Source identification of groundwater pollution with the aid of multivariate statistical analysis

With the aid of multivariate statistical analysis, this study attempted to predict possible underlying processes, attribute their influence, and isolate the distribution of sources that might threaten groundwater quality. Tainan County, Taiwan was employed as a case study, and 34 monitoring wells were sampled for routine lab analysis. Lab data of groundwater quality including pH, EC, hardness, chloride, sulfate, ammonia, nitrate, Fe, Mn, As, Zn, TOC and TDS 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. By utilizing PCA, the identified four major principal components (PCs) representing 78.8% 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 and sulfate in groundwater. PC 2 with the positive loadings of TOC and pH but negative loading of nitrate is thought to be representative of organic pollution within the aquifer. PC 3 is regarded as mineralization factor on the basis of the loadings of manganese and zinc. PC 4 shows a strong monotonic relationship with ammonia concentration in the groundwater revealing the linkage with agricultural activity. CA results illustrated that coastal area was partially salinized as a result of seawater intrusion and part of salinization zone was also subjected to the impact of mineral dissolution.