Improved Spatiotemporal Source-Based Air Pollutant Mixture Characterization for Health Studies
The growing availability of spatially resolved health data sets (i.e., resident and county level patient records) requires spatially resolved exposure or air quality metrics to investigate the impact of air pollution on health outcomes. While daily air quality data are essential in time-series epidemiologic analysis, the spatial distribution of the observations is limited. Air pollution modeling (i.e., chemical transport modeling (CTM)) addresses this by producing spatially resolved air quality predictions using terrain, emissions and meteorology inputs. However, predicted concentrations may be biased. This work incorporates unique data fusion approaches to combine air quality observations from regulatory monitoring networks (OBS) with the output from a CTM (CMAQ) to generate spatially and temporally resolved gaseous and PM species concentrations. Species concentrations alone cannot directly identify emission sources or characterize pollutant mixtures, therefore source apportionment (SA) models are required to estimate source impacts. The focus of this work is a comparison of SA results for three U.S. regions with differing air pollution sources, St. Louis, Missouri; Atlanta, Georgia; and Dallas-Fort Worth, Texas.
KeywordsOrdinary Kriging Species Concentration Source Apportionment Positive Matrix Factorization Mobile Source
This research was supported, in part, by USEPA grant R834799. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the USEPA. Further, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication. Additional support was made possible by grants from Georgia Power and the Southern Company.
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