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年度	99	專案性質	實驗性質
專案類別	研究專案	研究主題	調查
申請機構	國立台灣大學	申請系所	生物環境系統工程學系
專案主持人	林裕彬	職等/職稱	教授
專案中文名稱	區域尺度土壤重金屬空間採樣策略與汙染範圍劃定之研究
中文關鍵字	土壤汙染、 重金屬、分區條件拉丁超立方抽樣、條件模擬、空間不確定性
專案英文名稱
英文關鍵字	Soil pollution； Heavy metal； Stratified conditioned Latin hypercube sampling；Conditional simulation； Spatial uncertainty
執行金額
執行期間	2010/12/29 至 2011/12/28
計畫中文摘要	本研究以彰化地區為研究區域，以該地區多種土壤種金屬為研究變數，擬發展一同時考量多變數及其保留空間分布與統計特性的採樣策略，並評估以採樣資料於污染範圍的界定之可靠性分析方法。本採樣方法提出分區條件拉丁超立方採樣方法，首先於採樣過程中先將研究區分區如網格或灌區分區，再以原始土壤採樣資料或與污染相關之變數為條件拉丁超立方採樣方法之採樣變數，以期選取的樣本於空間特性及統計特性上能更接近於原始資料或污染相關之變數之空間特性及分佈，最後各分區中選取樣區所需之樣本。 本研究適用兩種採樣情境，一為有土壤重金屬採樣資料，另一為無土壤重金屬採樣資料，若資料為原始資料調查的土壤採樣資料，則將原始資料與不同採樣方式所得的資料以逐步指標模擬法模擬研究區內重金屬濃度空間分布情形，並比較分區條件拉丁超立方採樣與原始資料之變異圖及空間特性，且計算局部和空間不確定性，並探討其污染範圍劃設之可靠性。若資料為污染相關之變數(無土壤重金屬採樣資料)，則以與污染相關變數進行分區條件拉丁超立方採樣，比較分區條件拉丁超立方採樣與原始資料之統計特性與空間變異圖特性，並與有土壤資料之採樣結果進行比較以驗證可行性，最後探討各情境下適用之採樣策略。
計畫英文摘要	The research aims to resample the multiple soil heavy metals at Chang-Hua County. The sampling model that can consider multivariate, statistic distribution and spatial information is developed.Moreover, the method is reliable for the analysis in delineating the pollution hazard. The method is a stratified conditional Latin hypercube sampling (scLHS) that includes the stratified sampling and grid sampling. Meanwhile,the consideration in spatial aspect for sampling sites in conditioned Latin hypercube sampling is also unignorable. First, sampling is applied based on sampling data or the other correlated data, so the incorporation of spatial data, which is regarded as the spatial cLHS, might be able to drive the data closer to their original spatial allocation. Then, the spatial distribution and uncertainty of each technique, including original data without sampling, were evaluated by the sequential indicator simulation (SIS). Furthermore, the spatial cLHS could better imitate the distribution and spatial allocation of the original data. The study considers two scenarios: with and without soil pollution data. Wherever the soil pollution occurs, the model evaluates the variogram and the spatial distribution of soil pollution based on the information offered. And then the model compares both the variogram and the spatial distribution with original data. After all the local and spatial uncertainty of the model are calculated. If there is no soil pollution at all, the model evaluates variogram and spatial distribution of soil pollution based on other information and then compares them with the original data.