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

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

page7374This study provides a comprehensive assessment of NOinline-formula2 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 NOinline-formula2 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 NOinline-formula2 column changes with weather-normalized surface NOinline-formula2 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 NOinline-formula2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NOinline-formula2 pollution levels. Average reduction estimates based on either satellite observations (inline-formula−23 %), surface stations (inline-formula−43 %), or models (inline-formula−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 (inline-formula−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.

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Barré, Jérôme / Petetin, Hervé / Colette, Augustin / et al: Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models. 2021. Copernicus Publications.

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