title: Hierarchical Bayesian Approach To Reduce Uncertainty in the Aquatic Effect Assessment of Realistic Chemical Mixtures.
abstract: Species in the aquatic environment differ in their toxicological sensitivity to the various chemicals they encounter. In aquatic risk assessment, this interspecies variation is often quantified via species sensitivity distributions. Because the information available for the characterization of these distributions is typically limited, optimal use of information is essential to reduce uncertainty involved in the assessment. In the present study, we show that the credibility intervals on the estimated potentially affected fraction of species after exposure to a mixture of chemicals at environmentally relevant surface water concentrations can be extremely wide if a classical approach is followed, in which each chemical in the mixture is considered in isolation. As an alternative, we propose a hierarchical Bayesian approach, in which knowledge on the toxicity of chemicals other than those assessed is incorporated. A case study with a mixture of 13 pharmaceuticals demonstrates that this hierarchical approach results in more realistic estimations of the potentially affected fraction, as a result of reduced uncertainty in species sensitivity distributions for data-poor chemicals.
authors.affiliation: Department of Environmental Science, Institute for Wetland and Water Research, Radboud University , P.O. Box 9010, 6500 GL Nijmegen, The Netherlands.
authors.forename: Rik
authors.initials: R
authors.lastname: Oldenkamp
authors.affiliation: Department of Applied Stochastics, Institute for Mathematics, Astrophysics and Particle Physics, Radboud University , P.O. Box 9010, 6500 GL Nijmegen, The Netherlands.
authors.forename: Harrie W M
authors.initials: HW
authors.lastname: Hendriks
authors.affiliation: Department of Environmental Science, Institute for Wetland and Water Research, Radboud University , P.O. Box 9010, 6500 GL Nijmegen, The Netherlands.|Department of Ecological Risk Assessment, National Institute for Public Health and the Environment , P.O. Box 1, 3720 BA Bilthoven, The Netherlands.
authors.forename: Dik
authors.initials: D
authors.lastname: van de Meent
authors.affiliation: Department of Environmental Science, Institute for Wetland and Water Research, Radboud University , P.O. Box 9010, 6500 GL Nijmegen, The Netherlands.|Faculty of Management, Science & Technology, Open Universiteit , Valkenburgerweg 177, 6419 AT Heerlen, The Netherlands.
authors.forename: Ad M J
authors.initials: AM
authors.lastname: Ragas
mesh_terms: D000900:Anti-Bacterial Agents / Q000032:analysis; D000970:Antineoplastic Agents / Q000032:analysis; D001499:Bayes Theorem; D005843:Geography; D005858:Germany; D035501:Uncertainty; D014874:Water Pollutants, Chemical / Q000032:analysis* / Q000633:toxicity
chemical_list: D000900:Anti-Bacterial Agents; D000970:Antineoplastic Agents; D014874:Water Pollutants, Chemical