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Effects of corona virus disease‐19 control measures on air quality in North China

Author

Listed:
  • Xiangyu Zheng
  • Bin Guo
  • Jing He
  • Song Xi Chen

Abstract

Corona virus disease‐19 (COVID‐19) has substantially reduced human activities and the associated anthropogenic emissions. This study quantifies the effects of COVID‐19 control measures on six major air pollutants over 68 cities in North China by a Difference in Relative‐Difference method that allows estimation of the COVID‐19 effects while taking account of the general annual air quality trends, temporal and meteorological variations, and the spring festival effects. Significant COVID‐19 effects on all six major air pollutants are found, with NO2 having the largest decline (−39.6%), followed by PM2.5 (−30.9%), O3 (−16.3%), PM10 (−14.3%), CO (−13.9%), and the least in SO2 (−10.0%), which shows the achievability of air quality improvement by a large reduction in anthropogenic emissions. The heterogeneity of effects among the six pollutants and different regions can be partly explained by coal consumption and industrial output data.

Suggested Citation

  • Xiangyu Zheng & Bin Guo & Jing He & Song Xi Chen, 2021. "Effects of corona virus disease‐19 control measures on air quality in North China," Environmetrics, John Wiley & Sons, Ltd., vol. 32(2), March.
    • Handle: RePEc:wly:envmet:v:32:y:2021:i:2:n:e2673
    DOI: 10.1002/env.2673
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    References listed on IDEAS

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    Cited by:

    1. Ying Zhang & Song Xi Chen & Le Bao, 2023. "Air pollution estimation under air stagnation—A case study of Beijing," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.
    2. Hongya Niu & Chongchong Zhang & Wei Hu & Tafeng Hu & Chunmiao Wu & Sihao Hu & Luis F. O. Silva & Nana Gao & Xiaolei Bao & Jingsen Fan, 2022. "Air Quality Changes during the COVID-19 Lockdown in an Industrial City in North China: Post-Pandemic Proposals for Air Quality Improvement," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    3. Lauren Hoskovec & Sheena Martenies & Tori L. Burket & Sheryl Magzamen & Ander Wilson, 2022. "Association between air pollution and COVID‐19 disease severity via Bayesian multinomial logistic regression with partially missing outcomes," Environmetrics, John Wiley & Sons, Ltd., vol. 33(7), November.
    4. Guido Fioravanti & Michela Cameletti & Sara Martino & Giorgio Cattani & Enrico Pisoni, 2022. "A spatiotemporal analysis of NO2 concentrations during the Italian 2020 COVID‐19 lockdown," Environmetrics, John Wiley & Sons, Ltd., vol. 33(4), June.
    5. Rodeschini, Jacopo & Fassò, Alessandro & Finazzi, Francesco & Fusta Moro, Alessandro, 2024. "Scenario analysis of livestock-related PM2.5 pollution based on a new heteroskedastic spatiotemporal model," Socio-Economic Planning Sciences, Elsevier, vol. 96(C).
    6. Daniel Cirkovic & Thomas J. Fisher, 2021. "On testing for the equality of autocovariance in time series," Environmetrics, John Wiley & Sons, Ltd., vol. 32(7), November.

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