Geographic Information System and Multivariate Analysis Approach for Mapping Soil Contamination and Environmental Risk Assessment in Arid Regions

Heavy metal contamination in soil is a global issue threatening human health and ecosystems. Accurate spatial maps of heavy metals (HMs) are vital to mitigating the adverse effects on the ecosystem. This study utilizes GIS and multivariate analysis to evaluate HMs in agricultural soils from Al Ghat Governorate, Saudi Arabia, analyzing Al, As, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn using ICP-AES in 35 soil samples. Methods included contamination factor (CF), enrichment factor (EF), risk index (RI), geoaccumulation index (Igeo), pollution load index (PLI), soil quality guidelines (SQGs), and multivariate analysis. The soils, characterized by sandy texture, low organic matter, and alkalinity due to arid conditions and high calcium carbonate, had the following HM concentrations (mg/kg) in descending order: Fe (11,480) ˃ Al (7786) ˃ Mn (278) ˃ Zn (72.37) ˃ Ni (28.66) ˃ V (21.80) ˃ Cr (19.89) ˃ Co (19.00) ˃ Cu (12.46) ˃ Pb (5.46) ˃ As (2.69). EF, CF, and Igeo suggest natural sources for most HMs, predominantly from the sedimentary sequence, with localized Zn, Pb, Co, Mn, and Cu enrichment linked to mixed natural and agricultural influences. PLI and RI indicated acceptable contamination levels, posing no ecological risk. All samples fell below SQG thresholds for As, Cu, Pb, and Cr, confirming minimal ecological threat. Statistical analysis highlighted sedimentary cover as the primary HM source, with agricultural activities contributing to Co, Cu, Ni, and Pb levels.