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Treatment of Epistemic Uncertainty in Environmental Fate Models –Consequences on Chemical Safety Regulatory Strategies
Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
2012 (English)Conference paper, Presentation (Other academic)
Abstract [en]

The practical impact of treatment of epistemic uncertainty on decision making wasillustrated on two kinds of decisions from chemical regulation. First, regulatory strategies derivedfrom a simplified decision model based on toxicity and persistence showed that regulated level ofexposure is more conservative (safe) when uncertainty has been given a non-probabilistictreatment. Persistence and its uncertainty had been assessed by a Level II fugacity model forwhich input parameters had been quantified either by Bayesian probabilities, fuzzy numbers(non-probabilistic), or combinations of these (probability boxes). These findings are restricted tohow we let decision makers respond to uncertainty in model predictions by the chosen set ofdecision rules. Further, the use of either treatment depends on the quality and quantity ofbackground knowledge and the required level of detail on the assessment. In the absence ofexperimentally tested physicochemical endpoints, European chemical regulation REACH allowsthe use of non-testing strategies such as Quantitative Structure-Property Relationships (QSPR) topredict the required information. The second decision problem was to select which chemicalsubstances to prioritize for experimental testing in order to strengthen the background knowledgefor chemical regulation with respect to the uncertainty in QSPR predictions. We found that thevalue of reducing uncertainty, given by the expected gain in net benefit for society, was affectedby its treatment and there were no consistent order of testing of the three compounds. However,value of information is a Bayesian probabilistic approach that, unless developed further, loose itsinterpretability under other treatments of uncertainty. The framework of a predictive model, riskmodel, decision model and value of information analysis provides a computational template forfurther evaluation of the effect of treatment of uncertainty on decision making.

Place, publisher, year, edition, pages
Keyword [en]
Predictive Uncertainty, Value of Information, Chemical Regulation, Quantitative Structure-Property Relationships
National Category
Environmental Sciences
Research subject
Natural Science, Environmental Science
URN: urn:nbn:se:lnu:diva-21795OAI: diva2:556538
PSAM11 & ESREL 2012, Helsinki, Finland, 25-29 June 2012
Available from: 2012-09-25 Created: 2012-09-25 Last updated: 2014-02-07Bibliographically approved

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Iqbal, Muhammad SarfrazSahlin, Ullrika
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