A food chain model to predict the levels of lipophilic organic contaminants in humans

Czub, Gertje; McLachlan, Michael S.

doi:10.1897/03-317

Cited by 131 publications

(151 citation statements)

References 77 publications

(105 reference statements)

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“…Estimations by one-compartment mechanistic bioaccumulation models have been shown to be in reasonable agreement with measured bioaccumulation [1][2][3]. These models predict accumulation levels on the basis of first-order uptake and elimination kinetics, chemical partitioning, and ideally metabolic transformation.…”

Section: Introductionmentioning

confidence: 63%

Parameter uncertainty in modeling bioaccumulation factors of fish

Hauck

Hendriks

Huijbregts

et al. 2011

Enviro Toxic and Chemistry

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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.

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Section: Introductionmentioning

confidence: 63%

Parameter uncertainty in modeling bioaccumulation factors of fish

Hauck

Hendriks

Huijbregts

et al. 2011

Enviro Toxic and Chemistry

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“…12 The human exposure model ACC-HUMAN has been shown to predict PCB concentrations in food and human milk in Sweden from time trends of measured concentrations in air and water. 2 The coupled multimedia fate and exposure model CoZMoMAN was used to predict PCB behaviour in the western Baltic Sea drainage basin and reasonable agreement was found between measured PCB concentrations in most environmental media as well as in food and human milk. 7 The same model combination provides good estimates of PCB levels in Norwegians, effectively capturing the inuence of different dietary habits and exposure history on concentration in human plasma.…”

Section: -7mentioning

confidence: 94%

“…1 We have also developed exposure models that synthesize our understanding of processes that convey chemicals along exposure vectors from environmental media such as air, water and soil to humans. 2 In some cases multimedia fate and transport models have been combined with exposure models to create tools that allow us to describe the chemical's complete journey from emission to human tissue.…”

Section: Introductionmentioning

confidence: 99%

Predicting global scale exposure of humans to PCB 153 from historical emissions

McLachlan

Undeman

Zhao

et al. 2018

Environ. Sci.: Processes Impacts

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aPredicting human exposure to an environmental contaminant based on its emissions is one of the great challenges of environmental chemistry. It has been done successfully on a local or regional scale for some persistent organic pollutants. Here we assess whether it can be done at a global scale, using PCB 153 as a test chemical. The global multimedia fate model BETR Global and the human exposure model ACC-HUMAN were employed to predict the concentration of PCB 153 in human milk for 56 countries around the world from a global historical emissions scenario. The modeled concentrations were compared with measurements in pooled human milk samples from the UNEP/WHO Global Monitoring Plan. The modeled and measured concentrations were highly correlated (r ¼ 0.76, p < 0.0001), and the concentrations were predicted within a factor of 4 for 49 of 78 observations. Modeled concentrations of PCB 153 in human milk were higher than measurements for some European countries, which may reflect weaknesses in the assumptions made for food sourcing and an underestimation of the rate of decrease of concentrations in air during the last decades. Conversely, modeled concentrations were lower than measurements in West African countries, and more work is needed to characterize exposure vectors in this region. Environmental signicanceThis work shows that mathematical models can be used to predict the environmental fate and exposure of a persistent organic pollutant along the whole journey from emissions to human tissues on a global scale with reasonable accuracy. This increases our condence in our knowledge of the key processes governing the environmental chemistry of such contaminants and our ability to synthesize this knowledge in models. In addition, the work identies several areas where further work is needed: food sourcing patterns in regions with strong gradients in environmental contamination, exposure pathways to PCBs in Africa, and evaluation of the ability of the models to predict long term time trends.

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“…The original model for Crab Orchard Lake was based on fish tissue residues of total PCBs measured from channel catfish, common carp, and largemouth bass. The fish-consumption model generally followed that of Czub and McLachlan (2004), wherein individual exposure assessments for selected sensitive categories within human populations (for example, females of child-bearing age, infants, and preadolescent children) were evaluated with respect to their consumption of fish meals and modelled using standard model conditions. A first-tier evaluation would simplify to a typical food-chain model, including variables for chemical concentration in the edible part of fish, daily consumption rate of fish, and body weight of an individual consumer (appendix 1, eq.…”

Section: Fish-consumption Advisory Implementationmentioning

confidence: 99%