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研究生:周家宇
研究生(外文):Chia-Yu Chou
論文名稱:淡水河流域河川污染指數時間空間變異及分類之研究
論文名稱(外文):Study on Spatiotemporal Variability and Classification of River Pollution Index in the Tamsui River Watershed
指導教授:張誠信張誠信引用關係陳世楷陳世楷引用關係
指導教授(外文):Cheng-Shin JangShih-Kai Chen
口試委員:高雨瑄王聖瑋
口試委員(外文):Yu-Hsuan KaoSheng-Wei Wang
口試日期:2016-07-29
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:土木工程系土木與防災碩士班
學門:工程學門
學類:土木工程學類
論文種類:學術論文
畢業學年度:104
中文關鍵詞:局部熵指標克利金法河川污染指數感潮河段淡水河流域
外文關鍵詞:EntropyIndicator KrigingRiver Pollution IndexTidal StreamsTamsui River Watershed
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淡水河及其主要支流為北台灣各標的用水最重要之供水來源,為有效評估淡水河流域與感潮河段水質變化,如何利用現行河川污染指數(RPI)為基礎建立一個河川水質時間及空間變異推估與分類管理模式,為大台北都會區環境與資源經營管理之重要議題。本研究使用2010至2014年淡水河及其主要支流之月河川水質監測數據,首先將河川污染指數資料對於時間變化進行頻率分析得到第20、40、60、80百分位數之濃度值,再以RPI分類基準設定門檻並將各百分位數之濃度值二元化,而後進行指標克利金法推估,最後計算各門檻之局部熵(Entropy)以評估淡水河流域RPI之時間變異性。
本研究以各測站春(3-5月)、夏(6-8月)、秋(9-11月)、冬(12-2月)之中位數為污染狀態之基準值,將其對應計算之Entropy,以評估探討淡水河流域季節性RPI之時間變異性,結果顯示在淡水河流域之上游水質穩定無時變性,在中游河段之Entropy在第一門檻、第二門檻較高,在各季節間RPI於輕度與中度污染間變動,在中下游匯流處Entropy在第三門檻較高,但在基隆河與新店溪季節性之污染程度無變化,皆為中度污染,而大漢溪中下游季節性污染程度,在中度與嚴重污染間變動,以第50百分位數進行指標克利金推估做為常態污染程度,可發現大漢溪下游污染程度為嚴重污染,且位於人口密度最高之區域,顯示此河段受人為活動影響,造成此變動現象,而基隆河與新店溪中下游位於感潮河段內,可能受海水影響,使污染程度穩定處於中度污染。本研究藉由Entropy計算結果大致呈現淡水河流域各河段之時變性,此方法可提供環保主管機關完整掌握淡水河流域河川水質時間空間變化,以及未來擬訂相關水質環境與資源管理措施。
The Tamsui River and its tributaries are the important sources for various water resources demands in northern Taiwan. To effectively evaluate water quality variations in the Tamsui River and tidal streams, it is a critical issue in metropolitan Taipei regions that a river pollution index (RPI) is used to assess the river water quality in Taiwan, and can be adopted to regulate suitable utilization purposes of a river water body. This study used RPI in the Tamsui River Watershed during 2010-2014 to establish a management and classification model based on the spatiotemporal variability of RPI using non-parametric geostatistics. First, the 20th, 40th, 60th, and 80th percentiles of RPI proposed by the Taiwan Environmental Protection Administration (EPA) are determined according to a statistical frequency analysis of observed RPI data. Indicator kriging (IK) is then applied to spatially model the occurrence probability according to the pollution thresholds of RPI. Finally, IK combined with entropy is adopted to assess the seasonal variability and uncertainty of the river water quality. In addition, this study uses the 50th percentiles (i.e., medium) of RPI in spring, summer, fall, and winter to spatially determine the typical river water quality situations. The study results indicate that no seasonal variabilities are present in the upstream portion of the Tamsui River Watershed. The entropy is high at the 1st and 2nd thresholds in the midstream portion of the Tamsui River Watershed with the lightly polluted and moderately polluted categories during seasons. The entropy is high at the 3rd thresholds in the midstream and downstream portions of the Tamsui River Watershed with the moderately polluted categories during seasons. However, no seasonal variabilities occur in the Keelung River and Xindian Stream. The downstream portion of the Dahan Stream presents the moderately and severely polluted categories during seasons. According to the IK estimates using the 50th percentiles of RPI data, the downstream portion of the Dahan Stream are severely polluted because this region is highly populated, resulting in the high seasonal variation. Because seawater dilutes the severely polluted river water, the midstream and downstream portions of the Keelung River and Xindian Stream, which are the tidal stream, stably present the moderately polluted category. The assessed results can also provide government administrators with detailed and comprehensive information on analyzing the spatiotemporal variability of water quality and establishing strategies of environmental resources management of water quality in the Tamsui River Watershed.
摘要 i
ABSTRACT iii
誌謝 v
目錄 vi
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 前言 1
1.2 研究目的 2
1.3 論文架構及流程 2
第二章 文獻回顧 5
2.1 河川污染指數 5
2.2 地理統計應用於水質指數或河川污染指數之空間推估 7
2.3 指標克利金法之空間機率推估及分類模式 8
2.4 地理統計結合資訊熵之空間與時間變異推估模式 9
第三章 材料與方法 10
3.1 研究區域地理環境 10
3.1.1 流域概況 10
3.1.2 氣象與水文 10
3.1.3 感潮河段 11
3.2 水資源利用 13
3.3 河川污染指數與淡水河流域水體分類 13
3.3.1 河川污染指數 13
3.3.2 地面水體及水質標準 14
3.4 淡水河流域水質監測數據 17
3.5 地理統計理論 19
3.5.1 半變異元分析 19
3.5.2 半變異元模式驗證 20
3.5.3 指標克利金法 21
3.5.4 條件累積機率分佈函數之修正 22
3.5.5 時間不確定性評估 24
第四章 結果與討論 26
4.1 淡水河流域污染程度之概況 26
4.2 指標克利金之半變異元分析 34
4.3 指標克利金之推估及修正 51
4.4 結合局部熵分析淡水河流域RPI之時變性 57
4.5 綜合討論 63
第五章 結論與建議 66
5.1 結論 66
5.2 建議 67
參考文獻 68
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