REGRESSION OF CHEMICAL OXYGEN DEMAND AND TOTAL ORGANIC CARBON IN SELECTED MINING POLLUTED GROUNDWATER SOURCES (Journal of Pure and Applied Sciences (Science Forum), 2026)
Groundwater sources are vital resources for freshwater in many rural and semi-urban communities in developing countries. Pollution of these vital sources by mining activities is becoming a global challenge. This study examines the relationship between Total Organic Carbon (TOC), Colour, Turbidity, Microbial load, and Chemical Oxygen Demand (COD) in mining-contaminated groundwater, aiming to facilitate a rapid assessment of water quality, identify pollution levels, and propose treatment techniques required. Water samples were collected from groundwater sources (wells and boreholes) in selected Local Government Areas in Osun State (between July 2024 and May 2025) to account for spatial variations in water quality. Collected groundwater was characterized, and relationships between COD and selected water quality parameters (TOC, Colour, Turbidity, Microbial load) of the groundwater were developed, utilizing two different standard models (first and second orders). These models were solved using matrix functions in Microsoft Excel and compared with results from the Microsoft Excel Solver. The study revealed that for the first-order model, the regression coefficients for TOC, Microbial load, Colour, and Turbidity were 2.7687, -0.0022, 4.5262, and 0.0831 for Matrix and 2.8321, -00047, 2.0835, and 0.0559 for MES techniques, respectively. Statistical evaluation utilising analysis of variance established that there was no statistical difference between the coefficients utilising the two techniques at a 95 % (F1,6 = 0.2109, p = 0.6622) confidence level. In the case of second order, the coefficients for TOC, Microbial load, Colour, and Turbidity were 2.7687, -0.0022, 4.5262, and 0.0831 for Matrix and 2.8321, -00047, 2.0835, and 0.0559 for MES techniques, respectively. It was concluded that TOC can be used to predict Chemical Oxygen Demand based on high correlation and regression coefficients.