Data and Model-Driven Decision Support for Environmental Management of a Chromium Plume at Los Alamos National Laboratory–13264 (original) (raw)

A Combined Probabilistic/Nonprobabilistic Decision Analysis for Contaminant Remediation

SIAM/ASA Journal on Uncertainty Quantification, 2014

Groundwater contaminant remediation poses a significant challenge due in large part to ubiquitous uncertainties and unknowns. A number of remedial options are typically available at a given contamination site. However, choosing the best option is challenging, because it is not typically known beforehand how effective each remediation will be. Nonetheless, choices must be made. We present an approach to decision support consisting of three coupled layers accounting for model/parametric uncertainties and unknowns. The inner and outer layers employ an information-gap approach to uncertainty quantification, while the middle layer employs Bayes' theorem. Two synthetic remedial scenarios are explored to demonstrate the efficacy of the approach. Considered remedial actions are natural attenuation (NA) and an enhanced attenuation (EA).

On Bayesian Decision Analysis for Evaluating Alternative Actions at Contaminated Sites

2000

Today, contaminated land is a widespread infrastructural problem and it is widely recognised that returning all contaminated sites to background levels, or even to levels suitable for the most sensitive land use, is not technically or financially feasible. The large number of contaminated sites and the high costs of remediation, are strong incentives for applying cost-efficient investigation and remediation strategies that consider the inherent uncertainties.

Decision Support Systems for Risk-Based Management of Contaminated Sites

2009

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Contaminant remediation decision analysis using information gap theory

Stochastic Environmental Research and Risk Assessment, 2012

Decision making under severe lack of information is a ubiquitous situation in nearly every applied field of engineering, policy, and science. A severe lack of information precludes our ability to determine a frequency of occurrence of events or conditions that impact the decision; therefore, decision uncertainties due to a severe lack of information cannot be characterized probabilistically. To circumvent this problem, information gap (info-gap) theory has been developed to explicitly recognize and quantify the implications of information gaps in decision making. This paper presents a decision analysis based on info-gap theory developed for a contaminant remediation scenario. The analysis provides decision support in determining the fraction of contaminant mass to remove from the environment in the presence of a lack of information related to the contaminant mass flux into an aquifer. An info-gap uncertainty model is developed to characterize uncertainty due to a lack of information concerning the contaminant flux. The info-gap uncertainty model groups nested, convex sets of functions defining contaminant flux over time based on their level of deviation from a nominal contaminant flux. The nominal contaminant flux defines a reasonable contaminant flux over time based on existing information. A robustness function is derived to quantify the maximum level of deviation from nominal that still ensures compliance for each decision. An opportuneness function is derived to characterize the possibility of meeting a desired contaminant concentration level. The decision analysis evaluates how the robustness and opportuneness change as a function of time since remediation and as a function of the fraction of contaminant mass removed.

Overview of environmental decision support software

Regulatory exposure limits form the basis for making decisions on the characterization, monitoring, and remediation of environmental contamination. This paper discusses the development of Decision Support Software (DSS) tools developed to support decisions pertaining to environmental management. Decision support software packages are computer-based programs that facilitate the use of data, models, and structured decision processes in decision making. They incorporate the information into an integrated package that presents results in a format useful for making environmental decisions. Six major analysis functions of DSS tools have been identified: site characterization, plume characterization, risk assessment including regulatory compliance assessment, remedy selection, remedy design optimization, and cost/benefit analysis. Decision support software is relatively new and is now beginning to see application in the field. This paper discusses existing DSS and the strengths and limitat...

A development of a Decision Support System for Remediation Option Selection for Contaminated Sites 1

This paper presented the overview of different remediation technologies which are useful for the remediation of contaminated land that may be employed in a variety of contaminated site cleanup programs. This study summarized the advantages and disadvantages of different methods which are useful to manage a contaminated site by a geoenvironmental engineer. For a specific superfund site, there was not only one remediation technique is applicable but also more than one techniques are applicable on the basis of different criteria. The main objective of this paper was to provide the information for creation of a decision support system to remediate a superfund site. The criteria generated for different classes of remediation are cost effective methods, less time consuming methods and high risk reducing methods.

Los Alamos National Laboratory's RiskBased Decision Analysis for Groundwater Remediation and Monitoring

2006

Contaminant transport models can provide valuable information for decisions regarding environmental management. When coupled with quantitative uncertainty, sensitivity, and economic-worth analyses, contaminant transport models can be used to optimize overall performance of an environmental management system. This paper describes a risk-informed decision process developed and applied at Los Alamos National Laboratory in New Mexico, USA, to optimize the management of groundwater-protection program resources. A separate publication (Vesselinov & Birdsell, 2005) describes the groundwater transport model developed to assess uncertainty in this application, while this paper focuses on the decision analysis model developed to manage that uncertainty.

Decision support based on uncertainty quantification of model predictions of contaminant transport

The process of decision making to protect groundwater resources requires a detailed estimation of uncertainties in model predictions. Various uncertainties associated with model development, such as measurement and computational errors, uncertainties in the conceptual model and model-parameter estimates, simplifications in model setup and numerical representation of governing processes, influence the uncertainties in the model predictions. As a result, the predictive uncertainties are generally difficult to quantify. Quite frequently however, the uncertainties in only some of the model parameters and predictions are important to consider in the decision making process. We investigate and compare existing and newly-proposed methods for the quantification of predictive uncertainties in relation to decision support. The goal is to quantify predictive uncertainties affecting decision making related to locating new monitoring wells. Velimir V. Vesselinov et al.