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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/acp-2020-995
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-995
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Oct 2020

07 Oct 2020

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This preprint is currently under review for the journal ACP.

Estimating lockdown induced European NO2 changes

Jérôme Barré1, Hervé Petetin2, Augustin Colette3, Marc Guevara2, Vincent-Henri Peuch1, Laurence Rouil3, Richard Engelen1, Antje Inness1, Johannes Flemming1, Carlos Pérez García-Pando2,4, Dene Bowdalo2, Frederik Meleux3, Camilla Geels5, Jesper H. Christensen5, Michael Gauss6, Anna Benedictow6, Svetlana Tsyro6, Elmar Friese7, Joanna Struzewska8, Jacek W. Kaminski8,9, John Douros10, Renske Timmermans11, Lennart Robertson12, Mario Adani13, Oriol Jorba2, Mathieu Joly14, and Rostislav Kouznetsov15 Jérôme Barré et al.
  • 1European Centre for Medium-range Weather Forecast (ECMWF), Sinfield Park, Reading, UK
  • 2Barcelona Supercomputer Centre (BSC), Barcelona, Spain
  • 3National Institute for Industrial Environment and Risks (INERIS), Verneuil-en-Halatte, France
  • 4ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain
  • 5Department of Environmental Science, Aarhus University, Roskilde, Denmark
  • 6Norwegian Meteorological Institute, Oslo, Norway
  • 7Rhenish Institute for Environmental Research at the University of Cologne, Cologne, Germany
  • 8Institute of Environmental Protection – National Research Institute, Warsaw, Poland
  • 9Faculty of Environmental Engineering, Warsaw University of Technology, Warsaw, Poland
  • 10Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
  • 11Netherlands Organisation for Applied Scientific Research (TNO), Climate Air and Sustainability Unit, Utrecht, the Netherlands
  • 12Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden
  • 13Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Bologna, Italy
  • 14Météo-France, Toulouse, France
  • 15Finnish Meteorological Institute (FMI), Helsinki, Finland

Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by the COVID-19 lockdown using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI), surface site measurements and simulations from the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of NO2 changes have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite NO2 column changes with both weather-normalized surface NO2 concentration changes and simulated changes by the CAMS regional ensemble, composed of 11 models, using recently published emission reductions induced by the lockdown. We show that all estimates show the same tendency on NO2 reductions. Locations where the lockdown was stricter show stronger reductions and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Regarding average reductions, estimates based on either satellite observations (−23 %) surface stations (−43 %) or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %) pointing out the importance of the variability in time of such estimates. Observation based machine learning estimates show a stronger temporal variability than the model-based estimates.

Jérôme Barré et al.

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This study provides a comprehensive assessment of air quality changes across the main European urban areas induced by the COVID-19 lockdown using satellite observations, surface site measurements and forecasting system from the Copernicus Atmospheric Monitoring Service (CAMS). We demonstrate the importance of accounting for weather and seasonal variability in when calculating such estimates.
This study provides a comprehensive assessment of air quality changes across the main European...
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