Chen Haojia, Cao Yudong, Qin Wei, Lin Kunsen, Yang Yan, Liu Changqing, Ji Hongbing
School of Chemistry and Chemical Engineering, Guangxi Key Laboratory of Petrochemical Resource Processing and Process Intensification Technology, Guangxi University, Nanning 530004, China; School of Chemical Engineering and Light Industry, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China; Synergy Innovation Institute of Guangdong University of Technology, Shantou 515041, China.
School of Chemical Engineering and Light Industry, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China; Synergy Innovation Institute of Guangdong University of Technology, Shantou 515041, China.
Sci Total Environ. 2024 Jun 1;927:172173. doi: 10.1016/j.scitotenv.2024.172173. Epub 2024 Apr 3.
Among various remediation methods for organic-contaminated soil, thermal desorption stands out due to its broad treatment range and high efficiency. Nonetheless, analyzing the contribution of factors in complex soil remediation systems and deducing the results under multiple conditions are challenging, given the complexities arising from diverse soil properties, heating conditions, and contaminant types. Machine learning (ML) methods serve as a powerful analytical tool that can extract meaningful insights from datasets and reveal hidden relationships. Due to insufficient research on soil thermal desorption for remediation of organic sites using ML methods, this study took organic pollutants represented by polycyclic aromatic hydrocarbons (PAHs) as the research object and sorted out a comprehensive data set containing >700 data points on the thermal desorption of soil contaminated with PAHs from published literature. Several ML models, including artificial neural network (ANN), random forest (RF), and support vector regression (SVR), were applied. Model optimization and regression fitting centered on soil remediation efficiency, with feature importance analysis conducted on soil and contaminant properties and heating conditions. This approach enabled the quantitative evaluation and prediction of thermal desorption remediation effects on soil contaminated with PAHs. Results indicated that ML models, particularly the RF model (R = 0.90), exhibited high accuracy in predicting remediation efficiency. The hierarchical significance of the features within the RF model is elucidated as follows: heating conditions account for 52 %, contaminant properties for 28 %, and soil properties for 20 % of the model's predictive power. A comprehensive analysis suggests that practical applications should emphasize heating conditions for efficient soil remediation. This research provides a crucial reference for optimizing and implementing thermal desorption in the quest for more efficient and reliable soil remediation strategies.
在各种有机污染土壤修复方法中，热脱附因其处理范围广、效率高而脱颖而出。然而，鉴于复杂的土壤性质、加热条件和污染物类型所带来的复杂性，分析复杂土壤修复系统中各因素的贡献并推导多种条件下的结果具有挑战性。机器学习（ML）方法是一种强大的分析工具，能够从数据集中提取有意义的见解并揭示隐藏的关系。由于利用ML方法对有机污染场地进行土壤热脱附的研究不足，本研究以多环芳烃（PAHs）为代表的有机污染物为研究对象，从已发表的文献中整理出一个包含700多个关于PAHs污染土壤热脱附数据点的综合数据集。应用了几种ML模型，包括人工神经网络（ANN）、随机森林（RF）和支持向量回归（SVR）。以土壤修复效率为中心进行模型优化和回归拟合，并对土壤和污染物性质以及加热条件进行特征重要性分析。这种方法能够对PAHs污染土壤的热脱附修复效果进行定量评估和预测。结果表明，ML模型，特别是RF模型（R = 0.90），在预测修复效率方面表现出高精度。RF模型中各特征的层次重要性如下：加热条件占模型预测能力的52%，污染物性质占28%，土壤性质占20%。综合分析表明，实际应用应强调加热条件以实现高效的土壤修复。本研究为优化和实施热脱附以寻求更高效、可靠的土壤修复策略提供了关键参考。
