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.

本研究的目的是开发一个耦合优化和模拟风险管理模型，用于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）设计选项，范围从自然衰减到工程羽流控制和修复，以及这些方法的组合。将探索提高模型计算效率的理论进展，以便考虑大规模现场。 噪声遗传算法将与称为 Modflow 和 RT3D 的综合流动、命运和运输模拟模型相结合。 噪声遗传算法与遗传算法类似，但每个候选解的适应度（价值）是通过概率分布采样来评估的。 对于此应用，概率分布代表空间变化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。 提高待研究模型计算效率的方法包括开发高效且有效的采样策略，测试采样容差对数值模型中网格近似的影响，以及研究在多个条件下使用多个解群的注入岛遗传算法的性能有效识别和微调强大解决方案的决议。 拟议的工作预计将为监管机构和受监管社区提供一个有价值的风险管理工具，确定既具有成本效益又足以保护人类健康的风险降低策略。