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.

COVID-19 大流行期间交通量的大幅波动为评估车辆排放控制功效提供了无与伦比的机会。在这里，我们基于 COVID-19 期间的大量实时观测数据开发了一个随机森林回归模型，以预测洛杉矶大城市的地表 NO ₂、O ₃和细颗粒浓度。我们的模型在再现洛杉矶盆地的污染物浓度方面表现出高保真度，并确定了控制每个物种的主要因素。在最严格的封锁期间，交通减少导致 NO ₂和空气动力学直径 <2.5 μm 的颗粒物分别减少了 –30.1% 和 –17.5%，但 O ₃增加了 5.7%. 重型卡车排放主要导致这些变化。据估计，未来的交通排放控制会产生与 COVID-19 封锁期间观察到的类似影响，但幅度较小。汽车电气化将进一步降低NO ₂水平。