Spring Festival and COVID‐19 Lockdown: Disentangling PM Sources in Major Chinese Cities

Dai, Qili; Hou, Linlu; Liu, Bowen; Zhang, Yufen; Song, Congbo; Shi, Zongbo; Hopke, Philip K.; Feng, Yinchang

Responding to the 2020 COVID‐19 outbreak, China imposed an unprecedented lockdown producing reductions in air pollutant emissions. However, the lockdown driven air pollution changes have not been fully quantified. We applied machine learning to quantify the effects of meteorology on surface air quality data in 31 major Chinese cities. The meteorologically normalized NO(2), O(3), and PM(2.5) concentrations changed by −29.5%, +31.2%, and −7.0%, respectively, after the lockdown began. However, part of this effect was also associated with emission changes due to the Chinese Spring Festival, which led to ∼14.1% decrease in NO(2), ∼6.6% increase in O(3) and a mixed effect on PM(2.5) in the studied cities that largely resulted from festival associated fireworks. After decoupling the weather and Spring Festival effects, changes in air quality attributable to the lockdown were much smaller: −15.4%, +24.6%, and −9.7% for NO(2), O(3), and PM(2.5), respectively.