📖 ABSTRACT/OVERVIEW
The Sokoto Basin contains one of the most extensive sedimentary aquifer systems in Nigeria, supplying groundwater to millions of people, yet comprehensive hydrogeochemical characterisation using multivariate statistical approaches remains limited. This study employs multivariate statistical evaluation of physicochemical and trace metal data to classify hydrogeochemical facies and delineate contamination in shallow and deep aquifers across six local government areas of Sokoto and Kebbi States. A total of 72 groundwater samples were collected from boreholes and hand-dug wells and analysed for 28 parameters including major cations, anions, trace metals, and stable isotope ratios of oxygen-18 and deuterium. Principal component analysis, hierarchical cluster analysis, and discriminant analysis were applied using standardised data matrices. Piper, Stiff, and Gibbs diagrams were used to characterise hydrogeochemical processes. Results from principal component analysis identify four components explaining 76 percent of total variance, representing mineral dissolution, anthropogenic contamination, redox processes, and evaporation enrichment. Hierarchical cluster analysis classifies groundwater into three distinct hydrogeochemical groups: calcium-bicarbonate type reflecting carbonate dissolution, sodium-chloride type in deep confined aquifers, and mixed-type waters indicating anthropogenic influence. Fluoride and arsenic concentrations in deep aquifer samples indicate geogenic inputs from volcanic tuff formations. Stable isotope data confirm that recharge occurs predominantly from local precipitation. The multivariate framework developed provides a replicable tool for regional aquifer management and targeted monitoring programme design in the Sokoto Basin. Keywords: hydrogeochemistry, multivariate statistics, Sokoto Basin, groundwater, principal component analysis
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