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@ARTICLE{Hickman:1052339,
      author       = {Hickman, Sebastian H. M. and Kelp, Makoto M. and Griffiths,
                      Paul T. and Doerksen, Kelsey and Miyazaki, Kazuyuki and
                      Pennington, Elyse A. and Koren, Gerbrand and
                      Iglesias-Suarez, Fernando and Schultz, Martin G. and Chang,
                      Kai-Lan and Cooper, Owen R. and Archibald, Alex and
                      Sommariva, Roberto and Carlson, David and Wang, Hantao and
                      West, J. Jason and Liu, Zhenze},
      title        = {{A}pplications of {M}achine {L}earning and {A}rtificial
                      {I}ntelligence in {T}ropospheric {O}zone {R}esearch},
      journal      = {Geoscientific model development},
      volume       = {18},
      number       = {22},
      issn         = {1991-959X},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2026-00942},
      pages        = {8777 - 8800},
      year         = {2025},
      abstract     = {Machine learning (ML) is transforming atmospheric
                      chemistry, offering powerful tools to address challenges in
                      tropospheric ozone research, a critical area for climate
                      resilience and public health. As in adjacent fields, ML
                      approaches complement existing research by learning patterns
                      from ever-increasing volumes of atmospheric and
                      environmental data relevant to ozone. We highlight the rapid
                      progress made in the field since Phase 1 of the Tropospheric
                      Ozone Assessment Report (TOAR), focussing particularly on
                      the most active areas of research, namely short-term ozone
                      forecasting, emulation of atmospheric chemistry and the use
                      of remote sensing for ozone estimation. This review provides
                      a comprehensive synthesis of recent advancements, highlights
                      critical challenges, and proposes actionable pathways to
                      develop ML in ozone research. Further advances hinge on
                      addressing domain-specific issues such as the dependence of
                      ozone concentrations on several poorly observed precursor
                      species, as well as making progress on generic ML challenges
                      such as the definition of suitable benchmarks and developing
                      robust, explainable models. Reaping the full potential of ML
                      for ozone research and operational applications will require
                      close collaborations across atmospheric chemistry, ML and
                      computational science and vigilant pursuit of the rapid
                      developments in adjacent fields.},
      cin          = {JSC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Earth System Data
                      Exploration (ESDE) / IntelliAQ - Artificial Intelligence for
                      Air Quality (787576)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel-1)ESDE /
                      G:(EU-Grant)787576},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.5194/gmd-18-8777-2025},
      url          = {https://juser.fz-juelich.de/record/1052339},
}