Heavy Metals and Water Quality Assessment Using Multivariate Statistical Techniques and Water Quality Index of the Semenyih River, Peninsular Malaysia

The present study was carried out to investigate and determine the water quality and the pollution sources affected on􀀃Semenyih River using multivariate statistical techniques and water quality index (WQI). Temperature, pH, dissolved oxygen (DO), conductivity, total dissolved solids (TDS), sulfate (SO4 ), nitrate 2 (NO3 ), nitrite (NO2), phosphate (PO4 ), turbidity, ammonia-nitrogen (NH3-N), total suspended solids (TSS), 3 chemical oxygen demand (COD), biochemical oxygen demand (BOD), total hardness (TH), oil and grease (O&G), Escherichia coli and total Colifor (TC) as water quality variables and Cd, Cu, Ni, Zn, Fe, Pb, Mn, Cr and Hg as heavy metals variables have been analyzed in the collected water samples during the year 2012 from 8 sampling stations along􀀃Semenyih River. Cluster analysis (CA) categorized 8 stations into three clusters based on the similarity of water quality characteristics and categorized 27 variables analyzed to four clusters to determine the relationship among the variables and their possible sources. Principal component analysis (PCA) determined that 96.63% of the total variance was accounted for five factors which pointed to the variables responsible for deterioration of water quality attributed to anthropogenic activities associated with urbanization, industrialization, agriculture, livestock husbandry and mining activities. In addition, WQI classified the river as clean (Class I) at station 1, slightly polluted (Class II) at stations 2 and 3 and as moderately polluted (Class III) at stations 4-8; in general; however, the river falls into class III and thus is required􀀃extensive treatment before using for domestic purposes. Therefore, this study verified that the multivariate statistical techniques and water quality index are mainly required for interpreting complex data sets for the purpose of analysis of water quality variations.