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Estimating lockdown-induced European NOchanges using satellite and surface observations and air quality models

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  • Jérôme Barré, European Centre for Medium-Range Weather Forecasts
  • ,
  • Hervé Petetin, Barcelona Supercomputing Center
  • ,
  • Augustin Colette, Institut National de l'Environnement Industriel et des Risques
  • ,
  • Marc Guevara, Barcelona Supercomputing Center
  • ,
  • Vincent Henri Peuch, European Centre for Medium-Range Weather Forecasts
  • ,
  • Laurence Rouil, Institut National de l'Environnement Industriel et des Risques
  • ,
  • Richard Engelen, European Centre for Medium-Range Weather Forecasts
  • ,
  • Antje Inness, European Centre for Medium-Range Weather Forecasts
  • ,
  • Johannes Flemming, European Centre for Medium-Range Weather Forecasts
  • ,
  • Carlos Pérez García-Pando, Barcelona Supercomputing Center, ICREA
  • ,
  • Dene Bowdalo, Barcelona Supercomputing Center
  • ,
  • Frederik Meleux, Institut National de l'Environnement Industriel et des Risques
  • ,
  • Camilla Geels
  • Jesper H. Christensen
  • Michael Gauss, Norwegian Meteorological Institute
  • ,
  • Anna Benedictow, Norwegian Meteorological Institute
  • ,
  • Svetlana Tsyro, Norwegian Meteorological Institute
  • ,
  • Elmar Friese, University of Cologne
  • ,
  • Joanna Struzewska, National Research Institute
  • ,
  • Jacek W. Kaminski, Institute of Geophysics of the Polish Academy of Sciences, National Research Institute
  • ,
  • John Douros, Royal Netherlands Meteorological Institute
  • ,
  • Renske Timmermans, Netherlands Organisation for Applied Scientific Research
  • ,
  • Lennart Robertson, Swedish Meteorological and Hydrological Institute
  • ,
  • Mario Adani, Agenzia nazionale per le nuove tecnologie, l'energia e lo sviluppo economico sostenibile
  • ,
  • Oriol Jorba, Barcelona Supercomputing Center
  • ,
  • Mathieu Joly, Université de Toulouse
  • ,
  • Rostislav Kouznetsov, Finnish Meteorological Institute

This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations 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-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction 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 model-based estimates.

OriginalsprogEngelsk
TidsskriftAtmospheric Chemistry and Physics
Vol/bind21
Nummer9
Sider (fra-til)7373-7394
Antal sider22
ISSN1680-7316
DOI
StatusUdgivet - maj 2021

Bibliografisk note

Funding Information:
Financial support. This research has been supported by the Min-

Funding Information:
Acknowledgements. The research leading to these results has received funding from the Copernicus Atmosphere Monitoring Service (CAMS), which is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. We acknowledge support from the Ministe-rio de Ciencia, Innovación y Universidades (MICINN), as part of the BROWNING project RTI2018-099894-B-I00 and NUTRIENT project CGL2017-88911-R; the AXA Research Fund; and the 620 European Research Council (grant no. 773051, FRAGMENT). We also acknowledge PRACE and RES for awarding access to Marenostrum4 based in Spain at the Barcelona Supercomputing Center through the eFRAGMENT2 and AECT-2020-1-0007 projects. This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. Carlos Pérez García-Pando also acknowledges the support received through the Ramón y Cajal programme (grant no. RYC-2015-18690) of the MICINN. Modelling and satellite data were produced by the Copernicus Atmosphere Monitoring Service. We thank the three anonymous reviewers for their helpful comments that improved this paper.

Publisher Copyright:
© Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

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