Research on driving factors of ozone in Henan province based on explainable machine learning
説明可能な機械学習に基づく河南省のオゾン濃度の駆動要因に関する研究 (AI 翻訳)
Jun Yan, Shi Yan, Shihan He, Xiaoyong Liu, Xue Yang
🤖 gxceed AI 要約
日本語
本研究は河南省の地上オゾン汚染の特性と駆動要因を解明するため、2022～2024年の大気質・気象データを用いてXGBoostとSHAPによる解釈可能な機械学習分析を実施した。その結果、オゾン濃度の変動の83.3%は気象要因で説明され、特に地表気温と日射量が主要な寄与因子であることが明らかになった。地域的・季節的な差異を踏まえた対策の必要性が示唆された。
English
This study uses XGBoost and SHAP to analyze ozone pollution in Henan Province (2022-2024). Meteorological factors explain 83.3% of ozone variation, with temperature and solar radiation as key drivers. Regional and seasonal differences highlight the need for targeted pollution control measures.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本研究は中国河南省のオゾン汚染に焦点を当てているが、XGBoostとSHAPを用いた解釈可能な機械学習手法は、日本の大気質分析や環境影響評価にも応用可能である。ただし、日本のGX政策（SSBJ等）との直接的な関連は薄い。
In the global GX context
This study applies explainable ML to ozone pollution in Henan, China. The methodology is transferable to air quality and environmental assessment globally, though the specific case is China. It does not directly address climate disclosure or transition finance frameworks.
👥 読者別の含意
🔬研究者:The SHAP-enhanced XGBoost approach offers a replicable framework for analyzing air pollution drivers in other regions.
🏛政策担当者:Provides evidence for region- and season-specific ozone control strategies, applicable to local environmental policy.
📄 Abstract（原文）
Near-surface ozone pollution poses a severe threat to human health and the ecological environment. To investigate the characteristics and driving factors of O3 concentrations in Henan province in recent years, this study combined the extreme gradient boosting model with the SHapley Additive exPlanations interpretability framework, using air quality monitoring data and meteorological reanalysis datasets from 2022 to 2024. The impacts of meteorological variables and atmospheric pollutant emissions on O3 were systematically examined. Results showed significant O3 pollution in Henan Province, with ρ (O3−8 h) of 170.6, 169.8, and 183.2 μg m−3 for 2022, 2023, and 2024, respectively. Spatially, ρ (O3−8 h) in central and northern Henan exceeded that in southern regions, with Jiaozuo, Xinxiang, and Zhengzhou exhibiting the highest values among 17 cities. Temporally, monthly variations in ρ (O3−8 h) followed an inverted ‘V’ pattern, peaking in summer (summer > spring > autumn > winter). Meteorological factors accounted for 83.3% of the variance in ρ (O3−8 h), while pollutant emissions accounted for 16.7%. Key meteorological factors exhibited complex nonlinear relationships with O3, including 2 m surface temperature, surface solar radiation downwards, surface thermal radiation downwards, and 2 m dew point temperature. Although city-specific driving factors varied, T2M and SSRD remained dominant across all regions, with cumulative contributions ranging from 52.7% to 57.0%. Seasonally, T2M was the primary driver in spring (31.4%), while T2M dominated in summer, autumn, and winter, reaching maximum influence in winter (41.5%). The prevention and control of ozone pollution require the adoption of targeted measures based on regional and seasonal differences.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://iopscience.iop.org/article/10.1088/2515-7620/ae6d91/pdffirst seen 2026-07-25 06:02:34
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