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Application of Bioavailability Models to Derive Chronic Guideline Values for Nickel in Freshwaters of Australia and New Zealand.

Jenny StauberLisa GoldingAdam PetersGraham MerringtonMerrin AdamsMonique T BinetGraeme BatleyFrancesca GissiKitty McKnightEmily GarmanEllie MiddletonJennifer GaddChris Schlekat
Published in: Environmental toxicology and chemistry (2020)
There has been an increased emphasis on incorporating bioavailability-based approaches into freshwater guideline value derivations for metals in the Australian and New Zealand water quality guidelines. Four bioavailability models were compared: the existing European biotic ligand model (European Union BLM) and a softwater BLM, together with 2 newly developed multiple linear regressions (MLRs)-a trophic level-specific MLR and a pooled MLR. Each of the 4 models was used to normalize a nickel ecotoxicity dataset (combined tropical and temperate data) to an index condition of pH 7.5, 6 mg Ca/L, 4 mg Mg/L, (i.e., approximately 30 mg CaCO3 /L hardness), and 0.5 mg DOC/L. The trophic level-specific MLR outperformed the other 3 models, with 79% of the predicted 10% effect concentration (EC10) values within a factor of 2 of the observed EC10 values. All 4 models gave similar normalized species sensitivity distributions and similar estimates of protective concentrations (PCs). Based on the index condition water chemistry proposed as the basis of the national guideline value, a protective concentration for 95% of species (PC95) of 3 µg Ni/L was derived. This guideline value can be adjusted up and down to account for site-specific water chemistries. Predictions of PC95 values for 20 different typical water chemistries for Australia and New Zealand varied by >40-fold, which confirmed that correction for nickel bioavailability is critical for the derivation of site-specific guideline values. Environ Toxicol Chem 2021;40:100-112. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Keyphrases
  • water quality
  • randomized controlled trial
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  • quality improvement
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  • heavy metals
  • genetic diversity
  • deep learning
  • open label
  • neural network
  • meta analyses