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10.13227/j.hjkx.202012288 http://scihub22266oqcxt.onion/10.13227/j.hjkx.202012288 34414711!ä!34414711 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34414711&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538 Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538       Huan+Jing+Ke+Xue 2021 ; 42 (9): 4126-4139 Nephropedia Template TP {{tp|p=34414711|t=2021. Simulation Analysis of the Impact of COVID-19 Pandemic Control on Air Quality in Henan Province based on Machine Learning Algorithm |pdf=|usr=}}{{34414711}} gab.com Text Simulation Analysis of the Impact of COVID-19 Pandemic Control on Air Quality in Henan Province based on Machine Learning Algorithm JO: Huan Jing Ke Xue http://www.kidney.de/pget.php?&tw=1&pmid=34414711 #FOAvip To reduce the risks of COVID-19 on society and the health of the general public, necessary prevention and control measures were implemented throughout China in 2020. Consequently, air quality was greatly improved due to lower emissions. However, the improvement of air quality could also be closely related to meteorological conditions. During quarantine (January 27 to February, 2020), reductions were observed in the concentration of all air pollutants in Henan Province (PM(2.5), PM(10), SO(2), CO, and NO(2) decreased by 36.89%, 34.18%, 19.43%, 29.85%, and 58.51%, respectively) relative to measurements taken from January 1 to 26, 2020. The only exception was for the concentration of O(3), which increased by 69.64%. This study evaluates the importance of meteorological conditions in air pollution, through simulation with a long-and-short-term memory network (LSTM) and a machine learning algorithm. Results show that meteorological conditions play a crucial role in air pollutant formation. Given favorable meteorological factors, the concentrations of pollutants could be reduced by 15%-30%, while the reduction due to anthropogenic emission control ranges from 6%-40%. During the epidemic, meteorological conditions and human emissions accounted for 34.84% and 34.81% of the increase in O(3) concentration, respectively. The results show that primary pollutant concentrations are more sensitive to the intensity of anthropogenic emissions. However, secondary pollutants are more dependent on meteorological factors. Furthermore, a nonlinear relationship has been identified between O(3) concentration and to emission intensity. Further investigation into the causes of the rise in O(3) concentration is necessary to gain a greater understanding and better control of particulate matter and O(3) pollution. Twit Text FOAVip Simulation Analysis of the Impact of COVID-19 Pandemic Control on Air Quality in Henan Province based on Machine Learning Algorithm ????Huan Jing Ke Xue http://www.kidney.de/pget.php?&tw=1&pmid=34414711 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34414711 #FOAvip English Wikipedia <ref>{{Cite pmid|34414711}}</ref> Simulation Analysis of the Impact of COVID-19 Pandemic Control on Air Quality in Henan Province based on Machine Learning Algorithm #MMPMID34414711 Wei Y; Xu QX; Zhao JS; Zhang RQ Huan Jing Ke Xue 2021[Sep]; 42 (9): 4126-4139 PMID34414711show ga To reduce the risks of COVID-19 on society and the health of the general public, necessary prevention and control measures were implemented throughout China in 2020. Consequently, air quality was greatly improved due to lower emissions. However, the improvement of air quality could also be closely related to meteorological conditions. During quarantine (January 27 to February, 2020), reductions were observed in the concentration of all air pollutants in Henan Province (PM(2.5), PM(10), SO(2), CO, and NO(2) decreased by 36.89%, 34.18%, 19.43%, 29.85%, and 58.51%, respectively) relative to measurements taken from January 1 to 26, 2020. The only exception was for the concentration of O(3), which increased by 69.64%. This study evaluates the importance of meteorological conditions in air pollution, through simulation with a long-and-short-term memory network (LSTM) and a machine learning algorithm. Results show that meteorological conditions play a crucial role in air pollutant formation. Given favorable meteorological factors, the concentrations of pollutants could be reduced by 15%-30%, while the reduction due to anthropogenic emission control ranges from 6%-40%. During the epidemic, meteorological conditions and human emissions accounted for 34.84% and 34.81% of the increase in O(3) concentration, respectively. The results show that primary pollutant concentrations are more sensitive to the intensity of anthropogenic emissions. However, secondary pollutants are more dependent on meteorological factors. Furthermore, a nonlinear relationship has been identified between O(3) concentration and to emission intensity. Further investigation into the causes of the rise in O(3) concentration is necessary to gain a greater understanding and better control of particulate matter and O(3) pollution. |*Air Pollution[MESH] |*COVID-19[MESH] |Algorithms[MESH] |Humans[MESH] |Machine Learning[MESH] |Pandemics[MESH] Simulation Analysis of the Impact of COVID-19 Pandemic Control on Air(epmc).pdf DeepDyve Pubget Overpricing