Wu Jian, Huang Chengmin
Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China.
Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China.
J Hazard Mater. 2025 Apr 5;487:137276. doi: 10.1016/j.jhazmat.2025.137276. Epub 2025 Jan 18.
Heavy metal natural background values play a crucial role in distinguishing anthropogenic sources from natural sources to assess human impacts in polluted areas, thereby accurately formulating environmental policies. However, due to limitations imposed by human activities, research methods, and regional constraints, the determination of heavy metal background values, particularly on site or profile scale, is often challenging, highlighting the urgent need for new methodologies. To establish a comprehensive dataset containing heavy metal concentrations and soil properties, the study systematically collected and screened 82 soil profiles from areas minimally affected by human activities, resulting in a total of 2185 data sets. Using soil depth, pH, organic matter, weathering indices (SAF, BA), FeO, MgO, NaO, CaO, and KO as model input variables, the predictive performance for site-specific background levels of Cd, Cr, Cu, Ni, Pb, and Zn was compared across four advanced machine learning models (RF (random forest), XGBoost (extreme gradient boosting), ANN (artificial neural network), SVR (support vector regression)). The results indicated that the optimal model for predicting background values of Cd, Cr, and Ni was XGBoost (MAE = 0.14 - 0.17; MSE = 0.04 - 0.06; R² = 0.82 - 0.87), while RF was used for Cu, Pb, and Zn (MAE = 0.01 - 0.18; MSE = 0.02 - 0.06; R² = 0.89 - 0.95). Importance assessments using RF and SHAP revealed that pH is a key controlling factor for Cd and Ni, FeO significantly impacts Cr, Cu, and Zn background levels, and KO is the main controlling factor for Pb. The machine learning models developed can effectively predict the background levels of these six heavy metals based on major elemental and soil physicochemical properties, particularly achieving accurate predictions for Cu and Zn using just two input variables. This machine learning prediction framework is based on major elemental compositions and the physical/chemical properties of soil, enables precise and cost-effective point-to-point environmental assessments, thereby offering significant potential for practical applications.
重金属自然背景值在区分人为源和自然源以评估污染地区的人类影响方面起着关键作用，从而能够准确制定环境政策。然而，由于人类活动、研究方法和区域限制等因素，重金属背景值的确定，尤其是在现场或剖面尺度上，往往具有挑战性，这凸显了对新方法的迫切需求。为了建立一个包含重金属浓度和土壤性质的综合数据集，该研究系统地收集并筛选了82个受人类活动影响最小地区的土壤剖面，共获得2185个数据集。以土壤深度、pH值、有机质、风化指数（SAF、BA）、FeO、MgO、NaO、CaO和KO作为模型输入变量，比较了四种先进的机器学习模型（随机森林（RF）、极端梯度提升（XGBoost）、人工神经网络（ANN）、支持向量回归（SVR））对特定地点镉、铬、铜、镍、铅和锌背景水平的预测性能。结果表明，预测镉、铬和镍背景值的最佳模型是XGBoost（平均绝对误差（MAE）=0.14 - 0.17；均方误差（MSE）=0.04 - 0.06；决定系数（R²）=0.82 - 0.87），而预测铜、铅和锌则使用随机森林（MAE = 0.01 - 0.18；MSE = 0.02 - 0.06；R² = 0.89 - 0.95）。使用随机森林和SHAP进行的重要性评估表明，pH值是镉和镍的关键控制因素，FeO对铬、铜和锌的背景水平有显著影响，KO是铅的主要控制因素。所开发的机器学习模型能够基于主要元素和土壤理化性质有效地预测这六种重金属的背景水平，特别是仅使用两个输入变量就能对铜和锌进行准确预测。这种基于主要元素组成和土壤物理/化学性质的机器学习预测框架能够实现精确且经济高效的点对点环境评估，因此具有巨大的实际应用潜力。
