Either naturally occurring processes or human activities may have a significant impact on the quality of subsurface waters and further limit its use as water supply. With the aids of multivariate statistical techniques, this study attempted to puzzle out these processes and attribute their influence on groundwater quality. Kaohsiung County area holding parts of two main groundwater regions of Taiwan was selected for this study. Water quality data including temperature, pH, EC, DO, SS, turbidity, BOD, COD, NH3-N, NO2-N, NO3-N, TP, TKN, E-coli, anionic surfactant, Pb, Cd, Cr, Cu, Zn, Hg. from four monitoring stations were subjected to factor and cluster analysis. Principal component analysis (PCA) was utilized to reflect those chemical data with the greatest correlation, whereas cluster analysis (CA) was used to evaluate the similarities of water quality in groundwater samples. CA results illustrated that the overall quality of groundwater within hinterland was better than that within coastal area, where was partially salinized as a result of seawater intrusion. By utilizing PCA, the identified four major principal components (PCs) representing almost 94% of cumulative variance were able to interpret the most information contained in the data. PC 1 reflects the dominance of salinization, which was characterized by the elevated concentrations of EC, hardness, chloride, sulfate, sodium, potassium and magnesium in groundwater. PC 2 with the elevated concentrations of iron and manganese is thought to be representative of mineral dissolution within the aquifer. PC 3 shows a strong monotonic relationship with zinc concentration in the groundwater revealing the linkage of the oxidizing/reducing conditions within the aquifer. PC 4 describes the infiltration of organic matters that resulted in the enhancement of TOC on groundwater quality.

Water quality variation, principal component analysis; principal component