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 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, bu…