An indices-based water quality model to evaluate surface water quality: A case study in Vaalwaterspruit, Mpumalanga, South Africa

Abstract

Temporal variation assessments of surface water quality of the Vaalwaterspruit, a stream in Mpumalanga, South Africa, applied sixteen physicochemical parameters, anions, and trace metals, including pH, EC, TDS, Cl−, F−, alkalinity, hardness, Ca, Na, Mg, NO3−, SO42−, K, Al, Fe, and Mn, were measured monthly for a period of four years (January 2018–December 2021). Findings demonstrated water was scrambled under stress due to high amounts of Fe and Mn detected upstream. According to water quality indices, the stream category is poor and very poor (203–307.71) for the US-WQI, good and fair (76.34–82.35), for the CCME-WQI and fair state (0.61–0.87) for the CPI. CPI was best to categorize the water quality, which deteriorated more in the downstream area due to nutrients (Fluoride), trace metals (Al, Fe, and Mn), and particulate matter during the four-year study period. To evaluate the data and determine potential sources, the interrelation between the water quality data was applied using multivariate approaches. The findings showed that five eigenvalues were more significant than one, accounting for 74% of the overall variance. The five cluster results were TH-Ca-Mg, K–Fe–Mn, Cl–Na, F–SO4–Al, and TDS-Alkalinity. They were consistent with the PCA results, implying a common source and identifying summaries that sewage discharges and industrial effluents negatively affect surface water quality in addition to mining activities. Poor-quality water from anthropogenic sources should be carefully treated before releasing into surface water resources.