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Abstract:
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Fine particle matter (PM2.5) is an atmospheric pollutant linked to serious health problems, including mortality. PM2.5 is a mixture of pollutants with five main components: sulfate, nitrate, total carbonaceous mass, ammonium, and crustal materials. These components have complex spatial-temporal dependency and cross-dependency structures. It is important to gain insight and understanding about the spatial distribution of each component of total PM2.5, and to estimate how the composition of PM2.5 might change with location and season. Here, we introduce a multivariate spatiotemporal model for speciated PM2.5 and propose a Bayesian hierarchical framework with spatiotemporally varying coefficients. A linear coregionalization model is also developed to account for dependency structures. We apply our model to speciated PM2.5 monitoring data in the U.S. for the year 2004 from EPA.
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