From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model

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, O...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2021-06, Vol.118 (26), p.1-7
Main Authors: Yang, Jiani, Wen, Yifan, Wang, Yuan, Zhang, Shaojun, Pinto, Joseph P., Pennington, Elyse A., Wang, Zhou, Wu, Ye, Sander, Stanley P., Jiang, Jonathan H., Hao, Jiming, Yung, Yuk L., Seinfeld, John H.
Format: Article
Language:English
Subjects:
Air Pollutants - analysis
Air Pollution - analysis
Air quality
Airborne particulates
Algorithms
Coronaviruses
COVID-19
COVID-19 - epidemiology
Electricity
Electrification
Emission analysis
Emissions control
Forest management
Heavy duty trucks
Humans
Learning algorithms
Machine Learning
Megacities
Models, Theoretical
Nitrogen dioxide
Pandemics
Particulate emissions
Particulate matter
Particulate Matter - analysis
Physical Sciences
Pollutants
Regression models
Traffic control
Transportation
Vehicle Emissions
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Summary: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 particulate matter with aerodynamic diameters < 2.5 μm by –30.1% and –17.5%, respectively, but a 5.7% increase in O₃. Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission 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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.2102705118