Estimating lockdown-induced European NO2changes using satellite and surface observations and air quality models

TitleEstimating lockdown-induced European NO2changes using satellite and surface observations and air quality models
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2021
AuthorsBarré, J., Petetin H., Colette A., Guevara M., Peuch V.-H., Rouïl L., Engelen R., Inness A., Flemming J., C. García-Pando Pérez, Bowdalo D., Meleux F., Geels C., Christensen J.H., Gauss M., Benedictow A., Tsyro S., Friese E., Struzewska J., Kaminski J.W., Douros J., Timmermans R., Robertson L., Adani M., Jorba O., Joly M., and Kouznetsov R.
JournalAtmospheric Chemistry and Physics
Volume21
Pagination7373-7394
ISSN16807316
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(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License.

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85106045612&doi=10.5194%2facp-21-7373-2021&partnerID=40&md5=e83b588ba18e39583437b7ace3c84ea9
DOI10.5194/acp-21-7373-2021