Open AccessFrom COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model
Author(s) -
Jiani Yang,
Yifan Wen,
Yuan Wang,
Shaojun Zhang,
Joseph P. Pinto,
Elyse A. Pennington,
Zhou Wang,
Stanley P. Sander,
Jonathan H. Jiang,
Jiming Hao,
Yuk L. Yung,
John H. Seinfeld
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2102705118
Subject(s) - air quality index , air pollution , electrification , covid-19 , environmental science , meteorology , quality (philosophy) , air traffic control , transport engineering , computer science , engineering , electricity , geography , chemistry , medicine , disease , pathology , infectious disease (medical specialty) , electrical engineering , philosophy , organic chemistry , epistemology , aerospace engineering
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.