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A study was initiated which combined elements of stochastic hydrology, risk assessment, simulation modeling, cost analysis and decision making to define the optimum remediation choice(s) for a Superfund site in east Texas. The underlying premise of this effort was that environmental decision making is inherently complex due to uncertainties in contaminant concentrations and resultant exposures. The technical analyst should supply the decision maker with estimates of these uncertainties as well as the cost penalties required to reduce them to manageable levels.
This study employed Monte Carlo transport modeling to define the probability of contaminant excursions from the site, applied geostatistical simulation to existing data sets, used Bayesian modeling to define the worth of additional data and Decision Modeling to define optimum configurations. These individual components were combined to produce a decision model which defined remediation alternatives given levels of risk tolerance which could be supplied by the decision maker or affected community.
risk assessment, stochastic hydrology, decision modeling, uncertainty analysis
Captain, U.S. Army Corps of Engineers, Colorado Springs, Colorado
Professor, School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, Oklahoma
Professor, Oklahoma State University, Stillwater, Oklahoma