The Effects of COVID-19 Lockdown on Air Pollutant Concentrations across China: A Google Earth Engine-Based Analysis

Int J Environ Res Public Health. 2022 Dec 19;19(24):17056. doi: 10.3390/ijerph192417056.

Abstract

To overcome the spread of the severe COVID-19 outbreak, various lockdown measures have been taken worldwide. China imposed the strictest home-quarantine measures during the COVID-19 outbreak in the year 2020. This provides a valuable opportunity to study the impact of anthropogenic emission reductions on air quality. Based on the GEE platform and satellite imagery, this study analyzed the changes in the concentrations of NO2, O3, CO, and SO2 in the same season (1 February-1 May) before and after the epidemic control (2019-2021) for 16 typical representative cities of China. The results showed that NO2 concentrations significantly decreased by around 20-24% for different types of metropolises, whereas O3 increased for most of the studied metropolises, including approximately 7% in megacities and other major cities. Additionally, the concentrations of CO and SO2 showed no statistically significant changes during the study intervals. The study also indicated strong variations in air pollutants among different geographic regions. In addition to the methods in this study, it is essential to include the differences in meteorological impact factors in the study to identify future references for air pollution reduction measures.

Keywords: COVID-19; China; Google Earth Engine; air pollutants; remote sensing; urban.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Air Pollutants* / analysis
  • Air Pollution* / analysis
  • COVID-19* / epidemiology
  • China / epidemiology
  • Cities
  • Communicable Disease Control
  • Environmental Monitoring / methods
  • Humans
  • Nitrogen Dioxide / analysis
  • Particulate Matter / analysis
  • Search Engine

Substances

  • Air Pollutants
  • Nitrogen Dioxide
  • Particulate Matter

Grants and funding

This research was supported by a grant from the State Key Laboratory of Resources and Environmental Information Systems, China; the National Natural Science Foundation of China, grant number 4210010491; the Education Department of Jilin Province, China, grant number JJKH20211289KJ; and the Natural Science Foundation of Jilin Scientific Institute, grant number YDZJ202101ZYTS104.