Estimating lockdown-induced European NO.sub.2 changes using satellite and surface observations and air quality models

• Published in: Atmospheric chemistry and physics Vol. 21; no. 9; pp. 7373 - 14745
• Main Authors: Barré, Jérôme, Petetin, Hervé, Colette, Augustin, Guevara, Marc, Peuch, Vincent-Henri, Rouil, Laurence, Engelen, Richard, Inness, Antje, Flemming, Johannes, Pérez García-Pando, Carlos, Bowdalo, Dene, Meleux, Frederik, Geels, Camilla, Christensen, Jesper H, Gauss, Michael, Benedictow, Anna, Tsyro, Svetlana, Friese, Elmar, Struzewska, Joanna, Kaminski, Jacek W, Douros, John, Timmermans, Renske, Robertson, Lennart, Adani, Mario, Jorba, Oriol, Joly, Mathieu, Kouznetsov, Rostislav
• Format: Journal Article
• Language: English
• Published: Copernicus GmbH 17.05.2021
• Subjects: Air pollution; Air quality; Analysis; Machine learning; Weather
• ISSN: 1680-7316; 1680-7324

This study provides a comprehensive assessment of NO.sub.2 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 NO.sub.2 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 NO.sub.2 column changes with weather-normalized surface NO.sub.2 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 NO.sub.2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO.sub.2 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.