The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO₂, O₃, and PM_2.5 concentrations arising from these emission changes in 11 cities globally by applying a deweathering machine learning technique. Sudden decreases in deweathered NO₂ concentrations and increases in O₃ were observed in almost all cities. However, the decline in NO₂ concentrations attributable to the lockdowns was not as large as expected, at reductions of 10 to 50%. Accordingly, O₃ increased by 2 to 30% (except for London), the total gaseous oxidant (O_x = NO₂ + O₃) showed limited change, and PM_2.5 concentrations decreased in most cities studied but increased in London and Paris. Our results demonstrate the need for a sophisticated analysis to quantify air quality impacts of interventions and indicate that true air quality improvements were notably more limited than some earlier reports or observational data suggested.

COVID-19 封锁导致空气污染物排放量大幅减少。在这里，我们通过应用除风化机器学习技术，定量评估了全球 11 个城市的排放变化引起的环境 NO ₂ 、O ₃和 PM _2.5浓度的变化。几乎所有城市都观察到去风化NO ₂浓度突然下降而O ₃浓度突然上升。然而，封锁导致的 NO ₂浓度下降幅度并不像预期的那么大，下降了 10% 至 50%。因此，O ₃增加了 2% 至 30%（伦敦除外），总气态氧化剂（O _x = NO ₂ + O ₃ ）变化有限，大多数研究城市的 PM _2.5浓度下降，但伦敦和巴黎有所上升。我们的结果表明需要进行复杂的分析来量化干预措施对空气质量的影响，并表明真正的空气质量改善明显比一些早期报告或观测数据所建议的要有限。