Cost Effective Risk Based Corrective Action Design for Contaminated Groundwater

针对受污染地下水的具有成本效益的基于风险的纠正措施设计

• 批准号: 9903889
• 负责人: Barbara Minsker
• 金额: $ 21.3万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-15 至 2002-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9903889&HistoricalAwards=false
• 关键词: Cost; Effective; Risk; Based; Corrective

The objective of this research is to develop a coupled optimization and simulation risk management model for investigating the relationships between human health risk and corrective action design under conditions of uncertainty. The methodology will identify optimal risk-based corrective action (RBCA) design options ranging from natural attenuation to engineered plume control and remediation, as well as combinations of these approaches. Theoretical advancements for improving computational efficiency of the model will be explored so that large-scale field sites can be considered. A noisy genetic algorithm will be coupled with comprehensive flow and fate and transport simulation models called Modflow and RT3D. Noisy genetic algorithms are similar to genetic algorithms, but the fitness (worth) of each candidate solution is evaluated through sampling from probability distributions. For this application, probability distributions represent uncertainty in spatially-variable hydraulic conductivity measurements and variability in parameters used to estimate human health risks. Methods for improving computational efficiency of the model to be investigated include developing efficient and effective sampling strategies, testing the implications of sampling tolerance on grid approximations in the numerical models and investigating the performance of an injection island genetic algorithm that uses multiple populations of solutions at multiple resolutions to efficiently identify and fine-tune strong solutions. The proposed work is expected to result in a valuable risk management tool for both regulators and the regulated community, identifying risk reduction strategies that are both cost-effective and sufficiently protective of human health.

本研究的目的是建立一个耦合优化和模拟风险管理模型，以研究不确定条件下人类健康风险与纠正措施设计之间的关系。该方法将确定最佳的基于风险的纠正措施（RBCA）设计方案，从自然衰减到工程羽流控制和补救，以及这些方法的组合。探讨提高模型计算效率的理论进展，以便考虑大规模的现场。噪声遗传算法将与综合流量、命运和运输模拟模型Modflow和RT3D相结合。噪声遗传算法与遗传算法相似，但每个候选解的适应度（值）是通过从概率分布中抽样来评估的。对于这种应用，概率分布代表了空间可变的水力传导率测量的不确定性和用于估计人类健康风险的参数的可变性。提高模型计算效率的方法包括开发高效的采样策略，在数值模型中测试采样容限对网格近似的影响，以及研究注射岛遗传算法的性能，该算法使用多分辨率的多种群解来有效地识别和微调强解。拟议的工作预计将为监管机构和受监管社区提供一个宝贵的风险管理工具，确定既具有成本效益又充分保护人类健康的减少风险战略。