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Abstract
Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO2 compared to PM2.5 and PM10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe.
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1 London School of Hygiene and Tropical Medicine, Department of Public Health, Environments and Society, London, United Kingdom (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X); European Space Agency, Frascati, Italy (GRID:grid.423784.e) (ISNI:0000 0000 9801 3133); London School of Hygiene and Tropical Medicine, Centre on Climate Change and Planetary Health, London, United Kingdom (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X); European Centre for Medium-Range Weather Forecast, Reading, United Kingdom (GRID:grid.42781.38) (ISNI:0000 0004 0457 8766)
2 London School of Hygiene and Tropical Medicine, Department of Public Health, Environments and Society, London, United Kingdom (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X)
3 University of Bern, Institute of Social and Preventive Medicine, Bern, Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157); University of Bern, Oeschger Center for Climate Change Research, Bern, Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157)
4 London School of Hygiene and Tropical Medicine, Department of Public Health, Environments and Society, London, United Kingdom (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X); University of Florence, Department of Statistics, Computer Science and Applications “G. Parenti”, Florence, Italy (GRID:grid.8404.8) (ISNI:0000 0004 1757 2304)
5 Imperial College London, MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, London, United Kingdom (GRID:grid.7445.2) (ISNI:0000 0001 2113 8111)
6 Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands (GRID:grid.8653.8) (ISNI:0000000122851082)
7 Barcelona Supercomputing Centre, Barcelona, Spain (GRID:grid.10097.3f) (ISNI:0000 0004 0387 1602)
8 Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Bologna, Italy (GRID:grid.5196.b) (ISNI:0000 0000 9864 2490)
9 Finnish Meteorological Institute (FMI), Helsinki, Finland (GRID:grid.8657.c) (ISNI:0000 0001 2253 8678); A.M. Obukhov Institute for Atmospheric Physics (IAPh), Moscow, Russia (GRID:grid.459329.0) (ISNI:0000 0004 0485 5946)
10 National Institute for Industrial Environment and Risks (INERIS), Verneuil-en-Halatte, France (GRID:grid.8453.a) (ISNI:0000 0001 2177 3043)
11 University of Toulouse, Météo-France, CNRS, UMR 3589, National Center for Meteorological Research (CNRM), Toulouse, France (GRID:grid.8453.a)
12 European Centre for Medium-Range Weather Forecast, Reading, United Kingdom (GRID:grid.42781.38) (ISNI:0000 0004 0457 8766)
13 London School of Hygiene and Tropical Medicine, Department of Public Health, Environments and Society, London, United Kingdom (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X); London School of Hygiene and Tropical Medicine, Centre on Climate Change and Planetary Health, London, United Kingdom (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X); London School of Hygiene and Tropical Medicine, Centre for Statistical Methodology, London, United Kingdom (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X)