| Summary: | 碩士 === 崑山科技大學 === 環境工程研究所 === 97 === Statistical multivariate analysis can reduce data dimension on the basis of variable correlation, classify data clusters according to their similarity, and predict the temporal trend of specific variable variations. For the sustainable use of groundwater, this study employed multivariate analysis as a tool of anatomizing groundwater quality data to realize the characteristics, sources, and temporal trend of groundwater contamination. The established monitoring wells in Chianan Plain groundwater subregion were all subjected to principal component analysis and cluster analysis by the SPSS 12.0 software. As a result, the characteristics of groundwater quality as well as the linkage of contaminant sources and local distribution can be discovered. Groundwater quality data including pH, electrical conductivity (EC), hardness, total dissolved solid (TDS), total organic carbon (TOC), ammonia, nitrate, chloride, sulfate, Fe, Mn, As, Na, K, Ca and Mg were extracted for statistical analysis. By using principal component analysis, the obtained four principal components (PCs) account for 82.4% of the variance or information contained in the original data set. The obtained four PCs represent four identified patterns of groundwater contamination as salinization, arsenic dissolution, organic pollution, and mineralization. The 2-step cluster analysis is utilized to classify the similarity among samples, and eighty four monitoring wells were accordingly classified into four clusters. Associating with the locations of monitoring wells, the result showed that the overall groundwater quality in highland region is superior to the coastal area. Seawater intrusion or salinization is the common case in Chianan Plain coastal area, and the potential organic pollution of groundwater is found around the crowd districts in Kaohsiung, Fengshan, Rende, Shinying, Taiban cities. The measured arsenic level of groundwater exceeds drinking water standard in Chigu, Beimen, and Budai coastal regions where geographically matches up with the reported historical Blackfoot disease region.
Time series analysis is used as data mining tool, and the Chianan Blackfoot disease region was selected as study area. Blackfoot disease is caused by the high uptake of arsenic in groundwater, and thus the temporal trend of arsenic concentration in groundwater is examined by time series analysis. ARMA and ARIMA, the common time series modeling methods, were employed to interpret the information beneath the monitoring data. Thirty-nine monitoring wells around the Chianan Blackfoot disease region were subjected to time series analysis, and the input data was extracted from historical monitoring data of arsenic concentration in groundwater during the time period of 2000 and 2007. The Akaike’s information criterion (AIC) is generally served as a criterion for assessing the quality of model fitting. It is based on residual log-likelihood function for model comparison. As a usual rule, the smaller AIC and simpler model tends better fitness for a give data set. Through further verification, the selected ARMA(1,1) model fits the data set well over the other three models ARMA(2,1), ARMA(1,2) and ARIMA(1,1,1). The result showed that this developed numerical model can effectively interpret and forecast the arsenic level in groundwater from area affected by salinization and high arsenic level in Chianan Plain based on the known information.
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