Accurate estimation of ozone (O₃) concentrations and quantitative meteorological contribution are crucial for effective control of O₃ pollution. In recent years, there has been a growing interest in leveraging machine learning for O₃ pollution research due to its advantages, such as high accuracy, strong generalization, and ease of use. In this study, we utilized meteorological parameters obtained from european center for medium—range weather forecasts (EMCWF) Reanalysis v5 data as input and employed five distinct machine learning methods to estimate values of maximum daily 8-hr average (MDA8) O₃ concentrations and analyze meteorological contributions. To improve the accuracy and generalization capabilities of the estimation, we employed Grid SearchCV techniques to select optimal parameters and mitigate the risk of overfitting. Additionally, we incorporated meteorological normalization and the SHAP model to quantify the influence of various parameters. Among the models evaluated, the Extreme Gradient Boost model exhibited exceptional performance from 2015 to 2022, yielding determination coefficients of 0.85 and 0.80 for the training and test data sets, respectively. The outcomes of meteorological normalization revealed that meteorological parameters accounted for 87.7% of the impacts in 2018, while emission-related factors constituted 80.8% of the impacts in 2021. Over the period spanning 2015–2022, 2 m temperature emerged as the most influential parameter affecting daily MDA8 O₃ concentration, with an average contribution of 9.4 μg m⁻³.

中文翻译：

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基于机器学习模型的京津冀地区臭氧浓度估算及气象影响量化

准确估算臭氧(O _{3 )浓度和定量气象贡献对于有效控制O 3}污染至关重要。近年来，由于机器学习具有精度高、泛化性强、易于使用等优点，利用机器学习进行O ₃污染研究越来越受到人们的关注。在这项研究中，我们利用从欧洲中期天气预报中心 (EMCWF) Reanalysis v5 数据获得的气象参数作为输入，并采用五种不同的机器学习方法来估计最大每日 8 小时平均 (MDA8) O 3 浓度_值和分析气象贡献。为了提高估计的准确性和泛化能力，我们采用Grid SearchCV技术来选择最佳参数并降低过度拟合的风险。此外，我们结合气象归一化和SHAP模型来量化各种参数的影响。在评估的模型中，Extreme Gradient Boost模型在2015年至2022年期间表现出了出色的性能，训练和测试数据集的决定系数分别为0.85和0.80。气象常态化结果显示，2018年气象参数占87.7%，2021年排放相关因素占80.8%。2015-2022年期间，2 m温度成为影响最大的参数影响每日MDA8 O ₃浓度，平均贡献为9.4 μg m ^-3。