Identification of spatial clusters of potentially toxic elements in different soil types using unsupervised machine learning and compositional data analysis

Abstract

Soil health is important, with soil chemical composition data, including potentially toxic elements (PTEs) being one of its conceptual components. This study aims to reveal the spatial distribution patterns of soil PTEs contents, identify their potential sources, and unveil their geochemical associations in Aragatsotn region, Armenia. For that purpose, the contents of Cr, V, Ti, As, Zn, Cu, Co, Fe, Mn, Ba, and Pb were determined using an X-ray fluorescence spectrometer. The mean contents of Cr and As exceeded their upper continental crust by 1.5 and 3.1 times and their maximum acceptable values by 1.5 and 1.5 times. The analysis demonstrated the presence of sites where all these elements displayed comparatively higher values. The combined application of compositional data analysis and geospatial mapping revealed multivariate outliers, which were located in structural-metallogenic zones with active ore exploitation . The application of unsupervised machine learning algorithm unveiled three groups within the main dataset and the clr-biplot identified the source-specific elements. Particularly, Group I included Cu and displayed the highest mean value among the identified groups. The soil samples included in Group I were in areas where Calcisols were developed and comparatively high Cu contents were attributed to agricultural activities and vehicle emissions. Group II is represented by the geochemical association of Fe, Co, Cr, Mn, Zn, and As. The formation of this group is conditioned by volcanic rocks of the local geological origin. However, no spatial pattern was identified in Group II distribution aligned with soil types. Group III included Ti, V, Pb, and Ba, which may have a mixed origin as it is spatially distributed in areas where regional highways pass through and where Group II elements also exhibit their higher values.