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Abstract:
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Previous studies have suggested that fine particulate matter air pollution (PM2.5) can negatively impact health. Estimates of source-specific contributions to PM2.5 are needed to estimate source-specific health effects and can help identify sources with the most harmful contributions. They allow regulators to focus limited resources on reducing emissions of sources that contribute the most to morbidity and mortality. However, we often only have measurements of ambient PM2.5 concentrations from national monitoring networks and not individual source emissions. Multivariate receptor models, or source apportionment models, can be used to estimate source-specific contributions from ambient air pollution measurements. We provide a brief introduction to source apportionment methods used to estimate the contributions of sources of air pollution. We then focus on a Bayesian approach that can more easily accommodate the incorporation of a priori information compared to non-Bayesian approaches, such as PCA and PMF. The Bayesian model incorporates information from national databases containing data on both the composition of source emissions and the amount of emissions from known sources of
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