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

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

Research output: Contribution to journalArticlepeer-review


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 NO2, O3, and PM2.5 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 NO2, ∼6.6% increase in O3 and a mixed effect on PM2.5 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 NO2, O3, and PM2.5, respectively.

Original languageEnglish
Article numbere2021GL093403
Pages (from-to)1-12
Number of pages12
JournalGeophysical Research Letters
Issue number11
Publication statusPublished - 16 Jun 2021


  • air quality
  • COVID-19
  • machine learning
  • meteorological normalization
  • source
  • spring festival


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