環保專案成果報告查詢系統

專案計畫編號	EPA-940201-2
經費年度	094
計畫經費	795,000 千元
專案開始日期	2005/03/01
專案結束日期	2005/12/31
專案主持人	陳瑞仁
主辦單位	屏東縣政府環境保護局
承辦人
執行單位	陳瑞仁,黃益助
專案分類	水質保護
中文關鍵字	河川水質變異、因子分析、主成份分析、最大變異轉軸
英文關鍵字	Water quality variation, principal component analysis; principal component
協同主持人
共同主持人	黃益助
計畫聯絡人	陳瑞仁
計畫聯絡電話	08)7703202#7086
計畫聯絡信箱	chensj@mail.npust.edu.tw

中文摘要	由於各種不同污染量及型態排入河川，造成水質惡化產生程度上的差異，其變異程度可藉由統計軟體（如SPSS或SAS）進行各項統計分析，並可針對各監測站水質變異量進行分析及預測。本研究主要依據屏東縣東港溪歷年水質監測資料，進行統計分析，以利於水質資料之判讀及應用。在東港溪四個監測站（成德大橋、萬巒大橋、五魁橋、港東二號橋）所進行的河川水質採樣分析中，水質分析項目眾多，共22項，因此可利用統計軟體將大量數據資料予以簡化，分析影響河川水質變異的主要因子，進一步掌握東港溪的污染型態，作為未來重點監測項目與改善的參考。由變異數分析結果顯示，不同年度之氨氮和總磷經多重比較法（Tukey method）分析後可得到顯著的差異，若將東港溪各測站氨氮和總磷的濃度依季節整理分析，發現東港溪水質明顯受豐枯水期的影響。各水質監測項目以因子分析分類程序找出新的綜合指標，結果指出在相關性分析中原先與懸浮固體有高度相關約（0.89）的濁度經最大變異轉軸後，與該第三因子對於水質變異具較高的相關性（負荷值約0.94），懸浮固體與該第三因子對於水質變異則具有較高的相關性（負荷值約0.93），顯示因子分析得到的結果與相關性分析相符。第一因子中優氧化因子；第二因子屬於除了溶解性參數外亦包含了耗氧性參數，COD因子負荷值約0.60，硝酸鹽氮相對呈現高度負相關性（負荷值約-0.79）。因子分析可得到新的4個主分量及相關矩陣，其性質可分成第一因子屬溶解性及耗氧性因子，第二因子屬於除了溶解性參數外亦包含了耗氧性參數，第三因子屬於混濁性因子，第四因子屬於物理性或自然因子，由此可得到代表東港溪水質變異之主要污染因素所在。
中文關鍵字	河川水質變異、因子分析、主成份分析、最大變異轉軸

中文摘要

由於各種不同污染量及型態排入河川，造成水質惡化產生程度上的差異，其變異程度可藉由統計軟體（如SPSS或SAS）進行各項統計分析，並可針對各監測站水質變異量進行分析及預測。本研究主要依據屏東縣東港溪歷年水質監測資料，進行統計分析，以利於水質資料之判讀及應用。在東港溪四個監測站（成德大橋、萬巒大橋、五魁橋、港東二號橋）所進行的河川水質採樣分析中，水質分析項目眾多，共22項，因此可利用統計軟體將大量數據資料予以簡化，分析影響河川水質變異的主要因子，進一步掌握東港溪的污染型態，作為未來重點監測項目與改善的參考。由變異數分析結果顯示，不同年度之氨氮和總磷經多重比較法（Tukey method）分析後可得到顯著的差異，若將東港溪各測站氨氮和總磷的濃度依季節整理分析，發現東港溪水質明顯受豐枯水期的影響。各水質監測項目以因子分析分類程序找出新的綜合指標，結果指出在相關性分析中原先與懸浮固體有高度相關約（0.89）的濁度經最大變異轉軸後，與該第三因子對於水質變異具較高的相關性（負荷值約0.94），懸浮固體與該第三因子對於水質變異則具有較高的相關性（負荷值約0.93），顯示因子分析得到的結果與相關性分析相符。第一因子中優氧化因子；第二因子屬於除了溶解性參數外亦包含了耗氧性參數，COD因子負荷值約0.60，硝酸鹽氮相對呈現高度負相關性（負荷值約-0.79）。因子分析可得到新的4個主分量及相關矩陣，其性質可分成第一因子屬溶解性及耗氧性因子，第二因子屬於除了溶解性參數外亦包含了耗氧性參數，第三因子屬於混濁性因子，第四因子屬於物理性或自然因子，由此可得到代表東港溪水質變異之主要污染因素所在。

中文關鍵字

河川水質變異、因子分析、主成份分析、最大變異轉軸

類型	檔名	檔案大小	說明	最新版公開日期
期末報告	94年度屏東縣河川水質監測計畫期末報告.pdf	2MB	公開版	2006/3/1 上午 12:00:00

摘要	Either naturally occurring processes or human activities may have a significant impact on the quality of subsurface waters and further limit its use as water supply. With the aids of multivariate statistical techniques, this study attempted to puzzle out these processes and attribute their influence on groundwater quality. Kaohsiung County area holding parts of two main groundwater regions of Taiwan was selected for this study. Water quality data including temperature, pH, EC, DO, SS, turbidity, BOD, COD, NH3-N, NO2-N, NO3-N, TP, TKN, E-coli, anionic surfactant, Pb, Cd, Cr, Cu, Zn, Hg. from four monitoring stations were subjected to factor and cluster analysis. Principal component analysis (PCA) was utilized to reflect those chemical data with the greatest correlation, whereas cluster analysis (CA) was used to evaluate the similarities of water quality in groundwater samples. CA results illustrated that the overall quality of groundwater within hinterland was better than that within coastal area, where was partially salinized as a result of seawater intrusion. By utilizing PCA, the identified four major principal components (PCs) representing almost 94% of cumulative variance were able to interpret the most information contained in the data. PC 1 reflects the dominance of salinization, which was characterized by the elevated concentrations of EC, hardness, chloride, sulfate, sodium, potassium and magnesium in groundwater. PC 2 with the elevated concentrations of iron and manganese is thought to be representative of mineral dissolution within the aquifer. PC 3 shows a strong monotonic relationship with zinc concentration in the groundwater revealing the linkage of the oxidizing/reducing conditions within the aquifer. PC 4 describes the infiltration of organic matters that resulted in the enhancement of TOC on groundwater quality.
英文關鍵字	Water quality variation, principal component analysis; principal component

摘要

Either naturally occurring processes or human activities may have a significant impact on the quality of subsurface waters and further limit its use as water supply. With the aids of multivariate statistical techniques, this study attempted to puzzle out these processes and attribute their influence on groundwater quality. Kaohsiung County area holding parts of two main groundwater regions of Taiwan was selected for this study. Water quality data including temperature, pH, EC, DO, SS, turbidity, BOD, COD, NH3-N, NO2-N, NO3-N, TP, TKN, E-coli, anionic surfactant, Pb, Cd, Cr, Cu, Zn, Hg. from four monitoring stations were subjected to factor and cluster analysis. Principal component analysis (PCA) was utilized to reflect those chemical data with the greatest correlation, whereas cluster analysis (CA) was used to evaluate the similarities of water quality in groundwater samples. CA results illustrated that the overall quality of groundwater within hinterland was better than that within coastal area, where was partially salinized as a result of seawater intrusion. By utilizing PCA, the identified four major principal components (PCs) representing almost 94% of cumulative variance were able to interpret the most information contained in the data. PC 1 reflects the dominance of salinization, which was characterized by the elevated concentrations of EC, hardness, chloride, sulfate, sodium, potassium and magnesium in groundwater. PC 2 with the elevated concentrations of iron and manganese is thought to be representative of mineral dissolution within the aquifer. PC 3 shows a strong monotonic relationship with zinc concentration in the groundwater revealing the linkage of the oxidizing/reducing conditions within the aquifer. PC 4 describes the infiltration of organic matters that resulted in the enhancement of TOC on groundwater quality.

英文關鍵字

Water quality variation, principal component analysis; principal component