{Reference Type}: Journal Article
{Title}: Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment.
{Author}: Wen Y;Zhang S;Wang Y;Yang J;He L;Wu Y;Hao J;
{Journal}: Environ Sci Technol
{Volume}: 0
{Issue}: 0
{Year}: 2024 Jan 23
{Factor}: 11.357
{DOI}: 10.1021/acs.est.3c07545
{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 time-varying 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 (NO2), maximum daily 8-h average ozone (MDA8 O3), and fine particulate matter (PM2.5) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM2.5 levels exceeding limits due to heavy traffic activities and providing an "out-of-limit 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 PM2.5. 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.