An emission-weighted proximity model for air pollution exposure assessment

Central South University, School of Info-Physics and Geomatics Engineering, Changsha, Hunan 410086, China; Texas State University, Texas Center for Geographic Information Science, Department of Geography, 601 University Drive, San Marcos, TX 78666, USA; University of Texas at Brownsville, Department of Chemistry and Environmental Sciences, Brownsville, TX 78520, USA; Wuhan University, School of Resource and Environmental Science, Wuhan, Hubei, 430079, China
Science of The Total Environment 01/2009; DOI:10.1016/j.scitotenv.2009.05.014
Source: PubMed

ABSTRACT 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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