Spatial Distribution, Contamination Assessment, and Sustainability Perspectives of Potentially Toxic Elements in Arid Soils: An Integrated GIS and Multivariate Statistical Approach

This investigation was conducted to evaluate the environmental risks associated with cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) in the Faifa region in southwestern Saudi Arabia. To accomplish this objective, several contamination indices were applied in addition to principal component analysis (PCA) and hierarchical cluster analysis (HCA). A total of thirty surface soil samples were collected and analyzed to determine their potential toxic element (PTE) concentrations. Moreover, the Normalized Difference Vegetation Index (NDVI) was derived from satellite imagery of the study area using the Google Earth Engine (GEE) platform. NDVI values ranged from −0.02 to 1, indicating generally high vegetation cover, particularly in the northwestern section of the study area where higher NDVI values were observed. After Box–Cox transformation of the raw concentrations (required because most elements departed from normality), the multivariate analysis retained two principal components explaining 59.6% (PC1) and 24.2% (PC2) of the total variance. Cobalt, chromium, copper, manganese, nickel, lead, and zinc all loaded strongly on PC1, a pattern more consistent with a shared lithogenic (parent-material) control than with a single dominant anthropogenic source, while PC2 was dominated by cadmium and iron, which behaved comparatively independently of the other elements. Cd results are reported as indicative because all values fall below the limit of quantification. Attributing these axes to specific anthropogenic or natural sources with confidence would require additional evidence, such as land-use or isotopic data, which lie beyond the scope of the present dataset. This study revealed that the level of PTEs varied from unpolluted to slightly polluted levels, according to the Pollution Load Index (PLI) and the modified Contamination Index (mCd). Decision-makers can use the spatial distribution maps of pollutant concentrations to plan future monitoring campaigns, which will help prevent soil contamination and promote the achievement of the Sustainable Development Goals.