Assessing model uncertainty of bioaccumulation models by combining chemical space visualization with a process-based diagnostic approach.

As models describing human exposure to organic chemicals gain wider use in chemical risk assessment and management, it becomes important to understand their uncertainty. Although evaluation of parameter sensitivity/uncertainty is increasingly common, model uncertainty is rarely assessed. When it is, the assessment is generally limited to a handful of chemicals. In this study, a strategy for more comprehensive model uncertainty assessment was developed. A regulatory model (EUSES) was compared with a research model based on more recent science. Predicted human intake was used as the model end point. Chemical space visualization techniques showed that the extent of disagreement between the models varied strongly with chemical partitioning properties. For each region of disagreement, the primary human exposure vector was determined. The differences between the models' process algorithms describing these exposure vectors were identified and evaluated. The equilibrium assumption for root crops in EUSES caused overestimations in daily intake of superhydrophobic chemicals (log K(OW) > 11, log K(OA) > 10), whereas EUSES's approach to calculating bioaccumulation in fish prey resulted in underestimations for hydrophobic compounds (log K(OW) ∼ 6-8). Uptake of hydrophilic chemicals from soil and bioaccumulation of superhydrophobic chemicals in zooplankton were identified as important research areas to enable further reduction of model uncertainty in bioaccumulation models.