Groundwater Quality Prediction Using Water Quality Index and Multiple Regression Modelling in Trans Amadi Industrial Area, Port Harcourt, Nigeria

Abstract

Groundwater quality assessment is essential for ensuring safe drinking water and mitigating contamination associated with industrialization and urbanization. This study developed a predictive model for groundwater quality assessment using the Water Quality Index (WQI) and Multiple Regression Modelling (MRM) within Trans Amadi Industrial Layout, Port Harcourt, Nigeria. Groundwater samples were collected from fifteen (15) boreholes and analyzed for selected physicochemical parameters following standard procedures. Results indicated considerable spatial variation in groundwater quality, with elevated concentrations of heavy metals such as lead (Pb) and cadmium (Cd) observed at several locations. WQI values revealed that most sampling points were unsuitable for drinking, indicating widespread groundwater deterioration. The developed regression model demonstrated excellent predictive performance (R² = 0.9998; Adjusted R² = 0.9989), with Pb, Cd, dissolved oxygen (DO), sulphate (SO₄), and zinc (Zn) identified as major predictors influencing WQI. The close agreement between observed and predicted WQI values confirms the model’s reliability for groundwater quality prediction. The findings demonstrate that integrating WQI and MRM provides an effective and cost-efficient approach for groundwater monitoring, contamination assessment, and sustainable water resource management in industrial environments.