title: Uncertainty reduction in environmental data with conflicting information.
abstract: The assessment of ecotoxicological effects of chemicals for regulatory purposes requires large amounts of experimental data which are expensive to obtain and eventually might entail exhaustive animal testing. The required decision-making processes in this regulatory context, must often be carried out with limited or even contradictory sources of information. To benefit from all sources of information without compromising the quality of the decision process, uncertainty management and reduction techniques, such as the Dempster-Shafer theory of evidence, have to be applied. This theory was applied to both experimental and in silico biodegradation data sources to assess chemical persistence. Uncertainties of the initially lass uncertain estimates for biodegradation rates in water were reduced by as much as 20-60%. The analysis showed that conflicting evidence can be detected, quantified, and redistributed proportionally among all the feasible subsets of hypotheses. The advantages of the Dempster-Shafer theory over Bayesian approaches to represent evidence concerning hypotheses by assigning probabilities were also analyzed.
authors: Departament d'Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalunya, Spain.
authors: Alberto
authors: A
authors: Fernández
authors: Robert
authors: R
authors: Rallo
authors: Francesc
authors: F
authors: Giralt
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001673:Biodegradation, Environmental; D004781:Environmental Exposure / Q000517:prevention & control; D004782:Environmental Health; D004784:Environmental Monitoring / Q000379:methods*; D004785:Environmental Pollutants; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D011336:Probability; D015203:Reproducibility of Results; D012984:Software; D014873:Water Pollutants / Q000032:analysis*; D014876:Water Pollution
chemical_list: D004785:Environmental Pollutants; D014873:Water Pollutants