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

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Estimating lockdown-induced European NOchanges 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.

I: Atmospheric Chemistry and Physics, Bind 21, Nr. 9, 05.2021, s. 7373-7394.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Barré, J, Petetin, H, Colette, A, Guevara, M, Peuch, VH, Rouil, L, Engelen, R, Inness, A, Flemming, J, Pérez García-Pando, C, Bowdalo, D, Meleux, F, Geels, C, Christensen, JH, Gauss, M, Benedictow, A, Tsyro, S, Friese, E, Struzewska, J, Kaminski, JW, Douros, J, Timmermans, R, Robertson, L, Adani, M, Jorba, O, Joly, M & Kouznetsov, R 2021, 'Estimating lockdown-induced European NOchanges using satellite and surface observations and air quality models', Atmospheric Chemistry and Physics, bind 21, nr. 9, s. 7373-7394. https://doi.org/10.5194/acp-21-7373-2021

APA

Barré, J., Petetin, H., Colette, A., Guevara, M., Peuch, V. H., Rouil, L., Engelen, R., Inness, A., Flemming, J., Pérez García-Pando, C., Bowdalo, D., Meleux, F., Geels, C., Christensen, J. H., Gauss, M., Benedictow, A., Tsyro, S., Friese, E., Struzewska, J., ... Kouznetsov, R. (2021). Estimating lockdown-induced European NOchanges using satellite and surface observations and air quality models. Atmospheric Chemistry and Physics, 21(9), 7373-7394. https://doi.org/10.5194/acp-21-7373-2021

CBE

Barré J, Petetin H, Colette A, Guevara M, Peuch VH, Rouil L, Engelen R, Inness A, Flemming J, Pérez García-Pando C, Bowdalo D, Meleux F, Geels C, Christensen JH, Gauss M, Benedictow A, Tsyro S, Friese E, Struzewska J, Kaminski JW, Douros J, Timmermans R, Robertson L, Adani M, Jorba O, Joly M, Kouznetsov R. 2021. Estimating lockdown-induced European NOchanges using satellite and surface observations and air quality models. Atmospheric Chemistry and Physics. 21(9):7373-7394. https://doi.org/10.5194/acp-21-7373-2021

MLA

Vancouver

Barré J, Petetin H, Colette A, Guevara M, Peuch VH, Rouil L o.a. Estimating lockdown-induced European NOchanges using satellite and surface observations and air quality models. Atmospheric Chemistry and Physics. 2021 maj;21(9):7373-7394. https://doi.org/10.5194/acp-21-7373-2021

Author

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. / Estimating lockdown-induced European NOchanges using satellite and surface observations and air quality models. I: Atmospheric Chemistry and Physics. 2021 ; Bind 21, Nr. 9. s. 7373-7394.

Bibtex

@article{8ad556fd349d4c3e9e6cd44227c58046,
title = "Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models",
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. ",
author = "J{\'e}r{\^o}me Barr{\'e} and Herv{\'e} Petetin and Augustin Colette and Marc Guevara and Peuch, {Vincent Henri} and Laurence Rouil and Richard Engelen and Antje Inness and Johannes Flemming and {P{\'e}rez Garc{\'i}a-Pando}, Carlos and Dene Bowdalo and Frederik Meleux and Camilla Geels and Christensen, {Jesper H.} and Michael Gauss and Anna Benedictow and Svetlana Tsyro and Elmar Friese and Joanna Struzewska and Kaminski, {Jacek W.} and John Douros and Renske Timmermans and Lennart Robertson and Mario Adani and Oriol Jorba and Mathieu Joly and Rostislav Kouznetsov",
note = "Publisher Copyright: {\textcopyright} Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = may,
doi = "10.5194/acp-21-7373-2021",
language = "English",
volume = "21",
pages = "7373--7394",
journal = "Atmospheric Chemistry and Physics",
issn = "1680-7316",
publisher = "Copernicus GmbH",
number = "9",

}

RIS

TY - JOUR

T1 - Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models

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

N1 - Publisher Copyright: © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/5

Y1 - 2021/5

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=85106045612&partnerID=8YFLogxK

U2 - 10.5194/acp-21-7373-2021

DO - 10.5194/acp-21-7373-2021

M3 - Journal article

AN - SCOPUS:85106045612

VL - 21

SP - 7373

EP - 7394

JO - Atmospheric Chemistry and Physics

JF - Atmospheric Chemistry and Physics

SN - 1680-7316

IS - 9

ER -