{
  "abstract": "Ozone (O3) pollution in relatively high-latitude industrial cities shows spatiotemporal patterns associated with coupled meteorological, transport, and chemical processes, yet its dominant formation mechanisms remain unresolved. This study developed an interpretable diagnostic framework integrating process-resolved Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) simulations, observations, and Extreme Gradient Boosting (XGBoost) interpreted using Shapley additive explanations (SHAP) for Changchun, Northeast China (September 2023–August 2024). Surface daily maximum 8-h average (MDA8) O3 exceedances occurred from April to July, peaking above 200 μg/m3. Lidar monthly means at 0.3–1.0 km peaked from April to June (108.9–111.4 μg/m3), while the 1.0–2.0 and 2.0–3.0 km layers peaked in May (97.3 and 76.4 μg/m3), indicating that lower-layer intensity and vertical extent peaked asynchronously. Across five analysis windows (E1–E5), WRF-Chem diagnosed vertical mixing as the largest positive contribution in E1 and E3 (+19.6 and +12.2 µg/m3), concurrent positive contributions in E2 led by vertical mixing (+41.3 µg/m3) and chemistry (+21.1 µg/m3), and chemistry as the largest positive contribution in E5 (+21.8 µg/m3). In E4, upper-quartile O3 coincided with predominantly negative contributions, particularly vertical advection (−24.0 µg/m3), showing that high concentrations and hourly O3 changes were asynchronous. XGBoost–SHAP identified 2-m temperature (T2) and NO2 as the leading nonlinear predictors: T2-related SHAP values became positive near 18.5 °C, whereas the NO2-related values shifted from positive at 10–20 μg/m3 to predominantly negative above 26 μg/m3. The framework integrates vertical observations, statistical attribution, and modeled tendencies to support event-scale O3 diagnosis.",
  "abstract_shown": true,
  "affiliations": [
    "Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences , Changchun, Jilin, 130102 ,",
    "University of Chinese Academy of Sciences , Beijing, 100049 ,",
    "Public Meteorological Service Centre, China Meteorological Administration , Beijing, 100081 ,",
    "Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences , Hefei, Anhui, 230031 ,",
    "Jilin Province Ecological Environment Monitoring Center , Changchun, Jilin, 130032 ,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Bowen Shi"
    },
    {
      "affs": [
        2
      ],
      "name": "Shunqiang Pei"
    },
    {
      "affs": [
        3
      ],
      "name": "Tianshu Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Lin Liang"
    },
    {
      "affs": [
        4
      ],
      "name": "Pingping An"
    },
    {
      "affs": [
        4
      ],
      "name": "Xiaopeng Ding"
    },
    {
      "affs": [
        4
      ],
      "name": "Li Shen"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jing Fu"
    },
    {
      "affs": [
        0
      ],
      "name": "Bo You"
    },
    {
      "affs": [
        0
      ],
      "name": "Weiwei Chen",
      "orcid": "0000-0002-8763-7527"
    },
    {
      "affs": [
        0
      ],
      "name": "Lingjian Duanmu"
    },
    {
      "affs": [
        0
      ],
      "name": "Luyan He"
    },
    {
      "affs": [
        0
      ],
      "name": "Xue Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4y20tcted0sjr.bib",
  "cited_by": 0,
  "doi": "10.1093/pnasnexus/pgag337",
  "fulltext_url": null,
  "id": "4y20tcted0sjr",
  "issns": [
    "2752-6542"
  ],
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  "language": null,
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1902|Atmospheric chemistry and aerosols"
    ]
  ],
  "openalex": "W7215096179",
  "pdf_url": "https://academic.oup.com/pnasnexus/advance-article-pdf/doi/10.1093/pnasnexus/pgag337/71544835/pgag337.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/4y20tcted0sjr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1093/pnasnexus/pgag337",
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  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1902|Atmospheric chemistry and aerosols",
      "subfield": "atmospheric"
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  ],
  "title": "Integrated diagnostic framework for resolving distinct ozone formation mechanisms via observations, modeling, and machine learning",
  "title_text": "Integrated diagnostic framework for resolving distinct ozone formation mechanisms via observations, modeling, and machine learning",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/4y20tcted0sjr",
  "venue": "PNAS Nexus",
  "versions": []
}