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.
Highlights
The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy
Vehicular traffic is a principal source of air pollutants, including nitrogen oxides (NOx = NO + NO2), carbon monoxide (CO), and carbonaceous particles
Machine-learned geostatistical models are developed here to predict the concentrations of three major pollutants: NO2, O3, and PM2.5 in the Los Angeles (LA) basin, using traffic information, meteorological conditions, and other socioeconomic factors as inputs (SI Appendix, Fig. S1)
Summary
The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. We capitalize on large variations of urban air quality during the COVID-19 pandemic and real-time observations of traffic, meteorology, and air pollution in Los Angeles to develop a machine-learning air pollution prediction model.
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