Value of Information as a Context-Specific Measure of Uncertainty in Groundwater Remediation

Abstract

The remediation of groundwater sites has been recognized as a difficult and expensive task for years. One of the challenges is that the success of remediation is usually contingent upon an appropriate level of characterization of the physical, chemical, and biological site properties. For example, thermal treatment cannot be economically applied if the location of a non-aqueous phase liquid (NAPL) source is unknown. Both characterization and remediation are expensive. Thus, efforts need to be prioritized and optimized taking effects of uncertainty into consideration. Traditional measures of uncertainty, such as variance and correlation coefficients, do not fully depict the significance of uncertainty. For example, a small error in a parameter to which performance is sensitive may affect the prospect for remediation success much more than a large error in a parameter that has minor influence. In this paper, we quantify uncertainty as the expected increase in the cost of achieving clean-up objectives that is associated with uncertainty in performance prediction models, i.e., the minimum expected cost attainable with the present state of uncertainty minus the expected cost achievable if uncertainty were fully or partially removed. This measure, a.k.a., the value of information (VOI), is context-specific, i.e., it is dependent on site conditions and remediation strategies as well as specific remediation objectives and unit costs. We consider clean-up objectives, cost formulations, and sensitivity of costs to uncertainty in parameters, measurements, and the model itself and seek to minimize expected cost under conditions of incomplete information. We present results from a synthetic case study of dense non-aqueous phase liquid (DNAPL) plume treatment. The results quantify the cost attributable to uncertainty, thus setting an upper limit on how much one should pay for characterization, and helping decision makers to decide whether the data should be collected or not.