Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China
https://orcid.org/0000-0001-8352-9820
https://orcid.org/0000-0001-9393-2701
https://orcid.org/0000-0003-3648-6770
https://orcid.org/0000-0002-5820-3869
Abstract 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 NO2 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 NO2 mass concentrations in Shanghai was 21.7, 22.5, 11.3 (μg/m3) 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.
Highlights The interruption and recovery of air pollution during the COVID-19 was evaluated. Regression Discontinuity Design (RDD) has been used for statistical analysis. Data of four major Chinese cites were utilized for characterizing and modelling. The year-on-year NO2 and PM2.5 showed 25.3%–48.8% and 23.1%–46.2% drop separately.
Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China
https://orcid.org/0000-0001-8352-9820
https://orcid.org/0000-0001-9393-2701
https://orcid.org/0000-0003-3648-6770
https://orcid.org/0000-0002-5820-3869
Abstract 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 NO2 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 NO2 mass concentrations in Shanghai was 21.7, 22.5, 11.3 (μg/m3) 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.
Highlights The interruption and recovery of air pollution during the COVID-19 was evaluated. Regression Discontinuity Design (RDD) has been used for statistical analysis. Data of four major Chinese cites were utilized for characterizing and modelling. The year-on-year NO2 and PM2.5 showed 25.3%–48.8% and 23.1%–46.2% drop separately.
Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China
https://orcid.org/0000-0001-8352-9820
https://orcid.org/0000-0001-9393-2701
https://orcid.org/0000-0003-3648-6770
https://orcid.org/0000-0002-5820-3869
Abstract 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 NO2 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 NO2 mass concentrations in Shanghai was 21.7, 22.5, 11.3 (μg/m3) 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.
Highlights The interruption and recovery of air pollution during the COVID-19 was evaluated. Regression Discontinuity Design (RDD) has been used for statistical analysis. Data of four major Chinese cites were utilized for characterizing and modelling. The year-on-year NO2 and PM2.5 showed 25.3%–48.8% and 23.1%–46.2% drop separately.
Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China
Cai, Wan-Jin (Autor:in) / Wang, Hong-Wei (Autor:in) / Wu, Cui-Lin (Autor:in) / Lu, Kai-Fa (Autor:in) / Peng, Zhong-Ren (Autor:in) / He, Hong-Di (Autor:in)
Building and Environment ; 205
04.08.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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