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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張尊國(Tsun-Kuo Chang) | |
| dc.contributor.author | Chih-Hao Chen | en |
| dc.contributor.author | 陳志豪 | zh_TW |
| dc.date.accessioned | 2021-06-13T08:04:02Z | - |
| dc.date.available | 2005-07-27 | |
| dc.date.copyright | 2005-07-27 | |
| dc.date.issued | 2005 | |
| dc.date.submitted | 2005-07-21 | |
| dc.identifier.citation | 1. 行政院環境保護署, 2002。「農地土壤重金屬調查與場址列管計畫」。
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Deutsch, C. V., Journel, A. G., 1998, GSLIB: Geostatistical Software Library and User’s Guide, 2nd Edition, Oxford University Press, New York. 23. European Environment Agency, 2000, Management of contaminated sites in Western Europe. Copenhagen. 24. Ferguson C. C., 1992, “The statistical basis for spatial sampling of contaminated land,” Ground Engineering, Vol. 25, No. 5, pp. 34-38. 25. Ferguson, C. C., 1994, Contaminated Land Research Report No.4: Sampling Strategies For Contaminated Land, The Nottingham Trent University, Nottingham. 26. Gilbert, R.O., 1987. Statistical Methods for Environmental Pollution Monitoring, Van Nostrand Reinhold, New York. 27. Gilbert, R. O., Bates, D. J., Wilson, J. E., Pulsipher, B. A., O’Brien, R. F., McKinstry, C. A., Carlson, D. K., 2002, Version 2.0 Visual Sample Plan (VSP): Models and Code Verification, PNNL-13991, Pacific Northwest National Laboratory, Richland, Washington. 28. Gogolak, C. V., Powers, G. E., Huffert, A. M., 1997, A Nonparametric Statistical Methodology for the Design and Analysis of Final Status Decommissioning Surveys, NUREG-1505, U.S. Nuclear Regulatory Commission, Washington. 29. Gomez-Hernandez, J.J., 1997, “Issues on environmental risk assessment,” Geostatistics Wollongong ’96, Kluwer Academic Publishing, Dordrecht, pp. 15-26. 30. Goovaerts, P., Journel, A.G., 1995. “Integrating soil map information in modelling the spatial variation of continuous soil properties,” Eur. J. Soil Sci., Vol. 46, pp. 397-414. 31. Goovaerts, P., 1997, Geostatistics for natural resources evalution, Oxford University Press, New York. 32. Goovaerts, P., 2001, “Geostatistical modelling of uncertainty in soil science,” Geoderma, Vol. 103, pp. 3-26. 33. Guenther, W. C. 1977, “Sampling Inspection in Statistical Quality Control,” Griffin’s Statistical Monographs and Courses, No. 37, London. 34. Guenther, W. C., 1981, “Sample Size Formulas for Normal Theory T-Tests,” The American Statistician, Vol. 35, No.4, pp. 243-244. 35. Hassig, N. L., Wilson, J. E., Gilbert, R.O., Pulsipher, B. A., 2004, Visual Sample Plan Version 3.0 User’s Guide, PNNL-14970, Pacific Northwest National Laboratory, Richland, Washington. 36. Journel, A. G., 1988, “Nonparametric geostatistics for risk and additional sampling assessment,” In L. Keith (ed.) Principles of environmental sampling, American Chemical Society, pp. 45-72. 37. Juang, K. W., Chen, Y. S., Lee, D. Y., 2004, “Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils,” Environmental Pollution, Vol. 127, pp. 229-238. 38. Kirkpatrick, S., Gelatt Jr., C. D., and Vecchi, M. P., 1983, “Optimization by simulated annealing,” Science, Vol. 220, No. 4598, pp. 671-680. 39. Lin, Y. P., Chang, T. K., 2000, “Simulated annealing and kriging method for identifying the spatial patterns and variability of soil heavy metal,” J. Environ. Sci., Vol. 35, No.7, pp. 1089-1115. 40. Ministry for the Environment, 2004, Contaminated Land Management Guidelines No.5: Site Investigation and Analysis of Soils, New Zealand. 41. Noether, G. E., 1987, “Sample Size Determination for Some Common Nonparametric Tests,” Journal of the American Statistical Association, Vol. 82, pp. 645-647. 42. Pannatier, Y., 1996, VARIOWIN: Software for Spatial Data Analysis in 2D, Springer-Verlag, New York. 43. Singer, D. A., Wickman, F. E., 1969, Probability Tables for Locating Elliptical Targets with Square, Rectangular and Hexagonal Point Nets, Pennsylvania State University, University Park, Pennsylvania. 44. Singer, D. A., 1972, “ELIPGRID: A Fortran IV program for calculating the probability of success in locating elliptical targets with square and rectangular and hexagonal grids,” Geocom Programs, Vol. 4, pp. 1-16. 45. Singer, D. A., 1975, “Relative efficiencies of square and triangular grids in the search for elliptically shaped resource targets,” Journal of Research of the U.S. Geological Survey, Vol. 3, No. 2, pp. 163- 167. 46. Singer, D. A., Drew, L. J., 1976, “The Area of Influence of an Exploratory Hole,” Economic Geology, Vol. 71, pp. 642- 647. 47. STATISTICA, 2001, STATISTICA 6 Electronic Manual, StatSoft Inc., Tulsa. 48. Steven, S., 2004, “Expected Confidence and Required Probability of Sampling Patterns,” Paper Presented at EIGG conference on the Non-Invasive Investigation and Monitoring of Waste Sites, Birmingham, UK. 49. Todd Mowrer, H., 1997, “Progating uncertainty through spatial estimation processes for old-growth forests using sequential Gaussian simulation in GIS,” Ecological Modelling, Vol. 98, pp. 73-86. 50. U.S. EPA, 1989, Methods for Evaluating the Attainment of Cleanup Standards, EPA 230-02-89-042, Washington. 51. U.S. EPA, 1992, Preparation of Soil Sampling Protocols: Sampling Techniques and Strategies, EPA 600-R-92-128, Washington. 52. U.S. EPA, 1997, Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM). EPA 402-R-97-016, NUREG-1575, Washington. 53. U.S. EPA, 2000a, Guidance for the Data Quality Objectives Process, EPA 600-R-96-055, Washington. 54. U.S. EPA, 2000b, Guidance for Data Quality Assessment-Practical Methods for Data Analysis, EPA 600-R-96-084, Washington. 55. U.S. EPA, 2002, Guidance on Choosing a Sampling Design for Environmental Data Collection, EPA-240-R-02-005, Washington. 56. Van Groenigen, J. W., Stein, A., 1998, “Constrained optimization of spatial sampling using continuous simulated annealing,” J. Environ. Qual., Vol.27, No. 5, pp.1078-1086. 57. Van Meirvenne, M., Goovaerts, P., 2001, “Evaluating the probability of exceeding a site-specific soil cadmium contamination threshold,” Geoderma, Vol. 102, pp. 63-88. 58. Wallis, W. A., 1947, “Uses of Variables in Acceptance Inspection for Percent Defective,” Techniques of Statistical Analysis, McGraw-Hill, New York. 59. Wang, G., Gertner, G., Parysow, P., Anderson, A. B., 2000. “Spatial prediction and uncertainty analysis of topographic factors for the revised universal soil loss equation (RUSEL),” J. Soil Water Conserv., Vol. 55, pp. 374-384. 60. Webster, R., Oliver, M. A., 1989. “Optimal interpolation and isarithmic mapping of soil properties: VI. Disjunctive kriging and mapping the conditional probability.” J. Soil Sci., Vol. 40, pp. 497-512. 61. Zirschky, J., Gilbert, R. O., 1984, “Decting hot spots at hazardous-waste sites,” Chemical Engineering, Vol. 91, pp. 97-100. 62. @RISK, 2000, @RISK 4.0.5 User’s Manual, Palisade Corporation, Newfield, New York. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/36522 | - |
| dc.description.abstract | 本文以不同的角度切入三個在土壤污染調查時常遇見的問題(1) 可否找到高濃度分佈的地點，以及其命中率；(2) 場址受到污染的程度；(3) 高濃度區分佈之範圍。分別進行採樣策略合宜性的探討。
在搜尋高污染區方面，應用空間分析的方式，進行採樣配置的高污染區命中率分析，並比較空間模擬退火佈點採樣策略與網格式佈點採樣策略在搜尋污染場址中高污染區之能力。本研究的結果指出空間模擬退火佈點法，在搜尋污染場址之高污染區方面確實比網格式佈點法更優良，在相同的條件下，相同的採樣密度和同一污染場址，模擬退火佈點法比網格式佈點法有較低的錯失率。 在判斷場址受到污染程度的部份，統計理論提供對場址資訊量化的科學性證據，可作為進行採樣調查時控制成本與效益的實用工具，研究中利用控制可容忍誤差範圍方式進行階段性採樣合宜性之探討，結果顯示，在規劃合理之信賴度及容許的誤差範圍下，評估調查土壤污染調查所需之樣本數量，能讓調查結果明確的代表母體的特性。 在探討高濃度區分佈範圍方面，於研究中分別使用克利金法與序列指標模擬進行污染範圍的推測，並透過實例分析兩種方法的特性。結果顯示，利用克利金法推測污染範圍能有效的輔助判斷性採樣之設計，而模擬法則能提供使決策者更能掌握空間變數在未採樣位置的變化情形，也提供設計採樣時更多元的資訊。 | zh_TW |
| dc.description.abstract | Three typical objectives of a sampling design for contaminated soil investigation are: (1) to identify the location of “hot spots”, (2) to estimate contamination levels, (3) to delineate the pollution patterns and range of contaminated sites. This research addresses the issue of suitability about the sampling strategy for above objectives.
In first case, objective of sampling is to determine where 'hot spots' are present. Spatial analysis method is presented to assess the capability of searching for hot spots of sampling strategy, includes Spatial Simulated Annealing (SSA) and grid sampling strategy. The results indicate the Spatial Simulated Annealing is better than grid sampling design when searching for hot spots. Grid sampling design has the higher miss rate than Spatial Simulated Annealing when deal with the same pollution site with same sampling density. When sampling strategy were aimed at estimating average concentrations or total amounts of pollutants in environmental media, Statistics theory provides a basis for balancing decision uncertainty with available resources. This section discusses optimize the design for obtaining data by specify limits on decision errors stage by stage. The results reflect that assessing sample size needed for investigation by well-planned confidence could make the outcome achieve sampling objective with required performance. To delineate the pollution patterns and range of contaminated sites, two approaches are presented: ordinary kriging (OK) and sequential indicator simulation (SIS) approach, and illustrated the characteristics by case studies. Mapping the values of continuous soil attributes by kriging perform well to assist judgmental sampling, while the simulation-based approach provide assessing the uncertainty about the value of soil properties at unsampled locations, and thus incorporate this assessment in subsequent decision-making processes. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T08:04:02Z (GMT). No. of bitstreams: 1 ntu-94-R92622031-1.pdf: 7737203 bytes, checksum: 6e980acbcb64fdb04b463c3dfca4b74a (MD5) Previous issue date: 2005 | en |
| dc.description.tableofcontents | 摘要 Ⅰ
英文摘要 Ⅱ 目錄 Ⅳ 圖目錄 Ⅶ 表目錄 Ⅹ 第一章 前言 1.1 研究動機 1 1.2 研究目的 1 第二章 文獻回顧 2.1 土壤重金屬調查概況 2 2.2 土壤採樣規範 4 2.2.1 國內土壤採樣規範 4 2.2.2 美國土壤採樣規範 6 2.2.3 英國土壤採樣規範 11 2.2.4 荷蘭土壤採樣規範 13 2.2.5 紐西蘭土壤採樣規範 14 2.2.6 採樣規範綜合分析 16 2.3 其他採樣之相關研究 24 2.3.1 空間模擬退火法 24 2.3.2 高污染區錯失率 29 2.3.3 地理統計 33 第三章 材料與方法 3.1 研究材料 37 3.1.1 資料來源 37 3.1.2 應用軟體 39 3.2 研究方法 40 3.2.1 研究流程 40 3.2.2 研究方法 41 3.2.3 研究範圍 42 第四章 理論模式 4.1 空間模擬退火法 43 4.1.1 模擬退火法演算流程 43 4.1.2 空間模擬退火法 45 4.1.2.1 建構問題模型 45 4.1.2.2 鄰近解產生與接受機制 48 4.1.2.3 建構退火程序 48 4.2 高污染區錯失率 51 4.2.1 最大miss圓之定義 51 4.2.2 Hot Spot 53 4.3 地理統計 55 4.3.1 一般克利金 55 4.3.2 指標克利金 57 4.3.3 隨機模擬 59 4.3.3.1 評估局部不確定性 59 4.3.3.2 評估空間不確定性 60 4.3.3.3 模擬運算原理 61 4.3.3.4 序列模擬 61 4.3.3.5 序列指標模擬 63 第五章 結果與討論 5.1 採樣設計之高污染區錯失率 66 5.1.1 採樣之空間配置與數量：推論命中率 66 5.1.2 研究案例一 於不規則場址比較採樣策略之高污染區錯失率 69 5.2 給定誤判機率下所需之最少樣本數 77 5.2.1 統計學原理：推論所需樣本數量 77 5.2.2 研究案例二 兩階段隨機抽樣 78 5.3 界定高濃度區分佈之範圍 85 5.3.1 地理統計方法： 考量空間分佈進行採樣點配置 85 5.3.2 研究案例三 多重目標下的採樣設計：兩階段判斷式採樣 85 5.3.3 研究案例四 考量不確定性進行採樣配置 95 第六章 結論與建議 6.1 結論 110 6.2 建議 112 附錄A：統計檢定相關樣本數計算公式 113 附錄B： 與所對應的SignP值 120 參考文獻 121 | |
| dc.language.iso | zh-TW | |
| dc.title | 土壤污染調查之採樣策略研究 | zh_TW |
| dc.title | A Study on Sampling Strategy for Contaminated Soil Investigation | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 93-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳尊賢(Zueng-Sang Chen),李達源(Dar-Yuan Lee),林裕彬(Yu-Pin Lin),張文亮(Wen-Lian Chang) | |
| dc.subject.keyword | 土壤污染,採樣策略,空間模擬退火法,地理統計, | zh_TW |
| dc.subject.keyword | Soil contamination,Sampling Strategy,Spatial Simulated Annealing,Geostatistics, | en |
| dc.relation.page | 126 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2005-07-21 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物環境系統工程學系 | |
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