Multi-criteria decision analysis with probabilistic risk assessment for the management of contaminated ground water

Traditionally, environmental decision analysis in subsurface contamination scenarios is performed using cost–benefit analysis. In this paper, we discuss some of the limitations associated with cost–benefit analysis, especially its definition of risk, its definition of cost of risk, and its poor ability to communicate risk-related information. This paper presents an integrated approach for management of contaminated ground water resources using health risk assessment and economic analysis through a multi-criteria decision analysis framework. The methodology introduces several important concepts and definitions in decision analysis related to subsurface contamination. These are the trade-off between population risk and individual risk, the trade-off between the residual risk and the cost of risk reduction, and cost-effectiveness as a justification for remediation. The proposed decision analysis framework integrates probabilistic health risk assessment into a comprehensive, yet simple, cost-based multi-criteria decision analysis framework. The methodology focuses on developing decision criteria that provide insight into the common questions of the decision-maker that involve a number of remedial alternatives. The paper then explores three potential approaches for alternative ranking, a structured explicit decision analysis, a heuristic approach of importance of the order of criteria, and a fuzzy logic approach based on fuzzy dominance and similarity analysis. Using formal alternative ranking procedures, the methodology seeks to present a structured decision analysis framework that can be applied consistently across many different and complex remediation settings. A simple numerical example is presented to demonstrate the proposed methodology. The results showed the importance of using an integrated approach for decision-making considering both costs and risks. Future work should focus on the application of the methodology to a variety of complex field conditions to better evaluate the proposed methodology

Water quality modeling under hydrologic variability and parameter uncertainty using export coefficients

Water quality modeling is important to assess the health of a watershed and to make necessary management decisions to control existing and future pollution of receiving water bodies. The existing export coefficient approach is attractive due to minimum data requirements; however, this method does not account for hydrologic variability. In this paper, an erosion-scaled export coefficient approach is proposed that can model and explain the hydrologic variability in predicting the annual phosphorus (P) loading to the receiving stream. Here sediment discharge was introduced into the export coefficient model as a surrogate for hydrologic variability. Application of this approach to model P in the Fishtrap Creek of Washington State showed the superiority of this approach compared to the traditional export coefficient approach, while maintaining its simplicity and low data requirement characteristics. In addition, a Bayesian framework is proposed to assess the parameter uncertainty of the export coefficient method instead of subjective assignment of uncertainty. This work also showed through a joint variability-uncertainty analysis the importance of separate consideration of hydrologic variability and parameter uncertainty, as these represent two independent and important characteristics of the overall model uncertainty. The paper also recommends the use of a longitudinal data collection scheme to reduce the uncertainty in export coefficients

Applicability of risk-based management and the need for risk-based economic decision analysis at hazardous waste contaminated sites

Decision analysis in subsurface contamination management is generally carried out through a traditional engineering economic viewpoint. However, new advances in human health risk assessment, namely, the probabilistic risk assessment, and the growing awareness of the importance of soft data in the decision-making process, require decision analysis methodologies that are capable of accommodating non-technical and politically biased qualitative information. In this work, we discuss the major limitations of the currently practiced decision analysis framework, which evolves around the definition of risk and cost of risk, and its poor ability to communicate risk-related information. A demonstration using a numerical example was conducted to provide insight on these limitations of the current decision analysis framework. The results from this simple ground water contamination and remediation scenario were identical to those obtained from studies carried out on existing Superfund sites, which suggests serious flaws in the current risk management framework. In order to provide a perspective on how these limitations may be avoided in future formulation of the management framework, more matured and well-accepted approaches to decision analysis in dam safety and the utility industry, where public health and public investment are of great concern, are presented and their applicability in subsurface remediation management is discussed. Finally, in light of the success of the application of risk-based decision analysis in dam safety and the utility industry, potential options for decision analysis in subsurface contamination management are discussed