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<br />Environmental Science Policy ' <br />A central question in environmental polity analysis is how to interpret and use scientific information ' <br />in the decision making process - a process based on the integration of physical, chemical, and ' <br />biological data that may be subject to uncertainty. Uncertainties may stem from a lack of basic data, <br />from disagreement among "experts" regarding explanatory models, from the natural variability <br />inherent in biological systems, from lack of a precise regulatory framework, or from the absence: of <br />a coherent decision making calculus. Highly experienced in statistical, regulatory, and quantitative ' <br />uncertainty analysis, RCG/Hagler, Bailly staff members collect and interpret scientific data relevant <br />to specific polity goals, thus providing a vital interface between the sciences and environmental <br />decision making polity. Examples include: ' <br />^ Quantitatlve Approaches to Addressing Uncertainty , <br />RCG/Hagler, Bailly utilizes quantitative approaches to incorporate uncertainty into health <br />and ecological risk assessments for municipal sewage sludge disposal. Monte Carlo/Iatin- <br />Hypercube analysis was used to models uncertainties in exposure models for uptake of organic ' <br />pollutants by marine biota and to address the policy ramifications of uncertainties to risk- <br />based decision making. The results were published in Environmental Toxicology and <br />Chemistry. ' <br />^ Theoetlc Models For Addressirg Environmental Decision-Making Under Uncertainty ' <br />This theoretic examination focused on applying stochastic dominance, entropy, Bayesian <br />decision theory, fuzzy set theory, and weighted "decision rules" to environmental decision- ' <br />making. A concurrent analysis determined what levels of uncertainty in risk assessment were <br />deemed "acceptable" by the EPA. The results of this latter study were published in <br />Regulatory Toxicology and Pharmacology. <br />^ Technical Guidance on Moute-Carlo Simulatlou in Exposure Assessmeut ' <br />Technical guidance documents were prepared for the EPA to support the increased use of <br />Monte-Carlo analysis in conducting exposure assessments. This guidance addressed <br />simulation techniques, methods for selecting input distnbutioas, and random-number ' <br />generation <br />^ Ecological R1sk Assessment and Natural Resource Dsmage Assessmeat <br />Ecological risk assessments currently are performed by the EPA as part of Remedial ' <br />Investigations (RI) at National Priority List (Superfund) sites. In this project, technical <br />guidance was provided to EPA to identify differences and similarities between ecological risk <br />of natural resource injury assessment conducted as part of the NRDA process. ' <br />6 <br /> <br />