From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model
Creators
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1.
California Institute of Technology
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2.
Tsinghua University
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3.
Jet Propulsion Lab
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4.
University of North Carolina at Chapel Hill
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Johannes Gutenberg University of Mainz
Abstract
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.
Additional Information
© 2021 National Academy of Sciences. Published under the PNAS license. Contributed by John H. Seinfeld, May 3, 2021 (sent for review February 12, 2021; reviewed by Russell R. Dickerson and Alma Hodzic). Y. Wang, S.P.S., J.H.J., and Y.L.Y. acknowledge support by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. E.A.P. and J.H.S. acknowledge support by the Samsung Corporation (award SAMS.2019GRO). S.Z. acknowledges support by the National Key Research and Development Program of China (grant 2017YFC0212100), the National Natural Science Foundation of China (grant 41977180), and Ford Motor Company. J.Y. acknowledges Leo Gallagher and Daniel Kitowski at the California Department of Transportation, Thomas E. Morrell at the Caltech Library, Jin Tao for helpful information on data inputs, and Yu Zhou at Tsinghua University for useful discussions. Data Availability: All study data are included in the article and/or SI Appendix. J.Y. and Y.W. contributed equally to this work. Author contributions: J.Y., Y. Wang, S.Z., and J.H.S. designed research; J.Y., Y. Wen, Y. Wang, and S.Z. performed research; J.Y., Y. Wen, Y. Wang, S.Z., J.P.P., E.A.P., Z.W., Y. Wu, S.P.S., J.H.J., J.H., Y.L.Y., and J.H.S. analyzed data; J.Y., Y. Wen, and S.Z. developed the RF model; and J.Y., Y. Wen, Y. Wang, S.Z., and J.H.S. wrote the paper. Reviewers: R.R.D., University of Maryland, College Park; and A.H., National Center for Atmospheric Research. The authors declare no competing interest. This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2102705118/-/DCSupplemental.Attached Files
Published - e2102705118.full.pdf
Supplemental Material - pnas.2102705118.sapp.pdf
Files
e2102705118.full.pdf
Additional details
Identifiers
- PMCID
- PMC8256029
- Eprint ID
- 109562
- Resolver ID
- CaltechAUTHORS:20210623-180037244
Funding
- NASA/JPL/Caltech
- Samsung Corporation
- SAMS.2019GRO
- National Key Research and Development Program of China
- 2017YFC0212100
- National Natural Science Foundation of China
- 41977180
- Ford Motor Company
Dates
- Created
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2021-06-23Created from EPrint's datestamp field
- Updated
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2022-02-08Created from EPrint's last_modified field
Caltech Custom Metadata
- Caltech groups
- COVID-19 , Division of Geological and Planetary Sciences (GPS)