Cargando…

Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China

Coronavirus disease 2019 (COVID-19) was first detected in December 2019 in Wuhan, China, with 11,669,259 positive cases and 539,906 deaths globally as of July 8, 2020. The objective of the present study was to determine whether meteorological parameters and air quality affect the transmission of COV...

Descripción completa

Detalles Bibliográficos
Autores principales: Lin, Shaowei, Wei, Donghong, Sun, Yi, Chen, Kun, Yang, Le, Liu, Bang, Huang, Qing, Paoliello, Monica Maria Bastos, Li, Huangyuan, Wu, Siying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7406240/
https://www.ncbi.nlm.nih.gov/pubmed/32768746
http://dx.doi.org/10.1016/j.ecoenv.2020.111035
_version_ 1783567395007234048
author Lin, Shaowei
Wei, Donghong
Sun, Yi
Chen, Kun
Yang, Le
Liu, Bang
Huang, Qing
Paoliello, Monica Maria Bastos
Li, Huangyuan
Wu, Siying
author_facet Lin, Shaowei
Wei, Donghong
Sun, Yi
Chen, Kun
Yang, Le
Liu, Bang
Huang, Qing
Paoliello, Monica Maria Bastos
Li, Huangyuan
Wu, Siying
author_sort Lin, Shaowei
collection PubMed
description Coronavirus disease 2019 (COVID-19) was first detected in December 2019 in Wuhan, China, with 11,669,259 positive cases and 539,906 deaths globally as of July 8, 2020. The objective of the present study was to determine whether meteorological parameters and air quality affect the transmission of COVID-19, analogous to SARS. We captured data from 29 provinces, including numbers of COVID-19 cases, meteorological parameters, air quality and population flow data, between Jan 21, 2020 and Apr 3, 2020. To evaluate the transmissibility of COVID-19, the basic reproductive ratio (R(0)) was calculated with the maximum likelihood “removal” method, which is based on chain-binomial model, and the association between COVID-19 and air pollutants or meteorological parameters was estimated by correlation analyses. The mean estimated value of R(0) was 1.79 ± 0.31 in 29 provinces, ranging from 1.08 to 2.45. The correlation between R(0) and the mean relative humidity was positive, with coefficient of 0.370. In provinces with high flow, indicators such as carbon monoxide (CO) and 24-h average concentration of carbon monoxide (CO_24 h) were positively correlated with R(0), while nitrogen dioxide (NO(2)), 24-h average concentration of nitrogen dioxide (NO(2)_24 h) and daily maximum temperature were inversely correlated to R(0), with coefficients of 0.644, 0.661, −0.636, −0.657, −0.645, respectively. In provinces with medium flow, only the weather factors were correlated with R(0), including mean/maximum/minimum air pressure and mean wind speed, with coefficients of −0.697, −0.697, −0.697 and −0.841, respectively. There was no correlation with R(0) and meteorological parameters or air pollutants in provinces with low flow. Our findings suggest that higher ambient CO concentration is a risk factor for increased transmissibility of the novel coronavirus, while higher temperature and air pressure, and efficient ventilation reduce its transmissibility. The effect of meteorological parameters and air pollutants varies in different regions, and requires that these issues be considered in future modeling disease transmissibility.
format Online
Article
Text
id pubmed-7406240
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Elsevier Inc.
record_format MEDLINE/PubMed
spelling pubmed-74062402020-08-06 Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China Lin, Shaowei Wei, Donghong Sun, Yi Chen, Kun Yang, Le Liu, Bang Huang, Qing Paoliello, Monica Maria Bastos Li, Huangyuan Wu, Siying Ecotoxicol Environ Saf Article Coronavirus disease 2019 (COVID-19) was first detected in December 2019 in Wuhan, China, with 11,669,259 positive cases and 539,906 deaths globally as of July 8, 2020. The objective of the present study was to determine whether meteorological parameters and air quality affect the transmission of COVID-19, analogous to SARS. We captured data from 29 provinces, including numbers of COVID-19 cases, meteorological parameters, air quality and population flow data, between Jan 21, 2020 and Apr 3, 2020. To evaluate the transmissibility of COVID-19, the basic reproductive ratio (R(0)) was calculated with the maximum likelihood “removal” method, which is based on chain-binomial model, and the association between COVID-19 and air pollutants or meteorological parameters was estimated by correlation analyses. The mean estimated value of R(0) was 1.79 ± 0.31 in 29 provinces, ranging from 1.08 to 2.45. The correlation between R(0) and the mean relative humidity was positive, with coefficient of 0.370. In provinces with high flow, indicators such as carbon monoxide (CO) and 24-h average concentration of carbon monoxide (CO_24 h) were positively correlated with R(0), while nitrogen dioxide (NO(2)), 24-h average concentration of nitrogen dioxide (NO(2)_24 h) and daily maximum temperature were inversely correlated to R(0), with coefficients of 0.644, 0.661, −0.636, −0.657, −0.645, respectively. In provinces with medium flow, only the weather factors were correlated with R(0), including mean/maximum/minimum air pressure and mean wind speed, with coefficients of −0.697, −0.697, −0.697 and −0.841, respectively. There was no correlation with R(0) and meteorological parameters or air pollutants in provinces with low flow. Our findings suggest that higher ambient CO concentration is a risk factor for increased transmissibility of the novel coronavirus, while higher temperature and air pressure, and efficient ventilation reduce its transmissibility. The effect of meteorological parameters and air pollutants varies in different regions, and requires that these issues be considered in future modeling disease transmissibility. Elsevier Inc. 2020-11 2020-08-05 /pmc/articles/PMC7406240/ /pubmed/32768746 http://dx.doi.org/10.1016/j.ecoenv.2020.111035 Text en © 2020 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Lin, Shaowei
Wei, Donghong
Sun, Yi
Chen, Kun
Yang, Le
Liu, Bang
Huang, Qing
Paoliello, Monica Maria Bastos
Li, Huangyuan
Wu, Siying
Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China
title Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China
title_full Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China
title_fullStr Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China
title_full_unstemmed Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China
title_short Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China
title_sort region-specific air pollutants and meteorological parameters influence covid-19: a study from mainland china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7406240/
https://www.ncbi.nlm.nih.gov/pubmed/32768746
http://dx.doi.org/10.1016/j.ecoenv.2020.111035
work_keys_str_mv AT linshaowei regionspecificairpollutantsandmeteorologicalparametersinfluencecovid19astudyfrommainlandchina
AT weidonghong regionspecificairpollutantsandmeteorologicalparametersinfluencecovid19astudyfrommainlandchina
AT sunyi regionspecificairpollutantsandmeteorologicalparametersinfluencecovid19astudyfrommainlandchina
AT chenkun regionspecificairpollutantsandmeteorologicalparametersinfluencecovid19astudyfrommainlandchina
AT yangle regionspecificairpollutantsandmeteorologicalparametersinfluencecovid19astudyfrommainlandchina
AT liubang regionspecificairpollutantsandmeteorologicalparametersinfluencecovid19astudyfrommainlandchina
AT huangqing regionspecificairpollutantsandmeteorologicalparametersinfluencecovid19astudyfrommainlandchina
AT paoliellomonicamariabastos regionspecificairpollutantsandmeteorologicalparametersinfluencecovid19astudyfrommainlandchina
AT lihuangyuan regionspecificairpollutantsandmeteorologicalparametersinfluencecovid19astudyfrommainlandchina
AT wusiying regionspecificairpollutantsandmeteorologicalparametersinfluencecovid19astudyfrommainlandchina