Liu Junqin, Zhao Jiang, Du Jiapan, Peng Suyi, Wu Jiahui, Zhang Wenchao, Yan Xu, Lin Zhang
School of Metallurgy and Environment, Central South University, Changsha, Hunan 410083, China.
School of Mathmatics and Statistics, Beijing Technology and Business University, Beijing 100048, China.
J Hazard Mater. 2024 Apr 15;468:133797. doi: 10.1016/j.jhazmat.2024.133797. Epub 2024 Feb 15.
Heavy metals raise a global concern and can be easily retained by ubiquitous iron (oxyhydr)oxides in natural and engineered systems. The complex interaction between iron (oxyhydr)oxides and heavy metals results in various mineral-metal binding configurations, such as outer-sphere complexes and edge-sharing inner-sphere complexes, which determine the accumulation and release of heavy metals in the environment. However, traditional experimental approaches are time-consuming and inadequate to elucidate the complex binding relationships and configurations between iron (oxyhydr)oxides and heavy metals. Herein, a workflow that integrates the binding configuration data of 11 heavy metals on 7 iron (oxyhydr)oxides and then trains machine learning models to predict unknown binding configurations was proposed. The well-trained multi-grained cascade forest models exhibited high accuracy (> 90%) and predictive performance (R ∼ 0.75). The underlying effects of mineral properties, metal ion species, and environmental conditions on mineral-metal binding configurations were fully interpreted with data mining. Moreover, the metal release rate was further successfully predicted based on mineral-metal binding configurations. This work provides a method to accurately and quickly predict the binding configuration of heavy metals on iron (oxyhydr)oxides, which would provide guidance for estimating the potential release behavior of heavy metals and remediating heavy metal pollution in natural and engineered environments.
重金属引发了全球关注，并且在自然和工程系统中很容易被普遍存在的铁（氢）氧化物所吸附。铁（氢）氧化物与重金属之间的复杂相互作用导致了各种矿物 - 金属结合构型，如外层配合物和边共享内层配合物，这些构型决定了重金属在环境中的积累和释放。然而，传统的实验方法耗时且不足以阐明铁（氢）氧化物与重金属之间复杂的结合关系和构型。在此，提出了一种工作流程，该流程整合了11种重金属在7种铁（氢）氧化物上的结合构型数据，然后训练机器学习模型来预测未知的结合构型。训练良好的多粒度级联森林模型表现出高精度（> 90%）和预测性能（R ∼ 0.75）。通过数据挖掘充分解释了矿物性质、金属离子种类和环境条件对矿物 - 金属结合构型的潜在影响。此外，基于矿物 - 金属结合构型进一步成功预测了金属释放速率。这项工作提供了一种准确快速预测重金属在铁（氢）氧化物上结合构型的方法，这将为估计自然和工程环境中重金属的潜在释放行为以及修复重金属污染提供指导。
