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10.1016/j.ecoenv.2020.111035 http://scihub22266oqcxt.onion/10.1016/j.ecoenv.2020.111035 32768746!7406240!32768746     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32768746&cmd=llinks): Failed to open stream: HTTP request failed! 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Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China |pdf=|usr=}}{{32768746}} gab.com Text Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China JO: Ecotoxicol Environ Saf http://www.kidney.de/pget.php?&tw=1&pmid=32768746 #FOAvip 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. Twit Text FOAVip Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China ????Ecotoxicol Environ Saf http://www.kidney.de/pget.php?&tw=1&pmid=32768746 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32768746 #FOAvip English Wikipedia <ref>{{Cite pmid|32768746}}</ref> Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China #MMPMID32768746 Lin S; Wei D; Sun Y; Chen K; Yang L; Liu B; Huang Q; Paoliello MMB; Li H; Wu S Ecotoxicol Environ Saf 2020[Nov]; 204 (ä): 111035 PMID32768746show ga 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. |Air Pollutants/*analysis[MESH] |Air Pollution/analysis/*statistics & numerical data[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Carbon Monoxide/analysis[MESH] |China/epidemiology[MESH] |Coronavirus[MESH] |Coronavirus Infections/*epidemiology[MESH] |Environmental Exposure/*statistics & numerical data[MESH] |Humans[MESH] |Nitrogen Dioxide/analysis[MESH] |Pandemics[MESH] |Particulate Matter/analysis[MESH] |Pneumonia, Viral/*epidemiology[MESH] |SARS-CoV-2[MESH] |Temperature[MESH]Region-specific air pollutants and meteorological parameters influence(epmc).pdf DeepDyve Pubget Overpricing