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10.3390/ijerph17176274 http://scihub22266oqcxt.onion/10.3390/ijerph17176274 32872261!7503249!32872261     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 284.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 284.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 284.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 284.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2020 ; 17 (17): ä Nephropedia Template TP {{tp|p=32872261|t=2020. How Did Distribution Patterns of Particulate Matter Air Pollution (PM(2 5) and PM(10)) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level |pdf=|usr=}}{{32872261}} gab.com Text How Did Distribution Patterns of Particulate Matter Air Pollution (PM(2 5) and PM(10)) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32872261 #FOAvip Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM(2.5) and PM(10) during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM(2.5) and PM(10) concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R(2) of 0.711 and 0.732 for PM(2.5) and PM(10), respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM(2.5) in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM(10) in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM(10) drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future. Twit Text FOAVip How Did Distribution Patterns of Particulate Matter Air Pollution (PM(2 5) and PM(10)) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32872261 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32872261 #FOAvip English Wikipedia <ref>{{Cite pmid|32872261}}</ref> How Did Distribution Patterns of Particulate Matter Air Pollution (PM(2 5) and PM(10)) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level #MMPMID32872261 Fan Z; Zhan Q; Yang C; Liu H; Zhan M Int J Environ Res Public Health 2020[Aug]; 17 (17): ä PMID32872261show ga Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM(2.5) and PM(10) during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM(2.5) and PM(10) concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R(2) of 0.711 and 0.732 for PM(2.5) and PM(10), respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM(2.5) in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM(10) in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM(10) drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future. |*Environmental Monitoring[MESH] |Air Pollutants/*analysis[MESH] |Air Pollution/analysis[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |China[MESH] |Cities[MESH] |Coronavirus Infections/*epidemiology[MESH] |Humans[MESH] |Pandemics[MESH] |Particulate Matter/*analysis[MESH] |Pneumonia, Viral/*epidemiology[MESH]How Did Distribution Patterns of Particulate Matter Air Pollution (PM((epmc).pdf DeepDyve Pubget Overpricing