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10.1007/s10668-021-01366-4 http://scihub22266oqcxt.onion/10.1007/s10668-021-01366-4 33841040!8019339!33841040     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Environ+Dev+Sustain 2021 ; 23 (11): 16632-16645 Nephropedia Template TP {{tp|p=33841040|t=2021. Strong link between coronavirus count and bad air: a case study of India |pdf=|usr=}}{{33841040}} gab.com Text Strong link between coronavirus count and bad air: a case study of India JO: Environ Dev Sustain http://www.kidney.de/pget.php?&tw=1&pmid=33841040 #FOAvip The present study aims to highlight the contrast relationship between COVID-19 (Coronavirus Disease-2019) infections and air pollutants for the Indian region. The COVID-19 data (cumulative, confirmed cases and deaths), air pollutants (PM(10), PM(2.5), NO(2) and SO(2)) and meteorological data (temperature and relative humidity) were collected from January 2020 to August 2020 for all 28 states and the union territory of India during the pandemic. Now, to understand the relationship between air pollutant concentration, meteorological factor, and COVID-19 cases, the nonparametric Spearman's and Kendall's rank correlation were used. The COVID-19 shows a favourable temperature (0.55-0.79) and humidity (0.14-0.52) over the Indian region. The PM(2.5) and PM(10) gave a strong and negative correlation with COVID-19 cases in the range of 0.64-0.98. Similarly, the NO(2) shows a strong and negative correlation in the range of 0.64-0.98. Before the lockdown, the concentration of pollution parameters is high due to the shallow boundary layer height. But after lockdown, the overall reduction was reported up to 33.67% in air quality index (AQI). The background metrological parameters showed a crucial role in the variation of pollutant parameters (SO(2), NO(2), PM(10) and PM(2.5)) and the COVID-19 infection with the economic aspects. The European Centre for Medium-Range Weather Forecasts derived monthly average wind speed was also plotted. It can see that January and February of 2020 show the least variation of air mass in the range of 1-2 m/s. The highest wind speed was reported during July and August 2020. India's western and southern parts experienced an air mass in the range of 4-8 m/s. The precipitation/wet deposition of atmospheric aerosols further improves the AQI over India. According to a study, the impact of relative humidity among all other metrological parameters is positively correlated with Cases and death. Outcomes of the proposed work had the aim of supporting national and state governance for healthcare policymakers. Twit Text FOAVip Strong link between coronavirus count and bad air: a case study of India ????Environ Dev Sustain http://www.kidney.de/pget.php?&tw=1&pmid=33841040 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33841040 #FOAvip English Wikipedia <ref>{{Cite pmid|33841040}}</ref> Strong link between coronavirus count and bad air: a case study of India #MMPMID33841040 Gautam S; Samuel C; Gautam AS; Kumar S Environ Dev Sustain 2021[]; 23 (11): 16632-16645 PMID33841040show ga The present study aims to highlight the contrast relationship between COVID-19 (Coronavirus Disease-2019) infections and air pollutants for the Indian region. The COVID-19 data (cumulative, confirmed cases and deaths), air pollutants (PM(10), PM(2.5), NO(2) and SO(2)) and meteorological data (temperature and relative humidity) were collected from January 2020 to August 2020 for all 28 states and the union territory of India during the pandemic. Now, to understand the relationship between air pollutant concentration, meteorological factor, and COVID-19 cases, the nonparametric Spearman's and Kendall's rank correlation were used. The COVID-19 shows a favourable temperature (0.55-0.79) and humidity (0.14-0.52) over the Indian region. The PM(2.5) and PM(10) gave a strong and negative correlation with COVID-19 cases in the range of 0.64-0.98. Similarly, the NO(2) shows a strong and negative correlation in the range of 0.64-0.98. Before the lockdown, the concentration of pollution parameters is high due to the shallow boundary layer height. But after lockdown, the overall reduction was reported up to 33.67% in air quality index (AQI). The background metrological parameters showed a crucial role in the variation of pollutant parameters (SO(2), NO(2), PM(10) and PM(2.5)) and the COVID-19 infection with the economic aspects. The European Centre for Medium-Range Weather Forecasts derived monthly average wind speed was also plotted. It can see that January and February of 2020 show the least variation of air mass in the range of 1-2 m/s. The highest wind speed was reported during July and August 2020. India's western and southern parts experienced an air mass in the range of 4-8 m/s. The precipitation/wet deposition of atmospheric aerosols further improves the AQI over India. According to a study, the impact of relative humidity among all other metrological parameters is positively correlated with Cases and death. Outcomes of the proposed work had the aim of supporting national and state governance for healthcare policymakers. äStrong link between coronavirus count and bad air: a case study of Ind(epmc).pdf DeepDyve Pubget Overpricing