Assessment of Groundwater Quality Using Multivariate Statistical Techniques

Abstract

AbstractThe most vital source of freshwater for domestic, agriculture and industry is groundwater. The physical and chemical compositions of this main source of water are primarily determined by natural and anthropogenic activities. This study is carried out to monitor the groundwater quality in the Jaen district of Nigeria using several multivariate statistical procedures including principal component analysis (PCA), factor analysis (FA), and cluster analysis (CA). Thirty different samples were obtained to analyse the physical and chemical parameters during raining season in the year 2020. The collected parameters include electric conductivity (EC), pH, total dissolved solids (TDS), temperature (Temp.), carbonate (CO3), magnesium (Mg), sodium (Na), potassium (K), calcium (Ca), bicarbonate (HCO3), chloride (Cl), sulphate (SO4), fluoride (F), nitrate (NO3), total hardness (TH), chromium (Cr), iron (Fe), zinc (Zn), manganese (Mn), and copper (Cu). The result from PCA identified major pollutants which account for 56.4% of the three first components of differences altogether, the first component accounting for 24.9%, the second for 18.2% and the third for 13.3% of the contribution, respectively. The major groundwater parameters responsible for water pollution in the study area were Cr, Cu, Zn, Mn, Fe, EC, TDS, Temp., Mg, Ca, Na, HCO3 and F compared to other parameters. In addition, PCA showed that the main sources of groundwater contamination in the study area are industrial and domestic activities. Furthermore, based on similarities in pollution levels, CA grouped the sampling stations into three distinct groups; less polluted, moderately polluted, and highly polluted.KeywordsCuster analysisGroundwater qualityMonitoringMultivariate statisticsPollution