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10.1038/s41598-020-72271-5 http://scihub22266oqcxt.onion/10.1038/s41598-020-72271-5 33024128!7539013!33024128     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2020 ; 10 (1): 16574 Nephropedia Template TP {{tp|p=33024128|t=2020. Spatial and temporal variations of air pollution over 41 cities of India during the COVID-19 lockdown period |pdf=|usr=}}{{33024128}} gab.com Text Spatial and temporal variations of air pollution over 41 cities of India during the COVID-19 lockdown period JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33024128 #FOAvip In this study, we characterize the impacts of COVID-19 on air pollution using NO(2) and Aerosol Optical Depth (AOD) from TROPOMI and MODIS satellite datasets for 41 cities in India. Specifically, our results suggested a 13% NO(2) reduction during the lockdown (March 25-May 3rd, 2020) compared to the pre-lockdown (January 1st-March 24th, 2020) period. Also, a 19% reduction in NO(2) was observed during the 2020-lockdown as compared to the same period during 2019. The top cities where NO(2) reduction occurred were New Delhi (61.74%), Delhi (60.37%), Bangalore (48.25%), Ahmedabad (46.20%), Nagpur (46.13%), Gandhinagar (45.64) and Mumbai (43.08%) with less reduction in coastal cities. The temporal analysis revealed a progressive decrease in NO(2) for all seven cities during the 2020 lockdown period. Results also suggested spatial differences, i.e., as the distance from the city center increased, the NO(2) levels decreased exponentially. In contrast, to the decreased NO(2) observed for most of the cities, we observed an increase in NO(2) for cities in Northeast India during the 2020 lockdown period and attribute it to vegetation fires. The NO(2) temporal patterns matched the AOD signal; however, the correlations were poor. Overall, our results highlight COVID-19 impacts on NO(2), and the results can inform pollution mitigation efforts across different cities of India. Twit Text FOAVip Spatial and temporal variations of air pollution over 41 cities of India during the COVID-19 lockdown period ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33024128 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33024128 #FOAvip English Wikipedia <ref>{{Cite pmid|33024128}}</ref> Spatial and temporal variations of air pollution over 41 cities of India during the COVID-19 lockdown period #MMPMID33024128 Vadrevu KP; Eaturu A; Biswas S; Lasko K; Sahu S; Garg JK; Justice C Sci Rep 2020[Oct]; 10 (1): 16574 PMID33024128show ga In this study, we characterize the impacts of COVID-19 on air pollution using NO(2) and Aerosol Optical Depth (AOD) from TROPOMI and MODIS satellite datasets for 41 cities in India. Specifically, our results suggested a 13% NO(2) reduction during the lockdown (March 25-May 3rd, 2020) compared to the pre-lockdown (January 1st-March 24th, 2020) period. Also, a 19% reduction in NO(2) was observed during the 2020-lockdown as compared to the same period during 2019. The top cities where NO(2) reduction occurred were New Delhi (61.74%), Delhi (60.37%), Bangalore (48.25%), Ahmedabad (46.20%), Nagpur (46.13%), Gandhinagar (45.64) and Mumbai (43.08%) with less reduction in coastal cities. The temporal analysis revealed a progressive decrease in NO(2) for all seven cities during the 2020 lockdown period. Results also suggested spatial differences, i.e., as the distance from the city center increased, the NO(2) levels decreased exponentially. In contrast, to the decreased NO(2) observed for most of the cities, we observed an increase in NO(2) for cities in Northeast India during the 2020 lockdown period and attribute it to vegetation fires. The NO(2) temporal patterns matched the AOD signal; however, the correlations were poor. Overall, our results highlight COVID-19 impacts on NO(2), and the results can inform pollution mitigation efforts across different cities of India. äSpatial and temporal variations of air pollution over 41 cities of Ind(epmc).pdf DeepDyve Pubget Overpricing