Department of Civil and Environmental Engineering, Stanford University, USA.
J Contam Hydrol. 2010 Apr 1;113(1-4):25-43. doi: 10.1016/j.jconhyd.2009.11.004. Epub 2010 Jan 20.
Dense non-aqueous phase liquid (DNAPL) spills represent a potential long-term source of aquifer contamination, and successful low-cost remediation may require a combination of both plume management and source treatment. In addition, substantial uncertainty exists in many of the parameters that control field-scale behavior of DNAPL sources and plumes. For these reasons, cost optimization of DNAPL cleanup needs to consider multiple treatment options and their associated costs while also gauging the influence of prediction uncertainty on expected costs. In this paper, we present a management methodology for field-scale DNAPL source and plume management under uncertainty. Using probabilistic methods, historical data and prior information are combined to produce a set of equally likely realizations of true field conditions (i.e., parameter sets). These parameter sets are then used in a simulation-optimization framework to produce DNAPL cleanup solutions that have the lowest possible expected net present value (ENPV) cost and that are suitably cautious in the presence of high uncertainty. For simulation, we utilize a fast-running semi-analytic field-scale model of DNAPL source and plume evolution that also approximates the effects of remedial actions. The degree of model prediction uncertainty is gauged using a restricted maximum likelihood method, which helps to produce suitably cautious remediation strategies. We test our methodology on a synthetic field-scale problem with multiple source architectures, for which source zone thermal treatment and electron donor injection are considered as remedial actions. The lowest cost solution found utilizes a combination of source and plume remediation methods, and is able to successfully meet remediation constraints for a majority of possible scenarios. Comparisons with deterministic optimization results show that not taking into account uncertainty can result in optimization strategies that are not aggressive enough and result in greater overall total cost.
非水相液体（DNAPL）泄漏是含水层污染的潜在长期污染源，成功的低成本修复可能需要同时采用羽流管理和源处理。此外，在控制 DNAPL 源和羽流的场尺度行为的许多参数中存在大量不确定性。出于这些原因，DNAPL 清理的成本优化需要考虑多种处理方案及其相关成本，同时衡量预测不确定性对预期成本的影响。在本文中，我们提出了一种在不确定性下进行场尺度 DNAPL 源和羽流管理的管理方法。使用概率方法，将历史数据和先验信息结合起来，生成一组真实场条件（即参数集）的等可能实现。然后，这些参数集在模拟-优化框架中使用，以生成具有最低可能净现值（ENPV）成本的 DNAPL 清理解决方案，并在存在高度不确定性的情况下谨慎行事。对于模拟，我们利用快速运行的半分析场尺度 DNAPL 源和羽流演化模型，该模型还近似考虑了补救措施的影响。使用受限最大似然方法来衡量模型预测不确定性的程度，这有助于生成适当谨慎的补救策略。我们在具有多种源架构的合成场尺度问题上测试了我们的方法，其中考虑了源区热处理和电子供体注入作为补救措施。发现的最低成本解决方案利用了源和羽流修复方法的组合，并且能够成功满足大多数可能情况下的修复约束。与确定性优化结果的比较表明，不考虑不确定性可能导致优化策略不够积极，并导致总成本增加。
