نبذة مختصرة : 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 PM2.5levels 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. ; © 2024 American Chemical Society. ; We are grateful to the National Key Research and Development Program of China (Grant No. 2022YFC3703600), the National Natural Science Foundation of China (grant No. 42261160645), the China National Postdoctoral Program for Innovative Talents (No. BX20220179), and the Shuimu Tsinghua Scholar Program (No. 2022SM011). ; S.Z., Y.W., and Y.W. conceived the research idea; Y.W., J.Y., and L.H. collected and prepared the data; Y.W. developed the script for data analysis; Y.W. developed the models; Y.W., S.Z., and Y.W. wrote the manuscript through contributions of all authors; all authors provided comments and contributed to the final version of the article. ; The authors declare no competing financial ...
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