From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model.
In: Proceedings of the National Academy of Sciences of the United States of America, Jg. 118 (2021-06-29), Heft 26, S. 1-7
Online
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Zugriff:
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 NO 2 , O 3 , 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 NO 2 and particulate matter with aerodynamic diameters <2.5 µm by -30.1% and -17.5%, respectively, but a 5.7% increase in O 3 . 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 NO 2 levels. [ABSTRACT FROM AUTHOR]
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Titel: |
From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model.
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Autor/in / Beteiligte Person: | 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. |
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Zeitschrift: | Proceedings of the National Academy of Sciences of the United States of America, Jg. 118 (2021-06-29), Heft 26, S. 1-7 |
Veröffentlichung: | 2021 |
Medientyp: | academicJournal |
ISSN: | 0027-8424 (print) |
DOI: | 10.1073/pnas.2102705118 |
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