Sampling and inversion methods for quantifying effect of incomplete subsurface characterization on uncertainty associated with recovery of contamination history.

用于量化不完整的地下表征对与污染历史恢复相关的不确定性的影响的采样和反演方法。

• 批准号: 0607002
• 负责人: Anna Michalak
• 金额: --
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0607002&HistoricalAwards=false
• 关键词: Sampling; inversion; methods; quantifying; effect

0607002MichalakThis project will employ inverse modeling to trace contaminants in groundwater to the source of the contamination. Three methods will be developed to incorporate uncertainties in transport parameters into the modeling: Monte Carlo simulation, small perturbation approximation, and co-kriging. Results of these three methods will be tested with field data and hypothetical applications at Dover Air Force Base, Delaware, and Gloucester Landfill, Canada. The results of this research also will be used to develop guidelines to determine the extent to which the history of contamination can be quantified given uncertainties associated with transport parameters. Development of capabilities to trace contaminants to their source will contribute to the identification of responsible parties and could lead to improved practices that correct such problems.

0607002Michalak该项目将采用逆向建模来追踪地下水中的污染物到污染源。将开发三种方法，以将不确定性的传输参数的建模：蒙特卡罗模拟，小扰动近似，协同克里格。这三种方法的结果将在特拉华州的多佛空军基地和加拿大的格洛斯特垃圾填埋场进行现场数据和假设应用的测试。这项研究的结果还将用于制定准则，以确定在与运输参数有关的不确定性的情况下，污染历史可以量化的程度。发展追踪污染物来源的能力将有助于查明责任方，并可改进纠正这些问题的做法。