Parameter uncertainty in modeling bioaccumulation factors of fish

Abstract

We quantified the uncertainty due to biota-related parameters in estimated bioaccumulation factors (BAFs) of persistent organic pollutants for fish through Monte Carlo simulations. For this purpose, the bioaccumulation model OMEGA (Optimal Modeling for EcotoxicoloGical Applications) was parameterized based on data from the existing literature, analysis of allometric data, and maximum likelihood estimation. Lipid contents, fractions of food assimilated, the allometric rate exponent, normalized food intakes, respiration and growth dilution rates, and partial mass transfer resistances in water and lipid layers were included as uncertain parameters. The uncertainty in partial resistances was particularly important in the estimation of the rate constants for chemical intake from water by fish. Uncertainties in the fractions of food assimilated and partial water layer resistances from and to food were particularly important in the estimation of the rate constants of chemical intake from food. The uncertainty in the model outcomes for the bioaccumulation factors for fish was a factor of 10 (ratio of 95th and fifth percentile estimates), which was mainly caused by the uncertainty in the lipid fraction. For chemicals with a K(OW) of 10(3) to 10(6), the uncertainty in the lipid contents of fish accounted for more than 50% of the uncertainty in the estimated bioaccumulation factor. For chemicals with a high K(OW) (10(7) and higher), the fractions of food assimilated and partial resistances also contributed to uncertainty in the estimated bioaccumulation factor (up to 60%). A case study showed that uncertainty in estimated BAF for nonpersistent substances can be dominated by uncertainty in the rate constants for metabolic transformation.

No measurable “cleaning” of polychlorinated biphenyls from Rainbow Trout in a 9 week depuration study with dietary exposure to 40% polyethylene microspheres

“…A factor of 3.7 difference between the two groups is within the range of uncertainty of bioaccumulation studies [38][39][40] which suggests that the difference between the two groups might exist and not have been detected in our experiment. A factor of 3.7 difference between the two groups is within the range of uncertainty of bioaccumulation studies [38][39][40] which suggests that the difference between the two groups might exist and not have been detected in our experiment.…”

Explaining differences between bioaccumulation measurements in laboratory and field data through use of a probabilistic modeling approach

“…Our simulations had a much narrower range for pyrene (2.86 for pyrene and 3.67 for PCB‐153) compared to Hauck and coworkers (2011), which probably is because these authors did not include biotransformation in their simulations. For PCB‐153, our simulations had a wider range (6.50 and 7.35), which probably reflects that we included 3 diets, whereas Hauck et al (2011) only included 1 diet. The mean biomagnification factor, expressed as the fugacity ratio of animal‐to‐average diet, was 1.28 (±1.90: range: 0.06–7.2) for PCB‐153, and the mean predicted biomagnification factor was 0.004 (±2.09; range: 0.0002–0.108) for pyrene (Table 3).…”

“…The PCB‐153 value closely approximated the observed BAF reported for yellow perch by Arnot and Gobas (2004) from Lake St. Clair, Canada, and Lake Erie in the range of 6.4 to 7.04. Hauck et al (2011) simulated a model fish using 24 parameters and a range of K OW values. Based on their figure 2 and the K OW values corresponding to pyrene and PCB‐153, we estimate that the 10th and 90th percentile for log BAF for the model fish was 3.15 and 4.48, and 4.60 and approximately 6.26 for pyrene and PCB‐153, respectively.…”

How to quantify uncertainty and variability in life cycle assessment: the case of greenhouse gas emissions of gas power generation in the US

“…The contribution of uncertainties in individual parameters to the range in impacts associated with the LCGHG emissions of the comprehensive system (Y) was also assessed. This analysis consisted of a MC simulation in combination with a Rank correlation (expressed as percentage of total variance) (Ragas et al 1999, Hauck et al 2011. The contribution to variance is a combination of the model's sensitivity to a parameter and the uncertainty range of the parameter.…”