School of Minerals Processing and Bioengineering, Central South University, Changsha, 410083, China.
Atmospheric Environment Monitoring Department, Changsha Environmental Monitoring Centre of Hunan Province, Changsha, 410001, China.
Chemosphere. 2022 Dec;308(Pt 1):136353. doi: 10.1016/j.chemosphere.2022.136353. Epub 2022 Sep 6.
Particulate matter (PM) pollution greatly endanger human physical and mental health, and it is of great practical significance to predict PM concentrations accurately. This study measured one-year monitoring data of six main meteorological parameters and PM2.5 concentrations independently at two monitoring sites in central China's Hunan Province. These datasets were then employed to train, validate, and evaluate the proposed extreme gradient boosting (XGBoost) machine learning model and the fully connected neural network deep learning model, respectively. The performances of the two models were compared, analyzed, and optimized through model parameter tuning. The XGBoost model had better prediction ability with R higher than 0.761 in the complete test dataset. When the complete dataset was divided into stratified sub-sets by daytime-nighttime periods, the value of R increased to 0.856 in the nighttime test dataset. The feature importance and influential mechanism of meteorological variables on PM2.5 concentrations were analyzed and discussed.
颗粒物（PM）污染严重危害人类身心健康，准确预测 PM 浓度具有重要的现实意义。本研究在中国中部湖南省的两个监测点分别独立测量了一年的六项主要气象参数和 PM2.5 浓度监测数据。然后，使用这些数据集分别训练、验证和评估所提出的极端梯度提升（XGBoost）机器学习模型和全连接神经网络深度学习模型。通过模型参数调整，比较、分析和优化了这两个模型的性能。XGBoost 模型在完整测试数据集的 R 值高于 0.761，具有更好的预测能力。当将完整数据集按日夜时段划分为分层子数据集时，夜间测试数据集的 R 值增加到 0.856。分析并讨论了气象变量对 PM2.5 浓度的特征重要性和影响机制。
