Integrating GIS–Based Inverse Distance Weighting and Multivariate Statistical Techniques to Assess Surface Water Quality Within a Sub–Tropical River System

Freshwater ecosystems worldwide are facing significant pressures due to the ever–increasing human activities. Land–use intensification has been closely associated with water quality degradation and loss of freshwater biodiversity. In this study, we utilised inverse distance weighted method to assess spatio–temporal concentrations of metals, nutrients and other physicochemical variables in the Komati River, Mpumalanga Province, South Africa. Water quality measurements were taken from nine sampling sites along the Nkomati River and its tributaries –across three seasons i.e, cool–dry (June 2021), hot–wet (December 2021) and hot–dry (February 2024). The results of two–way ANOVA revealed that physicochemical, nutrients and metals variables such as temperature, salinity, potassium, calcium, magnesium, aluminium and sediment phosphorus differed significantly across sites and seasons. Correlation matrix indicated significant relationships between water quality parameters and different land cover types, with pH and temperature exhibiting a strong positive correlation with settlement and agricultural areas. Elevated nitrate concentrations observed in the Nkomati River suggest significant nutrient enrichment, likely linked to adjacent agricultural activities. The findings of this study offer valuable scientific insight for guiding land use planning and policy development aimed at protecting freshwater resources in sub–tropical river systems. This highlights the need for targeted conservation and management interventions, particularly within the lowveld regions, to mitigate nutrient loading into the river systems.