Beyond air pollution at home: Assessment of personal exposure to PM2.5 using activity-based travel demand model and low-cost air sensor network data

Abstract

Assessing personal exposure to air pollution is challenging due to the limited availability of human movement data and the complexity of modeling air pollution at high spatiotemporal resolution. Most health studies rely on residential estimates of outdoor air pollution instead which introduces exposure measurement error. Personal exposure for 100,784 individuals in Los Angeles County was estimated by integrating human movement data simulated from the Southern California Association of Governments (SCAG) activity-based travel demand model with hourly PM2.5 predictions from my 500 m gridded model incorporating low-cost sensor monitoring data. Individual exposures were assigned considering PM2.5 levels at homes, workplaces, and other activity locations. These dynamic exposures were compared to the residence-based exposures, which do not consider human movement, to examine the degree of exposure estimation bias. The results suggest that exposures were underestimated by 13% (range 5–22%) on average when human movement was not considered, and much of the error was eliminated by accounting for work location. Exposure estimation bias increased for people who exhibited higher mobility levels, especially for workers with long commute distances. Overall, the personal exposures of workers were underestimated by 22% (5–61%) relative to their residence-based exposures. For workers who commute >20 miles, their exposure levels can be at most underestimated by 61%. Omitting mobility resulted in underestimating exposures for people who reside in areas with cleaner air but work in more polluted areas. Similarly, exposures were overestimated for people living in areas with poorer air quality and working in cleaner areas. These could lead to differential estimation biases across racial, ethnic and socioeconomic lines that typically correlate with where people live and work and lead to important exposure and health disparities. This study demonstrates that ignoring human movement and spatiotemporal variability of air pollution could lead to differential exposure misclassification potentially biasing health risk assessments. These improved dynamic approaches can help planners and policymakers identify disadvantaged populations for which exposures are typically misrepresented and might lead to targeted policy and planning implications.