UC Berkeley

Recently, concerns have centered on how to expand knowledge on the limited science related to the cumulative impact of multiple air pollution exposures and the potential vulnerability of poor communities to their toxic effects. The highly intercorrelated nature of exposures makes application of standard regression-based methods to these questions problematic due to well-known issues related to multicollinearity. Our paper addresses these problems by using, as its basic unit of inference, a profile consisting of a pattern of exposure values. These profiles are grouped into clusters and associated with a deprivation outcome. Specifically, we examine how profiles of NO(2)-, PM(2.5)-, and diesel- (road and off-road) based exposures are associated with the number of individuals living under poverty in census tracts (CT's) in Los Angeles County. Results indicate that higher levels of pollutants are generally associated with higher poverty counts, though the association is complex and nonlinear. Our approach is set in the Bayesian framework, and as such the entire model can be fit as a unit using modern Bayesian multilevel modeling techniques via the freely available WinBUGS software package, (1) though we have used custom-written C++ code (validated with WinBUGS) to improve computational speed. The modeling approach proposed thus goes beyond single-pollutant models in that it allows us to determine the association between entire multipollutant profiles of exposures with poverty levels in small geographic areas in Los Angeles County.