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Comparison of two models for predicting bioaccumulation of hydrophobic organic chemicals in a great lakes food web

Environmental Toxicology and Chemistry, ISSN: 0730-7268, Vol: 17, Issue: 3, Page: 383-393
1998
  • 71
    Citations
  • 0
    Usage
  • 27
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    71
    • Citation Indexes
      59
    • Policy Citations
      12
      • Policy Citation
        12
  • Captures
    27

Article Description

The steady-state models of Gobas and Thomann for predicting chemical residues in aquatic food webs were compared. Bioaccumulation factors (BAFs) predicted on the basis of freely dissolved chemical in the water were not significantly different for piscivorous fish, based upon the 10th and 90th percentile predictions, except for chemicals with log n-octanol/water partition coefficients K(ows) ranging from 6.5 to 6.9, the region of maximum differences for chemicals with log K(ow)s less than 8. For chemicals with log K(ow)s greater than 8, the BAFs predicted by the models diverged. The BAFs predicted using the Gobas model were in slightly better agreement with measured BAFs (determined from Lake Ontario data) than those predicted using the Thomann model. Sensitivities of the input parameters used by both models were very similar. The most sensitive input parameters were lipid contents, K(ow), and sediment-water column chemical concentration quotient (II(socw)) for both models, and the feeding preferences for benthic invertebrates (Diporeia) for the Thomann model. Simulations performed using uncertainties for the input parameters demonstrated that the K(ow) and II(socw) were the dominant sources of uncertainties for predicted BAFs by both models for the Great Lakes food web. For piscivorous fish, overall uncertainties in the predicted BAFs ranged from a factor of 3.3 to 5.5 (Gobas model) and from a factor of 3.3 to 8.7 (Thomann model) for chemicals with log K(ow)s less than 7.6 (based upon the 10th and 90th percentile predictions).

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