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Special Issue "Air Pollution Modeling"

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (15 August 2014)

Special Issue Editors

Guest Editor
Dr. Michael S. Breen

National Exposure Research Laboratory Office of Research and Development US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Website | E-Mail
Interests: air pollution exposure modeling; building air exchange rate modeling; integrating personal sensor technologies with computational exposure models; applying exposure modeling tools for health studies and citizen scientists
Guest Editor
Dr. Vlad Isakov

Atmospheric Modeling and Analysis Division National Exposure Research Laboratory US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Website | E-Mail
Interests: development; testing through applications; innovative air quality modeling approaches in support of public health and epidemiological research studies; developing improved techniques to estimate concentrations near major roadways and testing these methods with state of art measurements; developing techniques to estimate uncertainty

Special Issue Information

Dear Colleagues,

A major challenge in environmental epidemiology research is improving exposure assessments. This is especially important for epidemiologic studies focusing on exposure to air pollutants. Due to cost and participant burden of personal air pollution measurements, air pollution health studies often estimate exposures based on measurements from outdoor air monitors at central-site locations. This can lead to substantial exposure misclassifications since concentrations inside buildings can significantly differ from central-site concentrations, and people spend the majority of their time indoors. To improve exposure assessments, air quality and exposure models can be used. Air quality models can improve the spatial and temporal resolution of outdoor air pollutant concentrations. Air pollution exposure models, which consider human time-location data and indoor/outdoor relationships of air pollutants, can better account for human mobility and indoor air pollutant concentrations. Therefore, linking of air quality models with exposure models can improve exposure assessments for populations of interest.

The goal of this Special Issue on air pollution modeling is to highlight novel research and analyses on development and evaluation of air quality and exposure models, with an emphasis on applications for health studies. For air quality models, particular interests include innovative approaches for the emission, deposition, and transport aspects of air pollutants. This Special Issue represents an effort to capture current developments in the field and provide a forum for cutting edge contributions to the literature. Research papers, analytical reviews, case studies, conceptual frameworks, and policy-relevant articles are encouraged.

Dr. Michael Breen
Dr. Vlad Isakov
Guest Editors

Submission

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs).


Published Papers (12 papers)

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Research

Open AccessArticle Future Premature Mortality Due to O3, Secondary Inorganic Aerosols and Primary PM in Europe — Sensitivity to Changes in Climate, Anthropogenic Emissions, Population and Building Stock
Int. J. Environ. Res. Public Health 2015, 12(3), 2837-2869; doi:10.3390/ijerph120302837
Received: 10 November 2014 / Revised: 9 February 2015 / Accepted: 25 February 2015 / Published: 4 March 2015
Cited by 4 | PDF Full-text (3476 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Air pollution is an important environmental factor associated with health impacts in Europe and considerable resources are used to reduce exposure to air pollution through emission reductions. These reductions will have non-linear effects on exposure due, e.g., to interactions between climate and atmospheric
[...] Read more.
Air pollution is an important environmental factor associated with health impacts in Europe and considerable resources are used to reduce exposure to air pollution through emission reductions. These reductions will have non-linear effects on exposure due, e.g., to interactions between climate and atmospheric chemistry. By using an integrated assessment model, we quantify the effect of changes in climate, emissions and population demography on exposure and health impacts in Europe. The sensitivity to the changes is assessed by investigating the differences between the decades 2000–2009, 2050–2059 and 2080–2089. We focus on the number of premature deaths related to atmospheric ozone, Secondary Inorganic Aerosols and primary PM. For the Nordic region we furthermore include a projection on how population exposure might develop due to changes in building stock with increased energy efficiency. Reductions in emissions cause a large significant decrease in mortality, while climate effects on chemistry and emissions only affects premature mortality by a few percent. Changes in population demography lead to a larger relative increase in chronic mortality than the relative increase in population. Finally, the projected changes in building stock and infiltration rates in the Nordic indicate that this factor may be very important for assessments of population exposure in the future. Full article
(This article belongs to the Special Issue Air Pollution Modeling)
Open AccessArticle Preliminary Evaluation of a Regional Atmospheric Chemical Data Assimilation System for Environmental Surveillance
Int. J. Environ. Res. Public Health 2014, 11(12), 12795-12816; doi:10.3390/ijerph111212795
Received: 20 September 2014 / Revised: 2 December 2014 / Accepted: 3 December 2014 / Published: 11 December 2014
Cited by 1 | PDF Full-text (1385 KB) | HTML Full-text | XML Full-text
Abstract
We report the progress of an ongoing effort by the Air Resources Laboratory, NOAA to build a prototype regional Chemical Analysis System (ARLCAS). The ARLCAS focuses on providing long-term analysis of the three dimensional (3D) air-pollutant concentration fields over the continental U.S. It
[...] Read more.
We report the progress of an ongoing effort by the Air Resources Laboratory, NOAA to build a prototype regional Chemical Analysis System (ARLCAS). The ARLCAS focuses on providing long-term analysis of the three dimensional (3D) air-pollutant concentration fields over the continental U.S. It leverages expertise from the NASA Earth Science Division-sponsored Air Quality Applied Science Team (AQAST) for the state-of-science knowledge in atmospheric and data assimilation sciences. The ARLCAS complies with national operational center requirement protocols and aims to have the modeling system to be maintained by a national center. Meteorology and chemistry observations consist of land-, air- and space-based observed and quality-assured data. We develop modularized testing to investigate the efficacies of the various components of the ARLCAS. The sensitivity testing of data assimilation schemes showed that with the increment of additional observational data sets, the accuracy of the analysis chemical fields also increased incrementally in varying margins. The benefit is especially noted for additional data sets based on a different platform and/or a different retrieval algorithm. We also described a plan to apply the analysis chemical fields in environmental surveillance at the Centers for Disease Control and Prevention. Full article
(This article belongs to the Special Issue Air Pollution Modeling)
Open AccessArticle Creating Locally-Resolved Mobile-Source Emissions Inputs for Air Quality Modeling in Support of an Exposure Study in Detroit, Michigan, USA
Int. J. Environ. Res. Public Health 2014, 11(12), 12739-12766; doi:10.3390/ijerph111212739
Received: 15 August 2014 / Revised: 27 November 2014 / Accepted: 3 December 2014 / Published: 9 December 2014
Cited by 3 | PDF Full-text (6313 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This work describes a methodology for modeling the impact of traffic-generated air pollutants in an urban area. This methodology presented here utilizes road network geometry, traffic volume, temporal allocation factors, fleet mixes, and emission factors to provide critical modeling inputs. These inputs, assembled
[...] Read more.
This work describes a methodology for modeling the impact of traffic-generated air pollutants in an urban area. This methodology presented here utilizes road network geometry, traffic volume, temporal allocation factors, fleet mixes, and emission factors to provide critical modeling inputs. These inputs, assembled from a variety of sources, are combined with meteorological inputs to generate link-based emissions for use in dispersion modeling to estimate pollutant concentration levels due to traffic. A case study implementing this methodology for a large health study is presented, including a sensitivity analysis of the modeling results reinforcing the importance of model inputs and identify those having greater relative impact, such as fleet mix. In addition, an example use of local measurements of fleet activity to supplement model inputs is described, and its impacts to the model outputs are discussed. We conclude that with detailed model inputs supported by local traffic measurements and meteorology, it is possible to capture the spatial and temporal patterns needed to accurately estimate exposure from traffic-related pollutants. Full article
(This article belongs to the Special Issue Air Pollution Modeling)
Open AccessArticle Health Risk Assessment of Inhalable Particulate Matter in Beijing Based on the Thermal Environment
Int. J. Environ. Res. Public Health 2014, 11(12), 12368-12388; doi:10.3390/ijerph111212368
Received: 5 July 2014 / Revised: 18 November 2014 / Accepted: 19 November 2014 / Published: 28 November 2014
Cited by 5 | PDF Full-text (979 KB) | HTML Full-text | XML Full-text
Abstract
Inhalable particulate matter (PM10) is a primary air pollutant closely related to public health, and an especially serious problem in urban areas. The urban heat island (UHI) effect has made the urban PM10 pollution situation more complex and severe. In
[...] Read more.
Inhalable particulate matter (PM10) is a primary air pollutant closely related to public health, and an especially serious problem in urban areas. The urban heat island (UHI) effect has made the urban PM10 pollution situation more complex and severe. In this study, we established a health risk assessment system utilizing an epidemiological method taking the thermal environment effects into consideration. We utilized a remote sensing method to retrieve the PM10 concentration, UHI, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). With the correlation between difference vegetation index (DVI) and PM10 concentration, we utilized the established model between PM10 and thermal environmental indicators to evaluate the PM10 health risks based on the epidemiological study. Additionally, with the regulation of UHI, NDVI and NDWI, we aimed at regulating the PM10 health risks and thermal environment simultaneously. This study attempted to accomplish concurrent thermal environment regulation and elimination of PM10 health risks through control of UHI intensity. The results indicate that urban Beijing has a higher PM10 health risk than rural areas; PM10 health risk based on the thermal environment is 1.145, which is similar to the health risk calculated (1.144) from the PM10 concentration inversion; according to the regulation results, regulation of UHI and NDVI is effective and helpful for mitigation of PM10 health risk in functional zones. Full article
(This article belongs to the Special Issue Air Pollution Modeling)
Open AccessArticle Comparing Multipollutant Emissions-Based Mobile Source Indicators to Other Single Pollutant and Multipollutant Indicators in Different Urban Areas
Int. J. Environ. Res. Public Health 2014, 11(11), 11727-11752; doi:10.3390/ijerph111111727
Received: 15 August 2014 / Revised: 5 November 2014 / Accepted: 6 November 2014 / Published: 14 November 2014
Cited by 1 | PDF Full-text (1159 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A variety of single pollutant and multipollutant metrics can be used to represent exposure to traffic pollutant mixtures and evaluate their health effects. Integrated mobile source indicators (IMSIs) that combine air quality concentration and emissions data have recently been developed and evaluated using
[...] Read more.
A variety of single pollutant and multipollutant metrics can be used to represent exposure to traffic pollutant mixtures and evaluate their health effects. Integrated mobile source indicators (IMSIs) that combine air quality concentration and emissions data have recently been developed and evaluated using data from Atlanta, Georgia. IMSIs were found to track trends in traffic-related pollutants and have similar or stronger associations with health outcomes. In the current work, we apply IMSIs for gasoline, diesel and total (gasoline + diesel) vehicles to two other cities (Denver, Colorado and Houston, Texas) with different emissions profiles as well as to a different dataset from Atlanta. We compare spatial and temporal variability of IMSIs to single-pollutant indicators (carbon monoxide (CO), nitrogen oxides (NOx) and elemental carbon (EC)) and multipollutant source apportionment factors produced by Positive Matrix Factorization (PMF). Across cities, PMF-derived and IMSI gasoline metrics were most strongly correlated with CO (r = 0.31–0.98), while multipollutant diesel metrics were most strongly correlated with EC (r = 0.80–0.98). NOx correlations with PMF factors varied across cities (r = 0.29–0.67), while correlations with IMSIs were relatively consistent (r = 0.61–0.94). In general, single-pollutant metrics were more correlated with IMSIs (r = 0.58–0.98) than with PMF-derived factors (r = 0.07–0.99). A spatial analysis indicated that IMSIs were more strongly correlated (r > 0.7) between two sites in each city than single pollutant and PMF factors. These findings provide confidence that IMSIs provide a transferable, simple approach to estimate mobile source air pollution in cities with differing topography and source profiles using readily available data. Full article
(This article belongs to the Special Issue Air Pollution Modeling)
Open AccessArticle Modeling Spatial and Temporal Variability of Residential Air Exchange Rates for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)
Int. J. Environ. Res. Public Health 2014, 11(11), 11481-11504; doi:10.3390/ijerph111111481
Received: 4 August 2014 / Revised: 24 October 2014 / Accepted: 27 October 2014 / Published: 7 November 2014
Cited by 4 | PDF Full-text (1857 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. The residential air exchange rate
[...] Read more.
Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. The residential air exchange rate (AER), which is the rate of exchange of indoor air with outdoor air, is an important determinant for house-to-house (spatial) and temporal variations of air pollution infiltration. Our goal was to evaluate and apply mechanistic models to predict AERs for 213 homes in the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS), a cohort study of traffic-related air pollution exposures and respiratory effects in asthmatic children living near major roads in Detroit, Michigan. We used a previously developed model (LBL), which predicts AER from meteorology and questionnaire data on building characteristics related to air leakage, and an extended version of this model (LBLX) that includes natural ventilation from open windows. As a critical and novel aspect of our AER modeling approach, we performed a cross validation, which included both parameter estimation (i.e., model calibration) and model evaluation, based on daily AER measurements from a subset of 24 study homes on five consecutive days during two seasons. The measured AER varied between 0.09 and 3.48 h−1 with a median of 0.64 h−1. For the individual model-predicted and measured AER, the median absolute difference was 29% (0.19 h‑1) for both the LBL and LBLX models. The LBL and LBLX models predicted 59% and 61% of the variance in the AER, respectively. Daily AER predictions for all 213 homes during the three year study (2010–2012) showed considerable house-to-house variations from building leakage differences, and temporal variations from outdoor temperature and wind speed fluctuations. Using this novel approach, NEXUS will be one of the first epidemiology studies to apply calibrated and home-specific AER models, and to include the spatial and temporal variations of AER for over 200 individual homes across multiple years into an exposure assessment in support of improving risk estimates. Full article
(This article belongs to the Special Issue Air Pollution Modeling)
Open AccessArticle A Method for Estimating Urban Background Concentrations in Support of Hybrid Air Pollution Modeling for Environmental Health Studies
Int. J. Environ. Res. Public Health 2014, 11(10), 10518-10536; doi:10.3390/ijerph111010518
Received: 29 July 2014 / Revised: 29 September 2014 / Accepted: 30 September 2014 / Published: 15 October 2014
Cited by 5 | PDF Full-text (6219 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Exposure studies rely on detailed characterization of air quality, either from sparsely located routine ambient monitors or from central monitoring sites that may lack spatial representativeness. Alternatively, some studies use models of various complexities to characterize local-scale air quality, but often with poor
[...] Read more.
Exposure studies rely on detailed characterization of air quality, either from sparsely located routine ambient monitors or from central monitoring sites that may lack spatial representativeness. Alternatively, some studies use models of various complexities to characterize local-scale air quality, but often with poor representation of background concentrations. A hybrid approach that addresses this drawback combines a regional-scale model to provide background concentrations and a local-scale model to assess impacts of local sources. However, this approach may double-count sources in the study regions. To address these limitations, we carefully define the background concentration as the concentration that would be measured if local sources were not present, and to estimate these background concentrations we developed a novel technique that combines space-time ordinary kriging (STOK) of observations with outputs from a detailed chemistry-transport model with local sources zeroed out. We applied this technique to support an exposure study in Detroit, Michigan, for several pollutants (including NOx and PM2.5), and evaluated the estimated hybrid concentrations (calculated by combining the background estimates that addresses this issue of double counting with local-scale dispersion model estimates) using observations. Our results demonstrate the strength of this approach specifically by eliminating the problem of double-counting reported in previous hybrid modeling approaches leading to improved estimates of background concentrations, and further highlight the relative importance of NOx vs. PM2.5 in their relative contributions to total concentrations. While a key limitation of this approach is the requirement for another detailed model simulation to avoid double-counting, STOK improves the overall characterization of background concentrations at very fine spatial scales. Full article
(This article belongs to the Special Issue Air Pollution Modeling)
Figures

Open AccessArticle A Comparison of Exposure Metrics for Traffic-Related Air Pollutants: Application to Epidemiology Studies in Detroit, Michigan
Int. J. Environ. Res. Public Health 2014, 11(9), 9553-9577; doi:10.3390/ijerph110909553
Received: 6 June 2014 / Revised: 25 August 2014 / Accepted: 26 August 2014 / Published: 15 September 2014
Cited by 11 | PDF Full-text (4088 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding
[...] Read more.
Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding of the key information and metrics needed to assess exposures, as well as the strengths and limitations of alternate exposure metrics. This study develops and evaluates several metrics for characterizing exposure to traffic-related air pollutants for the 218 residential locations of participants in the NEXUS epidemiology study conducted in Detroit (MI, USA). Exposure metrics included proximity to major roads, traffic volume, vehicle mix, traffic density, vehicle exhaust emissions density, and pollutant concentrations predicted by dispersion models. Results presented for each metric include comparisons of exposure distributions, spatial variability, intraclass correlation, concordance and discordance rates, and overall strengths and limitations. While showing some agreement, the simple categorical and proximity classifications (e.g., high diesel/low diesel traffic roads and distance from major roads) do not reflect the range and overlap of exposures seen in the other metrics. Information provided by the traffic density metric, defined as the number of kilometers traveled (VKT) per day within a 300 m buffer around each home, was reasonably consistent with the more sophisticated metrics. Dispersion modeling provided spatially- and temporally-resolved concentrations, along with apportionments that separated concentrations due to traffic emissions and other sources. While several of the exposure metrics showed broad agreement, including traffic density, emissions density and modeled concentrations, these alternatives still produced exposure classifications that differed for a substantial fraction of study participants, e.g., from 20% to 50% of homes, depending on the metric, would be incorrectly classified into “low”, “medium” or “high” traffic exposure classes. These and other results suggest the potential for exposure misclassification and the need for refined and validated exposure metrics. While data and computational demands for dispersion modeling of traffic emissions are non-trivial concerns, once established, dispersion modeling systems can provide exposure information for both on- and near-road environments that would benefit future traffic-related assessments. Full article
(This article belongs to the Special Issue Air Pollution Modeling)
Open AccessArticle A Synthetic Method for Atmospheric Diffusion Simulation and Environmental Impact Assessment of Accidental Pollution in the Chemical Industry in a WEBGIS Context
Int. J. Environ. Res. Public Health 2014, 11(9), 9238-9255; doi:10.3390/ijerph110909238
Received: 9 June 2014 / Revised: 15 August 2014 / Accepted: 27 August 2014 / Published: 5 September 2014
PDF Full-text (818 KB) | HTML Full-text | XML Full-text
Abstract
The chemical industry poses a potential security risk to factory personnel and neighboring residents. In order to mitigate prospective damage, a synthetic method must be developed for an emergency response. With the development of environmental numeric simulation models, model integration methods, and modern
[...] Read more.
The chemical industry poses a potential security risk to factory personnel and neighboring residents. In order to mitigate prospective damage, a synthetic method must be developed for an emergency response. With the development of environmental numeric simulation models, model integration methods, and modern information technology, many Decision Support Systems (DSSs) have been established. However, existing systems still have limitations, in terms of synthetic simulation and network interoperation. In order to resolve these limitations, the matured simulation model for chemical accidents was integrated into the WEB Geographic Information System (WEBGIS) platform. The complete workflow of the emergency response, including raw data (meteorology information, and accident information) management, numeric simulation of different kinds of accidents, environmental impact assessments, and representation of the simulation results were achieved. This allowed comprehensive and real-time simulation of acute accidents in the chemical industry. The main contribution of this paper is that an organizational mechanism of the model set, based on the accident type and pollutant substance; a scheduling mechanism for the parallel processing of multi-accident-type, multi-accident-substance, and multi-simulation-model; and finally a presentation method for scalar and vector data on the web browser on the integration of a WEB Geographic Information System (WEBGIS) platform. The outcomes demonstrated that this method could provide effective support for deciding emergency responses of acute chemical accidents. Full article
(This article belongs to the Special Issue Air Pollution Modeling)
Open AccessArticle Study on an Air Quality Evaluation Model for Beijing City Under Haze-Fog Pollution Based on New Ambient Air Quality Standards
Int. J. Environ. Res. Public Health 2014, 11(9), 8909-8923; doi:10.3390/ijerph110908909
Received: 24 July 2014 / Revised: 22 August 2014 / Accepted: 25 August 2014 / Published: 28 August 2014
Cited by 4 | PDF Full-text (365 KB) | HTML Full-text | XML Full-text
Abstract
Since 2012, China has been facing haze-fog weather conditions, and haze-fog pollution and PM2.5 have become hot topics. It is very necessary to evaluate and analyze the ecological status of the air environment of China, which is of great significance for environmental
[...] Read more.
Since 2012, China has been facing haze-fog weather conditions, and haze-fog pollution and PM2.5 have become hot topics. It is very necessary to evaluate and analyze the ecological status of the air environment of China, which is of great significance for environmental protection measures. In this study the current situation of haze-fog pollution in China was analyzed first, and the new Ambient Air Quality Standards were introduced. For the issue of air quality evaluation, a comprehensive evaluation model based on an entropy weighting method and nearest neighbor method was developed. The entropy weighting method was used to determine the weights of indicators, and the nearest neighbor method was utilized to evaluate the air quality levels. Then the comprehensive evaluation model was applied into the practical evaluation problems of air quality in Beijing to analyze the haze-fog pollution. Two simulation experiments were implemented in this study. One experiment included the indicator of PM2.5 and was carried out based on the new Ambient Air Quality Standards (GB 3095-2012); the other experiment excluded PM2.5 and was carried out based on the old Ambient Air Quality Standards (GB 3095-1996). Their results were compared, and the simulation results showed that PM2.5 was an important indicator for air quality and the evaluation results of the new Air Quality Standards were more scientific than the old ones. The haze-fog pollution situation in Beijing City was also analyzed based on these results, and the corresponding management measures were suggested. Full article
(This article belongs to the Special Issue Air Pollution Modeling)
Open AccessArticle Air Quality Modeling in Support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)
Int. J. Environ. Res. Public Health 2014, 11(9), 8777-8793; doi:10.3390/ijerph110908777
Received: 16 July 2014 / Revised: 15 August 2014 / Accepted: 18 August 2014 / Published: 27 August 2014
Cited by 7 | PDF Full-text (2544 KB) | HTML Full-text | XML Full-text
Abstract
A major challenge in traffic-related air pollution 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. A hybrid air
[...] Read more.
A major challenge in traffic-related air pollution 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. A hybrid air quality modeling approach was used to estimate exposure to traffic-related air pollutants in support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) conducted in Detroit (Michigan, USA). Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and Research LINE-source dispersion model for near-surface releases (RLINE) 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 Multi-scale Air Quality (CMAQ) and the Space-Time Ordinary Kriging (STOK) models. To capture the near-road pollutant gradients, refined “mini-grids” of model receptors were placed around participant homes. Exposure metrics for CO, NOx, PM2.5 and its components (elemental and organic carbon) were predicted at each home location for multiple time periods including daily and rush hours. The exposure metrics were evaluated for their ability to characterize the spatial and temporal variations of multiple ambient air pollutants compared to measurements across the study area. Full article
(This article belongs to the Special Issue Air Pollution Modeling)
Open AccessArticle Humidity and Gravimetric Equivalency Adjustments for Nephelometer-Based Particulate Matter Measurements of Emissions from Solid Biomass Fuel Use in Cookstoves
Int. J. Environ. Res. Public Health 2014, 11(6), 6400-6416; doi:10.3390/ijerph110606400
Received: 10 February 2014 / Revised: 16 May 2014 / Accepted: 9 June 2014 / Published: 19 June 2014
Cited by 6 | PDF Full-text (501 KB) | HTML Full-text | XML Full-text
Abstract
Great uncertainty exists around indoor biomass burning exposure-disease relationships due to lack of detailed exposure data in large health outcome studies. Passive nephelometers can be used to estimate high particulate matter (PM) concentrations during cooking in low resource environments. Since passive nephelometers do
[...] Read more.
Great uncertainty exists around indoor biomass burning exposure-disease relationships due to lack of detailed exposure data in large health outcome studies. Passive nephelometers can be used to estimate high particulate matter (PM) concentrations during cooking in low resource environments. Since passive nephelometers do not have a collection filter they are not subject to sampler overload. Nephelometric concentration readings can be biased due to particle growth in high humid environments and differences in compositional and size dependent aerosol characteristics. This paper explores relative humidity (RH) and gravimetric equivalency adjustment approaches to be used for the pDR-1000 used to assess indoor PM concentrations for a cookstove intervention trial in Nepal. Three approaches to humidity adjustment performed equivalently (similar root mean squared error). For gravimetric conversion, the new linear regression equation with log-transformed variables performed better than the traditional linear equation. In addition, gravimetric conversion equations utilizing a spline or quadratic term were examined. We propose a humidity adjustment equation encompassing the entire RH range instead of adjusting for RH above an arbitrary 60% threshold. Furthermore, we propose new integrated RH and gravimetric conversion methods because they have one response variable (gravimetric PM2.5 concentration), do not contain an RH threshold, and is straightforward. Full article
(This article belongs to the Special Issue Air Pollution Modeling)

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