Evaluation of Groundwater Quality at an Industrial Park Site Zone Using Statistical Analyses: A Case Study in Taiwan

Multivariate statistical analysis explains the huge and complicated current situation of the original data efficiently, concisely, and explicitly. It simplifies the original data into representative factors, or bases on the similarity between data to cluster and identify clustering outcome. In this study, the statistical software SPSS 12.0 was used to perform the multivariate statistical analysis to evaluate characteristics of groundwater quality at an industrial park site located in Kaohsiung, Taiwan. Results from the principal component analysis (PCA) and factor analyses (FA) show that seven principal components could be compiled from 20 groundwater quality indicators obtained from groundwater analyses, which included background factor, salt residua factor, hardness factor, ethylene chloride factor, alkalinity factor, organic pollutant factor, and chloroform factor. Among the seven principal components, the major influencing components were salinization factor and acid-base factor. Results show that the seven principal component factors were able to represent 89.6% of the total variability for 20 different groundwater quality indicators. Groundwater monitoring wells were classified into seven groups according to the partition of homogeneity and similarity using the two-phase cluster analysis (CA). The clustering results indicate that chlorides such as 1,1-dichloroethylene, 1,1-dichloroethane, and cis-1,2-dichloroethylene had the highest concentrations among the clusters. This indicates that groundwater at nearby areas may be polluted by chlorinated organic compounds. Results from the correlation analysis by Fisher coefficient formula show that the cluster results of seven groups of groundwater wells had 100 and 80% accuracies using discriminant and cross-validation analyses, respectively. This implies that high accuracy can be obtained when discriminant and cluster analyses are applied for data evaluation.