RISK-BASED ENVIRONMENTAL REMEDIATION - BAYESIAN MONTE-CARLO ANALYSIS AND THE EXPECTED VALUE OF SAMPLE INFORMATION

A methodology that simulates outcomes from future data collection prog rams, utilizes Bayesian Monte Carlo analysis to predict the resulting reduction in uncertainty in an environmental fate-and-transport model, and estimates the expected value of this reduction in uncertainty to a risk-based environmental remediation decision is illustrated conside ring polychlorinated biphenyl (PCB) sediment contamination and uptake by winter flounder in New Bedford Harbor, MA. The expected value of sa mple information (EVSI), the difference between the expected loss of t he optimal decision based on the prior uncertainty analysis and the ex pected loss of the optimal decision from an updated information state, is calculated for several sampling plan. For the illustrative applica tion we have posed, the EVSI for a sampling plan of two data points is $9.4 million, for five data points is $10.4 million, and for ten data points is $11.5 million. The EVSI for sampling plans involving larger numbers of data points is bounded by the expected value of perfect in formation, $15.6 million. A sensitivity analysis is conducted to exami ne the effect of selected model structure and parametric assumptions o n the optimal decision and the EVSI. The optimal decision (total area to be dredged) is sensitive to the assumption of linearity between PCB sediment concentration and flounder PCB body burden and to the assume d relationship between area dredged and the harbor-wide average sedime nt PCB concentration; these assumptions also have a moderate impact on the computed EVSI. The EVSI is most sensitive to the unit cost of rem ediation and rather insensitive to the penalty cost associated with un der-remediation.