Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China.
Identifieur interne : 002587 ( Main/Exploration ); précédent : 002586; suivant : 002588Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China.
Auteurs : Yongjian Zhu [République populaire de Chine] ; Jingui Xie [Royaume-Uni] ; Fengming Huang [République populaire de Chine] ; Liqing Cao [République populaire de Chine]Source :
- The Science of the total environment [ 1879-1026 ] ; 2020.
Descripteurs français
- KwdFr :
- MESH :
- Wicri :
- geographic : République populaire de Chine.
English descriptors
- KwdEn :
- MESH :
- chemical : Air Pollutants, Particulate Matter.
- geographic : China, Cities.
- Air Pollution, Betacoronavirus, COVID-19, Coronavirus Infections, Environmental Exposure, Humans, Pandemics, Pneumonia, Viral, SARS-CoV-2.
Abstract
The novel coronavirus pneumonia, namely COVID-19, has become a global public health problem. Previous studies have found that air pollution is a risk factor for respiratory infection by carrying microorganisms and affecting body's immunity. This study aimed to explore the relationship between ambient air pollutants and the infection caused by the novel coronavirus. Daily confirmed cases, air pollution concentration and meteorological variables in 120 cities were obtained from January 23, 2020 to February 29, 2020 in China. We applied a generalized additive model to investigate the associations of six air pollutants (PM2.5, PM10, SO2, CO, NO2 and O3) with COVID-19 confirmed cases. We observed significantly positive associations of PM2.5, PM10, NO2 and O3 in the last two weeks with newly COVID-19 confirmed cases. A 10-μg/m3 increase (lag0-14) in PM2.5, PM10, NO2, and O3 was associated with a 2.24% (95% CI: 1.02 to 3.46), 1.76% (95% CI: 0.89 to 2.63), 6.94% (95% CI: 2.38 to 11.51), and 4.76% (95% CI: 1.99 to 7.52) increase in the daily counts of confirmed cases, respectively. However, a 10-μg/m3 increase (lag0-14) in SO2 was associated with a 7.79% decrease (95% CI: -14.57 to -1.01) in COVID-19 confirmed cases. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which could partially explain the effect of national lockdown and provide implications for the control and prevention of this novel disease.
DOI: 10.1016/j.scitotenv.2020.138704
PubMed: 32315904
PubMed Central: PMC7159846
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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<term>Betacoronavirus (MeSH)</term>
<term>COVID-19 (MeSH)</term>
<term>China (MeSH)</term>
<term>Cities (MeSH)</term>
<term>Coronavirus Infections (MeSH)</term>
<term>Environmental Exposure (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Pandemics (MeSH)</term>
<term>Particulate Matter (MeSH)</term>
<term>Pneumonia, Viral (MeSH)</term>
<term>SARS-CoV-2 (MeSH)</term>
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<keywords scheme="KwdFr" xml:lang="fr"><term>Betacoronavirus (MeSH)</term>
<term>Chine (MeSH)</term>
<term>Exposition environnementale (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Infections à coronavirus (MeSH)</term>
<term>Matière particulaire (MeSH)</term>
<term>Pandémies (MeSH)</term>
<term>Pneumopathie virale (MeSH)</term>
<term>Polluants atmosphériques (MeSH)</term>
<term>Pollution de l'air (MeSH)</term>
<term>Villes (MeSH)</term>
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<front><div type="abstract" xml:lang="en">The novel coronavirus pneumonia, namely COVID-19, has become a global public health problem. Previous studies have found that air pollution is a risk factor for respiratory infection by carrying microorganisms and affecting body's immunity. This study aimed to explore the relationship between ambient air pollutants and the infection caused by the novel coronavirus. Daily confirmed cases, air pollution concentration and meteorological variables in 120 cities were obtained from January 23, 2020 to February 29, 2020 in China. We applied a generalized additive model to investigate the associations of six air pollutants (PM<sub>2.5</sub>
, PM<sub>10</sub>
, SO<sub>2</sub>
, CO, NO<sub>2</sub>
and O<sub>3</sub>
) with COVID-19 confirmed cases. We observed significantly positive associations of PM<sub>2.5</sub>
, PM<sub>10</sub>
, NO<sub>2</sub>
and O<sub>3</sub>
in the last two weeks with newly COVID-19 confirmed cases. A 10-μg/m<sup>3</sup>
increase (lag0-14) in PM<sub>2.5</sub>
, PM<sub>10</sub>
, NO<sub>2</sub>
, and O<sub>3</sub>
was associated with a 2.24% (95% CI: 1.02 to 3.46), 1.76% (95% CI: 0.89 to 2.63), 6.94% (95% CI: 2.38 to 11.51), and 4.76% (95% CI: 1.99 to 7.52) increase in the daily counts of confirmed cases, respectively. However, a 10-μg/m<sup>3</sup>
increase (lag0-14) in SO<sub>2</sub>
was associated with a 7.79% decrease (95% CI: -14.57 to -1.01) in COVID-19 confirmed cases. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which could partially explain the effect of national lockdown and provide implications for the control and prevention of this novel disease.</div>
</front>
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<Abstract><AbstractText>The novel coronavirus pneumonia, namely COVID-19, has become a global public health problem. Previous studies have found that air pollution is a risk factor for respiratory infection by carrying microorganisms and affecting body's immunity. This study aimed to explore the relationship between ambient air pollutants and the infection caused by the novel coronavirus. Daily confirmed cases, air pollution concentration and meteorological variables in 120 cities were obtained from January 23, 2020 to February 29, 2020 in China. We applied a generalized additive model to investigate the associations of six air pollutants (PM<sub>2.5</sub>
, PM<sub>10</sub>
, SO<sub>2</sub>
, CO, NO<sub>2</sub>
and O<sub>3</sub>
) with COVID-19 confirmed cases. We observed significantly positive associations of PM<sub>2.5</sub>
, PM<sub>10</sub>
, NO<sub>2</sub>
and O<sub>3</sub>
in the last two weeks with newly COVID-19 confirmed cases. A 10-μg/m<sup>3</sup>
increase (lag0-14) in PM<sub>2.5</sub>
, PM<sub>10</sub>
, NO<sub>2</sub>
, and O<sub>3</sub>
was associated with a 2.24% (95% CI: 1.02 to 3.46), 1.76% (95% CI: 0.89 to 2.63), 6.94% (95% CI: 2.38 to 11.51), and 4.76% (95% CI: 1.99 to 7.52) increase in the daily counts of confirmed cases, respectively. However, a 10-μg/m<sup>3</sup>
increase (lag0-14) in SO<sub>2</sub>
was associated with a 7.79% decrease (95% CI: -14.57 to -1.01) in COVID-19 confirmed cases. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which could partially explain the effect of national lockdown and provide implications for the control and prevention of this novel disease.</AbstractText>
<CopyrightInformation>Copyright © 2020 Elsevier B.V. All rights reserved.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhu</LastName>
<ForeName>Yongjian</ForeName>
<Initials>Y</Initials>
<AffiliationInfo><Affiliation>School of Management, University of Science and Technology of China, Hefei, China. Electronic address: ustczyj@mail.ustc.edu.cn.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Xie</LastName>
<ForeName>Jingui</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China; Brunel Business School, Brunel University London, Uxbridge, United Kingdom. Electronic address: xiej@ustc.edu.cn.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Huang</LastName>
<ForeName>Fengming</ForeName>
<Initials>F</Initials>
<AffiliationInfo><Affiliation>The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China. Electronic address: 3234119867@qq.com.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Cao</LastName>
<ForeName>Liqing</ForeName>
<Initials>L</Initials>
<AffiliationInfo><Affiliation>The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China. Electronic address: caoliqing@ustc.edu.cn.</Affiliation>
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<CoiStatement>Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</CoiStatement>
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