Machine Learning-Based Bias-Corrected Future Projections of Ozone Concentrations from a Chemistry-Climate Model.

Journal: Environmental science & technology
Published Date: Jan 16, 2026

Abstract

Reliable projections of future surface ozone are crucial for air quality management and health risk assessment. However, potential biases in spatial distribution, magnitude, and trends in ozone simulated by global chemistry-climate models limit their applicability in regional evaluations. In this study, LightGBM, a machine learning (ML) algorithm, is applied to correct biases in CESM2-simulated ozone over China, the United States, and Europe and to calibrate future projections under two Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) from 2020 to 2060. The ML-based correction significantly improves spatial distribution and reduces bias by 40 to 60%, also reversing the potentially incorrect trend under SSP1-2.6 in eastern China. When the ML-based correction is applied to CESM2 projections, the warm-season mean ozone shows substantial changes from 2020 to 2060. Under SSP1-2.6, corrected ozone decreases by 13.5, 17.9, and 13.7 μg/m3 in China, the United States, and Europe, respectively. In contrast, under SSP5-8.5, ozone increases over the same period by 9.4, 2.0, and 5.2 μg/m3 in these regions. The decomposition analysis shows that anthropogenic emission changes dominate future ozone trends, while a strong climate penalty occurs in polluted eastern China and climate benefits are found in western China, the United States, and Europe under SSP5-8.5. These findings demonstrate the value of combining ML with chemistry-climate models to produce more accurate air quality projections, indicating more effective and region-specific environmental protection strategies.

Authors

  • Yiqian Ni
    State Key Laboratory of Climate System Prediction and Risk Management/Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control/Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology/Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Hailong Wang
    Wenzhou Medical University, Wenzhou, Zhejiang, China.
  • Pinya Wang
    School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China.
  • Ke Li
    School of Ideological and Political Education, Shanghai Maritime University, Shanghai, China.
  • Lei Chen
    Department of Chemistry, Stony Brook University Stony Brook NY USA.
  • Jia Zhu
    School of Computer Science, South China Normal University, Guangzhou, China. Electronic address: [email protected].
  • Baojie Li
    School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China. [email protected].
  • Hong Liao
    Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of the University of Electronic Science and Technology of China, Chengdu, China. [email protected].

Keywords

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