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10.1016/j.buildenv.2021.108231 http://scihub22266oqcxt.onion/10.1016/j.buildenv.2021.108231 34393324!8354860!34393324     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 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Build+Environ 2021 ; 205 (ä): 108231 Nephropedia Template TP {{tp|p=34393324|t=2021. Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China |pdf=|usr=}}{{34393324}} gab.com Text Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China JO: Build Environ http://www.kidney.de/pget.php?&tw=1&pmid=34393324 #FOAvip The COVID-19 pandemic provides an opportunity to study the effects of urban lockdown policies on the variation in pollutant concentrations and to characterize the recovery patterns of urban air pollution under the interruption of COVID-19 lockdown policies. In this paper, interruption-recovery models and regression discontinuity design were developed to characterize air pollution interruption-recovery patterns and analyze environmental impacts of the COVID-19 lockdown, using air pollution data from four Chinese metropolises (i.e., Shanghai, Wuhan, Tianjin, and Guangzhou). The results revealed the air pollutant interruption-recovery curve represented by the three lockdown response periods (Level I, Level II and Level III) during COVID-19. The curve decreased during Level I (A 25.3%-48.8% drop in the concentration of NO(2) has been observed in the four metropolises compared with the same period in 2018-2019.), then recovered around reopening, but decreased again during Level III. Moreover, the interruption-recovery curve of the year-on-year air pollution difference suggests a process of first decreasing during Level I and gradually recovering to a new equilibrium during Level III (e.g., the unit cumulative difference of NO(2) mass concentrations in Shanghai was 21.7, 22.5, 11.3 (mug/m(3)) during Level I, II, and III and other metropolises shared similar results). Our findings reveal general trends in the air quality externality of different lockdown policies, hence could provide valuable insights into air pollutant interruption-recovery patterns and clear scientific guides for policymakers to estimate the effect of different lockdown policies on urban air quality. Twit Text FOAVip Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China ????Build Environ http://www.kidney.de/pget.php?&tw=1&pmid=34393324 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34393324 #FOAvip English Wikipedia <ref>{{Cite pmid|34393324}}</ref> Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China #MMPMID34393324 Cai WJ; Wang HW; Wu CL; Lu KF; Peng ZR; He HD Build Environ 2021[Nov]; 205 (ä): 108231 PMID34393324show ga The COVID-19 pandemic provides an opportunity to study the effects of urban lockdown policies on the variation in pollutant concentrations and to characterize the recovery patterns of urban air pollution under the interruption of COVID-19 lockdown policies. In this paper, interruption-recovery models and regression discontinuity design were developed to characterize air pollution interruption-recovery patterns and analyze environmental impacts of the COVID-19 lockdown, using air pollution data from four Chinese metropolises (i.e., Shanghai, Wuhan, Tianjin, and Guangzhou). The results revealed the air pollutant interruption-recovery curve represented by the three lockdown response periods (Level I, Level II and Level III) during COVID-19. The curve decreased during Level I (A 25.3%-48.8% drop in the concentration of NO(2) has been observed in the four metropolises compared with the same period in 2018-2019.), then recovered around reopening, but decreased again during Level III. Moreover, the interruption-recovery curve of the year-on-year air pollution difference suggests a process of first decreasing during Level I and gradually recovering to a new equilibrium during Level III (e.g., the unit cumulative difference of NO(2) mass concentrations in Shanghai was 21.7, 22.5, 11.3 (mug/m(3)) during Level I, II, and III and other metropolises shared similar results). Our findings reveal general trends in the air quality externality of different lockdown policies, hence could provide valuable insights into air pollutant interruption-recovery patterns and clear scientific guides for policymakers to estimate the effect of different lockdown policies on urban air quality. äCharacterizing the interruption-recovery patterns of urban air polluti(epmc).pdf DeepDyve Pubget Overpricing