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.
Keywords: Cluster analysis; Conditional inferences tree (CIT); Heavy metals pattern; Soil contamination.
© 2021. The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.