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@ARTICLE{Barr:902390,
      author       = {Barré, Jérôme and Petetin, Hervé and Colette, Augustin
                      and Guevara, Marc and Peuch, Vincent-Henri and Rouil,
                      Laurence and Engelen, Richard and Inness, Antje and
                      Flemming, Johannes and Pérez García-Pando, Carlos and
                      Bowdalo, Dene and Meleux, Frederik and Geels, Camilla and
                      Christensen, Jesper H. and Gauss, Michael and Benedictow,
                      Anna and Tsyro, Svetlana and Friese, Elmar and Struzewska,
                      Joanna and Kaminski, Jacek W. and Douros, John and
                      Timmermans, Renske and Robertson, Lennart and Adani, Mario
                      and Jorba, Oriol and Joly, Mathieu and Kouznetsov,
                      Rostislav},
      title        = {{E}stimating lockdown-induced {E}uropean
                      ${NO}\<sub\>2\</sub\>$ changes using satellite and surface
                      observations and air quality models},
      journal      = {Atmospheric chemistry and physics},
      volume       = {21},
      number       = {9},
      issn         = {1680-7324},
      address      = {Katlenburg-Lindau},
      publisher    = {EGU},
      reportid     = {FZJ-2021-04224},
      pages        = {7373 - 7394},
      year         = {2021},
      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.},
      cin          = {IEK-8},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IEK-8-20101013},
      pnm          = {2111 - Air Quality (POF4-211)},
      pid          = {G:(DE-HGF)POF4-2111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000653523100002},
      doi          = {10.5194/acp-21-7373-2021},
      url          = {https://juser.fz-juelich.de/record/902390},
}