Hierarchical Bayesian Approach To Reduce Uncertainty in the Aquatic Effect Assessment of Realistic Chemical Mixtures

Rik Oldenkamp*, Harrie W. M. Hendriks, Dik van de Meent, Ad M. J. Ragas

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)10457-10465
Number of pages9
JournalEnvironmental Science & Technology
Volume49
Issue number17
DOIs
Publication statusPublished - 1 Sep 2015

Keywords

  • SPECIES-SENSITIVITY DISTRIBUTIONS
  • POTENTIALLY AFFECTED FRACTION
  • NOEC TOXICITY DATA
  • RISK-ASSESSMENT
  • MONITORING CONVERGENCE
  • ECOLOGICAL RISK
  • ECX
  • ECOTOXICOLOGY
  • SIMULATIONS
  • ECOSYSTEMS

Cite this

@article{a30e5300c01d4627a16b5bc1fa01b87a,
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.",
keywords = "SPECIES-SENSITIVITY DISTRIBUTIONS, POTENTIALLY AFFECTED FRACTION, NOEC TOXICITY DATA, RISK-ASSESSMENT, MONITORING CONVERGENCE, ECOLOGICAL RISK, ECX, ECOTOXICOLOGY, SIMULATIONS, ECOSYSTEMS",
author = "Rik Oldenkamp and Hendriks, {Harrie W. M.} and {van de Meent}, Dik and Ragas, {Ad M. J.}",
year = "2015",
month = "9",
day = "1",
doi = "10.1021/acs.est.5b02651",
language = "English",
volume = "49",
pages = "10457--10465",
journal = "Environmental Science & Technology",
issn = "0013-936X",
publisher = "AMER CHEMICAL SOC",
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}

Hierarchical Bayesian Approach To Reduce Uncertainty in the Aquatic Effect Assessment of Realistic Chemical Mixtures. / Oldenkamp, Rik; Hendriks, Harrie W. M.; van de Meent, Dik; Ragas, Ad M. J.

In: Environmental Science & Technology, Vol. 49, No. 17, 01.09.2015, p. 10457-10465.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Hierarchical Bayesian Approach To Reduce Uncertainty in the Aquatic Effect Assessment of Realistic Chemical Mixtures

AU - Oldenkamp, Rik

AU - Hendriks, Harrie W. M.

AU - van de Meent, Dik

AU - Ragas, Ad M. J.

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AB - 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.

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KW - POTENTIALLY AFFECTED FRACTION

KW - NOEC TOXICITY DATA

KW - RISK-ASSESSMENT

KW - MONITORING CONVERGENCE

KW - ECOLOGICAL RISK

KW - ECX

KW - ECOTOXICOLOGY

KW - SIMULATIONS

KW - ECOSYSTEMS

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