Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment

Abstract

Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial-and timevarying patterns of ambient pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO 2 ), maximum daily 8-h average ozone (MDA8 O 3 ), and fine particulate matter (PM 2.5 ) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM 2.5 levels exceeding limits due to heavy traffic activities and providing an "out-oflimit map" tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents' exposure to PM 2.5 . The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.