Statistical methods for assessing source-specific health effects of air pollution

A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles, or particulate matter (PM). Quantifying source-specific risk is a challenge, because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions based on a collection of observed chemical species. Methods such as source apportionment and multivariate receptor modeling use standard factor analytic techniques to estimate the source-specific contributions from complex mixtures of exposure. In the interest of a more flexible source apportionment, we propose a multiplicative factor analysis with a mixed model on the latent source contributions. Modeling exposure in this way maintains the non-negativity of the measured chemical concentrations and adjusts for systematic effects on source activity as well as residual correlation in the source-specific exposures. We propose a structural equation approach to estimate the health effects associated with source-specific PM. By modeling the observed exposures and measured health outcomes jointly as a function of the unobserved source contributions, inference on the health effects account for the fact that uncertainty is associated with the source-specific exposures. Since the structural equation model typically involves a large number of parameters, for small sample settings we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We conduct simulation studies to evaluate the statistical properties of the health effect estimates obtained using the proposed methods. Finally, we apply our methods to data collected from animal toxicology studies investigating the mechanisms of morbidity and mortality associated with inhalation of ambient air PM.