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

• DOI: 10.1021/acs.est.3c07545
• Publication Date: 2024-01-23T20:19:56Z
• Authors: Yifan Wen; Shaojun Zhang; Yuan Wang; Jiani Yang; Liyin He; Ye Wu; Jiming Hao
• Keywords: Environmental Justice; Environmental Monitoring

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 community scale for megacities. Here, we developed machine-learning approach that leverages dynamic traffic profiles continuously estimate community-level year-long air concentrations Los Angeles, U.S. We found introduction real-world data significantly improved spatial fidelity nitrogen dioxide (NO