Integrated diagnostic framework for resolving distinct ozone formation mechanisms via observations, modeling, and machine learning

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ID: 330744
2026
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Abstract
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.
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openalex_W7215096179 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bowen Shi, Shunqiang Pei, Tianshu Zhang, Lin Liang, Pingping An, Xiaopeng Ding, Li Shen, Jing Fu, Bo You, Weiwei Chen, Lingjian Duanmu, Luyan He, Xue Yang
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Journal PNAS nexus
Year 2026
DOI
10.1093/pnasnexus/pgag337
URL
https://doi.org/10.1093/pnasnexus/pgag337
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