Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models

Jérôme Barré (1) , Hervé Petetin (2) , Augustin Colette (3) , Marc Guevara (2) , Vincent-Henri Peuch (1) , Laurence Rouil (3) , Richard Engelen (1) , Antje Inness (1) , Johannes Flemming (1) , Carlos Pérez García-Pando (2, 4) , Dene Bowdalo (2) , Frederik Meleux (3) , Camilla Geels (5) , Jesper Christensen (5) , Michael Gauss (6) , Anna Benedictow (6) , Svetlana Tsyro (6) , Elmar Friese (7) , Joanna Struzewska (8) , Jacek Kaminski (8, 9) , John Douros (10) , Renske Timmermans (11) , Lennart Robertson (12) , Mario Adani (13) , Oriol Jorba (2) , Mathieu Joly (14) , Rostislav Kouznetsov (15)

1 ECMWF - European Centre for Medium-Range Weather Forecasts
2 BSC-CNS - Barcelona Supercomputing Center - Centro Nacional de Supercomputacion
3 INERIS - Institut National de l'Environnement Industriel et des Risques
4 ICREA - Institució Catalana de Recerca i Estudis Avançats = Catalan Institution for Research and Advanced Studies
5 Aarhus University [Aarhus]
6 MET - Norwegian Meteorological Institute [Oslo]
7 RIU - Rhenish Institute for Environmental Research
8 INSTITUTE OF ENVIRONMENTAL PROTECTION WARSAW POL
9 Institute of Geophysics [Warsaw]
10 KNMI - Royal Netherlands Meteorological Institute
11 TNO - The Netherlands Organisation for Applied Scientific Research
12 SMHI - Swedish Meteorological and Hydrological Institute
13 ENEA - Agenzia Nazionale per le nuove Tecnologie, l’energia e lo sviluppo economico sostenibile = Italian National Agency for New Technologies, Energy and Sustainable Economic Development
14 CNRM - Centre national de recherches météorologiques
15 FMI - 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.

Domaines: Sciences de l'environnement

Jérôme Barré, Hervé Petetin, Augustin Colette, Marc Guevara, Vincent-Henri Peuch, et al.. Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models. Atmospheric Chemistry and Physics, 2021, 21 (9), pp.7373-7394. ⟨10.5194/acp-21-7373-2021⟩. ⟨ineris-03267344⟩