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

Zongbo Shi, Congbo Song, Bowen Liu, Gongda Lu, Jingsha Xu, Van Tuan VU, Robert J R Elliott, Weijun Li, William Bloss, Roy Harrison

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19 Citations (Scopus)
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The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO2, O3, and PM2.5concentrations arising from these emission changes in 11 cities globally by applying a deweathering machine learning technique. Sudden decreases in deweathered NO2concentrations and increases in O3 were observed in almost all cities. However, the decline in NO2concentrations attributable to the lockdowns was not as large as expected, at reductions of 10 to 50%. Accordingly, O3increased by 2 to 30% (except for London), the total gaseous oxidant (Ox= NO2+ O3) showed limited change, and PM2.5concentrations 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.

Original languageEnglish
Article numbereabd6696
JournalScience Advances
Issue number3
Publication statusPublished - 13 Jan 2021

Bibliographical note

Funding Information:
This work is funded by the Natural Environment Research Council (NE/ N007190/1, NE/R005281/1, and NE/S006699/1) as part of the Atmospheric Pollution and Human Health in a Chinese Megacity program. We also acknowledge the support from the IGI Clean Air Theme in the University of Birmingham. G.L. thanks the PhD studentship funded by the China Scholarship Council.


  • Air Pollutants/analysis
  • Air Pollution
  • COVID-19/epidemiology
  • Cities
  • Environmental Monitoring/methods
  • Gases/analysis
  • Humans
  • London
  • Machine Learning
  • Nitrogen Dioxide/analysis
  • Ozone/analysis
  • Paris
  • Particulate Matter
  • Temperature


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