Barre, Jerome, Petetin, Herve, Colette, Augustin ORCID: 0000-0002-0162-0098, Guevara, Marc, Peuch, Vincent-Henri, Rouil, Laurence, Engelen, Richard, Inness, Antje, Flemming, Johannes, Garcia-Pando, Carlos Perez, Bowdalo, Dene, Meleux, Frederik, Geels, Camilla ORCID: 0000-0003-2549-1750, Christensen, Jesper H., Gauss, Michael, Benedictow, Anna, Tsyro, Svetlana, Friese, Elmar, Struzewska, Joanna ORCID: 0000-0003-3538-0122, Kaminski, Jacek W., Douros, John, Timmermans, Renske, Robertson, Lennart, Adani, Mario, Jorba, Oriol, Joly, Mathieu and Kouznetsov, Rostislav (2021). Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models. Atmos. Chem. Phys., 21 (9). S. 7373 - 7395. GOTTINGEN: COPERNICUS GESELLSCHAFT MBH. ISSN 1680-7324

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Abstract

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.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Barre, JeromeUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Petetin, HerveUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Colette, AugustinUNSPECIFIEDorcid.org/0000-0002-0162-0098UNSPECIFIED
Guevara, MarcUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Peuch, Vincent-HenriUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Rouil, LaurenceUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Engelen, RichardUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Inness, AntjeUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Flemming, JohannesUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Garcia-Pando, Carlos PerezUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bowdalo, DeneUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Meleux, FrederikUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Geels, CamillaUNSPECIFIEDorcid.org/0000-0003-2549-1750UNSPECIFIED
Christensen, Jesper H.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Gauss, MichaelUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Benedictow, AnnaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Tsyro, SvetlanaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Friese, ElmarUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Struzewska, JoannaUNSPECIFIEDorcid.org/0000-0003-3538-0122UNSPECIFIED
Kaminski, Jacek W.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Douros, JohnUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Timmermans, RenskeUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Robertson, LennartUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Adani, MarioUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Jorba, OriolUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Joly, MathieuUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kouznetsov, RostislavUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-604335
DOI: 10.5194/acp-21-7373-2021
Journal or Publication Title: Atmos. Chem. Phys.
Volume: 21
Number: 9
Page Range: S. 7373 - 7395
Date: 2021
Publisher: COPERNICUS GESELLSCHAFT MBH
Place of Publication: GOTTINGEN
ISSN: 1680-7324
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
METEOROLOGICAL NORMALIZATION; COVID-19Multiple languages
Environmental Sciences; Meteorology & Atmospheric SciencesMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/60433

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