A multivariate statistical approach and water quality index for water quality assessment for the Rokel river in Sierra Leone

The main aim of the study was to use multivariate statistical approach to determine the relationship between parameters, identify the factors affecting the quality of water and interpret and group the water quality parameters. Water quality data was collected during two seasons; wet season spanning from June to August 2019 and dry season spanning from February to April 2019. The physiochemical and microbial parameters measured from the sampling process were turbidity, temperature, pH, electric conductivity, total hardness, calcium carbonates, total dissolved solid (TDS), dissolved oxygen (DO), total suspended solids (TSS), iron, nitrate, phosphate, potassium, sulphate, chromium, fluoride, e. coli and coliform. A total of 406 data set were collected and analysed using Principal Component Analysis, water quality index, cluster analysis and analysis of variance (ANOVA). These data sets were tested for sampling adequacy using Kaiser-Meyer-Olkin and Bartlett's Test and the result on the Kaiser-Meyer-Olkin Measure of Sampling Adequacy obtained was 0.615. The analysis yields Five PCs extraction with eigenvalues >1. These components explained 82.628% of the total variance of the entire components. The maximum water quality index 13 which indicated a grade A and can be treated for water supply. The following parameters Chromium 0.39 mg/l, Iron 1.88 mg/l, turbidity 18.66NTU, Phosphates 26.00 mg/l and fluorides 1.75 mg/l exceeded the WHO guidelines for drinking water. The mean values electrical conductivity is 12.26 μS/cm, 31.8 μS/cm for rain and dry seasons respectively., The following parameters Turbidity, Total Dissolved Solid, Total Suspended solids, Iron, Phosphate, Fluoride and Sulphate shows variation with High during the rain and low during the dry season with significant statical difference with a p value < 0.05. Whereas there is difference between the seasonal values of chromium, Nitrate and Potassium. The ANOVA resulted in P-value >0.05 which indicated no statistically significant different for chromium, Nitrate and Potassium. The seasonal variation was corroborated by cluster analysis with two clusters of C1 and C2. The PCs analysis, cluster analysis and ANOVA gave detailed characterization of the source and group correlation amongst the physiochemical and microbial parameters.