Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns.

True air quality improvements during the COVID-19 lockdowns in global cities are more limited than we thought. The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO2, O3, and PM2.5 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 (Ox = NO2 + O3) showed limited change, and PM2.5 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.

全球城市在新冠疫情封锁期间空气质量的真实改善程度比我们想象的更为有限。 新冠疫情封锁导致空气污染物排放大幅减少。在此，我们通过应用一种去气象影响的机器学习技术，对全球11个城市因这些排放变化而导致的环境中二氧化氮、臭氧和细颗粒物（PM2.5）浓度的变化进行了定量评估。几乎在所有城市都观察到去气象影响后的二氧化氮浓度突然下降以及臭氧浓度上升。然而，因封锁导致的二氧化氮浓度下降幅度不如预期的大，仅下降了10%到50%。相应地，臭氧浓度上升了2%到30%（伦敦除外），总气态氧化剂（Ox = 二氧化氮 + 臭氧）变化有限，大多数被研究城市的PM2.5浓度下降，但在伦敦和巴黎却上升了。我们的研究结果表明，需要进行精细的分析来量化干预措施对空气质量的影响，并指出空气质量的真实改善程度明显比一些早期报告或观测数据所显示的更为有限。