Variation of groundwater quality in seawater intrusion area using cluster and multivariate factor analysis

Numerous monitoring data and water quality index obtained from 76 regional shallow-layer monitoring wells are simplified and classified by applying the multivariate statistical methods such as factor and cluster analysis to search for the interrelation between the water quality parameters, factors representing the characteristics as well as possible pollution sources of groundwater quality in Pingtung Champaign, Taiwan. Four principal factors were recognized in concerned area using principal component analysis (PCA) including salinization factor, mineralization factor, inorganic factor, and inorganic reduction factor. All four factors can interpret 81.0% variances of the integrated groundwater characteristics. In addition four clusters were classified according to the similar and dissimilar characteristics of water quality of monitoring wells in Pingtung Champaign. The results showed that the groundwater quality of hinterland was better than that of coastal area. Some coastal areas have already been affected by the seawater intrusion resulting in aquifer salinization. Multivariate statistical methods provided by this study can not only reduce the harassment of the missing items of monitoring water quality, but also refer as a management alternative for groundwater resources.