A study of air quality variations during the COVID-19 pandemic finds controls on air quality as well as implications for air quality benefits expected from future vehicle electrification. The COVID-19 pandemic provided an opportunity to study real-world effects of decreased vehicle emission air pollution. John H. Seinfeld, Yuan Wang, Shaojun Zhang, and colleagues incorporated the highly variable measurements of air quality over the course of the pandemic in the Los Angeles area into a machine-learning model. The model exhibited high fidelity in reproducing pollutant concentrations and identified factors controlling the concentrations of the chemical components of air pollution. Further, the model accounted for the nonlinear relationships between emission rates, atmospheric chemistry, and meteorological conditions. From observations of 30.1% reduction in NO2 and 17.5% reduction in PM2.5 along with 5.7% increase in ozone during the strictest lockdown period, the authors found that heavy-duty truck emissions were primarily responsible for emissions variations. The authors suggest that future vehicle electrification may produce air quality effects similar to those at the beginning of the pandemic but with decreased magnitude. According to the authors, the full air quality benefits of vehicle electrification will additionally require mitigation of off-road emissions.
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Article #21-02705: "From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model," by Jiani Yang et al.
MEDIA CONTACTS: John H. Seinfeld, California Institute of Technology, Pasadena, CA; tel: 626-808-1446; email: < seinfeld@caltech.edu >; Yuan Wang, California Institute of Technology, Pasadena CA, tel: 979-450-9106; email: < Yuan.wang@caltech.edu >
Proceedings of the National Academy of Sciences