Soil Pollution Source Identification in Southern Iran Using Geochemical Data as a Global Study Model

Geochemical data and accurate statistics are critical in environmental studies to identify sources of soil contamination and reduce costs. A comprehensive understanding of investigation methods and data interpretation is a current challenge. This study presents innovative techniques for investigating soil contamination that can be applied worldwide. Using sediment samples from the southern region of Iran, we analysed 38 samples with X-ray fluorescence (XRF) and inductively coupled plasma mass spectrometry (ICP-MS). Statistical analysis, including descriptive statistics, Kolmogorov-Smirnov test (K-S), correlation coefficients, cluster and factor analyses, was performed using SPSS software. Cluster analysis revealed two primary element sources: lithogenic and anthropogenic, likely from the Karun River, the Gachsaran Formation, and contamination from nearby oil platforms. The pollution indices showed moderate pollution values for Mg, Sr, Cr, Ni, Pr, and Cd. Geochemical indices RI, PLI, Igeo, EF, Cf, and NIPI assessed the extent of pollution. Integration of sedimentary and geochemical data identified the source of sand particles as reworked Neogene deposits and silt from marl layers in upstream evaporites. These techniques have potential global applications.