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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/22942


    Title: 統計診斷技術應用於離島工業區地下水質剖析之研究案例
    其他題名: A Case Study: Statistical Diagnosis of Groundwater Quality Data in an Offshore Industrial Park
    Authors: 鄭曜德
    Cheng, Yao-Te
    指導教授: 吳庭年
    Ting-Nien Wu
    Contributor: 環境工程研究所
    Keywords: 因子分析;群集分析;多變量統計分析;溶礦因子;鹽化因子
    factor analysis;cluster analysis;principal component analysis;mineralization factor;salinization factor
    Date: 2014
    Issue Date: 2014-12-19 11:01:36 (UTC+8)
    Abstract: 離島工業區自民國88年營運以來,環保問題一直飽受各界關注,離島工業區眾多產業類別中多為高污染潛勢業別,而在眾多污染類型中,土壤及地下水往往成為環境中最終受體且難以察覺,亦可能造成周遭環境及居民健康之衝擊。本研究係利用統計診斷技術剖析離島工業區地下水水質資料,監測數據源自於雲林縣環境保護局「離島工業區土壤及地下水污染調查報告」,針對73口地下水監測井之19項水質測項資料,進行主成分分析歸納出影響地下水水質的可能污染成因,並利用群集分析標定出各污染成因影響的區域位置,藉此發揮監測井預警的功能,以即時強化地下水水質管理的機制。主成分分析將19項水質測項縮減為6個主成分因子:第一主成分鑑別為「鹽化因子」,可能是抽砂填海造陸致使地下水呈現有鹽化特徵。第二主成分歸類為「除銹污染因子」,研判為定期管路除銹意外造成鋅、鎳、銅等重金屬之環境釋出。第三主成分屬於「溶礦因子」,鐵與錳元素普遍存在於地殼地質礦物層中,與地下水長時間接觸易從礦物層中溶解出來,導致地下水水質中含鐵質與錳。第四主成分界定為「有機污染因子」,疑似製程有機污染物洩漏造成。第五主成分為「氨氮污染因子」,與鄰近地區土地的利用型態有關,氨氮的可能來源包含農、漁業活動,亦不排除為製程洩漏所造成。第六主成分為「硝酸鹽氮污染因子」,初步研判為氨氮硝化的產物。對應6個主成分因子,運用群集分析將73口地下水監測井區分為6個群集,並標定出各群集監測井之相關位置。歸屬於群集二的監測井計有66口,相關位置多數位於儲槽及管路區域,對應於第二主成分,研判為定期管路除銹意外釋出鋅、鎳、銅等重金屬,進而影響地下水水質。其他群集多為一口監測井,反映主成分因子鑑別之污染成因,且多屬於局部性影響。利用統計診斷技術可以鑑別區域地下水水質之可能污染成因,並界定各污染成因影響相關區域位置,有助於提升離島工業區地下水水質的管理工作。
    The environmental issues have drawn much attention in the studied offshore industrial park since its starting in 1999. Many high-potentially polluted plants were accommodated in the studied offshore industrial park. Soil and groundwater is the ultimate containment of environmental pollutants, and soil and groundwater contamination may directly threat to the surroundings and public health. This study utilized statistical diagnosis technology to anatomize groundwater quality data from 19 water quality measurements of 73 monitoring wells in Yulin offshore industrial park. Principal component analysis is able to derive the potential sources affecting groundwater quality, and cluster analysis is able to delineate the spatial domains of each potential sources. The integration of principal component analysis and cluster analysis is capable of achieving the forecasting of groundwater monitoring and intensifying the management mechanism of groundwater quality.By using principal component analysis, 19 water quality measurements in the original data set were extracted into 6 principal components (PCs). The first PC was recognized as salinization factor, which the characteristics of groundwater salinization is ascribed to the reclamation by using sea sand. The second PC was recognized as rust pollution factor, which resulted from the release of Zn, Ni and Cu during pipeline rust recovery processes. The third PC was identified as mineralization factor, which the most abundant Fe and Mn in the earth shell might gradually dissolve in groundwater. The fourth PC was defined as organic pollution factor, which is likely released from the leakages in the manufacturing processes. The fifth PC was defined as ammonia pollution factor, which the sources of ammonia might include agricultural operation, fishery activity, and industrial leakages. The sixth PC is identified as nitrate pollution factor, which was the nitrification product of ammonia.In order to correspond to 6 PCs, 73 monitoring wells were classified into 6 groups by using cluster analysis. Based on the locations of monitoring wells, the domains of 6 groups were spatially allocated. 66 monitoring wells located at the regions of tanks and pipelines were classified into cluster 2, which was corresponding to the potential release of Zn, Ni and Cu during pipeline rust recovery processes in the second PC. The other clusters contained only 1 monitoring well, which were corresponding to the identified source in the individual PC. Statistical diagnosis technology was proved its feasibility of identifying potential sources, allocating the affecting domain of potential sources, and improving groundwater quality management.
    Appears in Collections:[環境工程系所] 博碩士論文

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