Application of Principal Component Analysis on Source Identification and Tracking of Groundwater Contamination from Southern Taiwan Science Park

This paper demonstrated a case study on how to utilize principal component analysis (PCA) as mining tool to extract the patterns of groundwater contamination. Southern Taiwan Science Park was selected as study area, and forty-five groundwater monitoring wells within the study area were used to build up groundwater quality database. Lab data of routine groundwater analysis including pH, electrical conductivity, temperature, total dissolved solid, total organic carbon, fluoride, ammonia, nitrite, nitrate, and phenols were subjected to principal component analysis. Based on the monitoring data between 2005 and 2007, the extracted information from the PCA mirrored the potential sources of groundwater contamination as salinization, arsenic dissolution, industrial leakage, mineralization, and agricultural activities. According to the depth of monitoring wells, the PCA results of two stratification groups were matched up to track the movement of groundwater contamination in the vertical profile. The vertical distribution of groundwater contamination revealed that fluoride and phenols was transported to the bottom of the unconfined aquifer. On the basis of yearly monitoring data, the PCA results of each year were matched up to track the shift of groundwater contamination with time. The possible sources of groundwater contamination resulted from agricultural activities and mineralization processes before 2005, and its significance is gradually fading out and turning to be the type of potential industrial leakage.