See the published version of this preprint at Environmental Science and Pollution Research.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
Research Article
Qiansheng Huang
Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3788-3164
License:
This work is licensed under a CC BY 4.0 License. Read Full License
There is a rising concern that air pollution plays an important role in the COVID-19 pandemic. However, the results weren’t consistent on the association between air pollution and the spread of COVID-19. In the study, air pollution data and the confirmed cases of COVID-19 were both gathered from five severe cities across three countries in South America. Daily real-time population regeneration (Rt) were calculated to assess the spread of COVID-19. Two frequently used model, generalized additive models (GAM) and multiple linear regression, were both used to explore the impact of environmental pollutants on the epidemic. Wide ranges of all the six air pollutants were detected across the five cities. Spearman's correlation analysis confirmed the positive correlation within six pollutants. Rt value showed a gradual decline in all the five cities. Further analysis showed that the association between air pollution and COVID-19 varied across five cities. Multiple linear regression and GAM did not give the same trend in a specific city. For example, in Sao Paulo, the GAM model shows that PM10 has a nonlinear negative correlation with Rt, while PM10 has no significant correlation in the multiple linear model. According to our research results, even for the same region, varied models gave inconsistent results. Moreover, in the case of multiple regions, current used models should be selected according to local conditions.
Keywords
COVID-19, air pollution, generalized additive model, multiple linear regression, South America, Daily real-time population regeneration
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Environmental Science and Pollution Research.
Version 1
Posted 13 May, 2021
No community comments so far
Editorial decision: Major Revision
On 31 May, 2021
Reviews received
Received 11 May, 2021
Reviewers invited
Invitations sent on 11 May, 2021
Editor invited
On 07 May, 2021
Editor assigned
On 28 Apr, 2021
First submitted
On 23 Apr, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Environmental Policy
Learn more about our company.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at Environmental Science and Pollution Research.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Qiansheng Huang
Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3788-3164
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Environmental Science and Pollution Research.
Version 1
Posted 13 May, 2021
No community comments so far
Editorial decision: Major Revision
On 31 May, 2021
Reviews received
Received 11 May, 2021
Reviewers invited
Invitations sent on 11 May, 2021
Editor invited
On 07 May, 2021
Editor assigned
On 28 Apr, 2021
First submitted
On 23 Apr, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Environmental Policy
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Environmental Science and Pollution Research.
There is a rising concern that air pollution plays an important role in the COVID-19 pandemic. However, the results weren’t consistent on the association between air pollution and the spread of COVID-19. In the study, air pollution data and the confirmed cases of COVID-19 were both gathered from five severe cities across three countries in South America. Daily real-time population regeneration (Rt) were calculated to assess the spread of COVID-19. Two frequently used model, generalized additive models (GAM) and multiple linear regression, were both used to explore the impact of environmental pollutants on the epidemic. Wide ranges of all the six air pollutants were detected across the five cities. Spearman's correlation analysis confirmed the positive correlation within six pollutants. Rt value showed a gradual decline in all the five cities. Further analysis showed that the association between air pollution and COVID-19 varied across five cities. Multiple linear regression and GAM did not give the same trend in a specific city. For example, in Sao Paulo, the GAM model shows that PM10 has a nonlinear negative correlation with Rt, while PM10 has no significant correlation in the multiple linear model. According to our research results, even for the same region, varied models gave inconsistent results. Moreover, in the case of multiple regions, current used models should be selected according to local conditions.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Loading...