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@ARTICLE{Tao:1037651,
      author       = {Tao, Chenliang and Peng, Yanbo and Zhang, Qingzhu and
                      Zhang, Yuqiang and Gong, Bing and Wang, Qiao and Wang,
                      Wenxing},
      title        = {{D}iagnosing ozone–{NO} x –{VOC}–aerosol sensitivity
                      and uncovering causes of urban–nonurban discrepancies in
                      {S}handong, {C}hina, using transformer-based estimations},
      journal      = {Atmospheric chemistry and physics},
      volume       = {24},
      number       = {7},
      issn         = {1680-7316},
      address      = {Katlenburg-Lindau},
      publisher    = {EGU},
      reportid     = {FZJ-2025-00816},
      pages        = {4177 - 4192},
      year         = {2024},
      abstract     = {Narrowing surface ozone disparities between urban and
                      nonurban areas escalate health risks in densely populated
                      urban zones. A comprehensive understanding of the impact of
                      ozone photochemistry on this transition remains constrained
                      by current knowledge of aerosol effects and the availability
                      of surface monitoring. Here we reconstructed spatiotemporal
                      gapless air quality concentrations using a novel transformer
                      deep learning (DL) framework capable of perceiving
                      spatiotemporal dynamics to analyze ozone urban–nonurban
                      differences. Subsequently, the photochemical effect on these
                      discrepancies was analyzed by elucidating shifts in ozone
                      regimes inferred from an interpretable machine learning
                      method. The evaluations of the model exhibited an average
                      out-of-sample cross-validation coefficient of determination
                      of 0.96, 0.92, and 0.95 for ozone, nitrogen dioxide, and
                      fine particulate matter (PM2.5), respectively. The ozone
                      sensitivity in nonurban areas, dominated by a
                      nitrogen-oxide-limited (NOx-limited) regime, was observed to
                      shift towards increased sensitivity to volatile organic
                      compounds (VOCs) when extended to urban areas. A third
                      “aerosol-inhibited” regime was identified in the
                      Jiaodong Peninsula, where the uptake of hydroperoxyl
                      radicals onto aerosols suppressed ozone production under low
                      NOx levels during summertime. The reduction of PM2.5 could
                      increase the sensitivity of ozone to VOCs, necessitating
                      more stringent VOC emission abatement for urban ozone
                      mitigation. In 2020, urban ozone levels in Shandong
                      surpassed those in nonurban areas, primarily due to a more
                      pronounced decrease in the latter resulting from stronger
                      aerosol suppression effects and less reduction in PM2.5.
                      This case study demonstrates the critical need for advanced
                      spatially resolved models and interpretable analysis in
                      tackling ozone pollution challenges.},
      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)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001198245900001},
      doi          = {10.5194/acp-24-4177-2024},
      url          = {https://juser.fz-juelich.de/record/1037651},
}