TY  - JOUR
AU  - Barré, Jérôme
AU  - Petetin, Hervé
AU  - Colette, Augustin
AU  - Guevara, Marc
AU  - Peuch, Vincent-Henri
AU  - Rouil, Laurence
AU  - Engelen, Richard
AU  - Inness, Antje
AU  - Flemming, Johannes
AU  - Pérez García-Pando, Carlos
AU  - Bowdalo, Dene
AU  - Meleux, Frederik
AU  - Geels, Camilla
AU  - Christensen, Jesper H.
AU  - Gauss, Michael
AU  - Benedictow, Anna
AU  - Tsyro, Svetlana
AU  - Friese, Elmar
AU  - Struzewska, Joanna
AU  - Kaminski, Jacek W.
AU  - Douros, John
AU  - Timmermans, Renske
AU  - Robertson, Lennart
AU  - Adani, Mario
AU  - Jorba, Oriol
AU  - Joly, Mathieu
AU  - Kouznetsov, Rostislav
TI  - Estimating lockdown-induced European NO<sub>2</sub> changes using satellite and surface observations and air quality models
JO  - Atmospheric chemistry and physics
VL  - 21
IS  - 9
SN  - 1680-7324
CY  - Katlenburg-Lindau
PB  - EGU
M1  - FZJ-2021-04224
SP  - 7373 - 7394
PY  - 2021
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000653523100002
DO  - DOI:10.5194/acp-21-7373-2021
UR  - https://juser.fz-juelich.de/record/902390
ER  -