Application of Principal Component Analysis & Multiple Regression Models in Surface Water Quality Assessment

Abstract

Principal component analysis (PCA) and multiple linear regressions were applied on the surface water quality data with the aim of identifying the pollution sources and their contribution toward water quality variation. Surface water samples were collected from four different sampling points along Jakara River. Fifteen physico-chemical water quality parameters were selected for analysis: dissolved oxygen (DO), biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), suspended solids (SS), pH, conductivity, salinity, temperature, nitrogen in the form of ammonia (NH₃), turbidity, dissolved solids (DS), total solids (TS), nitrates (NO₃), chloride (Cl) and phosphates (PO₄^3-). PCA was used to investigate the origin of each water quality parameters and yielded five varimax factors with 83.1% total variance and in addition PCA identified five latent pollution sources namely: ionic, erosion, domestic, dilution effect and agricultural run-off. Multiple linear regressions identified the contribution of each variable with significant value (r 0.970, R² 0.942, p < 0.01).

Keywords: River, Stepwise regression, Varimax factor, Varimax rotation, Water pollution