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Fine-scale characterization of traffic-related mortality associated with exposure to PM2.5
Citation:
Chang, S., S. Arunachalam, M. Serre, AND V. Isakov. Fine-scale characterization of traffic-related mortality associated with exposure to PM2.5. A&WMA Air Quality Measurement Methods and Technology, Chapel Hill, NC, March 15 - 17, 2016.
Impact/Purpose:
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
Description:
Emission from on-road vehicles is a major contributor of air pollution-related premature death. Previous studies have estimated that on-road emissions in the U.S. cause 29,000 to 53,000 ozone and PM2.5-related premature deaths. In these studies, air quality chemical transport models (CTM) were used to provide ambient concentration estimates. These grid-based models were usually run at a relatively coarse spatial resolution (i.e. 36 km x 36 km or 12 km x 12 km) that fails to fully capture the concentration hotspots at the proximity of emission sources, and thus high-risk areas were not captured. Further, studies have shown that people living close to major roads have higher risk to develop respiratory diseases than those living several hundred meters away. To capture this sharp gradient and improve characterization of the exposure and risk from traffic-related air pollutants, fine-resolution modeling is required. Fine-resolution modeling with CTM, however, is resource intensive because of the increased grid number and is not viable for large-scale applications. In this study, we will use a line source dispersion model, R-LINE, to provide PM2.5 concentration estimates in the North Carolina Piedmont region at the Census block level. While R-LINE will be used to provide estimates of primary PM2.5 due to on-road emissions, secondary PM2.5 will be estimated with a two-step approach combining CMAQ and space-time ordinary kriging (STOK). In the first step, brute-force CMAQ simulations will be performed with and without on-road emissions to estimate the fraction of secondary PM2.5 from on-road source to total PM2.5 at a coarse resolution. In the second step, the estimated fraction from Step 1 will be applied to the observed PM2.5 from surrounding monitoring sites to generate the (soft data) for STOK to estimate Census block-level secondary PM2.5 from on-road source. Concentration-response functions from the literature will be used to estimate premature death based on predicted ambient concentration. The resultant premature death estimate will be compared to the estimate using CTM model to evaluate the potential underestimates by using only coarse-resolution grid-based modeling.
