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Further Development and Refinement of Interspecies Correlation Estimation Models for Current‐Use Dispersants
Author(s) -
Bejarano Adriana C.
Publication year - 2019
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.4452
Subject(s) - current (fluid) , estimation , environmental science , biochemical engineering , geology , oceanography , engineering , systems engineering
Limited species diversity in aquatic toxicity data for most current‐use dispersants leads to uncertainties in hazard assessments, which impacts the broader discussion on dispersant use. Sufficient toxicity data are available for a re‐evaluation of previously developed dispersant–interspecies correlation estimation (ICE) models. These models increase species diversity because toxicity predictions for untested species are made from the known toxicity for surrogates. Data were used to refine 4 and develop 25 new dispersant‐ICE models. Most of the new models are for species not included in the >2000 existing ICE models, and contain a higher species diversity than the original dispersant‐ICE models (19 vs 7 species). Dispersant‐ICE models predict toxicity with reasonably accuracy: predictions were within 3‐fold of observed values (new models: 70% of 132 predictions; refined models: 88% of 83 predictions), and species sensitivity distributions developed with ICE‐predicted data only were in most cases not statistically significantly different from those developed with empirical data (83% of 23 paired comparisons). Examples of the practical application of dispersant‐ICE models, including laboratory‐to‐field comparisons within the context of operational dispersant application rates, are also presented. The significance of these results is that dispersant‐ICE models could fill gaps in species diversity, and thus help to address concerns about species sensitivities related to the use of dispersants. Environ Toxicol Chem 2019;38:1682–1691. © 2019 SETAC

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