气候变化领域集成服务门户(CCPortal): From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model

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DOI	10.1073/pnas.2102705118
	From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model
	Yang J.; Wen Y.; Wang Y.; Zhang S.; Pinto J.P.; Pennington E.A.; Wang Z.; Wu Y.; Sander S.P.; Jiang J.H.; Hao J.; Yung Y.L.; Seinfeld J.H.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:26
英文摘要	The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID- 19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulatematterwith aerodynamic diameters <2.5 μmby -30.1%and -17.5%, respectively, but a 5.7% increase in O3. Heavy-duty truck emissions contribute primarily to these variations. Future trafficemission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Air pollution; COVID-19; Machine learning; Traffic emissions; Vehicular electrification
语种	英语
scopus关键词	air pollutant; air pollution; algorithm; electricity; epidemiology; exhaust gas; human; machine learning; particulate matter; theoretical model; traffic and transport; Air Pollutants; Air Pollution; Algorithms; COVID-19; Electricity; Humans; Machine Learning; Models, Theoretical; Particulate Matter; Transportation; Vehicle Emissions
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/238501
作者单位	Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, United States; School of Environment, Tsinghua University, Beijing, 100084, China; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, United States; Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, United States; Department of Geography, University of Mainz, Mainz, 55099, Germany
推荐引用方式 GB/T 7714	Yang J.,Wen Y.,Wang Y.,et al. From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model[J],2021,118(26).
APA	Yang J..,Wen Y..,Wang Y..,Zhang S..,Pinto J.P..,...&Seinfeld J.H..(2021).From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model.Proceedings of the National Academy of Sciences of the United States of America,118(26).
MLA	Yang J.,et al."From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model".Proceedings of the National Academy of Sciences of the United States of America 118.26(2021).