An emission-weighted proximity model for air pollution exposure assessment

University of Texas at Brownsville, Department of Chemistry and Environmental Sciences, Brownsville, TX 78520, USA
Science of The Total Environment (Impact Factor: 4.1). 08/2009; 407(17):4939-4945. DOI: 10.1016/j.scitotenv.2009.05.014
Source: PubMed


BackgroundAmong the most common spatial models for estimating personal exposure are Traditional Proximity Models (TPMs). Though TPMs are straightforward to configure and interpret, they are prone to extensive errors in exposure estimates and do not provide prospective estimates.MethodTo resolve these inherent problems with TPMs, we introduce here a novel Emission Weighted Proximity Model (EWPM) to improve the TPM, which takes into consideration the emissions from all sources potentially influencing the receptors. EWPM performance was evaluated by comparing the normalized exposure risk values of sulfur dioxide (SO2) calculated by EWPM with those calculated by TPM and monitored observations over a one-year period in two large Texas counties. In order to investigate whether the limitations of TPM in potential exposure risk prediction without recorded incidence can be overcome, we also introduce a hybrid framework, a ‘Geo-statistical EWPM’. Geo-statistical EWPM is a synthesis of Ordinary Kriging Geo-statistical interpolation and EWPM. The prediction results are presented as two potential exposure risk prediction maps. The performance of these two exposure maps in predicting individual SO2 exposure risk was validated with 10 virtual cases in prospective exposure scenarios.ResultsRisk values for EWPM were clearly more agreeable with the observed concentrations than those from TPM. Over the entire study area, the mean SO2 exposure risk from EWPM was higher relative to TPM (1.00 vs. 0.91). The mean bias of the exposure risk values of 10 virtual cases between EWPM and ‘Geo-statistical EWPM’ are much smaller than those between TPM and ‘Geo-statistical TPM’ (5.12 vs. 24.63).ConclusionEWPM appears to more accurately portray individual exposure relative to TPM. The ‘Geo-statistical EWPM’ effectively augments the role of the standard proximity model and makes it possible to predict individual risk in future exposure scenarios resulting in adverse health effects from environmental pollution.

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Available from: Bin Zou, Jun 18, 2014
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    • "The advantage of EWPM is that it can assess individual exposure risk with emission data only. However, unlike AERMOD, the performance of EWPM has not been examined (Zou et al., 2009b). Therefore, there is also the need for a comparative evaluation to examine whether EWPM can be used as an alternative measure of exposure risk when researchers cannot use AERMOD for air pollution exposure assessment for any reason. "
    International Journal of Environmental Research 08/2011; 5(e):769-778. · 1.10 Impact Factor
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    • "The performance of EWPM in assessing air pollution exposure risk of any receptor was evaluated in our earlier work [7], indicating that, exposure risks calculated by EWPM can match those from observational concentration well at annual scale. Fig. 2(a) and Fig. 2 "
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    ABSTRACT: This paper proposed a framework potential for assessing regional spatial and temporal variations of air pollution exposure risk by combing Geo-information Technology Aided Proximity Model (GTAPM) and GIS interpolation technology in areas with limited access to data inputs (e.g. emission data, meteorological data). In this process, the exposure risks to sulfur dioxide (SO2) for Dallas and Ellis counties in 1996 and 2002 were assessed firstly at annual scale based on GTAPM; then, the spatial and temporal variations of annual SO2 exposure risk in Dallas and Ellis counties were evaluated based on GIS spatial interpolation technology and overlay analysis operation. The results indicate that the annual SO2 exposure risk in Dallas and Ellis counties are spatially different in both 1996 and 2002. While areas with lower levels of variations were mainly distributed in most central parts of study area, the areas with relative higher levels of variations emerged in the northern part of Dallas county and southeastern of Ellis County. The results also suggest that while the annual SO2 exposure risk in minority areas with relative high exposure risks in 1996 seemed to be reduced over the period of 1996 to 2002, those in majority areas with relative lower exposure risks in 1996 increased a lot. The annual SO2 exposure risk of entire study area appeared to increase. Therefore, it can be concluded that GTAPM can effectively and efficiently evaluate air pollution exposure risk and its spatial and temporal variations in area without air quality observation data and related input data for air dispersion modeling.
    The 18th International Conference on Geoinformatics: GIScience in Change, Geoinformatics 2010, Peking University, Beijing, China, June, 18-20, 2010; 01/2010
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    • "The objective of this study is twofold. The first objective was to undertake a comparative evaluation of the performance of three commonly used exposure assessment methods [i.e., traditional proximity model (TPM), American Meteorological Society/EPA regulatory model (AERMOD), ordinary kriging interpolation (OKI)] and one novel method—emission weighted proximity model (EWPM)—we recently proposed (Zou et al. 2009b). The second objective was to identify the potential appropriate method for exposure scenarios with different data availability . "
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    ABSTRACT: Methods commonly used to assess the environmental exposure risk at a location (e.g., proximity models) are usually based on different assumptions, leading to conflicting results and recommendations in epidemiological studies. In this case study, a comparative evaluation of the accuracy levels associated with four commonly used exposure risk estimate models [i.e., traditional proximity model (TPM), emission weighted proximity model (EWPM), the American Meteorological Society/EPA regulatory model (AERMOD), and ordinary kriging interpolation (OKI)] were conducted. Results show that at the annual and the monthly scales, the normalized exposure risk values simulated by AERMOD and EWPM have higher accuracy levels than the simulations from the TPM and OKI methods. However, AERMOD has higher accuracy than that of the EWPM, and this was attributed to the differences of input data. EWPM provided the most accurate simulations when analysts have access to only point emission source data. The results also indicate that the accuracies of the exposure risks simulated by AERMOD and EWPM can be influenced by factors such as the modeling extent, the distance settings, and so forth.
    Environmental Monitoring and Assessment 06/2009; 166(1-4):159-67. DOI:10.1007/s10661-009-0992-8 · 1.68 Impact Factor
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