Impacts of distinct travel behaviors on potential air pollution exposure measurement error

Abstract

Prior studies typically estimate air pollution exposure at places of residence only; however, people are likely to conduct daily activities at out-of-home locations. Overlooking human mobility in exposure estimation can introduce measurement error in health analyses and may lead to ineffective public policies. We simulated travel mobility patterns for 100,784 residents of Los Angeles County and their exposure to outdoor PM2.5 on a typical weekday. We used K-means clustering via principal components analysis (PCA) to subdivide subjects into seven travel behavior clusters and compared their mobility-based dynamic PM2.5 exposures to their residence-based static exposure using our previously developed spatiotemporal model. Travel behavior correlated with exposure to outdoor PM2.5 and with the degree of PM2.5 exposure measurement error. Workers with long commuting distance (and highest income) had the lowest dynamic PM2.5 exposure (8.1 μg/m3), while workers who relied on public transit (and were generally lower income) had the highest exposure (8.8 μg/m3). In general, lower income individuals lived in more polluted neighborhoods and took longer duration (but shorter distance) commutes to work locations with similarly elevated PM2.5 concentrations, while higher income individuals took longer distance commutes (similar duration) from less polluted residential to more polluted occupational neighborhoods. “Worker” and frequent traveler clusters with highest mobility had the highest exposure measurement error, and the non-worker “stay-at-home” cluster and student clusters had the lowest. The extent of exposure measurement error generally correlated with pollution levels at the residential location and distance (less so duration) of travel away from the residential neighborhood. Our results contribute to the literature on effects of travel behavior on exposure measurement error. The generated activity clusters combined with sociodemographic information provide additional information for researchers to understand which groups of people may be more challenging to model or predict their exposures accurately.