Impact of the 2020 COVID-19 lockdown on NO2 and PM10 concentrations in Berlin, Germany

Mona Schatke; Fred Meier; Boris Schröder; Stephan Weber

doi:10.1016/j.atmosenv.2022.119372

Impact of the 2020 COVID-19 lockdown on NO₂ and PM₁₀ concentrations in Berlin, Germany

Atmos Environ (1994). 2022 Dec 1:290:119372. doi: 10.1016/j.atmosenv.2022.119372. Epub 2022 Sep 7.

Authors

Mona Schatke¹, Fred Meier², Boris Schröder^{3

4}, Stephan Weber¹

Affiliations

¹ Climatology and Environmental Meteorology, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, 38106, Braunschweig, Germany.
² Chair of Climatology, Institute of Ecology, Technische Universität Berlin, Rothenburgstraße 12, 12165, Berlin, Germany.
³ Landscape Ecology and Environmental Systems Analysis, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, 38106, Braunschweig, Germany.
⁴ Berlin-Brandenburg Institute of Advanced Biodiversity Research BBIB, Altensteinstraße 6, D, 14195, Berlin, Germany.

Abstract

In March 2020, the World Health Organization declared a pandemic due to the rapid and worldwide spread of the SARS-CoV-2 virus. To prevent spread of the infection social contact restrictions were enacted worldwide, which suggest a significant effect on the anthropogenic emission of gaseous and particulate pollutants in urban areas. To account for the influence of meteorological conditions on airborne pollutant concentrations, we used a Random Forest machine learning technique for predicting business as usual (BAU) pollutant concentrations of NO₂ and PM₁₀ at five observation sites in the city of Berlin, Germany, during the 2020 COVID-19 lockdown periods. The predictor variables were based on meteorological and traffic data from the period of 2017-2019. The differences between BAU and observed concentrations were used to quantify lockdown-related effects on average pollutant concentrations as well as spatial variation between individual observation sites. The comparison between predicted and observed concentrations documented good overall model performance for different evaluation periods, but better performance for NO₂ (R² = 0.72) than PM₁₀ concentrations (R² = 0.35). The average decrease of NO₂ was 21.9% in the spring lockdown and 22.3% in the winter lockdown in 2020. PM₁₀ concentrations showed a smaller decrease, with an average of 12.8% in the spring as well as the winter lockdown. The model results were found sensitive to depict local variation of pollutant reductions at the different sites that were mainly related to locally varying modifications in traffic intensity.