Key Takeaways & Executive Findings
- •• Machine learning models (XGBoost, GBDT, CatBoost) accurately predict heavy metal occurrence forms in tailings, with R² > 0.859 on test sets. • Electronegativity is the most influential feature, with an average importance of 0.4522, highlighting its role in HM speciation. • Stacking ensemble learning further improves prediction accuracy, increasing R² to 0.879, demonstrating the benefit of model integration. • The developed empirical method offers a rapid, cost-effective alternative to traditional sequential extraction, aiding environmental assessment and tailings recycling.
Abstract
Tailings produced by mining and ore smelting are a major source of soil pollution. Understanding the speciation of heavy metals (HMs) in tailings is essential for soil remediation and sustainable development. Given the complex and time-consuming nature of traditional sequential laboratory extraction methods for determining the forms of HMs in tailings, a rapid and precise identification approach is urgently required. To address this issue, a general empirical prediction method for HM occurrence was developed using machine learning (ML). The compositional information of the tailings, properties of the HMs, and sequential extraction steps were used as inputs to calculate the percentages of the seven forms of HMs. After the models were tuned and compared, extreme gradient boosting, gradient boosting decision tree, and categorical boosting methods were found to be the top three performing ML models, with the coefficient of determination (R2) values on the testing set exceeding 0.859. Feature importance analysis for these three optimal models indicated that electronegativity was the most important factor affecting the occurrence of HMs, with an average feature importance of 0.4522. The subsequent use of stacking as a model integration method enabled the ability of the ML models to predict HM occurrence forms to be further improved, and resulting in an increase of R2 to 0.879. Overall, this study developed a robust technique for predicting the occurrence forms in tailings and provides an important reference for the environmental assessment and recycling of tailings.
1. Introduction
Modern industrial development has boosted the demand for mineral resources. However, the resulting generation of large tailings volumes by mining and metallurgical industries has been causing severe environmental pollution [1–3]. An estimated five to seven billion tons of tailings are produced annually [4], which cover large areas of land, destroy habitats, and pollute soil and water resources [5–7]. The exposure of tailings to the environment under open stockpile conditions accelerates the weathering process, during which residues of heavy metals (HMs) such as Cu, Cr, Mn, Fe, Al, Pb, and other toxic elements present in the tailings may be released [8–9]. Previous studies have highlighted that toxic elements, including HMs, can pollute soil and water environments through infiltration and leakage [10], thereby posing a threat to nearby ecosystems and human health [11–14]. The leaching of HMs from tailings is affected by multiple factors, including the mineralogical properties of the tailings, total amount of HMs present, and nature of the metals in the tailings.
The mobility of HMs in tailings can be categorized into various forms (e.g., mobile, easily deliverable, and easily reducible), and these forms are commonly estimated using sequential extraction methods [15–16]. Among these, the Tessier and Community Bureau of Reference (BCR) approaches are the most widely used [17–19]. However, the use of sequential extraction techniques to determine the occurrence forms of HMs is typically labor-intensive and time-consuming. To the best of our knowledge, few empirical methods that can efficiently and accurately determine the occurrence forms of HMs have been reported to date. Therefore, the development of an empirical method that can efficiently predict the occurrence forms has become an urgent issue.
With the rapid development and widespread availability of computational power [20], machine learning (ML) has become an increasingly powerful approach for processing complex datasets [21–23]. For example, Wang et al. [24] used a ML method to predict the migration routes of HMs in soil. Xiao et al. [25] quantified the particle sizes and particle size distribution of tailings with the aid of ML. Tan et al. [26] followed an ensemble learning approach based on stacking technology to estimate the distribution trends of heavy metal concentrations in soils surrounding mining sites. Razanamahandry et al. [27] estimated the key factors influencing the spatial distribution of soil contamination in gold mining areas using logistic regression. Many other previous studies demonstrated the potential of ML for solving problems related to HMs in tailings (Table S1). In our previous publication, we presented a preliminary study on the prediction of the HM occurrence forms [28]. However, the models available in this research field
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Pengxin Zhao, Kechao Li, Nana Zhou, Qiusong Chen, Min Zhou, Chongchong Qi (2025). Enhanced prediction of occurrence forms of heavy metals in tailings: A systematic comparison of machine learning methods and model integration. Int. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报). https://doi.org/10.1007/s12613-025-3136-4
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Frequently Asked Questions
What is the main objective of this study?
The study aims to develop a rapid and accurate empirical method using machine learning to predict the occurrence forms of heavy metals in tailings, overcoming the limitations of traditional sequential extraction methods.
Which machine learning models performed best in predicting heavy metal occurrence forms?
Extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), and categorical boosting (CatBoost) were the top three models, with R² values exceeding 0.859 on the testing set.
What was the most important feature influencing heavy metal occurrence forms?
Electronegativity was identified as the most important factor, with an average feature importance of 0.4522 across the three optimal models.
How did model integration improve prediction performance?
By using stacking ensemble learning, the prediction accuracy was further improved, increasing the R² value to 0.879, demonstrating the benefit of combining multiple models.
What are the practical implications of this research?
The developed method provides a fast and cost-effective alternative for assessing heavy metal speciation in tailings, which is crucial for environmental risk assessment and the recycling of tailings resources.
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