Final Program - Society for Risk Analysis

ogy capable of quant ifying ecological risks related to rare events such as industrialaccidents. We use population modeling to simulate future changes in the populationabundance of key species at risk and therefore estimate their probability of extinctionor decline, time to extinct ion and other measures, for each accidental scenario.Thus, it was possible to develop an approach that links the ecological damage (predictedvia ecological modeling) with the frequency of occurrence of the accidentalscenario (estimated via historical data and reliability analysis). The result is a FN riskcurve similar to the result of a human quantitative risk assessment for industrial accidents.However, in our context, N is the average population decline number and Fthe cumulative frequency of accidents with N or greater abundance decline. Secondly,the work presents an application of the methodology using a project of a petroleumrefinery to be constructed in the Northeast of Brazil, which estimates a processing of200 thousand barrels of oil per day. This facility is located near a very rich aquatic ecosystemwith a high biodiversity. A population of a key species was strategically chosento represent the ecosystem, some accidental scenarios of a great amount of oil spillwere simulated and their frequencies of occurrence estimated. For each scenario, theconcentration of oil that reaches the population was predicted via fate and transportmodeling. Finally, the ecological risks were quantified and presented as a FN curve.T2-I.1 Dudley SE; sdudley@gwu.eduThe George Washington UniversityREGULATORY SCIENCE AND POLICY - A CASE STUDY OF THE NA-TIONAL AMBIENT AIR QUALITY STANDARDSThis paper will explore the motivations and institutional incentives of participantsinvolved in the development of regulation aimed at reducing health risks, witha goal of understanding and identifying institutional solutions to what the BipartisanPolicy Center has characterized as “a tendency to frame regulatory issues as debatessolely about science, regardless of the actual subject in dispute, [that] is at the rootof the stalemate and acrimony all too present in the regulatory system today.” Wewill focus our analysis with a case study of the procedures for developing NationalAmbient Air Quality Standards under the Clean Air Act, and attempt to identify proceduralapproaches that bring greater diversity (in data, expertise and experience) intothe decision process.M2-E.2 Dutt V, Gonzalez C; varundutt@cmu.eduCarnegie Mellon UniversityENABLING ECO-FRIENDLY CHOICES BY USING HUMAN PSYCHO-LOGICAL BIASESEcological (eco) taxes are promising mechanisms to enable eco-friendly decisions;however, they do not enjoy popular support. In this study, we make use oftwo psychological biases to enable more eco-friendly choices: loss aversion and thetendency to respond linearly to non-linear problems (i.e., proportional thinking). Par-92ticipants were asked to choose between two eco-tax increases in two decision problems:in one, the smaller eco-tax increase resulted in greater CO2 emissions reduction,while in the other, the smaller increase resulted in lesser reduction. Although largereco-tax increases did not always save more CO2 emissions, a majority of participantspreferred the smaller eco-tax increases, while judging larger tax increases to causegreater reductions in CO2 emissions. Therefore, participants rely on loss aversionand proportional thinking biases in their preferences and judgments about eco-taxes,and eco-tax policies might benefit by presenting information such that smaller taxincreases cause greater CO2 emissions reductions.T2-C.4 Dyck R, Sadiq R, Zargar A, Islam S, Mohapatra A; asish.mohapatra@hc-sc.gc.caHealth Canada Alberta RegionAPPLICATION OF A DATA FUSION FRAMEWORK TO INTEGRATETOXICITY DATA FOR A PETROLEUM HYDROCARBON MIXTUREA modified Joint Director Laboratories (JDL) data fusion (DF) framework wasdeveloped to integrate exposure and toxicity data from disparate sources for humanhealth risk assessments (HHRA). The framework was used to detect patterns andintegration of various toxicological datasets from the F1 group of hydrocarbons. F1toxicological data were fused where available. The objective of our research was todemonstrate the suitability and applicability of the proposed DF HHRA framework.Traditionally, health risk assessments of mixtures are evaluated using a surrogate ofchemical mixture data (current practice of F1 hydrocarbons assessment) or throughcomponents of mixture data. Neurotoxicity response analysis, neurotoxic metabolitestoxicological data were fused with predictive toxicological data. Probability-boxes (pbox)were developed to represent the toxicity of each compound. The neurotoxicresponse was given a rating of “low”, “medium” or “high”. These responses werethen weighted by the percent composition in the F1 hydrocarbon mixture. The resultingp-boxes were fused according to Dempster-Shafer Mixture rule of combination.The p boxes were fused again with toxicity data for n-hexane. Furthermore,n-hexane datasets were requested for curation from the Comparative ToxicogenomicsDatabase for preliminary analysis and integration of system biology datasets. Keyinteracting genes (BAX, BCL2, CASP3, CYP1A1, and CYP1A2 in rats; CYP2E1 inmice, and CYP2B1, CYP2B6, and CYP2E1 in humans) were identified. Additionalanalysis were conducted for altered protein expression, metabolic changes, and genepolymorphisms in CYP2E1 leading to potential chemical susceptibility to n-hexaneexposure. Further analysis of other health effects end points such as respiratory irritancy,respiratory lining and lungs inflammation, peripheral nervous system and hepaticdiseases are required. Some preliminary results were presented at the Alliancefor Risk Assessment workshops.