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Abstract:
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Characterizing the variation in human exposure to toxic substances over large populations often requires an understanding of the geographic variation in environmental levels of toxicants. This knowledge is essential when the primary routes of exposure are through interaction with environmental media, as opposed to more individual-specific exposure routes (e.g., occupational exposure). In this study, we focus on modeling the spatial variation in the levels of heavy metals (arsenic, cadmium, chromium, and lead) in air, soil, and water. We draw on a variety of different types of relevant data, including emission inventories and ambient environmental measurements, and develop hierarchical Bayesian models that allow us to combine these different types of information in a coherent and scientifically appropriate manner.
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