Application of Multivariate Statistical Methods for Investigation of Hydrochemical Variability and Water-Quality Deterioration in the Upper Olifants River Catchment in the Mpumalanga Province, South Africa

This study was conducted in the Upper Olifants River Catchment, located in the Mpumalanga Province of South Africa. The area faces groundwater pollution associated with the current land use and land cover. The primary aim of the present study is to apply multivariate statistical methods to investigate hydrochemical variability and identify the dominant natural and anthropogenic processes associated with water-quality deterioration. In this study, Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were applied to hydrochemical data to explore the key factors driving variability in the physical and chemical characteristics of water resources in the Upper Olifants River Catchment. Factor analysis identified three underlying common factors with Eigenvalues > 1, accounting for 80.132% of the total variance in the hydrochemical data. Component 1 explains 52.567% and shows high loadings for TDS, Ca, Mg, Mn, K, and SO4. Component 2 accounts for 15.978% and shows high loadings for Na and Cl. Component 3 accounts for 11.587% of the variance and shows a loading on Fe. Hierarchical clustering yields two clusters: cluster 1 with TDS, Mg, SO4, and Ca; cluster 2 with Cl, Fe, Na, K, and Mn. The presence of TDS, Mg, SO4, and Ca in component 1 and cluster 1 indicates that chemical interactions between water and rocks dominate water chemistry. Higher Na and Cl in component 2 and cluster 2 result from ion exchange and groundwater residence time effects on water quality. Additionally, inverse modelling indicates that sulphate mainly exists as a free ion in water and strongly binds to divalent metals such as Ca, Mg, and Mn. The saturation index indicates that the water is supersaturated with sulphur-rich minerals such as gypsum, while alkaline water is supersaturated with carbonate minerals. Sulphur-rich minerals can cause heavy metals to dissolve into the water, whereas carbonate minerals serve as a buffer, decreasing the water’s acidity. The integrated application of PCA, HCA, and inverse geochemical modelling helps identify spatial patterns in groundwater chemistry and the main processes affecting water quality, thereby offering a solid basis for understanding the causes of groundwater deterioration in the study area.