Citation:

Russell, A., J. Mulholland, R. Huand, M. Strickland, Y. Lin, L. Darrow, J. Warren, H. Chang, P. Tolbert, R. Devlin, L. McGuinn, L. Neas, D. Diaz-Sanchez, W. Cascio, C. Ward-Caviness, W. Kraus, AND E. Hauser. Using Fused Chemical Transport Models to Estimate Spatially and Temporally Resolved Ambient Air Pollution in Georgia and North Carolina. International Society for Exposure Science, Ottawa, Ontario, CANADA, August 26 - 30, 2018.

Impact/Purpose:

This abstract makes a modest contribution to the evolving knowledge about the application of model-derived estimates of exposures to ambient air pollutants.

Description:

A number of adverse health impacts, including acute cardiovascular disease-related events, have been associated with exposure to fine particulate matter (e.g., PM2.5). Given the prevalence of data for PM2.5 mass concentrations, most of these studies are based upon total PM2.5 mass, not on individual species. However, a number of studies have found differing associations of disease outcomes with PM2.5 components (or species), and PM properties (e.g., size and oxidative potential: OP). Data assimilation using chemical transport modelling (CMAQ) and observations is used to develop concentration fields of major PM2.5 components for two areas: Georgia and North Carolina. As part of a birthweight study, speciated PM2.5 and gaseous pollutant fields were constructed over Georgia for 2002-2006. Increases in air pollutant concentrations were associated with decreases in mean birth weight. In this study, speciated PM fields are also constructed over North Carolina for 2002-2009. This extends a prior study that used a similar approach to provide unspeciated PM2.5 fields in an epidemiologic study using health data from the CATHGEN cohort of patients who had undergone a cardiac catheterization in North Carolina and in a Birth Cohort Study in Georgia. As part of a comparison of approaches, the model-observation fusing approach used here for PM2.5, performance was similar to using a method including satellite data as well as more coarse modelling. The added benefit of using a chemical transport model is that it can provide speciated PM2.5 fields as well as gaseous pollutants that are not as readily observed using remote sensing methods, or where such observations are temporally constrained. Speciated spatiotemporal fields developed here include elemental and organic carbon (EC/OC), sulfate, nitrate, ammonium, and crustal material. The CMAQ approach also provides NO2, O3 and CO fields. Using the chemical transport modelling, along with data assimilation, addresses issues of limited monitoring of speciated PM and other pollutants. This abstract does not necessarily reflect EPA policy.

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