Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany

Atmospheric Pollution Research

article

Xin Cao¹  Xiansheng Liu³  Hadiatullah Hadiatullah⁵  Yanning Xu⁶  Xun Zhang⁷  Josef Cyrys⁸  Ralf Zimmermann²  Thomas Adam² 

[1] School of Sport Science, Beijing Sport University;Joint Mass Spectrometry Center, Cooperation Group Comprehensive Molecular Analytics, Helmholtz Zentrum München, German Research Center for Environmental Health;University of the Bundeswehr Munich, Faculty for Mechanical Engineering, Institute of Chemical and Environmental Engineering;Institute of Environmental Assessment and Water Research;School of Pharmaceutical Science and Technology, Tianjin University;School of Environmental and Municipal Engineering, Qingdao University of Technology;Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science and Engineering, Beijing Technology and Business University;Research Unit Analytical BioGeoChemistry, German Research Center for Environmental Health;Joint Mass Spectrometry Center, Chair of Analytical Chemistry, University of Rostock

关键词: COVID-19;    Lockdown;    Air pollution;    Random forest;    Traffic volume;   

DOI  :  10.1016/j.apr.2022.101536

学科分类：农业科学（综合）

来源: Dokuz Eylul Universitesi * Department of Environmental Engineering

【 摘 要 】

The COVID-19 pandemic in Germany in 2020 brought many regulations to impede its transmission such as lockdown. Hence, in this study, we compared the annual air pollutants (CO, NO, NO 2 , O 3 , PM 10 , PM 2.5 , and BC) in Augsburg in 2020 to the record data in 2010–2019. The annual air pollutants in 2020 were significantly (p < 0.001) lower than that in 2010–2019 except O 3 , which was significantly (p = 0.02) higher than that in 2010–2019. In a depth perspective, we explored how lockdown impacted air pollutants in Augsburg. We simulated air pollutants based on the meteorological data, traffic density, and weekday and weekend/holiday by using four different models (i.e. Random Forest, K-nearest Neighbors, Linear Regression, and Lasso Regression). According to the best fitting effects, Random Forest was used to predict air pollutants during two lockdown periods (16/03/2020–19/04/2020, 1st lockdown and 02/11/2020–31/12/2020, 2nd lockdown) to explore how lockdown measures impacted air pollutants. Compared to the predicted values, the measured CO, NO 2 , and BC significantly reduced 18.21%, 21.75%, and 48.92% in the 1st lockdown as well as 7.67%, 32.28%, and 79.08% in the 2nd lockdown. It could be owing to the reduction of traffic and industrial activities. O 3 significantly increased 15.62% in the 1st lockdown but decreased 40.39% in the 2nd lockdown, which may have relations with the fluctuations the NO titration effect and photochemistry effect. PM 10 and PM 2.5 were significantly increased 18.23% an 10.06% in the 1st lockdown but reduced 34.37% and 30.62% in the 2nd lockdown, which could be owing to their complex generation mechanisms.

【 授权许可】

CC BY