Statistical Diagnosis of Groundwater Quality Data in Kaohsiung Science Park

This study integrated conventional statistical tools with neighbourhood linkage to propose the statistical diagnosis approach. Fourteen monitoring wells in Kaohsiung Science Park were selected as study case, and lab data of routine groundwater analysis including pH, EC, hardness, TDS, TOC, ammonia, nitrate, nitrite, chloride, sulphate, fluoride, phenols, Fe, Mn, As, and temperature were subjected to principal component and cluster analysis. Principal component analysis (PCA) was utilized to reflect those chemical data with the greatest correlation, and PCA results identified five major principal components (PCs) representing 74.6% of cumulative variance. Based on the monitoring data between 2005 and 2008, the extracted information from the PCA mirrored the potential sources of groundwater contamination as acid leakage, arsenic dissolution, salinization, mineralization, and fluoride release. Cluster analysis (CA) was used to evaluate the similarities of water quality in groundwater samples, and five clusters were assigned in two-step clustering for corresponding with the number of PCs, i.e. the potential sources of groundwater contamination. The interpreted facts from CA illustrated that the classified monitoring wells in each cluster properly match up with the identified processes. With the aid of neighbourhood linkage, the domain of groundwater contamination can be spatially outlined by mapping the neighbouring wells within the identical cluster. Therefore, the nature of underlying processes affecting groundwater quality was explored by statistical diagnosis.