrophication. Assessments to support development of strategies to mitigate these effects require spatially and temporally continuous values of nitrogen and sulfur deposition. In the U.S., national monitoring networks exist that provide values of wet and dry deposition at discrete locations. While wet deposition can be interpolated between the monitoring locations, dry deposition cannot. Additionally, monitoring networks do not measure the complete suite of chemicals that contribute to total sulfur and nitrogen deposition. Regional air quality models provide spatially continuous values of deposition of monitored species as well as important unmeasured species. However, air quality modeling values are not generally available for an extended continuous time period. Air quality modeling results may also be biased for some chemical species. We developed a novel approach for estimating dry deposition using data from monitoring networks such as the Clean Air Status and Trends Network (CASTNET), the National Atmospheric Deposition Program (NADP) Ammonia Monitoring Network (AMoN), and the Southeastern Aerosol Research and Characterization (SEARCH) network and modeled data from the Community Multiscale Air Quality (CMAQ) model. These dry deposition values estimates are then combined with wet deposition values from the NADP National Trends Networ

A Novel Hybrid Approach for Estimating Total Deposition in ...

EPA Pesticide Factsheets

Atmospheric deposition of nitrogen and sulfur causes many deleterious effects on ecosystems including acidification and excess eutrophication. Assessments to support development of strategies to mitigate these effects require spatially and temporally continuous values of nitrogen and sulfur deposition. In the U.S., national monitoring networks exist that provide values of wet and dry deposition at discrete locations. While wet deposition can be interpolated between the monitoring locations, dry deposition cannot. Additionally, monitoring networks do not measure the complete suite of chemicals that contribute to total sulfur and nitrogen deposition. Regional air quality models provide spatially continuous values of deposition of monitored species as well as important unmeasured species. However, air quality modeling values are not generally available for an extended continuous time period. Air quality modeling results may also be biased for some chemical species. We developed a novel approach for estimating dry deposition using data from monitoring networks such as the Clean Air Status and Trends Network (CASTNET), the National Atmospheric Deposition Program (NADP) Ammonia Monitoring Network (AMoN), and the Southeastern Aerosol Research and Characterization (SEARCH) network and modeled data from the Community Multiscale Air Quality (CMAQ) model. These dry deposition values estimates are then combined with wet deposition values from the NADP National Trends Networ

A novel hybrid approach for estimating total deposition in the United States

NASA Astrophysics Data System (ADS)

Schwede, Donna B.; Lear, Gary G.

2014-08-01

Atmospheric deposition of nitrogen and sulfur causes many deleterious effects on ecosystems including acidification and excess eutrophication. Assessments to support development of strategies to mitigate these effects require spatially and temporally continuous values of nitrogen and sulfur deposition. In the U.S., national monitoring networks exist that provide values of wet and dry deposition at discrete locations. While wet deposition can be interpolated between the monitoring locations, dry deposition cannot. Additionally, monitoring networks do not measure the complete suite of chemicals that contribute to total sulfur and nitrogen deposition. Regional air quality models provide spatially continuous values of deposition of monitored species as well as important unmeasured species. However, air quality modeling values are not generally available for an extended continuous time period. Air quality modeling results may also be biased for some chemical species. We developed a novel approach for estimating dry deposition using data from monitoring networks such as the Clean Air Status and Trends Network (CASTNET), the National Atmospheric Deposition Program (NADP) Ammonia Monitoring Network (AMoN), and the Southeastern Aerosol Research and Characterization (SEARCH) network and modeled data from the Community Multiscale Air Quality (CMAQ) model. These dry deposition values estimates are then combined with wet deposition values from the NADP National Trends Network (NTN) to develop values of total deposition of sulfur and nitrogen. Data developed using this method are made available via the CASTNET website.

Some applications of remote sensing in atmospheric monitoring programs

NASA Technical Reports Server (NTRS)

Heller, A. N.; Bryson, J. C.; Vasuki, N. C.

1972-01-01

The applications of remote sensing in atmospheric monitoring programs are described. The organization, operations, and functions of an air quality monitoring network at New Castle County, Delaware is discussed. The data obtained by the air quality monitoring network ground stations and the equipment used to obtain atmospheric data are explained. It is concluded that correlation of the information obtained by the network will make it possible to anticipate air pollution problems in the Chesapeake Bay area before a crisis develops.

Air Pollution over the States

ERIC Educational Resources Information Center

Environmental Science and Technology, 1972

1972-01-01

State plans for implementing air quality standards are evaluated together with problems in modeling procedures and enforcement. Monitoring networks, standards, air quality regions, and industrial problems are also discussed. (BL)

Monitoring air quality in Southeast Alaska’s National Parks and Forests: Linking atmospheric pollutants with ecological effects

Treesearch

D. Schirokauer; L. Geiser; A. Bytnerowicz; M. Fenn; K. Dillman

2014-01-01

Air quality and air quality related values are important resources to the National Park Service (NPS) units and Wilderness areas in northern Southeast Alaska. Air quality monitoring was prioritized as a high-priority Vital Sign at the Southeast Alaska Network’s (SEAN) Inventory and Monitoring Program’s terrestrial scoping workshop (Derr and Fastie 2006). Air quality...

Definition of air quality measurements for monitoring space shuttle launches

NASA Technical Reports Server (NTRS)

Thorpe, R. D.

1978-01-01

A description of a recommended air quality monitoring network to characterize the impact on ambient air quality in the Kennedy Space Center (KSC) (area) of space shuttle launch operations is given. Analysis of ground cloud processes and prevalent meteorological conditions indicates that transient HCl depositions can be a cause for concern. The system designed to monitor HCl employs an extensive network of inexpensive detectors combined with a central analysis device. An acid rain network is also recommended. A quantitative measure of projected minimal long-term impact involves the limited monitoring of NOx and particulates. All recommended monitoring is confined ti KSC property.

INTEGRATION OF SATELLITE-DERIVED AEROSOL DATA INTO THE AIR QUALITY APPLICATIONS

EPA Science Inventory

Historically, the only source of aerosol air quality data available on an ongoing and systematic basis at national levels was generated by ambient air monitoring networks put in place for the US EPA's Air Quality Programs. Over the past several years, the remote sensing of aeros...

40 CFR 58.13 - Monitoring network completion.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 6 2012-07-01 2012-07-01 false Monitoring network completion. 58.13 Section 58.13 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.13 Monitoring network completion. (a...

40 CFR 58.13 - Monitoring network completion.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 6 2014-07-01 2014-07-01 false Monitoring network completion. 58.13 Section 58.13 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.13 Monitoring network completion. (a...

40 CFR 58.13 - Monitoring network completion.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 6 2013-07-01 2013-07-01 false Monitoring network completion. 58.13 Section 58.13 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.13 Monitoring network completion. (a...

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial, California, USA.

PubMed

Carvlin, Graeme N; Lugo, Humberto; Olmedo, Luis; Bejarano, Ester; Wilkie, Alexa; Meltzer, Dan; Wong, Michelle; King, Galatea; Northcross, Amanda; Jerrett, Michael; English, Paul B; Hammond, Donald; Seto, Edmund

2017-12-01

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM 2.5 and PM 10 (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R 2 for converted hourly averaged Dylos mass measurements versus a PM 2.5 BAM was 0.79 and that versus a PM 10 BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM 2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R 2 = 0.35-0.81). The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.

40 CFR 58.16 - Data submittal and archiving requirements.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... Other Federal agencies may request access to filters for purposes of supporting air quality management... PROGRAMS (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.16 Data submittal and..., via AQS all ambient air quality data and associated quality assurance data for SO2; CO; O3; NO2; NO...

40 CFR 58.10 - Annual monitoring network plan and periodic network assessment.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 5 2011-07-01 2011-07-01 false Annual monitoring network plan and periodic network assessment. 58.10 Section 58.10 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.10 Annual...

40 CFR 58.10 - Annual monitoring network plan and periodic network assessment.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 5 2010-07-01 2010-07-01 false Annual monitoring network plan and periodic network assessment. 58.10 Section 58.10 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.10 Annual...

Amperometric Gas Sensors as a Low Cost Emerging Technology Platform for Air Quality Monitoring Applications: A Review.

PubMed

Baron, Ronan; Saffell, John

2017-11-22

This review examines the use of amperometric electrochemical gas sensors for monitoring inorganic gases that affect urban air quality. First, we consider amperometric gas sensor technology including its development toward specifically designed air quality sensors. We then review recent academic and research organizations' studies where this technology has been trialed for air quality monitoring applications: early studies showed the potential of electrochemical gas sensors when colocated with reference Air Quality Monitoring (AQM) stations. Spatially dense networks with fast temporal resolution provide information not available from sparse AQMs with longer recording intervals. We review how this technology is being offered as commercial urban air quality networks and consider the remaining challenges. Sensors must be sensitive, selective, and stable; air quality monitors/nodes must be electronically and mechanically well designed. Data correction is required and models with differing levels of sophistication are being designed. Data analysis and validation is possibly the biggest remaining hurdle needed to deliver reliable concentration readings. Finally, this review also considers the roles of companies, urban infrastructure requirements, and public research in the development of this technology.

ASSESSING THE COMPARABILITY OF AMMONIUM, NITRATE AND SULFATE CONCENTRATIONS MEASURED BY THREE AIR QUALITY MONITORING NETWORKS

EPA Science Inventory

Airborne fine particulate matter across the United States is monitored by different networks, the three prevalent ones presently being the Clean Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE) and the Speciati...

Low-cost, high-density sensor network for urban emission monitoring: BEACO2N

NASA Astrophysics Data System (ADS)

Kim, J.; Shusterman, A.; Lieschke, K.; Newman, C.; Cohen, R. C.

2017-12-01

In urban environments, air quality is spatially and temporally heterogeneous as diverse emission sources create a high degree of variability even at the neighborhood scale. Conventional air quality monitoring relies on continuous measurements with limited spatial resolution or passive sampling with high-density and low temporal resolution. Either approach averages the air quality information over space or time and hinders our attempts to understand emissions, chemistry, and human exposure in the near-field of emission sources. To better capture the true spatio-temporal heterogeneity of urban conditions, we have deployed a low-cost, high-density air quality monitoring network in San Francisco Bay Area distributed at 2km horizontal spacing. The BErkeley Atmospheric CO2 Observation Network (BEACO2N) consists of approximately 50 sensor nodes, measuring CO2, CO, NO, NO2, O­3, and aerosol. Here we describe field-based calibration approaches that are consistent with the low-cost strategy of the monitoring network. Observations that allow inference of emission factors and identification of specific local emission sources will also be presented.

Quality control and gap-filling of PM10 daily mean concentrations with the best linear unbiased estimator.

PubMed

Sozzi, R; Bolignano, A; Ceradini, S; Morelli, M; Petenko, I; Argentini, S

2017-10-15

According to the European Directive 2008/50/CE, the air quality assessment consists in the measurement of the concentration fields, and the evaluation of the mean, number of exceedances, etc. of some chemical species dangerous to human health. The measurements provided by an air quality ground-based monitoring network are the main information source but the availability of these data is often limited by several technical and operational problems. In this paper, the best linear unbiased estimator (BLUE) is proposed to validate the pollutant concentration values and to fill the gaps in the measurement of time series collected by a monitoring network. The BLUE algorithm is tested using the daily mean concentrations of particulate matter having aerodynamic diameter less than 10 μ (PM 10 concentrations) measured by the air quality monitoring sensors operating in the Lazio Region in Italy. The comparison between the estimated and measured data evidences an error comparable with the measurement uncertainty. Due to its simplicity and reliability, the BLUE will be used in the routine quality test procedures of the Lazio air quality monitoring network measurements.

Forecasting PM10 in metropolitan areas: Efficacy of neural networks.

PubMed

Fernando, H J S; Mammarella, M C; Grandoni, G; Fedele, P; Di Marco, R; Dimitrova, R; Hyde, P

2012-04-01

Deterministic photochemical air quality models are commonly used for regulatory management and planning of urban airsheds. These models are complex, computer intensive, and hence are prohibitively expensive for routine air quality predictions. Stochastic methods are becoming increasingly popular as an alternative, which relegate decision making to artificial intelligence based on Neural Networks that are made of artificial neurons or 'nodes' capable of 'learning through training' via historic data. A Neural Network was used to predict particulate matter concentration at a regulatory monitoring site in Phoenix, Arizona; its development, efficacy as a predictive tool and performance vis-à-vis a commonly used regulatory photochemical model are described in this paper. It is concluded that Neural Networks are much easier, quicker and economical to implement without compromising the accuracy of predictions. Neural Networks can be used to develop rapid air quality warning systems based on a network of automated monitoring stations. Copyright © 2011 Elsevier Ltd. All rights reserved.

Air pollution and public health: a guidance document for risk managers.

PubMed

Craig, Lorraine; Brook, Jeffrey R; Chiotti, Quentin; Croes, Bart; Gower, Stephanie; Hedley, Anthony; Krewski, Daniel; Krupnick, Alan; Krzyzanowski, Michal; Moran, Michael D; Pennell, William; Samet, Jonathan M; Schneider, Jurgen; Shortreed, John; Williams, Martin

2008-01-01

This guidance document is a reference for air quality policymakers and managers providing state-of-the-art, evidence-based information on key determinants of air quality management decisions. The document reflects the findings of five annual meetings of the NERAM (Network for Environmental Risk Assessment and Management) International Colloquium Series on Air Quality Management (2001-2006), as well as the results of supporting international research. The topics covered in the guidance document reflect critical science and policy aspects of air quality risk management including i) health effects, ii) air quality emissions, measurement and modeling, iii) air quality management interventions, and iv) clean air policy challenges and opportunities.

AN INTEGRATED APPROACH TO AIR QUALITY USING IN SITU, SATELLITE, AND MODELED DATA - FOCUSED ON THE FUTURE OF EARTH OBSERVATIONS SYSTEM (EOS)

EPA Science Inventory

EPA through statutory mandates has monitored air, water, land and human health for the past several decades. The design of the ambient air monitoring networks, for the most part, has been loosely tied single-pollutant networks focused on large urban areas. These networks supply t...

Observational Needs for Four-Dimensional Air Quality Characterization

EPA Science Inventory

Surface-based monitoring programs provide the foundation for associating air pollution and causal effects in human health studies, and they support the development of air quality standards and the preparation of emission reduction strategies. While surface oriented networks remai...

Forest fires and air quality issues in southern Europe

Treesearch

Ana Isabel Miranda; Enrico Marchi; Marco Ferretti; Millán M. Millán

2009-01-01

Each summer forest fires in southern Europe emit large quantities of pollutants to the atmosphere. These fires can generate a number of air pollution episodes as measured by air quality monitoring networks. We analyzed the impact of forest fires on air quality of specific regions of southern Europe. Data from several summer seasons were studied with the aim of...

Application of ESE Data and Tools to Air Quality Management: Services for Helping the Air Quality Community use ESE Data (SHAirED)

NASA Technical Reports Server (NTRS)

Falke, Stefan; Husar, Rudolf

2011-01-01

The goal of this REASoN applications and technology project is to deliver and use Earth Science Enterprise (ESE) data and tools in support of air quality management. Its scope falls within the domain of air quality management and aims to develop a federated air quality information sharing network that includes data from NASA, EPA, US States and others. Project goals were achieved through a access of satellite and ground observation data, web services information technology, interoperability standards, and air quality community collaboration. In contributing to a network of NASA ESE data in support of particulate air quality management, the project will develop access to distributed data, build Web infrastructure, and create tools for data processing and analysis. The key technologies used in the project include emerging web services for developing self describing and modular data access and processing tools, and service oriented architecture for chaining web services together to assemble customized air quality management applications. The technology and tools required for this project were developed within DataFed.net, a shared infrastructure that supports collaborative atmospheric data sharing and processing web services. Much of the collaboration was facilitated through community interactions through the Federation of Earth Science Information Partners (ESIP) Air Quality Workgroup. The main activities during the project that successfully advanced DataFed, enabled air quality applications and established community-oriented infrastructures were: develop access to distributed data (surface and satellite), build Web infrastructure to support data access, processing and analysis create tools for data processing and analysis foster air quality community collaboration and interoperability.

Evaluation of Data Replacement Strategies for CASTNET Dry Deposition Modeling

EPA Science Inventory

The U.S. Environmental Protection Agency (EPA) established the Clean Air Status and Trends Network (CASTNET) and its predecessor, the National Dry Deposition Network (NDDN), as national air quality and meteorological monitoring networks. The purpose of CASTNET is to track the pr...

Twenty years of measurement of polycyclic aromatic hydrocarbons (PAHs) in UK ambient air by nationwide air quality networks.

PubMed

Brown, Andrew S; Brown, Richard J C; Coleman, Peter J; Conolly, Christopher; Sweetman, Andrew J; Jones, Kevin C; Butterfield, David M; Sarantaridis, Dimitris; Donovan, Brian J; Roberts, Ian

2013-06-01

The impact of human activities on the health of the population and of the wider environment has prompted action to monitor the presence of toxic compounds in the atmosphere. Toxic organic micropollutants (TOMPs) are some of the most insidious and persistent of these pollutants. Since 1991 the United Kingdom has operated nationwide air quality networks to assess the presence of TOMPs, including polycyclic aromatic hydrocarbons (PAHs), in ambient air. The data produced in 2010 marked 20 years of nationwide PAH monitoring. This paper marks this milestone by providing a novel and critical review of the data produced since nationwide monitoring began up to the end of 2011 (the latest year for which published data is available), discussing how the networks performing this monitoring has evolved, and elucidating trends in the concentrations of the PAHs measured. The current challenges in the area and a forward look to the future of air quality monitoring for PAHs are also discussed briefly.

Assessing the Performance of a Network of Low Cost Particulate Matter Sensors Deployed in Sacramento, California

NASA Astrophysics Data System (ADS)

Mukherjee, A. D.; Brown, S. G.; McCarthy, M. C.

2017-12-01

A new generation of low cost air quality sensors have the potential to provide valuable information on the spatial-temporal variability of air pollution - if the measurements have sufficient quality. This study examined the performance of a particulate matter sensor model, the AirBeam (HabitatMap Inc., Brooklyn, NY), over a three month period in the urban environment of Sacramento, California. Nineteen AirBeam sensors were deployed at a regulatory air monitoring site collocated with meteorology measurements and as a local network over an 80 km2 domain in Sacramento, CA. This study presents the methodology to evaluate the precision, accuracy, and reliability of the sensors over a range of meteorological and aerosol conditions. The sensors demonstrated a robust degree of precision during collocated measurement periods (R2 = 0.98 - 0.99) and a moderate degree of correlation against a Beta Attenuation Monitor PM2.5 monitor (R2 0.6). A normalization correction is applied during the study period so that each AirBeam sensor in the network reports a comparable value. The role of the meteorological environment on the accuracy of the sensor measurements is investigated, along with the possibility of improving the measurements through a meteorology weighted correction. The data quality of the network of sensors is examined, and the spatial variability of particulate matter through the study domain derived from the sensor network is presented.

Use of Whatman-41 filters in air quality sampling networks (with applications to elemental analysis)

NASA Technical Reports Server (NTRS)

Neustadter, H. E.; Sidik, S. M.; King, R. B.; Fordyce, J. S.; Burr, J. C.

1974-01-01

The operation of a 16-site parallel high volume air sampling network with glass fiber filters on one unit and Whatman-41 filters on the other is reported. The network data and data from several other experiments indicate that (1) Sampler-to-sampler and filter-to-filter variabilities are small; (2) hygroscopic affinity of Whatman-41 filters need not introduce errors; and (3) suspended particulate samples from glass fiber filters averaged slightly, but not statistically significantly, higher than from Whatman-41-filters. The results obtained demonstrate the practicability of Whatman-41 filters for air quality monitoring and elemental analysis.

Smoke monitoring network on 2006 Northern California fires

Treesearch

Brenda Belongie; Suraj Ahuja

2007-01-01

Long-duration fire activity during the 2006 northern California fire season presented an excellent opportunity to create a temporary air-quality/smoke-monitoring network in the complex terrain across northwestern California. The network was established through cooperative interagency coordination of Federal officials, the California Air Resources Board (CARB), and...

Towards an agent based traffic regulation and recommendation system for the on-road air quality control.

PubMed

Sadiq, Abderrahmane; El Fazziki, Abdelaziz; Ouarzazi, Jamal; Sadgal, Mohamed

2016-01-01

This paper presents an integrated and adaptive problem-solving approach to control the on-road air quality by modeling the road infrastructure, managing traffic based on pollution level and generating recommendations for road users. The aim is to reduce vehicle emissions in the most polluted road segments and optimizing the pollution levels. For this we propose the use of historical and real time pollution records and contextual data to calculate the air quality index on road networks and generate recommendations for reassigning traffic flow in order to improve the on-road air quality. The resulting air quality indexes are used in the system's traffic network generation, which the cartography is represented by a weighted graph. The weights evolve according to the pollution indexes and path properties and the graph is therefore dynamic. Furthermore, the systems use the available pollution data and meteorological records in order to predict the on-road pollutant levels by using an artificial neural network based prediction model. The proposed approach combines the benefits of multi-agent systems, Big data technology, machine learning tools and the available data sources. For the shortest path searching in the road network, we use the Dijkstra algorithm over Hadoop MapReduce framework. The use Hadoop framework in the data retrieve and analysis process has significantly improved the performance of the proposed system. Also, the agent technology allowed proposing a suitable solution in terms of robustness and agility.

High Efficiency, Transparent, Reusable, and Active PM2.5 Filters by Hierarchical Ag Nanowire Percolation Network.

PubMed

Jeong, Seongmin; Cho, Hyunmin; Han, Seonggeun; Won, Phillip; Lee, Habeom; Hong, Sukjoon; Yeo, Junyeob; Kwon, Jinhyeong; Ko, Seung Hwan

2017-07-12

Air quality has become a major public health issue in Asia including China, Korea, and India. Particulate matters are the major concern in air quality. We present the first environmental application demonstration of Ag nanowire percolation network for a novel, electrical type transparent, reusable, and active PM2.5 air filter although the Ag nanowire percolation network has been studied as a very promising transparent conductor in optoelectronics. Compared with previous particulate matter air filter study using relatively weaker short-range intermolecular force in polar polymeric nanofiber, Ag nanowire percolation network filters use stronger long-range electrostatic force to capture PM2.5, and they are highly efficient (>99.99%), transparent, working on an active mode, low power consumption, antibacterial, and reusable after simple washing. The proposed new particulate matter filter can be applied for a highly efficient, reusable, active and energy efficient filter for wearable electronics application.

High Electricity Demand in the Northeast U.S.: PJM Reliability Network and Peaking Unit Impacts on Air Quality.

PubMed

Farkas, Caroline M; Moeller, Michael D; Felder, Frank A; Henderson, Barron H; Carlton, Annmarie G

2016-08-02

On high electricity demand days, when air quality is often poor, regional transmission organizations (RTOs), such as PJM Interconnection, ensure reliability of the grid by employing peak-use electric generating units (EGUs). These "peaking units" are exempt from some federal and state air quality rules. We identify RTO assignment and peaking unit classification for EGUs in the Eastern U.S. and estimate air quality for four emission scenarios with the Community Multiscale Air Quality (CMAQ) model during the July 2006 heat wave. Further, we population-weight ambient values as a surrogate for potential population exposure. Emissions from electricity reliability networks negatively impact air quality in their own region and in neighboring geographic areas. Monitored and controlled PJM peaking units are generally located in economically depressed areas and can contribute up to 87% of hourly maximum PM2.5 mass locally. Potential population exposure to peaking unit PM2.5 mass is highest in the model domain's most populated cities. Average daily temperature and national gross domestic product steer peaking unit heat input. Air quality planning that capitalizes on a priori knowledge of local electricity demand and economics may provide a more holistic approach to protect human health within the context of growing energy needs in a changing world.

Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN)

PubMed Central

Mad Saad, Shaharil; Melvin Andrew, Allan; Md Shakaff, Ali Yeon; Mohd Saad, Abdul Rahman; Muhamad Yusof @ Kamarudin, Azman; Zakaria, Ammar

2015-01-01

Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity. PMID:26007724

Monitoring air quality in mountains: Designing an effective network

USGS Publications Warehouse

Peterson, D.L.

2000-01-01

A quantitatively robust yet parsimonious air-quality monitoring network in mountainous regions requires special attention to relevant spatial and temporal scales of measurement and inference. The design of monitoring networks should focus on the objectives required by public agencies, namely: 1) determine if some threshold has been exceeded (e.g., for regulatory purposes), and 2) identify spatial patterns and temporal trends (e.g., to protect natural resources). A short-term, multi-scale assessment to quantify spatial variability in air quality is a valuable asset in designing a network, in conjunction with an evaluation of existing data and simulation-model output. A recent assessment in Washington state (USA) quantified spatial variability in tropospheric ozone distribution ranging from a single watershed to the western third of the state. Spatial and temporal coherence in ozone exposure modified by predictable elevational relationships ( 1.3 ppbv ozone per 100 m elevation gain) extends from urban areas to the crest of the Cascade Range. This suggests that a sparse network of permanent analyzers is sufficient at all spatial scales, with the option of periodic intensive measurements to validate network design. It is imperative that agencies cooperate in the design of monitoring networks in mountainous regions to optimize data collection and financial efficiencies.

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.

Air quality in California forests: current efforts to initiate biomonitoring with lichens.

Treesearch

Sarah Jovan

2002-01-01

The primary objective of the Forest Health Monitoring indicator project is to develop models that use the composition of epiphytic lichen communities to detect and monitor air quality in forests. The designs of existing air quality monitoring networks in California do not provide adequate representation of rural areas to assess impacts to forests. This article is...

Air-Sense: indoor environment monitoring evaluation system based on ZigBee network

NASA Astrophysics Data System (ADS)

Huang, Yang; Hu, Liang; Yang, Disheng; Liu, Hengchang

2017-08-01

In the modern life, people spend most of their time indoors. However, indoor environmental quality problems have always been affecting people’s social activities. In general, indoor environmental quality is also related to our indoor activities. Since most of the organic irritants and volatile gases are colorless, odorless and too tiny to be seen, because we have been unconsciously overlooked indoor environment quality. Consequently, our body suffer a great health problem. In this work, we propose Air-Sense system which utilizes the platform of ZigBee Network to collect and detect the real-time indoor environment quality. What’s more, Air-Sense system can also provide data analysis, and visualizing the results of the indoor environment to the user.

A PRELIMINARY EVALUATION OF MODELS-3 CMAQ USING PARTICULATE MATTER DATA FROM THE IMPROVE NETWORK

EPA Science Inventory

The Clean Air Act and its Amendments require the United States Environmental Protection Agency (EPA) to establish National Ambient Air Quality Standards for Particulate Matter (PM) and to assess current and future air quality regulations designed to protect human health and wel...

Feedbacks between Air Pollution and Weather, Part 2: Effects on Chemistry.

EPA Science Inventory

Fully-coupled air-quality models running in “feedback” and “no-feedback” configurations were compared against each other and observation network data as part of Phase 2 of the Air Quality Model Evaluation International Initiative. In the “no-feedback” mode, interactions between m...

Air quality monitor and acid rain networks

NASA Technical Reports Server (NTRS)

Rudolph, H.

1980-01-01

The air quality monitor program which consists of two permanent air monitor stations (PAMS's) and four mobile shuttle pollutant air monitor stations (SPAMS's) is evaluated. The PAMS measures SO sub X, NO sub X particulates, CO, O3, and nonmethane hydrocarbons. The SPAMS measures O3, SO2, HCl, and particulates. The collection and analysis of data in the rain monitor program are discussed.

WSN based indoor air quality monitoring in classrooms

NASA Astrophysics Data System (ADS)

Wang, S. K.; Chew, S. P.; Jusoh, M. T.; Khairunissa, A.; Leong, K. Y.; Azid, A. A.

2017-03-01

Indoor air quality monitoring is essential as the human health is directly affected by indoor air quality. This paper presents the investigations of the impact of undergraduate students' concentration during lecture due to the indoor air quality in classroom. Three environmental parameters such as temperature, relative humidity and concentration of carbon dioxide are measured using wireless sensor network based air quality monitoring system. This simple yet reliable system is incorporated with DHT-11 and MG-811 sensors. Two classrooms were selected to install the monitoring system. The level of indoor air quality were measured and students' concentration was assessed using intelligent test during normal lecturing section. The test showed significant correlation between the collected environmental parameters and the students' level of performances in their study.

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

Assessing the Value of Enhancing AirNow Data with NASA Satellite Data

NASA Astrophysics Data System (ADS)

Pasch, A. N.; Burke, B.; Huang, S.; Dye, T.; Dawes, S. S.; DeWinter, J. L.; Zahn, P. H.; Haderman, M.; Szykman, J.; White, J. E.; Dickerson, P.; van Donkelaar, A.; Martin, R.

2013-12-01

We will describe the methodology and findings from a study that addressed how satellite-enhanced air quality information provided through the U.S. Environmental Protection Agency's (EPA) AirNow Satellite Data Processor (ASDP) program could contribute to greater socioeconomic benefits. This study was funded by the National Aeronautics and Space Administration (NASA) and conducted, in partnership with the EPA, by the Center for Technology in Government at the University at Albany (CTG) and Sonoma Technology, Inc. (STI). AirNow is the national repository of real-time air quality data and forecasts for the United States. While mainly a public outreach and awareness tool, AirNow relies on the same network of ground-based air quality monitors that is used by federal, state, local, and tribal governments throughout the United States. Extensive as the monitoring network is, considerable gaps exist in certain parts of the United States. Even areas with monitors considered adequate for regulatory purposes can lack information needed to resolve localized air quality issues or give forecasters sufficient confidence about the potential air quality impact of specific events. Monitors are expensive to deploy and maintain; thus, EPA is seeking other ways to improve coverage and detail. Satellite-estimated data can provide information for many places where ground monitors do not exist, and supplement ground monitors, providing additional information for use in analysis and forecasting. ASDP uses satellite-derived estimates for fine-particle pollution (PM2.5) and provides coverage for a small window of time during the day. As satellite capabilities improve in terms of different types of sensors and increased coverage throughout the day, the ASDP program is prepared to extend its scope to additional pollutants and provide greater enhancements to the ground-based networks. In this study, CTG assessed the socioeconomic benefits of air quality data at a community level through three case studies in the Denver, Atlanta, and Kansas City regions by interviewing people at EPA regional offices, state environmental and public health agencies, local public health authorities, regional planning and non-profit outreach organizations, and universities. The interviews focused on the existing uses of air quality information and the potential value of incorporating NASA satellite-enhanced AirNow data to support and enhance the missions of the organizations interviewed. STI analyzed the economic benefit of using satellite data to fill in gaps in the current air quality monitoring network used to provide information to the public. This presentation will discuss how the findings can be used to improve estimation of the socioeconomic benefits derived from Earth observation science in policy and management decisions.

Outlier Detection in Urban Air Quality Sensor Networks.

PubMed

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

2018-01-01

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

A black carbon air quality network

NASA Astrophysics Data System (ADS)

Kirchstetter, T.; Caubel, J.; Cados, T.; Preble, C.; Rosen, A.

2016-12-01

We developed a portable, power efficient black carbon sensor for deployment in an air quality network in West Oakland, California. West Oakland is a San Francisco Bay Area residential/industrial community adjacent to regional port and rail yard facilities, and is surrounded by major freeways. As such, the community is affected by diesel particulate matter emissions from heavy-duty diesel trucks, locomotives, and ships associated with freight movement. In partnership with Environmental Defense Fund, the Bay Area Air Quality Management District, and the West Oakland Environmental Indicators Project, we are collaborating with community members to build and operate a 100-sensor black carbon measurement network for a period of several months. The sensor employs the filter-based light transmission method to measure black carbon. Each sensor node in the network transmits data hourly via SMS text messages. Cost, power consumption, and performance are considered in choosing components (e.g., pump) and operating conditions (e.g., sample flow rate). In field evaluation trials over several weeks at three monitoring locations, the sensor nodes provided black carbon concentrations comparable to commercial instruments and ran autonomously for a week before sample filters and rechargeable batteries needed to be replaced. Buildup to the 100-sensor network is taking place during Fall 2016 and will overlap with other ongoing air monitoring projects and monitoring platforms in West Oakland. Sensors will be placed along commercial corridors, adjacent to freeways, upwind of and within the Port, and throughout the residential community. Spatial and temporal black carbon concentration patterns will help characterize pollution sources and demonstrate the value of sensing networks for characterizing intra-urban air pollution concentrations and exposure to air pollution.

A state of the art regarding urban air quality prediction models

NASA Astrophysics Data System (ADS)

Croitoru, Cristiana; Nastase, Ilinca

2018-02-01

Urban pollution represents an increasing risk to residents of urban regions, particularly in large, over-industrialized cities knowing that the traffic is responsible for more than 25% of air gaseous pollutants and dust particles. Air quality modelling plays an important role in addressing air pollution control and management approaches by providing guidelines for better and more efficient air quality forecasting, along with smart monitoring sensor networks. The advances in technology regarding simulations, forecasting and monitoring are part of the new smart cities which offers a healthy environment for their occupants.

Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings.

PubMed

Challoner, Avril; Pilla, Francesco; Gill, Laurence

2015-12-01

NO₂ and particulate matter are the air pollutants of most concern in Ireland, with possible links to the higher respiratory and cardiovascular mortality and morbidity rates found in the country compared to the rest of Europe. Currently, air quality limits in Europe only cover outdoor environments yet the quality of indoor air is an essential determinant of a person's well-being, especially since the average person spends more than 90% of their time indoors. The modelling conducted in this research aims to provide a framework for epidemiological studies by the use of publically available data from fixed outdoor monitoring stations to predict indoor air quality more accurately. Predictions are made using two modelling techniques, the Personal-exposure Activity Location Model (PALM), to predict outdoor air quality at a particular building, and Artificial Neural Networks, to model the indoor/outdoor relationship of the building. This joint approach has been used to predict indoor air concentrations for three inner city commercial buildings in Dublin, where parallel indoor and outdoor diurnal monitoring had been carried out on site. This modelling methodology has been shown to provide reasonable predictions of average NO₂ indoor air quality compared to the monitored data, but did not perform well in the prediction of indoor PM2.5 concentrations. Hence, this approach could be used to determine NO₂ exposures more rigorously of those who work and/or live in the city centre, which can then be linked to potential health impacts.

Resolving uncertainties in the urban air quality, climate, and vegetation nexus through citizen science, satellite imagery, and atmospheric modeling

NASA Astrophysics Data System (ADS)

Jenerette, D.; Wang, J.; Chandler, M.; Ripplinger, J.; Koutzoukis, S.; Ge, C.; Castro Garcia, L.; Kucera, D.; Liu, X.

2017-12-01

Large uncertainties remain in identifying the distribution of urban air quality and temperature risks across neighborhood to regional scales. Nevertheless, many cities are actively expanding vegetation with an expectation to moderate both climate and air quality risks. We address these uncertainties through an integrated analysis of satellite data, atmospheric modeling, and in-situ environmental sensor networks maintained by citizen scientists. During the summer of 2017 we deployed neighborhood-scale networks of air temperature and ozone sensors through three campaigns across urbanized southern California. During each five-week campaign we deployed six sensor nodes that included an EPA federal equivalent method ozone sensor and a suite of meteorological sensors. Each node was further embedded in a network of 100 air temperature sensors that combined a randomized design developed by the research team and a design co-created by citizen scientists. Between 20 and 60 citizen scientists were recruited for each campaign, with local partners supporting outreach and training to ensure consistent deployment and data gathering. We observed substantial variation in both temperature and ozone concentrations at scales less than 4km, whole city, and the broader southern California region. At the whole city scale the average spatial variation with our ozone sensor network just for city of Long Beach was 26% of the mean, while corresponding variation in air temperature was only 7% of the mean. These findings contrast with atmospheric model estimates of variation at the regional scale of 11% and 1%. Our results show the magnitude of fine-scale variation underestimated by current models and may also suggest scaling functions that can connect neighborhood and regional variation in both ozone and temperature risks in southern California. By engaging citizen science with high quality sensors, satellite data, and real-time forecasting, our results help identify magnitudes of climate and air quality risk variation across scales and can guide individual decisions and urban policies surrounding vegetation to moderate these risks.

Mapping urban air quality in near real-time using observations from low-cost sensors and model information.

PubMed

Schneider, Philipp; Castell, Nuria; Vogt, Matthias; Dauge, Franck R; Lahoz, William A; Bartonova, Alena

2017-09-01

The recent emergence of low-cost microsensors measuring various air pollutants has significant potential for carrying out high-resolution mapping of air quality in the urban environment. However, the data obtained by such sensors are generally less reliable than that from standard equipment and they are subject to significant data gaps in both space and time. In order to overcome this issue, we present here a data fusion method based on geostatistics that allows for merging observations of air quality from a network of low-cost sensors with spatial information from an urban-scale air quality model. The performance of the methodology is evaluated for nitrogen dioxide in Oslo, Norway, using both simulated datasets and real-world measurements from a low-cost sensor network for January 2016. The results indicate that the method is capable of producing realistic hourly concentration fields of urban nitrogen dioxide that inherit the spatial patterns from the model and adjust the prior values using the information from the sensor network. The accuracy of the data fusion method is dependent on various factors including the total number of observations, their spatial distribution, their uncertainty (both in terms of systematic biases and random errors), as well as the ability of the model to provide realistic spatial patterns of urban air pollution. A validation against official data from air quality monitoring stations equipped with reference instrumentation indicates that the data fusion method is capable of reproducing city-wide averaged official values with an R 2 of 0.89 and a root mean squared error of 14.3 μg m -3 . It is further capable of reproducing the typical daily cycles of nitrogen dioxide. Overall, the results indicate that the method provides a robust way of extracting useful information from uncertain sensor data using only a time-invariant model dataset and the knowledge contained within an entire sensor network. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

CityAir app: Mapping air-quality perception using people as sensors

NASA Astrophysics Data System (ADS)

Castell, Nuria; Fredriksen, Mirjam; Cole-Hunter, Thomas; Robinson, Johanna; Keune, Hans; Nieuwenhuijsen, Mark; Bartonova, Alena

2016-04-01

Outdoor air pollution is a major environmental health problem affecting all people in developed and developing countries alike. Ambient (outdoor) air pollution in both cities and rural areas was estimated to cause 3.7 million premature deaths worldwide in 2012. In modern society, people are expending an increasing amount of time in polluted urban environments, thus increasing their exposure and associated health responses. Some cities provide information about air pollution levels to their citizens using air quality monitoring networks. However, due to their high cost and maintenance, the density of the monitoring networks is very low and not capable to capture the high temporal and spatial variability of air pollution. Thus, the citizen lacks a specific answer to the question of "how the air quality is in our surroundings". In the framework of the EU-funded CITI-SENSE project the innovative concept of People as Sensors is being applied to the field of outdoor air pollution. This is being done in eight European cities, including Barcelona, Belgrade, Edinburgh, Haifa, Ljubljana, Oslo, Ostrava and Vienna. People as Sensors defines a measurement model, in which measurements are not only taken by hardware sensors, but in which also humans can contribute with their individual "measurements" such as their subjective perception of air quality and other personal observations. In order to collect the personal observations a mobile app, CityAir, has been developed. CityAir allows citizens to rate the air quality in their surroundings with colour at their current location: green if air quality is very good, yellow if air quality is good, orange if air quality is poor and red if air quality is very poor. The users have also the possibility of indicating the source of pollution (i.e. traffic, industry, wood burning) and writing a comment. The information is on-line and accessible for other app users, thus contributing to create an air-quality map based on citizens' perception. Currently, 400 + Android OS and 180+ iOS smartphone users in 12+ countries have downloaded, installed and used CityAir. The central advantage of the People as Sensors approach is that it can complement costly physical sensor networks. The observations made in smartphones are shared and other persons can consult those to take decisions as for instance choosing a cleaner route to bicycle to work or avoid exercising in certain areas that day. The drawbacks are limited comparability and interpretability, and the inherent uncertainty. CityAir can be seen as a democratic platform for empowering citizens to contribute to environmental governance, facilitating the communication between the citizen and the decision makers. Citizens are encouraged to participate in sharing their perception on the air quality in their city. Citizens become agents of change by uncovering and sharing their perception of air quality in a place that matters to them. We discuss the current challenges: how to involve citizens in the use of the app and how to communicate and visualize the information in a way that is useful for the citizens; point out possible solutions, and pin-point directions for future research.

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.

Routing UAVs to Co-Optimize Mission Effectiveness and Network Performance with Dynamic Programming

DTIC Science & Technology

2011-03-01

Heuristics on Hexagonal Connected Dominating Sets to Model Routing Dissemination," in Communication Theory, Reliability, and Quality of Service (CTRQ...24] Matthew Capt. USAF Compton, Improving the Quality of Service and Security of Military Networks with a Network Tasking Order Process, 2010. [25...Wesley, 2006. [32] James Haught, "Adaptive Quality of Service Engine with Dynamic Queue Control," Air Force Institute of Technology, Wright

Deep learning architecture for air quality predictions.

PubMed

Li, Xiang; Peng, Ling; Hu, Yuan; Shao, Jing; Chi, Tianhe

2016-11-01

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.

Forecasting daily source air quality using multivariate statistical analysis and radial basis function networks.

PubMed

Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A

2008-12-01

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.

Of moss and men: Using moss as a bioindicator of toxic heavy metals at the city scale

Treesearch

Natasha Vizcarra; Sarah Jovan; Demetrios Gatziolis; Vicente Monleon

2018-01-01

Air quality is a critical issue affecting the health of billions of people worldwide, yet often little is known about what is in the air we breathe. To reduce air pollution’s health impacts, pollution sources must first be reliably identified. Otherwise, it is impossible to design and effectively enforce environmental standards. However, urban networks of air quality...

Coordinated profiling of stratospheric intrusions and transported pollution by the Tropospheric Ozone Lidar Network (TOLNet) and NASA Alpha Jet experiment (AJAX): Observations and comparison to HYSPLIT, RAQMS, and FLEXPART

NASA Astrophysics Data System (ADS)

Langford, A. O.; Alvarez, R. J.; Brioude, J.; Evan, S.; Iraci, L. T.; Kirgis, G.; Kuang, S.; Leblanc, T.; Newchurch, M. J.; Pierce, R. B.; Senff, C. J.; Yates, E. L.

2018-02-01

Ground-based lidars and ozonesondes belonging to the NASA-supported Tropospheric Ozone Lidar Network (TOLNet) are used in conjunction with the NASA Alpha Jet Atmospheric eXperiment (AJAX) to investigate the transport of stratospheric ozone and entrained pollution into the lower troposphere above the United States on May 24-25, 2013. TOLNet and AJAX measurements made in California, Nevada, and Alabama are compared to tropospheric ozone retrievals from the Atmospheric Infrared Sounder (AIRS), to back trajectories from the NOAA Air Resources Laboratory (ARL) Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, and to analyses from the NOAA/NESDIS Real-time Air Quality Modeling System (RAQMS) and FLEXPART particle dispersion model. The measurements and model analyses show much deeper descent of ozone-rich upper tropospheric/lower stratospheric air above the Desert Southwest than above the Southeast, and comparisons to surface measurements from regulatory monitors reporting to the U.S. EPA Air Quality System (AQS) suggest that there was a much greater surface impact in the Southwest including exceedances of the 2008 National Ambient Air Quality Standard (NAAQS) of 0.075 ppm in both Southern California and Nevada. Our analysis demonstrates the potential benefits to be gained by supplementing the existing surface ozone network with coordinated upper air observations by TOLNet.

ADVANCEMENTS IN SOURCE-TO-DOSE ANALYSIS OF POPULATION EXPOSURES TO OZONE

EPA Science Inventory

The current study takes advantage of the observations from regional air quality monitoring networks, the data from the NE-OPS (North East Oxidant and Particulate Study) Project in the Philadelphia region, and regional photochemical air quality model predictions to obtain and co...

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

40 CFR 58.15 - Annual air monitoring data certification.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 5 2010-07-01 2010-07-01 false Annual air monitoring data certification. 58.15 Section 58.15 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.15 Annual air monitoring data...

40 CFR 58.15 - Annual air monitoring data certification.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 5 2011-07-01 2011-07-01 false Annual air monitoring data certification. 58.15 Section 58.15 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.15 Annual air monitoring data...

Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings

PubMed Central

Challoner, Avril; Pilla, Francesco; Gill, Laurence

2015-01-01

NO2 and particulate matter are the air pollutants of most concern in Ireland, with possible links to the higher respiratory and cardiovascular mortality and morbidity rates found in the country compared to the rest of Europe. Currently, air quality limits in Europe only cover outdoor environments yet the quality of indoor air is an essential determinant of a person’s well-being, especially since the average person spends more than 90% of their time indoors. The modelling conducted in this research aims to provide a framework for epidemiological studies by the use of publically available data from fixed outdoor monitoring stations to predict indoor air quality more accurately. Predictions are made using two modelling techniques, the Personal-exposure Activity Location Model (PALM), to predict outdoor air quality at a particular building, and Artificial Neural Networks, to model the indoor/outdoor relationship of the building. This joint approach has been used to predict indoor air concentrations for three inner city commercial buildings in Dublin, where parallel indoor and outdoor diurnal monitoring had been carried out on site. This modelling methodology has been shown to provide reasonable predictions of average NO2 indoor air quality compared to the monitored data, but did not perform well in the prediction of indoor PM2.5 concentrations. Hence, this approach could be used to determine NO2 exposures more rigorously of those who work and/or live in the city centre, which can then be linked to potential health impacts. PMID:26633448

Quantifying the effect of air quality control measures during the 2010 Commonwealth Games at Delhi, India

NASA Astrophysics Data System (ADS)

Beig, Gufran; Chate, Dilip M.; Ghude, Sachin. D.; Mahajan, A. S.; Srinivas, R.; Ali, K.; Sahu, S. K.; Parkhi, N.; Surendran, D.; Trimbake, H. R.

2013-12-01

In 2010, the XIX Commonwealth Games (CWG-2010) were held in India for the first time at Delhi and involved 71 commonwealth nations and dependencies with more than 6000 athletes participating in 272 events. This was the largest international multi-sport event to be staged in India and strict emission controls were imposed during the games in order to ensure improved air quality for the participating athletes as a significant portion of the population in Delhi is regularly exposed to elevated levels of pollution. The air quality control measures ranged from vehicular and traffic controls to relocation of factories and reduction of power plant emissions. In order to understand the effects of these policy induced control measures, a network of air quality and weather monitoring stations was set-up across different areas in Delhi under the Government of India's System of Air quality Forecasting And Research (SAFAR) project. Simultaneous measurements of aerosols, reactive trace gases (e.g. NOx, O3, CO) and meteorological parameters were made before, during and after CWG-2010. Contrary to expectations, the emission controls implemented were not sufficient to reduce the pollutants, instead in some cases, causing an increase. The measured pollutants regularly exceeded the National Ambient Air Quality limits over the games period. The reasons for this increase are attributed to an underestimation of the required control measures, which resulted in inadequate planning. The results indicate that any future air quality control measures need to be well planned and strictly imposed in order to improve the air quality in Delhi, which affects a large population and is deteriorating rapidly. Thus, the presence of systematic high resolution data and realistic emission inventories through networks such as SAFAR will be directly useful for the future.

A multi-objective assessment of an air quality monitoring network using environmental, economic, and social indicators and GIS-based models.

PubMed

Pope, Ronald; Wu, Jianguo

2014-06-01

In the United States, air pollution is primarily measured by Air Quality Monitoring Networks (AQMN). These AQMNs have multiple objectives, including characterizing pollution patterns, protecting the public health, and determining compliance with air quality standards. In 2006, the U.S. Environmental Protection Agency issued a directive that air pollution agencies assess the performance of their AQMNs. Although various methods to design and assess AQMNs exist, here we demonstrate a geographic information system (GIS)-based approach that combines environmental, economic, and social indicators through the assessment of the ozone (O3) and particulate matter (PM10) networks in Maricopa County, Arizona. The assessment was conducted in three phases: (1) to evaluate the performance of the existing networks, (2) to identify areas that would benefit from the addition of new monitoring stations, and (3) to recommend changes to the AQMN. A comprehensive set of indicators was created for evaluating differing aspects of the AQMNs' objectives, and weights were applied to emphasize important indicators. Indicators were also classified according to their sustainable development goal. Our results showed that O3 was well represented in the county with some redundancy in terms of the urban monitors. The addition of weights to the indicators only had a minimal effect on the results. For O3, urban monitors had greater social scores, while rural monitors had greater environmental scores. The results did not suggest a need for adding more O3 monitoring sites. For PM10, clustered urban monitors were redundant, and weights also had a minimal effect on the results. The clustered urban monitors had overall low scores; sites near point sources had high environmental scores. Several areas were identified as needing additional PM10 monitors. This study demonstrates the usefulness of a multi-indicator approach to assess AQMNs. Network managers and planners may use this method to assess the performance of air quality monitoring networks in urban regions. The U.S. Environmental Protection Agency issued a directive in 2006 that air pollution agencies assess the performance of their AQMNs; as a result, we developed a GIS-based, multi-objective assessment approach that integrates environmental, economic, and social indicators, and demonstrates its use through assessing the O3 and PM10 monitoring networks in the Phoenix metropolitan area. We exhibit a method of assessing network performance and identifying areas that would benefit from new monitoring stations; also, we demonstrate the effect of adding weights to the indicators. Our study shows that using a multi-indicator approach gave detailed assessment results for the Phoenix AQMN.

Web Information Systems for Monitoring and Control of Indoor Air Quality at Subway Stations

NASA Astrophysics Data System (ADS)

Choi, Gi Heung; Choi, Gi Sang; Jang, Joo Hyoung

In crowded subway stations indoor air quality (IAQ) is a key factor for ensuring the safety, health and comfort of passengers. In this study, a framework for web-based information system in VDN environment for monitoring and control of IAQ in subway stations is suggested. Since physical variables that describing IAQ need to be closely monitored and controlled in multiple locations in subway stations, concept of distributed monitoring and control network using wireless media needs to be implemented. Connecting remote wireless sensor network and device (LonWorks) networks to the IP network based on the concept of VDN can provide a powerful, integrated, distributed monitoring and control performance, making a web-based information system possible.

40 CFR 58.13 - Monitoring network completion.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 5 2010-07-01 2010-07-01 false Monitoring network completion. 58.13... (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.13 Monitoring network completion. (a) The network of NCore multipollutant sites must be physically established no later than January 1, 2011...

40 CFR 58.13 - Monitoring network completion.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 5 2011-07-01 2011-07-01 false Monitoring network completion. 58.13... (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.13 Monitoring network completion. (a) The network of NCore multipollutant sites must be physically established no later than January 1, 2011...

RadNet Air Quality (Fixed Station) Data

EPA Pesticide Factsheets

RadNet is a national network of monitoring stations that regularly collect air for analysis of radioactivity. The RadNet network, which has stations in each State, has been used to track environmental releases of radioactivity from nuclear weapons tests and nuclear accidents. RadNet also documents the status and trends of environmental radioactivity

ASSESSMENT OF ETA-CMAQ FORECASTS OF PARTICULATE MATTER DISTRIBUTIONS THROUGH COMPARISONS WITH SURFACE NETWORK AND SPECIALIZED MEASUREMENTS

EPA Science Inventory

An air-quality forecasting (AQF) system based on the National Weather Service (NWS) National Centers for Environmental Prediction's (NCEP's) Eta model and the U.S. EPA's Community Multiscale Air Quality (CMAQ) Modeling System is used to simulate the distributions of tropospheric ...

40 CFR 58.11 - Network technical requirements.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 5 2010-07-01 2010-07-01 false Network technical requirements. 58.11... (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.11 Network technical requirements. (a)(1... A to this part when operating the SLAMS networks. (2) Beginning January 1, 2009, State and local...

40 CFR 58.11 - Network technical requirements.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 5 2011-07-01 2011-07-01 false Network technical requirements. 58.11... (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.11 Network technical requirements. (a)(1... A to this part when operating the SLAMS networks. (2) Beginning January 1, 2009, State and local...

The GCOS Reference Upper-Air Network (GRUAN)

NASA Astrophysics Data System (ADS)

Vömel, H.; Berger, F. H.; Immler, F. J.; Seidel, D.; Thorne, P.

2009-04-01

While the global upper-air observing network has provided useful observations for operational weather forecasting for decades, its measurements lack the accuracy and long-term continuity needed for understanding climate change. Consequently, the scientific community faces uncertainty on such key issues as the trends of temperature in the upper troposphere and stratosphere or the variability and trends of stratospheric water vapour. To address these shortcomings, and to ensure that future climate records will be more useful than the records to date, the Global Climate Observing System (GCOS) program initiated the GCOS Reference Upper Air Network (GRUAN). GRUAN will be a network of about 30-40 observatories with a representative sampling of geographic regions and surface types. These stations will provide upper-air reference observations of the essential climate variables, i.e. temperature, geopotential, humidity, wind, radiation and cloud properties using specialized radiosondes and complementary remote sensing profiling instrumentation. Long-term stability, quality assurance / quality control, and a detailed assessment of measurement uncertainties will be the key aspects of GRUAN observations. The network will not be globally complete but will serve to constrain and adjust data from more spatially comprehensive global observing systems including satellites and the current radiosonde networks. This paper outlines the scientific rationale for GRUAN, its role in the Global Earth Observation System of Systems, network requirements and likely instrumentation, management structure, current status and future plans.

Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm

NASA Astrophysics Data System (ADS)

Hao, Yufang; Xie, Shaodong

2018-03-01

Air quality monitoring networks play a significant role in identifying the spatiotemporal patterns of air pollution, and they need to be deployed efficiently, with a minimum number of sites. The revision and optimal adjustment of existing monitoring networks is crucial for cities that have undergone rapid urban expansion and experience temporal variations in pollution patterns. The approach based on the Weather Research and Forecasting-California PUFF (WRF-CALPUFF) model and genetic algorithm (GA) was developed to design an optimal monitoring network. The maximization of coverage with minimum overlap and the ability to detect violations of standards were developed as the design objectives for redistributed networks. The non-dominated sorting genetic algorithm was applied to optimize the network size and site locations simultaneously for Shijiazhuang city, one of the most polluted cities in China. The assessment on the current network identified the insufficient spatial coverage of SO2 and NO2 monitoring for the expanding city. The optimization results showed that significant improvements were achieved in multiple objectives by redistributing the original network. Efficient coverage of the resulting designs improved to 60.99% and 76.06% of the urban area for SO2 and NO2, respectively. The redistributing design for multi-pollutant including 8 sites was also proposed, with the spatial representation covered 52.30% of the urban area and the overlapped areas decreased by 85.87% compared with the original network. The abilities to detect violations of standards were not improved as much as the other two objectives due to the conflicting nature between the multiple objectives. Additionally, the results demonstrated that the algorithm was slightly sensitive to the parameter settings, with the number of generations presented the most significant effect. Overall, our study presents an effective and feasible procedure for air quality network optimization at a city scale.

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian

2005-04-01

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.

Local-Scale Air Quality Modeling in Support of Human Health and Exposure Research (Invited)

NASA Astrophysics Data System (ADS)

Isakov, V.

2010-12-01

Spatially- and temporally-sparse information on air quality is a key concern for air-pollution-related environmental health studies. Monitor networks are sparse in both space and time, are costly to maintain, and are often designed purposely to avoid detecting highly localized sources. Recent studies have shown that more narrowly defining the geographic domain of the study populations and improvements in the measured/estimated ambient concentrations can lead to stronger associations between air pollution and hospital admissions and mortality records. Traditionally, ambient air quality measurements have been used as a primary input to support human health and exposure research. However, there is increasing evidence that the current ambient monitoring network is not capturing sharp gradients in exposure due to the presence of high concentration levels near, for example, major roadways. Many air pollutants exhibit large concentration gradients near large emitters such as major roadways, factories, ports, etc. To overcome these limitations, researchers are now beginning to use air quality models to support air pollution exposure and health studies. There are many advantages to using air quality models over traditional approaches based on existing ambient measurements alone. First, models can provide spatially- and temporally-resolved concentrations as direct input to exposure and health studies and thus better defining the concentration levels for the population in the geographic domain. Air quality models have a long history of use in air pollution regulations, and supported by regulatory agencies and a large user community. Also, models can provide bidirectional linkages between sources of emissions and ambient concentrations, thus allowing exploration of various mitigation strategies to reduce risk to exposure. In order to provide best estimates of air concentrations to support human health and exposure studies, model estimates should consider local-scale features, regional-scale transport, and photochemical transformations. Since these needs are currently not met by a single model, hybrid air quality modeling has recently been developed to combine these capabilities. In this paper, we present the results of two studies where we applied the hybrid modeling approach to provide spatial and temporal details in air quality concentrations to support exposure and health studies: a) an urban-scale air quality accountability study involving near-source exposures to multiple ambient air pollutants, and b) an urban-scale epidemiological study involving human health data based on emergency department visits.

Mobile Sensors and Applications for Air Pollutants

EPA Science Inventory

Executive Summary The public has long been interested in understanding what pollutants are in the air they breathe so they can best protect their environmental health and welfare. The current air quality monitoring network consists of discrete stations with expensive equipment ...

76 FR 54293 - Review of National Ambient Air Quality Standards for Carbon Monoxide

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-31

...This rule is being issued at this time as required by a court order governing the schedule for completion of this review of the air quality criteria and the national ambient air quality standards (NAAQS) for carbon monoxide (CO). Based on its review, the EPA concludes the current primary standards are requisite to protect public health with an adequate margin of safety, and is retaining those standards. After review of the air quality criteria, EPA further concludes that no secondary standard should be set for CO at this time. EPA is also making changes to the ambient air monitoring requirements for CO, including those related to network design, and is updating, without substantive change, aspects of the Federal reference method.

Enhancing wind erosion monitoring and assessment for U.S. rangelands

USGS Publications Warehouse

Webb, Nicholas P.; Van Zee, Justin W.; Karl, Jason W.; Herrick, Jeffrey E.; Courtright, Ericha M.; Billings, Benjamin J.; Boyd, Robert C.; Chappell, Adrian; Duniway, Michael C.; Derner, Justin D.; Hand, Jenny L.; Kachergis, Emily; McCord, Sarah E.; Newingham, Beth A.; Pierson, Frederick B.; Steiner, Jean L.; Tatarko, John; Tedela, Negussie H.; Toledo, David; Van Pelt, R. Scott

2017-01-01

On the GroundWind erosion is a major resource concern for rangeland managers because it can impact soil health, ecosystem structure and function, hydrologic processes, agricultural production, and air quality.Despite its significance, little is known about which landscapes are eroding, by how much, and when.The National Wind Erosion Research Network was established in 2014 to develop tools for monitoring and assessing wind erosion and dust emissions across the United States.The Network, currently consisting of 13 sites, creates opportunities to enhance existing rangeland soil, vegetation, and air quality monitoring programs.Decision-support tools developed by the Network will improve the prediction and management of wind erosion across rangeland ecosystems.

78 FR 5346 - Approval and Promulgation of Air Quality Implementation Plans; Massachusetts and New Hampshire...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-25

...-FRL-9754-7] Approval and Promulgation of Air Quality Implementation Plans; Massachusetts and New... (EPA). ACTION: Proposed rule. SUMMARY: EPA is proposing to approve State Implementation Plan (SIP... repair network for an on-board diagnostic (OBD2) testing program for model year 1996 and newer vehicles...

Real-Time ambient carbon monoxide and ultrafine particle concentration mapping in a near-road environment

EPA Science Inventory

Ambient air quality has traditionally been monitored using a network of fixed point sampling sites that are strategically placed to represent regional (e.g., county or town) rather than local (e.g., neighborhood) air quality trends. This type of monitoring data has been used to m...

Learning Principal Component Analysis by Using Data from Air Quality Networks

ERIC Educational Resources Information Center

Perez-Arribas, Luis Vicente; Leon-González, María Eugenia; Rosales-Conrado, Noelia

2017-01-01

With the final objective of using computational and chemometrics tools in the chemistry studies, this paper shows the methodology and interpretation of the Principal Component Analysis (PCA) using pollution data from different cities. This paper describes how students can obtain data on air quality and process such data for additional information…

40 CFR 58.14 - System modification.

Code of Federal Regulations, 2010 CFR

2010-07-01

...) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.14 System modification. (a) The State, or where... monitoring network that complies with the findings of the network assessments required every 5 years by § 58... schedule with respect to the SLAMS network are subject to the approval of the EPA Regional Administrator...

40 CFR 58.14 - System modification.

Code of Federal Regulations, 2011 CFR

2011-07-01

...) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.14 System modification. (a) The State, or where... monitoring network that complies with the findings of the network assessments required every 5 years by § 58... schedule with respect to the SLAMS network are subject to the approval of the EPA Regional Administrator...

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.

Development of wireless sensor network for monitoring indoor air pollutant

NASA Astrophysics Data System (ADS)

Saad, Shaharil Mad; Shakaff, Ali Yeon Md; Saad, Abdul Rahman Mohd; Yusof @ Kamarudin, Azman Muhamad

2015-05-01

The air that we breathe with everyday contains variety of contaminants and particles. Some of these contaminants and particles are hazardous to human health. Most of the people don't realize that the content of air they being exposed to whether it was a good or bad air quality. The air quality whether in indoor or outdoor environment can be influenced by physical factors like dust particles, gaseous pollutants (including carbon dioxide, carbon monoxide and volatile organic compounds) and biological like molds and bacteria growth which largely depend on temperature and humidity condition of a room. These kinds of pollutants can affect human health, physical reaction, comfort or work performance. In this study, a wireless sensor network (WSN) monitoring system for monitor air pollutant in indoor environment was developed. The system was divided into three parts: web-based interface program, sensing module and a base station. The measured data was displayed on the web which is can be accessed by the user. The result shows that the overall measured parameters were meet the acceptable limit, requirement and criteria of indoor air pollution inside the building. The research can be used to improve the indoor air quality level in order to create a comfortable working and healthy environment for the occupants inside the building.

Air quality measurements-From rubber bands to tapping the rainbow.

PubMed

Hidy, George M; Mueller, Peter K; Altshuler, Samuel L; Chow, Judith C; Watson, John G

2017-06-01

It is axiomatic that good measurements are integral to good public policy for environmental protection. The generalized term for "measurements" includes sampling and quantitation, data integrity, documentation, network design, sponsorship, operations, archiving, and accessing for applications. Each of these components has evolved and advanced over the last 200 years as knowledge of atmospheric chemistry and physics has matured. Air quality was first detected by what people could see and smell in contaminated air. Gaseous pollutants were found to react with certain materials or chemicals, changing the color of dissolved reagents such that their light absorption at selected wavelengths could be related to both the pollutant chemistry and its concentration. Airborne particles have challenged the development of a variety of sensory devices and laboratory assays for characterization of their enormous range of physical and chemical properties. Advanced electronics made possible the sampling, concentration, and detection of gases and particles, both in situ and in laboratory analysis of collected samples. Accurate and precise measurements by these methods have made possible advanced air quality management practices that led to decreasing concentrations over time. New technologies are leading to smaller and cheaper measurement systems that can further expand and enhance current air pollution monitoring networks. Ambient air quality measurement systems have a large influence on air quality management by determining compliance, tracking trends, elucidating pollutant transport and transformation, and relating concentrations to adverse effects. These systems consist of more than just instrumentation, and involve extensive support efforts for siting, maintenance, calibration, auditing, data validation, data management and access, and data interpretation. These requirements have largely been attained for criteria pollutants regulated by National Ambient Air Quality Standards, but they are rarely attained for nonroutine measurements and research studies.

Meet EPA Researcher Rachelle Duvall, Ph.D.

EPA Pesticide Factsheets

EPA Research Physical Scientist Dr. Rachelle Duvall works with equipment that measures air pollution—evaluating, testing, and approving of methods that are used in air quality monitoring networks across the U.S.

The Imperial County Community Air Monitoring Network: A Model for Community-based Environmental Monitoring for Public Health Action

PubMed Central

Olmedo, Luis; Bejarano, Ester; Lugo, Humberto; Murillo, Eduardo; Seto, Edmund; Wong, Michelle; King, Galatea; Wilkie, Alexa; Meltzer, Dan; Carvlin, Graeme; Jerrett, Michael; Northcross, Amanda

2017-01-01

Summary: The Imperial County Community Air Monitoring Network (the Network) is a collaborative group of community, academic, nongovernmental, and government partners designed to fill the need for more detailed data on particulate matter in an area that often exceeds air quality standards. The Network employs a community-based environmental monitoring process in which the community and researchers have specific, well-defined roles as part of an equitable partnership that also includes shared decision-making to determine study direction, plan research protocols, and conduct project activities. The Network is currently producing real-time particulate matter data from 40 low-cost sensors throughout Imperial County, one of the largest community-based air networks in the United States. Establishment of a community-led air network involves engaging community members to be citizen-scientists in the monitoring, siting, and data collection process. Attention to technical issues regarding instrument calibration and validation and electronic transfer and storage of data is also essential. Finally, continued community health improvements will be predicated on facilitating community ownership and sustainability of the network after research funds have been expended. https://doi.org/10.1289/EHP1772 PMID:28886604

Analysis of feature selection with Probabilistic Neural Network (PNN) to classify sources influencing indoor air quality

NASA Astrophysics Data System (ADS)

Saad, S. M.; Shakaff, A. Y. M.; Saad, A. R. M.; Yusof, A. M.; Andrew, A. M.; Zakaria, A.; Adom, A. H.

2017-03-01

There are various sources influencing indoor air quality (IAQ) which could emit dangerous gases such as carbon monoxide (CO), carbon dioxide (CO2), ozone (O3) and particulate matter. These gases are usually safe for us to breathe in if they are emitted in safe quantity but if the amount of these gases exceeded the safe level, they might be hazardous to human being especially children and people with asthmatic problem. Therefore, a smart indoor air quality monitoring system (IAQMS) is needed that able to tell the occupants about which sources that trigger the indoor air pollution. In this project, an IAQMS that able to classify sources influencing IAQ has been developed. This IAQMS applies a classification method based on Probabilistic Neural Network (PNN). It is used to classify the sources of indoor air pollution based on five conditions: ambient air, human activity, presence of chemical products, presence of food and beverage, and presence of fragrance. In order to get good and best classification accuracy, an analysis of several feature selection based on data pre-processing method is done to discriminate among the sources. The output from each data pre-processing method has been used as the input for the neural network. The result shows that PNN analysis with the data pre-processing method give good classification accuracy of 99.89% and able to classify the sources influencing IAQ high classification rate.

Evaluation and intercomparison of five major dry deposition ...

EPA Pesticide Factsheets

Dry deposition of various pollutants needs to be quantified in air quality monitoring networks as well as in chemical transport models. The inferential method is the most commonly used approach in which the dry deposition velocity (Vd) is empirically parameterized as a function of meteorological and biological conditions and pollutant species’ chemical properties. Earlier model intercomparison studies suggested that existing dry deposition algorithms produce quite different Vd values, e.g., up to a factor of 2 for monthly to annual average values for ozone, and sulfur and nitrogen species (Flechard et al., 2011; Schwede et al., 2011; Wu et al., 2011). To further evaluate model discrepancies using available flux data, this study compared the five dry deposition algorithms commonly used in North America and evaluated the models using five-year Vd(O3) and Vd(SO2) data generated from concentration gradient measurements above a temperate mixed forest in Canada. The five algorithms include: (1) the one used in the Canadian Air and Precipitation Monitoring Network (CAPMoN) and several Canadian air quality models based on Zhang et al. (2003), (2) the one used in the US Clean Air Status and Trends Network (CASTNET) based on Meyers et al. (1998), (3) the one used in the Community Multiscale Air Quality (CMAQ) model described in Pleim and Ran (2011), (4) the Noah land surface model coupled with a photosynthesis-based Gas Exchange Model (Noah-GEM) described in Wu et a

Air quality assessment of Estarreja, an urban industrialized area, in a coastal region of Portugal.

PubMed

Figueiredo, M L; Monteiro, A; Lopes, M; Ferreira, J; Borrego, C

2013-07-01

Despite the increasing concern given to air quality in urban and industrial areas in recent years, particular emphasis on regulation, control, and reduction of air pollutant emissions is still necessary to fully characterize the chain emissions-air quality-exposure-dose-health effects, for specific sources. The Estarreja region was selected as a case study because it has one of the largest chemical industrial complexes in Portugal that has been recently expanded, together with a growing urban area with an interesting location in the Portuguese coastland and crossed by important road traffic and rail national networks. This work presents the first air quality assessment for the region concerning pollutant emissions and meteorological and air quality monitoring data analysis, over the period 2000-2009. This assessment also includes a detailed investigation and characterization of past air pollution episodes for the most problematic pollutants: ozone and PM10. The contribution of different emission sources and meteorological conditions to these episodes is investigated. The stagnant meteorological conditions associated with local emissions, namely industrial activity and road traffic, are the major contributors to the air quality degradation over the study region. A set of measures to improve air quality--regarding ozone and PM10 levels--is proposed as an air quality management strategy for the study region.

Classification of air quality using fuzzy synthetic multiplication.

PubMed

Abdullah, Lazim; Khalid, Noor Dalina

2012-11-01

Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.

Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki.

PubMed

Voukantsis, Dimitris; Karatzas, Kostas; Kukkonen, Jaakko; Räsänen, Teemu; Karppinen, Ari; Kolehmainen, Mikko

2011-03-01

In this paper we propose a methodology consisting of specific computational intelligence methods, i.e. principal component analysis and artificial neural networks, in order to inter-compare air quality and meteorological data, and to forecast the concentration levels for environmental parameters of interest (air pollutants). We demonstrate these methods to data monitored in the urban areas of Thessaloniki and Helsinki in Greece and Finland, respectively. For this purpose, we applied the principal component analysis method in order to inter-compare the patterns of air pollution in the two selected cities. Then, we proceeded with the development of air quality forecasting models for both studied areas. On this basis, we formulated and employed a novel hybrid scheme in the selection process of input variables for the forecasting models, involving a combination of linear regression and artificial neural networks (multi-layer perceptron) models. The latter ones were used for the forecasting of the daily mean concentrations of PM₁₀ and PM₂.₅ for the next day. Results demonstrated an index of agreement between measured and modelled daily averaged PM₁₀ concentrations, between 0.80 and 0.85, while the kappa index for the forecasting of the daily averaged PM₁₀ concentrations reached 60% for both cities. Compared with previous corresponding studies, these statistical parameters indicate an improved performance of air quality parameters forecasting. It was also found that the performance of the models for the forecasting of the daily mean concentrations of PM₁₀ was not substantially different for both cities, despite the major differences of the two urban environments under consideration. Copyright © 2011 Elsevier B.V. All rights reserved.

A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems

PubMed Central

Yi, Wei Ying; Lo, Kin Ming; Mak, Terrence; Leung, Kwong Sak; Leung, Yee; Meng, Mei Ling

2015-01-01

The air quality in urban areas is a major concern in modern cities due to significant impacts of air pollution on public health, global environment, and worldwide economy. Recent studies reveal the importance of micro-level pollution information, including human personal exposure and acute exposure to air pollutants. A real-time system with high spatio-temporal resolution is essential because of the limited data availability and non-scalability of conventional air pollution monitoring systems. Currently, researchers focus on the concept of The Next Generation Air Pollution Monitoring System (TNGAPMS) and have achieved significant breakthroughs by utilizing the advance sensing technologies, MicroElectroMechanical Systems (MEMS) and Wireless Sensor Network (WSN). However, there exist potential problems of these newly proposed systems, namely the lack of 3D data acquisition ability and the flexibility of the sensor network. In this paper, we classify the existing works into three categories as Static Sensor Network (SSN), Community Sensor Network (CSN) and Vehicle Sensor Network (VSN) based on the carriers of the sensors. Comprehensive reviews and comparisons among these three types of sensor networks were also performed. Last but not least, we discuss the limitations of the existing works and conclude the objectives that we want to achieve in future systems. PMID:26703598

A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems.

PubMed

Yi, Wei Ying; Lo, Kin Ming; Mak, Terrence; Leung, Kwong Sak; Leung, Yee; Meng, Mei Ling

2015-12-12

The air quality in urban areas is a major concern in modern cities due to significant impacts of air pollution on public health, global environment, and worldwide economy. Recent studies reveal the importance of micro-level pollution information, including human personal exposure and acute exposure to air pollutants. A real-time system with high spatio-temporal resolution is essential because of the limited data availability and non-scalability of conventional air pollution monitoring systems. Currently, researchers focus on the concept of The Next Generation Air Pollution Monitoring System (TNGAPMS) and have achieved significant breakthroughs by utilizing the advance sensing technologies, MicroElectroMechanical Systems (MEMS) and Wireless Sensor Network (WSN). However, there exist potential problems of these newly proposed systems, namely the lack of 3D data acquisition ability and the flexibility of the sensor network. In this paper, we classify the existing works into three categories as Static Sensor Network (SSN), Community Sensor Network (CSN) and Vehicle Sensor Network (VSN) based on the carriers of the sensors. Comprehensive reviews and comparisons among these three types of sensor networks were also performed. Last but not least, we discuss the limitations of the existing works and conclude the objectives that we want to achieve in future systems.

End-user perspective of low-cost sensors for outdoor air pollution monitoring.

PubMed

Rai, Aakash C; Kumar, Prashant; Pilla, Francesco; Skouloudis, Andreas N; Di Sabatino, Silvana; Ratti, Carlo; Yasar, Ansar; Rickerby, David

2017-12-31

Low-cost sensor technology can potentially revolutionise the area of air pollution monitoring by providing high-density spatiotemporal pollution data. Such data can be utilised for supplementing traditional pollution monitoring, improving exposure estimates, and raising community awareness about air pollution. However, data quality remains a major concern that hinders the widespread adoption of low-cost sensor technology. Unreliable data may mislead unsuspecting users and potentially lead to alarming consequences such as reporting acceptable air pollutant levels when they are above the limits deemed safe for human health. This article provides scientific guidance to the end-users for effectively deploying low-cost sensors for monitoring air pollution and people's exposure, while ensuring reasonable data quality. We review the performance characteristics of several low-cost particle and gas monitoring sensors and provide recommendations to end-users for making proper sensor selection by summarizing the capabilities and limitations of such sensors. The challenges, best practices, and future outlook for effectively deploying low-cost sensors, and maintaining data quality are also discussed. For data quality assurance, a two-stage sensor calibration process is recommended, which includes laboratory calibration under controlled conditions by the manufacturer supplemented with routine calibration checks performed by the end-user under final deployment conditions. For large sensor networks where routine calibration checks are impractical, statistical techniques for data quality assurance should be utilised. Further advancements and adoption of sophisticated mathematical and statistical techniques for sensor calibration, fault detection, and data quality assurance can indeed help to realise the promised benefits of a low-cost air pollution sensor network. Copyright © 2017 Elsevier B.V. All rights reserved.

Evaluation of near surface ozone and particulate matter in air ...

EPA Pesticide Factsheets

In this study, techniques typically used for future air quality projections are applied to a historical 11-year period to assess the performance of the modeling system when the driving meteorological conditions are obtained using dynamical downscaling of coarse-scale fields without correcting toward higher-resolution observations. The Weather Research and Forecasting model and the Community Multiscale Air Quality model are used to simulate regional climate and air quality over the contiguous United States for 2000–2010. The air quality simulations for that historical period are then compared to observations from four national networks. Comparisons are drawn between defined performance metrics and other published modeling results for predicted ozone, fine particulate matter, and speciated fine particulate matter. The results indicate that the historical air quality simulations driven by dynamically downscaled meteorology are typically within defined modeling performance benchmarks and are consistent with results from other published modeling studies using finer-resolution meteorology. This indicates that the regional climate and air quality modeling framework utilized here does not introduce substantial bias, which provides confidence in the method’s use for future air quality projections. This paper shows that if emissions inputs and coarse-scale meteorological inputs are reasonably accurate, then air quality can be simulated with acceptable accuracy even wi

40 CFR 58.12 - Operating schedules.

Code of Federal Regulations, 2010 CFR

2010-07-01

...) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.12 Operating schedules. State and local... part. Area-specific PAMS operating schedules must be included as part of the PAMS network description... remains once every six days. No less frequently than as part of each 5-year network assessment, the most...

40 CFR 58.12 - Operating schedules.

Code of Federal Regulations, 2011 CFR

2011-07-01

...) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.12 Operating schedules. State and local... part. Area-specific PAMS operating schedules must be included as part of the PAMS network description... remains once every six days. No less frequently than as part of each 5-year network assessment, the most...

40 CFR 52.70 - Identification of plan.

Code of Federal Regulations, 2011 CFR

2011-07-01

... nonattainment areas submitted by the Governor of Alaska on January 18, 1980 as follows: Volume II. Analysis of...: Volume II. Analysis of Problems, Control Actions Section V. Ambient Air Monitoring A. Purpose C. Air Monitoring Network E. Annual Review (9) Provisions of a State Air Quality Control Plan submitted by the...

40 CFR 52.70 - Identification of plan.

Code of Federal Regulations, 2012 CFR

2012-07-01

... nonattainment areas submitted by the Governor of Alaska on January 18, 1980 as follows: Volume II. Analysis of...: Volume II. Analysis of Problems, Control Actions Section V. Ambient Air Monitoring A. Purpose C. Air Monitoring Network E. Annual Review (9) Provisions of a State Air Quality Control Plan submitted by the...

QUALITY ASSURANCE PERFORMANCE AUDIT REPORT FOR THE SECRETARIA DEL MEDIO AMBIENTE CIUDAD DE MEXICO, DF, MEXICO RED AUTOMATICA DE MONITOREO ATMOSFERICO (RAMA) AIR QUALITY MONITORING STATIONS

EPA Science Inventory

The United States Environmental Protection Agency (U.S. EPA) conducted this evaluation of the air monitoring network, known as RAM (Red Automatica de Monitoreo Atmosferico) at the request of the Mexico City Secretariat of the Environment on October 16-27, 2000. This evaluation...

Low-Cost Sensor Units for Measuring Urban Air Quality

NASA Astrophysics Data System (ADS)

Popoola, O. A.; Mead, M.; Stewart, G.; Hodgson, T.; McLoed, M.; Baldovi, J.; Landshoff, P.; Hayes, M.; Calleja, M.; Jones, R.

2010-12-01

Measurements of selected key air quality gases (CO, NO & NO2) have been made with a range of miniature low-cost sensors based on electrochemical gas sensing technology incorporating GPS and GPRS for position and communication respectively. Two types of simple to operate sensors units have been designed to be deployed in relatively large numbers. Mobile handheld sensor units designed for operation by members of the public have been deployed on numerous occasions including in Cambridge, London and Valencia. Static sensor units have also been designed for long-term autonomous deployment on existing street furniture. A study was recently completed in which 45 sensor units were deployed in the Cambridge area for a period of 3 months. Results from these studies indicate that air quality varies widely both spatially and temporally. The widely varying concentrations found suggest that the urban environment cannot be fully understood using limited static site (AURN) networks and that a higher resolution, more dispersed network is required to better define air quality in the urban environment. The results also suggest that higher spatial and temporal resolution measurements could improve knowledge of the levels of individual exposure in the urban environment.

78 FR 57631 - Information Collection Request Submitted to OMB for Review and Approval; Comment Request; Ambient...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-19

..., tribal entities, environmental groups, academic institutions, industrial groups) use the ambient air... System (AQS) database. Quality assurance/quality control records and monitoring network documentation are...

Correlation of gravestone decay and air quality 1960-2010

NASA Astrophysics Data System (ADS)

Mooers, H. D.; Carlson, M. J.; Harrison, R. M.; Inkpen, R. J.; Loeffler, S.

2017-03-01

Evaluation of spatial and temporal variability in surface recession of lead-lettered Carrara marble gravestones provides a quantitative measure of acid flux to the stone surfaces and is closely related to local land use and air quality. Correlation of stone decay, land use, and air quality for the period after 1960 when reliable estimates of atmospheric pollution are available is evaluated. Gravestone decay and SO2 measurements are interpolated spatially using deterministic and geostatistical techniques. A general lack of spatial correlation was identified and therefore a land-use-based technique for correlation of stone decay and air quality is employed. Decadally averaged stone decay is highly correlated with land use averaged spatially over an optimum radius of ≈7 km even though air quality, determined by records from the UK monitoring network, is not highly correlated with gravestone decay. The relationships among stone decay, air-quality, and land use is complicated by the relatively low spatial density of both gravestone decay and air quality data and the fact that air quality data is available only as annual averages and therefore seasonal dependence cannot be evaluated. However, acid deposition calculated from gravestone decay suggests that the deposition efficiency of SO2 has increased appreciably since 1980 indicating an increase in the SO2 oxidation process possibly related to reactions with ammonia.

A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction.

PubMed

Yang, Zhongshan; Wang, Jian

2017-10-01

Air pollution in many countries is worsening with industrialization and urbanization, resulting in climate change and affecting people's health, thus, making the work of policymakers more difficult. It is therefore both urgent and necessary to establish amore scientific air quality monitoring and early warning system to evaluate the degree of air pollution objectively, and predict pollutant concentrations accurately. However, the integration of air quality assessment and air pollutant concentration prediction to establish an air quality system is not common. In this paper, we propose a new air quality monitoring and early warning system, including an assessment module and forecasting module. In the air quality assessment module, fuzzy comprehensive evaluation is used to determine the main pollutants and evaluate the degree of air pollution more scientifically. In the air pollutant concentration prediction module, a novel hybridization model combining complementary ensemble empirical mode decomposition, a modified cuckoo search and differential evolution algorithm, and an Elman neural network, is proposed to improve the forecasting accuracy of six main air pollutant concentrations. To verify the effectiveness of this system, pollutant data for two cities in China are used. The result of the fuzzy comprehensive evaluation shows that the major air pollutants in Xi'an and Jinan are PM 10 and PM 2.5 respectively, and that the air quality of Xi'an is better than that of Jinan. The forecasting results indicate that the proposed hybrid model is remarkably superior to all benchmark models on account of its higher prediction accuracy and stability. Copyright © 2017 Elsevier Inc. All rights reserved.

Monitoring the effects of air-quality on forests: An overview of the Sierra Ancha Experimental Forest ICP-Level II Site

Treesearch

Peter E. Koestner; Karen A. Koestner; Daniel G. Neary

2012-01-01

The Sierra Ancha International Cooperative Program on Assessment and Monitoring of Air Pollution Effects on Forests study site or (SAEF-ICP II) is part of an international network of cooperative forest monitoring sites spread throughout Europe and the United States. The United Nations Economic Commission for Europe established the ICP II network in 1985 to monitor long...

The use of hierarchical clustering for the design of optimized monitoring networks

NASA Astrophysics Data System (ADS)

Soares, Joana; Makar, Paul Andrew; Aklilu, Yayne; Akingunola, Ayodeji

2018-05-01

Associativity analysis is a powerful tool to deal with large-scale datasets by clustering the data on the basis of (dis)similarity and can be used to assess the efficacy and design of air quality monitoring networks. We describe here our use of Kolmogorov-Zurbenko filtering and hierarchical clustering of NO2 and SO2 passive and continuous monitoring data to analyse and optimize air quality networks for these species in the province of Alberta, Canada. The methodology applied in this study assesses dissimilarity between monitoring station time series based on two metrics: 1 - R, R being the Pearson correlation coefficient, and the Euclidean distance; we find that both should be used in evaluating monitoring site similarity. We have combined the analytic power of hierarchical clustering with the spatial information provided by deterministic air quality model results, using the gridded time series of model output as potential station locations, as a proxy for assessing monitoring network design and for network optimization. We demonstrate that clustering results depend on the air contaminant analysed, reflecting the difference in the respective emission sources of SO2 and NO2 in the region under study. Our work shows that much of the signal identifying the sources of NO2 and SO2 emissions resides in shorter timescales (hourly to daily) due to short-term variation of concentrations and that longer-term averages in data collection may lose the information needed to identify local sources. However, the methodology identifies stations mainly influenced by seasonality, if larger timescales (weekly to monthly) are considered. We have performed the first dissimilarity analysis based on gridded air quality model output and have shown that the methodology is capable of generating maps of subregions within which a single station will represent the entire subregion, to a given level of dissimilarity. We have also shown that our approach is capable of identifying different sampling methodologies as well as outliers (stations' time series which are markedly different from all others in a given dataset).

Improving of local ozone forecasting by integrated models.

PubMed

Gradišar, Dejan; Grašič, Boštjan; Božnar, Marija Zlata; Mlakar, Primož; Kocijan, Juš

2016-09-01

This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.

TOLNet ozone lidar intercomparison during the discover-aq and frappé campaigns

NASA Astrophysics Data System (ADS)

Newchurch, Michael J.; Alvarez, Raul J.; Berkoff, Timothy A.; Carrion, William; DeYoung, Russell J.; Ganoe, Rene; Gronoff, Guillaume; Kirgis, Guillaume; Kuang, Shi; Langford, Andy O.; Leblanc, Thierry; McGee, Thomas J.; Pliutau, Denis; Senff, Christoph; Sullivan, John T.; Sumnicht, Grant; Twigg, Laurence W.; Wang, Lihua

2018-04-01

The Tropospheric Ozone Lidar Network (TOLNet) is a unique network of lidar systems that measure atmospheric profiles of ozone and aerosols, to contribute to air-quality studies, atmospheric modeling, and satellite validation efforts. The accurate characterization of these lidars is of critical interest, and is necessary to determine cross-instrument calibration uniformity. From July to August 2014, three lidars, the TROPospheric OZone (TROPOZ) lidar, the Tunable Optical Profiler for Aerosol and oZone (TOPAZ) lidar, and the Langley Mobile Ozone Lidar (LMOL), of TOLNet participated in the "Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality" (DISCOVER-AQ) mission and the "Front Range Air Pollution and Photochemistry Éxperiment" (FRAPPÉ) to measure sub-hourly ozone variations from near the surface to the top of the troposphere. Although large differences occur at few individual altitudes in the near field and far field range, the TOLNet lidars agree with each other within ±4%. These results indicate excellent measurement accuracy for the TOLNet lidars that is suitable for use in air-quality and ozone modeling efforts.

PM2.5 Monitors in New England | Air Quality Planning Unit ...

EPA Pesticide Factsheets

2017-04-10

The New England states are currently operating a network of 58 ambient PM2.5 air quality monitors that meet EPA's Federal Reference Method (FRM) for PM2.5, which is necessary in order for the resultant data to be used for attainment/non-attainment purposes. These monitors collect particles in the ambient air smaller than 2.5 microns in size on a filter, which is weighed prior and post sampling to produce a 24-hour sample concentration.

Seltzer_et_al_2016

EPA Pesticide Factsheets

This dataset supports the modeling study of Seltzer et al. (2016) published in Atmospheric Environment. In this study, techniques typically used for future air quality projections are applied to a historical 11-year period to assess the performance of the modeling system when the driving meteorological conditions are obtained using dynamical downscaling of coarse-scale fields without correcting toward higher resolution observations. The Weather Research and Forecasting model and the Community Multiscale Air Quality model are used to simulate regional climate and air quality over the contiguous United States for 2000-2010. The air quality simulations for that historical period are then compared to observations from four national networks. Comparisons are drawn between defined performance metrics and other published modeling results for predicted ozone, fine particulate matter, and speciated fine particulate matter. The results indicate that the historical air quality simulations driven by dynamically downscaled meteorology are typically within defined modeling performance benchmarks and are consistent with results from other published modeling studies using finer-resolution meteorology. This indicates that the regional climate and air quality modeling framework utilized here does not introduce substantial bias, which provides confidence in the method??s use for future air quality projections.This dataset is associated with the following publication:Seltzer, K., C

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

NASA Astrophysics Data System (ADS)

Sampson, Paul D.; Szpiro, Adam A.; Sheppard, Lianne; Lindström, Johan; Kaufman, Joel D.

2011-11-01

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM 2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.

Micro sensor node for air pollutant monitoring: hardware and software issues.

PubMed

Choi, Sukwon; Kim, Nakyoung; Cha, Hojung; Ha, Rhan

2009-01-01

Wireless sensor networks equipped with various gas sensors have been actively used for air quality monitoring. Previous studies have typically explored system issues that include middleware or networking performance, but most research has barely considered the details of the hardware and software of the sensor node itself. In this paper, we focus on the design and implementation of a sensor board for air pollutant monitoring applications. Several hardware and software issues are discussed to explore the possibilities of a practical WSN-based air pollution monitoring system. Through extensive experiments and evaluation, we have determined the various characteristics of the gas sensors and their practical implications for air pollutant monitoring systems.

40 CFR 52.74 - Original identification of plan section.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Governor of Alaska on January 18, 1980 as follows: Volume II. Analysis of Problems, Control Actions Section... requirements of Air Quality Monitoring, 40 CFR part 58, subpart C, § 58.20, as follows: Volume II. Analysis of Problems, Control Actions Section V. Ambient Air Monitoring A. Purpose C. Air Monitoring Network E. Annual...

40 CFR 52.70 - Identification of plan.

Code of Federal Regulations, 2013 CFR

2013-07-01

... submitted by the Governor of Alaska on January 18, 1980 as follows: Volume II. Analysis of Problems, Control... requirements of Air Quality Monitoring, 40 CFR part 58, subpart C, § 58.20, as follows: Volume II. Analysis of Problems, Control Actions Section V. Ambient Air Monitoring A. Purpose C. Air Monitoring Network E. Annual...

SEASONAL AND REGIONAL AIR QUALITY AND ATMOSPHERIC DEPOSITION IN THE EASTERN US

EPA Science Inventory

Dry concentration and dry and wet deposition of selected air pollutants monitored over two 5-year periods in the 1990s at or near 34 rural Clean Air Status and Trends Network (CASTNET) sites located in the eastern US are adjusted for known biases, composed into seasonal values, a...

Air quality impacted by local pollution sources and beyond - Using a prominent petro-industrial complex as a study case.

PubMed

Chen, Sheng-Po; Wang, Chieh-Heng; Lin, Wen-Dian; Tong, Yu-Huei; Chen, Yu-Chun; Chiu, Ching-Jui; Chiang, Hung-Chi; Fan, Chen-Lun; Wang, Jia-Lin; Chang, Julius S

2018-05-01

The present study combines high-resolution measurements at various distances from a world-class gigantic petrochemical complex with model simulations to test a method to assess industrial emissions and their effect on local air quality. Due to the complexity in wind conditions which were highly seasonal, the dominant wind flow patterns in the coastal region of interest were classified into three types, namely northeast monsoonal (NEM) flows, southwest monsoonal (SEM) flows and local circulation (LC) based on six years of monitoring data. Sulfur dioxide (SO 2 ) was chosen as an indicative pollutant for prominent industrial emissions. A high-density monitoring network of 12 air-quality stations distributed within a 20-km radius surrounding the petrochemical complex provided hourly measurements of SO 2 and wind parameters. The SO 2 emissions from major industrial sources registered by the monitoring network were then used to validate model simulations and to illustrate the transport of the SO 2 plumes under the three typical wind patterns. It was found that the coupling of observations and modeling was able to successfully explain the transport of the industrial plumes. Although the petrochemical complex was seemingly the only major source to affect local air quality, multiple prominent sources from afar also played a significant role in local air quality. As a result, we found that a more complete and balanced assessment of the local air quality can be achieved only after taking into account the wind characteristics and emission factors of a much larger spatial scale than the initial (20 km by 20 km) study domain. Copyright © 2018 Elsevier Ltd. All rights reserved.

Air quality mapping using GIS and economic evaluation of health impact for Mumbai City, India.

PubMed

Kumar, Awkash; Gupta, Indrani; Brandt, Jørgen; Kumar, Rakesh; Dikshit, Anil Kumar; Patil, Rashmi S

2016-05-01

Mumbai, a highly populated city in India, has been selected for air quality mapping and assessment of health impact using monitored air quality data. Air quality monitoring networks in Mumbai are operated by National Environment Engineering Research Institute (NEERI), Maharashtra Pollution Control Board (MPCB), and Brihanmumbai Municipal Corporation (BMC). A monitoring station represents air quality at a particular location, while we need spatial variation for air quality management. Here, air quality monitored data of NEERI and BMC were spatially interpolated using various inbuilt interpolation techniques of ArcGIS. Inverse distance weighting (IDW), Kriging (spherical and Gaussian), and spline techniques have been applied for spatial interpolation for this study. The interpolated results of air pollutants sulfur dioxide (SO2), nitrogen dioxide (NO2) and suspended particulate matter (SPM) were compared with air quality data of MPCB in the same region. Comparison of results showed good agreement for predicted values using IDW and Kriging with observed data. Subsequently, health impact assessment of a ward was carried out based on total population of the ward and air quality monitored data within the ward. Finally, health cost within a ward was estimated on the basis of exposed population. This study helps to estimate the valuation of health damage due to air pollution. Operating more air quality monitoring stations for measurement of air quality is highly resource intensive in terms of time and cost. The appropriate spatial interpolation techniques can be used to estimate concentration where air quality monitoring stations are not available. Further, health impact assessment for the population of the city and estimation of economic cost of health damage due to ambient air quality can help to make rational control strategies for environmental management. The total health cost for Mumbai city for the year 2012, with a population of 12.4 million, was estimated as USD8000 million.

Monitoring urban air quality using a high-density network of low-cost sensor nodes in Oslo, Norway.

NASA Astrophysics Data System (ADS)

Castell, Nuria; Schneider, Philipp; Vogt, Matthias; Dauge, Franck R.; Lahoz, William; Bartonova, Alena

2017-04-01

Urban air quality represents a major public health burden and is a long-standing concern to citizens. Air pollution is associated with a range of diseases, symptoms and conditions that impair health and quality of life. In Oslo, traffic, especially exhaust from heavy-duty and private diesel vehicles and dust resuspension from studded tyres, together with wood burning in winter, are the main sources of pollution. Norway, as part of the European Economic Area, is obliged to comply with the European air quality regulations and ensure clean air. Despite this, Oslo has exceeded both the NO2 and PM10 thresholds for health protection defined in the Directive 2008/50/EC. The air quality in the Oslo area is continuously monitored in 12 compliance monitoring stations. These stations provide reliable and accurate data but their density is too low to provide a detailed spatial distribution of air quality. The emergence of low-cost nodes enables observations at high spatial resolution, providing the opportunity to enhance existing monitoring systems. However, the data generated by these nodes is significantly less accurate and precise than the data provided by reference equipment. We have conducted an evaluation of low-cost nodes to monitor NO2 and PM10, comparing the data collected with low-cost nodes against CEN (European Standardization Organization) reference analysers. During January and March 2016, a network of 24 nodes was deployed in Oslo. During January, high NO2 levels were observed for several days in a row coinciding with the formation of a thermal inversion. During March, we observed an episode with high PM10 levels due to road dust resuspension. Our results show that there is a major technical challenge associated with current commercial low-cost sensors, regarding the sensor robustness and measurement repeatability. Despite this, low-cost sensor nodes are able to reproduce the NO2 and PM10 variability. The data from the sensors was employed to generate detailed NO2 and PM10 air quality maps using a data fusion technique. This way we were able to offer localized air quality information for the city of Oslo. The outlook for commercial low-cost sensors is promising, and our results show that currently some sensors are already capable of providing coarse information about air quality, indicating if the air quality is good, moderate or if the air is heavily polluted. This type of information could be suitable for applications that aim to raise awareness, or engage the community by monitoring local air quality, as such applications do not require the same accuracy as scientific or regulatory monitoring.

Evaluation of Day and Nighttime Lower Tropospheric Ozone from Air Quality Models using TES and Ozonesondes

NASA Astrophysics Data System (ADS)

Osterman, G. B.; Neu, J. L.; Eldering, A.; Pinder, R. W.; Tang, Y.; McQueen, J.

2012-12-01

At night, ozone can be transported long distances above the surface inversion layer without chemical destruction or deposition. As the boundary layer breaks up in the morning, this nocturnal ozone can be mixed down to the surface and rapidly increase ozone concentrations at a rate that can rival chemical ozone production. Most regional scale models that are used for air quality forecasts and ozone source attribution do not adequately capture nighttime ozone concentrations and transport. We combine ozone profile data from the NASA Earth Observing System (EOS) Tropospheric Emission Spectrometer (TES) and other sensors, ozonesonde data collected during the INTEX Ozonesonde Network Study (IONS), EPA AirNow ground station ozone data, the Community Multi-Scale Air Quality (CMAQ) model, and the National Air Quality Forecast Capability (NAQFC) model to examine air quality events during August 2006. We present both aggregated statistics and case-study analyses that assess the relationship between the models' ability to reproduce surface air quality events and their ability to capture the vertical distribution of ozone both during the day and at night. We perform the comparisons looking at the geospatial dependence in the differences between the measurements and models under different surface ozone conditions.

Investigation of environmental indices from the Earth Resources Technology Satellite

NASA Technical Reports Server (NTRS)

Greeley, R. S. (Principal Investigator); Riley, E. L.; Stryker, S.; Ward, E. A.

1973-01-01

The author has identified the following significant results. Land use, quality, and air quality trends are being deduced from both ERTS-1 MSS and computer compatible tapes. The data analysis plan and the preliminary data analysis phase were conducted in January 1973. Results from these two phases are: (1) Method of analysis has been selected and checked out. (2) Land use for two dates have been generated for one test site. (3) Water quality for one date has been produced partially. (4) Air quality for three has been produced and compared with ground truth. (5) One of the two DCP stations is in operation; the second station will be installed in March 1973. Land use classification exceeds pre-launch expectations. Water quality (turbidity) is not progressing as expected. Finally, mesoscale air quality results have shown correlation with NOAA/EPA turbidity network. If air quality correlations continue to show favorable results, a rapid means of global turbidity may be available from ERTS-1 MSS observations.

A low-cost sensing system for cooperative air quality monitoring in urban areas.

PubMed

Brienza, Simone; Galli, Andrea; Anastasi, Giuseppe; Bruschi, Paolo

2015-05-26

Air quality in urban areas is a very important topic as it closely affects the health of citizens. Recent studies highlight that the exposure to polluted air can increase the incidence of diseases and deteriorate the quality of life. Hence, it is necessary to develop tools for real-time air quality monitoring, so as to allow appropriate and timely decisions. In this paper, we present uSense, a low-cost cooperative monitoring tool that allows knowing, in real-time, the concentrations of polluting gases in various areas of the city. Specifically, users monitor the areas of their interest by deploying low-cost and low-power sensor nodes. In addition, they can share the collected data following a social networking approach. uSense has been tested through an in-field experimentation performed in different areas of a city. The obtained results are in line with those provided by the local environmental control authority and show that uSense can be profitably used for air quality monitoring.

One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system

NASA Astrophysics Data System (ADS)

Hu, Jianlin; Chen, Jianjun; Ying, Qi; Zhang, Hongliang

2016-08-01

China has been experiencing severe air pollution in recent decades. Although an ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research and Forecasting (WRF) model and the Community Multi-scale Air Quality (CMAQ) model was conducted to provide detailed temporal and spatial information of ozone (O3), total PM2.5, and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, overprediction of O3 generally occurs at low concentration range while underprediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in southern China than in northern China, central China, and Sichuan Basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of the CMAQ model to reproduce severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

40 CFR 58.14 - System modification.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 6 2012-07-01 2012-07-01 false System modification. 58.14 Section 58.14 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.14 System modification. (a) The State, or where...

40 CFR 58.14 - System modification.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 6 2014-07-01 2014-07-01 false System modification. 58.14 Section 58.14 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.14 System modification. (a) The State, or where...

OXIDIZED NITROGEN DEPOSITION IN THE EASTERN UNITED STATES

EPA Science Inventory

Air quality and selected meteorological parameters have been monitored at rural sites in the United States (US) by EPA's Clean Air Status and Trends Network, (CASTNet) sites. The National Atmospheric Deposition Program (NADP) monitors wet deposition of numerous ions in precip...

"Total Deposition (TDEP) Maps" | Science Inventory | US EPA

EPA Pesticide Factsheets

The presentation provides an update on the use of a hybrid methodology that relies on measured values from national monitoring networks and modeled values from CMAQ to produce of maps of total deposition for use in critical loads and other ecological assessments. Additionally, comparisons of the deposition values from the hybrid approach are compared with deposition estimates from other methodologies. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the 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 used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

Large-scale monitoring of air pollution in remote and ecologically important areas

Treesearch

Andrzej Bytnerowicz; Witold Fraczek

2013-01-01

New advances in air quality monitoring techniques, such as passive samplers for nitrogenous (N) or sulphurous (S) pollutants and ozone (O3), have allowed for an improved understanding of concentrations of these pollutants in remote areas. Mountains create special problems with regard to the feasibility of establishing and maintaining air pollution monitoring networks,...

77 FR 45965 - Determination of Attainment for the Paul Spur/Douglas PM10

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-02

... plans, and based on the findings of our technical system audit report, ADEQ's monitoring network meets... to EPA's Air Quality System (AQS) database as quality- assured. Next, we reviewed the ambient PM 10...

40 CFR 58.16 - Data submittal and archiving requirements.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 5 2011-07-01 2011-07-01 false Data submittal and archiving requirements. 58.16 Section 58.16 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) AMBIENT AIR QUALITY SURVEILLANCE Monitoring Network § 58.16 Data submittal and...

Increasing the Use of Earth Science Data and Models in Air Quality Management.

PubMed

Milford, Jana B; Knight, Daniel

2017-04-01

In 2010, the U.S. National Aeronautics and Space Administration (NASA) initiated the Air Quality Applied Science Team (AQAST) as a 5-year, $17.5-million award with 19 principal investigators. AQAST aims to increase the use of Earth science products in air quality-related research and to help meet air quality managers' information needs. We conducted a Web-based survey and a limited number of follow-up interviews to investigate federal, state, tribal, and local air quality managers' perspectives on usefulness of Earth science data and models, and on the impact AQAST has had. The air quality managers we surveyed identified meeting the National Ambient Air Quality Standards for ozone and particulate matter, emissions from mobile sources, and interstate air pollution transport as top challenges in need of improved information. Most survey respondents viewed inadequate coverage or frequency of satellite observations, data uncertainty, and lack of staff time or resources as barriers to increased use of satellite data by their organizations. Managers who have been involved with AQAST indicated that the program has helped build awareness of NASA Earth science products, and assisted their organizations with retrieval and interpretation of satellite data and with application of global chemistry and climate models. AQAST has also helped build a network between researchers and air quality managers with potential for further collaborations. NASA's Air Quality Applied Science Team (AQAST) aims to increase the use of satellite data and global chemistry and climate models for air quality management purposes, by supporting research and tool development projects of interest to both groups. Our survey and interviews of air quality managers indicate they found value in many AQAST projects and particularly appreciated the connections to the research community that the program facilitated. Managers expressed interest in receiving continued support for their organizations' use of satellite data, including assistance in retrieving and interpreting data from future geostationary platforms meant to provide more frequent coverage for air quality and other applications.

Measurement results obtained from air quality monitoring system

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

Turzanski, P.K.; Beres, R.

1995-12-31

An automatic system of air pollution monitoring operates in Cracow since 1991. The organization, assembling and start-up of the network is a result of joint efforts of the US Environmental Protection Agency and the Cracow environmental protection service. At present the automatic monitoring network is operated by the Provincial Inspection of Environmental Protection. There are in total seven stationary stations situated in Cracow to measure air pollution. These stations are supported continuously by one semi-mobile (transportable) station. It allows to modify periodically the area under investigation and therefore the 3-dimensional picture of creation and distribution of air pollutants within Cracowmore » area could be more intelligible.« less

Journal Article: Quality Assurance Considerations for An Ambient Dioxin Monitoring Network

EPA Science Inventory

The U.S. Environmental Protection Agency initiated the National Dioxin Air Monitoring Network (NDAMN) in 1998. NDAMN has three primary purposes:
1. To provide measurements of background atmospheric levels of dioxin-like compounds in different geographic regions of the Unite...

AEROCAN, the Canadian sub-network of AERONET: Aerosol monitoring and air quality applications

NASA Astrophysics Data System (ADS)

Sioris, Christopher E.; Abboud, Ihab; Fioletov, Vitali E.; McLinden, Chris A.

2017-10-01

Previous studies have demonstrated the utility of AERONET (Aerosol Robotic Network) aerosol optical depth (AOD) data for monitoring the spatial variability of particulate matter (PM) in relatively polluted regions of the globe. AEROCAN, a Canadian sub-network of AERONET, was established 20 years ago and currently consists of twenty sites across the country. In this study, we examine whether the AEROCAN sunphotometer data provide evidence of anthropogenic contributions to ambient particulate matter concentrations in relatively clean Canadian locations. The similar weekly cycle of AOD and PM2.5 over Toronto provides insight into the impact of local pollution on observed AODs. High temporal correlations (up to r = 0.78) between daily mean AOD (or its fine-mode component) and PM2.5 are found at southern Ontario AEROCAN sites during May-August, implying that the variability in the aerosol load resides primarily in the boundary layer and that sunphotometers capture day-to-day PM2.5 variations at moderately polluted sites. The sensitivity of AEROCAN AOD data to anthropogenic surface-level aerosol enhancements is demonstrated using boundary-layer wind information for sites near sources of aerosol or its precursors. An advantage of AEROCAN relative to the Canadian in-situ National Air Pollution Surveillance (NAPS) network is the ability to detect free tropospheric aerosol enhancements, which can be large in the case of lofted forest fire smoke or desert dust. These aerosol plumes eventually descend to the surface, sometimes in populated areas, exacerbating air quality. In cases of large AOD (≥0.4), AEROCAN data are also useful in characterizing the aerosol type. The AEROCAN network includes three sites in the high Arctic, a region not sampled by the NAPS PM2.5 monitoring network. These polar sites show the importance of long-range transport and meteorology in the Arctic haze phenomenon. Also, AEROCAN sunphotometers are, by design and due to regular maintenance, the most valuable monitors available for long term aerosol trends. Using a variety of data analysis techniques and timescales, the usefulness of this ground-based remote-sensing sub-network for providing information relevant to air quality is demonstrated.

SAMIRA - SAtellite based Monitoring Initiative for Regional Air quality

NASA Astrophysics Data System (ADS)

Schneider, Philipp; Stebel, Kerstin; Ajtai, Nicolae; Diamandi, Andrei; Horalek, Jan; Nicolae, Doina; Stachlewska, Iwona; Zehner, Claus

2016-04-01

Here, we present a new ESA-funded project entitled Satellite based Monitoring Initiative for Regional Air quality (SAMIRA), which aims at improving regional and local air quality monitoring through synergetic use of data from present and upcoming satellites, traditionally used in situ air quality monitoring networks and output from chemical transport models. Through collaborative efforts in four countries, namely Romania, Poland, the Czech Republic and Norway, all with existing air quality problems, SAMIRA intends to support the involved institutions and associated users in their national monitoring and reporting mandates as well as to generate novel research in this area. Despite considerable improvements in the past decades, Europe is still far from achieving levels of air quality that do not pose unacceptable hazards to humans and the environment. Main concerns in Europe are exceedances of particulate matter (PM), ground-level ozone, benzo(a)pyrene (BaP) and nitrogen dioxide (NO2). While overall sulfur dioxide (SO2) emissions have decreased in recent years, regional concentrations can still be high in some areas. The objectives of SAMIRA are to improve algorithms for the retrieval of hourly aerosol optical depth (AOD) maps from SEVIRI, and to develop robust methods for deriving column- and near-surface PM maps for the study area by combining satellite AOD with information from regional models. The benefit to existing monitoring networks (in situ, models, satellite) by combining these datasets using data fusion methods will be tested for satellite-based NO2, SO2, and PM/AOD. Furthermore, SAMIRA will test and apply techniques for downscaling air quality-related EO products to a spatial resolution that is more in line with what is generally required for studying urban and regional scale air quality. This will be demonstrated for a set of study sites that include the capitals of the four countries and the highly polluted areas along the border of Poland and the Czech Republic, and the Gorj County in Romania. All data products shall undergo a quality control, i.e. robust and independent validation. The SAMIRA consortium will further work towards a pre-operational system for improved PM10 forecasts using observational (in situ and satellite) data assimilation. SAMIRA aims to maximize project benefits by liaison with national and regional environmental protection agencies and health institutions, as well as related ESA and European initiatives such as the Copernicus Atmospheric Monitoring Services (CAMS).

EVALUATION OF THE FILTER PACK FOR LONG-DURATION SAMPLING OF AMBIENT AIR

EPA Science Inventory

A 14-week filter pack (FP) sampler evaluation field study was conducted at a site near Bondville, IL to investigate the impact of weekly sampling duration. Simultaneous samples were collected using collocated filter packs (FP) from two independent air quality monitoring networks...

The Use of Remote Sensing Data for Modeling Air Quality in the Cities

NASA Astrophysics Data System (ADS)

Putrenko, V. V.; Pashynska, N. M.

2017-12-01

Monitoring of environmental pollution in the cities by the methods of remote sensing of the Earth is actual area of research for sustainable development. Ukraine has a poorly developed network of monitoring stations for air quality, the technical condition of which is deteriorating in recent years. Therefore, the possibility of obtaining data about the condition of air by remote sensing methods is of great importance. The paper considers the possibility of using the data about condition of atmosphere of the project AERONET to assess the air quality in Ukraine. The main pollution indicators were used data on fine particulate matter (PM2.5) and nitrogen dioxide (NO2) content in the atmosphere. The main indicator of air quality in Ukraine is the air pollution index (API). We have built regression models the relationship between indicators of NO2, which are measured by remote sensing methods and ground-based measurements of indicators. There have also been built regression models, the relationship between the data given to the land of NO2 and API. To simulate the relationship between the API and PM2.5 were used geographically weighted regression model, which allows to take into account the territorial differentiation between these indicators. As a result, the maps that show the distribution of the main types of pollution in the territory of Ukraine, were constructed. PM2.5 data modeling is complicated with using existing indicators, which requires a separate organization observation network for PM2.5 content in the atmosphere for sustainable development in cities of Ukraine.

Determination of beryllium concentrations in UK ambient air

NASA Astrophysics Data System (ADS)

Goddard, Sharon L.; Brown, Richard J. C.; Ghatora, Baljit K.

2016-12-01

Air quality monitoring of ambient air is essential to minimise the exposure of the general population to toxic substances such as heavy metals, and thus the health risks associated with them. In the UK, ambient air is already monitored under the UK Heavy Metals Monitoring Network for a number of heavy metals, including nickel (Ni), arsenic (As), cadmium (Cd) and lead (Pb) to ensure compliance with legislative limits. However, the UK Expert Panel on Air Quality Standards (EPAQS) has highlighted a need to limit concentrations of beryllium (Be) in air, which is not currently monitored, because of its toxicity. The aim of this work was to analyse airborne particulate matter (PM) sampled onto filter papers from the UK Heavy Metals Monitoring Network for quantitative, trace level beryllium determination and compare the results to the guideline concentration specified by EPAQS. Samples were prepared by microwave acid digestion in a matrix of 2% sulphuric acid and 14% nitric acid, verified by the use of Certified Reference Materials (CRMs). The digested samples were then analysed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The filters from the UK Heavy Metals Monitoring Network were tested using this procedure and the average beryllium concentration across the network for the duration of the study period was 7.87 pg m-3. The highest site average concentration was 32.0 pg m-3 at Scunthorpe Low Santon, which is significantly lower than levels that are thought to cause harm. However the highest levels were observed at sites monitoring industrial point sources, indicating that beryllium is being used and emitted, albeit at very low levels, from these point sources. Comparison with other metals concentrations and data from the UK National Atmospheric Emissions Inventory suggests that current emissions of beryllium may be significantly overestimated.

Analysis of Nitrogen Dioxide and Sulphur Dioxide in Lima, Peru: Trends and Seasonal Variations

NASA Astrophysics Data System (ADS)

Pacsi, S.; Rappenglueck, B.

2007-12-01

This research was carried out to show a general analysis of the monthly and yearly variation (1996-2002) and the tendency of the nitrogen dioxide (NO2) and sulfur dioxide (SO2) for the 5 stations of the air quality network of Lima. The SO2 and NO2 concentrations were measured by the Dirección General de Salud Ambiental (DIGESA), using the active sampling method and the chemical analysis has been determined by Turbidimetry and Colorimetry for the SO2 and NO2 respectively. The monthly average variation (1996-2001) of SO2 in the Lima Center station has a small annual range (32,4 mikrograms/m3) with maximum values in autumn (April) and minimum in winter (June). The NO2 presents a higher annual range (128,2 mikrograms/m3) and its minimum values occur in the summer and the maximum in spring. The annual averages analysis (2000-2002) of the air quality monitoring network of Lima shows that the SO2 and NO2 values are maximum in the Lima Center station and exceed the Peruvian air quality standard (ECAs) in 30% and 75% respectively. The yearly variation (1996-2001) in the Lima Center station show an increasing tendency in the SO2 (significant) and NO2 (not significant) values, which indicates the critical level of the air quality in Lima, therefore the implementation of the air pollution control programs is urgent.

TOLNet - A Tropospheric Ozone Lidar Profiling Network for Satellite Continuity and Process Studies

NASA Technical Reports Server (NTRS)

Newchurch, Michael J.; Kuang, Shi; Wang, Lihua; LeBlanc, Thierry; Alvarez II, Raul J.; Langford, Andrew O.; Senff, Christoph J.; Brown, Steve; Johnson, Bryan; Burris, John F.;

UTILIZING SATELLITE OBSERVATIONS TO EXPAND EPA'S AIR MONITORING NETWORK: A NEW PARTNERSHIP BETWEEN NASA AND EPA

EPA Science Inventory

Over the next decade, data requirements to inform air quality management decisions and policies will need to be expanded to large spatial domains to accommodate decisions which more frequently cross geo-political boundaries; from urban (local) and regional scales to regional, sup...

Evaluation of land use regression models (LURs) for nitrogen dioxide and benzene in four U.S. Cities.

EPA Science Inventory

Spatial analysis studies have included application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO2) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks ...

Managing air and water quality in the face of uncertain futures: perspectives, perceptions, reported action, and needs for climate adaptation at the local level

NASA Astrophysics Data System (ADS)

Bedsworth, L. W.; Ekstrom, J.

2017-12-01

As the climate continues to shift, projections show amplified and more frequent extreme events, including coastal and inland flooding, wildfires, prolonged droughts, and heatwaves. Vital public goods, both air quality and water quality, can be critically affected by such extreme events. Climate change will make it increasingly difficult for managers to achieve public health targets for air and water quality. Successfully preparing governance structures developed to maintain and improve air and water quality may benefit from preventative strategies to avoid public health impacts and costs of climate change locally. Perceptions of climate change and its risks, actions taken so far, and perceived barriers to adaptation give insight into the needs of managers for preparing for climate change impacts. This paper compares results of two surveys that looked at local level management of air quality and water quality in California. Air quality managers consistently reported to recognize the risks of climate change on their sector, where water quality managers' perceptions varied between no concern to high concern. We explore the differences in governance, capacity influence the ill-defined responsibility and assumed roles of water and air districts in adaptation to extreme events increasing with climate change. The chain and network of managing air quality is compared with that of water quality - laying out similarities and differences. Then we compare how the survey respondents differed in terms of extreme weather-influenced threats to environmental quality. We end with a discussion of responsibility - where in the chain of managing these life-critical ecosystem services, is the need greatest for adapting to climate change and what does this mean for the other levels in the chain beyond the local management.

Monitoring Indoor Air Quality for Enhanced Occupational Health.

PubMed

Pitarma, Rui; Marques, Gonçalo; Ferreira, Bárbara Roque

2017-02-01

Indoor environments are characterized by several pollutant sources. Because people spend more than 90% of their time in indoor environments, several studies have pointed out the impact of indoor air quality on the etiopathogenesis of a wide number of non-specific symptoms which characterizes the "Sick Building Syndrome", involving the skin, the upper and lower respiratory tract, the eyes and the nervous system, as well as many building related diseases. Thus, indoor air quality (IAQ) is recognized as an important factor to be controlled for the occupants' health and comfort. The majority of the monitoring systems presently available is very expensive and only allow to collect random samples. This work describes the system (iAQ), a low-cost indoor air quality monitoring wireless sensor network system, developed using Arduino, XBee modules and micro sensors, for storage and availability of monitoring data on a web portal in real time. Five micro sensors of environmental parameters (air temperature, humidity, carbon monoxide, carbon dioxide and luminosity) were used. Other sensors can be added for monitoring specific pollutants. The results reveal that the system can provide an effective indoor air quality assessment to prevent exposure risk. In fact, the indoor air quality may be extremely different compared to what is expected for a quality living environment. Systems like this would have benefit as public health interventions to reduce the burden of symptoms and diseases related to "sick buildings".

Transformations in Air Transportation Systems For the 21st Century

NASA Technical Reports Server (NTRS)

Holmes, Bruce J.

2004-01-01

Globally, our transportation systems face increasingly discomforting realities: certain of the legacy air and ground infrastructures of the 20th century will not satisfy our 21st century mobility needs. The consequence of inaction is diminished quality of life and economic opportunity for those nations unable to transform from the 20th to 21st century systems. Clearly, new thinking is required regarding business models that cater to consumers value of time, airspace architectures that enable those new business models, and technology strategies for innovating at the system-of-networks level. This lecture proposes a structured way of thinking about transformation from the legacy systems of the 20th century toward new systems for the 21st century. The comparison and contrast between the legacy systems of the 20th century and the transformed systems of the 21st century provides insights into the structure of transformation of air transportation. Where the legacy systems tend to be analog (versus digital), centralized (versus distributed), and scheduled (versus on-demand) for example, transformed 21st century systems become capable of scalability through technological, business, and policy innovations. Where air mobility in our legacy systems of the 20th century brought economic opportunity and quality of life to large service markets, transformed air mobility of the 21st century becomes more equitable available to ever-thinner and widely distributed populations. Several technological developments in the traditional aircraft disciplines as well as in communication, navigation, surveillance and information systems create new foundations for 21st thinking about air transportation. One of the technological developments of importance arises from complexity science and modern network theory. Scale-free (i.e., scalable) networks represent a promising concept space for modeling airspace system architectures, and for assessing network performance in terms of robustness, resilience, and other metrics. The lecture offers an air transportation system topology and a scale-free network linkage graphic as framework for transportation system innovation. Successful outcomes of innovation in air transportation could lay the foundations for new paradigms for aircraft and their operating capabilities, air transportation system topologies, and airspace architectures and procedural concepts. These new paradigms could support scalable alternatives for the expansion of future air mobility to more consumers in more parts of the world.

Ambient Greenhouse Gas (GHG) Observations in the San Francisco Bay Area of California Using a Fixed-site Monitoring Network

NASA Astrophysics Data System (ADS)

Martien, P. T.; Guha, A.; Bower, J.; Perkins, I.; Randall, S.; Young, A.; Hilken, H.; Stevenson, E.

2016-12-01

The Bay Area Air Quality Management District is the greater San Francisco Bay metropolitan area's chief air quality regulatory agency. Aligning itself with the Governor's Executive Order S-3-05, the Air District has set a goal to reduce the region's GHG emissions by 80% below 1990 levels by the year 2050. The Air District's 2016 Clean Air Plan will lay out the agency's vision and actions to put the region on a path forward towards achieving the 2050 goal while also reducing air pollution and related health impacts. The 2016 Plan has three overarching objectives: 1) develop a multi-pollutant emissions control strategy, (2) reduce population exposure to harmful air pollutants, especially in vulnerable communities, and (3) protect climate through a comprehensive Regional Climate Protection Strategy. To accomplish one of 2016 Plan's control measures (SL3 - Greenhouse Gas Monitoring and Measurement Network), the Air District has set up a long-term, ambient GHG monitoring network at four sites. The first site is located north and upwind of the urban core at Bodega Bay by the Pacific Coast. It mostly receives clean marine inflow and serves as the regional background site. The other three sites are strategically located at regional exit points for Bay Area plumes that presumably contain well-mixed GHG enhancements from local sources. CO2 and CH4are being measured continuously at the fixed-sites, along with combustion tracer CO and other air pollutants. In the longer term, the network will allow the Air District to monitor ambient concentrations of GHGs and thus evaluate the effectiveness of its policy, regulation and enforcement efforts. We present data trends from the first year of operation of the fixed-site monitoring network including monthly and seasonal patterns, diurnal variations and regional enhancements at individual sites above background concentrations. We also locate an isotopic methane instrument (Picarro, G132-i) for a short duration (a week) at each of the four fixed sites. A comparison of 13C/12C content of ambient CH4 at the downwind sites with the regional background isotopic methane signal at the coastal site provides a valuable top-down assessment of the relative distribution of biogenic versus fossil-fuel based sources of CH4 in the region.

Spatial and temporal trends from an air quality sensor network near a heavily trafficked intersection

NASA Astrophysics Data System (ADS)

Orlando, P.; Vo, D.; Giossi, C.; George, L.

2017-12-01

With the world-wide increase in urbanization and the increasing usage of combustion vehicles in urban areas, traffic-related air pollution is a growing health hazard. However, there are limited studies that examine the spatial and temporal impacts of traffic-related pollutants within cities. In particular, there are few studies that look at traffic management and its potential for pollution mitigation. In a previous study we examined roadway pollution and traffic parameters with one roadway station instrumented with standard measurement instruments. With the advent of low-cost air pollution sensors, we have expanded our work by observing multiple sites within a neighborhood to understand spatial and temporal exposures. We have deployed a high-density sensor network around urban arterial corridors in SE Portland, Oregon. This network consisted of ten nodes measuring CO, NO, NO2 and O3, and ten nodes measuring CO, CO2, VOC and PM2.5. The co-location of standard measurement instruments provided insight towards the utility of our low-cost sensor network, as the different nodes varied in cost, and potentially in quality. We have identified near-real-time temporal trends and local-scale spatial patterns during the summer of 2017. Meteorological and traffic data were included to further characterize these patterns, exploring the potential for pollution mitigation.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Novel Method for Detection of Air Pollution using Cellular Communication Networks

NASA Astrophysics Data System (ADS)

David, N.; Gao, O. H.

2016-12-01

Air pollution can lead to a wide spectrum of severe and chronic health impacts. Conventional tools for monitoring the phenomenon do not provide a sufficient monitoring solution in a global scale since they are, for example, not representative of the larger space or due to limited deployment as a result of practical limitations, such as: acquisition, installation, and ongoing maintenance costs. Near ground temperature inversions are directly identified with air pollution events since they suppress vertical atmospheric movement and trap pollutants near the ground. Wireless telecommunication links that comprise the data transfer infrastructure in cellular communication networks operate at frequencies of tens of GHz and are affected by different atmospheric phenomena. These systems are deployed near ground level across the globe, including in developing countries such as India, countries in Africa, etc. Many cellular providers routinely store data regarding the received signal levels in the network for quality assurance needs. Temperature inversions cause atmospheric layering, and change the refractive index of the air when compared to standard conditions. As a result, the ducts that are formed can operate, in essence, as atmospheric wave guides, and cause interference (signal amplification / attenuation) in the microwaves measured by the wireless network. Thus, this network is in effect, an existing system of environmental sensors for monitoring temperature inversions and the episodes of air pollution identified with them. This work presents the novel idea, and demonstrates it, in operation, over several events of air pollution which were detected by a standard cellular communication network during routine operation. Reference: David, N. and Gao, H.O. Using cellular communication networks to detect air pollution, Environmental Science & Technology, 2016 (accepted).

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.

Profile and Remote Sensing Observation Datasets (Trace Gases and Aerosols) for Regional- Scale Model Evaluation under the Air Quality Model Evaluation International Initiative (AQMEII)- North American and European Perspectives

EPA Science Inventory

While the vast majority of operational air-pollution networks across the world are designed to measure relevant metrics at the surface, the air pollution problem is a three-dimensional phenomenon. The lack of adequate observations aloft to routinely characterize the nature of ai...

Modeling study of biomass burning plumes and their impact on urban air quality; a case study of Santiago de Chile

NASA Astrophysics Data System (ADS)

Cuchiara, Gustavo C.; Rappenglück, Bernhard; Angelica Rubio, Maria; Lissi, Eduardo; Gramsch, Ernesto; Garreaud, Rene D.

2017-04-01

Wildfires are a significant direct source of atmospheric pollutants; on a global scale biomass burning is believed to be the largest source of primary fine particles in the atmosphere and the second largest source of trace gases after anthropogenic emission sources. During the summer of 2014, an intense forest and dry pasture wildfire occurred nearby the city of Santiago de Chile. The biomass-burning plume was transported towards the metropolitan area of Santiago and exacerbated the air quality in this region. In this study, we investigated this wildfire event using a forward plume-rise and a chemistry (WRF/Chem) simulation. These data sets provided an opportunity to validate a regional air-quality simulation over Santiago, and a unique case to assess the performance of biomass burning plume modeling in complex topography and validated against an established air quality network. The results from both meteorological and air quality models provide insights about the transport of biomass-burning plumes from the wildfire region towards the metropolitan region of Santiago de Chile. We studied a seven-day period between January 01-07, 2014, and the impact of biomass burning plume emissions estimated by Fire Inventory from NCAR version 1 (FINNv1) on the air quality of Santiago de Chile.

Solutions Network Formulation Report. NASA's Potential Contributions for Using Solar Ultraviolet Radiation in Conjunction with Photocatalysis for Urban Air Pollution Mitigation and Increasing Air Quality

NASA Technical Reports Server (NTRS)

Underwood, Lauren; Ryan, Robert E.

2007-01-01

This Candidate Solution is based on using NASA Earth science research on atmospheric ozone and aerosols data as a means to predict and evaluate the effectiveness of photocatalytically created surfaces (building materials like glass, tile and cement) for air pollution mitigation purposes. When these surfaces are exposed to near UV light, organic molecules, like air pollutants and smog precursors, will degrade into environmentally friendly compounds. U.S. EPA (Environmental Protection Agency) is responsible for forecasting daily air quality by using the Air Quality Index (AQI) that is provided by AIRNow. EPA is partnered with AIRNow and is responsible for calculating the AQI for five major air pollutants that are regulated by the Clean Air Act. In this Solution, UV irradiance data acquired from the satellite mission Aura and the OMI Surface UV algorithm will be used to help understand both the efficacy and efficiency of the photocatalytic decomposition process these surfaces facilitate, and their ability to reduce air pollutants. Prediction models that estimate photocatalytic function do not exist. NASA UV irradiance data will enable this capability, so that air quality agencies that are run by state and local officials can develop and implement programs that utilize photocatalysis for urban air pollution control and, enable them to make effective decisions about air pollution protection programs.

Developing and Transitioning Numerical Air Quality Models to Improve Air Quality and Public Health Decision-Making in El Salvador and Costa Rica As Part of the Servir Applied Sciences Team

NASA Astrophysics Data System (ADS)

Thomas, A.; Huff, A. K.; Gomori, S. G.; Sadoff, N.

2014-12-01

In order to enhance the capacity for air quality modeling and improve air quality monitoring and management in the SERVIR Mesoamerica region, members of SERVIR's Applied Sciences Team (AST) are developing national numerical air quality models for El Salvador and Costa Rica. We are working with stakeholders from the El Salvador Ministry of the Environment and Natural Resources (MARN); National University of Costa Rica (UNA); the Costa Rica Ministry of the Environment, Energy, and Telecommunications (MINAET); and Costa Rica National Meteorological Institute (IMN), who are leaders in air quality monitoring and management in the Mesoamerica region. Focusing initially on these institutions will build sustainability in regional modeling activities by developing air quality modeling capability that can be shared with other countries in Mesoamerica. The air quality models are based on the Community Multi-scale Air Quality (CMAQ) model and incorporate meteorological inputs from the Weather Research and Forecasting (WRF) model, as well as national emissions inventories from El Salvador and Costa Rica. The models are being optimized for urban air quality, which is a priority of decision-makers in Mesoamerica. Once experimental versions of the modeling systems are complete, they will be transitioned to servers run by stakeholders in El Salvador and Costa Rica. The numerical air quality models will provide decision support for stakeholders to identify 1) high-priority areas for expanding national ambient air monitoring networks, 2) needed revisions to national air quality regulations, and 3) gaps in national emissions inventories. This project illustrates SERVIR's goal of the transition of science to support decision-making through capacity building in Mesoamerica, and it aligns with the Group on Earth Observations' health societal benefit theme. This presentation will describe technical aspects of the development of the models and outline key steps in our successful collaboration with the Mesoamerican stakeholders, including the processes of identifying and engaging decision-makers, understanding their requirements and limitations, communicating status updates on a regular basis, and providing sufficient training for end users to be able to utilize the models in a decision-making context.

Comparison of Satellite Observations of Aerosol Optical Depth to Surface Monitor Fine Particle Concentration

NASA Technical Reports Server (NTRS)

Kleb, Mary M.; AlSaadi, Jassim A.; Neil, Doreen O.; Pierce, Robert B.; Pippin, Margartet R.; Roell, Marilee M.; Kittaka, Chieko; Szykman, James J.

2004-01-01

Under NASA's Earth Science Applications Program, the Infusing satellite Data into Environmental Applications (IDEA) project examined the relationship between satellite observations and surface monitors of air pollutants to facilitate a more capable and integrated observing network. This report provides a comparison of satellite aerosol optical depth to surface monitor fine particle concentration observations for the month of September 2003 at more than 300 individual locations in the continental US. During September 2003, IDEA provided prototype, near real-time data-fusion products to the Environmental Protection Agency (EPA) directed toward improving the accuracy of EPA s next-day Air Quality Index (AQI) forecasts. Researchers from NASA Langley Research Center and EPA used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument combined with EPA ground network data to create a NASA-data-enhanced Forecast Tool. Air quality forecasters used this tool to prepare their forecasts of particle pollution, or particulate matter less than 2.5 microns in diameter (PM2.5), for the next-day AQI. The archived data provide a rich resource for further studies and analysis. The IDEA project uses data sets and models developed for tropospheric chemistry research to assist federal, state, and local agencies in making decisions concerning air quality management to protect public health.

A New Black Carbon Sensor for Dense Air Quality Monitoring Networks

PubMed Central

Caubel, Julien J.; Cados, Troy E.; Kirchstetter, Thomas W.

2018-01-01

Low-cost air pollution sensors are emerging and increasingly being deployed in densely distributed wireless networks that provide more spatial resolution than is typical in traditional monitoring of ambient air quality. However, a low-cost option to measure black carbon (BC)—a major component of particulate matter pollution associated with adverse human health risks—is missing. This paper presents a new BC sensor designed to fill this gap, the Aerosol Black Carbon Detector (ABCD), which incorporates a compact weatherproof enclosure, solar-powered rechargeable battery, and cellular communication to enable long-term, remote operation. This paper also demonstrates a data processing methodology that reduces the ABCD’s sensitivity to ambient temperature fluctuations, and therefore improves measurement performance in unconditioned operating environments (e.g., outdoors). A fleet of over 100 ABCDs was operated outdoors in collocation with a commercial BC instrument (Magee Scientific, Model AE33) housed inside a regulatory air quality monitoring station. The measurement performance of the 105 ABCDs is comparable to the AE33. The fleet-average precision and accuracy, expressed in terms of mean absolute percentage error, are 9.2 ± 0.8% (relative to the fleet average data) and 24.6 ± 0.9% (relative to the AE33 data), respectively (fleet-average ± 90% confidence interval). PMID:29494528

A New Black Carbon Sensor for Dense Air Quality Monitoring Networks.

PubMed

Caubel, Julien J; Cados, Troy E; Kirchstetter, Thomas W

2018-03-01

Low-cost air pollution sensors are emerging and increasingly being deployed in densely distributed wireless networks that provide more spatial resolution than is typical in traditional monitoring of ambient air quality. However, a low-cost option to measure black carbon (BC)-a major component of particulate matter pollution associated with adverse human health risks-is missing. This paper presents a new BC sensor designed to fill this gap, the Aerosol Black Carbon Detector (ABCD), which incorporates a compact weatherproof enclosure, solar-powered rechargeable battery, and cellular communication to enable long-term, remote operation. This paper also demonstrates a data processing methodology that reduces the ABCD's sensitivity to ambient temperature fluctuations, and therefore improves measurement performance in unconditioned operating environments (e.g., outdoors). A fleet of over 100 ABCDs was operated outdoors in collocation with a commercial BC instrument (Magee Scientific, Model AE33) housed inside a regulatory air quality monitoring station. The measurement performance of the 105 ABCDs is comparable to the AE33. The fleet-average precision and accuracy, expressed in terms of mean absolute percentage error, are 9.2 ± 0.8% (relative to the fleet average data) and 24.6 ± 0.9% (relative to the AE33 data), respectively (fleet-average ± 90% confidence interval).

75 FR 43062 - Approval and Promulgation of Air Quality Implementation Plans; Texas; Revisions to Emissions...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-23

... ozone precursor gases during the winter and summer months, respectively. The revisions also allow for... dioxide, ozone, lead (Pb), particulate matter (PM), and sulfur dioxide (SO 2 ). A SIP is a set of air... supporting information such as emissions inventories, monitoring networks, and modeling demonstrations. Each...

Air quality real-time forecast before and during the G-20 ...

EPA Pesticide Factsheets

The 2016 G-20 Hangzhou summit, the eleventh annual meeting of the G-20 heads of government, will be held during September 3-5, 2016 in Hangzhou, China. For a successful summit, it is important to ensure good air quality. To achieve this goal, governments of Hangzhou and its surrounding provinces will enforce a series of emission reductions, such as a forced closure of major highly-polluting industries and also limiting car and construction emissions in the cities and surroundings during the 2016 G-20 Hangzhou summit. Air quality forecast systems consisting of the two-way coupled WRF-CMAQ and online-coupled WRF-Chem have been applied to forecast air quality in Hangzhou regularly. This study will present the results of real-time forecasts of air quality over eastern China using 12-km grid spacing and for Hangzhou area using 4-km grid spacing with these two modeling systems using emission inventories for base and 2016 G-20 scenarios before and during the 2016 G-20 Hangzhou summit. Evaluations of models’ performance for both cases for PM2.5, PM10, O3, SO2, NO2, CO, air quality index (AQI), and aerosol optical depth (AOD) are carried out by comparing them with observations obtained from satellites, such as MODIS, and surface monitoring networks. The effects of the emission reduction efforts on expected air quality improvements during the2016 G-20 Hangzhou summit will be studied in depth. This study provides insights on how air quality will be improved by a plan

Sharing is Winning: Cooperative Learning about Atmospheric Composition Change

NASA Astrophysics Data System (ADS)

Schuepbach, E.

2010-09-01

This contribution presents evolving good practice in disseminating the body of know-how, skills and competencies within the networked community of atmospheric scientists as established in ACCENT. The promotion of early-career scientists, and encouraging the next generation to move into the field were among the key issues addressed by the "Training and Education" programme in the European Network of Excellence in Atmospheric Composition Change (ACCENT). Dissemination avenues include a virtual knowledge train carrying the wealth of high-quality scientific learning material developed with experts involved in the ACCENT network. Learning opportunities on current research in atmospheric composition change in Europe were also created during face-to-face training workshops. Real-life examples of pressing air quality issues were addressed in meetings with stakeholder groups that offered opportunities for mutual learning in inspiring partnerships. In order to increase the expertise in atmospheric composition change across Europe, activities were organized with the general public (e.g., Café Scientifique), where the participating early-career scientists were confronted with questions from lay people. For interested teachers, didactic translations of compact overviews on air quality science topics developed in ACCENT offer links with the typical European science curriculum and go beyond school book content. Some of the educational events, methods and tools are described in a booklet published in 2009 ("We Care for Clean Air!", ISBN 978-88-95665-01-6). The electronic version and all training material can be downloaded from www.accent-network.org/portal/education - a valuable resource for teachers and learners around the globe.

78 FR 54173 - Approval and Promulgation of Air Quality Implementation Plans; Indiana; Maintenance Plan Update...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-03

... direct final rule in the Federal Register informing the public that the rule will not take effect... maintenance period, a commitment to maintain the existing monitoring network, factors and procedures to be... Network Indiana currently operates two SO 2 monitors in Lake County, Indiana. Indiana has committed to...

Community Air Sensor Network (CAIRSENSE) project ...

EPA Pesticide Factsheets

Advances in air pollution sensor technology have enabled the development of small and low cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low cost, continuous and commercially-available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ~2 km area in Southeastern U.S. Co-location of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as whether multiple identical sensors reproduced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, -0.25 to 0.76, -0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in sampling days could be used in a correction algorihm to im

Matrix Factorisation-based Calibration For Air Quality Crowd-sensing

NASA Astrophysics Data System (ADS)

Dorffer, Clement; Puigt, Matthieu; Delmaire, Gilles; Roussel, Gilles; Rouvoy, Romain; Sagnier, Isabelle

2017-04-01

Internet of Things (IoT) is extending internet to physical objects and places. The internet-enabled objects are thus able to communicate with each other and with their users. One main interest of IoT is the ease of production of huge masses of data (Big Data) using distributed networks of connected objects, thus making possible a fine-grained yet accurate analysis of physical phenomena. Mobile crowdsensing is a way to collect data using IoT. It basically consists of acquiring geolocalized data from the sensors (from or connected to the mobile devices, e.g., smartphones) of a crowd of volunteers. The sensed data are then collectively shared using wireless connection—such as GSM or WiFi—and stored on a dedicated server to be processed. One major application of mobile crowdsensing is environment monitoring. Indeed, with the proliferation of miniaturized yet sensitive sensors on one hand and, on the other hand, of low-cost microcontrollers/single-card PCs, it is easy to extend the sensing abilities of smartphones. Alongside the conventional, regulated, bulky and expensive instruments used in authoritative air quality stations, it is then possible to create a large-scale mobile sensor network providing insightful information about air quality. In particular, the finer spatial sampling rate due to such a dense network should allow air quality models to take into account local effects such as street canyons. However, one key issue with low-cost air quality sensors is the lack of trust in the sensed data. In most crowdsensing scenarios, the sensors (i) cannot be calibrated in a laboratory before or during their deployment and (ii) might be sparsely or continuously faulty (thus providing outliers in the data). Such issues should be automatically handled from the sensor readings. Indeed, due to the masses of generated data, solving the above issues cannot be performed by experts but requires specific data processing techniques. In this work, we assume that some mobile sensors share some information using the APISENSE® crowdsensing platform and we aim to calibrate the sensor responses from the data directly. For that purpose, we express the sensor readings as a low-rank matrix with missing entries and we revisit self-calibration as a Matrix Factorization (MF) problem. In our proposed framework, one factor matrix contains the calibration parameters while the other is structured by the calibration model and contains some values of the sensed phenomenon. The MF calibration approach also uses the precise measurements from ATMO—the French public institution—to drive the calibration of the mobile sensors. MF calibration can be improved using, e.g., the mean calibration parameters provided by the sensor manufacturers, or using sparse priors or a model of the physical phenomenon. All our approaches are shown to provide a better calibration accuracy than matrix-completion-based and robust-regression-based methods, even in difficult scenarios involving a lot of missing data and/or very few accurate references. When combined with a dictionary of air quality patterns, our experiments suggest that MF is not only able to perform sensor network calibration but also to provide detailed maps of air quality.

An overview of the CalNex 2010 field mission: research to extend the understanding of climate and air quality issues in California (Invited)

NASA Astrophysics Data System (ADS)

Ryerson, T. B.

2009-12-01

CalNex is a major field study planned for May and June of 2010. The study, led by NOAA and the California Air Resources Board, coordinates many interagency partners with independent but complementary capabilities and goals. Observations from surface-, aircraft-, ship-, and space-based sensors, along with Lagrangian and Eulerian modeling studies, will be used to extend previous studies and contribute new data and understanding to issues relevant to climate change and air quality in California. This overview will present the integrated approach that will be taken in CalNex, utilizing long-term surface observations, instrumented tall towers, several major intensive ground sites, a network of daily ozonesonde launches, a radar profiling network, multiple instrumented research aircraft, a research vessel, and retrievals from several satellites. The primary CalNex science questions and experimental strategies to address them will be presented, with illustrations and data examples taken from recent field projects in California to provide context for the upcoming study.

Atlanta Rail Yard Study: Evaluation of local-scale air pollution ...

EPA Pesticide Factsheets

Intermodal rail yards are important nodes in the freight transportation network, where freight is organized and moved from one mode of transport to another, critical equipment is serviced, and freight is routed to its next destination. Rail yard environments are also areas with multiple sources of air pollutant emissions (e.g., heavy-duty vehicles, locomotives, cranes), which may affect local air quality in residential areas nearby. In order to understand emissions and related air quality impacts, two field studies took place over the time span of 2010-2012 to measure air pollution trends in close proximity to the Inman and Tilford rail yard complex in Atlanta, GA. One field study involved long-term stationary monitoring of black carbon, fine particles, and carbon dioxide at two stations nearby the rail yard. In addition, a second field study performed intensive mobile air monitoring for a one month period in the summer of 2012 at a roadway network surrounding the rail yard complex and measured a comprehensive array of pollutants. Real-time mobile particulate measurements included particle counts, extinction coefficient, black carbon via light-absorption and particle incandescence, and particle composition derived by aerosol mass spectrometry. Gas-phase measurements included oxides of nitrogen, sulfur dioxide, carbon dioxide, and air toxics (e.g., benzene). Both sets of measurements determined detectable local influence from rail yard-related emissions.

A Modular IoT Platform for Real-Time Indoor Air Quality Monitoring.

PubMed

Benammar, Mohieddine; Abdaoui, Abderrazak; Ahmad, Sabbir H M; Touati, Farid; Kadri, Abdullah

2018-02-14

The impact of air quality on health and on life comfort is well established. In many societies, vulnerable elderly and young populations spend most of their time indoors. Therefore, indoor air quality monitoring (IAQM) is of great importance to human health. Engineers and researchers are increasingly focusing their efforts on the design of real-time IAQM systems using wireless sensor networks. This paper presents an end-to-end IAQM system enabling measurement of CO₂, CO, SO₂, NO₂, O₃, Cl₂, ambient temperature, and relative humidity. In IAQM systems, remote users usually use a local gateway to connect wireless sensor nodes in a given monitoring site to the external world for ubiquitous access of data. In this work, the role of the gateway in processing collected air quality data and its reliable dissemination to end-users through a web-server is emphasized. A mechanism for the backup and the restoration of the collected data in the case of Internet outage is presented. The system is adapted to an open-source Internet-of-Things (IoT) web-server platform, called Emoncms, for live monitoring and long-term storage of the collected IAQM data. A modular IAQM architecture is adopted, which results in a smart scalable system that allows seamless integration of various sensing technologies, wireless sensor networks (WSNs) and smart mobile standards. The paper gives full hardware and software details of the proposed solution. Sample IAQM results collected in various locations are also presented to demonstrate the abilities of the system.

Measurements of Atmospheric NH3, NOy/NOx, and NO2 and Deposition of Total Nitrogen at the Beaufort, NC CASTNET Site (BFT142)

EPA Science Inventory

The Clean Air Status and Trends Network (CASTNET) is a long-term environmental monitoring program that measures trends in ambient air quality and atmospheric dry pollutant deposition across the United States. CASTNET has been operating since 1987 and currently consists of 89 moni...

Air quality remote sensing over alpine regions with METEOSAT SEVIRI

NASA Astrophysics Data System (ADS)

Emili, E.; Popp, C.; Petitta, M.; Riffler, M.; Wunderle, S.

2009-04-01

It is well demonstrated that small aerosol particles or particulate matter (PM10 and PM2.5) affect air quality and can have severe effects on human's health. Hence, it is of great interest for public institutions to have an efficient PM monitoring network. In the last decades this data has been provided from ground-based instruments. Moreover, due to the fast development of space-borne remote sensing instruments, we can now be able to take advantage of air pollution measurements from space, which bears the potential to fill up the gap of spatial coverage from ground-based networks. This also improves the capability to assess air pollutants transport properties together with a better implementation in forecasting data assimilation procedures. In this study we examine the possibility of using data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI), on-board of the geostationary Meteosat Second Generation (MSG) platform, to provide PM concentrations values over Switzerland. SEVIRI's high temporal resolution (15 minutes) could be very useful in investigating the daily behaviour of air pollutants and therefore be a good complement to measurements from polar orbiting sensors (e.g. MODIS). Switzerland is of particular interest because of its mountainous orography that hampers pollutants dispersion. Further, major transalpine connection routes, often characterised by high traffic load, act as a significant air pollution source. The south of Switzerland is also occasionally influenced by pollutants transported from the highly industrialised Po Valley in northern Italy. We investigate the existence of a linear relation between the SEVIRI retrieved AOD (Aerosol Optical Depth) and the PM concentration obtained from the ground-based air quality network NABEL (Nationales Beobachtungsnetz fuer Luftfremdstoffe). The temporal trend of this two quantities shows a significant relationship over various locations. The correlation coefficient is in some cases higher than 0.6, indicating the possibility of estimating PM concentrations from SEVIRI AOD with a reasonable uncertainty using a statistical empirical linear model. The quality of this approach is highly influenced by the seasonal variability and by the meteorological conditions. We also include meteorological data in order to investigate the observed correlation and to improve the statistical empirical model. Finally, the possible sources of errors for this approach are examined.

Improving Environmental Literacy through GO3 Citizen Science Project

NASA Astrophysics Data System (ADS)

Wilkening, B.

2011-12-01

In the Global Ozone (GO3) Project students measure ground-level ozone on a continuous basis and upload their results to a global network used by atmospheric scientists and schools. Students learn important concepts such as chemical measurement methods; instrumentation; calibration; data acquisition using computers; data quality; statistics; data analysis and graphing; posting of data to the web; the chemistry of air pollution; stratospheric ozone depletion and global climate change. Students collaborate with researchers and other students globally in the GO3 network. Wilson K-8 School is located in a suburban area in Pima County, Arizona. Throughout the year we receive high ozone alert days. Prior to joining the GO3 project, my students were unaware of air pollution alerts, risks and causes. In the past when Pima County issued alerts to the school, they were posted on signs around the school. No explanation was provided to the students and the signs were often left up for days. This discounted the potential health effects of the situation, resulting in the alerts effectively being ignored. The GO3 project is transforming both my students and our school community. Now my students are:

• Performing science research
• Utilizing technology and increasing their skills
• Collaborating in a responsible manner on the global GO3 social network
• Communicating their work to the community
• Issuing their own ozone alerts to their school
• Advocating for actions that will improve air quality

Evaluating A Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals

NASA Technical Reports Server (NTRS)

Johnson, Matthew S.; Sullivan, John T.; Liu, Xiong; Newchurch, Mike; Kuang, Shi; McGee, Thomas J.; Langford, Andrew O'Neil; Senff, Christoph J.; Leblanc, Thierry; Berkoff, Timothy;

Evaluating a Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals

NASA Technical Reports Server (NTRS)

Johnson, Matthew S.; Sullivan, John; Liu, Xiong; Newchurch, Mike; Kuang, Shi; McGee, Thomas; Langford, Andrew; Senff, Chris; Leblanc, Thierry; Berkoff, Timothy;

Evaluating A Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals

NASA Astrophysics Data System (ADS)

Johnson, M. S.; Sullivan, J. T.; Liu, X.; Newchurch, M.; Kuang, S.; McGee, T. J.; Langford, A. O.; Senff, C. J.; Leblanc, T.; Berkoff, T.; Gronoff, G.; Chen, G.; Strawbridge, K. B.

2016-12-01

Ozone (O3) is a greenhouse gas and toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is primarily conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address these limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME, GOME-2, and OMI. This algorithm uses a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB) O3 climatology). It has been shown that satellite O3 retrievals are sensitive to a priori O3 profiles and covariance matrices. During this work we investigate the climatological data to be used in TEMPO algorithms (TB O3) and simulated data from the NASA GMAO Goddard Earth Observing System (GEOS-5) Forward Processing (FP) near-real-time (NRT) model products. These two data products will be evaluated with ground-based lidar data from the Tropospheric Ozone Lidar Network (TOLNet) at various locations of the US. This study evaluates the TB climatology, GEOS-5 climatology, and 3-hourly GEOS-5 data compared to lower tropospheric observations to demonstrate the accuracy of a priori information to potentially be used in TEMPO O3 algorithms. Here we present our initial analysis and the theoretical impact on TEMPO retrievals in the lower troposphere.

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

PubMed

Broday, David M

2017-10-02

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

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

PubMed Central

2017-01-01

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

FIELD EVALUATION OF SAMPLERS FOR EPA'S NATIONAL PM 2.5 CHEMICAL SPECIATION NETWORK-PRELIMINARY RESULTS FROM ATLANTA

EPA Science Inventory

The US EPA bas established a national network at nearly 1100 sites to monitor PM2.5 mass for testing compliance with the PM2.5 National Ambient Air Quality Standards. The objective of the field evaluation is to determine the performance characteristics for the collection of the...

Children’s Environmental Health: Online Resources for Healthcare Providers

EPA Pesticide Factsheets

Free online resources, many produced in the North American Pediatric Environmental Health Specialty Unit (PEHSU) network, covering general information, air quality, asthma, climate change, lead, mercury, mold, pesticides, and water.

DEVELOPMENT OF QUALITY ASSURANCE AND QUALITY CONTROL GUIDANCE FOR GROUND-BASED REMOTE SENSORS FOR USE IN REGULATORY MONITORING

EPA Science Inventory

The U.S. Environmental Protection Agency's (EPA) authority for enhanced monitoring activities is provided for in Title I, Section 182 of the Clean Air Act Amendment of 1990. or example, the Photochemical Assessment Monitoring Station (PAMS) network is one such program which requi...

Hybrid Air Quality Modeling Approach For Use in the Near ...

EPA Pesticide Factsheets

The Near-road EXposures to Urban air pollutant Study (NEXUS) investigated whether children with asthma living in close proximity to major roadways in Detroit, MI, (particularly near roadways with high diesel traffic) have greater health impacts associated with exposure to air pollutants than those living farther away. A major challenge in such health and exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. This paper presents a hybrid air quality modeling approach and its application in NEXUS in order to provide spatial and temporally varying exposure estimates and identification of the mobile source contribution to the total pollutant exposure. Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the AERMOD and R-LINE dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the Community Multiscale Air Quality (CMAQ) model and the Space/Time Ordinary Kriging (STOK) model. To capture the near-road pollutant gradients, refined “mini-grids” of model recep

3D Air Quality and the Clean Air Interstate Rule: Lagrangian Sampling of CMAQ Model Results to Aid Regional Accountability Metrics

NASA Technical Reports Server (NTRS)

Fairlie, T. D.; Szykman, Jim; Pierce, Robert B.; Gilliland, A. B.; Engel-Cox, Jill; Weber, Stephanie; Kittaka, Chieko; Al-Saadi, Jassim A.; Scheffe, Rich; Dimmick, Fred;

An Overview of the 3C-STAR project

NASA Astrophysics Data System (ADS)

Zhang, Y.

2009-04-01

Over the past three decades, city clusters have played a leading role in the economic growth of China, owing to their collective economic capacity and interdependency. However, pollution prevention lags behind the economic boom, led to a general decline in air quality in city clusters. As a result, industrial emissions and traffic exhausts together contribute to high levels of ozone (O3) and fine particulate matter (PM2.5) pollution problems ranging from urban to regional scale. Such high levels of both primary and secondary airborne pollutants lead to the development of a (perhaps typically Chinese) "air pollution complex" concept. Air pollution complex is particularly true and significant in Beijing-Tianjin area, Pearl River Delta (PRD) and Yangtze River Delta. The concurrent high concentrations of O3 and PM2.5 in PRD as well as in other China city clusters have led to rather unique pollution characteristics due to interactions between primary emissions and photochemical processes, between gaseous compounds and aerosol phase species, and between local and regional scale processes. The knowledge and experience needed to find solutions to the unique pollution complex in China are still lacking. Starting from 2007, we launch a major project "Synthesized Prevention Techniques for Air Pollution Complex and Integrated Demonstration in Key City-Cluster Region" (3C-STAR) to address those problems scientifically and technically. The purpose of the project is to build up the capacity of regional air pollution control and to establish regional coordination mechanism for joint implementation of pollution control. The project includes a number of key components technically: regional air quality monitoring network and super-sites, regional dynamic emission inventory of multi-pollutants, regional ensemble air quality forecasting model system, and regional management system supported by decision making platform. The 3C-STAR project selected PRD as a core area to have technical demonstration, and thus provide opportunities as well as challenges for PRD to improve its regional air quality. An integrated field measurement campaign 3C-STAR2008 was organized during October 15-November 19, 2008, including 3-D regional air quality monitoring network, two super-sites, and in-site meteorological and air quality forecasting. With the efforts of more than 100 scientists and students from 12 research institutes, the 3C-STAR2008 was conducted with great success. A great amount of data with rigorous QA/QC procedures has been obtained and data analysis is underway. In this talk, an overview of the 3C-STAR project will be presented, together with major findings from previous PRD campaigns (PRD2004 and PRD2006).

Aerosol Absorption by Black Carbon and Dust: Implications of Climate Change and Air Quality in Asia

NASA Technical Reports Server (NTRS)

Chin, Mian

2010-01-01

Atmospheric aerosol distributions from 2000 to 2007 are simulated with the global model GOCART to attribute light absorption by aerosol to its composition and sources. We show the seasonal and interannual variations of absorbing aerosols in the atmosphere over Asia, mainly black carbon and dust. and their linkage to the changes of anthropogenic and dust emissions in the region. We compare our results with observations from satellite and ground-based networks, and estimate the importance of black carbon and dust on regional climate forcing and air quality.

Air quality impact assessment of multiple open pit coal mines in northern Colombia.

PubMed

Huertas, José I; Huertas, María E; Izquierdo, Sebastián; González, Enrique D

2012-01-01

The coal mining region in northern Colombia is one of the largest open pit mining regions of the world. In 2009, there were 8 mining companies in operation with an approximate coal production of ∼70 Mtons/year. Since 2007, the Colombian air quality monitoring network has reported readings that exceed the daily and annual air quality standards for total suspended particulate (TSP) matter and particles with an equivalent aerodynamic diameter smaller than 10 μm (PM₁₀) in nearby villages. This paper describes work carried out in order to establish an appropriate clean air program for this region, based on the Colombian national environmental authority requirement for modeling of TSP and PM(10) dispersion. A TSP and PM₁₀ emission inventory was initially developed, and topographic and meteorological information for the region was collected and analyzed. Using this information, the dispersion of TSP was modeled in ISC3 and AERMOD using meteorological data collected by 3 local stations during 2008 and 2009. The results obtained were compared to actual values measured by the air quality monitoring network. High correlation coefficients (>0.73) were obtained, indicating that the models accurately described the main factors affecting particle dispersion in the region. The model was then used to forecast concentrations of particulate matter for 2010. Based on results from the model, areas within the modeling region were identified as highly, fairly, moderately and marginally polluted according to local regulations. Additionally, the contribution particulate matter to the pollution at each village was estimated. Using these predicted values, the Colombian environmental authority imposed new decontamination measures on the mining companies operating in the region. These measures included the relocation of three villages financed by the mine companies based on forecasted pollution levels. Copyright © 2011. Published by Elsevier Ltd.

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

PubMed

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

2015-01-01

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

Ground-Based Aerosol Measurements | Science Inventory ...

EPA Pesticide Factsheets

Atmospheric particulate matter (PM) is a complex chemical mixture of liquid and solid particles suspended in air (Seinfeld and Pandis 2016). Measurements of this complex mixture form the basis of our knowledge regarding particle formation, source-receptor relationships, data to test and verify complex air quality models, and how PM impacts human health, visibility, global warming, and ecological systems (EPA 2009). Historically, PM samples have been collected on filters or other substrates with subsequent chemical analysis in the laboratory and this is still the major approach for routine networks (Chow 2005; Solomon et al. 2014) as well as in research studies. In this approach, air, at a specified flow rate and time period, is typically drawn through an inlet, usually a size selective inlet, and then drawn through filters, 1 INTRODUCTION Atmospheric particulate matter (PM) is a complex chemical mixture of liquid and solid particles suspended in air (Seinfeld and Pandis 2016). Measurements of this complex mixture form the basis of our knowledge regarding particle formation, source-receptor relationships, data to test and verify complex air quality models, and how PM impacts human health, visibility, global warming, and ecological systems (EPA 2009). Historically, PM samples have been collected on filters or other substrates with subsequent chemical analysis in the laboratory and this is still the major approach for routine networks (Chow 2005; Solomo

76 FR 28393 - Proposed Approval of Air Quality Implementation Plan; Ohio and West Virginia; Determinations of...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-17

... substituted for missing data. See 40 CFR Part 58, appendix D for network design criteria. EPA has approved the... certified ambient air monitoring data for the 2007-2009 period showing that the areas have monitored attainment of the annual PM 2.5 NAAQS. EPA also evaluated incomplete data from this period from other...

77 FR 33808 - Agency Information Collection; Activity Under OMB Review: Airline Service Quality Performance...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-07

... Administration uses Part 234 data to pinpoint and analyze air traffic delays. Wheels-up and wheels-down times are... elapsed flight time, wheels-down minus wheels- up time, is compared to scheduled elapsed flight time to... the air network, which enables the FAA to study the ripple effects of delays at hub airports. The data...

A smart indoor air quality sensor network

NASA Astrophysics Data System (ADS)

Wen, Jin

2006-03-01

The indoor air quality (IAQ) has an important impact on public health. Currently, the indoor air pollution, caused by gas, particle, and bio-aerosol pollutants, is considered as the top five environmental risks to public health and has an estimated cost of $2 billion/year due to medical cost and lost productivity. Furthermore, current buildings are especially vulnerable for chemical and biological warfare (CBW) agent contamination because the central air conditioning and ventilation system serve as a nature carrier to spread the released agent from one location to the whole indoor environment within a short time period. To assure the IAQ and safety for either new or existing buildings, real time comprehensive IAQ and CBW measurements are needed. With the development of new sensing technologies, economic and reliable comprehensive IAQ and CBW sensors become promising. However, few studies exist that examine the design and evaluation issues related to IAQ and CBW sensor network. In this paper, relevant research areas including IAQ and CBW sensor development, demand control ventilation, indoor CBW sensor system design, and sensor system design for other areas such as water system protection, fault detection and diagnosis, are reviewed and summarized. Potential research opportunities for IAQ and CBW sensor system design and evaluation are discussed.

Air quality, primary air pollutants and ambient concentrations inventory for Romania

NASA Astrophysics Data System (ADS)

Năstase, Gabriel; Șerban, Alexandru; Năstase, Alina Florentina; Dragomir, George; Brezeanu, Alin Ionuț

2018-07-01

Air pollution is among the greatest risk factors for human health, but it also poses risks to the food security, the economy and the environment. The majority of the pollutants emitted by human activities derive from the production and use of fossil-fuel-based energy. Most energy-related emissions contain sulfur dioxide and nitrogen oxides. The principal source of sulfur dioxide originates from coal, and the main sources of nitrogen oxide emissions are power generation and use of vehicles. Other important pollutants are the inhalable coarse particles (PM10) and the fine particulate matter (PM2.5), which arises from the building sector. Over the last decade, since Romania joined the European Union on the 1st of January 2007, the use of fossil fuels has decreased dramatically, as consumers switched to either natural gas or biomass. This was as a result of the European Commission encouraging the member countries to make use of renewable sources (including biomass). To reduce the PM emissions, in April 2015 EC has extended the EcoDesign Directive to solid-fuel boilers and solid-fuel space heaters. The boilers need to generally meet certain requirements that will be introduced by 1 January 2020. In this article, we are highlighting the fluctuations in air pollution in Romania from the European WebDAB - EMAP database and trends in ambient concentrations of air pollutants using Romania's national air pollution monitoring network. Romania's Air Pollutants/Air Quality Monitoring Network consists of 142 automatic air quality monitoring stations. The results indicate that Romania's annual average mass emissions of CO decreased from 3186 Gg in 1990 to 774 in 2014 (decrease by <76%), SOx decreased from 1311 Gg-176 Gg (decrease by ∼60%), NOx decreased from 546 Gg to 218 (decrease by ∼87%), CO2 decreased from 66.226 Gg/year in 2007 to 38.916 Gg/year in 2014 (decrease by <41%).

WebDMS: A Web-Based Data Management System for Environmental Data

NASA Astrophysics Data System (ADS)

Ekstrand, A. L.; Haderman, M.; Chan, A.; Dye, T.; White, J. E.; Parajon, G.

2015-12-01

DMS is an environmental Data Management System to manage, quality-control (QC), summarize, document chain-of-custody, and disseminate data from networks ranging in size from a few sites to thousands of sites, instruments, and sensors. The server-client desktop version of DMS is used by local and regional air quality agencies (including the Bay Area Air Quality Management District, the South Coast Air Quality Management District, and the California Air Resources Board), the EPA's AirNow Program, and the EPA's AirNow-International (AirNow-I) program, which offers countries the ability to run an AirNow-like system. As AirNow's core data processing engine, DMS ingests, QCs, and stores real-time data from over 30,000 active sensors at over 5,280 air quality and meteorological sites from over 130 air quality agencies across the United States. As part of the AirNow-I program, several instances of DMS are deployed in China, Mexico, and Taiwan. The U.S. Department of State's StateAir Program also uses DMS for five regions in China and plans to expand to other countries in the future. Recent development has begun to migrate DMS from an onsite desktop application to WebDMS, a web-based application designed to take advantage of cloud hosting and computing services to increase scalability and lower costs. WebDMS will continue to provide easy-to-use data analysis tools, such as time-series graphs, scatterplots, and wind- or pollution-rose diagrams, as well as allowing data to be exported to external systems such as the EPA's Air Quality System (AQS). WebDMS will also provide new GIS analysis features and a suite of web services through a RESTful web API. These changes will better meet air agency needs and allow for broader national and international use (for example, by the AirNow-I partners). We will talk about the challenges and advantages of migrating DMS to the web, modernizing the DMS user interface, and making it more cost-effective to enhance and maintain over time.

40 CFR Appendix D to Part 58 - Network Design Criteria for Ambient Air Quality Monitoring

Code of Federal Regulations, 2010 CFR

2010-07-01

... residential district. In this case, one location is representative of a neighborhood of small scale sites and... least one of the required FRM/FEM/ARM monitors is itself a continuous FEM or ARM monitor in which case... studies. 4.7.5Special Network Considerations Required When Using PM2.5 Spatial Averaging Approaches. (a...

40 CFR Appendix D to Part 58 - Network Design Criteria for Ambient Air Quality Monitoring

Code of Federal Regulations, 2012 CFR

2012-07-01

... residential district. In this case, one location is representative of a neighborhood of small scale sites and... least one of the required FRM/FEM/ARM monitors is itself a continuous FEM or ARM monitor in which case... studies. 4.7.5Special Network Considerations Required When Using PM2.5 Spatial Averaging Approaches. (a...

40 CFR Appendix D to Part 58 - Network Design Criteria for Ambient Air Quality Monitoring

Code of Federal Regulations, 2011 CFR

2011-07-01

... residential district. In this case, one location is representative of a neighborhood of small scale sites and... least one of the required FRM/FEM/ARM monitors is itself a continuous FEM or ARM monitor in which case... studies. 4.7.5Special Network Considerations Required When Using PM2.5 Spatial Averaging Approaches. (a...

Building and evaluating sensor-based Citizens' Observatories for improving quality of life in cities

NASA Astrophysics Data System (ADS)

Castell, Nuria; Lahoz, William; Schneider, Philipp; Høiskar, Britt Ann; Grossberndt, Sonja; Naderer, Clemens; Robinson, Johanna; Kocman, David; Horvat, Milena; Bartonova, Alena

2014-05-01

Urban air quality, the environmental quality of public spaces and indoor areas such as schools, are areas of great concern to citizens and policymakers. However, access to information addressing these areas is not always available in a user-friendly manner. In particular, the quality and quantity of this information is not consistent across these areas, and does not reflect differences in needs among users. The EU-funded CITI-SENSE project will build on the concept of the Citizens' Observatories to empower citizens to contribute to and participate in environmental governance, and enable them to support and influence decision making by policymakers. To achieve this goal, CITI-SENSE will develop, test, demonstrate and validate a community-based environmental monitoring and information system using low-cost sensors and Earth Observation applications. Key to achieving this goal is the chain "sensors-platforms-products-users" linking providers of technology to users: (i) technologies for distributed monitoring (sensors); (ii) information and communication technologies (platform); (iii) information products and services (products); (iv) and citizen involvement in both monitoring and societal decisions (users). The CITI-SENSE observatories cover three empowerment initiatives: urban air quality; public spaces; and school indoor quality. The empowerment initiatives are being performed at nine locations across Europe. Each location has adapted the generic case study to their local circumstances and has contacted the urban stakeholders needed to run the study. The empowerment initiatives are divided into two phases: a first phase (Pilot Study), and a second phase (Full Implementation). The main goal of the Pilot Study is to test and evaluate the chain "sensors-platform-products-users". To assess the results of the empowerment initiatives, key performance indicators (KPIs) are being developed; these include questionnaires for users. The KPIs will be used to design the full implementation phase of the project. First results from the Pilot Study will be presented for three participating cities: Ljubljana (Slovenia), Vienna (Austria) and Oslo (Norway), which differ in size, environmental conditions and social perception on local air quality. Ljubljana and Oslo empowerment initiatives include urban air quality, and school indoor air quality, while Vienna only includes urban air quality. For the area of urban air quality, the three cities will deploy a wireless network of five static sensor nodes and distribute five personal sensors among people to be carried while performing daily activities in the pilot study. The data will be accessible to users through mobile phones, web services and other devices. For the full implementation phase the sensor network will comprise a total of 20 to 40 static nodes, depending on the size of the city, and 20 personal nodes. For the school indoor air quality three sensors will be allocated inside the school and one outside. The data will be visible provided in school classrooms giving the students a unique and innovative approach to learn about air quality by being involved. Acknowledgements: CITI-SENSE is a Collaborative Project partly funded by the EU FP7-ENV-2012 under grant agreement no 308524. www.citi-sense.eu.

Indoor Air Quality Tribal Partners Program

EPA Pesticide Factsheets

IAQ Tribal Partners Program. Empowering champions of healthy IAQ in tribal communities with tools for networking, sharing innovative and promising programs and practices and a reservoir of the best available tribal-specific IAQ information and materials.

Global Aerosol Observations

Atmospheric Science Data Center

2013-04-19

... atmosphere, directly influencing global climate and human health. Ground-based networks that accurately measure column aerosol amount and ... being used to improve Air Quality Models and for regional health studies. To assess the human-health impact of chronic aerosol exposure, ...

Satellite-Surface Perspectives of Air Quality and Aerosol-Cloud Effects on the Environment: An Overview of 7-SEAS BASELInE

NASA Technical Reports Server (NTRS)

Tsay, Si-Chee; Maring, Hal B.; Lin, Neng-Huei; Buntoung, Sumaman; Chantara, Somporn; Chuang, Hsiao-Chi; Gabriel, Philip M.; Goodloe, Colby S.; Holben, Brent N.; Hsiao, Ta-Chih;

A Modular IoT Platform for Real-Time Indoor Air Quality Monitoring

PubMed Central

Abdaoui, Abderrazak; Ahmad, Sabbir H.M.; Touati, Farid; Kadri, Abdullah

2018-01-01

The impact of air quality on health and on life comfort is well established. In many societies, vulnerable elderly and young populations spend most of their time indoors. Therefore, indoor air quality monitoring (IAQM) is of great importance to human health. Engineers and researchers are increasingly focusing their efforts on the design of real-time IAQM systems using wireless sensor networks. This paper presents an end-to-end IAQM system enabling measurement of CO2, CO, SO2, NO2, O3, Cl2, ambient temperature, and relative humidity. In IAQM systems, remote users usually use a local gateway to connect wireless sensor nodes in a given monitoring site to the external world for ubiquitous access of data. In this work, the role of the gateway in processing collected air quality data and its reliable dissemination to end-users through a web-server is emphasized. A mechanism for the backup and the restoration of the collected data in the case of Internet outage is presented. The system is adapted to an open-source Internet-of-Things (IoT) web-server platform, called Emoncms, for live monitoring and long-term storage of the collected IAQM data. A modular IAQM architecture is adopted, which results in a smart scalable system that allows seamless integration of various sensing technologies, wireless sensor networks (WSNs) and smart mobile standards. The paper gives full hardware and software details of the proposed solution. Sample IAQM results collected in various locations are also presented to demonstrate the abilities of the system. PMID:29443893

Solutions Network Formulation Report. Integration of OMI and TES Aerosol Products into the EPA Regional Planning Organizations' FASTNET Aerosol Tracking and Analysis Tool

NASA Technical Reports Server (NTRS)

Knowlton, Kelly; Andrews, Jane C.

2006-01-01

Every year, more than 280 million visitors tour our Nation s most treasured parks and wilderness areas. Unfortunately, many visitors are unable to see the spectacular vistas they expect because of white or brown haze in the air. Most of this haze is not natural; it is air pollution, carried by the wind often hundreds of miles from its origin. Some of the pollutants have been linked to serious health problems, such as asthma and other lung disorders, and even premature death. In addition, nitrates and sulfates contribute to acid rain formation, which contaminates rivers and lakes and erodes buildings and historical monuments. The U.S. Environmental Protection Agency RPOs (Regional Planning Organizations) have been tasked with monitoring and determining the nature and origin of haze in Class I scenic areas, and finding ways to reduce haze in order to improve visibility in these areas. The RPOs have developed an Internet-based air quality DST (Decision Support Tool) called FASTNET (Fast Aerosol Sensing Tools for Natural Event Tracking). While FASTNET incorporates a few satellite datasets, most of the data utilized by this DST comes from ground-based instrument networks. The problem is that in many areas the sensors are sparsely located, with long distances between them, causing difficulties in tracking haze over the United States, determining its source, and analyzing its content. Satellite data could help to fill in the data gaps and to supplement and verify ground-recorded air quality data. Although satellite data are now being used for air quality research applications, such data are not routinely used for environmental decision support, in part because of limited resources, difficulties with interdisciplinary data interpretation, and the need for advanced inter-agency partnerships. As a result, the validation and verification of satellite data for air quality operational system applications has been limited This candidate solution evaluates the usefulness of OMI (Ozone Monitoring Instrument) and TES (Tropospheric Emission Spectrometer) air quality data for the RPOs by comparing OMI and TES data with ground-based data that are acquired during identified episodes of air pollution. The air quality data from OMI and TES are of different spectral ranges than data from satellites currently included in FASTNET, giving them potential advantages over the existing satellites. If the OMI and TES data are shown to be useful to the RPOs, they would then be integrated into the FASTNET DST for use on an operational basis.

Warm Dry Weather Conditions Cause of 2016 Fort McMurray Wild Forest Fire and Associated Air Quality

NASA Astrophysics Data System (ADS)

de Azevedo, S. C.; Singh, R. P.; da Silva, E. A., Sr.

2016-12-01

The climate change is evident from the increasing temperature around the world, day to day life and increasing frequency of natural hazards. The warm and dry conditions are the cause of frequent forest fires around the globe. Forest fires severely affect the air quality and human health. Multi sensor satellites and dense network of ground stations provide information about vegetation health, meteorological, air quality and atmospheric parameters. We have carried out detailed analysis of satellite and ground data of wild forest fire that occurred in May 2016 in Fort McMurray, Alberta, Canada. This wild forest fire destroyed 10 per cent of Fort McMurray's housing and forced more than 90,000 people to evacuate the surrounding areas. Our results show that the warm and dry conditions with low rainfall were the cause of Fort McMurray wild fire. The air quality parameters (particulate matter, CO, ozone, NO2, methane) and greenhouse gases measured from Atmospheric Infrared Sounder (AIRS) satellite show enhanced levels soon after the forest fire. The emissions from the forest fire affected health of population living in surrounding areas up to 300 km radius.

Air quality measurements and monitoring network in the Republic of Latvia

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

Grinman, A.; Lyulko, J.; Dubrovskaja, R.

1996-12-31

The territory of Latvia is covered with a wide environmental monitoring network, that falls under 2 main categories: (1) regional network featuring the region and involved in international monitoring programs, including EMEP, GAW, IM; (2) state network providing for local pollution monitoring of the atmosphere (19 posts), precipitation (5 station) and radioactivity (46 station). In 1994, measurements were made at 20 stationary posts located in Daugavpils (2), Jekabpils (2), Jurmala, (2), Liepaja (2), Nigrande (1), Olaine (1), Rezekne (1), Riga (5), Valn-dera (2), Ventspils (2). This atmospheric air observation network covers mostly towns densely populated with industrial objects and othermore » pollutant emitting sources. Thus, the observation programs encompass measurements of pollutants that have higher concentrations in the ambient air. Results indicate that the annual pollution dynamics are closely connected with concentration fluctuations in the seasons. The sulfur dioxide and nitrogen dioxide concentrations increased during the heating season in Jekabpils, Jurmala and Valmiera, i.e., in the town that have many small heating installations. The data obtained allow to trace a dependence of measurement values upon the location of the observational posts vis-a-vis the pollutant emitting sources.« less

Air concentrations of volatile compounds near oil and gas production: a community-based exploratory study.

PubMed

Macey, Gregg P; Breech, Ruth; Chernaik, Mark; Cox, Caroline; Larson, Denny; Thomas, Deb; Carpenter, David O

2014-10-30

Horizontal drilling, hydraulic fracturing, and other drilling and well stimulation technologies are now used widely in the United States and increasingly in other countries. They enable increases in oil and gas production, but there has been inadequate attention to human health impacts. Air quality near oil and gas operations is an underexplored human health concern for five reasons: (1) prior focus on threats to water quality; (2) an evolving understanding of contributions of certain oil and gas production processes to air quality; (3) limited state air quality monitoring networks; (4) significant variability in air emissions and concentrations; and (5) air quality research that misses impacts important to residents. Preliminary research suggests that volatile compounds, including hazardous air pollutants, are of potential concern. This study differs from prior research in its use of a community-based process to identify sampling locations. Through this approach, we determine concentrations of volatile compounds in air near operations that reflect community concerns and point to the need for more fine-grained and frequent monitoring at points along the production life cycle. Grab and passive air samples were collected by trained volunteers at locations identified through systematic observation of industrial operations and air impacts over the course of resident daily routines. A total of 75 volatile organics were measured using EPA Method TO-15 or TO-3 by gas chromatography/mass spectrometry. Formaldehyde levels were determined using UMEx 100 Passive Samplers. Levels of eight volatile chemicals exceeded federal guidelines under several operational circumstances. Benzene, formaldehyde, and hydrogen sulfide were the most common compounds to exceed acute and other health-based risk levels. Air concentrations of potentially dangerous compounds and chemical mixtures are frequently present near oil and gas production sites. Community-based research can provide an important supplement to state air quality monitoring programs.

Do Individual and Neighborhood Characteristics Influence Perceived Air Quality?

PubMed Central

Deguen, Séverine; Padilla, Manon; Kihal-Talantikite, Wahida

2017-01-01

Background: Despite improvements, air pollution still remains a major public health issue. Numerous epidemiological studies have demonstrated the adverse health effects of air pollution exposure based on modeled measures, but only a few have considered the health impact of perceived air quality. Improving our knowledge of individual perceptions is crucial to defining targeted actions and promoting appropriate intervention measures. Our objective is to investigate the relationship between subjective and objective measures of air pollution and to focus on how individual characteristics combined with the neighborhood socioeconomic deprivation index, measured at a fine spatial scale, may or may not alter this relationship. Materials and Methods: The subjective measures of air quality reported by a sample of Lyon residents were collected via an individual questionnaire. The objective measures of air pollution were modeled by the local air quality monitoring network of the Rhône-Alpes region at census block level. We used a socioeconomic deprivation index to capture the different socioeconomic dimensions at census block level. The statistical analysis was structured in two steps: (1) identification of individual determinants of the subjective measures of air quality using multiple correspondence analysis followed by hierarchical clustering; (2) identification of individual and contextual characteristics that may alter the relationship between the objective and subjective measures of air pollution. Results: Among the youngest and the middle aged population (ages 30 to 59), consistent results between level of satisfaction, perceived air quality and objective measures of air pollution were found whatever the individual characteristics of the population. It is less clear among the oldest population: globally no significant difference between the NO2 concentrations and the level of satisfaction was observed. Conclusions: We found a significant relationship between the subjective and objective measures of air pollution in many population sub-groups with different combinations of individual characteristics. The relationship is less clear among the oldest population, which confirms previous findings. Our finding highlights that age combined with low level of education and unemployment, or women or health problems as well as the neighborhood deprivation index influence the level of air quality satisfaction. PMID:29231899

Do Individual and Neighborhood Characteristics Influence Perceived Air Quality?

PubMed

Deguen, Séverine; Padilla, Manon; Padilla, Cindy; Kihal-Talantikite, Wahida

2017-12-12

Background : Despite improvements, air pollution still remains a major public health issue. Numerous epidemiological studies have demonstrated the adverse health effects of air pollution exposure based on modeled measures, but only a few have considered the health impact of perceived air quality. Improving our knowledge of individual perceptions is crucial to defining targeted actions and promoting appropriate intervention measures. Our objective is to investigate the relationship between subjective and objective measures of air pollution and to focus on how individual characteristics combined with the neighborhood socioeconomic deprivation index, measured at a fine spatial scale, may or may not alter this relationship. Materials and Methods : The subjective measures of air quality reported by a sample of Lyon residents were collected via an individual questionnaire. The objective measures of air pollution were modeled by the local air quality monitoring network of the Rhône-Alpes region at census block level. We used a socioeconomic deprivation index to capture the different socioeconomic dimensions at census block level. The statistical analysis was structured in two steps: (1) identification of individual determinants of the subjective measures of air quality using multiple correspondence analysis followed by hierarchical clustering; (2) identification of individual and contextual characteristics that may alter the relationship between the objective and subjective measures of air pollution. Results : Among the youngest and the middle aged population (ages 30 to 59), consistent results between level of satisfaction, perceived air quality and objective measures of air pollution were found whatever the individual characteristics of the population. It is less clear among the oldest population: globally no significant difference between the NO₂ concentrations and the level of satisfaction was observed. Conclusion s : We found a significant relationship between the subjective and objective measures of air pollution in many population sub-groups with different combinations of individual characteristics. The relationship is less clear among the oldest population, which confirms previous findings. Our finding highlights that age combined with low level of education and unemployment, or women or health problems as well as the neighborhood deprivation index influence the level of air quality satisfaction.

Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment.

PubMed

Shi, Yuan; Lau, Kevin Ka-Lun; Ng, Edward

2017-08-01

Urban air quality serves as an important function of the quality of urban life. Land use regression (LUR) modelling of air quality is essential for conducting health impacts assessment but more challenging in mountainous high-density urban scenario due to the complexities of the urban environment. In this study, a total of 21 LUR models are developed for seven kinds of air pollutants (gaseous air pollutants CO, NO 2 , NO x , O 3 , SO 2 and particulate air pollutants PM 2.5 , PM 10 ) with reference to three different time periods (summertime, wintertime and annual average of 5-year long-term hourly monitoring data from local air quality monitoring network) in Hong Kong. Under the mountainous high-density urban scenario, we improved the traditional LUR modelling method by incorporating wind availability information into LUR modelling based on surface geomorphometrical analysis. As a result, 269 independent variables were examined to develop the LUR models by using the "ADDRESS" independent variable selection method and stepwise multiple linear regression (MLR). Cross validation has been performed for each resultant model. The results show that wind-related variables are included in most of the resultant models as statistically significant independent variables. Compared with the traditional method, a maximum increase of 20% was achieved in the prediction performance of annual averaged NO 2 concentration level by incorporating wind-related variables into LUR model development. Copyright © 2017 Elsevier Inc. All rights reserved.

Summarising climate and air quality (ozone) data on self-organising maps: a Sydney case study.

PubMed

Jiang, Ningbo; Betts, Alan; Riley, Matt

2016-02-01

This paper explores the classification and visualisation utility of the self-organising map (SOM) method in the context of New South Wales (NSW), Australia, using gridded NCEP/NCAR geopotential height reanalysis for east Australia, together with multi-site meteorological and air quality data for Sydney from the NSW Office of Environment and Heritage Air Quality Monitoring Network. A twice-daily synoptic classification has been derived for east Australia for the period of 1958-2012. The classification has not only reproduced the typical synoptic patterns previously identified in the literature but also provided an opportunity to visualise the subtle, non-linear change in the eastward-migrating synoptic systems influencing NSW (including Sydney). The summarisation of long-term, multi-site air quality/meteorological data from the Sydney basin on the SOM plane has identified a set of typical air pollution/meteorological spatial patterns in the region. Importantly, the examination of these patterns in relation to synoptic weather types has provided important visual insights into how local and synoptic meteorological conditions interact with each other and affect the variability of air quality in tandem. The study illustrates that while synoptic circulation types are influential, the within-type variability in mesoscale flows plays a critical role in determining local ozone levels in Sydney. These results indicate that the SOM can be a useful tool for assessing the impact of weather and climatic conditions on air quality in the regional airshed. This study further promotes the use of the SOM method in environmental research.

Environment quality monitoring using ARM processor

NASA Astrophysics Data System (ADS)

Vinaya, C. H.; Krishna Thanikanti, Vamsi; Ramasamy, Sudha

2017-11-01

This paper of air quality monitoring system describes a model of sensors network to continuously monitoring the environment with low cost developed model. At present time all over the world turned into a great revolution in industrial domain and on the other hand environment get polluting in a dangerous value. There are so many technologies present to reduce the polluting contents but still there is no completely reduction of that pollution. Even there are different methods to monitor the pollution content; these are much costly that not everyone can adapt those methods or devices. Now we are proposing a sensors connected network to monitor the environment continuously and displaying the pollutant gases percentage in air surroundings and can transmit the results to our mobiles by message. The advantage of this system is easy to design, establish at area to monitor, maintenance and most cost effective as well.

Exploring the potential relationship between indoor air quality and the concentration of airborne culturable fungi: a combined experimental and neural network modeling study.

PubMed

Liu, Zhijian; Cheng, Kewei; Li, Hao; Cao, Guoqing; Wu, Di; Shi, Yunjie

2018-02-01

Indoor airborne culturable fungi exposure has been closely linked to occupants' health. However, conventional measurement of indoor airborne fungal concentration is complicated and usually requires around one week for fungi incubation in laboratory. To provide an ultra-fast solution, here, for the first time, a knowledge-based machine learning model is developed with the inputs of indoor air quality data for estimating the concentration of indoor airborne culturable fungi. To construct a database for statistical analysis and model training, 249 data groups of air quality indicators (concentration of indoor airborne culturable fungi, indoor/outdoor PM 2.5 and PM 10 concentrations, indoor temperature, indoor relative humidity, and indoor CO 2 concentration) were measured from 85 residential buildings of Baoding (China) during the period of 2016.11.15-2017.03.15. Our results show that artificial neural network (ANN) with one hidden layer has good prediction performances, compared to a support vector machine (SVM). With the tolerance of ± 30%, the prediction accuracy of the ANN model with ten hidden nodes can at highest reach 83.33% in the testing set. Most importantly, we here provide a quick method for estimating the concentration of indoor airborne fungi that can be applied to real-time evaluation.

A Comparative Study of Three Spatial Interpolation Methodologies for the Analysis of Air Pollution Concentrations in Athens, Greece

NASA Astrophysics Data System (ADS)

Deligiorgi, Despina; Philippopoulos, Kostas; Thanou, Lelouda; Karvounis, Georgios

2010-01-01

Spatial interpolation in air pollution modeling is the procedure for estimating ambient air pollution concentrations at unmonitored locations based on available observations. The selection of the appropriate methodology is based on the nature and the quality of the interpolated data. In this paper, an assessment of three widely used interpolation methodologies is undertaken in order to estimate the errors involved. For this purpose, air quality data from January 2001 to December 2005, from a network of seventeen monitoring stations, operating at the greater area of Athens in Greece, are used. The Nearest Neighbor and the Liner schemes were applied to the mean hourly observations, while the Inverse Distance Weighted (IDW) method to the mean monthly concentrations. The discrepancies of the estimated and measured values are assessed for every station and pollutant, using the correlation coefficient, the scatter diagrams and the statistical residuals. The capability of the methods to estimate air quality data in an area with multiple land-use types and pollution sources, such as Athens, is discussed.

Air quality concerns of unconventional oil and natural gas production.

PubMed

Field, R A; Soltis, J; Murphy, S

2014-05-01

Increased use of hydraulic fracturing ("fracking") in unconventional oil and natural gas (O & NG) development from coal, sandstone, and shale deposits in the United States (US) has created environmental concerns over water and air quality impacts. In this perspective we focus on how the production of unconventional O & NG affects air quality. We pay particular attention to shale gas as this type of development has transformed natural gas production in the US and is set to become important in the rest of the world. A variety of potential emission sources can be spread over tens of thousands of acres of a production area and this complicates assessment of local and regional air quality impacts. We outline upstream activities including drilling, completion and production. After contrasting the context for development activities in the US and Europe we explore the use of inventories for determining air emissions. Location and scale of analysis is important, as O & NG production emissions in some US basins account for nearly 100% of the pollution burden, whereas in other basins these activities make up less than 10% of total air emissions. While emission inventories are beneficial to quantifying air emissions from a particular source category, they do have limitations when determining air quality impacts from a large area. Air monitoring is essential, not only to validate inventories, but also to measure impacts. We describe the use of measurements, including ground-based mobile monitoring, network stations, airborne, and satellite platforms for measuring air quality impacts. We identify nitrogen oxides, volatile organic compounds (VOC), ozone, hazardous air pollutants (HAP), and methane as pollutants of concern related to O & NG activities. These pollutants can contribute to air quality concerns and they may be regulated in ambient air, due to human health or climate forcing concerns. Close to well pads, emissions are concentrated and exposure to a wide range of pollutants is possible. Public health protection is improved when emissions are controlled and facilities are located away from where people live. Based on lessons learned in the US we outline an approach for future unconventional O & NG development that includes regulation, assessment and monitoring.

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.

Transportation Network Topologies

NASA Technical Reports Server (NTRS)

Holmes, Bruce J.; Scott, John M.

2004-01-01

A discomforting reality has materialized on the transportation scene: our existing air and ground infrastructures will not scale to meet our nation's 21st century demands and expectations for mobility, commerce, safety, and security. The consequence of inaction is diminished quality of life and economic opportunity in the 21st century. Clearly, new thinking is required for transportation that can scale to meet to the realities of a networked, knowledge-based economy in which the value of time is a new coin of the realm. This paper proposes a framework, or topology, for thinking about the problem of scalability of the system of networks that comprise the aviation system. This framework highlights the role of integrated communication-navigation-surveillance systems in enabling scalability of future air transportation networks. Scalability, in this vein, is a goal of the recently formed Joint Planning and Development Office for the Next Generation Air Transportation System. New foundations for 21PstP thinking about air transportation are underpinned by several technological developments in the traditional aircraft disciplines as well as in communication, navigation, surveillance and information systems. Complexity science and modern network theory give rise to one of the technological developments of importance. Scale-free (i.e., scalable) networks represent a promising concept space for modeling airspace system architectures, and for assessing network performance in terms of scalability, efficiency, robustness, resilience, and other metrics. The paper offers an air transportation system topology as framework for transportation system innovation. Successful outcomes of innovation in air transportation could lay the foundations for new paradigms for aircraft and their operating capabilities, air transportation system architectures, and airspace architectures and procedural concepts. The topology proposed considers air transportation as a system of networks, within which strategies for scalability of the topology may be enabled by technologies and policies. In particular, the effects of scalable ICNS concepts are evaluated within this proposed topology. Alternative business models are appearing on the scene as the old centralized hub-and-spoke model reaches the limits of its scalability. These models include growth of point-to-point scheduled air transportation service (e.g., the RJ phenomenon and the 'Southwest Effect'). Another is a new business model for on-demand, widely distributed, air mobility in jet taxi services. The new businesses forming around this vision are targeting personal air mobility to virtually any of the thousands of origins and destinations throughout suburban, rural, and remote communities and regions. Such advancement in air mobility has many implications for requirements for airports, airspace, and consumers. These new paradigms could support scalable alternatives for the expansion of future air mobility to more consumers in more places.

Transportation Network Topologies

NASA Technical Reports Server (NTRS)

Holmes, Bruce J.; Scott, John

2004-01-01

A discomforting reality has materialized on the transportation scene: our existing air and ground infrastructures will not scale to meet our nation's 21st century demands and expectations for mobility, commerce, safety, and security. The consequence of inaction is diminished quality of life and economic opportunity in the 21st century. Clearly, new thinking is required for transportation that can scale to meet to the realities of a networked, knowledge-based economy in which the value of time is a new coin of the realm. This paper proposes a framework, or topology, for thinking about the problem of scalability of the system of networks that comprise the aviation system. This framework highlights the role of integrated communication-navigation-surveillance systems in enabling scalability of future air transportation networks. Scalability, in this vein, is a goal of the recently formed Joint Planning and Development Office for the Next Generation Air Transportation System. New foundations for 21st thinking about air transportation are underpinned by several technological developments in the traditional aircraft disciplines as well as in communication, navigation, surveillance and information systems. Complexity science and modern network theory give rise to one of the technological developments of importance. Scale-free (i.e., scalable) networks represent a promising concept space for modeling airspace system architectures, and for assessing network performance in terms of scalability, efficiency, robustness, resilience, and other metrics. The paper offers an air transportation system topology as framework for transportation system innovation. Successful outcomes of innovation in air transportation could lay the foundations for new paradigms for aircraft and their operating capabilities, air transportation system architectures, and airspace architectures and procedural concepts. The topology proposed considers air transportation as a system of networks, within which strategies for scalability of the topology may be enabled by technologies and policies. In particular, the effects of scalable ICNS concepts are evaluated within this proposed topology. Alternative business models are appearing on the scene as the old centralized hub-and-spoke model reaches the limits of its scalability. These models include growth of point-to-point scheduled air transportation service (e.g., the RJ phenomenon and the Southwest Effect). Another is a new business model for on-demand, widely distributed, air mobility in jet taxi services. The new businesses forming around this vision are targeting personal air mobility to virtually any of the thousands of origins and destinations throughout suburban, rural, and remote communities and regions. Such advancement in air mobility has many implications for requirements for airports, airspace, and consumers. These new paradigms could support scalable alternatives for the expansion of future air mobility to more consumers in more places.

Rural and Urban Differences in Air Quality, 2008–2012, and Community Drinking Water Quality, 2010–2015 — United States

PubMed Central

Kennedy, Caitlin; Monti, Michele; Yip, Fuyuen

2017-01-01

Problem/Condition The places in which persons live, work, and play can contribute to the development of adverse health outcomes. Understanding the differences in risk factors in various environments can help to explain differences in the occurrence of these outcomes and can be used to develop public health programs, interventions, and policies. Efforts to characterize urban and rural differences have largely focused on social and demographic characteristics. A paucity of national standardized environmental data has hindered efforts to characterize differences in the physical aspects of urban and rural areas, such as air and water quality. Reporting Period 2008–2012 for air quality and 2010–2015 for water quality. Description of System Since 2002, CDC’s National Environmental Public Health Tracking Program has collaborated with federal, state, and local partners to gather standardized environmental data by creating national data standards, collecting available data, and disseminating data to be used in developing public health actions. The National Environmental Public Health Tracking Network (i.e., the tracking network) collects data provided by national, state, and local partners and includes 21 health outcomes, exposures, and environmental hazards. To assess environmental factors that affect health, CDC analyzed three air-quality measures from the tracking network for all counties in the contiguous United States during 2008–2012 and one water-quality measure for 26 states during 2010–2015. The three air-quality measures include 1) total number of days with fine particulate matter (PM2.5) levels greater than the U.S. Environmental Protection Agency’s (EPA’s) National Ambient Air Quality Standards (NAAQS) for 24-hour average PM2.5 (PM2.5 days); 2) mean annual average ambient concentrations of PM2.5 in micrograms per cubic meter (mean PM2.5); and 3) total number of days with maximum 8-hour average ozone concentrations greater than the NAAQS (ozone days). The water-quality measure compared the annual mean concentration for a community water system (CWS) to the maximum contaminant level (MCL) defined by EPA for 10 contaminants: arsenic, atrazine, di(2-ethylhexyl) phthalate (DEHP), haloacetic acids (HAA5), nitrate, perchloroethene (PCE), radium, trichloroethene (TCE), total trihalomethanes (TTHM), and uranium. Findings are presented by urban-rural classification scheme: four metropolitan (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) and two nonmetropolitan (micropolitan and noncore) categories. Regression modeling was used to determine whether differences in the measures by urban-rural categories were statistically significant. Results Patterns for all three air-quality measures suggest that air quality improves as areas become more rural (or less urban). The mean total number of ozone days decreased from 47.54 days in large central metropolitan counties to 3.81 days in noncore counties, whereas the mean total number of PM2.5 days decreased from 11.21 in large central metropolitan counties to 0.95 in noncore counties. The mean average annual PM2.5 concentration decreased from 11.15 μg/m3 in large central metropolitan counties to 8.87 μg/m3 in noncore counties. Patterns for the water-quality measure suggest that water quality improves as areas become more urban (or less rural). Overall, 7% of CWSs reported at least one annual mean concentration greater than the MCL for all 10 contaminants combined. The percentage increased from 5.4% in large central metropolitan counties to 10% in noncore counties, a difference that was significant, adjusting for U.S. region, CWS size, water source, and potential spatial correlation. Similar results were found for two disinfection by-products, HAA5 and TTHM. Arsenic was the only other contaminant with a significant result. Medium metropolitan counties had 3.1% of CWSs reporting at least one annual mean greater than the MCL, compared with 2.4% in large central counties. Interpretation Noncore (rural) counties experienced fewer unhealthy air-quality days than large central metropolitan counties, likely because of fewer air pollution sources in the noncore counties. All categories of counties had a mean annual average PM2.5 concentration lower than the EPA standard. Among all CWSs analyzed, the number reporting one or more annual mean contaminant concentrations greater the MCL was small. The water-quality measure suggests that water quality worsens as counties become more rural, in regards to all contaminants combined and for the two disinfection by-products individually. Although significant differences were found for the water-quality measure, the odds ratios were very small, making it difficult to determine whether these differences have a meaningful effect on public health. These differences might be a result of variations in water treatment practices in rural versus urban counties. Public Health Action Understanding the differences between rural and urban areas in air and water quality can help public health departments to identify, monitor, and prioritize potential environmental public health concerns and opportunities for action. These findings suggest a continued need to develop more geographically targeted, evidence-based interventions to prevent morbidity and mortality associated with poor air and water quality. PMID:28640797

Rural and Urban Differences in Air Quality, 2008-2012, and Community Drinking Water Quality, 2010-2015 - United States.

PubMed

Strosnider, Heather; Kennedy, Caitlin; Monti, Michele; Yip, Fuyuen

2017-06-23

The places in which persons live, work, and play can contribute to the development of adverse health outcomes. Understanding the differences in risk factors in various environments can help to explain differences in the occurrence of these outcomes and can be used to develop public health programs, interventions, and policies. Efforts to characterize urban and rural differences have largely focused on social and demographic characteristics. A paucity of national standardized environmental data has hindered efforts to characterize differences in the physical aspects of urban and rural areas, such as air and water quality. 2008-2012 for air quality and 2010-2015 for water quality. Since 2002, CDC's National Environmental Public Health Tracking Program has collaborated with federal, state, and local partners to gather standardized environmental data by creating national data standards, collecting available data, and disseminating data to be used in developing public health actions. The National Environmental Public Health Tracking Network (i.e., the tracking network) collects data provided by national, state, and local partners and includes 21 health outcomes, exposures, and environmental hazards. To assess environmental factors that affect health, CDC analyzed three air-quality measures from the tracking network for all counties in the contiguous United States during 2008-2012 and one water-quality measure for 26 states during 2010-2015. The three air-quality measures include 1) total number of days with fine particulate matter (PM 2.5 ) levels greater than the U.S. Environmental Protection Agency's (EPA's) National Ambient Air Quality Standards (NAAQS) for 24-hour average PM 2.5 (PM 2.5 days); 2) mean annual average ambient concentrations of PM 2.5 in micrograms per cubic meter (mean PM 2.5 ); and 3) total number of days with maximum 8-hour average ozone concentrations greater than the NAAQS (ozone days). The water-quality measure compared the annual mean concentration for a community water system (CWS) to the maximum contaminant level (MCL) defined by EPA for 10 contaminants: arsenic, atrazine, di(2-ethylhexyl) phthalate (DEHP), haloacetic acids (HAA5), nitrate, perchloroethene (PCE), radium, trichloroethene (TCE), total trihalomethanes (TTHM), and uranium. Findings are presented by urban-rural classification scheme: four metropolitan (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) and two nonmetropolitan (micropolitan and noncore) categories. Regression modeling was used to determine whether differences in the measures by urban-rural categories were statistically significant. Patterns for all three air-quality measures suggest that air quality improves as areas become more rural (or less urban). The mean total number of ozone days decreased from 47.54 days in large central metropolitan counties to 3.81 days in noncore counties, whereas the mean total number of PM 2.5 days decreased from 11.21 in large central metropolitan counties to 0.95 in noncore counties. The mean average annual PM 2.5 concentration decreased from 11.15 μg/m 3 in large central metropolitan counties to 8.87 μg/m 3 in noncore counties. Patterns for the water-quality measure suggest that water quality improves as areas become more urban (or less rural). Overall, 7% of CWSs reported at least one annual mean concentration greater than the MCL for all 10 contaminants combined. The percentage increased from 5.4% in large central metropolitan counties to 10% in noncore counties, a difference that was significant, adjusting for U.S. region, CWS size, water source, and potential spatial correlation. Similar results were found for two disinfection by-products, HAA5 and TTHM. Arsenic was the only other contaminant with a significant result. Medium metropolitan counties had 3.1% of CWSs reporting at least one annual mean greater than the MCL, compared with 2.4% in large central counties. Noncore (rural) counties experienced fewer unhealthy air-quality days than large central metropolitan counties, likely because of fewer air pollution sources in the noncore counties. All categories of counties had a mean annual average PM 2.5 concentration lower than the EPA standard. Among all CWSs analyzed, the number reporting one or more annual mean contaminant concentrations greater the MCL was small. The water-quality measure suggests that water quality worsens as counties become more rural, in regards to all contaminants combined and for the two disinfection by-products individually. Although significant differences were found for the water-quality measure, the odds ratios were very small, making it difficult to determine whether these differences have a meaningful effect on public health. These differences might be a result of variations in water treatment practices in rural versus urban counties. Understanding the differences between rural and urban areas in air and water quality can help public health departments to identify, monitor, and prioritize potential environmental public health concerns and opportunities for action. These findings suggest a continued need to develop more geographically targeted, evidence-based interventions to prevent morbidity and mortality associated with poor air and water quality.

Evolving Best Practice in Learning About Air Quality and Climate Change Science in ACCENT

NASA Astrophysics Data System (ADS)

Schuepbach, E.

2008-12-01

Learning about air quality and climate change science has developed into a transdisciplinary impact generator, moulded by academic-stakeholder partnerships, where complementary skills and competences lead to a culture of dialogue, mutual learning and decision-making. These sweeping changes are mirrored in the evolving best practice within the European Network of Excellence on Atmospheric Composition Change (ACCENT). The Training and Education Programme in ACCENT pursues an integrated approach and innovative avenues to sharing knowledge and communicating air quality and climate change science to various end-user groups, including teachers, policy makers, stakeholders, and the general public. Early career scientists are involved in the process, and are trained to acquire new knowledge in a variety of learning communities and environments. Here, examples of both the open system of teaching within ACCENT training workshops for early career scientists, and the engagement of non-academic audiences in the joint learning process are presented.

Impact of Asian Dust on Global Surface Air Quality and Radiation Budget

NASA Technical Reports Server (NTRS)

Chin, Mian; Diehl, Thomas; Yu, Hongbin; Ginoux, Paul

2006-01-01

Dust originating from Asian deserts and desertification areas can be transported regionally and globally to affect surface air quality, visibility, and radiation budget not only at immediate downwind locations (e.g., eastern Asia) but also regions far away from the sources (e.g., North America). Deposition of Asian dust to the North Pacific Ocean basin influences the ocean productivity. In this study, we will use the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model, remote sensing data form satellite and from the ground-based network, and in-situ data from aircraft and surface observations to address the following questions: - What are the effects of Asian dust on the surface air quality and visibility over Asia and North America? - What are the seasonal and spatial variations of dust deposition to the North Pacific Ocean? How does the Asian dust affect surface radiation budget?

Analysis of local bus markets : volume II – rider characteristics : final report.

DOT National Transportation Integrated Search

2017-07-04

Despite having an extensive network of public transit, traffic congestion and transportation-related greenhouse gas (GHG) emissions are significant concerns in New Jersey. This research hypothesizes that traffic congestion and air quality concerns in...

Analysis of local bus markets : volume I – methodology and findings : final report.

DOT National Transportation Integrated Search

2017-07-04

Despite having an extensive network of public transit, traffic congestion and transportation-related greenhouse gas (GHG) emissions are significant concerns in New Jersey. This research hypothesizes that traffic congestion and air quality concerns in...

Analysis of local bus markets : volume III – travel characteristics : final report.

DOT National Transportation Integrated Search

2017-07-04

Despite having an extensive network of public transit, traffic congestion and transportation-related greenhouse gas (GHG) emissions are significant concerns in New Jersey. This research hypothesizes that traffic congestion and air quality concerns in...

Observations and modeling of air quality trends over 1990-2010 across the Northern Hemisphere: China, the United States and Europe

NASA Astrophysics Data System (ADS)

Xing, J.; Mathur, R.; Pleim, J.; Hogrefe, C.; Gan, C.-M.; Wong, D. C.; Wei, C.; Gilliam, R.; Pouliot, G.

2015-03-01

Trends in air quality across the Northern Hemisphere over a 21-year period (1990-2010) were simulated using the Community Multiscale Air Quality (CMAQ) multiscale chemical transport model driven by meteorology from Weather Research and Forecasting (WRF) simulations and internally consistent historical emission inventories obtained from EDGAR. Thorough comparison with several ground observation networks mostly over Europe and North America was conducted to evaluate the model performance as well as the ability of CMAQ to reproduce the observed trends in air quality over the past 2 decades in three regions: eastern China, the continental United States and Europe. The model successfully reproduced the observed decreasing trends in SO2, NO2, 8 h O3 maxima, SO42- and elemental carbon (EC) in the US and Europe. However, the model fails to reproduce the decreasing trends in NO3- in the US, potentially pointing to uncertainties of NH3 emissions. The model failed to capture the 6-year trends of SO2 and NO2 in CN-API (China - Air Pollution Index) from 2005 to 2010, but reproduced the observed pattern of O3 trends shown in three World Data Centre for Greenhouse Gases (WDCGG) sites over eastern Asia. Due to the coarse spatial resolution employed in these calculations, predicted SO2 and NO2 concentrations are underestimated relative to all urban networks, i.e., US-AQS (US - Air Quality System; normalized mean bias (NMB) = -38% and -48%), EU-AIRBASE (European Air quality data Base; NMB = -18 and -54%) and CN-API (NMB = -36 and -68%). Conversely, at the rural network EU-EMEP (European Monitoring and Evaluation Programme), SO2 is overestimated (NMB from 4 to 150%) while NO2 is simulated well (NMB within ±15%) in all seasons. Correlations between simulated and observed O3 wintertime daily 8 h maxima (DM8) are poor compared to other seasons for all networks. Better correlation between simulated and observed SO42- was found compared to that for SO2. Underestimation of summer SO42- in the US may be associated with the uncertainty in precipitation and associated wet scavenging representation in the model. The model exhibits worse performance for NO3- predictions, particularly in summer, due to high uncertainties in the gas/particle partitioning of NO3- as well as seasonal variations of NH3 emissions. There are high correlations (R > 0.5) between observed and simulated EC, although the model underestimates the EC concentration by 65% due to the coarse grid resolution as well as uncertainties in the PM speciation profile associated with EC emissions. The almost linear response seen in the trajectory of modeled O3 changes in eastern China over the past 2 decades suggests that control strategies that focus on combined control of NOx and volatile organic compound (VOC) emissions with a ratio of 0.46 may provide the most effective means for O3 reductions for the region devoid of nonlinear response potentially associated with NOx or VOC limitation resulting from alternate strategies. The response of O3 is more sensitive to changes in NOx emissions in the eastern US because the relative abundance of biogenic VOC emissions tends to reduce the effectiveness of VOC controls. Increasing NH3 levels offset the relative effectiveness of NOx controls in reducing the relative fraction of aerosol NO3- formed from declining NOx emissions in the eastern US, while the control effectiveness was assured by the simultaneous control of NH3 emission in Europe.

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.

Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia.

PubMed

Syed Abdul Mutalib, Sharifah Norsukhairin; Juahir, Hafizan; Azid, Azman; Mohd Sharif, Sharifah; Latif, Mohd Talib; Aris, Ahmad Zaharin; Zain, Sharifuddin M; Dominick, Doreena

2013-09-01

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.

Using an epiphytic moss to identify previously unknown sources of atmospheric cadmium pollution

Treesearch

Geoffrey H. Donovan; Sarah E. Jovan; Demetrios Gatziolis; Igor Burstyn; Yvonne L. Michael; Michael C. Amacher; Vicente J. Monleon

2016-01-01

Urban networks of air-quality monitors are often too widely spaced to identify sources of air pollutants, especially if they do not disperse far from emission sources. The objectives of this study were to test the use of moss bio-indicators to develop a fine-scale map of atmospherically-derived cadmium and to identify the sources of cadmium in a complex urban setting....

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.

Changes in O3 and NO2 due to emissions from Fracking in the UK.

NASA Astrophysics Data System (ADS)

Archibald, Alexander; Ordonez, Carlos

2016-04-01

Poor air quality is a problem that affects millions of people around the world. Understanding the driving forces behind air pollution is complicated as the precursor gases which combine to produce air pollutants react in a highly non-linear manner and are subject to a range of atmospheric transport mechanisms compounded by the weather. A great deal of money has been spent on mitigating air pollution and so it's important to assess the impacts that new technologies that emit air pollutant precursors may have on local and regional air pollution. One of the most highly discussed new technologies that could impact air quality is the adoption of wide-scale hydraulic fracturing or "fracking" for natural gas. Indeed in regions of the USA where fracking is commonplace large levels of ozone (O3 - a key air pollutant) have been observed and attributed directly to the fracking process. In this study, a numerical modelling framework was used to assess possible impacts of fracking in the UK where at present no large scale fracking facilities are in operation. A number of emissions scenarios were developed for the principle gas phase air pollution precursors: the oxides of nitrogen (NOx) and volatile organic compounds (VOCs). These emissions scenarios were then used in a state-of-the-art numerical air quality model (the UK Met Office operational air quality forecasting model AQUM) to determine potential impacts related to fracking on UK air quality. Comparison of base model results and observations for the year 2013 of NOx, O3 and VOCs from the UK Automatic Urban and Rural Network (AURN) showed that AQUM has good skill at simulating these gas phase air pollutants (O3 r=0.64, NMGE=0.3; NO2 r=0.62, NMGE=0.51). Analysis of the simulations with fracking emissions demonstrate that there are large changes in 1hr max NO2 (11.6±6.6 ppb) with modest increases in monthly mean NO2, throughout the British Isles (150±100 ppt). These results highlight that stringent measures should be applied to prevent deleterious impacts on air quality from emissions related to fracking in the UK.

Monitoring Street-Level Spatial-Temporal Variations of Carbon Monoxide in Urban Settings Using a Wireless Sensor Network (WSN) Framework

PubMed Central

Wen, Tzai-Hung; Jiang, Joe-Air; Sun, Chih-Hong; Juang, Jehn-Yih; Lin, Tzu-Shiang

2013-01-01

Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot reflect the detailed variations in air quality that may be induced by traffic jams. By deploying wireless sensors on crossroads and main roads, this study established a pilot framework for a wireless sensor network (WSN)-based real-time monitoring system to understand street-level spatial-temporal changes of carbon monoxide (CO) in urban settings. The system consists of two major components. The first component is the deployment of wireless sensors. We deployed 44 sensor nodes, 40 transmitter nodes and four gateway nodes in this study. Each sensor node includes a signal processing module, a CO sensor and a wireless communication module. In order to capture realistic human exposure to traffic pollutants, all sensors were deployed at a height of 1.5 m on lampposts and traffic signs. The study area covers a total length of 1.5 km of Keelung Road in Taipei City. The other component is a map-based monitoring platform for sensor data visualization and manipulation in time and space. Using intensive real-time street-level monitoring framework, we compared the spatial-temporal patterns of air pollution in different time periods. Our results capture four CO concentration peaks throughout the day at the location, which was located along an arterial and nearby traffic sign. The hourly average could reach 5.3 ppm from 5:00 pm to 7:00 pm due to the traffic congestion. The proposed WSN-based framework captures detailed ground information and potential risk of human exposure to traffic-related air pollution. It also provides street-level insights into real-time monitoring for further early warning of air pollution and urban environmental management. PMID:24287859

Monitoring street-level spatial-temporal variations of carbon monoxide in urban settings using a wireless sensor network (WSN) framework.

PubMed

Wen, Tzai-Hung; Jiang, Joe-Air; Sun, Chih-Hong; Juang, Jehn-Yih; Lin, Tzu-Shiang

2013-11-27

Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot reflect the detailed variations in air quality that may be induced by traffic jams. By deploying wireless sensors on crossroads and main roads, this study established a pilot framework for a wireless sensor network (WSN)-based real-time monitoring system to understand street-level spatial-temporal changes of carbon monoxide (CO) in urban settings. The system consists of two major components. The first component is the deployment of wireless sensors. We deployed 44 sensor nodes, 40 transmitter nodes and four gateway nodes in this study. Each sensor node includes a signal processing module, a CO sensor and a wireless communication module. In order to capture realistic human exposure to traffic pollutants, all sensors were deployed at a height of 1.5 m on lampposts and traffic signs. The study area covers a total length of 1.5 km of Keelung Road in Taipei City. The other component is a map-based monitoring platform for sensor data visualization and manipulation in time and space. Using intensive real-time street-level monitoring framework, we compared the spatial-temporal patterns of air pollution in different time periods. Our results capture four CO concentration peaks throughout the day at the location, which was located along an arterial and nearby traffic sign. The hourly average could reach 5.3 ppm from 5:00 pm to 7:00 pm due to the traffic congestion. The proposed WSN-based framework captures detailed ground information and potential risk of human exposure to traffic-related air pollution. It also provides street-level insights into real-time monitoring for further early warning of air pollution and urban environmental management.

THE US EPA'S SUPERSITES PROGRAM

EPA Science Inventory

The PM2.5 monitoring program is dominated by gravimetric measurements (over 1000 mass samplers nationwide) specific for mass, where the primary objective is comparisons with the PMZ 5 National Ambient Air Quality Standards (NAAQS). The other major component of the network is th...

The Long Term Agroecosystem Research Network - Shared research strategy

USDA-ARS?s Scientific Manuscript database

Agriculture faces tremendous challenges in meeting multiple societal goals, including a safe and plentiful food supply; climate change adaptation and mitigation; supplying sources of bioenergy; improving water, air, and soil quality; and maintaining biodiversity. The Long Term Agroecosystem Research...

Basic Information about the Indoor Air Quality Tribal Partners Program

EPA Pesticide Factsheets

IAQ Tribal Partners Program. This website aims to further empower champions of healthy IAQ in tribal communities with tools for networking, sharing programs and practices, and by serving as a reservoir of the best available tribal-specific IAQ information.

Merging Air Quality and Public Health Decision Support Systems

NASA Astrophysics Data System (ADS)

Hudspeth, W. B.; Bales, C. L.

2003-12-01

The New Mexico Air Quality Mapper (NMAQM) is a Web-based, open source GIS prototype application that Earth Data Analysis Center is developing under a NASA Cooperative Agreement. NMAQM enhances and extends existing data and imagery delivery systems with an existing Public Health system called the Rapid Syndrome Validation Project (RSVP). RSVP is a decision support system operating in several medical and public health arenas. It is evolving to ingest remote sensing data as input to provide early warning of human health threats, especially those related to anthropogenic atmospheric pollutants and airborne pathogens. The NMAQM project applies measurements of these atmospheric pollutants, derived from both remotely sensed data as well as from in-situ air quality networks, to both forecasting and retrospective analyses that influence human respiratory health. NMAQM provides a user-friendly interface for visualizing and interpreting environmentally-linked epidemiological phenomena. The results, and the systems made to provide the information, will be applicable not only to decision-makers in the public health realm, but also to air quality organizations, demographers, community planners, and other professionals in information technology, and social and engineering sciences. As an accessible and interactive mapping and analysis application, it allows environment and health personnel to study historic data for hypothesis generation and trend analysis, and then, potentially, to predict air quality conditions from daily data acquisitions. Additional spin off benefits to such users include the identification of gaps in the distribution of in-situ monitoring stations, the dissemination of air quality data to the public, and the discrimination of local vs. more regional sources of air pollutants that may bear on decisions relating to public health and public policy.

A Fleet of Low-Cost Sensor Based Air Quality Monitors Is Used to Measure Carbon Dioxide and Carbon Monoxide in Two Settings: In the Ambient Environment to Explore the Regional-Scale Spatial Variability of These Compounds Via a Distributed Network, and in Homes to Investigate How Heating during Winter Months can Impact Indoor Air Quality.

NASA Astrophysics Data System (ADS)

Casey, J. G.; Hannigan, M.; Collier, A. M.; Coffey, E.; Piedrahita, R.

2016-12-01

Affordable, small, portable, quiet tools to measure atmospheric trace gases and air quality enable novel experimental design and new findings. Members of the Hannigan Lab at the University of Colorado in Boulder have been working over the last few years to integrate emerging affordable gas sensors into such an air quality monitor. Presented here are carbon monoxide (CO) and carbon dioxide (CO2) measurements from two field experiments that utilized these tools. In the first experiment, ten air quality monitors were located northeast of Boulder throughout the Denver Julesburg oil and gas basin. The Colorado Department of Health and Environment has several air quality monitoring sites in this broader region, each in an Urban center. One goal of the experiment was to determine whether or not significant spatial variability of EPA criteria pollutants like CO, exists on a sub-regulatory monitoring grid scale. Another goal of the experiment was to compare rural sampling locations with urban sites. The monitors collected continuous data (sampling every 15 seconds) at each location over the course of several months. Our sensor calibration procedures are presented along with our observations and an analysis of the spatial and temporal variability in CO and CO2. In the second experiment, we used eight of our air quality monitors to better understand how home heating fuel type can impact indoor air quality in two communities on the Navajo Nation. We sought to compare air quality in homes using one of four different fuels for heat (wood, wood plus coal, pellet, and gas). There are many factors that contribute to indoor air quality and the impact of an emission source, like a woodstove, within a home. Having multiple, easily deployable, air quality monitors allowed us to account for many of these factors. We sampled four homes at a time, aiming for one home from each of our fuel groups in each sampling period. We sampled inside and outside of each home for a period of 3-4 days. In this way, we hoped to account for possible weather and outdoor air quality biases. CO and CO2 were measured and are put into context with acceptable levels. During periods when there were no emissions of CO and CO2, we used their rates of decay to calculate the home's air exchange rate via the tracer gas technique. The air exchange rate was then used to calculate emission rates for CO.

How to most effectively expand the global surface ozone observing network

NASA Astrophysics Data System (ADS)

Sofen, E. D.; Bowdalo, D.; Evans, M. J.

2016-02-01

Surface ozone observations with modern instrumentation have been made around the world for more than 40 years. Some of these observations have been made as one-off activities with short-term, specific science objectives and some have been made as part of wider networks which have provided a foundational infrastructure of data collection, calibration, quality control, and dissemination. These observations provide a fundamental underpinning to our understanding of tropospheric chemistry, air quality policy, atmosphere-biosphere interactions, etc. brought together eight of these networks to provide a single data set of surface ozone observations. We investigate how representative this combined data set is of global surface ozone using the output from a global atmospheric chemistry model. We estimate that on an area basis, 25 % of the globe is observed (34 % land, 21 % ocean). Whereas Europe and North America have almost complete coverage, other continents, Africa, South America, Australia, and Asia (12-17 %) show significant gaps. Antarctica is surprisingly well observed (78 %). Little monitoring occurs over the oceans, with the tropical and southern oceans particularly poorly represented. The surface ozone over key biomes such as tropical forests and savanna is almost completely unmonitored. A chemical cluster analysis suggests that a significant number of observations are made of polluted air masses, but cleaner air masses whether over the land or ocean (especially again in the tropics) are significantly under-observed. The current network is unlikely to see the impact of the El Niño-Southern Oscillation (ENSO) but may be capable of detecting other planetary-scale signals. Model assessment and validation activities are hampered by a lack of observations in regions where the models differ substantially, as is the ability to monitor likely changes in surface ozone over the next century. Using our methodology we are able to suggest new sites which would help to close the gap in our ability to measure global surface ozone. An additional 20 surface ozone monitoring sites (a 20 % increase in the World Meteorological Organization Global Atmosphere Watch (WMO GAW) ozone sites or a 1 % increase in the total background network) located on 10 islands and in 10 continental regions would almost double the area observed. The cost of this addition to the network is small compared to other expenditure on atmospheric composition research infrastructure and would provide a significant long-term benefit to our understanding of the composition of the atmosphere, information which will also be available for consideration by air quality control managers and policy makers.

[Estimating emergency hospital admissions to gauge short-term effects of air pollution: evaluation of health data quality].

PubMed

Bois de Fer, Béatrice; Host, Sabine; Chardon, Benoît; Chatignoux, Edouard; Beaujouan, Laure; Brun-Ney, Dominique; Grémy, Isabelle

2009-01-01

The study of the short-term effects and health impact of air pollution is carrier out by the ERPURS regional surveillance program which utilizes hospitalization data obtained from the French hospital information system (PMSI) to determine these links. This system does not permit the distinction between emergency hospital admissions from scheduled ones, which cannot be related to short term changes in air pollution levels. This study examines how scheduled admissions affect the quality of the health indicators used to estimate air pollution effects. This indicator is compared to three new emergency hospitalisation indicators reconstructed based on data from the public hospitals in Paris, partly from the PMSI data and partly with data from an on-line emergency network that regroups all of the computerized emergency services. According to the pathology, scheduled admissions present a difficulty which affects the capacity to highlight the weakest risks with any precision.

Asthma and Air Quality in the Presence of Fires - A Foundation for Public Health Policy in Florida

NASA Technical Reports Server (NTRS)

Crosson, William; Al-Hamdan, Mohammad; Estes, Maurice, Jr.; Estes, Sue; Luvall, Jeffrey; Sifford, Cody; Young, Linda

2012-01-01

Outdoor air quality and its associated impacts on respiratory problems in Florida are of public health significance. Air quality in Florida can be poor during the extended wildfire season, threatening persons with compromised respiratory systems each year. Studies have demonstrated that particulate matter, which is generally elevated in the vicinity of wildfires, is associated with increases in hospital admissions and occurrences of acute asthma exacerbations. However, few studies have examined the modifying effect of socio-demographic characteristics of cities or regional areas on the relationship between air quality and health outcomes. In an ongoing university/multi-agency project, asthma hospital/emergency room (patient) data are being used to create a health outcome indicator of human response to environmental air quality. Environmental data are derived from satellite measurements, with special attention being given to the effect of wildfires and prescribed burns on air quality. This presentation will focus on the environmental data sets particulate matter, location of fires, smoke plumes that are being collected and processed for linkage with health data. After this linkage has been performed, space-time models of asthma rates as a function of air quality data and socio-demographic variables will be developed and validated. The Florida Department of Health (FDOH) will work with county health department staff and representatives from the medical community to establish a protocol with triggers for issuing public health advisories/alerts based on the developed and validated health outcome indicators. From this effort, a science-based policy for issuing public health advisories/alerts for asthma relating to air quality will be developed, giving FDOH the ability to (1) predict, with stated levels of uncertainty, case load of hospital admissions based on air quality, (2) reduce asthma exacerbations by forewarning asthmatics to limit outside activities on poor air quality days, (3) apply management practices on the rates of hospital/emergency room visits for asthma, and (4) provide information that would help translate interventions into policy decisions, thereby reducing the economic burden and increasing well being of asthmatics. Further, the results of the study will be incorporated into Florida s Environmental Public Health Tracking (EPHT) program, which is part of the Centers for Disease Control and Prevention's (CDC's) EPHT network.

Evaluating Ambient Concentrations and Local Emissions of Greenhouse Gases (GHGs) in the San Francisco Bay Area of California Using a Comprehensive Fixed-site and Mobile Monitoring Network

NASA Astrophysics Data System (ADS)

Guha, A.; Bower, J. P.; Martien, P. T.; Randall, S.; Young, A.; Hilken, H.; Stevenson, E.

2015-12-01

The Bay Area Air Quality Management District (hence the Air District) is the greater San Francisco Bay metropolitan region's chief air quality regulatory agency. Aligning itself with Executive Order S-3-05, the Air District has set a goal to reduce the region's GHG emissions by 80% below 1990 levels by the year 2050. The Air District's 10-point Climate Action Work Program lays out the agency's priorities, actions and coordination with regional stakeholders. The Program has three core objectives: (1) to develop a technical and monitoring program to document the region's GHG sources and related emissions, (2) to implement a policy and rule-based approach to control and regulate GHG emissions, and finally, (3) to utilize local governance, incentives and partnerships to encourage GHG emissions reductions.As part of the technical program, the Air District has set up a long term, ambient GHG monitoring network at four sites. The first site is located north and upwind of the urban core at Bodega Bay by the Pacific Coast. It mostly receives clean marine inflow and serves as the regional background site. The other three sites are strategically located at regional exit points for Bay Area plumes that presumably contain GHG enhancements from local sources. These stations are at San Martin, located south of the San Jose metropolitan area; at Patterson Pass at the cross section with California's Central Valley; and at Bethel Island at the mouth of the Sacramento-San Joaquin Delta. At all sites, carbon dioxide (CO2) and methane (CH4) are being measured continuously, along with combustion tracer CO and other air pollutants. The GHG measurements are performed with high precision and fast laser instruments (Picarro Inc). In the longer term, the network will allow the Air District to monitor ambient concentrations of GHGs and thus evaluate the effectiveness of its policy, regulation and enforcement efforts. We present data from the sites in their first few months of operation and demonstrate the efficacy and utility of this monitoring network. We also present our progress on the design and fabrication of a dedicated mobile GHG measurement platform (a research van) equipped with state of the art analyzers capable of measuring isotopic methane (13C - CH4), CH4, CO2 and also nitrous oxide (N2O) in ambient air at fast temporal rates.

Urban-rural variations in air quality and health impacts in northern India

NASA Astrophysics Data System (ADS)

Karambelas, A. N.; Holloway, T.; Fiore, A. M.; Kinney, P.; DeFries, R. S.; Kiesewetter, G.; Heyes, C.

2017-12-01

Ambient air pollution in India is a severe problem, contributing to negative health impacts and early death. Ground-based monitors often used to quantify health impacts are often located in urban regions, however approximately 70% of India's population resides in rural areas. We use high-resolution concentrations from the regional Community Multi-scale Air Quality (CMAQ) model over densely-populated northern India to estimate air quality and health impacts due to anthropogenic emission sectors separately for urban and rural regions. Modeled concentrations inform relative risk calculations and exposure estimates as performed in the Global Burden of Disease. Anthropogenic emissions from the International Institute for Applied Systems Analysis (IIASA) Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model following version 5a of the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants project gridding structure are updated to reflect urban- and rural-specific activity information for transportation and residential combustion, and industrial and electrical generating unit location and magnitude information. We estimate 314,000 (95% Confidence Interval: 304,000—323,000) and 58,000 (CI: 39,000—70,000) adults (25 years or older) die prematurely each year from PM2.5 and O3 respectively in northern India, with the greatest impacts along the Indo-Gangetic Plain. Using urban and rural population distributions, we estimate that the majority of premature deaths resulting from PM2.5 and O3 are in rural (292,000) as opposed to urban (79,000) regions. These findings indicate the need for designing monitoring networks and ground-based health studies in rural areas of India to more accurately quantify the true health implications of ambient air pollution, in addition to supporting model evaluation. Using this urban-versus-rural emissions framework, we are assessing anthropogenic contributions to regional air quality and health impacts, and examining mitigation strategies to reduce anthropogenic emissions, improve air quality, and reduce PM2.5 and O3 attributable premature death in the near-term.

Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

PubMed Central

Zhang, Jiangshe; Ding, Weifu

2017-01-01

With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R2 increased and root mean square error values decreased respectively. PMID:28125034

Regulatory Impact Analysis (RIA) for the Proposed Revisions to the Sulfur Dioxide National Ambient Air Quality Standards (NAAQS)

EPA Pesticide Factsheets

This Regulatory Impact Analysis (RIA) provides estimates of the incremental costs and monetized human health benefits of attaining a revised short‐term Sulfur Dioxide (SO2) NAAQS within the current monitoring network.

Final Regulatory Impact Analysis (RIA) for the NO2 National Ambient Air Quality Standards (NAAQS)

EPA Pesticide Factsheets

This RIA provides illustrative estimates, as of January 2010, of the incremental costs and monetized human health benefits of attaining the revised NO2 NAAQS within the the existing community-wide monitoring network of 409 monitors.

Intercontinental Transport of Aerosols: Implication for Regional Air Quality

NASA Technical Reports Server (NTRS)

Chin, Mian; Diehl, Thomas; Ginoux, Paul

2006-01-01

Aerosol particles, also known as PM2.5 (particle diameter less than 2.5 microns) and PM10 (particle diameter less than 10 microns), is one of the key atmospheric components that determine ambient air quality. Current US air quality standards for PM10 (particles with diameter < 10 microns) and PM2.5 (particles with diameter 2.5 microns) are 50 pg/cu m and 15 pg/cu m, respectively. While local and regional emission sources are the main cause of air pollution problems, aerosols can be transported on a hemispheric or global scale. In this study, we use the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model to quantify contributions of long-range transport vs. local/regional pollution sources and from natural vs. anthropogenic sources to PM concentrations different regions. In particular, we estimate the hemispheric impact of anthropogenic sulfate aerosols and dust from major source areas on other regions in the world. The GOCART model results are compared with satellite remote sensing and ground-based network measurements of aerosol optical depth and concentrations.

Tropospheric Ozone Lidar Network (TOLNet) - Long-term Tropospheric Ozone and Aerosol Profiling for Satellite Continuity and Process Studies

NASA Astrophysics Data System (ADS)

Newchurch, M.; Al-Saadi, J. A.; Alvarez, R. J.; Burris, J.; Cantrell, W.; Chen, G.; De Young, R.; Hardesty, R.; Hoff, R. M.; Kaye, J. A.; kuang, S.; Langford, A. O.; LeBlanc, T.; McDermid, I. S.; McGee, T. J.; Pierce, R.; Senff, C. J.; Sullivan, J. T.; Szykman, J.; Tonnesen, G.; Wang, L.

2012-12-01

An interagency research initiative for ground-based ozone and aerosol lidar profiling recently funded by NASA has important applications to air-quality studies in addition to the goal of serving the GEO-CAPE and other air-quality missions. Ozone is a key trace-gas species, a greenhouse gas, and an important pollutant in the troposphere. High spatial and temporal variability of ozone affected by various physical and photochemical processes motivates the high spatio-temporal lidar profiling of tropospheric ozone for improving the simulation and forecasting capability of the photochemical/air-quality models, especially in the boundary layer where the resolution and precision of satellite retrievals are fundamentally limited. It is well known that there are large discrepancies between the surface and upper-air ozone due to titration, surface deposition, diurnal processes, free-tropospheric transport, and other processes. Near-ground ozone profiling has been technically challenging for lidars due to some engineering difficulties, such as near-range saturation, field-of-view overlap, and signal processing issues. This initiative provides an opportunity for us to solve those engineering issues and redesign the lidars aimed at long-term, routine ozone/aerosol observations from the near surface to the top of the troposphere at multiple stations (i.e., NASA/GSFC, NASA/LaRC, NASA/JPL, NOAA/ESRL, UAHuntsville) for addressing the needs of NASA, NOAA, EPA and State/local AQ agencies. We will present the details of the science investigations, current status of the instrumentation development, data access/protocol, and the future goals of this lidar network. Ozone lidar/RAQMS comparison of laminar structures.

Importance of a Priori Vertical Ozone Profiles for TEMPO Air Quality Retrievals

NASA Technical Reports Server (NTRS)

Johnson, Matthew S.; Sullivan, John; Liu, Xiong; Zoogman, Peter; Newchurch, Mike; Kuang, Shi; McGee, Thomas; Leblanc, Thierry

2017-01-01

Ozone (O3) is a toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address the limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME (Global Ozone Monitoring Experiment), GOME-2, and OMI (Ozone Monitoring Instrument). This algorithm is suggested to use a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB-Clim) O3 climatology). This study evaluates the TB-Clim dataset and model simulated O3 profiles, which could potentially serve as a priori O3 profile information in TEMPO retrievals, from near-real-time data assimilation model products (NASA GMAO's (Global Modeling and Assimilation Office) operational GEOS-5 (Goddard Earth Observing System, Version 5) FP (Forecast Products) model and reanalysis data from MERRA2 (Modern-Era Retrospective analysis for Research and Applications, Version 2)) and a full chemical transport model (CTM), GEOS-Chem. In this study, vertical profile products are evaluated with surface (0-2 kilometers) and tropospheric (0-10 kilometers) TOLNet (Tropospheric Ozone Lidar Network) observations and the theoretical impact of individual a priori profile sources on the accuracy of TEMPO O3 retrievals in the troposphere and at the surface are presented. Results indicate that while the TB-Clim climatological dataset can replicate seasonally-averaged tropospheric O3 profiles, model-simulated profiles from a full CTM resulted in more accurate tropospheric and surface-level O3 retrievals from TEMPO when compared to hourly and daily-averaged TOLNet observations. Furthermore, it is shown that when large surface O3 mixing ratios are observed, TEMPO retrieval values at the surface are most accurate when applying CTM a priori profile information compared to all other data products.

Achieving QoS for Aeronautical Telecommunication Networks Over Differentiated Services

NASA Technical Reports Server (NTRS)

Bai, Haowei; Atiquzzaman, Mohammed; Ivanic, William

2001-01-01

Aeronautical Telecommunication Network (ATN) has been developed by the International Civil Aviation Organization to integrate Air-Ground and Ground-Ground data communication for aeronautical applications into a single network serving Air Traffic Control and Aeronautical Operational Communications. To carry time critical information required for aeronautical applications, ATN provides different Quality of Services (QoS) to applications. ATN has therefore, been designed as a stand alone network which implies building an expensive separate network for ATN However, the cost of operating ATN can be reduced if it can be run over a public network such as the Internet. Although the current Internet does not provide QoS the next generation Internet is expected to provide QoS to applications. The objective of this paper is to investigate the possibility of providing QoS to ATN applications when it is run over the next generation Internet. Differentiated Services (DiffServ), one of the protocols proposed for the next generation Internet, will allow network service providers to offer different QoS to customers. Our results show that it is possible to provide QoS to ATN applications when they run over a DiffServ backbone.

Achieving QoS for Aeronautical Telecommunication Networks over Differentiated Services

NASA Technical Reports Server (NTRS)

Bai, Haowei; Atiquzzaman, Mohammed; Ivancic, William

2001-01-01

Aeronautical Telecommunication Network (ATN) has been developed by the International Civil Aviation Organization to integrate Air-Ground and Ground-Ground data communication for aeronautical applications into a single network serving Air Traffic Control and Aeronautical Operational Communications. To carry time critical information required for aeronautical applications, ATN provides different Quality of Services (QoS) to applications. ATN has therefore, been designed as a standalone network which implies building an expensive separate network for ATN. However, the cost of operating ATN can be reduced if it can be run over a public network such as the Internet. Although the current Internet does not provide QoS, the next generation Internet is expected to provide QoS to applications. The objective of this paper is to investigate the possibility of providing QoS to ATN applications when it is run over the next generation Internet. Differentiated Services (DiffServ), one of the protocols proposed for the next generation Internet, will allow network service providers to offer different QoS to customers. Our results show that it is possible to provide QoS to ATN applications when they run over a DiffServ backbone.

Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States

NASA Astrophysics Data System (ADS)

Jiao, Wan; Hagler, Gayle; Williams, Ronald; Sharpe, Robert; Brown, Ryan; Garver, Daniel; Judge, Robert; Caudill, Motria; Rickard, Joshua; Davis, Michael; Weinstock, Lewis; Zimmer-Dauphinee, Susan; Buckley, Ken

2016-11-01

Advances in air pollution sensor technology have enabled the development of small and low-cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low-cost, continuous, and commercially available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ˜ 2 km area in the southeastern United States. Collocation of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as the degree to which multiple identical sensors produced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, -0.25 to 0.76, and -0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r < 0.5) with a reference monitor and erroneously high concentration values. A wide variety of particulate matter (PM) sensors were tested with variable results - some sensors had very high agreement (e.g., r = 0.99) between identical sensors but moderate agreement with a reference PM2.5 monitor (e.g., r = 0.65). For select sensors that had moderate to strong correlation with reference monitors (r > 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in number of sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2adj-orig = 0.57, R2adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (two nodes) and PM (four nodes) data for an 8-month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to nearby traffic emissions. Overall, this study demonstrates the performance of emerging air quality sensor technologies in a real-world setting; the variable agreement between sensors and reference monitors indicates that in situ testing of sensors against benchmark monitors should be a critical aspect of all field studies.

Solution to the Problem of Calibration of Low-Cost Air Quality Measurement Sensors in Networks.

PubMed

Miskell, Georgia; Salmond, Jennifer A; Williams, David E

2018-04-27

We provide a simple, remote, continuous calibration technique suitable for application in a hierarchical network featuring a few well-maintained, high-quality instruments ("proxies") and a larger number of low-cost devices. The ideas are grounded in a clear definition of the purpose of a low-cost network, defined here as providing reliable information on air quality at small spatiotemporal scales. The technique assumes linearity of the sensor signal. It derives running slope and offset estimates by matching mean and standard deviations of the sensor data to values derived from proxies over the same time. The idea is extremely simple: choose an appropriate proxy and an averaging-time that is sufficiently long to remove the influence of short-term fluctuations but sufficiently short that it preserves the regular diurnal variations. The use of running statistical measures rather than cross-correlation of sites means that the method is robust against periods of missing data. Ideas are first developed using simulated data and then demonstrated using field data, at hourly and 1 min time-scales, from a real network of low-cost semiconductor-based sensors. Despite the almost naïve simplicity of the method, it was robust for both drift detection and calibration correction applications. We discuss the use of generally available geographic and environmental data as well as microscale land-use regression as means to enhance the proxy estimates and to generalize the ideas to other pollutants with high spatial variability, such as nitrogen dioxide and particulates. These improvements can also be used to minimize the required number of proxy sites.

The Use of AMET and Automated Scripts for Model Evaluation

EPA Science Inventory

The Atmospheric Model Evaluation Tool (AMET) is a suite of software designed to facilitate the analysis and evaluation of meteorological and air quality models. AMET matches the model output for particular locations to the corresponding observed values from one or more networks ...

A Performance Evaluation of Lightning-NO Algorithms in CMAQ

EPA Science Inventory

In the Community Multiscale Air Quality (CMAQv5.2) model, we have implemented two algorithms for lightning NO production; one algorithm is based on the hourly observed cloud-to-ground lightning strike data from National Lightning Detection Network (NLDN) to replace the previous m...

Understanding sources of organic aerosol during CalNex-2010 using the CMAQ-VBS

EPA Science Inventory

Community Multiscale Air Quality (CMAQ) model simulations utilizing the traditional organic aerosol (OA) treatment (CMAQ-AE6) and a volatility basis set (VBS) treatment for OA (CMAQ-VBS) were evaluated against measurements collected at routine monitoring networks (Chemical Specia...

75 FR 54773 - Approval and Promulgation of Air Quality Implementation Plans; Minnesota; Carbon Monoxide (CO...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-09

.... Monitoring Network and Verification of Continued Attainment 4. Contingency Plan 5. Conformity Determination... Continued Attainment, Contingency Plan, and Conformity Determinations Under Limited Maintenance Plans. These.../Verification of Continued Attainment, Contingency Plan, and Conformity Determination Under Limited Maintenance...

A study on the impact of parameter uncertainty on the emission-based ranking of transportation projects.

DOT National Transportation Integrated Search

2014-01-01

With the growing concern with air quality levels and, hence, the livability of urban regions in the nation, it has become increasingly common to incorporate vehicular emission considerations in the ranking of transportation projects. Network assignme...

Re: Request Under the Data Quality Act and EPA's Information Quality Guidelines

EPA Pesticide Factsheets

In light of recent statements from EPA in the rulemaking for the Mercury and Air Toxics Standards (MATS Rule), Environmental Integrity Project (EIP) and Chesapeake Climate Action Network (CCAN) submit this Request for Correction to ask EPA to resolve the conflict between the statements from the MATS rulemaking and EPA's earlier position on the accuracy of monitoring of sulfur dioxide (SO2) under the Acid Rain program.

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.

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus.

PubMed

Kadiyala, Akhil; Kaur, Devinder; Kumar, Ashok

2013-02-01

The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3-0.4 microm sized particle numbers, 0.4-0.5 microm sized particle numbers, particulate matter (PM) concentrations less than 1.0 microm (PM10), and PM concentrations less than 2.5 microm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm-based neural network IAQ models outperformed the traditional ANN methods of the back-propagation and the radial basis function networks. The novelty of this research is the development of a novel approach to modeling vehicular indoor air quality by integration of the advanced methods of genetic algorithms, regression trees, and the analysis of variance for the monitored in-vehicle gaseous and particulate matter contaminants, and comparing the results obtained from using the developed approach with conventional artificial intelligence techniques of back propagation networks and radial basis function networks. This study validated the newly developed approach using holdout and threefold cross-validation methods. These results are of great interest to scientists, researchers, and the public in understanding the various aspects of modeling an indoor microenvironment. This methodology can easily be extended to other fields of study also.

Integration of Satellite, Modeled, and Ground Based Aerosol Data for use in Air Quality and Public Health Applications

NASA Astrophysics Data System (ADS)

Garcia, V.; Kondragunta, S.; Holland, D.; Dimmick, F.; Boothe, V.; Szykman, J.; Chu, A.; Kittaka, C.; Al-Saadi, J.; Engel-Cox, J.; Hoff, R.; Wayland, R.; Rao, S.; Remer, L.

2006-05-01

Advancements in remote sensing over the past decade have been recognized by governments around the world and led to the development of the international Global Earth Observation System of Systems 10-Year Implementation Plan. The plan for the U.S. contribution to GEOSS has been put forth in The Strategic Plan for the U.S. Integrated Earth Observation System (IEOS) developed under IWGEO-CENR. The approach for the development of the U.S. IEOS is to focus on specific societal benefits that can be achieved by integrating the nation's Earth observation capabilities. One such challenge is our ability to understand the impact of poor air quality on human health and well being. Historically, the air monitoring networks put in place for the Nations air quality programs provided the only aerosol air quality data on an ongoing and systematic basis at national levels. However, scientific advances in the remote sensing of aerosols from space have improved dramatically. The MODIS sensor and GOES Imager aboard NASA and NOAA satellites, respectively, provide synoptic-scale measurements of aerosol optical depth (AOD) which have been demonstrated to correlate with high levels of PM10 and PM2.5 at the surface. The MODIS sensor has been shown to be capable of a 1 km x 1 km (at nadir) AOD product, while the GOES Imager can provide AOD at 4 km x 4 km every 30 minutes. Within the next several years NOAA and EPA will begin to issue PM2.5 air quality forecasts over the entire domain of the eastern United States, eventually extending to national coverage. These forecasts will provide continuous estimated values of PM2.5 on a daily basis. A multi-agency collaborative project among government and academia is underway to improve the spatial prediction of fine particulate matter through the integration of multi-sensor and multi-platform aerosol observations (MODIS and GOES), numerical model output, and air monitoring data. By giving more weight to monitoring data in monitored areas and relying on adjusted model output and satellite data in non-monitored areas, a Bayesian hierarchical space-time model will be used to improve the accuracy of prediction and associated prediction errors. The improved spatial predictions will be tested as estimates of exposure for input to modeling relationships between air quality and asthma/other respiratory diseases through CDC under the Environmental Public Health Tracking Network. We will also focus on the use of the predictive spatial maps within the EPA AIRNow program which provides near real-time spatial maps of daily average PM2.5 concentrations across the US. We will present the overall project plan and preliminary results with emphasis on how GEOSS framework is facilitating this effort.

An Indoor Monitoring System for Ambient Assisted Living Based on Internet of Things Architecture

PubMed Central

Marques, Gonçalo; Pitarma, Rui

2016-01-01

The study of systems and architectures for ambient assisted living (AAL) is undoubtedly a topic of great relevance given the aging of the world population. The AAL technologies are designed to meet the needs of the aging population in order to maintain their independence as long as possible. As people typically spend more than 90% of their time in indoor environments, indoor air quality (iAQ) is perceived as an imperative variable to be controlled for the inhabitants’ wellbeing and comfort. Advances in networking, sensors, and embedded devices have made it possible to monitor and provide assistance to people in their homes. The continuous technological advancements make it possible to build smart objects with great capabilities for sensing and connecting several possible advancements in ambient assisted living systems architectures. Indoor environments are characterized by several pollutant sources. Most of the monitoring frameworks instantly accessible are exceptionally costly and only permit the gathering of arbitrary examples. iAQ is an indoor air quality system based on an Internet of Things paradigm that incorporates in its construction Arduino, ESP8266, and XBee technologies for processing and data transmission and micro sensors for data acquisition. It also allows access to data collected through web access and through a mobile application in real time, and this data can be accessed by doctors in order to support medical diagnostics. Five smaller scale sensors of natural parameters (air temperature, moistness, carbon monoxide, carbon dioxide, and glow) were utilized. Different sensors can be included to check for particular contamination. The results reveal that the system can give a viable indoor air quality appraisal in order to anticipate technical interventions for improving indoor air quality. Indeed indoor air quality might be distinctively contrasted with what is normal for a quality living environment. PMID:27869682

An Indoor Monitoring System for Ambient Assisted Living Based on Internet of Things Architecture.

PubMed

Marques, Gonçalo; Pitarma, Rui

2016-11-17

The study of systems and architectures for ambient assisted living (AAL) is undoubtedly a topic of great relevance given the aging of the world population. The AAL technologies are designed to meet the needs of the aging population in order to maintain their independence as long as possible. As people typically spend more than 90% of their time in indoor environments, indoor air quality (iAQ) is perceived as an imperative variable to be controlled for the inhabitants' wellbeing and comfort. Advances in networking, sensors, and embedded devices have made it possible to monitor and provide assistance to people in their homes. The continuous technological advancements make it possible to build smart objects with great capabilities for sensing and connecting several possible advancements in ambient assisted living systems architectures. Indoor environments are characterized by several pollutant sources. Most of the monitoring frameworks instantly accessible are exceptionally costly and only permit the gathering of arbitrary examples. iAQ is an indoor air quality system based on an Internet of Things paradigm that incorporates in its construction Arduino, ESP8266, and XBee technologies for processing and data transmission and micro sensors for data acquisition. It also allows access to data collected through web access and through a mobile application in real time, and this data can be accessed by doctors in order to support medical diagnostics. Five smaller scale sensors of natural parameters (air temperature, moistness, carbon monoxide, carbon dioxide, and glow) were utilized. Different sensors can be included to check for particular contamination. The results reveal that the system can give a viable indoor air quality appraisal in order to anticipate technical interventions for improving indoor air quality. Indeed indoor air quality might be distinctively contrasted with what is normal for a quality living environment.

Global ozone and air quality: a multi-model assessment of risks to human health and crops

NASA Astrophysics Data System (ADS)

Ellingsen, K.; Gauss, M.; van Dingenen, R.; Dentener, F. J.; Emberson, L.; Fiore, A. M.; Schultz, M. G.; Stevenson, D. S.; Ashmore, M. R.; Atherton, C. S.; Bergmann, D. J.; Bey, I.; Butler, T.; Drevet, J.; Eskes, H.; Hauglustaine, D. A.; Isaksen, I. S. A.; Horowitz, L. W.; Krol, M.; Lamarque, J. F.; Lawrence, M. G.; van Noije, T.; Pyle, J.; Rast, S.; Rodriguez, J.; Savage, N.; Strahan, S.; Sudo, K.; Szopa, S.; Wild, O.

2008-02-01

Within ACCENT, a European Network of Excellence, eighteen atmospheric models from the U.S., Europe, and Japan calculated present (2000) and future (2030) concentrations of ozone at the Earth's surface with hourly temporal resolution. Comparison of model results with surface ozone measurements in 14 world regions indicates that levels and seasonality of surface ozone in North America and Europe are characterized well by global models, with annual average biases typically within 5-10 nmol/mol. However, comparison with rather sparse observations over some regions suggest that most models overestimate annual ozone by 15-20 nmol/mol in some locations. Two scenarios from the International Institute for Applied Systems Analysis (IIASA) and one from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) have been implemented in the models. This study focuses on changes in near-surface ozone and their effects on human health and vegetation. Different indices and air quality standards are used to characterise air quality. We show that often the calculated changes in the different indices are closely inter-related. Indices using lower thresholds are more consistent between the models, and are recommended for global model analysis. Our analysis indicates that currently about two-thirds of the regions considered do not meet health air quality standards, whereas only 2-4 regions remain below the threshold. Calculated air quality exceedances show moderate deterioration by 2030 if current emissions legislation is followed and slight improvements if current emissions reduction technology is used optimally. For the "business as usual" scenario severe air quality problems are predicted. We show that model simulations of air quality indices are particularly sensitive to how well ozone is represented, and improved accuracy is needed for future projections. Additional measurements are needed to allow a more quantitative assessment of the risks to human health and vegetation from changing levels of surface ozone.

Ozone's Threat Hits Back Mexico City

NASA Astrophysics Data System (ADS)

Velasco, E.; Retama, A.; Guzman, D.

2016-12-01

Last March the Mexican authorities activated after 13 years the environmental alarm when ozone (O3) reached 210 ppb. The emergency measures created confusion among the public, who had lost memory of previous air quality crisis. Despite Mexico City has experienced a significant progress towards achieving cleaner air during the last 20 years, a recent relaxation in traffic regulations and meteorology favorable for photochemical activity triggered this new episode. All criteria pollutants of primary origin have been controlled and are in compliance with the Mexican Air Quality Standards. However, O3 and fine particles still exceed the standard threshold concentrations. For instance, 49-64% of the days have exceeded the 1-hour O3 standard of 95 ppb during the last 5 years. The current control policies, which responded to the integration of air quality information by authorities and scientists, have apparently started to lose effectiveness. Although precursor gases, such as alkanes and aromatics have shown an important decrease, reactive olefins have gained importance. The increase of motor-vehicles in recent years seems to fuel again the atmosphere's reactivity. This paper analyses the effectiveness of the emergency measures during the crisis based on the knowledge obtained from previous large field studies and the comprehensive data collected by the local air quality monitoring network. It is 10 years from MILAGRO, the last interdisciplinary study that examined the air pollution of the most populous city in North America. We call for a new collaborative research initiative based on a major field measurement campaign with the target of revealing new insights into the meteorology, emission of primary pollutants and precursor gases, photochemical production and formation of secondary particles in the atmosphere of Mexico City to improve its air quality, as well as of similar cities in the developing world.

An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning

PubMed Central

Jo, ByungWan

2018-01-01

The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality. PMID:29561777

An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning.

PubMed

Jo, ByungWan; Khan, Rana Muhammad Asad

2018-03-21

The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH₄, CO, SO₂, and H₂S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R ² and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.

Urban air quality measurements using a sensor-based system

NASA Astrophysics Data System (ADS)

Ródenas, Mila; Hernández, Daniel; Gómez, Tatiana; López, Ramón; Muñoz, Amalia

2017-04-01

Air pollution levels in urban areas have increased the interest, not only of the scientific community but also of the general public, and both at the regional and at the European level. This interest has run in parallel to the development of miniaturized sensors, which only since very recently are suitable for air quality measurements. Certainly, their small size and price allows them to be used as a network of sensors capable of providing high temporal and spatial frequency measurements to characterize an area or city and with increasing potential, under certain considerations, as a complement of conventional methods. Within the frame of the LIFE PHOTOCITYTEX project (use of photocatalytic textiles to help reducing air pollution), CEAM has developed a system to measure gaseous compounds of importance for urban air quality characterization. This system, which allows an autonomous power supply, uses commercial NO, NO2, O3 and CO2 small sensors and incorporates measurements of temperature and humidity. A first version, using XBee boards (Radiofrequency) for communications has been installed in the urban locations defined by the project (tunnel and school), permitting the long-term air quality characterization of sites in the presence of the textiles. An improved second version of the system which also comprises a sensor for measuring particles and which uses GPRS for communications, has been developed and successfully installed in the city center of Valencia. Data are sent to a central server where they can be accessed by citizens in nearly real time and online and, in general, they can be utilized in the air quality characterization, for decision-making related to decontamination (traffic regulation, photocatalytic materials, etc.), in air quality models or in mobile applications of interest for the citizens. Within this work, temporal trends obtained with this system in different urban locations will be shown, discussing the impact of the characteristics of the selected sites and the seasonal variability on the air quality levels observed. Acknowledgements EUPHORE staff is acknowledged. PHOTOCITYTEX project (LIFE13 ENV/ES/000603) is acknowledged for supporting this work. Fundación CEAM is partly supported by Generalitat Valenciana - Spain.

Quantifying TOLNet Ozone Lidar Accuracy During the 2014 DISCOVER-AQ and FRAPPE Campaigns

NASA Technical Reports Server (NTRS)

Wang, Lihua; Newchurch, Michael J.; Alvarez, Raul J., II; Berkoff, Timothy A.; Brown, Steven S.; Carrion, William; De Young, Russell J.; Johnson, Bryan J.; Ganoe, Rene; Gronoff, Guillaume;

Quantifying TOLNet ozone lidar accuracy during the 2014 DISCOVER-AQ and FRAPPÉ campaigns

NASA Astrophysics Data System (ADS)

Wang, Lihua; Newchurch, Michael J.; Alvarez, Raul J., II; Berkoff, Timothy A.; Brown, Steven S.; Carrion, William; De Young, Russell J.; Johnson, Bryan J.; Ganoe, Rene; Gronoff, Guillaume; Kirgis, Guillaume; Kuang, Shi; Langford, Andrew O.; Leblanc, Thierry; McDuffie, Erin E.; McGee, Thomas J.; Pliutau, Denis; Senff, Christoph J.; Sullivan, John T.; Sumnicht, Grant; Twigg, Laurence W.; Weinheimer, Andrew J.

2017-10-01

The Tropospheric Ozone Lidar Network (TOLNet) is a unique network of lidar systems that measure high-resolution atmospheric profiles of ozone. The accurate characterization of these lidars is necessary to determine the uniformity of the network calibration. From July to August 2014, three lidars, the TROPospheric OZone (TROPOZ) lidar, the Tunable Optical Profiler for Aerosol and oZone (TOPAZ) lidar, and the Langley Mobile Ozone Lidar (LMOL), of TOLNet participated in the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission and the Front Range Air Pollution and Photochemistry Éxperiment (FRAPPÉ) to measure ozone variations from the boundary layer to the top of the troposphere. This study presents the analysis of the intercomparison between the TROPOZ, TOPAZ, and LMOL lidars, along with comparisons between the lidars and other in situ ozone instruments including ozonesondes and a P-3B airborne chemiluminescence sensor. The TOLNet lidars measured vertical ozone structures with an accuracy generally better than ±15 % within the troposphere. Larger differences occur at some individual altitudes in both the near-field and far-field range of the lidar systems, largely as expected. In terms of column average, the TOLNet lidars measured ozone with an accuracy better than ±5 % for both the intercomparison between the lidars and between the lidars and other instruments. These results indicate that these three TOLNet lidars are suitable for use in air quality, satellite validation, and ozone modeling efforts.

A Visual Analytics Approach for Station-Based Air Quality Data

PubMed Central

Du, Yi; Ma, Cuixia; Wu, Chao; Xu, Xiaowei; Guo, Yike; Zhou, Yuanchun; Li, Jianhui

2016-01-01

With the deployment of multi-modality and large-scale sensor networks for monitoring air quality, we are now able to collect large and multi-dimensional spatio-temporal datasets. For these sensed data, we present a comprehensive visual analysis approach for air quality analysis. This approach integrates several visual methods, such as map-based views, calendar views, and trends views, to assist the analysis. Among those visual methods, map-based visual methods are used to display the locations of interest, and the calendar and the trends views are used to discover the linear and periodical patterns. The system also provides various interaction tools to combine the map-based visualization, trends view, calendar view and multi-dimensional view. In addition, we propose a self-adaptive calendar-based controller that can flexibly adapt the changes of data size and granularity in trends view. Such a visual analytics system would facilitate big-data analysis in real applications, especially for decision making support. PMID:28029117

A Visual Analytics Approach for Station-Based Air Quality Data.

PubMed

Du, Yi; Ma, Cuixia; Wu, Chao; Xu, Xiaowei; Guo, Yike; Zhou, Yuanchun; Li, Jianhui

2016-12-24

With the deployment of multi-modality and large-scale sensor networks for monitoring air quality, we are now able to collect large and multi-dimensional spatio-temporal datasets. For these sensed data, we present a comprehensive visual analysis approach for air quality analysis. This approach integrates several visual methods, such as map-based views, calendar views, and trends views, to assist the analysis. Among those visual methods, map-based visual methods are used to display the locations of interest, and the calendar and the trends views are used to discover the linear and periodical patterns. The system also provides various interaction tools to combine the map-based visualization, trends view, calendar view and multi-dimensional view. In addition, we propose a self-adaptive calendar-based controller that can flexibly adapt the changes of data size and granularity in trends view. Such a visual analytics system would facilitate big-data analysis in real applications, especially for decision making support.

Assessing the air quality impact of nitrogen oxides and benzene from road traffic and domestic heating and the associated cancer risk in an urban area of Verona (Italy)

NASA Astrophysics Data System (ADS)

Schiavon, Marco; Redivo, Martina; Antonacci, Gianluca; Rada, Elena Cristina; Ragazzi, Marco; Zardi, Dino; Giovannini, Lorenzo

2015-11-01

Simulations of emission and dispersion of nitrogen oxides (NOx) are performed in an urban area of Verona (Italy), characterized by street canyons and typical sources of urban pollutants. Two dominant source categories are considered: road traffic and, as an element of novelty, domestic heaters. Also, to assess the impact of urban air pollution on human health and, in particular, the cancer risk, simulations of emission and dispersion of benzene are carried out. Emissions from road traffic are estimated by the COPERT 4 algorithm, whilst NOx emission factors from domestic heaters are retrieved by means of criteria provided in the technical literature. Then maps of the annual mean concentrations of NOx and benzene are calculated using the AUSTAL2000 dispersion model, considering both scenarios representing the current situation, and scenarios simulating the introduction of environmental strategies for air pollution mitigation. The simulations highlight potentially critical situations of human exposure that may not be detected by the conventional network of air quality monitoring stations. The proposed methodology provides a support for air quality policies, such as planning targeted measurement campaigns, re-locating monitoring stations and adopting measures in favour of better air quality in urban planning. In particular, the estimation of the induced cancer risk is an important starting point to conduct zoning analyses and to detect the areas where population is more directly exposed to potential risks for health.

Method to Select Metropolitan Areas of Epidemiologic Interest for Enhanced Air Quality Monitoring

EPA Science Inventory

The U.S. Environmental Protection Agency’s current Speciation Trends Network (STN) covers most major U.S. metropolitan areas and a wide range of particulate matter (PM) constituents and gaseous co-pollutants. However, using filter-based methods, most PM constituents are measured ...

Open hardware, low cost, air quality stations for monitoring ozone in coastal area

NASA Astrophysics Data System (ADS)

Lima, Marco; Donzella, Davide; Pintus, Fabio; Fedi, Adriano; Ferrari, Daniele; Massabò, Marco

2014-05-01

Ozone concentrations in urban and coastal area are a great concern for citizens and, consequently regulator. In the last 20 years the Ozone concentration is almost doubled and it has attracted the public attention because of the well know harmful impacts on human health and biosphere in general. Official monitoring networks usually comprise high precision, high accuracy observation stations, usually managed by public administrations and environmental agency; unfortunately due to their high costs of installation and maintenance, the monitoring stations are relatively sparse. This kind of monitoring networks have been recognized to be unsuitable to effectively characterize the high variability of air quality, especially in areas where pollution sources are various and often not static. We present a prototype of a low cost station for air quality monitoring, specifically developed for complementing the official monitoring stations improving the representation of air quality spatial distribution. We focused on a semi-professional product that could guarantee the highest reliability at the lowest possible cost, supported by a consistent infrastructure for data management. We test two type of Ozone sensor electrochemical and metal oxide. This work is integrated in the ACRONET Paradigm ® project: an open-hardware platform strongly oriented on environmental monitoring. All software and hardware sources will be available on the web. Thus, a computer and a small amount of work tools will be sufficient to create new monitoring networks, with the only constraint to share all the data obtained. It will so possible to create a real "sensing community". The prototype is currently able to measure ozone level, temperature and relative humidity, but soon, with the upcoming changes, it will be able also to monitor dust, carbon monoxide and nitrogen dioxide, always through the use of commercial sensors. The sensors are grouped in a compact board that interfaces with a data-logger able to transmit data to a dedicated server through a GPRS module (no ad hoc radio infrastructure needed). Due to the GPRS low latency transmission the data are transmitted in near-real time. The prototype has an independent power supply. The sensors outputs are directly compared with the measurement of the official fixed monitoring stations. We present preliminary tests of a ozone level assessment obtained without laboratory calibration during a first field campaign in Savona (Italy); the preliminary verification and test show reasonable agreement between low cost sensors and fixed monitoring station ozone level trends (low cost sensors detect gas concentration at ppb level). The preliminary results are promising for complementing the fixed official monitoring networks with low-cost sensors.

Competing Air Quality and Water Conservation Co-benefits from Power Sector Decarbonization

NASA Astrophysics Data System (ADS)

Peng, W.; Wagner, F.; Mauzerall, D. L.; Ramana, M. V.; Zhai, H.; Small, M.; Zhang, X.; Dalin, C.

2016-12-01

Decarbonizing the power sector can reduce fossil-based generation and associated air pollution and water use. However, power sector configurations that prioritize air quality benefits can be different from those that maximize water conservation benefits. Despite extensive work to optimize the generation mix under an air pollution or water constraint, little research has examined electricity transmission networks and the choice of which fossil fuel units to displace in order to achieve both environmental objectives simultaneously. When air pollution and water stress occur in different regions, the optimal transmission and displacement decisions still depend on priorities placed on air quality and water conservation benefits even if low-carbon generation planning is fixed. Here we use China as a test case, and develop a new optimization framework to study transmission and displacement decisions and the resulting air quality and water use impacts for six power sector decarbonization scenarios in 2030 ( 50% of national generation is low carbon). We fix low-carbon generation in each scenario (e.g. type, location, quantity) and vary technology choices and deployment patterns across scenarios. The objective is to minimize the total physical costs (transmission costs and coal power generation costs) and the estimated environmental costs. Environmental costs are estimated by multiplying effective air pollutant emissions (EMeff, emissions weighted by population density) and effective water use (Weff, water use weighted by a local water stress index) by their unit economic values, Vem and Vw. We are hence able to examine the effect of varying policy priorities by imposing different combinations of Vem and Vw. In all six scenarios, we find that increasing the priority on air quality co-benefits (higher Vem) reduces air pollution impacts (lower EMeff) at the expense of lower water conservation (higher Weff); and vice versa. Such results can largely be explained by differences in optimal transmission decisions due to different locations of air pollution and water stress in China (severe in the east and north respectively). To achieve both co-benefits simultaneously, it is therefore critical to coordinate policies that reduce air pollution (pollution tax) and water use (water pricing) with power sector planning.

Air pollution in Latin America: Bottom-up Vehicular Emissions Inventory and Atmospheric Modeling

NASA Astrophysics Data System (ADS)

Ibarra Espinosa, S.; Vela, A. V.; Calderon, M. G.; Carlos, G.; Ynoue, R.

2016-12-01

Air pollution is a global environmental and health problem. Population of Latin America are facing air quality risks due to high level of air pollution. According to World Health Organization (WHO; 2016), several Latin American cities have high level of pollution. Emissions inventories are a key tool for air quality, however they normally present lack of quality and adequate documentation in developing countries. This work aims to develop air quality assessments in Latin American countries by 1) develop a high resolution emissions inventory of vehicles, and 2) simulate air pollutant concentrations. The bottom-up vehicular emissions inventory used was obtained with the REMI model (Ibarra et al., 2016) which allows to interpolate traffic over road network of Open Street Map to estimate vehicular emissions 24-h, each day of the week. REMI considers several parameters, among them the average age of fleet which was associated with gross domestic product (GDP) per capita. The estimated pollutants are CO, NOx, HC, PM2.5, NO, NO2, CO2, N2O, COV, NH3 and Fuel Consumption. The emissions inventory was performed at the biggest cities, including every capital of Latin America's countries. Initial results shows that the cities with most CO emissions are Buenos Aires 162800 (t/year), São Paulo 152061 (t/year), Campinas 151567 (t/year) and Brasilia 144332 (t/year). The results per capita shows that the city with most CO emissions per capita is Campinas, with 130 (kgCO/hab/year), showed in figure 1. This study also cover high resolution air quality simulations with WRF-Chem main cities in Latin America. Results will be assessed comparing: fuel estimates with local fuel sales, traffic count interpolation with available traffic data set at each city, and comparison between air pollutant simulations with air monitoring observation data. Ibarra, S., R. Ynoue, and S. Mhartain. 2016: "High Resolution Vehicular Emissions Inventory for the Megacity of São Paulo." Manuscript submitted to Journal of Atmospheric Environment. (1-15) WHO. 2016: WHO Global Urban Ambient Air Pollution Database (update 2016). http://www.who.int/phe/health_topics/outdoorair/databases/cities/en/

Evaluation and intercomparison of five major dry deposition algorithms in North America

EPA Science Inventory

Dry deposition of various pollutants needs to be quantified in air quality monitoring networks as well as in chemical transport models. The inferential method is the most commonly used approach in which the dry deposition velocity (Vd) is empirically parameterized as a function o...

76 FR 42549 - Approval and Promulgation of Air Quality Implementation Plans; Louisiana; Section 110(a)(2...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-19

..., including emissions inventories, monitoring, and modeling to assure attainment and maintenance of the... of the Louisiana Environmental Action Network (LEAN, hereinafter referred to as ``the commenter... action'' subject to review by the Office of Management and Budget under Executive Order 12866 (58 FR...

40 CFR 52.2565 - Original identification of plan.

Code of Federal Regulations, 2013 CFR

2013-07-01

...) Amended Regulations VI and VII, and an Identification and Analysis of the Impact of the 1979 West Virginia... State of West Virginia on November 4, 1983 which establishes an Ambient Air Quality Monitoring Network...) Revision to the State implementation plan consisting of a good engineering practice (GEP) for stack heights...

40 CFR 52.2565 - Original identification of plan.

Code of Federal Regulations, 2011 CFR

2011-07-01

...) Amended Regulations VI and VII, and an Identification and Analysis of the Impact of the 1979 West Virginia... State of West Virginia on November 4, 1983 which establishes an Ambient Air Quality Monitoring Network...) Revision to the State implementation plan consisting of a good engineering practice (GEP) for stack heights...

40 CFR 52.2565 - Original identification of plan.

Code of Federal Regulations, 2010 CFR

2010-07-01

...) Amended Regulations VI and VII, and an Identification and Analysis of the Impact of the 1979 West Virginia... State of West Virginia on November 4, 1983 which establishes an Ambient Air Quality Monitoring Network...) Revision to the State implementation plan consisting of a good engineering practice (GEP) for stack heights...

40 CFR 52.2565 - Original identification of plan.

Code of Federal Regulations, 2012 CFR

2012-07-01

...) Amended Regulations VI and VII, and an Identification and Analysis of the Impact of the 1979 West Virginia... State of West Virginia on November 4, 1983 which establishes an Ambient Air Quality Monitoring Network...) Revision to the State implementation plan consisting of a good engineering practice (GEP) for stack heights...

The nitrogen footprint tool network: a multi-institution program to reduce nitrogen pollution

EPA Science Inventory

Anthropogenic sources of reactive nitrogen have local and global impacts on air and water quality and detrimental effects on human and ecosystem health. This paper uses the nitrogen footprint tool (NFT) to determine the amount of nitrogen (N) released as a result of institutional...

THE PASSIVE OZONE NETWORK IN DALLAS (POND CONCEPT) - A MODELING OPPORTUNITY WITH COMMUNITY INVOLVEMENT

EPA Science Inventory

Despite tremendous efforts towards regulating and controlling tropospheric ozone (O3) formation, over 70 million people currently live in U.S. counties which exceed the National Ambient Air Quality Standard (NAAQS) set for 03. These high 03 concentrations alone cost the U.S. ap...

DEVELOPMENT, EVALUATION AND APPLICATION OF AN AUTOMATED EVENT PRECIPITATION SAMPLER FOR NETWORK OPERATION

EPA Science Inventory

In 1993, the University of Michigan Air Quality Laboratory (UMAQL) designed a new wet-only precipitation collection system that was utilized in the Lake Michigan Loading Study. The collection system was designed to collect discrete mercury and trace element samples on an event b...

Impacts of a large boreal wildfire on ground level atmospheric concentrations of PAHs, VOCs and ozone

EPA Science Inventory

During May 2016 a very large boreal wildfire burned throughout the Athabasca Oil Sands Region (AOSR) in central Canada, and in close proximity to an extensive air quality monitoring network. This study examines speciated 24-h integrated polycyclic aromatic hydrocarbon (PAH) and v...

The Satellite based Monitoring Initiative for Regional Air quality (SAMIRA): Project summary and first results

NASA Astrophysics Data System (ADS)

Schneider, Philipp; Stebel, Kerstin; Ajtai, Nicolae; Diamandi, Andrei; Horalek, Jan; Nemuc, Anca; Stachlewska, Iwona; Zehner, Claus

2017-04-01

We present a summary and some first results of a new ESA-funded project entitled Satellite based Monitoring Initiative for Regional Air quality (SAMIRA), which aims at improving regional and local air quality monitoring through synergetic use of data from present and upcoming satellite instruments, traditionally used in situ air quality monitoring networks and output from chemical transport models. Through collaborative efforts in four countries, namely Romania, Poland, the Czech Republic and Norway, all with existing air quality problems, SAMIRA intends to support the involved institutions and associated users in their national monitoring and reporting mandates as well as to generate novel research in this area. The primary goal of SAMIRA is to demonstrate the usefulness of existing and future satellite products of air quality for improving monitoring and mapping of air pollution at the regional scale. A total of six core activities are being carried out in order to achieve this goal: Firstly, the project is developing and optimizing algorithms for the retrieval of hourly aerosol optical depth (AOD) maps from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard of Meteosat Second Generation. As a second activity, SAMIRA aims to derive particulate matter (PM2.5) estimates from AOD data by developing robust algorithms for AOD-to-PM conversion with the support from model- and Lidar data. In a third activity, we evaluate the added value of satellite products of atmospheric composition for operational European-scale air quality mapping using geostatistics and auxiliary datasets. The additional benefit of satellite-based monitoring over existing monitoring techniques (in situ, models) is tested by combining these datasets using geostatistical methods and demonstrated for nitrogen dioxide (NO2), sulphur dioxide (SO2), and aerosol optical depth/particulate matter. As a fourth activity, the project is developing novel algorithms for downscaling coarse-resolution satellite products of air quality with the help of high-resolution model information. This will add value to existing earth observation products of air quality by bringing them to spatial scales that are more in line with what is generally required for studying urban and regional scale air quality. In a fifth activity, we implement robust and independent validation schemes for evaluating the quality of the generated products. Finally, in a sixth activity the consortium is working towards a pre-operational system for improved PM forecasts using observational (in situ and satellite) data assimilation. SAMIRA aims to maximize project benefits by liaison with national and regional environmental protection agencies and health institutions, as well as related ESA and European initiatives such as the Copernicus Atmosphere Monitoring Service (CAMS).

Keynote Address: When Breath Becomes Air-As Physician Becomes Patient.

PubMed

Kalanithi, Lucy; Wakelee, Heather; Carlson, Robert W

2017-05-01

As part of the NCCN 22nd Annual Conference: Improving the Quality, Effectiveness, and Efficiency of Cancer Care, Lucy Kalanithi, MD, wife of now-deceased best-selling author Paul Kalanithi ( When Breath Becomes Air ), and Heather Wakelee, MD, Paul's oncologist, discussed-for the first time together in a public forum-Paul's experience of going from a neurosurgery resident to a patient with cancer with a terminal diagnosis. Robert Carlson, MD, moderated the discussion. Copyright © 2017 by the National Comprehensive Cancer Network.

Calibration of low-cost gas sensors for an urban air quality monitoring network

NASA Astrophysics Data System (ADS)

Scott, A.; Kelley, C.; He, C.; Ghugare, P.; Lehman, A.; Benish, S.; Stratton, P.; Dickerson, R. R.; Zuidema, C.; Azdoud, Y.; Ren, X.

2017-12-01

In a warming world, environmental pollution may be exacerbated by anthropogenic activities, such as climate change and the urban heat island effect, as well as natural phenomena such as heat waves. However, monitoring air pollution at federal reference standards (approximately 1 part per billion or ppb for ambient ozone) is cost-prohibitive in heterogeneous urban areas as many expensive devices are required to fully capture a region's geo-spatial variability. Innovation in low-cost sensors provide a potential solution, yet technical challenges remain to overcome possible imprecision in the data. We present the calibrations of ozone and nitrous dioxide from a low-cost air quality monitoring device designed for the Baltimore Open Air Project. The sensors used in this study are commercially available thin film electrochemical sensors from SPEC Sensor, which are amperometric, meaning they generate current proportional to volumetric fraction of gas. The results of sensor calibrations in the laboratory and field are presented.

Incorporating principal component analysis into air quality ...

EPA Pesticide Factsheets

The efficacy of standard air quality model evaluation techniques is becoming compromised as the simulation periods continue to lengthen in response to ever increasing computing capacity. Accordingly, the purpose of this paper is to demonstrate a statistical approach called Principal Component Analysis (PCA) with the intent of motivating its use by the evaluation community. One of the main objectives of PCA is to identify, through data reduction, the recurring and independent modes of variations (or signals) within a very large dataset, thereby summarizing the essential information of that dataset so that meaningful and descriptive conclusions can be made. In this demonstration, PCA is applied to a simple evaluation metric – the model bias associated with EPA's Community Multi-scale Air Quality (CMAQ) model when compared to weekly observations of sulfate (SO42−) and ammonium (NH4+) ambient air concentrations measured by the Clean Air Status and Trends Network (CASTNet). The advantages of using this technique are demonstrated as it identifies strong and systematic patterns of CMAQ model bias across a myriad of spatial and temporal scales that are neither constrained to geopolitical boundaries nor monthly/seasonal time periods (a limitation of many current studies). The technique also identifies locations (station–grid cell pairs) that are used as indicators for a more thorough diagnostic evaluation thereby hastening and facilitating understanding of the prob

Analysis of impacts of urban land use and land cover on air quality in the Las Vegas region using remote sensing information and ground observations

USGS Publications Warehouse

Xian, G.

2007-01-01

Urban development in the Las Vegas Valley of Nevada (USA) has expanded rapidly over the past 50 years. The air quality in the valley has suffered owing to increases from anthropogenic emissions of carbon monoxide, ozone and criteria pollutants of particular matter. Air quality observations show that pollutant concentrations have apparent heterogeneous characteristics in the urban area. Quantified urban land use and land cover information derived from satellite remote sensing data indicate an apparent local influence of urban development density on air pollutant distributions. Multi‐year observational data collected by a network of local air monitoring stations specify that ozone maximums develop in the May and June timeframe, whereas minimum concentrations generally occur from November to February. The fine particulate matter maximum occurs in July. Ozone concentrations are highest on the west and northwest sides of the valley. Night‐time ozone reduction contributes to the heterogeneous features of the spatial distribution for average ozone levels in the Las Vegas metropolitan area. Decreased ozone levels associated with increased urban development density suggest that the highest ozone and lowest nitrogen oxides concentrations are associated with medium to low density urban development in Las Vegas.

Simulating the dispersion of NOx and CO2 in the city of Zurich at building resolving scale

NASA Astrophysics Data System (ADS)

Brunner, Dominik; Berchet, Antoine; Emmenegger, Lukas; Henne, Stephan; Müller, Michael

2017-04-01

Cities are emission hotspots for both greenhouse gases and air pollutants. They contribute about 70% of global greenhouse gas emissions and are home to a growing number of people potentially suffering from poor air quality in the urban environment. High-resolution atmospheric transport modelling of greenhouse gases and air pollutants at the city scale has, therefore, several important applications such as air pollutant exposure assessment, air quality forecasting, or urban planning and management. When combined with observations, it also has the potential to quantify emissions and monitor their long-term trends, which is the main motivation for the deployment of urban greenhouse gas monitoring networks. We have developed a comprehensive atmospheric modeling model system for the city of Zurich, Switzerland ( 600,000 inhabitants including suburbs), which is composed of the mesoscale model GRAMM simulating the flow in a larger domain around Zurich at 100 m resolution, and the nested high-resolution model GRAL simulating the flow and air pollutant dispersion in the city at building resolving (5-10 m) scale. Based on an extremely detailed emission inventory provided by the municipality of Zurich, we have simulated two years of hourly NOx and CO2 concentration fields across the entire city. Here, we present a detailed evaluation of the simulations against a comprehensive network of continuous monitoring sites and passive samplers for NOx and analyze the sensitivity of the results to the temporal variability of the emissions. Furthermore, we present first simulations of CO2 and investigate the challenges associated with CO2 sources not covered by the inventory such as human respiration and exchange fluxes with urban vegetation.

Providing QoS guarantee in 3G wireless networks

NASA Astrophysics Data System (ADS)

Chuah, MooiChoo; Huang, Min; Kumar, Suresh

2001-07-01

The third generation networks and services present opportunities to offer multimedia applications and services that meet end-to-end quality of service requirements. In this article, we present UMTS QoS architecture and its requirements. This includes the definition of QoS parameters, traffic classes, the end-to-end data delivery model, and the mapping of end-to-end services to the services provided by the network elements of the UMTS. End-to-end QoS of a user flow is achieved by the combination of the QoS control over UMTS Domain and the IP core Network. In the Third Generation Wireless network, UMTS bearer service manager is responsible to manage radio and transport resources to QoS-enabled applications. The UMTS bearer service consists of the Radio Access Bearer Service between Mobile Terminal and SGSN and Core Network bearer service between SGSN and GGSN. The Radio Access Bearer Service is further realized by the Radio Bearer Service (mostly air interface) and Iu bearer service. For the 3G air interface, one can provide differentiated QoS via intelligent burst allocation scheme, adaptive spreading factor control and weighted fair queueing scheduling algorithms. Next, we discuss the requirements for the transport technologies in the radio access network to provide differentiated QoS to multiple classes of traffic. We discuss both ATM based and IP based transport solutions. Last but not least, we discuss how QoS mechanism is provided in the core network to ensure e2e quality of service requirements. We discuss how mobile terminals that use RSVP as QoS signaling mechanisms can be are supported in the 3G network which may implement only IETF diffserv mechanism. . We discuss how one can map UMTS QoS classes with IETF diffserv code points. We also discuss 2G/3G handover scenarios and how the 2G/3G QoS parameters can be mapped.

A Tale of Two Cities - HSI-DOAS Measurements of Air Quality

NASA Astrophysics Data System (ADS)

Graves, Rosemarie; Leigh, Roland; Anand, Jasdeep; McNally, Michael; Lawrence, James; Monks, Paul

2013-04-01

Differential Optical Absorption Spectroscopy is now commonly used as an air quality measuring system; primarily through the measurements of nitrogen dioxide (NO2) both as a ground-based and satellite technique. CityScan is a Hemispherical Scanning Imaging Differential Optical Absorption Spectrometer (HSI-DOAS) which has been optimised to measure concentrations of nitrogen dioxide. CityScan has a 95˚ field of view (FOV) between the zenith and 5˚ below the horizon. Across this FOV there are 128 resolved elements which are measured concurrently, the spectrometer is rotated azimuthally 1˚ per second providing full hemispherical coverage every 6 minutes. CityScan measures concentrations of nitrogen dioxide over specific lines of sight and due to the extensive field of view of the instrument this produces measurements which are representative over city-wide scales. Nitrogen dioxide is an important air pollutant which is produced in all combustion processes and can reduce lung function; especially in sensitised individuals. These instruments aim to bridge the gap in spatial scales between point source measurements of air quality and satellite measurements of air quality offering additional information on emissions, transport and the chemistry of nitrogen dioxide. More information regarding the CityScan technique can be found at http://www.leos.le.ac.uk/aq/index.html. CityScan has been deployed in both London and Bologna, Italy during 2012. The London deployment took place as part of the large NERC funded ClearfLo project in January and July/August. CityScan was deployed in Bologna in June as part of the large EU project PEGASOS. Analysis of both of these campaigns of data will be used to give unprecedented levels of spatial information to air quality measurements whilst also showing the difference in air quality between a relatively isolated mega city and a smaller city situated in a very polluted region; in this case the Po Valley. Results from multiple CityScan instruments will be used in conjunction with data from ground based in-situ monitor networks to evaluate the ability of in-situ monitors to effectively assess the air quality in an urban environment. Trend analysis will also be shown to demonstrate any changes in the air quality in London during the time of the Olympic Games in comparison with a normal summer.

Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks.

PubMed

Abderrahim, Hamza; Chellali, Mohammed Reda; Hamou, Ahmed

2016-01-01

Air quality forecasting system has acquired high importance in atmospheric pollution due to its negative impacts on the environment and human health. The artificial neural network is one of the most common soft computing methods that can be pragmatic for carving such complex problem. In this paper, we used a multilayer perceptron neural network to forecast the daily averaged concentration of the respirable suspended particulates with aerodynamic diameter of not more than 10 μm (PM10) in Algiers, Algeria. The data for training and testing the network are based on the data sampled from 2002 to 2006 collected by SAMASAFIA network center at El Hamma station. The meteorological data, air temperature, relative humidity, and wind speed, are used as inputs network parameters in the formation of model. The training patterns used correspond to 41 days data. The performance of the developed models was evaluated on the basis index of agreement and other statistical parameters. It was seen that the overall performance of model with 15 neurons is better than the ones with 5 and 10 neurons. The results of multilayer network with as few as one hidden layer and 15 neurons were quite reasonable than the ones with 5 and 10 neurons. Finally, an error around 9% has been reached.

AQA-PM: Extension of the Air-Quality Model For Austria with Satellite based Particulate Matter Estimates

NASA Astrophysics Data System (ADS)

Hirtl, Marcus; Mantovani, Simone; Krüger, Bernd C.; Triebnig, Gerhard; Flandorfer, Claudia

2013-04-01

Air quality is a key element for the well-being and quality of life of European citizens. Air pollution measurements and modeling tools are essential for assessment of air quality according to EU legislation. The responsibilities of ZAMG as the national weather service of Austria include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. The Air Quality model for Austria (AQA) is operated at ZAMG in cooperation with the University of Natural Resources and Life Sciences in Vienna (BOKU) by order of the regional governments since 2005. AQA conducts daily forecasts of gaseous and particulate (PM10) air pollutants over Austria. In the frame of the project AQA-PM (funded by FFG), satellite measurements of the Aerosol Optical Thickness (AOT) and ground-based PM10-measurements are combined to highly-resolved initial fields using regression- and assimilation techniques. For the model simulations WRF/Chem is used with a resolution of 3 km over the alpine region. Interfaces have been developed to account for the different measurements as input data. The available local emission inventories provided by the different Austrian regional governments were harmonized and used for the model simulations. An episode in February 2010 is chosen for the model evaluation. During that month exceedances of PM10-thresholds occurred at many measurement stations of the Austrian network. Different model runs (only model/only ground stations assimilated/satellite and ground stations assimilated) are compared to the respective measurements. The goal of this project is to improve the PM10-forecasts for Austria with the integration of satellite based measurements and to provide a comprehensive product-platform.

An Analysis of Seacions Ozonesonde Measurements from St. Louis MO: Providing Insight into How Cross Country Wildfires and Descending Stratospheric Air over the Great Plains Impact Regional Air Quality

NASA Astrophysics Data System (ADS)

Wilkins, J. L.; Morris, G.; de Foy, B.; Fishman, J.

2014-12-01

As part of the SouthEast American Consortium for Intensive Ozone Network Study (SEACIONS) mission, 32 ozonesondes were launched from Forest Park in mid-town St. Louis between 8 Aug and 23 Sept 2013. These launches were supported by concurrent co-located continuous ground level ozone measurements at Saint Louis University's St. Louis Ozone Garden. During the operation of this site, wildfires from both Idaho's Beaver Creek (~115K acres) and California's RIM fire (~258k acres) generated copious amounts of pollution. In addition, widespread agricultural fires in the Midwest were also taking place. To interpret our observations over St. Louis, we used multiple satellite-derived products and retrievals in conjunction with trajectory calculations from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We examined a blocking high pressure event [Aug 26-30] which led to ozonesonde profile changes resulting from Stratospheric-Troposphere Exchange (STE) in addition to the smoke from the fires. This case study involved two mixed layer O3 enhancements, which could be spotted at multiple sites within the SEACIONS ozonesonde network. Our findings illustrate how satellite measurements can be used to assess the contribution of the transport of pollution from various sources to local air quality.

Air quality in Beijing during the 2008 Olympic Games observed by satellites and ground monitors

NASA Astrophysics Data System (ADS)

Zhao, Q.; Liu, Y.; He, K.; Chen, L.; Wang, Z.; Koutrakis, P.; Christiani, D.

2008-12-01

Beijing's severe air pollution has been a major concern for hosting the 29th Olympic Games and Special Olympic Games from August 8 to August 24, 2008. It was generally expected that its air quality in 2008, at least around the period of Olympic Games, would be significantly improved through aggressive government control measures However, it is also expected that the improvement of air quality will not be sustainable due to high economic costs. Thus, the massive temporary improvement of air quality in Beijing metropolitan area induced by direct government intervention will serve as an extremely rare "natural experiment", generating a great contrast in air pollution levels in a short period of time. A ground measurement campaign was conducted to evaluate the variation of airborne particulate matters (PM2.5 and PM10) levels in Beijing from late July to early September of 2008. Satellite aerosol remote sensing data from MISR, MODIS, and OMI during this period were also analyzed to evaluate the spatial distribution of particles in Beijing and surrounding areas. Preliminary analysis indicated that city-wide ground PM10 level in August was 30% lower than that in 2007. During the Olympic Games, PM10 level was nearly 50% lower than the same period in 2007. There are a total of 14 days with daily PM10 concentrations below 50 micrograms per cubic meter, longest since the ground monitoring network was established in 2001. PM2.5 concentrations measured from three research sites showed a similar reduction. Satellite remote sensing data are limited during the Games due to extensive cloud cover. However, existing data in August and September show a substantial regional reduction of aerosol optical depth. In conclusion, the pollution control measures effectively improved the air quality in Beijing and provided insight on how the Chinese government may mitigate air pollution in many of its large cities.

Historical Analysis and Charaterization of Ground Level Ozone for Canada and United State

NASA Astrophysics Data System (ADS)

Lin, H.; Li, H.; Auld, H.

2003-12-01

Ground-level ozone has long been recognized as an important health and ecosystem-related air quality concern in Canada and the United States. In this work we seek to understand the characteristics of ground level ozone conditions for Canada and United States to support the Ozone Annex under the Canada-U.S. Air Quality Agreement. Our analyses are based upon the data collected by Canadian National Air Pollution Surveillance (NAPS, the NAPS database has also been expanded to include U.S. EPA ground level ozone data) network. Historical ozone data from 1974 to 2002 at a total of 538 stations (253 Canadian stations and 285 U.S. stations) were statistically analyzed using several methodologies including the Canada Wide Standard (CWS). A more detailed analysis including hourly, daily, monthly, seasonally and yearly ozone concentration distributions and trends was undertaken for 54 stations.

Diurnal and Intra-Annual Variations in Greenhouse Gases at Fixed Sites in the San Francisco Bay Area

NASA Astrophysics Data System (ADS)

Newman, S.; Guha, A.; Martien, P. T.; Bower, J.; Perkins, I.; Randall, S.; Young, A.; Stevenson, E.; Hilken, H.

2017-12-01

The Bay Area Air Quality Management District, the San Francisco Bay Area's air quality regulatory agency, has set a goal to reduce the region's greenhouse gas (GHG) emissions to 80% below 1990 levels by 2050, consistent with the State of California's climate goals. Recently, the Air District's governing board adopted a 2017 Clean Air Plan which lays out the agency's vision and includes actions to put the region on a path towards achieving the 2050 goal while also reducing air pollution and related health impacts. The Plan includes GHG rule-making efforts, policy initiatives, local government partnerships, outreach, grants, and incentives, encompassing over 250 specific implementation actions across all economic sectors to effect ambitious emission reductions in the region. To track trends in atmospheric observations of GHGs and associated species and monitor changes in regional emission patterns, the Air District has established a fixed site network (CO2, CH4, CO) of one generally upwind site (Bodega Bay - on the coast north of Marin County) and three receptor sites (Bethel Island - east of the major refineries, in the Sacramento River Delta; Livermore - east of the bulk of the East Bay cities; and San Martin - south of the major city of San Jose). Having collected over a year of data for each of the fixed sites, the Air District is now investigating spatial and temporal variations in GHG emissions. Concentrating on variations in diurnal cycles, we see the commonly observed pattern of seasonal changes in diurnal amplitude at all sites, with larger variations during the winter than the summer, consistent with seasonally varying daily changes in planetary boundary layer heights. Investigations explore the weekday/weekend effect on the diurnal patterns and the effect of seasonal wind direction changes on the intra-annual variations of the local enhancements. The Air District is beginning to investigate the ways in which the fixed site network reflects the dominant upwind GHG emissions.

Characterizing Spatial and Temporal Patterns of Thermal Environment and Air Quality in Taipei Metropolitan Area

NASA Astrophysics Data System (ADS)

Juang, J. Y.; Sun, C. H.; Jiang, J. A.; Wen, T. H.

2017-12-01

The urban heat island effect (UHI) caused by the regional-to-global environmental changes, dramatic urbanization, and shifting in land-use compositions has becoming an important environmental issue in recent years. In the past century, the coverage of urban area in Taipei Basin has dramatically increasing by ten folds. The strengthen of UHI effect significantly enhances the frequency of warm-night effect, and strongly influences the thermal environment of the residents in the Greater Taipei Metropolitan. In addition, the urban expansions due to dramatic increasing in urban populations and traffic loading significantly impacts the air quality and causes health issue in Taipei. In this study, the main objective is to quantify and characterize the temporal and spatial distributions of thermal environmental and air quality in the Greater Taipei Metropolitan Area by using monitoring data from Central Weather Bureau, Environmental Protection Administration. In addition, in this study, we conduct the analysis on the distribution of physiological equivalent temperature in the micro scale in the metropolitan area by using the observation data and quantitative simulation to investigate how the thermal environment is influenced under different conditions. Furthermore, we establish a real-time mobile monitoring system by using wireless sensor network to investigate the correlation between the thermal environment, air quality and other environmental factors, and propose to develop the early warning system for heat stress and air quality in the metropolitan area. The results from this study can be integrated into the management and planning system, and provide sufficient and important background information for the development of smart city in the metropolitan area in the future.

Data Verification Tools for Minimizing Management Costs of Dense Air-Quality Monitoring Networks.

PubMed

Miskell, Georgia; Salmond, Jennifer; Alavi-Shoshtari, Maryam; Bart, Mark; Ainslie, Bruce; Grange, Stuart; McKendry, Ian G; Henshaw, Geoff S; Williams, David E

2016-01-19

Aiming at minimizing the costs, both of capital expenditure and maintenance, of an extensive air-quality measurement network, we present simple statistical methods that do not require extensive training data sets for automated real-time verification of the reliability of data delivered by a spatially dense hybrid network of both low-cost and reference ozone measurement instruments. Ozone is a pollutant that has a relatively smooth spatial spread over a large scale although there can be significant small-scale variations. We take advantage of these characteristics and demonstrate detection of instrument calibration drift within a few days using a rolling 72 h comparison of hourly averaged data from the test instrument with that from suitably defined proxies. We define the required characteristics of the proxy measurements by working from a definition of the network purpose and specification, in this case reliable determination of the proportion of hourly averaged ozone measurements that are above a threshold in any given day, and detection of calibration drift of greater than ±30% in slope or ±5 parts-per-billion in offset. By analyzing results of a study of an extensive deployment of low-cost instruments in the Lower Fraser Valley, we demonstrate that proxies can be established using land-use criteria and that simple statistical comparisons can identify low-cost instruments that are not stable and therefore need replacing. We propose that a minimal set of compliant reference instruments can be used to verify the reliability of data from a much more extensive network of low-cost devices.

SmartAQnet: remote and in-situ sensing of urban air quality

NASA Astrophysics Data System (ADS)

Budde, Matthias; Riedel, Till; Beigl, Michael; Schäfer, Klaus; Emeis, Stefan; Cyrys, Josef; Schnelle-Kreis, Jürgen; Philipp, Andreas; Ziegler, Volker; Grimm, Hans; Gratza, Thomas

2017-10-01

Air quality and the associated subjective and health-related quality of life are among the important topics of urban life in our time. However, it is very difficult for many cities to take measures to accommodate today's needs concerning e.g. mobility, housing and work, because a consistent fine-granular data and information on causal chains is largely missing. This has the potential to change, as today, both large-scale basic data as well as new promising measuring approaches are becoming available. The project "SmartAQnet", funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI), is based on a pragmatic, data driven approach, which for the first time combines existing data sets with a networked mobile measurement strategy in the urban space. By connecting open data, such as weather data or development plans, remote sensing of influencing factors, and new mobile measurement approaches, such as participatory sensing with low-cost sensor technology, "scientific scouts" (autonomous, mobile smart dust measurement device that is auto-calibrated to a high-quality reference instrument within an intelligent monitoring network) and demand-oriented measurements by light-weight UAVs, a novel measuring and analysis concept is created within the model region of Augsburg, Germany. In addition to novel analytics, a prototypical technology stack is planned which, through modern analytics methods and Big Data and IoT technologies, enables application in a scalable way.

High Spectral Resolution LIDAR as a Tool for Air Quality Research

NASA Astrophysics Data System (ADS)

Eloranta, E. W.; Spuler, S.; Hayman, M. M.

2017-12-01

Many aspects of air quality research require information on the vertical distribution of pollution. Traditional measurements, obtained from surface based samplers, or passive satellite remote sensing, do not provide vertical profiles. Lidar can provide profiles of aerosol properties. However traditional backscatter lidar suffers from uncertain calibrations with poorly constrained algorithms. These problems are avoided using High Spectral Resolution Lidar (HSRL) which provides absolutely calibrated vertical profiles of aerosol properties. The University of Wisconsin HSRL systems measure 532 nm wavelength aerosol backscatter cross-sections, extinction cross-sections, depolarization, and attenuated 1064 nm backscatter. These instruments are designed for long-term deployment at remote sites with minimal local support. Processed data is provided for public viewing and download in real-time on our web site "http://hsrl.ssec.wisc.edu". Air pollution applications of HSRL data will be illustrated with examples acquired during air quality field programs including; KORUS-AQ, DISCOVER-AQ, LAMOS and FRAPPE. Observations include 1) long range transport of dust, air pollution and smoke. 2) Fumigation episodes where elevated pollution is mixed down to the surface. 3) visibility restrictions by aerosols and 4) diurnal variations in atmospheric optical depth. While HSRL is powerful air quality research tool, its application in routine measurement networks is hindered by the high cost of current systems. Recent technical advances promise a next generation HSRL using telcom components to greatly reduce system cost. This paper will present data generated by a prototype low cost system constructed at NCAR. In addition to lower cost, operation at a non-visible near 780 nm infrared wavelength removes all FAA restrictions on the operation.

Assessing the Impact of Fires on Air Quality in the Southeastern U.S. with a Unified Prescribed Burning Database

NASA Astrophysics Data System (ADS)

Garcia Menendez, F.; Afrin, S.

2017-12-01

Prescribed fires are used extensively across the Southeastern United States and are a major source of air pollutant emissions in the region. These land management projects can adversely impact local and regional air quality. However, the emissions and air pollution impacts of prescribed fires remain largely uncertain. Satellite data, commonly used to estimate fire emissions, is often unable to detect the low-intensity, short-lived prescribed fires characteristic of the region. Additionally, existing ground-based prescribed burn records are incomplete, inconsistent and scattered. Here we present a new unified database of prescribed fire occurrence and characteristics developed from systemized digital burn permit records collected from public and private land management organizations in the Southeast. This bottom-up fire database is used to analyze the correlation between high PM2.5 concentrations measured by monitoring networks in southern states and prescribed fire occurrence at varying spatial and temporal scales. We show significant associations between ground-based records of prescribed fire activity and the observational air quality record at numerous sites by applying regression analysis and controlling confounding effects of meteorology. Furthermore, we demonstrate that the response of measured PM2.5 concentrations to prescribed fire estimates based on burning permits is significantly stronger than their response to satellite fire observations from MODIS (moderate-resolution imaging spectroradiometer) and geostationary satellites or prescribed fire emissions data in the National Emissions Inventory. These results show the importance of bottom-up smoke emissions estimates and reflect the need for improved ground-based fire data to advance air quality impacts assessments focused on prescribed burning.

Modelled air pollution levels versus EC air quality legislation - results from high resolution simulation.

PubMed

Chervenkov, Hristo

2013-12-01

An appropriate method for evaluating the air quality of a certain area is to contrast the actual air pollution levels to the critical ones, prescribed in the legislative standards. The application of numerical simulation models for assessing the real air quality status is allowed by the legislation of the European Community (EC). This approach is preferable, especially when the area of interest is relatively big and/or the network of measurement stations is sparse, and the available observational data are scarce, respectively. Such method is very efficient for similar assessment studies due to continuous spatio-temporal coverage of the obtained results. In the study the values of the concentration of the harmful substances sulphur dioxide, (SO2), nitrogen dioxide (NO2), particulate matter - coarse (PM10) and fine (PM2.5) fraction, ozone (O3), carbon monoxide (CO) and ammonia (NH3) in the surface layer obtained from modelling simulations with resolution 10 km on hourly bases are taken to calculate the necessary statistical quantities which are used for comparison with the corresponding critical levels, prescribed in the EC directives. For part of them (PM2.5, CO and NH3) this is done for first time with such resolution. The computational grid covers Bulgaria entirely and some surrounding territories and the calculations are made for every year in the period 1991-2000. The averaged over the whole time slice results can be treated as representative for the air quality situation of the last decade of the former century.

EVALUATION OF SEVERAL PM 2.5 FORECAST MODELS USING DATA COLLECTED DURING THE ICARTT/NEAQS 2004 FIELD STUDY

EPA Science Inventory

Real-time forecasts of PM_2.5 aerosol mass from seven air-quality forecast models (AQFMs) are statistically evaluated against observations collected in the northeastern U.S. and southeastern Canada from two surface networks and aircraft data during the summer of 2004 IC...

77 FR 65656 - Determination of Attainment for the Nogales Nonattainment Area for the 2006 Fine Particle...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-30

...\\ Furthermore, we concluded in our Technical System Audit Report concerning ADEQ's ambient air quality... Monitoring Network Plan: For the Year 2011''). \\7\\ Technical System Audit Report transmitted via... an ``anonymous access'' system, and EPA will not know your identity or contact information unless you...

40 CFR 52.1100 - Original identification of plan section.

Code of Federal Regulations, 2012 CFR

2012-07-01

... establish an Ambient Air Quality Monitoring Network. (45) Recodification of the Maryland Regulations... Practice (GEP) Stack Height Regulations, COMAR 10.18.01.08 (Determination of Ground Level Concentrations... consumption analysis. The amendments to COMAR 26.11.01.01, 26.11.02.10 (C)(9), and 26.11.06.14 were effective...

COMPARISON OF CMAQ DERIVED CARBON MONOXIDE COLUMNS WITH MOPITT CARBON MONOXIDE DATA, SENSITIVITY TO WILDFIRE EMISSIONS

EPA Science Inventory

All model results need to be evaluated against observed data, no matter what the model

scale. Traditionally for air quality applications, the observed data have been limited to

concentrations measured by networks of ground stations. These are located mostly in

78 FR 57496 - Approval and Promulgation of Air Quality Implementation Plans; State of Colorado Second Ten-Year...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-19

... FURTHER INFORMATION CONTACT section to view the hard copy of the docket. You may view the hard copy of the... to illustrate your concerns, and suggest alternatives. g. Explain your views as clearly as possible..., Maintenance Demonstration, Monitoring Network/Verification of Continued Attainment, Contingency Plan, and...

ASSESSMENT OF AN ENSEMBLE OF SEVEN REAL-TIME OZONE FORECASTS OVER EASTERN NORTH AMERICA DURING THE SUMMER OF 2004

EPA Science Inventory

The real-time forecasts of ozone (O₃) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during July and August of 2004 (53 days) through the Aerometric Information Retrieval Now (AIRNow) network at roughly 340 mon...

The Network for the Detection of Atmospheric Composition Change (NDACC): history, status and perspectives

NASA Astrophysics Data System (ADS)

De Mazière, Martine; Thompson, Anne M.; Kurylo, Michael J.; Wild, Jeannette D.; Bernhard, Germar; Blumenstock, Thomas; Braathen, Geir O.; Hannigan, James W.; Lambert, Jean-Christopher; Leblanc, Thierry; McGee, Thomas J.; Nedoluha, Gerald; Petropavlovskikh, Irina; Seckmeyer, Gunther; Simon, Paul C.; Steinbrecht, Wolfgang; Strahan, Susan E.

2018-04-01

The Network for the Detection of Atmospheric Composition Change (NDACC) is an international global network of more than 90 stations making high-quality measurements of atmospheric composition that began official operations in 1991 after 5 years of planning. Apart from sonde measurements, all measurements in the network are performed by ground-based remote-sensing techniques. Originally named the Network for the Detection of Stratospheric Change (NDSC), the name of the network was changed to NDACC in 2005 to better reflect the expanded scope of its measurements. The primary goal of NDACC is to establish long-term databases for detecting changes and trends in the chemical and physical state of the atmosphere (mesosphere, stratosphere, and troposphere) and to assess the coupling of such changes with climate and air quality. NDACC's origins, station locations, organizational structure, and data archiving are described. NDACC is structured around categories of ground-based observational techniques (sonde, lidar, microwave radiometers, Fourier-transform infrared, UV-visible DOAS (differential optical absorption spectroscopy)-type, and Dobson-Brewer spectrometers, as well as spectral UV radiometers), timely cross-cutting themes (ozone, water vapour, measurement strategies, cross-network data integration), satellite measurement systems, and theory and analyses. Participation in NDACC requires compliance with strict measurement and data protocols to ensure that the network data are of high and consistent quality. To widen its scope, NDACC has established formal collaborative agreements with eight other cooperating networks and Global Atmosphere Watch (GAW). A brief history is provided, major accomplishments of NDACC during its first 25 years of operation are reviewed, and a forward-looking perspective is presented.

The Network for the Detection of Atmospheric Composition Change (NDACC): History, Status and Perspectives

NASA Technical Reports Server (NTRS)

Simon, Paul C.; De Maziere, Martine; Bernhard, Germar; Blumenstock, Thomas; McGee, Thomas J.; Petropavlovskikh, Irina; Steinbrecht, Wolfgang; Wild, Jeannette D.; Lambert, Jean-Christopher; Seckmeyer, Gunther;

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

PubMed

Ghaemi, Z; Alimohammadi, A; Farnaghi, M

2018-04-20

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

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

PubMed

Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

2017-12-01

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Ground-water hydrology and water quality of the southern high plains aquifer, Melrose Air Force Range, Cannon Air Force Base, Curry and Roosevelt Counties, New Mexico, 2002-03

USGS Publications Warehouse

Langman, Jeff B.; Gebhardt, Fredrick E.; Falk, Sarah E.

2004-01-01

In cooperation with the U.S. Air Force, the U.S. Geological Survey characterized the ground-water hydrology and water quality at Melrose Air Force Range in east-central New Mexico. The purpose of the study was to provide baseline data to Cannon Air Force Base resource managers to make informed decisions concerning actions that may affect the ground-water system. Five periods of water-level measurements and four periods of water-quality sample collection were completed at Melrose Air Force Range during 2002 and 2003. The water-level measurements and water-quality samples were collected from a 29-well monitoring network that included wells in the Impact Area and leased lands of Melrose Air Force Range managed by Cannon Air Force Base personnel. The purpose of this report is to provide a broad overview of ground-water flow and ground-water quality in the Southern High Plains aquifer in the Ogallala Formation at Melrose Air Force Range. Results of the ground-water characterization of the Southern High Plains aquifer indicated a local flow system in the unconfined aquifer flowing northeastward from a topographic high, the Mesa (located in the southwestern part of the Range), toward a regional flow system in the unconfined aquifer that flows southeastward through the Portales Valley. Ground water was less than 55 years old across the Range; ground water was younger (less than 25 years) near the Mesa and ephemeral channels and older (25 years to 55 years) in the Portales Valley. Results of water-quality analysis indicated three areas of different water types: near the Mesa and ephemeral channels, in the Impact Area of the Range, and in the Portales Valley. Within the Southern High Plains aquifer, a sodium/chloride-dominated ground water was found in the center of the Impact Area of the Range with water-quality characteristics similar to ground water from the underlying Chinle Formation. This sodium/chloride-dominated ground water of the unconfined aquifer in the Impact Area indicates a likely connection with the deeper water-producing zone. No pesticides, explosives, volatile organic compounds, semivolatile organic compounds, organic halogens, or perchlorate were found in water samples from the Southern High Plains aquifer at the Range.

AIRQino, a low-cost air quality mobile platform

NASA Astrophysics Data System (ADS)

Zaldei, Alessandro; Vagnoli, Carolina; Di Lonardo, Sara; Gioli, Beniamino; Gualtieri, Giovanni; Toscano, Piero; Martelli, Francesca; Matese, Alessandro

2015-04-01

Recent air quality regulations (Directive 2008/50/EC) enforce the transition from point-based monitoring networks to new tools that must be capable of mapping and forecasting air quality on the totality of land area, and therefore the totality of citizens. This implies new technologies such as models and additional indicative measurements, are needed in addition to accurate fixed air quality monitoring stations, that until now have been taken as reference by local administrators for the enforcement of various mitigation strategies. However, due to their sporadic spatial distribution, they cannot describe the highly resolved spatial pollutant variations within cities. Integrating additional indicative measurements may provide adequate information on the spatial distribution of the ambient air quality, also allowing for a reduction of the required minimum number of fixed sampling points, whose high cost and complex maintenance still remain a crucial concern for local administrators. New low-cost and small size sensors are becoming available, that could be employed in air quality monitoring including mobile applications. However, accurate assessment of their accuracy and performance both in controlled and real monitoring conditions is crucially needed. Quantifying sensor response is a significant challenge due to the sensitivity to ambient temperature and humidity and the cross-sensitivity to others pollutant species. This study reports the development of an Arduino compatible electronic board (AIRQino) which integrates a series of low-cost metal oxide and NDIR sensors for air quality monitoring, with sensors to measure air temperature, relative humidity, noise, solar radiation and vertical acceleration. A comparative assessment was made for CO2, CO, NO2, CH4, O3, VOCs concentrations, temperature and relative humidity. A controlled climatic chamber study (-80°C / +80°C) was performed to verify temperature and humidity interference using reference gas cylinders and high quality reference sensors. The AIRQino was installed on mobile vectors such as bikes, buses and trams in the cities of Firenze and Siracusa (Italy), that send data real-time to a Web portal. By integrating a microprocessor unit it is capable of directly updating calibration coefficients to provide corrected sensor output as digital string through RS232 serial port. Results from the lab tests and the 'real world' mobile applications are presented and discussed, to assess to what extent this sensor technology might be useful for the development of portable, compact, wireless and cost-effective system for air quality monitoring in urban areas at high spatio-temporal resolution.

2012 Nitrogen Dioxide Monitoring Site Design Values

EPA Pesticide Factsheets

A design value is a statistic that describes the air quality status of a given location relative to the level of the National Ambient Air Quality Standards (NAAQS). Design values are defined to be consistent with the individual NAAQS as described in CFR Part 50. They are typically used to designate and classify nonattainment areas, as well as to assess progress towards meeting the NAAQS. To view a list of areas designated nonattainment, see EPA's Green Book site. Design values are computed and published annually by EPA's Office of Air Quality Planning and Standards and reviewed in conjunction with the EPA Regional Offices. Some of these design values can change after the date of publication for a variety of reasons, including but not limited to: 1) EPA agreement that certain data were influenced by exceptional events and therefore not subject for comparison with the NAAQS, 2) States retroactively entering or changing erroneous data based on later findings, and/or 3) notification of a monitoring issue (e.g. network design, site combination, change in the regulatory status of a monitor) that would prompt a revision.

Multiple site receptor modeling with a minimal spanning tree combined with a Kohonen neural network

NASA Astrophysics Data System (ADS)

Hopke, Philip K.

1999-12-01

A combination of two pattern recognition methods has been developed that allows the generation of geographical emission maps form multivariate environmental data. In such a projection into a visually interpretable subspace by a Kohonen Self-Organizing Feature Map, the topology of the higher dimensional variables space can be preserved, but parts of the information about the correct neighborhood among the sample vectors will be lost. This can partly be compensated for by an additional projection of Prim's Minimal Spanning Tree into the trained neural network. This new environmental receptor modeling technique has been adapted for multiple sampling sites. The behavior of the method has been studied using simulated data. Subsequently, the method has been applied to mapping data sets from the Southern California Air Quality Study. The projection of a 17 chemical variables measured at up to 8 sampling sites provided a 2D, visually interpretable, geometrically reasonable arrangement of air pollution source sin the South Coast Air Basin.

Air Quality in the Puebla-Tlaxcala Airshed in Mexico during April 2009

NASA Astrophysics Data System (ADS)

Ruiz Suarez, L. G.; Torres Jardón, R.; Torres Jaramillo, J. A.; Barrera, H.; Castro, T.; Mar Morales, B. E.; García Reynoso, J. A.; Molina, L. T.

2012-04-01

East of the Mexico Megacity, is the metropolitan area of Puebla-Tlaxcala which is reproducing the same patterns of urban sprawl as in the Mexico City Metropolitan Area. Is an area of high industrial density, the fragmented urban sprawl boost the use of particular cars in detrimental of public transport use. Emissions inventories reflect this fact; they also show a considerable use of biomass energy in households and small using a set of industries and service business. In April 2009 we carried out a preliminary field campaign in the basin, we deployed three mobile units, one in the north, in a site connecting with the valley of Mexico basin, one in the south where it may connect with the Cuautla-Cuernavaca Airshed and one in a receptor site to the Puebla Metropolitan Area. In addition to the available data from local air quality network within the City of Puebla. Analysis of the 2009 data show a complex flow pattern induced by the Popocateptl and Iztaccihuatl volcanoes to the west and La Malinche volcano to the east. Excess NOx emissions in the urban and industrial core lead to very low ozone levels within but high ozone concentrations are observed in the peri-urban and rural areas, exceeding the Mexican Air Quality Standards. In our presentation we will describe and explain these observations and will describe a field campaign to be carried out in March-April 2012 aiming to better document the air quality in the Puebla-Tlaxcala Airshed. Hybrid observation-model maps for ozone critical levels show the population exposed to exeedences to the official standards. AOT40 maps also show that crops and forests in the region are exposed to unhealthy ozone levels. These results add to those from MILAGRO and CARIEM field campaigns on the regional scale of the air quality issues in central Mexico. A point is made on the need to update the Mexicp Air Quality Standard for ozone.

Measuring NO, NO2, CO2 and O3 with low-cost sensors

NASA Astrophysics Data System (ADS)

Müller, Michael; Graf, Peter; Hüglin, Christoph

2017-04-01

Inexpensive sensors measuring ambient gas concentrations can be integrated in sensor units forming dense sensor networks. The utilized sensors have to be sufficiently accurate as the value of such networks directly depends on the information they provide. Thus, thorough testing of sensors before bringing them into service and the application of effective strategies for performance monitoring and adjustments during service are key elements for operating the low-cost sensors that are currently available on the market. We integrated several types of low-cost sensors into sensor units (Alphasense NO2 B4/B42F/B43F, Alphasense NO B4, SensAir CO2 LP8, Aeroqual O3 SM50), run them in the field next to instruments of air quality monitoring stations and performed tests in the laboratory. The poster summarizes our findings regarding the achieved sensor accuracy, methods to improve sensor performance as well as strategies to monitor the current state of the sensor (drifts, sensitivity) within a sensor network.

Can natural gas save lives? Evidence from the deployment of a fuel delivery system in a developing country.

PubMed

Cesur, Resul; Tekin, Erdal; Ulker, Aydogan

2018-05-01

There has been a widespread displacement of coal by natural gas as space heating and cooking technology in Turkey in the last two decades, triggered by the deployment of natural gas networks. We examine the impact of this development on mortality among adults and the elderly by exploiting the variation in the timing of the deployment and the intensity of expansion of gas networks across provinces using data from 2001 to 2016. The results indicate that the expansion of natural gas has caused significant reductions in mortality among both adults and the elderly. These findings are supported by our auxiliary analysis, which demonstrates that the expansion of natural gas networks might have led to a significant improvement in air quality. Furthermore, we show that the mortality gains are primarily driven by reductions in cardio-respiratory deaths, which are more likely to be due to conditions caused or exacerbated by air pollution. Copyright © 2018 Elsevier B.V. All rights reserved.

Measurements of Rural Sulfur Dioxide and Particle Sulfate: Analysis of CASTNet Data, 1987 through 1996.

PubMed

Baumgardner, Ralph E; Isil, Selma S; Bowser, Jon J; Fitzgerald, Kelley M

1999-11-01

The Clean Air Status and Trends Network (CASTNet) was implemented by the U.S. Environmental Protection Agency (EPA) in 1991 in response to Title IX of the Clean Air Amendments of 1990, which mandated the deployment of a national ambient air monitoring network to track progress of the implementation of emission reduction programs in terms of deposition, air quality, and changes to affected ecosystems. CASTNet evolved from the National Dry Deposition Network (NDDN). CASTNet currently consists of 45 sites in the eastern United States and 28 sites in the West. Each site measures sulfur dioxide (SO 2 ), nitric acid (HNO 3 ), particle sulfate (SO 4 = ), particle nitrate (NO 3 - ), and ozone. Nineteen sites collect precipitation samples. NDDN/CASTNet uses a uniform set of site-selection criteria which provides the data user with consistent measures to compare each site. These criteria also ensure that, to the extent possible, CASTNet sites are located away from local emission sources. This paper presents an analysis of SO 2 and SO 4 = concentration data collected from 1987 through 1996 at rural NDDN/CASTNet sites. Annual and seasonal variability is examined. Gradients of SO 2 and SO 4 = are discussed. The variability of the atmospheric mix of SO 2 and SO 4 = is explored spatially and seasonally. Data from CASTNet are also compared to SO 2 and SO 4 = data from concurrent monitoring studies in rural areas.

The AirQuality SenseBox

NASA Astrophysics Data System (ADS)

Demuth, Dustin; Nuest, Daniel; Bröring, Arne; Pebesma, Edzer

2013-04-01

In the past year, a group of open hardware enthusiasts and citizen scientists had large success in the crowd-funding of an open hardware-based sensor platform for air quality monitoring, called the Air Quality Egg. Via the kickstarter platform, the group was able to collect triple the amount of money than needed to fulfill their goals. Data generated by the Air Quality Egg is pushed to the data logging platform cosm.com, which makes the devices a part of the Internet of Things. The project aims at increasing the participation of citizens in the collection of data, the development of sensors, the operation of sensor stations, and, as data on cosm is publicly available, the sharing, visualization and analysis of data. Air Quality Eggs can measure NO2 and CO concentrations, as well as relative humidity and temperature. The chosen sensors are low-cost and have limited precision and accurracy. The Air Quality Egg consists of a stationary outdoor and a stationary indoor unit. Each outdoor unit will wirelessly transmit air quality measurements to the indoor unit, which forwards the data to cosm. Most recent versions of the Air Quality Egg allow a rough calibration of the gas sensors and on-the-fly conversion from raw sensor readings (impedance) to meaningful air quality data expressed in units of parts per billion. Data generated by these low-cost platforms are not intended to replace well-calibrated official monitoring stations, but rather augment the density of the total monitoring network with citizen sensors. To improve the usability of the Air Quality Egg, we present a new and more advanced concept, called the AirQuality SenseBox. We made the outdoor platform more autonomous and location-aware by adding solarpanels and rechargeable batteries as a power source. The AirQuality SenseBox knows its own position from a GPS device attached to the platform. As a mobile sensor platform, it can for instance be attached to vehicles. A low-cost and low-power wireless chipset reads the sensors and broadcasts the data. The data is received by gateways that convert the data and forward it to services. Although cosm is still supported, we also use services that are more common in the scientific domain, in particular the OGC Sensor Observation Service. In contrast to the ``One Sender - One Receiver'' (pair) setup proposed by the platform developers, we follow a ``Many Senders - Many Receivers'' (mesh) solution. As data is broadcasted by the platforms, it can be received and processed by any gateway, and, as the sender is not bound to the receiver, applications different from the gateways can receive and evaluate the data measured by the platform. Advantages of our solution are: (i) prepared gateways, which have more precise data at hand, can send calibration instructions to the mobile sensor platforms when those are in proximity; (ii) redundancy is obtained by adding additional gateways, to avoid the loss of data if a gateway fails; (iii) autonomous stations can be ubiquitous, are robust, do not require frequent maintenance, and can be placed at arbitrary locations; (iv) the standardized interface is vendor-independent and allows direct integration into existing analysis software.

Scientific production on indoor air quality of environments used for physical exercise and sports practice: Bibliometric analysis.

PubMed

Andrade, Alexandro; Dominski, Fábio Hech; Coimbra, Danilo Reis

2017-07-01

In order to minimize adverse health effects and increase the benefits of physical activity, it is important to systematize indoor air quality study in environments used for physical exercise and sports. To investigate and analyze the scientific production related to indoor air quality of environments used for physical exercise and sports practice through a bibliometric analysis. The databases Scielo, Science Direct, Scopus, Lilacs, Medline via Pubmed, and SportDiscus were searched from their inception to March 2016. Bibliometric analysis was performed for authors, institutions, countries, and collaborative networks, in relation to publication year, theme, citation network, funding agency, and analysis of titles and keywords of publications. Country, area, and impact factor of the journals were analyzed. Of 1281 studies screened, 34 satisfied the inclusion criteria. The first publication occurred in 1975. An increase in publications was observed in the last 15 years. Most of the studies were performed by researchers in the USA, followed by Portugal and Italy. Seventeen different scientific journals have published studies on the subject, and most are in the area of Environmental Sciences. It was noted that the categories of author keywords associated with "Pollutants," "Sport Environment," and "Physical Exercise" were the most commonly used in most studies. A total of 68% of the studies had at least one funding agency, and 81% of studies published in the last decade had funding. Our results demonstrate that there is recent exponential growth, driven in the last decade by researchers in environmental science from European institutions. Copyright © 2017 Elsevier Ltd. All rights reserved.

The deployment of carbon monoxide wireless sensor network (CO-WSN) for ambient air monitoring.

PubMed

Chaiwatpongsakorn, Chaichana; Lu, Mingming; Keener, Tim C; Khang, Soon-Jai

2014-06-16

Wireless sensor networks are becoming increasingly important as an alternative solution for environment monitoring because they can reduce cost and complexity. Also, they can improve reliability and data availability in places where traditional monitoring methods are difficult to site. In this study, a carbon monoxide wireless sensor network (CO-WSN) was developed to measure carbon monoxide concentrations at a major traffic intersection near the University of Cincinnati main campus. The system has been deployed over two weeks during Fall 2010, and Summer 2011-2012, traffic data was also recorded by using a manual traffic counter and a video camcorder to characterize vehicles at the intersection 24 h, particularly, during the morning and evening peak hour periods. According to the field test results, the 1 hr-average CO concentrations were found to range from 0.1-1.0 ppm which is lower than the National Ambient Air Quality Standards (NAAQS) 35 ppm on a one-hour averaging period. During rush hour periods, the traffic volume at the intersection varied from 2,067 to 3,076 vehicles per hour with 97% being passenger vehicles. Furthermore, the traffic volume based on a 1-h average showed good correlation (R2 = 0.87) with the 1-h average CO-WSN concentrations for morning and evening peak time periods whereas CO-WSN results provided a moderate correlation (R2 = 0.42) with 24 hours traffic volume due to fluctuated changes of meteorological conditions. It is concluded that the performance and the reliability of wireless ambient air monitoring networks can be used as an alternative method for real time air monitoring.

Remote sensing of GHG over Paris megacity and Orléans forest using ground-based QualAir FTS and TCCON-Orléans

NASA Astrophysics Data System (ADS)

Te, Y.; Jeseck, P.; Da Costa, J.; Deutscher, N. M.; Warneke, T.; Notholt, J.

2012-04-01

In a growing world with more than 7 billion inhabitants and big emerging countries such as China, Brazil and India, emissions of anthropogenic pollutants are increasing continuously. Monitoring and control of atmospheric pollutants in megacities have become a major challenge for scientists and public health authorities in environmental research area. The QualAir platform at University Pierre et Marie Curie (UPMC), is an innovating experimental research platform dedicated to survey greenhouse gases (GHGs) and urban air quality. As one of the major instruments of the QualAir platform, the ground-based Fourier transform spectrometer (QualAir FTS, IFS 125HR model) analyses the composition of the urban atmosphere of Paris, which is the third European megacity. The continuous monitoring of atmospheric pollutants is essential to improve the understanding of urban air pollution processes. Associated with a sun-tracker, the QualAir remote sensing FTS operates in solar infrared absorption and enables to monitor many trace gases, and to follow up their variability in the Ile-de-France region. A description of the QualAir FTS will be given. Concentrations of atmospheric GHG, especially CO2 and CH4, are retrieved by the radiative transfer model PROFFIT. Located in the centre of Paris, the QualAir FTS can provide new and complementary urban measurements as compared to unpolluted ground-based stations of existing networks (NDACC and TCCON). The work made by LPMAA to join the TCCON network will also be presented. TCCON-Orléans is a ground-based FTS of the TCCON network located in the forest of Orléans (100 km south of Paris). Preliminary comparisons of GHGs measurements from both sites will be shown. Such ground-based information will help to better characterize regional GHGs, especially regarding anthropogenic emissions and trends.

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.

Temporal and spatial correlation patterns of air pollutants in Chinese cities

PubMed Central

Dai, Yue-Hua

2017-01-01

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

Birmingham Urban Climate Laboratory (BUCL): Experiences, Challenges and Applications of an Urban Temperature Network

NASA Astrophysics Data System (ADS)

Muller, Catherine; Chapman, Lee; Young, Duick; Grimmond, Sue; Cai, Xiaoming

2013-04-01

The Birmingham Urban Climate Laboratory (BUCL) has recently been established by the University of Birmingham. BUCL is an in-situ, real-time urban network that will incorporate 3 nested networks - a wide-array of 25 weather stations, a dense array of 131 low-cost air temperature sensors and a fine-array of temperature sensor across the city-centre (50/km^2) - with the primary aim of monitoring air temperatures across a morphologically-heterogeneous urban conurbation for a variety of applications. During its installation there have been a number of challenges to overcome, including siting equipment in suitable urban locations, ensuring that the measurements were 'representative' of the local-scale climate, managing a large, near real-time data set and implementing QA/QC procedures. From these experiences, the establishment of a standardised urban meteorological network metadata protocol has been proposed in order to improve data quality, to ensure the end-user has access to all the supplementary information they would require for conducting valid analyses and to encourage the adequate recording and documentation of any changes to in-situ urban networks over time. This paper will provide an introduction to the BUCL in-situ network, give an overview of the challenges and experiences gained from its implementation, and finally discuss the proposed applications of the network, including its use in remote sensing observations of urban temperatures, as well as health and infrastructure applications.

The 2010 California Research at the Nexus of Air Quality and Climate Change (CalNex) field study

NASA Astrophysics Data System (ADS)

Ryerson, T. B.; Andrews, A. E.; Angevine, W. M.; Bates, T. S.; Brock, C. A.; Cairns, B.; Cohen, R. C.; Cooper, O. R.; de Gouw, J. A.; Fehsenfeld, F. C.; Ferrare, R. A.; Fischer, M. L.; Flagan, R. C.; Goldstein, A. H.; Hair, J. W.; Hardesty, R. M.; Hostetler, C. A.; Jimenez, J. L.; Langford, A. O.; McCauley, E.; McKeen, S. A.; Molina, L. T.; Nenes, A.; Oltmans, S. J.; Parrish, D. D.; Pederson, J. R.; Pierce, R. B.; Prather, K.; Quinn, P. K.; Seinfeld, J. H.; Senff, C. J.; Sorooshian, A.; Stutz, J.; Surratt, J. D.; Trainer, M.; Volkamer, R.; Williams, E. J.; Wofsy, S. C.

2013-06-01

The California Research at the Nexus of Air Quality and Climate Change (CalNex) field study was conducted throughout California in May, June, and July of 2010. The study was organized to address issues simultaneously relevant to atmospheric pollution and climate change, including (1) emission inventory assessment, (2) atmospheric transport and dispersion, (3) atmospheric chemical processing, and (4) cloud-aerosol interactions and aerosol radiative effects. Measurements from networks of ground sites, a research ship, tall towers, balloon-borne ozonesondes, multiple aircraft, and satellites provided in situ and remotely sensed data on trace pollutant and greenhouse gas concentrations, aerosol chemical composition and microphysical properties, cloud microphysics, and meteorological parameters. This overview report provides operational information for the variety of sites, platforms, and measurements, their joint deployment strategy, and summarizes findings that have resulted from the collaborative analyses of the CalNex field study. Climate-relevant findings from CalNex include that leakage from natural gas infrastructure may account for the excess of observed methane over emission estimates in Los Angeles. Air-quality relevant findings include the following: mobile fleet VOC significantly declines, and NOx emissions continue to have an impact on ozone in the Los Angeles basin; the relative contributions of diesel and gasoline emission to secondary organic aerosol are not fully understood; and nighttime NO3 chemistry contributes significantly to secondary organic aerosol mass in the San Joaquin Valley. Findings simultaneously relevant to climate and air quality include the following: marine vessel emissions changes due to fuel sulfur and speed controls result in a net warming effect but have substantial positive impacts on local air quality.

A study on exposure assessment for fine dust by using Kriging method: The case of Seoul Metropolitan city

NASA Astrophysics Data System (ADS)

Seo, J. H.; Sohn, J. R.; Mo, R.

2017-12-01

According to the OECD, South Korea is expected to have the highest morality and the biggest economic damage due to the air pollution among OECD members in 2060. Korea's air quality monitoring network is provided by Air Korea of the Korea Environment Corporation under the Ministry of Environment. There are 323 measurement stations installed in 97 different places in Korea. The monitoring network is classified into city atmosphere, roadside, country background concentration, and suburban atmosphere monitoring network, which operate according to each measurement purpose. However, the data from this network shows a large difference in pollutant concentration by region and there is a limit to explain the concentration of pollutants in Seoul, which has a very high population density. The data of the fine dust concentration in Korea University is provided by Seongbuk-gu, but actually Korea University is closer to the measuring station in Dongdaemun-gu. Therefore, a difference will occur if the data from Seongbuk-gu is used to the exposure assessment of residents in nearby Korea University for air pollution. Therefore, this study is aimed to acquire estimated value about areas that have not been measured and implement more precise exposure assessment by comparing it with measured value. On May 8, 2017, when the fine dust concentration was the highest, we calculated the pollutant concentration estimates near Korea University by using measuring network of Seongbukgu and Dongdaemun through Kriging method and compared them with actual measured value which was acquired in this study. Analysis results showed that air pollution concentration near Korea University tends to be overestimated when using the data from Seongbukgu. On the other hand, it showed a similarity to measured value when using data from both Seongbukgu and Dongdaemungu through Kriging method. Therefore, it is necessary to estimate the data about blind spots through Kriging method rather than using the existing national atmospheric monitoring data. In addition, it is required to acquire measured data through government agencies and research institutes in addition to the measurement networking data to calculate more accurate pollution concentration and utilize it for the exposure evaluation.

Designed Curriculum and Local Culture: Acknowledging the Primacy of Classroom Culture.

ERIC Educational Resources Information Center

Squire, Kurt D.; MaKinster, James G.; Barnett, Michael; Luehmann, April Lynn; Barab, Sasha L.

2003-01-01

Examines four teachers implementing a project-based curriculum (Air Quality module) on a web-based platform (ActiveInk Network) in four very different settings. Discusses each case across two themes by examining how the project-level question was contextualized to meet local needs and the cultural context that surrounded the implementation of the…

Air quality indices from ERTS-1 MSS information

NASA Technical Reports Server (NTRS)

Riley, E. L.; Stryker, S.; Ward, E. A.

1973-01-01

Comparison between ground based atmospheric turbidity network measurements and the average scene grayness from MSS Channel 4 data is in progress. Correlation between these two sources is promising. If continued correlation occurs for other ERTS-1 overflight dates and ground test sites, a new operational use of ERTS-1 useful to Federal, state, and international organizations will become available.

40 CFR 52.2386 - Original identification of plan section.

Code of Federal Regulations, 2013 CFR

2013-07-01

... limitation or Fuel Burning Equipment, revision to Rule 6, “Rules of Practice,” of the Air Quality Variance... network which meets the requirements of 40 CFR part 58, submitted on March 21, 1979 by the Governor of... comments and any analyses submitted by any Federal Land Manager, filed in its adopted form on September 2...

40 CFR 52.2386 - Original identification of plan section.

Code of Federal Regulations, 2011 CFR

2011-07-01

... limitation or Fuel Burning Equipment, revision to Rule 6, “Rules of Practice,” of the Air Quality Variance... network which meets the requirements of 40 CFR part 58, submitted on March 21, 1979 by the Governor of... comments and any analyses submitted by any Federal Land Manager, filed in its adopted form on September 2...

40 CFR 52.2386 - Original identification of plan section.

Code of Federal Regulations, 2012 CFR

2012-07-01

... limitation or Fuel Burning Equipment, revision to Rule 6, “Rules of Practice,” of the Air Quality Variance... network which meets the requirements of 40 CFR part 58, submitted on March 21, 1979 by the Governor of... comments and any analyses submitted by any Federal Land Manager, filed in its adopted form on September 2...

40 CFR 52.2386 - Original identification of plan section.

Code of Federal Regulations, 2010 CFR

2010-07-01

... limitation or Fuel Burning Equipment, revision to Rule 6, “Rules of Practice,” of the Air Quality Variance... network which meets the requirements of 40 CFR part 58, submitted on March 21, 1979 by the Governor of... comments and any analyses submitted by any Federal Land Manager, filed in its adopted form on September 2...

40 CFR 52.2386 - Original identification of plan section.

Code of Federal Regulations, 2014 CFR

2014-07-01

... limitation or Fuel Burning Equipment, revision to Rule 6, “Rules of Practice,” of the Air Quality Variance... network which meets the requirements of 40 CFR part 58, submitted on March 21, 1979 by the Governor of... comments and any analyses submitted by any Federal Land Manager, filed in its adopted form on September 2...

40 CFR Appendix D to Part 58 - Network Design Criteria for Ambient Air Quality Monitoring

Code of Federal Regulations, 2013 CFR

2013-07-01

... determine the extent of regional pollutant transport among populated areas; and in support of secondary... sources within the area, transport of O3 and its precursors, and the photochemical processes related to O3... precursor concentrations entering the area and will identify those areas which are subjected to transport...

40 CFR Appendix D to Part 58 - Network Design Criteria for Ambient Air Quality Monitoring

Code of Federal Regulations, 2014 CFR

2014-07-01

... determine the extent of regional pollutant transport among populated areas; and in support of secondary... sources within the area, transport of O3 and its precursors, and the photochemical processes related to O3... precursor concentrations entering the area and will identify those areas which are subjected to transport...

Physical and mathematical modeling of pollutant emissions when burning peat

NASA Astrophysics Data System (ADS)

Vasilyev, A.; Lozhkin, V.; Tarkhov, D.; Lozhkina, O.; Timofeev, V.

2017-11-01

The article presents an original neural network model of CO dispersion around the experimentally simulated peat fire. It is a self-learning model considering both the measured CO concentrations in the smoke cloud and the refined coefficients of the main equation. The method is recommended for the development of air quality control and forecasting systems.

ECAIM : Air Quality Studies and its Impact in Central Mexico.

NASA Astrophysics Data System (ADS)

Ruiz-Suárez, L. G.; Torres, R.; Garcia-Reynoso, J. A.; Zavala-Hidalgo, J.; Grutter, M.; Delgado-Campos, J.; Molina, L. T.

2014-12-01

Mexico City Metropolitan Area has been the object of several well know intensive campaigns. Since MARI (1991) , IMADA (1997), MCMA 2003 and MILAGRO (2006). The spatial scope of these studies have gone from urban to regional to continental, with the focus on MCMA as an emissions source. During MILAGRO, the influence on MCMA of wildfires and agricultural biomass burning around the megacity was considered. However, around Mexico City a crown of metropolis and middle size cities make a region known as the Central Mexico Regional Crow (CRCM for its acronym in Spanish language) or Central Mexico City Belt. It contains 32 million inhabitants and produces 40% of national gross product. The region undergoes an uncontrolled urban sprawl. Evidence is building-up on complex air pollution transport processes between the air basins within CRCM. However, only MCMA counts with reliable long-term records of criteria pollutants monitoring. Only few intensive campaigns have been done in the air basins surrounding MCMA. ECAIM project has several goals: a) To use ground and satellite observations to assess emissions inventories; b) To use ground and satellite observations to assess the performance of air quality models for the whole region; c) to produce critical levels exceedence maps; d) To produce a preliminary diagnostic of air quality for the CRCM; e) to produce a preliminary estimate of the cost of air pollution within the CRCM. In this work we show the method approach to use the best available information from local AQM networks, field campaigns, satellite observations and modeling to achieve those goals. We show some preliminary results.

Decision-Making Using Real-Time Observations for Environmental Sustainability; an integrated 802.11 sensor network

NASA Astrophysics Data System (ADS)

Dominguez, A.; Kleissl, J.; Farhadi, M.; Kim, D.; Liu, W.; Mao, Y.; Nguyen, H. T.; Roshandell, M.; Sankur, M.; Shiga, Y.; Linden, P.; Hodgkiss, W.

2007-12-01

Meteorological conditions have important implications on human activities. They affect human comfort, productivity, and health, and contribute to material wear and tear. The University of California, San Diego (UCSD)'s proximity to the Pacific Ocean places it in a temperate microclimate which has unique advantages and disadvantages for campus water and energy use and air quality. In particular, the daily sea-breezes provide cool, moist, and salt-laden air to campus. For the Decision-Making Using Real-Time Observations for Environmental Sustainability (DEMROES) project a heterogeneous wireless network of monitoring stations is being set up across the UCSD campus and beyond. Conditions to be monitored include temperature, humidity, wind speed and direction, surface temperatures, solar radiation, particulate matter, CO, NO2, rainfall, and soil moisture. Stations are strategically placed on rooftops and lampposts across campus, as well as select off-campus locations and will transmit data over the UCSD 802.11 wireless network. In addition to rooftop and lamppost stations, mobile stations will be deployed via remotely controlled ground and air units, and stations affixed to campus shuttle busses. These mobile stations will allow for greater spatial resolution of the environmental conditions across campus and inter-sensor calibration. The hardware consists of meteorological, hydrological, and air quality sensors connected to (a) commercial Campbell datalogging systems with serial2IP modules and wireless bridges, and (b) sensor and 802.11 boards based on the dpac technology developed in-house. The measurements will serve campus facilities management with information to feed the energy management system (EMS) for building operation and energy conservation, and irrigation management. The technology developed for this project can be applied elsewhere thereby contributing to hydrologic and ecologic observatories. Through extensive student involvement a new generation of environmental scientists and engineers will be trained to work on the planning and execution of national observatories.

Baseline Air Quality Assessment of Goods Movement Activities before the Port of Charleston Expansion: A Community–University Collaborative

PubMed Central

Wilson, Sacoby M.; Tarver, Siobhan L.; Svendsen, Erik; Jiang, Chengsheng; Ogunsakin, Olalekan A.; Zhang, Hongmei; Campbell, Dayna; Fraser-Rahim, Herbert

2017-01-01

Abstract As the demand for goods continues to increase, a collective network of transportation systems is required to facilitate goods movement activities. This study examines air quality near the Port of Charleston before its expansion and briefly describes the establishment and structure of a community–university partnership used to monitor existing pollution. Particulate matter (PM) concentrations (PM2.5 and PM10) were measured using the Thermo Fisher Scientific Partisol 2000i-D Dichotomous Air Sampler, Thermo Scientific Dichotomous Sequential Air Sampler Partisol-Plus 2025-D, and Rupprecht & Patashnick TEOM Series 1400 Sampler at neighborhood (Union Heights, Rosemont, and Accabee) and reference (FAA2.5 and Jenkins Street) sites. Descriptive statistics were performed and an ANOVA (analysis of variance) was calculated to find the difference in overall mean 24-hour PM average concentrations in communities impacted by environmental injustice. PM2.5 (15.2 μg/m3) and PM10 (27.2 μg/m3) maximum concentrations were highest in neighborhoods such as Union Heights neighborhoods due to more goods movement activities. Nevertheless, there was no statistically significant difference in mean concentrations of PM2.5 and PM10 across neighborhood sites. In contrast, mean PM10 neighborhood concentrations were significantly lower than mean PM10 reference concentrations for Union Heights (p = 0.00), Accabee (p ≤ 0.0001), and Rosemont (p = 0.01). Although PM concentrations were lower than current National Ambient Air Quality Standards, this study demonstrated how community–university partners can work collectively to document baseline PM concentrations that will be used to examine changes in air quality after the port expansion brings additional goods movement activities to the area. PMID:29576842

Bayesian maximum entropy integration of ozone observations and model predictions: an application for attainment demonstration in North Carolina.

PubMed

de Nazelle, Audrey; Arunachalam, Saravanan; Serre, Marc L

2010-08-01

States in the USA are required to demonstrate future compliance of criteria air pollutant standards by using both air quality monitors and model outputs. In the case of ozone, the demonstration tests aim at relying heavily on measured values, due to their perceived objectivity and enforceable quality. Weight given to numerical models is diminished by integrating them in the calculations only in a relative sense. For unmonitored locations, the EPA has suggested the use of a spatial interpolation technique to assign current values. We demonstrate that this approach may lead to erroneous assignments of nonattainment and may make it difficult for States to establish future compliance. We propose a method that combines different sources of information to map air pollution, using the Bayesian Maximum Entropy (BME) Framework. The approach gives precedence to measured values and integrates modeled data as a function of model performance. We demonstrate this approach in North Carolina, using the State's ozone monitoring network in combination with outputs from the Multiscale Air Quality Simulation Platform (MAQSIP) modeling system. We show that the BME data integration approach, compared to a spatial interpolation of measured data, improves the accuracy and the precision of ozone estimations across the state.

An experimental study of permeability within an out-of-autoclave vacuum-bag-only CFRP laminate

NASA Astrophysics Data System (ADS)

Wallace, Landon F.

The out-of-autoclave vacuum-bag-only (OOA-VBO) manufacturing process is a process that eliminates an autoclave when manufacturing aerospace quality carbon fiber reinforced plastics (CFRP). OOA-VBO pre-impregnated resin tow systems rely on air channel networks that guide unwanted voids out of the laminate. The air path networks can be characterized by measuring the permeability of a pre-cured laminate. Permeability results were successfully obtained for a laminate with a compaction similar to that found in a typical vacuum bagging setup. A study was done to find the relationship between compaction of the laminate and permeability. Permeability was measured as the laminate cured, using a constant temperature ramp rate. An experimental nodal analysis was performed to find the permeability at the midpoint of the in-plane direction.

A novel hybrid forecasting model for PM₁₀ and SO₂ daily concentrations.

PubMed

Wang, Ping; Liu, Yong; Qin, Zuodong; Zhang, Guisheng

2015-02-01

Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM₁₀ (particles with a diameter of 10 μm or less) concentrations and SO₂ (sulfur dioxide) concentrations from four air pollution monitoring stations located in Taiyuan, China. The theoretical analysis and experimental results demonstrated that the forecasting accuracy of the proposed model is very promising. Copyright © 2014 Elsevier B.V. All rights reserved.

Prediction of PM10 grades in Seoul, Korea using a neural network model based on synoptic patterns

NASA Astrophysics Data System (ADS)

Hur, S. K.; Oh, H. R.; Ho, C. H.; Kim, J.; Song, C. K.; Chang, L. S.; Lee, J. B.

2016-12-01

As of November 2014, the Korean Ministry of Environment (KME) started forecasting the level of ambient particulate matter with diameters ≤ 10 μm (PM10) as four grades: low (PM10 ≤ 30 μg m-3), moderate (30 < PM10 ≤ 80 μg m-3), high (80 < PM10 ≤ 150 μg m-3), and very high (PM10 > 150 μg m-3). Due to short history of forecast, overall performance of the operational forecasting system and its hit rate for the four PM10 grades are difficult to evaluate. In attempt to provide a statistical reference for the current air quality forecasting system, we hindcasted the four PM10 grades for the cold seasons (October-March) of 2001-2014 in Seoul, Korea using a neural network model based on the synoptic patterns of meteorological fields such as geopotential height, air temperature, relative humidity, and wind. In the form of cosine similarity, the distinctive synoptic patterns for each PM10 grades are well quantified as predictors to train the neural network model. Using these fields as predictors and considering the PM10 concentration in Seoul from the day before prediction as an additional predictor, an overall hit rate of 69% was achieved; the hit rates for the low, moderate, high, and very high PM10 grades were 33%, 83%, 45%, and 33%, respectively. This study reveals that the synoptic patterns of meteorological fields are useful predictors for the identification of favorable conditions for each PM10 grade, and the associated transboundary transport and local accumulation of PM10 from the industrialized regions of China. Consequently, the assessments of predictability obtained from the neural network model in this study are reliable to use as a statistical reference for the current air quality forecasting system.

Effects of environmental alerts and pre-emergencies on pollutant concentrations in Santiago, Chile

NASA Astrophysics Data System (ADS)

Troncoso, Rodrigo; de Grange, Louis; Cifuentes, Luis A.

2012-12-01

To reduce air pollution levels in Santiago, Chile on days when the weather is expected to create poor ventilation conditions and increased air pollutant concentrations, the responsible authorities impose temporary restrictions on motor vehicles and certain industrial activities. We estimate the impact of these restrictions on the city's air quality using data collected by a network of monitoring stations. The estimates show that the restrictions do reduce the average concentrations of coarse and fine particulate matter, carbon monoxide and nitrogen oxide (both gases are emitted mainly by vehicles). However, no significant changes were found in the sulfur dioxide concentrations, which are primarily the result of industrial processes.

Identifying and closing gaps in environmental monitoring by means of metadata, ecological regionalization and geostatistics using the UNESCO biosphere reserve Rhoen (Germany) as an example.

PubMed

Schröder, Winfried; Pesch, Roland; Schmidt, Gunther

2006-03-01

In Germany, environmental monitoring is intended to provide a holistic view of the environmental condition. To this end the monitoring operated by the federal states must use harmonized, resp., standardized methods. In addition, the monitoring sites should cover the ecoregions without any geographical gaps, the monitoring design should have no gaps in terms of ecologically relevant measurement parameters, and the sample data should be spatially without any gaps. This article outlines the extent to which the Rhoen Biosphere Reserve, occupying a part of the German federal states of Bavaria, Hesse and Thuringia, fulfills the listed requirements. The investigation considered collection, data banking and analysis of monitoring data and metadata, ecological regionalization and geostatistics. Metadata on the monitoring networks were collected by questionnaires and provided a complete inventory and description of the monitoring activities in the reserve and its surroundings. The analysis of these metadata reveals that most of the monitoring methods are harmonized across the boundaries of the three federal states the Rhoen is part of. The monitoring networks that measure precipitation, surface water levels, and groundwater quality are particularly overrepresented in the central ecoregions of the biosphere reserve. Soil monitoring sites are more equally distributed within the ecoregions of the Rhoen. The number of sites for the monitoring of air pollutants is not sufficient to draw spatially valid conclusions. To fill these spatial gaps, additional data on the annual average values of the concentrations of air pollutants from monitoring sites outside of the biosphere reserve had therefore been subject to geostatistical analysis and estimation. This yields valid information on the spatial patterns and temporal trends of air quality. The approach illustrated is applicable to similar cases, as, for example, the harmonization of international monitoring networks.

Evaluation of multisectional and two-section particulate matter photochemical grid models in the Western United States.

PubMed

Morris, Ralph; Koo, Bonyoung; Yarwood, Greg

2005-11-01

Version 4.10s of the comprehensive air-quality model with extensions (CAMx) photochemical grid model has been developed, which includes two options for representing particulate matter (PM) size distribution: (1) a two-section representation that consists of fine (PM2.5) and coarse (PM2.5-10) modes that has no interactions between the sections and assumes all of the secondary PM is fine; and (2) a multisectional representation that divides the PM size distribution into N sections (e.g., N = 10) and simulates the mass transfer between sections because of coagulation, accumulation, evaporation, and other processes. The model was applied to Southern California using the two-section and multisection representation of PM size distribution, and we found that allowing secondary PM to grow into the coarse mode had a substantial effect on PM concentration estimates. CAMx was then applied to the Western United States for the 1996 annual period with a 36-km grid resolution using both the two-section and multisection PM representation. The Community Multiscale Air Quality (CMAQ) and Regional Modeling for Aerosol and Deposition (REMSAD) models were also applied to the 1996 annual period. Similar model performance was exhibited by the four models across the Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network monitoring networks. All four of the models exhibited fairly low annual bias for secondary PM sulfate and nitrate but with a winter overestimation and summer underestimation bias. The CAMx multisectional model estimated that coarse mode secondary sulfate and nitrate typically contribute <10% of the total sulfate and nitrate when averaged across the more rural IMPROVE monitoring network.

Automobile gross emitter screening with remote sensing data using objective-oriented neural network.

PubMed

Chen, Ho-Wen; Yang, Hsi-Hsien; Wang, Yu-Sheng

2009-11-01

One of the costs of Taiwan's massive economic development has been severe air pollution problems in many parts of the island. Since vehicle emissions are the major source of air pollution in most of Taiwan's urban areas, Taiwan's government has implemented policies to rectify the degrading air quality, especially in areas with high population density. To reduce vehicle pollution emissions an on-road remote sensing and monitoring system is used to check the exhaust emissions from gasoline engine automobiles. By identifying individual vehicles with excessive emissions for follow-up inspection and testing, air quality in the urban environment is expected to improve greatly. Because remote sensing is capable of measuring a large number of moving vehicles in a short period, it has been considered as an assessment technique in place of the stationary emission-sampling techniques. However, inherent measurement uncertainty of remote sensing instrumentation, compounded by the indeterminacy of monitoring site selection, plus the vagaries of weather, causes large errors in pollution discrimination and limits the application of the remote sensing. Many governments are still waiting for a novel data analysis methodology to clamp down on heavily emitting vehicles by using remote sensing data. This paper proposes an artificial neural network (ANN), with vehicle attributes embedded, that can be trained by genetic algorithm (GA) based on different strategies to predict vehicle emission violation. Results show that the accuracy of predicting emission violation is as high as 92%. False determinations tend to occur for vehicles aged 7-13 years, peaking at 10 years of age.

Assessment of ambient air quality in Eskişehir, Turkey.

PubMed

Ozden, O; Döğeroğlu, T; Kara, S

2008-07-01

This paper presents an assessment of air quality of the city Eskişehir, located 230 km southwest to the capital of Turkey. Only five of the major air pollutants, most studied worldwide and available for the region, were considered for the assessment. Available sulphur dioxide (SO(2)), particulate matter (PM), nitrogen dioxide (NO(2)), ozone (O(3)), and non-methane volatile organic carbons (NMVOCs) data from local emission inventory studies provided relative source contributions of the selected pollutants to the region. The contributions of these typical pollution parameters, selected for characterizing such an urban atmosphere, were compared with the data established for other cities in the nation and world countries. Additionally, regional ambient SO(2) and PM concentrations, determined by semi-automatic monitoring at two sites, were gathered from the National Ambient Air Monitoring Network (NAAMN). Regional data for ambient NO(2) (as a precursor of ozone as VOCs) and ozone concentrations, through the application of the passive sampling method, were provided by the still ongoing local air quality monitoring studies conducted at six different sites, as representatives of either the traffic-dense-, or coal/natural gas burning residential-, or industrial/rural-localities of the city. Passively sampled ozone data at a single rural site were also verified with the data from a continuous automatic ozone monitoring system located at that site. Effects of variations in seasonal-activities, newly established railway system, and switching to natural gas usage on the temporal changes of air quality were all considered for the assessment. Based on the comparisons with the national [AQCR (Air Quality Control Regulation). Ministry of Environment (MOE), Ankara. Official Newspaper 19269; 1986.] and a number of international [WHO (World Health Organization). Guidelines for Air Quality. Geneva; 2000. Downloaded in January 2006, website: http://www.who.int/peh/; EU (European Union). Council Directive 1999/30/EC relating to limit values for sulfur dioxide, nitrogen dioxide and lead in ambient air. Of J Eur Communities L 163: 14-30; 29.6.1999; EU (European Union). Council Directive 2002/3/EC relating to ozone in ambient air. Of J Eur Communities. L 67: 14-30; 9.3.2002.; USEPA (U.S. Environmental Protection Agency). National Ambient Air Quality Standards (NAAQS). Downloaded in January 2006, website: http://www.epa.gov/ttn/naaqs/] ambient air standards, among all the pollutants studied, only the annual average SO(2) concentration was found to exceed one specific limit value (EU limit for protection of the ecosystem). A part of the data (VOC/NO(x) ratio), for determining the effects of photochemical interactions, indicated that VOC-limited regime was prevailing throughout the city.

Effects of equipment performance on data quality from the National Atmospheric Deposition Program/National Trends Network and the Mercury Deposition Network

USGS Publications Warehouse

Wetherbee, Gregory A.; Rhodes, Mark F.

2013-01-01

The U.S. Geological Survey Branch of Quality Systems operates the Precipitation Chemistry Quality Assurance project (PCQA) to provide independent, external quality-assurance for the National Atmospheric Deposition Program (NADP). NADP is composed of five monitoring networks that measure the chemical composition of precipitation and ambient air. PCQA and the NADP Program Office completed five short-term studies to investigate the effects of equipment performance with respect to the National Trends Network (NTN) and Mercury Deposition Network (MDN) data quality: sample evaporation from NTN collectors; sample volume and mercury loss from MDN collectors; mercury adsorption to MDN collector glassware, grid-type precipitation sensors for precipitation collectors, and the effects of an NTN collector wind shield on sample catch efficiency. Sample-volume evaporation from an NTN Aerochem Metrics (ACM) collector ranged between 1.1–33 percent with a median of 4.7 percent. The results suggest that weekly NTN sample evaporation is small relative to sample volume. MDN sample evaporation occurs predominantly in western and southern regions of the United States (U.S.) and more frequently with modified ACM collectors than with N-CON Systems Inc. collectors due to differences in airflow through the collectors. Variations in mercury concentrations, measured to be as high as 47.5 percent per week with a median of 5 percent, are associated with MDN sample-volume loss. Small amounts of mercury are also lost from MDN samples by adsorption to collector glassware irrespective of collector type. MDN 11-grid sensors were found to open collectors sooner, keep them open longer, and cause fewer lid cycles than NTN 7-grid sensors. Wind shielding an NTN ACM collector resulted in collection of larger quantities of precipitation while also preserving sample integrity.

The international fine aerosol networks

NASA Astrophysics Data System (ADS)

Cahill, Thomas A.

1993-04-01

The adoption by the United States of a PIXE-based protocol for its fine aerosol network, after open competitions involving numerous laboratories and methods, has encouraged cooperation with other countries possessing similar capabilities and similar needs. These informal cooperative programs, involving about a dozen countries at the end of 1991, almost all use PIXE as a major component of the analytical protocols. The University of California, Davis, Air Quality Group assisted such programs through indefinite loans of a quality assurance sampler, the IMPROVE Channel A, and analyses at no cost of a small fraction of the samples taken in a side-by-side configuration. In December 1991, the World Meteorological Organization chose a protocol essentially identical to IMPROVE for the Global Atmospheric Watch (GAW) network and began deploying units, the IMPROVE Channel A, to sites around the world. Preferred analyses include fine (less than about 2.5 μm) mass, ions by ion chromatography and elements by PIXE + PESA (or, lacking that, XRF). This paper will describe progress in both programs, giving examples of the utility of the data and projecting the future expansion of the network to about 20 GAW sites by 1994.

Assessment of planetary boundary layer and residual layer heights in the Northeastern U.S. using Lidar, a network of surface observations, and the WRF-STILT model

NASA Astrophysics Data System (ADS)

Barrera, Y.; Nehrkorn, T.; Hegarty, J. D.; Wofsy, S. C.; Gottlieb, E.; Sargent, M. R.; Decola, P.; Jones, T.

2015-12-01

Simulation of the planetary boundary layer (PBL) and residual layer (RL) are key requirements for forecasting air quality in cities and detecting transboundary air pollution events. This study combines information from a network of Mini Micropulse Lidar (MPL) instruments, the CALIOP satellite, meteorological and air pollution measuring sensors, and a particle-transport model to critically test mesoscale transport models at the regional level. Aerosol backscattering measurements were continuously taken with MPL units in various locations within the Northeastern U.S., between September 2012 to August 2015. Data is analyzed using wavelet covariance transforms and image processing techniques. Initial results for the city of Boston show a PBL growth rate between approx. 150 and 300 meters per hour, in the morning to early afternoon (~12-19 UTC). The RL was present throughout the night and day at approx. 1.3 to 2.0 km. Transboundary air pollution events were detected and quantified, and variations in concentrations of greenhouse gases and aerosols were also evaluated. Results were compared to information retrieved from Weather and Research Forecasting (WRF) model and the Stochastic Time-Inverted Lagrangian Transport (STILT) model.

Monitoring of Air Quality in Passenger Cabins of the Athens Metro

NASA Astrophysics Data System (ADS)

Tsairidi, Evangelia; Assimakopoulos, Vasiliki D.; Assimakopoulos, Margarita-Niki; Barbaresos, Nicolaos; Karagiannis, Athanassios

2013-04-01

The air pollution induced by various transportation means combines the emission of pollutants with the simultaneous presence of people. In this respect, the scientific community has focused its efforts in studying both the air quality within busy streets and inside cars, buses and the underground railway network in order to identify the pollutants' sources and levels as well as the human exposure. The impact of the air pollution on commuters of the underground may be more severe because it is a confined space, extended mostly under heavily trafficked urban streets, relies on mechanical ventilation for air renewal and gathers big numbers of passengers. The purpose of the present work is to monitor the air quality of the city of Athens Metro Network cabins and platforms during the unusually hot summer of 2012. For that cause particulate matter (PM10, PM2.5, PM1), carbon dioxide (CO2), the number of commuters along with temperature (T) and humidity (RH) were recorded inside the Athens Metro Blue Line trains (covering a route from the centre of Athens (Aigaleo) to the Athens International Airport) and on the platforms of a central (Syntagma) and a suburban-traffic (Doukissis Plakentias) station between June and August. The data collection included six different experiments that took place for 2 consecutive working days each, for a time period of 6 weeks from 6:30 am too 7:00 pm in order to account for different outdoor climatic conditions and for morning and evening rush hours respectively. Measurements were taken in the middle car of the moving trains and the platform end of the selected stations. The results show PM concentrations to be higher (approximately 2 to 5 times) inside the cabins and o the platforms of the underground network as compared to the outdoor levels monitored routinely by the Ministry of Environment. Moreover, PM1, PM2.5 and PM10 average concentrations recorded at the Syntagma Station Platform were almost constantly higher reaching 11 μg m-3 47 μg m-3 and 246 μg m-3 respectively on July 11th, as opposed to the ones at Doukissis Plakentias (4 μg m-3, 15 μg m-3and 97 μg m-3 respectively). Interestingly enough, inside the trains PM1, PM2.5 and PM10 average concentrations were significantly lower compared to the Syntagma Station Platform, reaching 8 μg m-3, 27 μg m-3 and 90 μg m-3 . It was also observed that particulate levels were higher over the extent of the central part of the train route. Finally, as expected CO2 levels where found to be higher inside the trains compared to the platforms and in some cases surpassed the 1,000 ppm limit during the hottest days of the experimental campaign. Temperature and humidity remained relatively stable on the platforms whereas measurements inside the cabin fluctuated depending on the trains track locations reaching 34.8° C at the central sector of the route. KEYWORDS: Particulate pollution, Athens underground, indoor air quality, urban pollution, transportation

Structural Properties of the Brazilian Air Transportation Network.

PubMed

Couto, Guilherme S; da Silva, Ana Paula Couto; Ruiz, Linnyer B; Benevenuto, Fabrício

2015-09-01

The air transportation network in a country has a great impact on the local, national and global economy. In this paper, we analyze the air transportation network in Brazil with complex network features to better understand its characteristics. In our analysis, we built networks composed either by national or by international flights. We also consider the network when both types of flights are put together. Interesting conclusions emerge from our analysis. For instance, Viracopos Airport (Campinas City) is the most central and connected airport on the national flights network. Any operational problem in this airport separates the Brazilian national network into six distinct subnetworks. Moreover, the Brazilian air transportation network exhibits small world characteristics and national connections network follows a power law distribution. Therefore, our analysis sheds light on the current Brazilian air transportation infrastructure, bringing a novel understanding that may help face the recent fast growth in the usage of the Brazilian transport network.

A multi-scale hybrid neural network retrieval model for dust storm detection, a study in Asia

NASA Astrophysics Data System (ADS)

Wong, Man Sing; Xiao, Fei; Nichol, Janet; Fung, Jimmy; Kim, Jhoon; Campbell, James; Chan, P. W.

2015-05-01

Dust storms are known to have adverse effects on human health and significant impact on weather, air quality, hydrological cycle, and ecosystem. Atmospheric dust loading is also one of the large uncertainties in global climate modeling, due to its significant impact on the radiation budget and atmospheric stability. Observations of dust storms in humid tropical south China (e.g. Hong Kong), are challenging due to high industrial pollution from the nearby Pearl River Delta region. This study develops a method for dust storm detection by combining ground station observations (PM10 concentration, AERONET data), geostationary satellite images (MTSAT), and numerical weather and climatic forecasting products (WRF/Chem). The method is based on a hybrid neural network (NN) retrieval model for two scales: (i) a NN model for near real-time detection of dust storms at broader regional scale; (ii) a NN model for detailed dust storm mapping for Hong Kong and Taiwan. A feed-forward multilayer perceptron (MLP) NN, trained using back propagation (BP) algorithm, was developed and validated by the k-fold cross validation approach. The accuracy of the near real-time detection MLP-BP network is 96.6%, and the accuracies for the detailed MLP-BP neural network for Hong Kong and Taiwan is 74.8%. This newly automated multi-scale hybrid method can be used to give advance near real-time mapping of dust storms for environmental authorities and the public. It is also beneficial for identifying spatial locations of adverse air quality conditions, and estimates of low visibility associated with dust events for port and airport authorities.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Saturday Morning Television Advertisements Aired on English and Spanish Language Networks along the Texas-Mexico Border

PubMed Central

Barroso, Cristina S.; Rodriguez, Dianeth; Camacho, Perla L.

2011-01-01

Objectives The aim of this content analysis study is to characterize the TV advertisements aired to an at-risk child population along the Texas-Mexico border. Methods We characterized the early Saturday morning TV advertisements aired by three broadcast network categories (U.S. English language, U.S. Spanish language, and Mexican Spanish language) in Spring 2010. The number, type (food related vs. non-food related), target audience, and persuasion tactics used were recorded. Advertised foods, based on nutrition content, were categorized as meeting or not meeting current dietary guidelines. Results Most commercials were non-food related (82.7%, 397 of 480). The majority of the prepared foods (e.g., cereals, snacks, and drinks) advertised did not meet the current U.S. Dietary Guidelines. Additionally, nutrition content information was not available for many of the foods advertised on the Mexican Spanish language broadcast network category. Conclusions For U.S. children at risk for obesity along the Texas-Mexico border exposure to TV food advertisements may result in the continuation of sedentary behavior as well as an increased consumption of foods of poor nutritional quality. An international regulatory effort to monitor and enforce the reduction of child-oriented food advertising is needed. PMID:22209760

A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

NASA Astrophysics Data System (ADS)

Fan, J.; Li, Q.; Hou, J.; Feng, X.; Karimian, H.; Lin, S.

2017-10-01

Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers etc. In order to handle missing values in time series, as well as the lack of considering temporal properties in machine learning models, we propose a spatiotemporal prediction framework based on missing value processing algorithms and deep recurrent neural network (DRNN). By using missing tag and missing interval to represent time series patterns, we implement three different missing value fixing algorithms, which are further incorporated into deep neural network that consists of LSTM (Long Short-term Memory) layers and fully connected layers. Real-world air quality and meteorological datasets (Jingjinji area, China) are used for model training and testing. Deep feed forward neural networks (DFNN) and gradient boosting decision trees (GBDT) are trained as baseline models against the proposed DRNN. Performances of three missing value fixing algorithms, as well as different machine learning models are evaluated and analysed. Experiments show that the proposed DRNN framework outperforms both DFNN and GBDT, therefore validating the capacity of the proposed framework. Our results also provides useful insights for better understanding of different strategies that handle missing values.

ACTRIS non-methane hydrocarbon intercomparison experiment in Europe to support WMO-GAW and EMEP observation networks

NASA Astrophysics Data System (ADS)

Hoerger, C. C.; Werner, A.; Plass-Duelmer, C.; Reimann, S.; Eckart, E.; Steinbrecher, R.; Aalto, J.; Arduini, J.; Bonnaire, N.; Cape, J. N.; Colomb, A.; Connolly, R.; Diskova, J.; Dumitrean, P.; Ehlers, C.; Gros, V.; Hakola, H.; Hill, M.; Hopkins, J. R.; Jäger, J.; Junek, R.; Kajos, M. K.; Klemp, D.; Leuchner, M.; Lewis, A. C.; Locoge, N.; Maione, M.; Martin, D.; Michl, K.; Nemitz, E.; O'Doherty, S.; Pérez Ballesta, P.; Ruuskanen, T. M.; Sauvage, S.; Schmidbauer, N.; Spain, T. G.; Straube, E.; Vana, M.; Vollmer, M. K.; Wegener, R.; Wenger, A.

2014-10-01

The performance of 20 European laboratories involved in long-term non-methane hydrocarbon (NMHC) measurements within the framework of Global Atmosphere Watch (GAW) and European Monitoring and Evaluation Programme (EMEP) was assessed with respect to the ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure Network) and GAW data quality objectives (DQOs). Compared to previous intercomparisons the DQOs of ACTRIS are much more demanding with deviations to a reference value of less than 5% and repeatability of better than 2% for mole fractions above 0.1 nmol mol-1. The participants were asked to measure both a 30 component NMHC mixture in nitrogen (NMHC_N2) at approximately 1 nmol mol-1 and whole air (NMHC_air), following a standardised operation procedure including zero- and calibration gas measurements. Furthermore, they had to report details on their instruments and they were asked to assess measurement uncertainties. The NMHCs were analysed either by gas chromatography-flame ionisation detection or gas chromatography-mass spectrometer methods. Most systems performed well for the NMHC_N2 measurements (88% of the reported values were within the GAW DQOs and even 58% within the ACTRIS DQOs). For NMHC_air generally more frequent and larger deviations to the assigned values were observed compared to NMHC_N2 (77% of the reported values were within the GAW DQOs, but only 48% within the ACTRIS DQOs). Important contributors to the poorer performance in NMHC_air compared to NMHC_N2 were a more complex matrix and a larger span of NMHC mole fractions (0.03-2.5 nmol mol-1). Issues, which affected both NMHC mixtures, are the usage of direct vs. two-step calibration, breakthrough of C2-C3 hydrocarbons, blank values in zero-gas measurements (especially for those systems using a Nafion® Dryer), adsorptive losses of aromatic compounds, and insufficient chromatographic resolution. Essential for high-quality results are experienced operators, a comprehensive quality assurance and quality control, well characterised systems, and sufficient man-power to operate the systems and evaluate the data.

Measurements of 4 Atmospheric Trace Gases Outside Homes Adjacent to a Multiwell Pad During Drilling, Hydraulic Fracturing, and Production Phases, Using Low-Cost Sensors and Artificial Neural Network Quantification Techniques

NASA Astrophysics Data System (ADS)

Casey, J. G.; Ilie, A. M. C.; Coffey, E.; Collier-Oxandale, A. M.; Hannigan, M.; Vaccaro, C.

2017-12-01

In Colorado and elsewhere in North America, the oil and gas production industry has been growing alongside and in the midst of increasing urban and rural populations. These coinciding trends have resulted in a growing number of people living in close proximity to petroleum production and processing activities, leading to potential public health impacts. Combustion-related emissions from heavy-duty diesel vehicle traffic, generators, compressors, and production stream flaring can potentially lead to locally enhanced levels of nitrogen oxides (NOx), carbon monoxide (CO), and carbon dioxide (CO2). Venting and fugitive emissions of production stream constituents can potentially lead to locally enhanced levels of methane (CH4) and volatile organic compounds (VOCs), some of which (like benzene) are known carcinogens. NOx and VOC emissions can also potentially increase local ozone (O3) production. After learning of a large new multiwell pad on the outskirts of Greeley, Colorado, we were able to quickly mobilize portable air quality monitors outfitted with low-cost gas sensors that respond to CH4, CO2, CO, and O3. The air quality monitors were installed outside homes adjacent to the new multiwell pad several weeks prior to the first spud date. An anemometer was also installed outside one of the homes in order to monitor wind speed and direction. Measurements continued during drilling, hydraulic fracturing, and production phases. The sensors were periodically collocated with reference instruments at a nearby regulatory air quality monitoring site towards calibration via field normalization and validation. Artificial Neural Networks were employed to map sensor signals to trace gas mole fractions during collocation periods. We present measurements of CH4, CO2, CO, and O3 in context with wellpad activities and local meteorology. CO and O3 observations are presented in context with regional measurements and National Ambient Air Quality Standards for each. Wind speed and direction measurements were used to indicate when air masses originated from the direction of the multiwell pad. CO2 mole fractions were used to estimate planetary boundary layer height and CH4 mole fractions were used to identify periods conducive to the pooling and accumulation of production stream venting and fugitive emissions.

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.

"What We Breathe Impacts Our Health: Improving Understanding of the Link between Air Pollution and Health".

PubMed

West, J Jason; Cohen, Aaron; Dentener, Frank; Brunekreef, Bert; Zhu, Tong; Armstrong, Ben; Bell, Michelle L; Brauer, Michael; Carmichael, Gregory; Costa, Dan L; Dockery, Douglas W; Kleeman, Michael; Krzyzanowski, Michal; Künzli, Nino; Liousse, Catherine; Lung, Shih-Chun Candice; Martin, Randall V; Pöschl, Ulrich; Pope, C Arden; Roberts, James M; Russell, Armistead G; Wiedinmyer, Christine

2016-05-17

Air pollution contributes to the premature deaths of millions of people each year around the world, and air quality problems are growing in many developing nations. While past policy efforts have succeeded in reducing particulate matter and trace gases in North America and Europe, adverse health effects are found at even these lower levels of air pollution. Future policy actions will benefit from improved understanding of the interactions and health effects of different chemical species and source categories. Achieving this new understanding requires air pollution scientists and engineers to work increasingly closely with health scientists. In particular, research is needed to better understand the chemical and physical properties of complex air pollutant mixtures, and to use new observations provided by satellites, advanced in situ measurement techniques, and distributed micro monitoring networks, coupled with models, to better characterize air pollution exposure for epidemiological and toxicological research, and to better quantify the effects of specific source sectors and mitigation strategies.

Large inter annual variation in air quality during the annual festival 'Diwali' in an Indian megacity.

PubMed

Parkhi, Neha; Chate, Dilip; Ghude, Sachin D; Peshin, Sunil; Mahajan, Anoop; Srinivas, Reka; Surendran, Divya; Ali, Kaushar; Singh, Siddhartha; Trimbake, Hanumant; Beig, Gufran

2016-05-01

A network of air quality and weather monitoring stations was established under the System of Air Quality Forecasting and Research (SAFAR) project in Delhi. We report observations of ozone (O3), nitrogen oxides (NOx), carbon monoxide (CO) and particulate matter (PM2.5 and PM10) before, during and after the Diwali in two consecutive years, i.e., November 2010 and October 2011. The Diwali days are characterised by large firework displays throughout India. The observations show that the background concentrations of particulate matter are between 5 and 10 times the permissible limits in Europe and the United States. During the Diwali-2010, the highest observed PM10 and PM2.5 mass concentration is as high as 2070µg/m3 and 1620μg/m(3), respectively (24hr mean), which was about 20 and 27 times to National Ambient Air Quality Standards (NAAQS). For Diwali-2011, the increase in PM10 and PM2.5 mass concentrations was much less with their peaks of 600 and of 390μg/m(3) respectively, as compared to the background concentrations. Contrary to previous reports, firework display was not found to strongly influence the NOx, and O3 mixing ratios, with the increase within the observed variability in the background. CO mixing ratios showed an increase. We show that the large difference in 2010 and 2011 pollutant concentrations is controlled by weather parameters. Copyright © 2015. Published by Elsevier B.V.

40 CFR Appendix N to Part 50 - Interpretation of the National Ambient Air Quality Standards for PM2.5

Code of Federal Regulations, 2013 CFR

2013-07-01

... monitors utilize the same specific sampling and analysis method. Combined site data record is the data set... monitors are suitable monitors designated by a state or local agency in their annual network plan (and in... appendix. Seasonal sampling is the practice of collecting data at a reduced frequency during a season of...

SOURCE SAMPLING FINE PARTICULATE MATTER: A KRAFT PROCESS RECOVERY BOILER AT A PULP AND PAPER FACILITY, VOLUMES 1 AND 2

EPA Science Inventory

Fine particulate matter of aerodynamic diameter 2.5 m or less (PM-2.5) has been found harmful to human health, and a National Ambient Air Quality Standard for PM-2.5 was promulgated by the U.S. Environmental Protection Agency in July 1997. A national network of ambient monitorin...

SOURCE SAMPLING FINE PARTICULATE MATTER: A KRAFT PROCESS HOGGED FUEL BOILER AT A PULP AND PAPER FACILITY, VOLUMES 1 AND 2

EPA Science Inventory

Fine particulate matter of aerodynamic diameter 2.5 m or less (PM-2.5) has been found harmful to human health, and a National Ambient Air Quality Standard for PM-2.5 was promulgated by the U.S. Environmental Protection Agency in July 1997. A national network of ambient monitorin...

Relationship between meteorological phenomena and air pollution in an urbanized and industrialized coastal area in northern France

NASA Astrophysics Data System (ADS)

Gengembre, Cyril; Zhang, Shouwen; Dieudonné, Elsa; Sokolov, Anton; Augustin, Patrick; Riffault, Véronique; Dusanter, Sébastien; Fourmentin, Marc; Delbarre, Hervé

2016-04-01

Impacts of global climate evolution are quite uncertain at regional and local scales, especially on air pollution. Air quality is associated with local atmospheric dynamics at a time scale shorter than a few weeks, while the climate change time scale is on the order of fifty years. To infer consequences of climate evolution on air pollution, it is necessary to fill the gap between these different scales. Another challenge is to understand the effect of global warming on the frequency of meteorological phenomena that influence air pollution. In this work, we classified meteorological events related to air pollution during a one-year long field campaign in Dunkirk (northern France). Owing to its coastal location under urban and industrial exposures, the Dunkirk agglomeration is an interesting area for studying gaseous and aerosols pollutants and their relationship with weather events such as sea breezes, fogs, storms and fronts. The air quality in the northern region of France is also greatly influenced by highly populated and industrialized cities along the coast of the North Sea, and by London and Paris agglomerations. During a field campaign, we used simultaneously a three-dimensional sonic anemometer and a weather station network, along with a scanning Doppler Lidar system to analyse the vertical structure of the atmosphere. An Aerosol Chemical Speciation Monitor enabled investigating the PM1 behaviour during the studied events. Air contaminants such as NOx (NO and NO2) were also measured by the regional pollution monitoring network ATMO Nord Pas-de-Calais. The events were identified by finding specific criteria from meteorological and turbulent parameters. Over a hundred cases of sea breezes, fog periods, stormy days and atmospheric front passages were investigated. Variations of turbulent parameters (vertical sensible heat flux and momentum flux) give estimations on the transport and the dispersal of pollutants. As the fluxes are weak during fogs, an increase of PM1 concentrations was observed, which causes a deposition of the particles. Due to turbulence and horizontal dilution, PM1 concentrations were weak during storms.

Improving Air Quality Forecasts with AURA Observations

NASA Technical Reports Server (NTRS)

Newchurch, M. J.; Biazer, A.; Khan, M.; Koshak, W. J.; Nair, U.; Fuller, K.; Wang, L.; Parker, Y.; Williams, R.; Liu, X.

2008-01-01

Past studies have identified model initial and boundary conditions as sources of reducible errors in air-quality simulations. In particular, improving the initial condition improves the accuracy of short-term forecasts as it allows for the impact of local emissions to be realized by the model and improving boundary conditions improves long range transport through the model domain, especially in recirculating anticyclones. During the August 2006 period, we use AURA/OMI ozone measurements along with MODIS and CALIPSO aerosol observations to improve the initial and boundary conditions of ozone and Particulate Matter. Assessment of the model by comparison of the control run and satellite assimilation run to the IONS06 network of ozonesonde observations, which comprise the densest ozone sounding campaign ever conducted in North America, to AURA/TES ozone profile measurements, and to the EPA ground network of ozone and PM measurements will show significant improvement in the CMAQ calculations that use AURA initial and boundary conditions. Further analyses of lightning occurrences from ground and satellite observations and AURA/OMI NO2 column abundances will identify the lightning NOx signal evident in OMI measurements and suggest pathways for incorporating the lightning and NO2 data into the CMAQ simulations.

The Deployment of Carbon Monoxide Wireless Sensor Network (CO-WSN) for Ambient Air Monitoring

PubMed Central

Chaiwatpongsakorn, Chaichana; Lu, Mingming; Keener, Tim C.; Khang, Soon-Jai

2014-01-01

Wireless sensor networks are becoming increasingly important as an alternative solution for environment monitoring because they can reduce cost and complexity. Also, they can improve reliability and data availability in places where traditional monitoring methods are difficult to site. In this study, a carbon monoxide wireless sensor network (CO-WSN) was developed to measure carbon monoxide concentrations at a major traffic intersection near the University of Cincinnati main campus. The system has been deployed over two weeks during Fall 2010, and Summer 2011–2012, traffic data was also recorded by using a manual traffic counter and a video camcorder to characterize vehicles at the intersection 24 h, particularly, during the morning and evening peak hour periods. According to the field test results, the 1 hr-average CO concentrations were found to range from 0.1–1.0 ppm which is lower than the National Ambient Air Quality Standards (NAAQS) 35 ppm on a one-hour averaging period. During rush hour periods, the traffic volume at the intersection varied from 2,067 to 3,076 vehicles per hour with 97% being passenger vehicles. Furthermore, the traffic volume based on a 1-h average showed good correlation (R2 = 0.87) with the 1-h average CO-WSN concentrations for morning and evening peak time periods whereas CO-WSN results provided a moderate correlation (R2 = 0.42) with 24 hours traffic volume due to fluctuated changes of meteorological conditions. It is concluded that the performance and the reliability of wireless ambient air monitoring networks can be used as an alternative method for real time air monitoring. PMID:24937527

Effect of β-glucan-rich barley flour fraction on rheology and quality of frozen yeasted dough.

PubMed

Hamed, Abdelmagid; Ragaee, Sanaa; Abdel-Aal, El-Sayed M

2014-12-01

Research has shown that prolonged frozen storage of bread dough reduces the quality of the end product. In this study, the effect of air-classified barley flour fraction rich in β-glucan (approximately 25%) on rheology and quality of frozen yeasted bread dough was investigated. Wheat flour (W) was replaced by air-classified barley flour fraction (B) at 10% without or with 1.4% vital gluten to produce β-glucan enriched barley dough (WB) or barley dough plus gluten (WB + G). Dough products were stored at -18 ºC for 8 wk and their rheological properties were investigated weekly. During frozen storage dough extensibility increased, while elastic and viscous moduli decreased. Differential scanning calorimeter and nuclear magnetic resonance data indicated that WB and WB + G dough products contained approximately 10% less freezable water and 9% more bound water compared to the control dough (W). β-Glucan enriched dough also exhibited less changes in gluten network as shown by SEM photographs. The addition of air-classified barley flour fraction at 10% in frozen dough reduced deterioration effects caused by frozen storage via minimizing water redistribution and maintaining rheological properties of frozen dough. © 2014 Institute of Food Technologists®

Impact of the June 2013 Riau province Sumatera smoke haze event on regional air pollution

NASA Astrophysics Data System (ADS)

Dewi Ayu Kusumaningtyas, Sheila; Aldrian, Edvin

2016-07-01

Forest and land fires in Riau province of Sumatera increase along with the rapid deforestation, land clearing, and are induced by dry climate. Forest and land fires, which occur routinely every year, cause trans-boundary air pollution up to Singapore. Economic losses were felt by Indonesia and Singapore as the affected country thus creates tensions among neighboring countries. A high concentration of aerosols are emitted from fire which degrade the local air quality and reduce visibility. This study aimed to analyze the impact of the June 2013 smoke haze event on the environment and air quality both in Riau and Singapore as well as to characterize the aerosol properties in Singapore during the fire period. Air quality parameters combine with aerosols from Aerosol Robotic Network (AERONET) data and some environmental parameters, i.e. rainfall, visibility, and hotspot numbers are investigated. There are significant relationships between aerosol and environmental parameters both in Riau and Singapore. From Hysplit modeling and a day lag correlation, smoke haze in Singapore is traced back to fire locations in Riau province after propagated one day. Aerosol characterization through aerosol optical depth (AOD), Ångstrom parameter and particle size distribution indicate the presence of fine aerosols in a great number in Singapore, which is characteristic of biomass burning aerosols. Fire and smoke haze even impaired economic activity both in Riau and Singapore, thus leaving some accounted economic losses as reported by some agencies.

Predicting the Occurrence of Haze Events in Southeast Asia using Machine Learning Algorithms

NASA Astrophysics Data System (ADS)

Lee, H. H.; Chulakadabba, A.; Tonks, A.; Yang, Z.; Wang, C.

2017-12-01

Severe local- and regional-scale air pollution episodes typically originate from 1) high emissions of air pollutants, 2) poor dispersion conditions, and 3) trans-boundary pollutant transport. Biomass burning activities have become more frequent in Southeast Asia, especially in Sumatra, Borneo, and the mainland Southeast. Trans-boundary transport of biomass burning aerosols often lead to air quality problems in the region. Furthermore, particulate pollutants from human activities besides biomass burning also play an important role in the air quality of Southeast Asia. Singapore, for example, has a dynamic industrial sector including chemical, electric and metallurgic industries, and is the region's major petroleum-refining center. In addition, natural gas and oil power plants, waste incinerators, active port traffic, and a major regional airport further complicate Singapore's air quality issues. In this study, we compare five Machine Learning algorithms: k-Nearest Neighbors, Linear Support Vector Machine, Decision Tree, Random Forest and Artificial Neural Network, to identify haze patterns and determine variable importance. The algorithms were trained using local atmospheric data (i.e. months, atmospheric conditions, wind direction and relative humidity) from three observation stations in Singapore (Changi, Seletar and Paya Labar). We find that the algorithms reveal the associations in data within and between the stations, and provide in-depth interpretation of the haze sources. The algorithms also allow us to predict the probability of haze episodes in Singapore and to determine the correlation between this probability and atmospheric conditions.

A deep learning-based reconstruction of cosmic ray-induced air showers

NASA Astrophysics Data System (ADS)

Erdmann, M.; Glombitza, J.; Walz, D.

2018-01-01

We describe a method of reconstructing air showers induced by cosmic rays using deep learning techniques. We simulate an observatory consisting of ground-based particle detectors with fixed locations on a regular grid. The detector's responses to traversing shower particles are signal amplitudes as a function of time, which provide information on transverse and longitudinal shower properties. In order to take advantage of convolutional network techniques specialized in local pattern recognition, we convert all information to the image-like grid of the detectors. In this way, multiple features, such as arrival times of the first particles and optimized characterizations of time traces, are processed by the network. The reconstruction quality of the cosmic ray arrival direction turns out to be competitive with an analytic reconstruction algorithm. The reconstructed shower direction, energy and shower depth show the expected improvement in resolution for higher cosmic ray energy.

The GAW Aerosol Lidar Observation Network (GALION) as a source of near-real time aerosol profile data for model evaluation and assimilation

NASA Astrophysics Data System (ADS)

Hoff, R. M.; Pappalardo, G.

2010-12-01

In 2007, the WMO Global Atmospheric Watch’s Science Advisory Group on Aerosols described a global network of lidar networks called GAW Aerosol Lidar Observation Network (GALION). GALION has a purpose of providing expanded coverage of aerosol observations for climate and air quality use. Comprised of networks in Asia (AD-NET), Europe (EARLINET and CIS-LINET), North America (CREST and CORALNET), South America (ALINE) and with contribution from global networks such as MPLNET and NDACC, the collaboration provides a unique capability to define aerosol profiles in the vertical. GALION is designed to supplement existing ground-based and column profiling (AERONET, PHOTONS, SKYNET, GAWPFR) stations. In September 2010, GALION held its second workshop and one component of discussion focussed how the network would integrate into model needs. GALION partners have contributed to the Sand and Dust Storm Warning and Analysis System (SDS-WAS) and to assimilation in models such as DREAM. This paper will present the conclusions of those discussions and how these observations can fit into a global model analysis framework. Questions of availability, latency, and aerosol parameters that might be ingested into models will be discussed. An example of where EARLINET and GALION have contributed in near-real time observations was the suite of measurements during the Eyjafjallajokull eruption in Iceland and its impact on European air travel. Lessons learned from this experience will be discussed.

The expanding scope of air pollution monitoring can facilitate sustainable development.

PubMed

Knox, Andrew; Mykhaylova, Natalia; Evans, Greg J; Lee, Colin J; Karney, Bryan; Brook, Jeffrey R

2013-03-15

This paper explores technologies currently expanding the physical scope of air pollution monitoring and their potential contributions to the assessment of sustainable development. This potential lies largely in the ability of these technologies to address issues typically on the fringe of the air pollution agenda. Air pollution monitoring tends to be primarily focused on human health, and largely neglects other aspects of sustainable development. Sensor networks, with their relatively inexpensive monitoring nodes, allow for monitoring with finer spatiotemporal resolution. This resolution can support more conclusive studies of air pollution's effect on socio-ecological justice and human quality of life. Satellite observation of air pollution allows for wider geographical scope, and in doing so can facilitate studies of air pollution's effects on natural capital and ecosystem resilience. Many air pollution-related aspects of the sustainability of development in human systems are not being given their due attention. Opportunities exist for air pollution monitoring to attend more to these issues. Improvements to the resolution and scale of monitoring make these opportunities realizable. Copyright © 2012 Elsevier B.V. All rights reserved.

Gridded global surface ozone metrics for atmospheric chemistry model evaluation

NASA Astrophysics Data System (ADS)

Sofen, E. D.; Bowdalo, D.; Evans, M. J.; Apadula, F.; Bonasoni, P.; Cupeiro, M.; Ellul, R.; Galbally, I. E.; Girgzdiene, R.; Luppo, S.; Mimouni, M.; Nahas, A. C.; Saliba, M.; Tørseth, K.

2016-02-01

The concentration of ozone at the Earth's surface is measured at many locations across the globe for the purposes of air quality monitoring and atmospheric chemistry research. We have brought together all publicly available surface ozone observations from online databases from the modern era to build a consistent data set for the evaluation of chemical transport and chemistry-climate (Earth System) models for projects such as the Chemistry-Climate Model Initiative and Aer-Chem-MIP. From a total data set of approximately 6600 sites and 500 million hourly observations from 1971-2015, approximately 2200 sites and 200 million hourly observations pass screening as high-quality sites in regionally representative locations that are appropriate for use in global model evaluation. There is generally good data volume since the start of air quality monitoring networks in 1990 through 2013. Ozone observations are biased heavily toward North America and Europe with sparse coverage over the rest of the globe. This data set is made available for the purposes of model evaluation as a set of gridded metrics intended to describe the distribution of ozone concentrations on monthly and annual timescales. Metrics include the moments of the distribution, percentiles, maximum daily 8-hour average (MDA8), sum of means over 35 ppb (daily maximum 8-h; SOMO35), accumulated ozone exposure above a threshold of 40 ppbv (AOT40), and metrics related to air quality regulatory thresholds. Gridded data sets are stored as netCDF-4 files and are available to download from the British Atmospheric Data Centre (doi: 10.5285/08fbe63d-fa6d-4a7a-b952-5932e3ab0452). We provide recommendations to the ozone measurement community regarding improving metadata reporting to simplify ongoing and future efforts in working with ozone data from disparate networks in a consistent manner.

Gridded global surface ozone metrics for atmospheric chemistry model evaluation

NASA Astrophysics Data System (ADS)

Sofen, E. D.; Bowdalo, D.; Evans, M. J.; Apadula, F.; Bonasoni, P.; Cupeiro, M.; Ellul, R.; Galbally, I. E.; Girgzdiene, R.; Luppo, S.; Mimouni, M.; Nahas, A. C.; Saliba, M.; Tørseth, K.; Wmo Gaw, Epa Aqs, Epa Castnet, Capmon, Naps, Airbase, Emep, Eanet Ozone Datasets, All Other Contributors To

2015-07-01

The concentration of ozone at the Earth's surface is measured at many locations across the globe for the purposes of air quality monitoring and atmospheric chemistry research. We have brought together all publicly available surface ozone observations from online databases from the modern era to build a consistent dataset for the evaluation of chemical transport and chemistry-climate (Earth System) models for projects such as the Chemistry-Climate Model Initiative and Aer-Chem-MIP. From a total dataset of approximately 6600 sites and 500 million hourly observations from 1971-2015, approximately 2200 sites and 200 million hourly observations pass screening as high-quality sites in regional background locations that are appropriate for use in global model evaluation. There is generally good data volume since the start of air quality monitoring networks in 1990 through 2013. Ozone observations are biased heavily toward North America and Europe with sparse coverage over the rest of the globe. This dataset is made available for the purposes of model evaluation as a set of gridded metrics intended to describe the distribution of ozone concentrations on monthly and annual timescales. Metrics include the moments of the distribution, percentiles, maximum daily eight-hour average (MDA8), SOMO35, AOT40, and metrics related to air quality regulatory thresholds. Gridded datasets are stored as netCDF-4 files and are available to download from the British Atmospheric Data Centre (doi:10.5285/08fbe63d-fa6d-4a7a-b952-5932e3ab0452). We provide recommendations to the ozone measurement community regarding improving metadata reporting to simplify ongoing and future efforts in working with ozone data from disparate networks in a consistent manner.

A weighted higher-order network analysis of fine particulate matter (PM2.5) transport in Yangtze River Delta

NASA Astrophysics Data System (ADS)

Wang, Yufang; Wang, Haiyan; Zhang, Shuhua

2018-04-01

Specification of PM2.5 transmission characteristics is important for pollution control, policymaking and prediction. In this paper, we propose weights for motif instances, thereby to implement a weighted higher-order clustering algorithm for a weighted, directed PM2.5 network in the Yangtze River Delta (YRD) of China. The weighted, directed network we create in this paper includes information on meteorological conditions of wind speed and wind direction, plus data on geographic distance and PM2.5 concentrations. We aim to reveal PM2.5 mobility between cities in the YRD. Major potential PM2.5 contributors and closely interacted clusters are identified in the network of 178 air quality stations in the YRD. To our knowledge, it is the first work to incorporate weight information into the higher-order network analysis to study PM2.5 transport.

Study of the air quality in the surroundings of an urban park: A micrometeorological approach

NASA Astrophysics Data System (ADS)

Sastre, Mariano; Yagüe, Carlos; Arrillaga, Jon A.; Román-Cascón, Carlos; Maqueda, Gregorio; Artíñano, Begoña; Díaz-Ramiro, Elías; Gómez-Moreno, Francisco J.; Barreiro, Marcos; Borge, Rafael; Narros, Adolfo; Pérez, Javier; Quaassdorff, Christina

2017-04-01

In this work we study the differences showed by two types of pollutants, particulate matter (PM) and NOx, by comparing ambient concentration measurements within an urban park versus the corresponding values nearby (but outside) it. The results are linked to both proximity to emission sources, such as road traffic, and the microscale atmospheric conditions. The work is motivated by the fact that poor air quality is a crucial issue of current cities. For some of them it is not uncommon to face this problem with occasional traffic restrictions when high concentrations of pollutants are reached. These events occur more frequently with specific large-scale atmospheric conditions, for example when a strong anticyclone is present. As the meteorological conditions may significantly influence the pollutants concentrations, the research project TECNAIRE-CM (Innovative technologies for the assessment and improvement of urban air quality) aims to provide new approaches to obtain proper descriptions of the urban pollution and its dynamics at different spatial and temporal scales, not only the synoptic scale. So far, a few field campaigns have been developed within TECNAIRE-CM at two locations in the city of Madrid, which are considered hot spots according to the air quality network records. Here we use the data from a field campaign carried out during summer 2016, which consider standard pollution and meteorological measurements, as well as sonic anemometer data. The latter help to include atmospheric turbulence as a significant agent for air quality characterization. The instrumentation was deployed at a location with considerable traffic density, but nearby a border of the main urban park of the city, so that its influence might be investigated. Supplementary data considered for this work correspond to permanent instrumentation within the park. With this extra information we can compare both measurements inside and outside the park. Therefore, we study the effect on wind, turbulence or air quality when we measure at a site either directly exposed to traffic emissions or partly protected and with a reduced influence of typical atmospheric urban phenomena. This work has been funded by Madrid Regional Research Plan through TECNAIRE (P2013/MAE-2972).

Performance and diagnostic evaluation of ozone predictions by the Eta-Community Multiscale Air Quality Forecast System during the 2002 New England Air Quality Study.

PubMed

Yu, Shaocai; Mathur, Rohit; Kang, Daiwen; Schere, Kenneth; Eder, Brian; Pleim, Jonathan

2006-10-01

A real-time air quality forecasting system (Eta-Community Multiscale Air Quality [CMAQ] model suite) has been developed by linking the National Centers for Environmental Estimation Eta model to the U.S. Environmental Protection Agency (EPA) CMAQ model. This work presents results from the application of the Eta-CMAQ modeling system for forecasting ozone (O3) over the Northeastern United States during the 2002 New England Air Quality Study (NEAQS). Spatial and temporal performance of the Eta-CMAQ model for O3 was evaluated by comparison with observations from the EPA Air Quality System (AQS) network. This study also examines the ability of the model to simulate the processes governing the distributions of tropospheric O3 on the basis of the intensive datasets obtained at the four Atmospheric Investigation, Regional Modeling, Analysis, and Estimation (AIRMAP) and Harvard Forest (HF) surface sites. The episode analysis reveals that the model captured the buildup of O3 concentrations over the northeastern domain from August 11 and reproduced the spatial distributions of observed O3 very well for the daytime (8:00 p.m.) of both August 8 and 12 with most of normalized mean bias (NMB) within +/- 20%. The model reproduced 53.3% of the observed hourly O3 within a factor of 1.5 with NMB of 29.7% and normalized mean error of 46.9% at the 342 AQS sites. The comparison of modeled and observed lidar O3 vertical profiles shows that whereas the model reproduced the observed vertical structure, it tended to overestimate at higher altitude. The model reproduced 64-77% of observed NO2 photolysis rate values within a factor of 1.5 at the AIRMAP sites. At the HF site, comparison of modeled and observed O3/nitrogen oxide (NOx) ratios suggests that the site is mainly under strongly NOx-sensitive conditions (>53%). It was found that the modeled lower limits of the O3 production efficiency values (inferred from O3-CO correlation) are close to the observations.

Contribution of biomass burning to particles matter smaller than ten microns in Mexico City during April 2013.

NASA Astrophysics Data System (ADS)

Mendoza Campos, Alejandra; Agustin Garcia Reynoso, Jóse; Castro Romero, Telma; Carbajal Perez, Jóse Noel; Gerardo Ruiz Suarez, Luis; Peralta Rosales, Oscar Augusto

2015-04-01

A forest fire is a natural combustion process in a specific geographic area, it's depends on meteorological variables, topography and vegetation type, The wildfires are potential sources of large amounts of pollutants. The main air pollutants emitted in a forest fire are the particles (PM10 and PM2.5) Carbon Monoxide (CO), Nitrogen Oxides (NOx), Volatile Organic Compounds (VOCs) and a negligible amount of Sulfur Dioxide (SO2) (Chow 1995), The study of the impact of air quality in Mexico City for a forest fire occurred on April 14 of 2013 was conducted a duration of 26 hours of grassland burning and consuming an extension of 150 ha, the WRF-Chem, WRF-fire and METv3 models were used to perform the study, for the study two modeling were made, one including emissions from forest fires and the other one no emission-fire, when interpolation is made between the two modeling and obtained the impact of air quality in Mexico City, performing calculating emissions and modeling, the impact on air quality for PM10particles were observed arriving at a concentration of 350 mg/m3 due to wildfire occurred, this issue exceeds the maximum permissible limit of PM10particles governed by NOM-025-SSA1-1993 that establishes a maximum of 120 mg/m3 on average for 24 hours, the modeling results with measured data is corroborated weather Stations the environmental monitoring network of the Mexico City, that alerts an environmental contingency for particles for the post-wildfire day. Until now is review the rule which establishes a maximum of 75 mg/m3 on average for 24 hours, implying greater involvement in air quality.

Tracking the association between metro-railway construction works and PM levels in an urban Mediterranean environment.

PubMed

Paschalidou, A K; Kassomenos, P A; Kelessis, A

2016-10-15

Metro-railways are considered to be a sustainable means of public transportation, as they contribute substantially to the reduction of air pollutant emissions through the decrease in the number of cars and heavy vehicles circulating in the road network. However, the works related to their construction may pose an extra burden in air quality status and consequently in public health. In the present study, we studied the possible effects of the metro-railway construction works in Thessaloniki, Greece, on public health through 2 well-established air quality indices, namely the PI and DAQI. The analysis suggested that there were excess high levels of PM10 measured in the close vicinity of the construction-sites during the period studied (2008-2014). These concentrations are likely to have originated from local construction sources rather than transport or continental secondary dust sources and might have an adverse health impact, as according to the PI index, the majority of days in the construction sites were grouped as "low pollution" or "moderate pollution", while a small percentage of days (1.84%) were suggested to be unhealthy for the most vulnerable groups of the population. Similarly, the DAQI index revealed that the vast majority of days were grouped as "poor" air quality, while 5.50% of the days reflected the most oppressive conditions for public health, as they were characterized as "very poor" air quality. Given the need of reaching a compromise between future transportation sustainability and public health during the construction works, the feasibility of appropriate measures in the area should be examined. Copyright © 2015 Elsevier B.V. All rights reserved.

Influence of boundary conditions to multi-model simulations of ozone and PM2.5 levels over Europe and North America in frame of AQMEII3

NASA Astrophysics Data System (ADS)

Im, Ulas; Hansen, Kaj M.; Geels, Camilla; Christensen, Jesper H.; Brandt, Jørgen; Hogrefe, Christian; Galmarini, Stefano

2016-04-01

AQMEII (Air Quality Model Evaluation International Initiative) promotes research on regional air quality model evaluation across the European and North American atmospheric modelling communities, providing the ideal platform for advancing the evaluation of air quality models at the regional scale. In frame of the AQMEII3 model evaluation exercise, thirteen regional chemistry and transport models have simulated the air pollutant levels over Europe and/or North America for the year 2010, along with various sensitivity simulations of reductions in anthropogenic emissions and boundary conditions. All participating groups have performed sensitivity simulation with 20% reductions in global (GLO) anthropogenic emissions. In addition, various groups simulated sensitivity scenarios of 20% reductions in anthropogenic emissions in different HTAP-defined regions such as North America (NAM), Europe (EUR) and East Asia (EAS). The boundary conditions for the base case and the perturbation scenarios were derived from the MOZART-IFS global chemical model. The present study will evaluate the impact of these emission perturbations on regional surface ozone and PM2.5 levels as well as over individual surface measurement stations over both continents and vertical profiles over the radiosonde stations from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) and the Aerosol Robotic Network (AERONET) stations for ozone and for PM2.5, respectively.

Sensitivities of NOx transformation and the effects on surface ozone and nitrate

NASA Astrophysics Data System (ADS)

Lei, H.; Wang, J. X. L.

2013-08-01

As precursors for tropospheric ozone and nitrate aerosols, Nitrogen oxides (NOx) in present atmosphere and its transformation in responding to emission and climate perturbations are studied by CAM-Chem model and air quality measurements including National Emission Inventory (NEI), Clean Air Status and Trends Network (CASTNET) and Environmental Protection Agency Air Quality System (EPA AQS). It is found that not only the surface ozone formation but also the nitrate formation is associated with the relative emissions of NOx and volatile organic compounds (VOC). Due to the availability of VOC and associated NOx titration, ozone productions in industrial regions increase in warmer conditions and slightly decrease against NOx emission increase, which is converse to the response in farming region. The decrease or small increase in ozone concentrations over industrial regions result in the responded nitrate increasing rate staying above the increasing rate of NOx emissions. It is indicated that ozone concentration change is more directly affected by changes in climate and precursor emissions, while nitrate concentration change is also affected by local ozone production types and their seasonal transfer. The sensitivity to temperature perturbations shows that warmer climate accelerates the decomposition of odd nitrogen (NOy) during the night. As a result, the transformation rate of NOx to nitrate decreases. Examinations on the historical emission and air quality records on typical pollution areas further confirm the conclusion drawn from modeling experiments.

Wet deposition monitoring and modelling in New Brunswick — An area dominated by wet deposition due to long-range transport

NASA Astrophysics Data System (ADS)

Davis, Claude S.

Two wet deposition monitoring networks, the Coleson Cove Precipitation Monitoring Network (CCPMN) (12 stations) located in the Coleson Cove-Saint John area of south New Brunswick, and the Expanded New Brunswick Precipitation Monitoring Network (ENBPMN) (6 stations) covering the remainder of the province, were established in May 1988. The monitoring networks and a complementary modelling study were implemented to assess the relative contributions of local and distant sources to wet deposition in New Brunswick. Quality assurance/quality control activities for the networks included independent external audits, collocated samplers at one site and comparisons of weekly measurements at the ENBPMN sampler and the Canadian Air and Precipitation Monitoring Network (CAPMoN) sampler which makes daily measurements. The intercomparisons provided reassurance that the networks provided high quality data. Analysis of 2 years (June 1988-May 1990) data from the networks included routine statistical analyses for acid rain chemistry as well as analysis of 1 year of daily back trajectory data from Harcourt, New Brunswick. Three-day back trajectories determined at 12-h intervals from Harcourt on days with precipitatio showed that air masses originate mainly from regions in Quebec, Ontario and northeast U.S.A. which are known to have high sulphur oxide emissions. Some 18 trajectories were associated with 50% of the wet sulphate deposition and over 200 trajectories with 75% of the deposition in the 1-year period ending 31 May 1989. The MESOPUFF model, applied to an 800 km by 800 km domain that included the entire province of New Brunswick, was used to make predictions of wet sulphate and nitrate deposition at each of the wet deposition monitoring stations for a 2-year period, 1 June 1988-31 May 1990. Model predictions averaged over all receptors due to all sources in the model domain accounted for 7-25% of the measured seasonal average wet sulphate deposition and less than 3% of the measured wet nitrate deposition at all monitoring stations. Wet deposition in New Brunswick is thus dominated by distant sources through long-range transport. The model estimated that the oil-fired Coleson Cove thermal generating station contributed between 7% and 16% to the seasonal wet sulphur deposition and less than 3% of the seasonal wet nitrogen deposition at monitoring stations in the Coleson Cove-Saint John area. The estimates for wet nitrogen deposition are limited by the NO χ emissions information which is considered less reliable than SO 2 emissions information.

Quality-Controlled Upper-Air Sounding Dataset for DYNAMO/CINDY/AMIE: Development and Corrections

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

Ciesielski, Paul; Yu, Hungjui; Johnson, Richard

2014-04-01

The upper-air sounding network for DYNAMO (Dynamics of the Madden-Julian Oscillation or MJO) has provided an unprecedented set of observations for studying the MJO over the Indian Ocean (IO) where coupling of this oscillation with deep convection first occurs. With 72 sounding sites and dropsonde data from 13 aircraft mission, the sonde network covers the tropics from Eastern African to the West Pacific. In total nearly 26,000 sondes were collected from this network during the experiment’s 6-month extended observing period (from October 2011 to March 2012). Slightly more than half of the sondes, collected from 33 sites, are at highmore » vertical resolution. Rigorous post-field phase processing of the sonde data included several levels of quality checks and a variety of corrections which address a number of issues (e.g., daytime dry bias, baseline surface data errors, ship deck-heating effects, artificial dry spikes in slow ascent sondes). Because of the importance of an accurate description of the moisture field in meeting the scientific goals of the experiments, particular attention is given to humidity correction and its validation. The humidity corrections, though small relative to some previous field campaigns, produced high fidelity moisture analyses in which sonde precipitable water compared well with independent estimates. An assessment of model operational analyses moisture using corrected sonde data shows an overall good agreement with the exception at upper-levels where model moisture and clouds are more abundant than the sounding data would indicate.« less

Particulate matter in the rural settlement during winter time

NASA Astrophysics Data System (ADS)

Olszowski, Tomasz

2017-10-01

The objective of this study was to analyzed the variability of the ambient particulates mass concentration in an area occupied by rural development. The analysis applied daily and hourly PM2.5 and PM10 levels. Data were derived on the basis of measurement results with the application of stationary gravimetric samplers and optical dust meter. The obtained data were compared with the results from the urban air quality monitoring network in Opole. Principal Component Analysis was used for data analysis. Research hypotheses were checked using U Mann-Whitney. It was indicated that during the smog episodes, the ratio of the inhalable dust fraction in the rural aerosol is greater than for the case of the urban aerosol. It was established that the principal meteorological factors affecting the local air quality. Air temperature, atmospheric pressure, movement of air masses and occurrence of precipitation are the most important. It was demonstrated that the during the temperature inversion phenomenon, the values of the hourly and daily mass concentration of PM2.5 and PM10 are very improper. The decrease of the PM's concentration to a safe level is principally relative to the occurrence of wind and precipitation.

Air Quality and Road Emission Results for Fort Stewart, Georgia

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

Kirkham, Randy R.; Driver, Crystal J.; Chamness, Mickie A.

2004-02-02

The Directorate of Public Works Environmental & Natural Resources Division (Fort Stewart /Hunter Army Airfield) contracted with the Pacific Northwest National Laboratory (PNNL) to monitor particulate matter (PM) concentrations on Fort Stewart, Georgia. The purpose of this investigation was to establish a PM sampling network using monitoring equipment typically used in U.S. Environmental Protection Agency (EPA) ''saturation sampling'', to determine air quality on the installation. In this initial study, the emphasis was on training-generated PM, not receptor PM loading. The majority of PM samples were 24-hr filter-based samples with sampling frequency ranging from every other day, to once every sixmore » days synchronized with the EPA 6th day national sampling schedule. Eight measurement sites were established and used to determine spatial variability in PM concentrations and evaluate whether fluctuations in PM appear to result from training activities and forest management practices on the installation. Data collected to date indicate the average installation PM2.5 concentration is lower than that of nearby urban Savannah, Georgia. At three sites near the installation perimeter, analyses to segregate PM concentrations by direction of air flow across the installation boundary indicate that air (below 80 ft) leaving the installation contains less PM2.5 than that entering the installation. This is reinforced by the observation that air near the ground is cleaner on average than the air at the top of the canopy.« less

Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean.

PubMed

de Gennaro, Gianluigi; Trizio, Livia; Di Gilio, Alessia; Pey, Jorge; Pérez, Noemi; Cusack, Michael; Alastuey, Andrés; Querol, Xavier

2013-10-01

An artificial neural network (ANN) was developed and tested to forecast PM10 daily concentration in two contrasted environments in NE Spain, a regional background site (Montseny), and an urban background site (Barcelona-CSIC), which was highly influenced by vehicular emissions. In order to predict 24-h average PM10 concentrations, the artificial neural network previously developed by Caselli et al. (2009) was improved by using hourly PM concentrations and deterministic factors such as a Saharan dust alert. In particular, the model input data for prediction were the hourly PM10 concentrations 1-day in advance, local meteorological data and information about air masses origin. The forecasted performance indexes for both sites were calculated and they showed better results for the regional background site in Montseny (R(2)=0.86, SI=0.75) than for urban site in Barcelona (R(2)=0.73, SI=0.58), influenced by local and sometimes unexpected sources. Moreover, a sensitivity analysis conducted to understand the importance of the different variables included among the input data, showed that local meteorology and air masses origin are key factors in the model forecasts. This result explains the reason for the improvement of ANN's forecasting performance at the Montseny site with respect to the Barcelona site. Moreover, the artificial neural network developed in this work could prove useful to predict PM10 concentrations, especially, at regional background sites such as those on the Mediterranean Basin which are primarily affected by long-range transports. Hence, the artificial neural network presented here could be a powerful tool for obtaining real time information on air quality status and could aid stakeholders in their development of cost-effective control strategies. © 2013 Elsevier B.V. All rights reserved.

Impact of wildfires on regional air pollution | Science Inventory ...

EPA Pesticide Factsheets

We examine the impact of wildfires and agricultural/prescribed burning on regional air pollution and Air Quality Index (AQI) between 2006 and 2013. We define daily regional air pollution using monitoring sites for ozone (n=1595), PM2.5 collected by Federal Reference Method (n=1058), and constituents of PM2.5 from the Interagency Monitoring of PROtected Visual Environment (IMPROVE) network (n=264) and use satellite image analysis from the NOAA Hazard Mapping System (HMS) to determine days on which visible smoke plumes are detected in the vertical column of the monitoring site. To examine the impact of smoke from these fires on regional air pollution we use a two stage approach, accounting for within site (1st stage) and between site (2nd stage) variations. At the first stage we estimate a monitor-specific plume day effect describing the relative change in pollutant concentrations on the days impacted by smoke plume while accounting for confounding effects of season and temperature_. At the second stage we combine monitor-specific plume day effects with a Bayesian hierarchical model and estimate a pooled nationally-averaged effect. HMS visible smoke plumes were detected on 6% of ozone, 8% of PM2.5 and 6% of IMPROVE network monitoring days. Our preliminary results indicate that the long range transport of air pollutants from wildfires and prescribed burns increase ozone concentration by 11% and PM2.5 mass by 34%. On all of the days where monitoring sites were AQI

The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors

NASA Astrophysics Data System (ADS)

Kim, Jinsol; Shusterman, Alexis A.; Lieschke, Kaitlyn J.; Newman, Catherine; Cohen, Ronald C.

2018-04-01

The newest generation of air quality sensors is small, low cost, and easy to deploy. These sensors are an attractive option for developing dense observation networks in support of regulatory activities and scientific research. They are also of interest for use by individuals to characterize their home environment and for citizen science. However, these sensors are difficult to interpret. Although some have an approximately linear response to the target analyte, that response may vary with time, temperature, and/or humidity, and the cross-sensitivity to non-target analytes can be large enough to be confounding. Standard approaches to calibration that are sufficient to account for these variations require a quantity of equipment and labor that negates the attractiveness of the sensors' low cost. Here we describe a novel calibration strategy for a set of sensors, including CO, NO, NO2, and O3, that makes use of (1) multiple co-located sensors, (2) a priori knowledge about the chemistry of NO, NO2, and O3, (3) an estimate of mean emission factors for CO, and (4) the global background of CO. The strategy requires one or more well calibrated anchor points within the network domain, but it does not require direct calibration of any of the individual low-cost sensors. The procedure nonetheless accounts for temperature and drift, in both the sensitivity and zero offset. We demonstrate this calibration on a subset of the sensors comprising BEACO2N, a distributed network of approximately 50 sensor nodes, each measuring CO2, CO, NO, NO2, O3 and particulate matter at 10 s time resolution and approximately 2 km spacing within the San Francisco Bay Area.

Hourly air pollution concentrations and their important predictors over Houston, Texas using deep neural networks: case study of DISCOVER-AQ time period

NASA Astrophysics Data System (ADS)

Eslami, E.; Choi, Y.; Roy, A.

2017-12-01

Air quality forecasting carried out by chemical transport models often show significant error. This study uses a deep-learning approach over the Houston-Galveston-Brazoria (HGB) area to overcome this forecasting challenge, for the DISCOVER-AQ period (September 2013). Two approaches, deep neural network (DNN) using a Multi-Layer Perceptron (MLP) and Restricted Boltzmann Machine (RBM) were utilized. The proposed approaches analyzed input data by identifying features abstracted from its previous layer using a stepwise method. The approaches predicted hourly ozone and PM in September 2013 using several predictors of prior three days, including wind fields, temperature, relative humidity, cloud fraction, precipitation along with PM, ozone, and NOx concentrations. Model-measurement comparisons for available monitoring sites reported Indexes of Agreement (IOA) of around 0.95 for both DNN and RBM. A standard artificial neural network (ANN) (IOA=0.90) with similar architecture showed poorer performance than the deep networks, clearly demonstrating the superiority of the deep approaches. Additionally, each network (both deep and standard) performed significantly better than a previous CMAQ study, which showed an IOA of less than 0.80. The most influential input variables were identified using their associated weights, which represented the sensitivity of ozone to input parameters. The results indicate deep learning approaches can achieve more accurate ozone forecasting and identify the important input variables for ozone predictions in metropolitan areas.

Wireless Sensor Network Applications for the Combat Air Forces

DTIC Science & Technology

2006-06-13

WIRELESS SENSOR NETWORK APPLICATIONS FOR THE COMBAT AIR FORCES GRADUATE RESEARCH PROJECT...Government. AFIT/IC4/ENG/06-05 WIRELESS SENSOR NETWORK APPLICATIONS FOR THE COMBAT AIR FORCES GRADUATE RESEARCH PROJECT Presented to the...Major, USAF June 2006 APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED AFIT/IC4/ENG/06-05 WIRELESS SENSOR NETWORK APPLICATIONS

Using Google Location History to track personal exposure to air pollution

NASA Astrophysics Data System (ADS)

Marais, E. A.; Wiedinmyer, C.

2017-12-01

Big data is increasingly used in air pollution research to monitor air quality and develop mitigation strategies. Google Location History provides an archive of geolocation and time information from mobile devices that can be used to track personal exposure to air pollution. Here we demonstrate the utility of Google Location History for assessing true exposure of individuals to air pollution hazardous to human health in an increasingly mobile world. We use the GEOS-Chem chemical transport model at coarse resolution (2° × 2.5°; latitude × longitude) to calculate and sample surface concentrations of fine particle mass (PM2.5) and ozone concentrations at the same time and location of each of six volunteers for 2 years (June 2015 to May 2017) and compare this to annual mean PM2.5 and ozone estimated at their postal addresses. The latter is synonymous with Global Burden of Disease studies that use a static population distribution map. We find that mobile PM2.5 is higher than static PM2.5 for most (five out of six) volunteers and can lead to a 10% increase in the risk for ischemic heart disease and stroke mortality. The difference may be more if instead a high resolution CTM or an abundant air quality monitoring network is used. There is tremendous potential to exploit geolocation and time data from mobile devices for cohort health studies and to determine best practices for limiting personal exposure to air pollution.

Evaluation of WRF Parameterizations for Air Quality Applications over the Midwest USA

NASA Astrophysics Data System (ADS)

Zheng, Z.; Fu, K.; Balasubramanian, S.; Koloutsou-Vakakis, S.; McFarland, D. M.; Rood, M. J.

2017-12-01

Reliable predictions from Chemical Transport Models (CTMs) for air quality research require accurate gridded weather inputs. In this study, a sensitivity analysis of 17 Weather Research and Forecast (WRF) model runs was conducted to explore the optimum configuration in six physics categories (i.e., cumulus, surface layer, microphysics, land surface model, planetary boundary layer, and longwave/shortwave radiation) for the Midwest USA. WRF runs were initally conducted over four days in May 2011 for a 12 km x 12 km domain over contiguous USA and a nested 4 km x 4 km domain over the Midwest USA (i.e., Illinois and adjacent areas including Iowa, Indiana, and Missouri). Model outputs were evaluated statistically by comparison with meteorological observations (DS337.0, METAR data, and the Water and Atmospheric Resources Monitoring Network) and resulting statistics were compared to benchmark values from the literature. Identified optimum configurations of physics parametrizations were then evaluated for the whole months of May and October 2011 to evaluate WRF model performance for Midwestern spring and fall seasons. This study demonstrated that for the chosen physics options, WRF predicted well temperature (Index of Agreement (IOA) = 0.99), pressure (IOA = 0.99), relative humidity (IOA = 0.93), wind speed (IOA = 0.85), and wind direction (IOA = 0.97). However, WRF did not predict daily precipitation satisfactorily (IOA = 0.16). Developed gridded weather fields will be used as inputs to a CTM ensemble consisting of the Comprehensive Air Quality Model with Extensions to study impacts of chemical fertilizer usage on regional air quality in the Midwest USA.

Workshop on Agricultural Air Quality: State of the science

NASA Astrophysics Data System (ADS)

Aneja, Viney P.; Blunden, Jessica; Roelle, Paul A.; Schlesinger, William H.; Knighton, Raymond; Niyogi, Dev; Gilliam, Wendell; Jennings, Greg; Duke, Clifford S.

The first Workshop on Agricultural Air Quality: State of the Science was held at the Bolger Center in Potomac, Maryland from 4 to 8 June 2006. This international conference assembled approximately 350 people representing 25 nations from 5 continents, with disciplines ranging from atmospheric chemistry to soil science. The workshop was designed as an open forum in which participants could openly exchange the most current knowledge and learn about numerous international perspectives regarding agricultural air quality. Participants represented many stakeholder groups concerned with the growing need to assess agricultural impacts on the atmosphere and to develop beneficial policies to improve air quality. The workshop focused on identifying methods to improve emissions inventories and best management practices for agriculture. Workshop participants also made recommendations for technological and methodological improvements in current emissions measurement and modeling practices. The workshop commenced with a session on agricultural emissions and was followed by international perspectives from the United States, Europe, Australia, India, and South America. This paper summarizes the findings and issues of the workshop and articulates future research needs. These needs were identified in three general areas: (1) improvement of emissions measurement; (2) development of appropriate emission factors; and (3) implementation of best management practices (BMPs) to minimize negative environmental impacts. Improvements in the appropriate measurements will inform decisions regarding US farming practices. A need was demonstrated for a national/international network to monitor atmospheric emissions from agriculture and their subsequent depositions to surrounding areas. Information collected through such a program may be used to assess model performance and could be critical for evaluating any future regulatory policies or BMPs. The workshop concluded that efforts to maximize benefits and reduce detrimental effects of agricultural production need to transcend disciplinary, geographic, and political boundaries. Also, such efforts should involve natural and social scientists, economists, engineers, business leaders, and decision makers. The workshop came to the conclusion that through these collaborative efforts improvements in air quality from agricultural practices will begin to take effect.

Assessing measurement uncertainty in meteorology in urban environments

NASA Astrophysics Data System (ADS)

Curci, S.; Lavecchia, C.; Frustaci, G.; Paolini, R.; Pilati, S.; Paganelli, C.

2017-10-01

Measurement uncertainty in meteorology has been addressed in a number of recent projects. In urban environments, uncertainty is also affected by local effects which are more difficult to deal with than for synoptic stations. In Italy, beginning in 2010, an urban meteorological network (Climate Network®) was designed, set up and managed at national level according to high metrological standards and homogeneity criteria to support energy applications. The availability of such a high-quality operative automatic weather station network represents an opportunity to investigate the effects of station siting and sensor exposure and to estimate the related measurement uncertainty. An extended metadata set was established for the stations in Milan, including siting and exposure details. Statistical analysis on an almost 3-year-long operational period assessed network homogeneity, quality and reliability. Deviations from reference mean values were then evaluated in selected low-gradient local weather situations in order to investigate siting and exposure effects. In this paper the methodology is depicted and preliminary results of its application to air temperature discussed; this allowed the setting of an upper limit of 1 °C for the added measurement uncertainty at the top of the urban canopy layer.

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.

A strategic outlook for coordination of ground-based measurement networks of atmospheric state variables and atmospheric composition

NASA Astrophysics Data System (ADS)

Bodeker, G. E.; Thorne, P.; Braathen, G.; De Maziere, M.; Thompson, A. M.; Kurylo, M. J., III

2016-12-01

There are a number of ground-based global observing networks that collectively aim to make key measurements of atmospheric state variables and atmospheric chemical composition. These networks include, but are not limited to:NDACC: Network for the Detection of Atmospheric Composition Change GUAN: GCOS Upper Air Network GRUAN: GCOS Reference Upper Air Network EARLINET: the European Aerosol Research Lidar Network GAW: Global Atmosphere Watch SHADOZ: Southern Hemisphere ADditional OZonesondes TCCON: Total Carbon Column Observing Network BSRN: Baseline Surface Radiation Network While each network brings unique capabilities to the global observing system, there are many instances where the activities and capabilities of the networks overlap. These commonalities across multiple networks can confound funding agencies when allocating scarce financial resources. Overlaps between networks may also result in some duplication of effort and a resultant sub-optimal use of funding resource for the global observing system. While some degree of overlap is useful for quality assurance, it is essential to identify the degree to which one network can take on a specific responsibility on behalf of all other networks to avoid unnecessary duplication, to identify where expertise in any one network may serve other networks, and to develop a long-term strategy for the evolution of these networks that clarifies to funding agencies where new investment is required. This presentation will briefly summarise the key characteristics of each network listed above, adopt a matrix approach to identify commonalities and, in particular, where there may be a danger of duplication of effort, and where gaps between the networks may be compromising the services that these networks are expected to collectively deliver to the global atmospheric and climate science research communities. The presentation will also examine where sharing of data and tools between networks may result in a more efficient delivery of records of essential climate variables to the global research community. There are aspects of underpinning research that are needed across all of these networks, such as laboratory spectroscopy, that often do not receive the attention they deserve. The presentation will also seek to identify where that underpinning research is lacking.

Regional impacts of oil and gas development on ozone formation in the western United States.

PubMed

Rodriguez, Marco A; Barna, Michael G; Moore, Tom

2009-09-01

The Intermountain West is currently experiencing increased growth in oil and gas production, which has the potential to affect the visibility and air quality of various Class I areas in the region. The following work presents an analysis of these impacts using the Comprehensive Air Quality Model with extensions (CAMx). CAMx is a state-of-the-science, "one-atmosphere" Eulerian photochemical dispersion model that has been widely used in the assessment of gaseous and particulate air pollution (ozone, fine [PM2.5], and coarse [PM10] particulate matter). Meteorology and emissions inventories developed by the Western Regional Air Partnership Regional Modeling Center for regional haze analysis and planning are used to establish an ozone baseline simulation for the year 2002. The predicted range of values for ozone in the national parks and other Class I areas in the western United States is then evaluated with available observations from the Clean Air Status and Trends Network (CASTNET). This evaluation demonstrates the model's suitability for subsequent planning, sensitivity, and emissions control strategy modeling. Once the ozone baseline simulation has been established, an analysis of the model results is performed to investigate the regional impacts of oil and gas development on the ozone concentrations that affect the air quality of Class I areas. Results indicate that the maximum 8-hr ozone enhancement from oil and gas (9.6 parts per billion [ppb]) could affect southwestern Colorado and northwestern New Mexico. Class I areas in this region that are likely to be impacted by increased ozone include Mesa Verde National Park and Weminuche Wilderness Area in Colorado and San Pedro Parks Wilderness Area, Bandelier Wilderness Area, Pecos Wilderness Area, and Wheeler Peak Wilderness Area in New Mexico.

GCOS reference upper air network (GRUAN): Steps towards assuring future climate records

NASA Astrophysics Data System (ADS)

Thorne, P. W.; Vömel, H.; Bodeker, G.; Sommer, M.; Apituley, A.; Berger, F.; Bojinski, S.; Braathen, G.; Calpini, B.; Demoz, B.; Diamond, H. J.; Dykema, J.; Fassò, A.; Fujiwara, M.; Gardiner, T.; Hurst, D.; Leblanc, T.; Madonna, F.; Merlone, A.; Mikalsen, A.; Miller, C. D.; Reale, T.; Rannat, K.; Richter, C.; Seidel, D. J.; Shiotani, M.; Sisterson, D.; Tan, D. G. H.; Vose, R. S.; Voyles, J.; Wang, J.; Whiteman, D. N.; Williams, S.

2013-09-01

The observational climate record is a cornerstone of our scientific understanding of climate changes and their potential causes. Existing observing networks have been designed largely in support of operational weather forecasting and continue to be run in this mode. Coverage and timeliness are often higher priorities than absolute traceability and accuracy. Changes in instrumentation used in the observing system, as well as in operating procedures, are frequent, rarely adequately documented and their impacts poorly quantified. For monitoring changes in upper-air climate, which is achieved through in-situ soundings and more recently satellites and ground-based remote sensing, the net result has been trend uncertainties as large as, or larger than, the expected emergent signals of climate change. This is more than simply academic with the tropospheric temperature trends issue having been the subject of intense debate, two international assessment reports and several US congressional hearings. For more than a decade the international climate science community has been calling for the instigation of a network of reference quality measurements to reduce uncertainty in our climate monitoring capabilities. This paper provides a brief history of GRUAN developments to date and outlines future plans. Such reference networks can only be achieved and maintained with strong continuing input from the global metrological community.

Progress on the Development of Future Airport Surface Wireless Communications Network

NASA Technical Reports Server (NTRS)

Kerczewski, Robert J.; Budinger, James M.; Brooks, David E.; Franklin, Morgan; DeHart, Steve; Dimond, Robert P.; Borden, Michael

2009-01-01

Continuing advances in airport surface management and improvements in airport surface safety are required to enable future growth in air traffic throughout the airspace, as airport arrival and departure delays create a major system bottleneck. These airport management and safety advances will be built upon improved communications, navigation, surveillance, and weather sensing, creating an information environment supporting system automation. The efficient movement of the digital data generated from these systems requires an underlying communications network infrastructure to connect data sources with the intended users with the required quality of service. Current airport surface communications consists primarily of buried copper or fiber cable. Safety related communications with mobile airport surface assets occurs over 25 kHz VHF voice and data channels. The available VHF spectrum, already congested in many areas, will be insufficient to support future data traffic requirements. Therefore, a broadband wireless airport surface communications network is considered a requirement for the future airport component of the air transportation system. Progress has been made on defining the technology and frequency spectrum for the airport surface wireless communications network. The development of a test and demonstration facility and the definition of required testing and standards development are now underway. This paper will review the progress and planned future work.

Importance of A Priori Vertical Ozone Profiles for TEMPO Air Quality Retrievals

NASA Astrophysics Data System (ADS)

Johnson, M. S.; Sullivan, J. T.; Liu, X.; Zoogman, P.; Newchurch, M.; Kuang, S.; McGee, T. J.; Leblanc, T.

2017-12-01

Ozone (O3) is a toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address the limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME, GOME-2, and OMI. This algorithm is suggested to use a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB-Clim) O3 climatology). This study evaluates the TB-Clim dataset and model simulated O3 profiles, which could potentially serve as a priori O3 profile information in TEMPO retrievals, from near-real-time data assimilation model products (NASA GMAO's operational GEOS-5 FP model and reanalysis data from MERRA2) and a full chemical transport model (CTM), GEOS-Chem. In this study, vertical profile products are evaluated with surface (0-2 km) and tropospheric (0-10 km) TOLNet observations and the theoretical impact of individual a priori profile sources on the accuracy of TEMPO O3 retrievals in the troposphere and at the surface are presented. Results indicate that while the TB-Clim climatological dataset can replicate seasonally-averaged tropospheric O3 profiles, model-simulated profiles from a full CTM resulted in more accurate tropospheric and surface-level O3 retrievals from TEMPO when compared to hourly and daily-averaged TOLNet observations. Furthermore, it is shown that when large surface O3 mixing ratios are observed, TEMPO retrieval values at the surface are most accurate when applying CTM a priori profile information compared to all other data products.

Elemental composition and size distribution of particulates in Cleveland, Ohio

NASA Technical Reports Server (NTRS)

King, R. B.; Fordyce, J. S.; Neustadter, H. E.; Leibecki, H. F.

1975-01-01

Measurements were made of the elemental particle size distribution at five contrasting urban environments with different source-type distributions in Cleveland, Ohio. Air quality conditions ranged from normal to air pollution alert levels. A parallel network of high-volume cascade impactors (5-state) were used for simultaneous sampling on glass fiber surfaces for mass determinations and on Whatman-41 surfaces for elemental analysis by neutron activation for 25 elements. The elemental data are assessed in terms of distribution functions and interrelationships and are compared between locations as a function of resultant wind direction in an attempt to relate the findings to sources.

Elemental composition and size distribution of particulates in Cleveland, Ohio

NASA Technical Reports Server (NTRS)

Leibecki, H. F.; King, R. B.; Fordyce, J. S.; Neustadter, H. E.

1975-01-01

Measurements have been made of the elemental particle size distribution at five contrasting urban environments with different source-type distributions in Cleveland, Ohio. Air quality conditions ranged from normal to air pollution alert levels. A parallel network of high-volume cascade impactors (5-stage) were used for simultaneous sampling on glass fiber surfaces for mass determinations and on Whatman-41 surfaces for elemental analysis by neutron activation for 25 elements. The elemental data are assessed in terms of distribution functions and interrelationships and are compared between locations as a function of resultant wind direction in an attempt to relate the findings to sources.

Analysis of the Chinese air route network as a complex network

NASA Astrophysics Data System (ADS)

Cai, Kai-Quan; Zhang, Jun; Du, Wen-Bo; Cao, Xian-Bin

2012-02-01

The air route network, which supports all the flight activities of the civil aviation, is the most fundamental infrastructure of air traffic management system. In this paper, we study the Chinese air route network (CARN) within the framework of complex networks. We find that CARN is a geographical network possessing exponential degree distribution, low clustering coefficient, large shortest path length and exponential spatial distance distribution that is obviously different from that of the Chinese airport network (CAN). Besides, via investigating the flight data from 2002 to 2010, we demonstrate that the topology structure of CARN is homogeneous, howbeit the distribution of flight flow on CARN is rather heterogeneous. In addition, the traffic on CARN keeps growing in an exponential form and the increasing speed of west China is remarkably larger than that of east China. Our work will be helpful to better understand Chinese air traffic systems.

The NASA Lightning Nitrogen Oxides Model (LNOM): Application to Air Quality Modeling

NASA Technical Reports Server (NTRS)

Koshak, William; Peterson, Harold; Khan, Maudood; Biazar, Arastoo; Wang, Lihua

2011-01-01

Recent improvements to the NASA Marshall Space Flight Center Lightning Nitrogen Oxides Model (LNOM) and its application to the Community Multiscale Air Quality (CMAQ) modeling system are discussed. The LNOM analyzes Lightning Mapping Array (LMA) and National Lightning Detection Network(TradeMark)(NLDN) data to estimate the raw (i.e., unmixed and otherwise environmentally unmodified) vertical profile of lightning NO(x) (= NO + NO2). The latest LNOM estimates of lightning channel length distributions, lightning 1-m segment altitude distributions, and the vertical profile of lightning NO(x) are presented. The primary improvement to the LNOM is the inclusion of non-return stroke lightning NOx production due to: (1) hot core stepped and dart leaders, (2) stepped leader corona sheath, K-changes, continuing currents, and M-components. The impact of including LNOM-estimates of lightning NO(x) for an August 2006 run of CMAQ is discussed.

Enhanced Representation of Soil NO Emissions in the Community Multiscale Air Quality (CMAQ) Model Version 5.0.2

NASA Technical Reports Server (NTRS)

Rasool, Quazi Z.; Zhang, Rui; Lash, Benjamin; Cohan, Daniel S.; Cooter, Ellen J.; Bash, Jesse O.; Lamsal, Lok N.

2016-01-01

Modeling of soil nitric oxide (NO) emissions is highly uncertain and may misrepresent its spatial and temporal distribution. This study builds upon a recently introduced parameterization to improve the timing and spatial distribution of soil NO emission estimates in the Community Multiscale Air Quality (CMAQ) model. The parameterization considers soil parameters, meteorology, land use, and mineral nitrogen (N) availability to estimate NO emissions. We incorporate daily year-specific fertilizer data from the Environmental Policy Integrated Climate (EPIC) agricultural model to replace the annual generic data of the initial parameterization, and use a 12km resolution soil biome map over the continental USA. CMAQ modeling for July 2011 shows slight differences in model performance in simulating fine particulate matter and ozone from Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network (CASTNET) sites and NO2 columns from Ozone Monitoring Instrument (OMI) satellite retrievals. We also simulate how the change in soil NO emissions scheme affects the expected O3 response to projected emissions reductions.

Local Air Quality Conditions and Forecasts

MedlinePlus

... Monitor Location Archived Maps by Region Canada Air Quality Air Quality on Google Earth Links A-Z About AirNow AirNow International Air Quality Action Days / Alerts AirCompare Air Quality Index (AQI) ...

Wearable technology: role in respiratory health and disease.

PubMed

Aliverti, Andrea

2017-06-01

In the future, diagnostic devices will be able to monitor a patient's physiological or biochemical parameters continuously, under natural physiological conditions and in any environment through wearable biomedical sensors. Together with apps that capture and interpret data, and integrated enterprise and cloud data repositories, the networks of wearable devices and body area networks will constitute the healthcare's Internet of Things. In this review, four main areas of interest for respiratory healthcare are described: pulse oximetry, pulmonary ventilation, activity tracking and air quality assessment. Although several issues still need to be solved, smart wearable technologies will provide unique opportunities for the future or personalised respiratory medicine.

Wearable technology: role in respiratory health and disease

PubMed Central

2017-01-01

In the future, diagnostic devices will be able to monitor a patient’s physiological or biochemical parameters continuously, under natural physiological conditions and in any environment through wearable biomedical sensors. Together with apps that capture and interpret data, and integrated enterprise and cloud data repositories, the networks of wearable devices and body area networks will constitute the healthcare’s Internet of Things. In this review, four main areas of interest for respiratory healthcare are described: pulse oximetry, pulmonary ventilation, activity tracking and air quality assessment. Although several issues still need to be solved, smart wearable technologies will provide unique opportunities for the future or personalised respiratory medicine. PMID:28966692

Baseline results from the Lichen Community Indicator Program in the Pacific Northwest: Air quality patterns and evidence of a nitrogen pollution problem

Treesearch

Sarah Jovan

2009-01-01

Why Are Epiphytic Lichen Communities Important? Lichens are one of the bioindicators used by the Forest Inventory and Analysis (FIA) Program to monitor forest health. To obtain data for use in its Lichen Community Indicator Program, FIA samples a regular network of permanent field plots to determine the composition of epiphytic, i.e., tree dwelling, lichen communities...

An automatic aerosol classification for earlinet: application and results

NASA Astrophysics Data System (ADS)

Papagiannopoulos, Nikolaos; Mona, Lucia; Amiridis, Vassilis; Binietoglou, Ioannis; D'Amico, Giuseppe; Guma-Claramunt, P.; Schwarz, Anja; Alados-Arboledas, Lucas; Amodeo, Aldo; Apituley, Arnoud; Baars, Holger; Bortoli, Daniele; Comeron, Adolfo; Guerrero-Rascado, Juan Luis; Kokkalis, Panos; Nicolae, Doina; Papayannis, Alex; Pappalardo, Gelsomina; Wandinger, Ulla; Wiegner, Matthias

2018-04-01

Aerosol typing is essential for understanding the impact of the different aerosol sources on climate, weather system and air quality. An aerosol classification method for EARLINET (European Aerosol Research Lidar Network) measurements is introduced which makes use the Mahalanobis distance classifier. The performance of the automatic classification is tested against manually classified EARLINET data. Results of the application of the method to an extensive aerosol dataset will be presented.

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

PubMed

Lee, Hyung Joo; Son, Youn-Suk

2016-04-05

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

Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN).

PubMed

Park, Sechan; Kim, Minjeong; Kim, Minhae; Namgung, Hyeong-Gyu; Kim, Ki-Tae; Cho, Kyung Hwa; Kwon, Soon-Bark

2018-01-05

The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Control Reallocation Strategies for Damage Adaptation in Transport Class Aircraft

NASA Technical Reports Server (NTRS)

Gundy-Burlet, Karen; Krishnakumar, K.; Limes, Greg; Bryant, Don

2003-01-01

This paper examines the feasibility, potential benefits and implementation issues associated with retrofitting a neural-adaptive flight control system (NFCS) to existing transport aircraft, including both cable/hydraulic and fly-by-wire configurations. NFCS uses a neural network based direct adaptive control approach for applying alternate sources of control authority in the presence of damage or failures in order to achieve desired flight control performance. Neural networks are used to provide consistent handling qualities across flight conditions, adapt to changes in aircraft dynamics and to make the controller easy to apply when implemented on different aircraft. Full-motion piloted simulation studies were performed on two different transport models: the Boeing 747-400 and the Boeing C-17. Subjects included NASA, Air Force and commercial airline pilots. Results demonstrate the potential for improving handing qualities and significantly increased survivability rates under various simulated failure conditions.

Long-term Simulation of Photo-oxidants and Particulate Matter Over Europe With The Eurad Modeling System

NASA Astrophysics Data System (ADS)

Memmesheimer, M.; Friese, E.; Jakobs, H. J.; Feldmann, H.; Ebel, A.; Kerschgens, M. J.

During recent years the interest in long-term applications of air pollution modeling systems (AQMS) has strongly increased. Most of these models have been developed for the application to photo-oxidant episodes during the last decade. In this contribu- tion a long-term application of the EURAD modeling sytem to the year 1997 is pre- sented. Atmospheric particles are included using the Modal Aerosol Dynamics Model for Europe (MADE). Meteorological fields are simulated by the mesoscale meteoro- logical model MM5, gas-phase chemistry has been treated with the RACM mecha- nism. The nesting option is used to zoom in areas of specific interest. Horizontal grid sizes are 125 km for the reginal scale, and 5 km for the local scale covering the area of North-Rhine-Westfalia (NRW). The results have been compared to observations of the air quality network of the environmental agency of NRW for the year 1997. The model results have been evaluated using the data quality objectives of the EU direc- tive 99/30. Further improvement for application of regional-scale air quality models is needed with respect to emission data bases, coupling to global models to improve the boundary values, interaction between aerosols and clouds and multiphase modeling.

An air quality emission inventory of offshore operations for the exploration and production of petroleum by the Mexican oil industry

NASA Astrophysics Data System (ADS)

Villasenor, R.; Magdaleno, M.; Quintanar, A.; Gallardo, J. C.; López, M. T.; Jurado, R.; Miranda, A.; Aguilar, M.; Melgarejo, L. A.; Palmerín, E.; Vallejo, C. J.; Barchet, W. R.

An air quality screening study was performed to assess the impacts of emissions from the offshore operations of the oil and gas exploration and production by Mexican industry in the Campeche Sound, which includes the states of Tabasco and Campeche in southeast Mexico. The major goal of this study was the compilation of an emission inventory (EI) for elevated, boom and ground level flares, processes, internal combustion engines and fugitive emissions. This inventory is so far the most comprehensive emission register that has ever been developed for the Mexican petroleum industry in this area. The EI considered 174 offshore platforms, the compression station at Atasta, and the Maritime Ports at Dos Bocas and Cayo Arcas. The offshore facilities identified as potential emitters in the area were the following: (1) trans-shipment stations, (2) a maritime floating port terminal, (3) drilling platforms, (4) crude oil recovering platforms, (5) crude oil production platforms, (6) linking platforms, (7) water injection platforms, (8) pumping platforms, (9) shelter platforms, (10) telecommunication platforms, (11) crude oil measurement platforms, and (12) flaring platforms. Crude oil storage tanks, helicopters and marine ship tankers were also considered to have an EI accurate enough for air quality regulations and mesoscale modeling of atmospheric pollutants. Historical ambient data measure at two onshore petroleum facilities were analyzed to measure air quality impacts on nearby inhabited coastal areas, and a source-receptor relationship for flares at the Ixtoc marine complex was performed to investigate health-based standards for offshore workers. A preliminary air quality model simulation was performed to observe the transport and dispersion patterns of SO 2, which is the main pollutant emitted from the offshore platforms. The meteorological wind and temperature fields were generated with CALMET, a diagnostic meteorological model that used surface observations and upper air soundings from a 4-day field campaign conducted in February of 1999. The CALMET meteorological output and the generated EI drove the transport and dispersion model, CALPUFF. Model results were compared with SO 2 measurements taken from the monitoring network at Dos Bocas.

Impacts of the July 2012 Siberian fire plume on air quality in the Pacific Northwest

NASA Astrophysics Data System (ADS)

Teakles, Andrew D.; So, Rita; Ainslie, Bruce; Nissen, Robert; Schiller, Corinne; Vingarzan, Roxanne; McKendry, Ian; Macdonald, Anne Marie; Jaffe, Daniel A.; Bertram, Allan K.; Strawbridge, Kevin B.; Leaitch, W. Richard; Hanna, Sarah; Toom, Desiree; Baik, Jonathan; Huang, Lin

2017-02-01

Biomass burning emissions emit a significant amount of trace gases and aerosols and can affect atmospheric chemistry and radiative forcing for hundreds or thousands of kilometres downwind. They can also contribute to exceedances of air quality standards and have negative impacts on human health. We present a case study of an intense wildfire plume from Siberia that affected the air quality across the Pacific Northwest on 6-10 July 2012. Using satellite measurements (MODIS True Colour RGB imagery and MODIS AOD), we track the wildfire smoke plume from its origin in Siberia to the Pacific Northwest where subsidence ahead of a subtropical Pacific High made the plume settle over the region. The normalized enhancement ratios of O3 and PM1 relative to CO of 0.26 and 0.08 are consistent with a plume aged 6-10 days. The aerosol mass in the plume was mainly submicron in diameter (PM1 / PM2.5 = 0.96) and the part of the plume sampled at the Whistler High Elevation Monitoring Site (2182 m a.s.l.) was 88 % organic material. Stable atmospheric conditions along the coast limited the initial entrainment of the plume and caused local anthropogenic emissions to build up. A synthesis of air quality from the regional surface monitoring networks describes changes in ambient O3 and PM2.5 during the event and contrasts them to baseline air quality estimates from the AURAMS chemical transport model without wildfire emissions. Overall, the smoke plume contributed significantly to the exceedances in O3 and PM2.5 air quality standards and objectives that occurred at several communities in the region during the event. Peak enhancements in 8 h O3 of 34-44 ppbv and 24 h PM2.5 of 10-32 µg m-3 were attributed to the effects of the smoke plume across the Interior of British Columbia and at the Whistler Peak High Elevation Site. Lesser enhancements of 10-12 ppbv for 8 h O3 and of 4-9 µg m-3 for 24 h PM2.5 occurred across coastal British Columbia and Washington State. The findings suggest that the large air quality impacts seen during this event were a combination of the efficient transport of the plume across the Pacific, favourable entrainment conditions across the BC interior, and the large scale of the Siberian wildfire emissions. A warming climate increases the risk of increased wildfire activity and events of this scale reoccurring under appropriate meteorological conditions.

Development of a distributed air pollutant dry deposition modeling framework.

PubMed

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

2012-12-01

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

Intraregional links between the trends in air pollutants observed at the EANET network sites for 2000-2014

NASA Astrophysics Data System (ADS)

Gromov, Sergey A.; Trifonova-Yakovleva, Alisa; Gromov, Sergey S.

2016-04-01

Recent changes in economic development tendencies and environmental protection policies in the East Asian countries raise hopes for improvement of regional air quality in this vast region populated by more than 3 billion people. To recognize anticipated changes in atmospheric pollutants levels, deposition rates and impact on the environment, the Acid Deposition Monitoring Network in East Asia (EANET, http://www.eanet.asia/) is regularly operating region-wide since 2000 in 13 countries. The network provides continuous monitoring data on the air quality and precipitation (including gas-phase and particulate chemistry) at 55 monitoring sites, including 20 remote and 14 rural sites. Observation of soil and inland water environments are performed at more than 30 monitoring sites [1]. In this study we focus on 1) the data quality assessment and preparation and 2) analysis of temporal trends of compositions observed at selected 26 non-urban EANET stations. Speciation includes gas-phase (SO2, HNO3, HCl, NH3) and particulate matter (SO42-, NO3-, Cl-, NH4+, Na+, K+, Mg2+, Ca2+) abundances analysed in samples collected using filterpack technique with sampling duration/frequency of one-two weeks. Data quality assessment (distribution test and manual inspection) allowed us to remove/repair random and operator errors. Wrong sample timing was found for 0.37% (severe) and 34% (mild inconsistency) of the total of 7630 samples regarded. Erroneous data flagging (e.g. missing or below the detection limit) was repaired for 9.3%, respectively. Some 1.8% of severely affected data were corrected (where possible) or removed. Thus refined 15-year dataset is made available for the scientific community. For convenience, we also provide data in netCDF format (per station or in an assembly). Based on this refined dataset, we performed trend analysis using several statistical approaches including quantile regression which provides robust results against outliers and better understanding of trend origins. Our calculations indicate that about half of the median trends at EANET stations are significant, derived either for the entire observational period or for a given season, however not for the same species. The proportions of decreasing and increasing trends are comparable. The latter is the case for SO2, HCl, Cl-, NO3 (except for Russia), while marked decrease in K+ abundances is prevailing at all stations. Most unsystematic trends are seen for nitrogenated compounds, particularly HNO3, which calls for deeper data quality analysis. Interestingly, about the same statistic (half of significant trends) is obtained for the upper (0.9) quantile of the dataset, suggesting that trends pertain to the upper part of the data distribution usually linked to emission dynamics (i.e. bearing winter/spring compositions). We further apply an ad hoc cluster analysis to infer spatial patterns and colocation of the trends across the East Asian region. Finally, we provide a brief comparison of results with an evaluation of changes in major acidic compounds over EMEP region for the 1990-2012 provided by EMEP in its trend assessment for the UN ECE CLRTAP earlier this year [2]. References: 1. EANET: Data Report 2014. Network Center for EANET (ACAP), November 2015, 314 p. (http://www.eanet.asia/product/datarep/datarep14/datarep14.pdf) 2. EMEP: Air Pollution Trends in the EMEP region between 1990 and 2012. WMO/EMEP TFMM Trend Assessment Report. UN ECE Convention on LRTAP, 2016, 54 p.

Combining Community Engagement and Scientific Approaches in Next-Generation Monitor Siting: The Case of the Imperial County Community Air Network.

PubMed

Wong, Michelle; Bejarano, Esther; Carvlin, Graeme; Fellows, Katie; King, Galatea; Lugo, Humberto; Jerrett, Michael; Meltzer, Dan; Northcross, Amanda; Olmedo, Luis; Seto, Edmund; Wilkie, Alexa; English, Paul

2018-03-15

Air pollution continues to be a global public health threat, and the expanding availability of small, low-cost air sensors has led to increased interest in both personal and crowd-sourced air monitoring. However, to date, few low-cost air monitoring networks have been developed with the scientific rigor or continuity needed to conduct public health surveillance and inform policy. In Imperial County, California, near the U.S./Mexico border, we used a collaborative, community-engaged process to develop a community air monitoring network that attains the scientific rigor required for research, while also achieving community priorities. By engaging community residents in the project design, monitor siting processes, data dissemination, and other key activities, the resulting air monitoring network data are relevant, trusted, understandable, and used by community residents. Integration of spatial analysis and air monitoring best practices into the network development process ensures that the data are reliable and appropriate for use in research activities. This combined approach results in a community air monitoring network that is better able to inform community residents, support research activities, guide public policy, and improve public health. Here we detail the monitor siting process and outline the advantages and challenges of this approach.

Combining Community Engagement and Scientific Approaches in Next-Generation Monitor Siting: The Case of the Imperial County Community Air Network

PubMed Central

Wong, Michelle; Bejarano, Esther; Carvlin, Graeme; King, Galatea; Lugo, Humberto; Jerrett, Michael; Northcross, Amanda; Olmedo, Luis; Seto, Edmund; Wilkie, Alexa; English, Paul

2018-01-01

Air pollution continues to be a global public health threat, and the expanding availability of small, low-cost air sensors has led to increased interest in both personal and crowd-sourced air monitoring. However, to date, few low-cost air monitoring networks have been developed with the scientific rigor or continuity needed to conduct public health surveillance and inform policy. In Imperial County, California, near the U.S./Mexico border, we used a collaborative, community-engaged process to develop a community air monitoring network that attains the scientific rigor required for research, while also achieving community priorities. By engaging community residents in the project design, monitor siting processes, data dissemination, and other key activities, the resulting air monitoring network data are relevant, trusted, understandable, and used by community residents. Integration of spatial analysis and air monitoring best practices into the network development process ensures that the data are reliable and appropriate for use in research activities. This combined approach results in a community air monitoring network that is better able to inform community residents, support research activities, guide public policy, and improve public health. Here we detail the monitor siting process and outline the advantages and challenges of this approach. PMID:29543726

Towards a network of Urban Forest Eddy Covariance stations: a unique case study in Naples

NASA Astrophysics Data System (ADS)

Guidolotti, Gabriele; Pallozzi, Emanuele; Esposito, Raffaela; Mattioni, Michele; Calfapietra, Carlo

2015-04-01

Urban forests are by definition integrated in highly human-made areas, and interact with different components of our cities. Thanks to those interactions, urban forests provide to people and to the urban environment a number of ecosystem services, including the absorption of CO2 and air pollutants thus influencing the local air quality. Moreover, in urban areas a relevant role is played by the photochemical pollution which is strongly influenced by the interactions between volatile organic compounds (VOC) and nitrogen oxides (NOx). In several cities, a high percentage of VOC is of biogenic origin mainly emitted from the urban trees. Despite their importance, experimental sites monitoring fluxes of trace gases fluxes in urban forest ecosystems are still scarce. Here we show the preliminary results of an innovative experimental site located in the Royal Park of Capodimonte within the city of Naples (40°51'N-14°15'E, 130 m above sea level). The site is mainly characterised by Quercus ilex with some patches of Pinus pinea and equipped with an eddy-covariance tower measuring the exchange of CO2, H2O, N2O, CH4, O3, PM, VOCs and NOx using state-of-the art instrumentations; it is running since the end of 2014 and it is part of the large infrastructural I-AMICA project. We suggest that the experience gained with research networks such as Fluxnet and ICOS should be duplicated for urban forests. This is crucial for carbon as there is now the ambition to include urban forests in the carbon stocks accounting system. This is even more important to understand the difficult interactions between anthropogenic and biogenic sources that often have negative implications for urban air quality. Urban environment can thus become an extraordinary case study and a network of such kind of stations might represent an important strategy both from the scientific and the applicative point of view.

CLEAN-ROADS project: air quality considerations after the application of a novel MDSS on winter road maintenance activities

NASA Astrophysics Data System (ADS)

Pretto, Ilaria; Malloci, Elisa; Tonidandel, Gabriele; Benedetti, Guido; Di Napoli, Claudia; Piazza, Andrea; Apolloni, Roberto; Cavaliere, Roberto

2016-04-01

With this poster we present the environmental benefit on air quality derived by the application of the CLEAN-ROADS pilot project. The CLEAN-ROADS project addresses the problem of the environmental pollution caused by de-icing salts during winter road maintenance activities in the Province of Trento (Italy). A demonstrative Maintenance Decision Support System (MDSS) has been developed in order to improve the intervention procedures of the road management service. Specifically it aims to optimize the efficiency of how available resources (e.g., salt consumption) are currently used while guaranteeing the current level of road safety. The CLEAN-ROADS project has been tested and validated on a test area located in a valley bottom (Adige Valley), where the highest optimization margins are to be expected. The project supports current road maintenance practices, which has proved to be reliable and accurate, with a new scalable and energy-efficient road monitoring system. This system is based on a network of road weather stations (road weather information system, RWIS) installed on the test route. It is capable to collect real-time data about the road conditions and to perform short-term and now-cast road weather forecasts, which actively integrate weather data and bulletins covering the target area [1]. This poster presents the results obtained from a three-year monitoring activity with the aim to (1) determine the impact of de-icing salts on air quality and (2) quantify the improvements obtained by the application of the CLEAN-ROADS project on air quality. The Ambient Air Quality and Cleaner Air for Europe Directive (2008/50/EC) states that contributions to exceedances of particulate matter PM10 limit values that are attributable to road winter salting may be subtracted when assessing compliance with air quality limit values, once provided that reasonable measures have been taken to lower concentrations [2]. As the de-icing salts used in road maintenance are mainly based on sodium chloride, which releases Na+ and Cl-, the estimation of the contribution of road salting to PM10 concentration can be carried out considering only measured concentrations of Na+ and Cl-. However, the presence of these elements might not be due exclusively to salting activities. For this reason data collected during first winter campaign were analysed using the Positive Matrix Factorization (PMF) Model developed by United States Environmental Protection Agency (EPA) to identify the presence of Na+ and Cl- in emission profiles of other PM10 sources (e.g., biomass burning, traffic) [3]. Through this study new guidelines have been defined for the optimization of current road management operations, and their applicability to other area in the Province of Trento has been assessed for future purposes. [1] Pretto I. et al., SIRWEC 2014 conference proceedings, ID:0019 (2014) [2] Ambient Air Quality and Cleaner Air for Europe (CAFE) Directive (2008/50/EC) [3] http://www.epa.gov/air-research/positive-matrix-factorization-model-environmental-data-analyses

M-TraCE: a new tool for high-resolution computation and statistical elaboration of backward trajectories on the Italian domain

NASA Astrophysics Data System (ADS)

Vitali, Lina; Righini, Gaia; Piersanti, Antonio; Cremona, Giuseppe; Pace, Giandomenico; Ciancarella, Luisella

2017-12-01

Air backward trajectory calculations are commonly used in a variety of atmospheric analyses, in particular for source attribution evaluation. The accuracy of backward trajectory analysis is mainly determined by the quality and the spatial and temporal resolution of the underlying meteorological data set, especially in the cases of complex terrain. This work describes a new tool for the calculation and the statistical elaboration of backward trajectories. To take advantage of the high-resolution meteorological database of the Italian national air quality model MINNI, a dedicated set of procedures was implemented under the name of M-TraCE (MINNI module for Trajectories Calculation and statistical Elaboration) to calculate and process the backward trajectories of air masses reaching a site of interest. Some outcomes from the application of the developed methodology to the Italian Network of Special Purpose Monitoring Stations are shown to assess its strengths for the meteorological characterization of air quality monitoring stations. M-TraCE has demonstrated its capabilities to provide a detailed statistical assessment of transport patterns and region of influence of the site under investigation, which is fundamental for correctly interpreting pollutants measurements and ascertaining the official classification of the monitoring site based on meta-data information. Moreover, M-TraCE has shown its usefulness in supporting other assessments, i.e., spatial representativeness of a monitoring site, focussing specifically on the analysis of the effects due to meteorological variables.

Water Resources Data for Illinois - Water Year 2005 (Includes Historical Data)

USGS Publications Warehouse

LaTour, J.K.; Weldon, E.A.; Dupre, D.H.; Halfar, T.M.

2006-01-01

This annual Water-Data Report for Illinois contains current water year (Oct. 1, 2004, to Sept. 30, 2005) and historical data of discharge, stage, water quality and biology of streams; stage of lakes and reservoirs; levels and quality of ground water; and records of precipitation, air temperature, dew point, solar radiation, and wind speed. The current year's (2005) data provided in this report include (1) discharge for 182 surface-water gaging stations and for 9 crest-stage partial-record stations; (2) stage for 33 surface-water gaging stations; (3) water-quality records for 10 surface-water stations; (4) sediment-discharge records for 14 surface-water stations; (5) water-level records for 98 ground-water wells; (6) water-quality records for 17 ground-water wells; (7) precipitation records for 48 rain gages; (8) records of air temperature, dew point, solar radiation and wind speed for 1 meteorological station; and (9) biological records for 6 sample sites. Also included are miscellaneous data collected at various sites not in the systematic data-collection network. Data were collected and compiled as a part of the National Water Information System (NWIS) maintained by the U.S. Geological Survey in cooperation with Federal, State, and local agencies.

CITYZER - Services for effective decision making and environmental resilience

NASA Astrophysics Data System (ADS)

Harri, Ari-Matti; Turtiainen, Heikki; Turpeinen, Jani; Viitala, Erkki; Janka, Kauko; Palonen, Henry; Rönkkö, Topi; Laiho, Tiina; Laitinen, Teija; Haukka, Harri; Schmidt, Walter; Nousiainen, Timo

2016-04-01

The CITYZER project develops new digital services and products to support decision making processes related to weather and air quality in cities. This includes, e.g., early warnings and forecasts (0-24 h), which allow for avoiding weather-related accidents, mitigate human distress and costs from weather-related damage and bad air quality, and generally improve the resilience and safety of the society. The project takes advantage of the latest scientific know-how and directly exploits the expertise obtained from, e.g., Tekes-funded (MMEA [1], RAVAKE) and EU-funded (HAREN, EDHIT [2]) projects. Central to the project is the Observation Network Manager NM10 [3] developed by Vaisala Oyj within the Tekes/MMEA project, on which CITYZER defines and builds new commercial services and connects new sensor networks (e.g., air quality). The target groups of the services and products (e.g., public sector, real estate and energy companies, and distributors) and related business models will be analyzed and developed in collaboration with local player (e.g., Asia, South America) taking advantage of the pre-existing contacts by the Haaga-Helia, Vaisala Oyj and CLIC Innovation. Service models are designed to account for and adapt to the special needs of different areas and customers. The developed services will be scalable (most common platforms) and responsive. CITYZER project partners include Vaisala Oyj (observation instrumentation, systems and products), Sasken Ltd (mobile products), Emtele Ltd (Portable IoT ICT Service Operation Center/Environment and remote intelligent cabinet for sensor network-GW and connections), HSY (urban services), Haaga-Helia University of Applied Sciences (service business models including digital services), Finnish Meteorological Institute (implementation of and scientific research on meteorological & air quality products), and the Tampere University of Technology (definition of and scientific research on air quality products), Pegasor Ltd (support for air quality instrumentation and products), INNO-W Ltd (providing business services support), as well as the CLIC Innovation Ltd as a subcontractor for arranging cooperation with international partners and project information dissemination, as well as composing the consortium agreement and other legal issues. Additional project partners are welcomed to join the project and current consortium encourage all potential partners to contact project management for further details. The business impact of this project to existing markets is estimated to be substantial and it will also create totally new markets especially for weather information related services. The existing whole target market size at this point is estimated to be several billion USD and the size of the market is growing steadily. The key CITYZER outcomes are the piloted services and products with envisaged great commercial and export potential. Development of the services will be managed by Sasken, Emtele, Pegasor and Vaisala and supported by INNO-W. The user profiling and market assessment, including the most potential market area either from Asia or South America, will be led by Haaga-Helia and supported by industrial partners. FMI, Vaisala and Pegasor will use their expertise and current business relations to those foreign markets to speed up and guide the user and market evaluation. Essential potential players are local actors in e.g. Brazil, China and India that will be subcontracted to bring in local expertise in the user profiling and market assessment processes. This three year project is scheduled such that, overall, the first two years focus on implementing the technical basis as well as customer and market analyses. Throughout the course of the project a CityzerDemo test bed environment will be developed in the Helsinki metropolitan area, demonstrating the observational and modeling system and services built on them. In addition, the services and business models will be evaluated. Acknowledgements The project has received funding from TEKES, the Finnish Funding Agency for Innovation. References [1] http://mmea.fi/ [2] http://edhit.eu/ [3] http://www.vaisala.com/en/products/metdatamanagementsystems/Pages/NM10.aspx

CITYZER - Services for effective decision making and environmental resilience

NASA Astrophysics Data System (ADS)

Haukka, Harri; Turtiainen, Heikki; Janka, Kauko; Palonen, Henry; Turpeinen, Jani; Viitala, Erkki; Rönkkö, Topi; Laiho, Tiina; Laitinen, Teija; Harri, Ari-Matti; Schmidt, Walter; Nousiainen, Timo; Niemi, Jarkko

2017-04-01

The CITYZER project develops new digital services and products to support decision making processes related to weather and air quality in cities. This includes, e.g., early warnings and forecasts (0-24 h), which allow for avoiding weather-related accidents, mitigate human distress and costs from weather-related damage and bad air quality, and generally improve the resilience and safety of the society. The project takes advantage of the latest scientific know-how and directly exploits the expertise obtained from, e.g., Tekes-funded (MMEA [1], RAVAKE) and EU-funded (HAREN, EDHIT [2]) projects. Central to the project is the Observation Network Manager NM10 [3] developed by Vaisala within the Tekes/MMEA project, on which CITYZER defines and builds new commercial services and connects new sensor networks (e.g., air quality). The target groups of the services and products (e.g., public sector, real estate and energy companies, and distributors) and related business models will be analyzed and developed in collaboration with local players (e.g., India, South America, China) taking advantage of the pre-existing contacts by the Haaga-Helia, Vaisala Ltd and CLIC Innovation. Service models are designed to account for and adapt to the special needs of different areas and customers. The developed services will be scalable (most common platforms) and responsive. CITYZER project partners include Vaisala Ltd (weather observation instrumentation and products), Sasken Ltd (mobile products), Emtele Ltd (Portable IoT ICT Service Operation Center/Environment and remote intelligent cabinet for sensor network-GW and connections), HSY (urban services), Haaga-Helia University of Applied Sciences (service business models including digital services), Finnish Meteorological Institute (implementation of and scientific research on meteorological & air quality products), and the Tampere University of Technology (definition of and scientific research on air quality products), Pegasor Ltd (support for air quality instrumentation and products), INNO-W Ltd (business services support), as well as the CLIC Innovation Ltd as a subcontractor for arranging cooperation with international partners and project information dissemination, as well as composing the consortium agreement and other legal issues. The business impact of this project to existing markets is estimated to be substantial and it will also create totally new markets especially for weather information related services. The existing whole target market size at this point is estimated to be several billion USD and the size of the market is growing steadily. The key CITYZER outcomes are the piloted services and products with envisaged great commercial and export potential. Development of the services will be managed by INNO-W supported by Sasken, Emtele, Pegasor and Vaisala. The user profiling and market assessment, including Asia and South America, will be led by Haaga-Helia supported by INNO-W and Sasken. FMI, Vaisala and Pegasor will use their expertise and current business relations to those foreign markets to speed up and guide the user and market evaluation. Essential potential players are local business school teams in Brazil and India that will be subcontracted to bring in local expertise in the user profiling and market assessment processes. This three year project is scheduled such that, overall, the first two years focus on implementing the technical basis as well as customer and market analyses. Throughout the course of the project a CityzerDemo environment will be developed in the Helsinki metropolitan area, demonstrating the observational and modeling system and services built on them. In addition, the services and business models will be evaluated. Acknowledgements The project has received funding from TEKES, the Finnish Funding Agency for Innovation. References [1] http://mmea.fi/ [2] http://edhit.eu/ [3] http://www.vaisala.com/en/products/metdatamanagementsystems/Pages/NM10.aspx

Satellite skill in detecting extreme episodes in near-surface air quality

NASA Astrophysics Data System (ADS)

Ruiz, D. J.; Prather, M. J.

2017-12-01

Ozone (O3) contributes to ambient air pollution, adversely affecting public health, agriculture, and ecosystems. Reliable, long-term, densely distributed surface networks are required to establish the scale, intensity and repeatability of major pollution events (designated here in a climatological sense as air quality extremes, AQX as defined in Schnell's work). Regrettably, such networks are only available for North America (NA) and Europe (EU), which does not include many populated regions where the deaths associated with air pollution exposure are alarmingly high. Directly measuring surface pollutants from space without lidar is extremely difficult. Mapping of daily pollution events requires cross-track nadir scanners and these have limited sensitivity to surface O3 levels. This work examines several years of coincident surface and OMI satellite measurements over NA-EU, in combination with a chemistry-transport model (CTM) hindcast of that period to understand how the large-scale AQX episodes may extend into the free troposphere and thus be more amenable to satellite mapping. We show how extreme NA-EU episodes are measured from OMI and then look for such patterns over other polluted regions of the globe. We gather individual high-quality O3 surface site measurements from these other regions, to check on our satellite detection. Our approach with global satellite detection would avoid issues associated with regional variations in seasonality, chemical regime, data product biases; and it does not require defining a separate absolute threshold for each data product (surface site and satellite). This also enables coherent linking of the extreme events into large-scale pollution episodes whose magnitude evolves over 100's of km for several days. Tools used here include the UC Irvine CTM, which shows that much of the O3 surface variability is lost at heights above 2 km, but AQX local events are readily seen in a 0-3 km column average. The OMI data are taken from X. Liu's dataset using an improved algorithm for detection of tropospheric O3. Surface site observations outside NA and EU are taken from research stations where possible.

Final Environmental Assessment for Wide Area Coverage Construct Land Mobile Network Communications Infrastructure Malmstrom Air Force Base, Montana

DTIC Science & Technology

2008-02-01

FINAL ENVIRONMENTAL ASSESSMENT February 2008 Malmstrom ® AFB WIDE AREA COVERAGE CONSTRUCT LAND MOBILE NETWORK COMMUNICATIONS INFRASTRUCTURE...Wide Area Coverage Construct Land Mobile Network Communications Infrastructure Malmstrom Air Force Base, Montana 5a. CONTRACT NUMBER 5b. GRANT...SIGNIFICANT IMPACT WIDE AREA COVERAGE CONSTRUCT LAND MOBILE NETWORK COMMUNICATIONS INFRASTRUCTURE MALMSTROM AIR FORCE BASE, MONTANA The

A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

NASA Astrophysics Data System (ADS)

Zimmerman, Naomi; Presto, Albert A.; Kumar, Sriniwasa P. N.; Gu, Jason; Hauryliuk, Aliaksei; Robinson, Ellis S.; Robinson, Allen L.; Subramanian, R.

2018-01-01

Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16-19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross-sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single-pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF-model-calibrated sensors could detect differences in NO2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with carefully controlled state-of-the-art multipollutant sensor packages as in the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low-cost air quality sensors.

Air quality of Prague: traffic as a main pollution source.

PubMed

Branis, Martin

2009-09-01

Political and economical transition in the Central and Eastern Europe at the end of eighties significantly influenced all aspects of life as well as technological infrastructure. Collapse of outdated energy demanding industry and adoption of environmental legislation resulted in seeming improvements of urban environmental quality. Hand in hand with modernization the newly adopted regulations also helped to phase out low quality coal frequently used for domestic heating. However, at the same time, the number of vehicles registered in the city increased. The two processes interestingly acted as parallel but antagonistic forces. To interpret the trends in urban air quality of Prague, Czech capital, monthly averages of PM(10), SO(2), NO(2), NO, O(3) and CO concentrations from the national network of automated monitoring stations were analyzed together with long term trends in fuel consumption and number of vehicles registered in Prague within a period of 1992-2005. The results showed that concentrations of SO(2) (a pollutant strongly related to fossil fuel burning) dropped significantly during the period of concern. Similarly NO(X) and PM(10) concentrations decreased significantly in the first half of the nineties (as a result of solid fuel use drop), but remained rather stable or increased after 2000, presumably reflecting rapid increase of traffic density. In conclusion, infrastructural changes in early nineties had a strong positive effect on Prague air quality namely in the first half of the period studied, nevertheless, the current trend in concentrations of automotive exhaust related pollutants (such as PM(10), NO(X)) needs adoption of stricter measures.

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

78 FR 63934 - Approval of Air Quality Implementation Plans; California; El Dorado County Air Quality Management...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-25

...] Approval of Air Quality Implementation Plans; California; El Dorado County Air Quality Management District... California for the El Dorado County Air Quality Management District (EDAQMD) portion of the California SIP... 24, 1987 Federal Register, May 25, 1988, U.S. EPA, Air Quality Management Division, Office of Air...

The Effect of Central American Smoke Aerosols on the Air Quality and Climate over the Southeastern United States: First Results from RAMS-AROMA

NASA Astrophysics Data System (ADS)

Wang, J.; Christopher, S. A.; Nair, U. S.; Reid, J.; Prins, E. M.; Szykman, J.

2004-12-01

Observation shows that smoke aerosols from biomass burning activities in Central America can be transported to the Southeastern United States (SEUS). In this study, the Regional Atmospheric Modeling System - Assimilation and Radiation Online Modeling of Aerosols (RAMS-AROMA) is used to investigate the effect of transported smoke aerosols on climate and air quality over the SEUS. AROMA is an aerosol transport model with capabilities of online integration of aerosol radiation effects and online assimilation of satellite-derived aerosol and emission products. It is assembled within the RAMS, so two-way interactions between aerosol fields and other meteorology fields are achieved simultaneously during each model time step. RAMS-AROMA is a unique tool that can be used to examine the aerosol radiative impacts on the surface energy budget and atmospheric heating rate and to investigate how atmospheric thermal and dynamical processes respond to such impacts and consequently affect the aerosol distribution (so called feedbacks). First results regarding air quality effects and radiative forcing of transported smoke aerosols will be presented from RAMS-AROMA based on assimilation of smoke emission products from the Fire Locating and Modeling of Burning Emissions (FLAMBE) project and aerosol optical thickness data derived from the MODIS instrument on the Terra and Aqua satellites. Comparisons with PM2.5 data collected from the EPA observation network and the aerosol optical thickness data from the DOE Atmosphere Radiation Measurements in the Southern Great Plains (ARM SGP) showed that RAMS-AROMA can predict the timing and spatial distribution of smoke events very well, with an accuracy useful for air quality forecasts. The smoke radiative effects on the surface temperature and atmospheric heating rate as well as their feedbacks will also be discussed.

Real-Time Environmental Sensors to Improve Health in the Sensing City

NASA Astrophysics Data System (ADS)

Marek, L.; Campbell, M.; Epton, M.; Storer, M.; Kingham, S.

2016-06-01

The opportunity of an emerging smart city in post-disaster Christchurch has been explored as a way to improve the quality of life of people suffering Chronic Obstructive Pulmonary Disease (COPD), which is a progressive disease that affects respiratory function. It affects 1 in 15 New Zealanders and is the 4th largest cause of death, with significant costs to the health system. While, cigarette smoking is the leading cause of COPD, long-term exposure to other lung irritants, such as air pollution, chemical fumes, or dust can also cause and exacerbate it. Currently, we do know little what happens to the patients with COPD after they leave a doctor's care. By learning more about patients' movements in space and time, we can better understand the impacts of both the environment and personal mobility on the disease. This research is studying patients with COPD by using GPS-enabled smartphones, combined with the data about their spatiotemporal movements and information about their actual usage of medication in near real-time. We measure environmental data in the city, including air pollution, humidity and temperature and how this may subsequently be associated with COPD symptoms. In addition to the existing air quality monitoring network, to improve the spatial scale of our analysis, we deployed a series of low-cost Internet of Things (IoT) air quality sensors as well. The study demonstrates how health devices, smartphones and IoT sensors are becoming a part of a new health data ecosystem and how their usage could provide information about high-risk health hotspots, which, in the longer term, could lead to improvement in the quality of life for patients with COPD.

Estimating the effects of the transboundary transport and local emissions of atmospheric pollutants in South Korea during KORUS-AQ campaign

NASA Astrophysics Data System (ADS)

Lee, S.; Koo, J. H.; Hong, J.; Choi, M.; Kim, J.; Lim, H.; Holben, B. N.; Eck, T. F.; Ahn, J. Y.; Park, J.; Kim, S. K.

2017-12-01

The air quality of South Korea, located in the east of China, is affected by persistent westerlies, showing the relationship to the emission in upwind region. High aerosol concentration in South Korea is also attributed to local emissions. Particularly, the industrial complex and power plants are concentrated in the Chungcheongnam-do (CN), located by the southwest part of Seoul Metropolitan Area (SMA). In this study, we evaluate the contribution of both the transboundary transport of Chinese pollutants and local emissions in the CN to the air quality in South Korea during Korea-US Air Quality (KORUS-AQ) campaign, 1 May to 12 June in 2016. Based on the information of aerosol optical depth (AOD) obtained from ground-based Aerosol Robotic NETwork (AERONET) sunphotometer and surface in-situ Particulate Matter (PM) measurements at 19 stations, high and low aerosol pollution cases are classified first. Then, 2-day back-trajectories are calculated using National Ocean and Atmospheric Administration (NOAA) HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model at each AERONET site to investigate whether transport pattern is different in accordance with the classified cases about aerosol amounts. As a result, we find the distinct pathway of air-mass transport from eastern China; When high AOD is observed at station located in the western coast of South Korea, air masses are directly transported from Shandong peninsular to the Korean peninsula. In contrast, air masses are mostly transported from northwestern or northern China during the period of low AOD conditions. When PM2.5 detected at SMA sites is greater than Korean government criteria (50 micrograms per cubic meter for 24-hour average PM2.5), SMA sites are mostly affected by air mass flows through the CN area. These results indicate that transport pattern can be different vertically and surface aerosol concentration has different transport pattern from the transport pattern related to the variation of total column aerosol.

Air pollution and gastrointestinal diseases in Utah

NASA Astrophysics Data System (ADS)

Maestas, Melissa May

The valleys of northern Utah, where most of Utah's population resides, experience episodic air pollution events well in excess of the National Ambient Air Quality Standards. Most of the events are due to an accumulation of particulate matter during persistent cold air pools in winter from both direct emissions and secondary chemical reactions in the atmosphere. High wintertime ozone concentrations are occasionally observed in the Uintah Basin, in addition to particulate matter. At other times of the year, blowing dust, wildland fires, fireworks, and summertime ozone formation contribute to local air pollution. The objective of this dissertation is to investigate one facet of the health effects of Utah's air pollution on its residents: the acute impacts of air pollution on gastrointestinal (GI) disease. To study the health effects of these episodic pollution events, some measure of air pollution exposure must be matched to the health data. Time and place are used to link the health data for a person with the pollution data. This dissertation describes the method of kriging data from the sparse pollution monitoring network to estimate personal air pollution history based on the zip code of residence. This dissertation then describes the application of these exposure estimates to a health study on GI disease. The purpose of the GI study is to retrospectively look at two groups of patients during 2000-2014: those with autoimmune disease of the GI tract (inflammatory bowel disease, IBD) and those with allergic disease of the GI tract (eosinophilic esophagitis, EoE) to determine whether disease exacerbations occur more commonly during and following periods of poor air quality compared to periods of good air quality. The primary analysis method is case crossover design. In addition to using the kriged air pollution estimates, the analysis was repeated using simpler empirical estimation methods to assess whether the odds ratios are sensitive to the air pollution estimation method. The data suggests an association between particulate matter smaller than 2.5 microns and prednisone prescriptions, gastrointestinal infections in general, clostridium difficile infections specifically, and hospitalizations among people who have at least five entries of IBD diagnosis codes in their medical records. EoE exacerbations appear to be associated with high concentrations of particulate matter as well as ozone.

Compilation of climate data from heterogeneous networks across the Hawaiian Islands

PubMed Central

Longman, Ryan J.; Giambelluca, Thomas W.; Nullet, Michael A.; Frazier, Abby G.; Kodama, Kevin; Crausbay, Shelley D.; Krushelnycky, Paul D.; Cordell, Susan; Clark, Martyn P.; Newman, Andy J.; Arnold, Jeffrey R.

2018-01-01

Long-term, accurate observations of atmospheric phenomena are essential for a myriad of applications, including historic and future climate assessments, resource management, and infrastructure planning. In Hawai‘i, climate data are available from individual researchers, local, State, and Federal agencies, and from large electronic repositories such as the National Centers for Environmental Information (NCEI). Researchers attempting to make use of available data are faced with a series of challenges that include: (1) identifying potential data sources; (2) acquiring data; (3) establishing data quality assurance and quality control (QA/QC) protocols; and (4) implementing robust gap filling techniques. This paper addresses these challenges by providing: (1) a summary of the available climate data in Hawai‘i including a detailed description of the various meteorological observation networks and data accessibility, and (2) a quality controlled meteorological dataset across the Hawaiian Islands for the 25-year period 1990-2014. The dataset draws on observations from 471 climate stations and includes rainfall, maximum and minimum surface air temperature, relative humidity, wind speed, downward shortwave and longwave radiation data. PMID:29437162

Compilation of climate data from heterogeneous networks across the Hawaiian Islands

NASA Astrophysics Data System (ADS)

Longman, Ryan J.; Giambelluca, Thomas W.; Nullet, Michael A.; Frazier, Abby G.; Kodama, Kevin; Crausbay, Shelley D.; Krushelnycky, Paul D.; Cordell, Susan; Clark, Martyn P.; Newman, Andy J.; Arnold, Jeffrey R.

2018-02-01

Long-term, accurate observations of atmospheric phenomena are essential for a myriad of applications, including historic and future climate assessments, resource management, and infrastructure planning. In Hawai'i, climate data are available from individual researchers, local, State, and Federal agencies, and from large electronic repositories such as the National Centers for Environmental Information (NCEI). Researchers attempting to make use of available data are faced with a series of challenges that include: (1) identifying potential data sources; (2) acquiring data; (3) establishing data quality assurance and quality control (QA/QC) protocols; and (4) implementing robust gap filling techniques. This paper addresses these challenges by providing: (1) a summary of the available climate data in Hawai'i including a detailed description of the various meteorological observation networks and data accessibility, and (2) a quality controlled meteorological dataset across the Hawaiian Islands for the 25-year period 1990-2014. The dataset draws on observations from 471 climate stations and includes rainfall, maximum and minimum surface air temperature, relative humidity, wind speed, downward shortwave and longwave radiation data.

Complexity, Robustness, and Multistability in Network Systems with Switching Topologies: A Hierarchical Hybrid Control Approach

DTIC Science & Technology

2015-05-22

sensor networks for managing power levels of wireless networks ; air and ground transportation systems for air traffic control and payload transport and... network systems, large-scale systems, adaptive control, discontinuous systems 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF...cover a broad spectrum of ap- plications including cooperative control of unmanned air vehicles, autonomous underwater vehicles, distributed sensor

76 FR 44535 - Revisions to the California State Implementation Plan, Northern Sierra Air Quality Management...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-26

... the California State Implementation Plan, Northern Sierra Air Quality Management District, Sacramento Metropolitan Air Quality Management District, and South Coast Air Quality Management District AGENCY... the Northern Sierra Air Quality Management District (NSAQMD), Sacramento Metropolitan Air Quality...

Evolution of aerosol loading in Santiago de Chile between 1997 and 2014

NASA Astrophysics Data System (ADS)

Pistone, Kristina; Gallardo, Laura

2015-04-01

While aerosols produced by major cities are a significant component of anthropogenic climate forcing as well as an important factor in public health, many South American cities have not been a major focus of aerosol studies due in part to relatively few long-term observations in the region. Here we present a synthesis of the available data for the emerging megacity of Santiago, Chile. We report new results from a recent NASA AERONET (AErosol RObotic NETwork) site in the Santiago basin, combining these with previous AERONET observations in Santiago as well as with a new assessment of the 11-station air quality monitoring network currently administered by the Chilean Environment Ministry (MMA, Ministerio del Medio Ambiente) to assess changes in aerosol composition since 1997. While the average surface concentration of pollution components (specifically PM2.5 and PM10) has decreased, no significant change in total aerosol optical depth was observed. However, changes in aerosol size and composition are suggested by the proxy measurements. Previous studies have revealed limitations in purely satellite-based studies over Santiago due to biases from high surface reflection in the region, particularly in summer months (e.g. Escribano et al 2014). To overcome this difficulty and certain limitations in the air quality data, we next incorporate analysis of aerosol products from the Multi-angle Imaging SpectroRadiometer (MISR) instrument along with those from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, both on NASA's Terra satellite, to better quantify the high bias of MODIS. Thus incorporating these complementary datasets, we characterize the aerosol over Santiago over the period 1997 to 2014, including the evolution of aerosol properties over time and seasonal dependencies in the observed trends. References: Escribano et al (2014), "Satellite Retrievals of Aerosol Optical Depth over a Subtropical Urban Area: The Role of Stratification and Surface Reflectance," Aerosol and Air Quality Research, doi:10.4209/aaqr.2013.03.0082.

Ambient Air Quality Data Inventory

EPA Pesticide Factsheets

The Office of Air and Radiation's (OAR) Ambient Air Quality Data (Current) contains ambient air pollution data collected by EPA, other federal agencies, as well as state, local, and tribal air pollution control agencies. Its component data sets have been collected over the years from approximately 10,000 monitoring sites, of which approximately 5,000 are currently active. OAR's Office of Air Quality Planning and Standards (OAQPS) and other internal and external users, rely on this data to assess air quality, assist in Attainment/Non-Attainment designations, evaluate State Implementation Plans for Non-Attainment Areas, perform modeling for permit review analysis, and other air quality management functions. Air quality information is also used to prepare reports for Congress as mandated by the Clean Air Act. This data covers air quality data collected after 1980, when the Clean Air Act requirements for monitoring were significantly modified. Air quality data from the Agency's early years (1970s) remains available (see OAR PRIMARY DATA ASSET: Ambient Air Quality Data -- Historical), but because of technical and definitional differences the two data assets are not directly comparable. The Clean Air Act of 1970 provided initial authority for monitoring air quality for Conventional Air Pollutants (CAPs) for which EPA has promulgated National Ambient Air Quality Standards (NAAQS). Requirements for monitoring visibility-related parameters were added in 1977. Requiremen

78 FR 30770 - Approval and Promulgation of Air Quality Implementation Plans; Illinois; Air Quality Standards...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-23

... Promulgation of Air Quality Implementation Plans; Illinois; Air Quality Standards Revision AGENCY... Illinois state implementation plan (SIP) to reflect current National Ambient Air Quality Standards (NAAQS... Implementation Plan at 35 Illinois Administrative Code part 243, which updates National Ambient Air Quality...

75 FR 65572 - Approval and Promulgation of Air Quality Implementation Plans; Ohio; Ohio Ambient Air Quality...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-26

... Promulgation of Air Quality Implementation Plans; Ohio; Ohio Ambient Air Quality Standards AGENCY... Ohio Administrative Code (OAC) relating to the consolidation of Ohio's Ambient Air Quality Standards... apply to Ohio's SIP. Incorporating the air quality standards into Ohio's SIP helps assure that...

Multi-scale modeling of urban air pollution: development and application of a Street-in-Grid model (v1.0) by coupling MUNICH (v1.0) and Polair3D (v1.8.1)

NASA Astrophysics Data System (ADS)

Kim, Youngseob; Wu, You; Seigneur, Christian; Roustan, Yelva

2018-02-01

A new multi-scale model of urban air pollution is presented. This model combines a chemistry-transport model (CTM) that includes a comprehensive treatment of atmospheric chemistry and transport on spatial scales down to 1 km and a street-network model that describes the atmospheric concentrations of pollutants in an urban street network. The street-network model is the Model of Urban Network of Intersecting Canyons and Highways (MUNICH), which consists of two main components: a street-canyon component and a street-intersection component. MUNICH is coupled to the Polair3D CTM of the Polyphemus air quality modeling platform to constitute the Street-in-Grid (SinG) model. MUNICH is used to simulate the concentrations of the chemical species in the urban canopy, which is located in the lowest layer of Polair3D, and the simulation of pollutant concentrations above rooftops is performed with Polair3D. Interactions between MUNICH and Polair3D occur at roof level and depend on a vertical mass transfer coefficient that is a function of atmospheric turbulence. SinG is used to simulate the concentrations of nitrogen oxides (NOx) and ozone (O3) in a Paris suburb. Simulated concentrations are compared to NOx concentrations measured at two monitoring stations within a street canyon. SinG shows better performance than MUNICH for nitrogen dioxide (NO2) concentrations. However, both SinG and MUNICH underestimate NOx. For the case study considered, the model performance for NOx concentrations is not sensitive to using a complex chemistry model in MUNICH and the Leighton NO-NO2-O3 set of reactions is sufficient.

How Clean is your Local Air? Here's an app for that

NASA Astrophysics Data System (ADS)

Maskey, M.; Yang, E.; Christopher, S. A.; Keiser, K.; Nair, U. S.; Graves, S. J.

2011-12-01

Air quality is a vital element of our environment. Accurate and localized air quality information is critical for characterizing environmental impacts at the local and regional levels. Advances in location-aware handheld devices and air quality modeling have enabled a group of UAHuntsville scientists to develop a mobile app, LocalAQI, that informs users of current conditions and forecasts of up to twenty-four hours, of air quality indices. The air quality index is based on Community Multiscale Air Quality Modeling System (CMAQ). UAHuntsville scientists have used satellite remote sensing products as inputs to CMAQ, resulting in forecast guidance for particulate matter air quality. The CMAQ output is processed to compute a standardized air quality index. Currently, the air quality index is available for the eastern half of the United States. LocalAQI consists of two main views: air quality index view and map view. The air quality index view displays current air quality for the zip code of a location of interest. Air quality index value is translated into a color-coded advisory system. In addition, users are able to cycle through available hourly forecasts for a location. This location-aware app defaults to the current air quality of user's location. The map view displays color-coded air quality information for the eastern US with an ability to animate through the available forecasts. The app is developed using a cross-platform native application development tool, appcelerator; hence LocalAQI is available for iOS and Android-based phones and pads.

Assessment of SRS ambient air monitoring network

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

Abbott, K.; Jannik, T.

Three methodologies have been used to assess the effectiveness of the existing ambient air monitoring system in place at the Savannah River Site in Aiken, SC. Effectiveness was measured using two metrics that have been utilized in previous quantification of air-monitoring network performance; frequency of detection (a measurement of how frequently a minimum number of samplers within the network detect an event), and network intensity (a measurement of how consistent each sampler within the network is at detecting events). In addition to determining the effectiveness of the current system, the objective of performing this assessment was to determine what, ifmore » any, changes could make the system more effective. Methodologies included 1) the Waite method of determining sampler distribution, 2) the CAP88- PC annual dose model, and 3) a puff/plume transport model used to predict air concentrations at sampler locations. Data collected from air samplers at SRS in 2015 compared with predicted data resulting from the methodologies determined that the frequency of detection for the current system is 79.2% with sampler efficiencies ranging from 5% to 45%, and a mean network intensity of 21.5%. One of the air monitoring stations had an efficiency of less than 10%, and detected releases during just one sampling period of the entire year, adding little to the overall network intensity. By moving or removing this sampler, the mean network intensity increased to about 23%. Further work in increasing the network intensity and simulating accident scenarios to further test the ambient air system at SRS is planned« less

A Method for Correlation of Gravestone Weathering and Air Quality (SO2), West Amidlands, UK

NASA Astrophysics Data System (ADS)

Carlson, Michael John

From the beginning of the Industrial Revolution through the environmental revolution of the 1970s Britain suffered the effects of poor air quality primarily from particulate matter and acid in the form of NOx and SO x compounds. Air quality stations across the region recorded SO 2 beginning in the 1960s however the direct measurement of air quality prior to 1960 is lacking and only anecdotal notations exist. Proxy records including lung tissue samples, particulates in sediments cores, lake acidification studies and gravestone weathering have all been used to reconstruct the history of air quality. A 120-year record of acid deposition reconstructed from lead-lettered marble gravestone weathering combined with SO2 measurements from the air monitoring network across the West Midlands, UK region beginning in the 1960s form the framework for this study. The study seeks to create a spatial and temporal correlation between the gravestone weathering and measured SO 2. Successful correlation of the dataset from 1960s to the 2000s would allow a paleo-air quality record to be generated from the 120-year record of gravestone weathering. Decadal gravestone weathering rates can be estimated by non-linear regression analysis of stone loss at individual cemeteries. Gravestone weathering rates are interpolated across the region through Empirical Bayesian Kriging (EBK) methods performed through ArcGISRTM and through a land use based approach based on digitized maps of land use. Both methods of interpolation allow for the direct correlation of gravestone weathering and measured SO2 to be made. Decadal scale correlations of gravestone weathering rates and measured SO2 are very weak and non-existent for both EBK and the land use based approach. Decadal results combined together on a larger scale for each respective method display a better visual correlation. However, the relative clustering of data at lower SO2 concentrations and the lack of data at higher SO2 concentrations make the confidence in the correlations made too weak to rely on. The relation between surrounding land use and gravestone weathering rates was very strong for the 1960s-1980s with diminishing correlations approaching the 2000s. Gravestone weathering of cemeteries is highly influenced by the amount of industrial sources of pollution within a 7km radius. Reduced correlation of land use and weathering beyond the 1980s is solid grounds for the success of environmental regulation and control put in place across the UK during later parts of the 20th century.

Modeling study of biomass burning plumes and their impact on urban air quality; a case study of Santiago de Chile

NASA Astrophysics Data System (ADS)

Cuchiara, G. C.; Rappenglück, B.; Rubio, M. A.; Lissi, E.; Gramsch, E.; Garreaud, R. D.

2017-10-01

On January 4, 2014, during the summer period in South America, an intense forest and dry pasture wildfire occurred nearby the city of Santiago de Chile. On that day the biomass-burning plume was transported by low-intensity winds towards the metropolitan area of Santiago and impacted the concentration of pollutants in this region. In this study, the Weather Research and Forecasting model coupled with Chemistry (WRF/Chem) is implemented to investigate the biomass-burning plume associated with these wildfires nearby Santiago, which impacted the ground-level ozone concentration and exacerbated Santiago's air quality. Meteorological variables simulated by WRF/Chem are compared against surface and radiosonde observations, and the results show that the model reproduces fairly well the observed wind speed, wind direction air temperature and relative humidity for the case studied. Based on an analysis of the transport of an inert tracer released over the locations, and at the time the wildfires were captured by the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS), the model reproduced reasonably well the transport of biomass burning plume towards the city of Santiago de Chile within a time delay of two hours as observed in ceilometer data. A six day air quality simulation was performed: the first three days were used to validate the anthropogenic and biogenic emissions, and the last three days (during and after the wildfire event) to analyze the performance of WRF/Chem plume-rise model within FINNv1 fire emission estimations. The model presented a satisfactory performance on the first days of the simulation when contrasted against data from the well-established air quality network over the city of Santiago de Chile. These days represent the urban air quality base case for Santiago de Chile unimpacted by fire emissions. However, for the last three simulation days, which were impacted by the fire emissions, the statistical indices showed a decrease in the model performance. While the model showed a satisfactory evidence that wildfires plumes that originated in the vicinity of Santiago de Chile were transported towards the urban area and impacted the air quality, the model still underpredicted some pollutants substantially, likely due to misrepresentation of fire emission sources during those days. Potential uncertainties may include to the land use/land cover classifications and its characteristics, such as type and density of vegetation assigned to the region, where the fire spots are detected. The variability of the ecosystem type during the fire event might also play a role.

Quality Control of The Norwegian Uv Monitoring Network.

NASA Astrophysics Data System (ADS)

Johnsen, B.; Mikkelborg, O.; Dahlback, A.; Høiskar, B. A.; Kylling, A.; Edvardsen, K.; Olseth, J. A.; Kjeldstad, B.; Ørbæk, J. B.

A Norwegian UV-monitoring network of GUV multiband radiometers has been operating at locations between 59°N to 79°N since 1995-96. The purpose of the network is to obtain data of high scientific quality, to be used in further assessments related to health- and environmental issues. Maintenance of measurement quality is given priority. Spectral response functions, crucial for calibrations, have been obtained for each instrument. Calibrations are traceable to the Nordic intercomparison of UV radiometers held in Sweden in June 2000. Instruments are inspected daily or weekly. Once a year the instruments are compared to travelling standards operating side by side to the local network radiometers. This enables determination of the longterm drift in instrument responses. For the six years period of operation, the steadiest instrument performed stable within +/-3%, whereas the least steady had a response drop by 23%. Comparisons with a true cosine performing spectroradiometer demonstrate close agreement (+/- 2%) for solar zenith angles less than 80°. Good cosine performance, high spectral sensitivity and weatherproof design demonstrate that the GUV radiometers are particularly suitable for UV monitoring at high latitudes. Complete records of corrected daily CIE-effective doses and online measurements are presented on http://uvnett.nrpa.no/. Gaps in measurement series have been corrected for with a clear sky radiative transfer model and hourly UV sky transmittances estimated from pyranometer data. Measurement data and information about the monitoring network may be found by visiting websites at respectively NRPA, NILU and The University of Oslo; http://www.nrpa.no, http://www.nilu.no/uv, http://www.fys.uio.no/plasma/ozone/. At this stage the quality of the network has reached a satisfactory level and it is possible to move on using UV data in further assessments. Trend analyses and UV forecasting are topics for future work. The network is supported by the ministries of Health and Environment and is administered by The Norwegian Radiation Protection Authority and The Norwegian Pollution Control Authority, the latter through The Norwegian Institute for Air Research.

REMOTE SENSING MEASUREMENTS OF AEROSOL OPTICAL THICKNESS AND CORRELATION WITH IN-SITU AIR QUALITY PARAMETERS DURING A SMOKE HAZE EPISODE IN SOUTHEAST ASIA

NASA Astrophysics Data System (ADS)

Chew, B.; Salinas Cortijo, S. V.; Liew, S.

2009-12-01

Transboundary smoke haze due to biomass burning is a major environmental problem in Southeast Asia which has not only affected air quality in the source region, but also in the surrounding countries. Air quality monitoring stations and meteorological stations can provide valuable information on the concentrations of criteria pollutants such as sulphur dioxide, nitrogen oxide, carbon monoxide, ozone and particulate mass (PM10) as well as health advisory to the general public during the haze episodes. Characteristics of aerosol particles in the smoke haze such as the aerosol optical thickness (AOT), aerosol size distribution and Angstrom exponent are also measured or retrieved by sun-tracking photometers, such as those deployed in the world-wide AErosol RObotic NETwork (AERONET). However, due to the limited spatial coverage by the air quality monitoring stations and AERONET sites, it is difficult to study and monitor the spatial and temporal variability of the smoke haze during a biomass burning episode, especially in areas without ground-based instrumentation. As such, we combine the standard in-situ measurements of PM10 by air quality monitoring stations with the remote sensing imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's Terra and Aqua satellites. The columnar AOT is first derived from the MODIS images for regions where PM10 measurements are available. Empirical correlations between AOT and PM10 measurements are then established for 50 sites in both Malaysia and Singapore during the smoke haze episode in 2006. When available, vertical feature information from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) is used to examine the validity of the correlations. Aloft transport of aerosols, which can weaken the correlations between AOT and PM10 measurements, is also identified by CALIPSO and taken into consideration for the analysis. With this integrated approach, we hope to enhance and complement current capabilities in monitoring air quality during the haze episodes in Southeast Asia. This study was completed as a preliminary analysis of the biomass burning situation in Southeast Asia under the Seven SouthEast Asian Studies (7 SEAS) Mission. Through collaborations with scientific partners in Taiwan and various Southeast Asian countries such as Indonesia, Malaysia, Philippines, Singapore, Thailand and Vietnam, 7 SEAS is jointly initiated by NASA’s Radiation Science, Tropospheric Chemistry, Air Quality and Oceanography programmes as well as the Office of Naval Research (ONR), the Office of Naval Research - Global (ONRG) and the US State Department in an effort to investigate the complex interactions between aerosols (anthropogenic or natural) and meteorological systems, especially with clouds, and their impacts on air quality in the region. 7 SEAS is a multi-disciplinary regional science programme which operates with the integrative support of in-situ measurements, remote sensing and scientific modeling.

Aerosol indirect effect on the grid-scale clouds in the two-way coupled WRF–CMAQ: model description, development, evaluation and regional analysis

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

Yu, S.; Mathur, R.; Pleim, J.

This study implemented first, second and glaciation aerosol indirect effects (AIE) on resolved clouds in the two-way coupled Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) modeling system by including parameterizations for both cloud drop and ice number concentrations on the basis of CMAQ-predicted aerosol distributions and WRF meteorological conditions. The performance of the newly developed WRF–CMAQ model, with alternate Community Atmospheric Model (CAM) and Rapid Radiative Transfer Model for GCMs (RRTMG) radiation schemes, was evaluated with observations from the Clouds and the See http://ceres.larc.nasa.gov/. Earth's Radiant Energy System (CERES) satellite and surface monitoring networks (AQS, IMPROVE, CASTNET, STN,more » and PRISM) over the continental US (CONUS) (12 km resolution) and eastern Texas (4 km resolution) during August and September of 2006. The results at the Air Quality System (AQS) surface sites show that in August, the normalized mean bias (NMB) values for PM 2.5 over the eastern US (EUS) and the western US (WUS) are 5.3% (-0.1%) and 0.4% (-5.2%) for WRF–CMAQ/CAM (WRF–CMAQ/RRTMG), respectively. The evaluation of PM 2.5 chemical composition reveals that in August, WRF–CMAQ/CAM (WRF–CMAQ/RRTMG) consistently underestimated the observed SO 4 2- by -23.0% (-27.7%), -12.5% (-18.9%) and -7.9% (-14.8%) over the EUS at the Clean Air Status Trends Network (CASTNET), Interagency Monitoring of Protected Visual Environments (IMPROVE) and Speciated Trends Network (STN) sites, respectively. Both configurations (WRF–CMAQ/CAM, WRF–CMAQ/RRTMG) overestimated the observed mean organic carbon (OC), elemental carbon (EC) and and total carbon (TC) concentrations over the EUS in August at the IMPROVE sites. Both configurations generally underestimated the cloud field (shortwave cloud forcing, SWCF) over the CONUS in August due to the fact that the AIE on the subgrid convective clouds was not considered when the model simulations were run at the 12 km resolution. This is in agreement with the fact that both configurations captured SWCF and longwave cloud forcing (LWCF) very well for the 4 km simulation over eastern Texas, when all clouds were resolved by the finer resolution domain. The simulations of WRF–CMAQ/CAM and WRF–CMAQ/RRTMG show dramatic improvements for SWCF, LWCF, cloud optical depth (COD), cloud fractions and precipitation over the ocean relative to those of WRF default cases in August. The model performance in September is similar to that in August, except for a greater overestimation of PM 2.5 due to the overestimations of SO 4 2-, NH 4 +, NO 3 -, and TC over the EUS, less underestimation of clouds (SWCF) over the land areas due to the lower SWCF values, and fewer convective clouds in September. Finally, this work shows that inclusion of indirect aerosol effect treatments in WRF–CMAQ represents a significant advancement and milestone in air quality modeling and the development of integrated emissions control strategies for air quality management and climate change mitigation.« less

Air Quality uFIND: User-oriented Tool Set for Air Quality Data Discovery and Access

NASA Astrophysics Data System (ADS)

Hoijarvi, K.; Robinson, E. M.; Husar, R. B.; Falke, S. R.; Schultz, M. G.; Keating, T. J.

2012-12-01

Historically, there have been major impediments to seamless and effective data usage encountered by both data providers and users. Over the last five years, the international Air Quality (AQ) Community has worked through forums such as the Group on Earth Observations AQ Community of Practice, the ESIP AQ Working Group, and the Task Force on Hemispheric Transport of Air Pollution to converge on data format standards (e.g., netCDF), data access standards (e.g., Open Geospatial Consortium Web Coverage Services), metadata standards (e.g., ISO 19115), as well as other conventions (e.g., CF Naming Convention) in order to build an Air Quality Data Network. The centerpiece of the AQ Data Network is the web service-based tool set: user-oriented Filtering and Identification of Networked Data. The purpose of uFIND is to provide rich and powerful facilities for the user to: a) discover and choose a desired dataset by navigation through the multi-dimensional metadata space using faceted search, b) seamlessly access and browse datasets, and c) use uFINDs facilities as a web service for mashups with other AQ applications and portals. In a user-centric information system such as uFIND, the user experience is improved by metadata that includes the general fields for discovery as well as community-specific metadata to narrow the search beyond space, time and generic keyword searches. However, even with the community-specific additions, the ISO 19115 records were formed in compliance with the standard, so that other standards-based search interface could leverage this additional information. To identify the fields necessary for metadata discovery we started with the ISO 19115 Core Metadata fields and fields that were needed for a Catalog Service for the Web (CSW) Record. This fulfilled two goals - one to create valid ISO 19115 records and the other to be able to retrieve the records through a Catalog Service for the Web query. Beyond the required set of fields, the AQ Community added additional fields using a combination of keywords and ISO 19115 fields. These extensions allow discovery by measurement platform or observed phenomena. Beyond discovery metadata, the AQ records include service identification objects that allow standards-based clients, such as some brokers, to access the data found via OGC WCS or WMS data access protocols. uFIND, is one such smart client, this combination of discovery and access metadata allows the user to preview each registered dataset through spatial and temporal views; observe the data access and usage pattern and also find links to dataset-specific metadata directly in uFIND. The AQ data providers also benefit from this architecture since their data products are easier to find and re-use, enhancing the relevance and importance of their products. Finally, the earth science community at large benefits from the Service Oriented Architecture of uFIND, since it is a service itself and allows service-based interfacing with providers and users of the metadata, allowing uFIND facets to be further refined for a particular AQ application or completely repurposed for other Earth Science domains that use the same set of data access and metadata standards.

New Zealand traffic and local air quality.

PubMed

Irving, Paul; Moncrieff, Ian

2004-12-01

Since 1996 the New Zealand Ministry of Transport (MOT) has been investigating the effects of road transport on local air quality. The outcome has been the government's Vehicle Fleet Emissions Control Strategy (VFECS). This is a programme of measures designed to assist with the improvement in local air quality, and especially in the appropriate management of transport sector emissions. Key to the VFECS has been the development of tools to assess and predict the contribution of vehicle emissions to local air pollution, in a given urban situation. Determining how vehicles behave as an emissions source, and more importantly, how the combined traffic flows contribute to the total emissions within a given airshed location was an important element of the programme. The actual emissions output of a vehicle is more than that determined by a certified emission standard, at the point of manufacture. It is the engine technology's general performance capability, in conjunction with the local driving conditions, that determines its actual emissions output. As vehicles are a mobile emissions source, to understand the effect of vehicle technology, it is necessary to work with the average fleet performance, or "fleet-weighted average emissions rate". This is the unit measure of performance of the general traffic flow that could be passing through a given road corridor or network, as an average, over time. The flow composition can be representative of the national fleet population, but also may feature particular vehicle types in a given locality, thereby have a different emissions 'signature'. A summary of the range of work that has been completed as part of the VFECS programme is provided. The NZ Vehicle Fleet Emissions Model and the derived data set available in the NZ Traffic Emission Rates provide a significant step forward in the consistent analysis of practical, sustainable vehicle emissions policy and air-quality management in New Zealand.

Insights into Tropospheric Ozone from the INTEX Ozonesonde Network Study (IONS)

NASA Technical Reports Server (NTRS)

Thompson, Anne M.; Witte, J. C.; Kucsera, T. L.; Merrill, J. T.; Morris, G.; Newchurch, M. J.; Oltmans, S. J.; Schmidlin, F. J.; Tarasick, D. J.

2004-01-01

Ozone profile data from soundings integrate models, aircraft and other ground-based measurements for better interpretation of atmospheric chemistry and dynamics. A well-designed network of ozonesonde stations, with consistent sampling, can answer questions not possible with short campaigns or current satellite technology. The SHADOZ (Southern Hemisphere Additional Ozonesondes) project, for example, has led to these findings about tropical ozone: definition of the zonal tropospheric wave-one pattern in equatorial ozone, characterization of the "Atlantic ozone paradox" and establishment of a link between tropical Atlantic and Indian Ocean pollution. Building on the SHADOZ concept, a short-term ozone network was formed in July-August 2004 to coordinate ozonesonde launches during the ICARTT/INTEX/NEAQS (International Consortium on Atmospheric Research on Transport and Transformation)/Intercontinental Transport Experiment/New England Air Quality Study. In IONS (INTEX Ozonesonde Network Study), more than 250 soundings, with daily frequency at half the sites, were launched from eleven North American stations and an oceanographic ship in the Gulf of Maine. Although the goal was to examine pollution influences under stable high-pressure systems and transport associated with "warm conveyor belt" flows, the INTEX study region was dominated by a series of weak frontal system that mixed aged pollution with stratospheric ozone in the middle troposphere. Deconvoluting ozone sources provides new insights into ozone in the transition between mid-latitude and polar air.

Wireless data over RAM's Mobitex network

NASA Astrophysics Data System (ADS)

Khan, M. Mobeen

1995-12-01

Mobitex is a mobile data technology standard created by Eritel, now a wholly owned subsidiary of Ericsson, that has been in existence for about a decade. Originally designed as a low speed (1.2 kbps) data system with a voice dispatch overlay, it was significantly enhanced in 1990 for use in North America and the UK. The enhanced system is a data-only system using cellular architecture and multi-channel frequency reuse, store-and-forward capability, and an 8 kbps over-the-air data rate. The mission of RAM Mobile Data USA Limited Partnership ('RAM') is to provide high quality, cost efficient, wireless data communications solutions in its targeted market segments. RAM's Mobitex network is currently one of the two networks providing two way wireless data services nationwide using a long distance service provider of the customer's choice.

40 CFR 81.77 - Puerto Rico Air Quality Control Region.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 17 2011-07-01 2011-07-01 false Puerto Rico Air Quality Control Region... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.77 Puerto Rico Air Quality Control Region. The Puerto Rico Air Quality Control Region...

40 CFR 81.76 - State of Hawaii Air Quality Control Region.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 17 2011-07-01 2011-07-01 false State of Hawaii Air Quality Control... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.76 State of Hawaii Air Quality Control Region. The State of Hawaii Air Quality...

40 CFR 81.77 - Puerto Rico Air Quality Control Region.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 18 2012-07-01 2012-07-01 false Puerto Rico Air Quality Control Region... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.77 Puerto Rico Air Quality Control Region. The Puerto Rico Air Quality Control Region...

40 CFR 81.76 - State of Hawaii Air Quality Control Region.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 18 2012-07-01 2012-07-01 false State of Hawaii Air Quality Control... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.76 State of Hawaii Air Quality Control Region. The State of Hawaii Air Quality...

40 CFR 81.76 - State of Hawaii Air Quality Control Region.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 18 2013-07-01 2013-07-01 false State of Hawaii Air Quality Control... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.76 State of Hawaii Air Quality Control Region. The State of Hawaii Air Quality...

40 CFR 81.77 - Puerto Rico Air Quality Control Region.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 18 2013-07-01 2013-07-01 false Puerto Rico Air Quality Control Region... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.77 Puerto Rico Air Quality Control Region. The Puerto Rico Air Quality Control Region...

78 FR 19990 - Approval and Promulgation of Air Quality Implementation Plans; Ohio; Ohio Ambient Air Quality...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-03

... Promulgation of Air Quality Implementation Plans; Ohio; Ohio Ambient Air Quality Standards; Correction AGENCY... approved revisions to Ohio regulations that consolidated air quality standards in a new chapter of rules... State's air quality standards into Ohio Administrative Code (OAC) 3745-25 and modifying an assortment of...

77 FR 12482 - Approval and Promulgation of Air Quality Implementation Plans; Indiana; Lead Ambient Air Quality...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-01

... Promulgation of Air Quality Implementation Plans; Indiana; Lead Ambient Air Quality Standards AGENCY... incorporates the National Ambient Air Quality Standards (NAAQS) for Pb promulgated by EPA in 2008. DATES: This... FR 66964) and codified at 40 CFR 50.16, ``National primary and secondary ambient air quality...

40 CFR 81.77 - Puerto Rico Air Quality Control Region.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Puerto Rico Air Quality Control Region... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.77 Puerto Rico Air Quality Control Region. The Puerto Rico Air Quality Control Region...

40 CFR 81.76 - State of Hawaii Air Quality Control Region.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 17 2010-07-01 2010-07-01 false State of Hawaii Air Quality Control... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.76 State of Hawaii Air Quality Control Region. The State of Hawaii Air Quality...

Detecting volcanic sulfur dioxide plumes in the Northern Hemisphere using the Brewer spectrophotometers, other networks, and satellite observations

NASA Astrophysics Data System (ADS)

Zerefos, Christos S.; Eleftheratos, Kostas; Kapsomenakis, John; Solomos, Stavros; Inness, Antje; Balis, Dimitris; Redondas, Alberto; Eskes, Henk; Allaart, Marc; Amiridis, Vassilis; Dahlback, Arne; De Bock, Veerle; Diémoz, Henri; Engelmann, Ronny; Eriksen, Paul; Fioletov, Vitali; Gröbner, Julian; Heikkilä, Anu; Petropavlovskikh, Irina; Jarosławski, Janusz; Josefsson, Weine; Karppinen, Tomi; Köhler, Ulf; Meleti, Charoula; Repapis, Christos; Rimmer, John; Savinykh, Vladimir; Shirotov, Vadim; Siani, Anna Maria; Smedley, Andrew R. D.; Stanek, Martin; Stübi, René

2017-01-01

This study examines the adequacy of the existing Brewer network to supplement other networks from the ground and space to detect SO2 plumes of volcanic origin. It was found that large volcanic eruptions of the last decade in the Northern Hemisphere have a positive columnar SO2 signal seen by the Brewer instruments located under the plume. It is shown that a few days after the eruption the Brewer instrument is capable of detecting significant columnar SO2 increases, exceeding on average 2 DU relative to an unperturbed pre-volcanic 10-day baseline, with a mean close to 0 and σ = 0.46, as calculated from the 32 Brewer stations under study. Intercomparisons with independent measurements from the ground and space as well as theoretical calculations corroborate the capability of the Brewer network to detect volcanic plumes. For instance, the comparison with OMI (Ozone Monitoring Instrument) and GOME-2 (Global Ozone Monitoring Experiment-2) SO2 space-borne retrievals shows statistically significant agreement between the Brewer network data and the collocated satellite overpasses in the case of the Kasatochi eruption. Unfortunately, due to sparsity of satellite data, the significant positive departures seen in the Brewer and other ground networks following the Eyjafjallajökull, Bárðarbunga and Nabro eruptions could not be statistically confirmed by the data from satellite overpasses. A model exercise from the MACC (Monitoring Atmospheric Composition and Climate) project shows that the large increases in SO2 over Europe following the Bárðarbunga eruption in Iceland were not caused by local pollution sources or ship emissions but were clearly linked to the volcanic eruption. Sulfur dioxide positive departures in Europe following Bárðarbunga could be traced by other networks from the free troposphere down to the surface (AirBase (European air quality database) and EARLINET (European Aerosol Research Lidar Network)). We propose that by combining Brewer data with that from other networks and satellites, a useful tool aided by trajectory analyses and modelling could be created which can also be used to forecast high SO2 values both at ground level and in air flight corridors following future eruptions.

Global thermal analysis of air-air cooled motor based on thermal network

NASA Astrophysics Data System (ADS)

Hu, Tian; Leng, Xue; Shen, Li; Liu, Haidong

2018-02-01

The air-air cooled motors with high efficiency, large starting torque, strong overload capacity, low noise, small vibration and other characteristics, are widely used in different department of national industry, but its cooling structure is complex, it requires the motor thermal management technology should be high. The thermal network method is a common method to calculate the temperature field of the motor, it has the advantages of small computation time and short time consuming, it can save a lot of time in the initial design phase of the motor. The domain analysis of air-air cooled motor and its cooler was based on thermal network method, the combined thermal network model was based, the main components of motor internal and external cooler temperature were calculated and analyzed, and the temperature rise test results were compared to verify the correctness of the combined thermal network model, the calculation method can satisfy the need of engineering design, and provide a reference for the initial and optimum design of the motor.

Constructing a generalized network design model to study air distribution in ventilation networks in subway with a single-track tunnel

NASA Astrophysics Data System (ADS)

Lugin, IV

2018-03-01

In focus are the features of construction of the generalized design model for the network method to study air distribution in ventilation system in subway with the single-track tunnel. The generalizations, assumptions and simplifications included in the model are specified. The air distribution is calculated with regard to the influence of topology and air resistances of the ventilation network sections. The author studies two variants of the subway line: half-open and closed with dead end on the both sides. It is found that the total air exchange at a subway station depends on the station location within the line. The operating mode of fans remains unaltered in this case. The article shows that elimination of air leakage in the station ventilation room allows an increase in the air flow rate by 7–8% at the same energy consumption by fans. The influence of the stop of a train in the tunnel on the air distribution is illustrated.

Regional characteristics of the relationship between columnar AOD and surface PM2.5: Application of lidar aerosol extinction profiles over Baltimore-Washington Corridor during DISCOVER-AQ

NASA Astrophysics Data System (ADS)

Chu, D. Allen; Ferrare, Richard; Szykman, James; Lewis, Jasper; Scarino, Amy; Hains, Jennifer; Burton, Sharon; Chen, Gao; Tsai, Tzuchin; Hostetler, Chris; Hair, Johnathan; Holben, Brent; Crawford, James

2015-01-01

The first field campaign of DISCOVER-AQ (Deriving Information on Surface conditions from COlumn and VERtically resolved observations relevant to Air Quality) took place in July 2011 over Baltimore-Washington Corridor (BWC). A suite of airborne remote sensing and in-situ sensors was deployed along with ground networks for mapping vertical and horizontal distribution of aerosols. Previous researches were based on a single lidar station because of the lack of regional coverage. This study uses the unique airborne HSRL (High Spectral Resolution Lidar) data to baseline PM2.5 (particulate matter of aerodynamic diameter less than 2.5 μm) estimates and applies to regional air quality with satellite AOD (Aerosol Optical Depth) retrievals over BWC (∼6500 km2). The linear approximation takes into account aerosols aloft above AML (Aerosol Mixing Layer) by normalizing AOD with haze layer height (i.e., AOD/HLH). The estimated PM2.5 mass concentrations by HSRL AOD/HLH are shown within 2 RMSE (Root Mean Square Error ∼9.6 μg/m3) with correlation ∼0.88 with the observed over BWC. Similar statistics are shown when applying HLH data from a single location over the distance of 100 km. In other words, a single lidar is feasible to cover the range of 100 km with expected uncertainties. The employment of MPLNET-AERONET (MicroPulse Lidar NETwork - AErosol RObotic NETwork) measurements at NASA GSFC produces similar statistics of PM2.5 estimates as those derived by HSRL. The synergy of active and passive remote sensing aerosol measurements provides the foundation for satellite application of air quality on a daily basis. For the optimal range of 10 km, the MODIS-estimated PM2.5 values are found satisfactory at 27 (out of 36) sunphotometer locations with mean RMSE of 1.6-3.3 μg/m3 relative to PM2.5 estimated by sunphotometers. The remaining 6 of 8 marginal sites are found in the coastal zone, for which associated large RMSE values ∼4.5-7.8 μg/m3 are most likely due to overestimated AOD because of water-contaminated pixels.

Modelling the emissions from ships in ports and their impact on air quality in the metropolitan area of Hamburg

NASA Astrophysics Data System (ADS)

Ramacher, Martin; Karl, Matthias; Aulinger, Armin; Bieser, Johannes; Matthias, Volker; Quante, Markus

2016-04-01

Exhaust emissions from shipping contribute significantly to the anthropogenic burden of air pollutants such as nitrogen oxides (NOX) and particulate matter (PM). Ships emit not only when sailing on open sea, but also when approaching harbors, during port manoeuvers and at berth to produce electricity and heat for the ship's operations. This affects the population of harbor cities because long-term exposure to PM and NOX has significant effects on human health. The European Union has therefore has set air quality standards for air pollutants. Many port cities have problems meeting these standards. The port of Hamburg with around 10.000 ship calls per year is Germany's largest seaport and Europe's second largest container port. Air quality standard reporting in Hamburg has revealed problems in meeting limits for NO2 and PM10. The amount and contribution of port related ship emissions (38% for NOx and 17% for PM10) to the overall emissions in the metropolitan area in 2005 [BSU Hamburg (2012): Luftreinhalteplan für Hamburg. 1. Fortschreibung 2012] has been modelled with a bottom up approach by using statistical data of ship activities in the harbor, technical vessel information and specific emission algorithms [GAUSS (2008): Quantifizierung von gasförmigen Emissionen durch Maschinenanlagen der Seeschiffart an der deutschen Küste]. However, knowledge about the spatial distribution of the harbor ship emissions over the city area is crucial when it comes to air quality standards and policy decisions to protect human health. Hence, this model study examines the spatial distribution of harbor ship emissions (NOX, PM10) and their deposition in the Hamburg metropolitan area. The transport and chemical transformation of atmospheric pollutants is calculated with the well-established chemistry transport model TAPM (The Air Pollution Model). TAPM is a three-dimensional coupled prognostic meteorological and air pollution model with a condensed chemistry scheme including photochemistry. The model was applied to the Hamburg metropolitan area with a setup of 30 x 30 grid cells of 1 km² each and 30 vertical grid levels from 10 to 8,000 m, for a time period of one year. Emission inventories for traffic, industry, households and ships in 2013 were generated. To investigate the dispersion of ship emissions to air pollution two different model runs for 2013 were performed; one model run including land-based emissions and the ship emissions and a model run just including the land-based emissions. The modelling results were evaluated with air quality data from the monitoring station network of Hamburg (luft.hamburg.de). The results are presented in form of spatial distribution maps for the Hamburg metropolitan area highlighting the pollutants (PM and NOX) originating from harbor residential ships.

40 CFR 81.16 - Metropolitan Denver Intrastate Air Quality Control Region.

Code of Federal Regulations, 2014 CFR

2014-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.16 Metropolitan Denver Intrastate Air Quality Control Region. The Metropolitan Denver Intrastate Air Quality Control Region (Colorado) consists of the territorial area...

40 CFR 81.62 - Northeast Mississippi Intrastate Air Quality Control Region.

Code of Federal Regulations, 2012 CFR

2012-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.62 Northeast Mississippi Intrastate Air Quality Control Region. The Alabama-Mississippi-Tennessee Interstate Air Quality Control Region has been renamed the Northeast...