Complex mixture toxicity for single and multiple species: Proposed methodologies

Authors

  • Dick de Zwart,

    Corresponding author
    1. National Institute for Public Health and the Environment, Laboratory for Ecological Risk Assessment, P.O. Box 1, NL-3720 BA Bilthoven, The Netherlands
    • National Institute for Public Health and the Environment, Laboratory for Ecological Risk Assessment, P.O. Box 1, NL-3720 BA Bilthoven, The Netherlands
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  • Leo Posthuma

    1. National Institute for Public Health and the Environment, Laboratory for Ecological Risk Assessment, P.O. Box 1, NL-3720 BA Bilthoven, The Netherlands
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Abstract

Methods for the assessment of ecological risks associated with exposure to defined mixtures of toxicants are reviewed and formalized for single-species toxicity. Depending on the modes of action of toxicants in a mixture, these methods apply either the model for concentration additivity (CA) or the model for response additivity (RA). For complex mixtures, the present paper advocates the use of a new, two-step, mixed-model approach as a logical extension of model selection: Mixture toxicity for individual modes of action is evaluated with the CA model, and the toxicities of different modes of action are combined using the RA model. Using comparable mixture toxicity strategies in combination with the concept of species-sensitivity distributions, we develop a method to address and predict the risk for direct effects on the composition of species assemblages and biodiversity. The data needed for modeling can be obtained from existing databases, and lack of data can, in part, be addressed by the use of toxicity patterns in those databases. Both single- and multiple-species methods of mixture risk prediction are useful for risk management, because they allow ranking of polluted sites and affected species as well as identification of the most hazardous contaminants, at least in a comparative way. Validation of the proposed methods is feasible but currently limited because of a lack of appropriate data.

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