Special Issue on COVID-19 Aerosol Drivers, Impacts and Mitigation (IX)

Yihan Wu1, Qingming Zhan This email address is being protected from spambots. You need JavaScript enabled to view it.1, Qunshan Zhao2

1 School of Urban Design, Wuhan University,8 Donghu South Road, Wuhan 430072, China
2 Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow G12 8RZ, UK

Received: July 17, 2020
Revised: August 27, 2020
Accepted: August 31, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.2020.07.0413  

Cite this article:

Wu, Y., Zhan, Q., Zhao, Q. (2021). Long-term Air Pollution Exposure Impact on COVID-19 Morbidity in China. Aerosol Air Qual. Res. 21, 200143. https://doi.org/10.4209/aaqr.2020.07.0413


  • PM2.5, PM10, NO2 and O3 levels may significantly affect COVID-19 morbidity.
  • The robustness of the statistical outcome is adjusted for 16 confounders.
  • No significant difference was found between single- and two-pollutant model results.
  • All data used in this study is provided by public agencies.


Although previous studies have proved the association between air pollution and respiratory viral infection, given the relatively short history of human infection with the severe acute respiratory syndrome coronavirus (SARS-CoV-2), the linkage between long-term air pollution exposure and the morbidity of 2019 novel coronavirus (COVID-19) pneumonia remains poorly understood. To fill this gap, this study investigates the influences of particulate matters (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2) and carbon monoxide (CO) on COVID-19 incidence rate based on the prefecture-level morbidity count and air quality data in China. Annual means for ambient PM2.5, PM10, SO2, NO2, CO and O3 concentrations in each prefecture are used to estimate the population’s exposure. We leverage identical statistical methods, i.e., Spearman’s rank correlation and negative binomial regression model, to demonstrate that people who are chronically exposed to ambient air pollution are more likely to be infected by COVID-19. Our statistical analysis indicates that a 1 µg m–3 increase of PM2.5, PM10, NO2 and O3 can result in 1.95% (95% CI: 0.83–3.08%), 0.55% (95% CI: –0.05–1.17%), 4.63% (95% CI: 3.07–6.22%) rise and 2.05% (95% CI: 0.51–3.59%) decrease of COVID-19 morbidity. However, we observe nonsignificant association with long-term SO2 and CO exposure to COVID-19 morbidity in this study. Our results’ robustness is examined based on sensitivity analyses that adjust for a wide range of confounders, including socio-economic, demographic, weather, healthcare, and mobility-related variables. We acknowledge that more laboratory results are required to prove the etiology of these associations.

Keywords: Air pollution exposure, COVID-19 morbidity, Prefecture-level data, Negative binomial regression

Aerosol Air Qual. Res. 21 :200413 -. https://doi.org/10.4209/aaqr.2020.07.0413  

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