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Interspecies correlation estimates predict protective environmental concentrations.

Environmental risk assessments often use multiple single species toxicity test results and species sensitivity distributions (SSDs) to derive a predicted no-effect concentration in the environment, typically the 5th percentile of the SSD, termed the HC5. The shape and location of the distribution are best known when populated with numerous toxicity values. To help overcome the cost of multiple toxicity tests, we explored the potential of the U.S. EPA's Interspecies Correlation Estimation (ICE) program to predict single species toxicity values from a single known toxicity value. ICE uses the initial toxicity estimate for one species to produce correlation toxicity values for multiple species, which can be used to develop SSD and HC5. To test this approach to deriving HC5, we generated toxicity values based on measured toxicity values for three surrogate species Pimephales promelas (Fathead minnow), Onchorynchus mykiss (Rainbow trout), and Daphnia magna (water flea). Algal taxa were not used due to the paucity of high quality algal-aquatic invertebrate and algal-fish correlations. The compounds used (dodecyl linear alkylbenzenesulfonate (LAS), nonylphenol, fenvalerate, atrazine, and copper) have multiple measured toxicity values and diverse modes of action and toxicities. Distribution parameters and HC5 values from the measured toxicity values were compared with ICE predicted distributions and HC5 values. While distributional parameters (scale and intercept) differed between measured and predicted distributions, in general, the ICE-based SSDs had HC5 values that were within an order of magnitude of the measured HC5 values. Examination of species placements within the SSDs indicated that the most sensitive species were coldwater species (e.g., salmonids and Gammarus pseudolimnaeus). These results raise the potential of using quantitative structure activity models to estimate HC5s.[1]

References

  1. Interspecies correlation estimates predict protective environmental concentrations. Dyer, S.D., Versteeg, D.J., Belanger, S.E., Chaney, J.G., Mayer, F.L. Environ. Sci. Technol. (2006) [Pubmed]
 
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