Assessment of spatial patterns of potentially toxic metals is one of the most urgent tasks in soil chemistry. In this study, descriptive statistics and three methods of multivariate statistical analysis, such as the hierarchical cluster analysis (HCA), correlation analysis, and conditional inference tree (CIT), were used to identify patterns and potential sources of heavy metals (Co, Ni, Cu, Cr, Pb, MnO, and Zn). The investigation was carried out on 81 sample points, using 20 testing parameters. A strong positive correlation found among Ni, Cu, Zn, and HCA results has confirmed the common origin of the elements from waste discharge. Hierarchical CA divided the 81 test sites into 5 classes based on the soil quality and HMs contamination similarity. Regression trees for Cr, Pb, Zn, and Cu were verified by the splitting factor including HMs content and soil chemistry factors. The CIT has revealed that the elements (Cr, Pb, Zn, and Cu) concentration values are split at the first level by some other metal, indicating common anthropogenic impact resulting from industrial waste discharges. The factors at the next hierarchical level of splitting, in addition to the HMs, include compounds belonging to soil chemistry variables (SiO₂, Al₂O₃, and K₂O). The CIT nonlinear regression model is in good agreement with the data: R² values for log-transformed concentrations of Cr, Pb, Zn, and Cu are equal to 0.775; 0.774; 0.775; 0.804, respectively.

评估潜在有毒金属的空间格局是土壤化学中最紧迫的任务之一。本研究采用描述性统计和层次聚类分析 (HCA)、相关性分析和条件推理树 (CIT) 三种多元统计分析方法来识别重金属 (Co、Ni) 的模式和潜在来源。 、Cu、Cr、Pb、MnO 和 Zn)。使用 20 个测试参数对 81 个样本点进行了调查。在 Ni、Cu、Zn 和 HCA 结果之间发现的强正相关性证实了废物排放中元素的共同来源。分级 CA 根据土壤质量和 HMs 污染相似性将 81 个试验地点分为 5 类。Cr、Pb、Zn 的回归树，和Cu通过包括HMs含量和土壤化学因子在内的分裂因子进行验证。CIT 揭示了元素（Cr、Pb、Zn 和 Cu）的浓度值在第一级被一些其他金属分开，表明工业废物排放造成的常见人为影响。除了 HM 之外，下一个分裂层次的因素还包括属于土壤化学变量（SiO₂、Al ₂ O ₃和 K ₂ O)。CIT 非线性回归模型与数据非常吻合： Cr、Pb、Zn 和 Cu 的对数转换浓度的R ²值等于 0.775；0.774; 0.775; 0.804，分别。