Annotation of soil metallic contamination hotspots via VES datasets; A case study of marble quarries in Kajiado County, SW Kenya

This study outlines a series of workflows, utilizing 1D VES datasets to identify suitable soil geochemical sampling points. It examines how the VES datasets spatially align with resultant soil heavy metallic concentrations, thereby re-evaluating the effectiveness of resistivity measurements as tools for exploring soil contamination. The study was conducted in the Kenyan marble quarries, where heavy metals are mobilized from mining activities and released into surrounding soils and sediments. Low resistivity, (< 100 Ωm) suggests potential contamination zones, whereas higher resistivity values represent either a clean fractured/weathered layer or fresh rock. Soil samples obtained at 20 VES stations were analysed for heavy metal concentrations. By comparing the modelled VES logs with existing borehole logs, the study distinguished contamination levels from the expected lithological resistivity of the various layers. Interpolated VES maps were categorized into low, medium, and high resistivity zones. Heavy metal concentrations were normalized using the geo-accumulation index (Igeo), indicating negligible pollution from Cr and Ni, slight pollution from Zn, As, Cu, and Ba, and significant pollution from Mn and Pb. The map overlay function was applied to compare the peaks and depressions of the topsoil layer resistivity with those of each heavy metal. A good correlation was found between VES and As, Ba, Mn, Cu, Zn, Mn, and Pb, whereas only slight correlations were noted for Ni and Cr, buttressing their minimal background levels. The integration of geophysical and geochemical datasets revealed contamination hotspots for each of the heavy metals, highlighting areas requiring immediate remediation efforts.