Long-term Air Pollution Exposure Impact on COVID-19 Morbidity in China
Abstract 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 th...
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| Format: | Article |
| Language: | English |
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Springer
2020-09-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.2020.07.0413 |
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| _version_ | 1825197500221358080 |
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| author | Yihan Wu Qingming Zhan Qunshan Zhao |
| author_facet | Yihan Wu Qingming Zhan Qunshan Zhao |
| author_sort | Yihan Wu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-4d0c97a36fd947f1a01754bd015106ec |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2020-09-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-4d0c97a36fd947f1a01754bd015106ec2025-02-09T12:20:40ZengSpringerAerosol and Air Quality Research1680-85842071-14092020-09-0121111310.4209/aaqr.2020.07.0413Long-term Air Pollution Exposure Impact on COVID-19 Morbidity in ChinaYihan Wu0Qingming Zhan1Qunshan Zhao2School of Urban Design, Wuhan UniversitySchool of Urban Design, Wuhan UniversityUrban Big Data Centre, School of Social and Political Sciences, University of GlasgowAbstract 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.https://doi.org/10.4209/aaqr.2020.07.0413Air pollution exposureCOVID-19 morbidityPrefecture-level dataNegative binomial regression |
| spellingShingle | Yihan Wu Qingming Zhan Qunshan Zhao Long-term Air Pollution Exposure Impact on COVID-19 Morbidity in China Aerosol and Air Quality Research Air pollution exposure COVID-19 morbidity Prefecture-level data Negative binomial regression |
| title | Long-term Air Pollution Exposure Impact on COVID-19 Morbidity in China |
| title_full | Long-term Air Pollution Exposure Impact on COVID-19 Morbidity in China |
| title_fullStr | Long-term Air Pollution Exposure Impact on COVID-19 Morbidity in China |
| title_full_unstemmed | Long-term Air Pollution Exposure Impact on COVID-19 Morbidity in China |
| title_short | Long-term Air Pollution Exposure Impact on COVID-19 Morbidity in China |
| title_sort | long term air pollution exposure impact on covid 19 morbidity in china |
| topic | Air pollution exposure COVID-19 morbidity Prefecture-level data Negative binomial regression |
| url | https://doi.org/10.4209/aaqr.2020.07.0413 |
| work_keys_str_mv | AT yihanwu longtermairpollutionexposureimpactoncovid19morbidityinchina AT qingmingzhan longtermairpollutionexposureimpactoncovid19morbidityinchina AT qunshanzhao longtermairpollutionexposureimpactoncovid19morbidityinchina |