Use of Multivariate Statistical Approaches for Decoding Chemical Evolution of Groundwater near Underground Storage Caverns

Multivariate statistical analyses have been extensively applied to hydrochemical measurements to analyze andinterpret the data. This study examines anthropogenic factors obtained from applications of correspondence analysis (CA) andprincipal component analysis (PCA) to a hydrogeochemical data set. The goal was to synthesize the hydrogeochemicalinformation using these multivariate statistical techniques by incorporating hydrogeochemical speciation results calculated bythe program, commonly used, WATEQ4F included in the NETPATH. The selected case study was LPG underground storagecaverns, which is located in the southeastern Korea. The highly alkaline groundwaters at this study area are an analogue forthe repository system. High pH, speciation of Al and possible precipitation of calcite characterize these groundwaters. Available groundwater quality monitoring data were used to confirm these statistical models. The present study focused onunderstanding the hydrogeochemical attributes and establishing the changes of phase when two anthropogenic effects (i.e.,disinfection activity and cement pore water) in the study area have been introduced. Comparisons made between twostatistical results presented and the findings of previous investigations highlight the descriptive capabilities of PCA usingcalculated saturation index and CA as exploratory tools in hydrogeochemical research.