From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model
Jiani Yang, Yifan Wen, Yuan Wang, Shaojun Zhang, Joseph P. Pinto, Elyse A. Pennington, Zhou Wang, Ye Wu, Stanley P. Sander, Jonathan H. Jiang, Jiming Hao, Yuk L. Yung, John H. Seinfeld
Abstract
Significance We capitalize on large variations of urban air quality during the COVID-19 pandemic and real-time observations of traffic, meteorology, and air pollution in Los Angeles to develop a machine-learning air pollution prediction model. Such a model can adequately account for the nonlinear relationships between emissions, atmospheric chemistry, and meteorological factors. Moreover, this model enables us to identify key drivers of air-quality variations and assess the effect of future traffic-emission controls on air quality. We unambiguously demonstrate that the full benefit from fleet electrification cannot be attained if focused only on mitigation of local vehicle emissions. To continue to improve air quality in Los Angeles, off-road emissions and those from volatile chemical products need to be more strictly regulated.