Bioaccumulation potential of air contaminants: Combining biological allometry, chemical equilibrium and mass-balances to predict accumulation of air pollutants in various mammals

Abstract

In the present study we develop and test a uniform model intended for single compartment analysis in the context of human and environmental risk assessment of airborne contaminants. The new aspects of the model are the integration of biological allometry with fugacity-based mass-balance theory to describe exchange of contaminants with air. The developed model is applicable to various mammalian species and a range of chemicals, while requiring few and typically well-known input parameters, such as the adult mass and composition of the species, and the octanol-water and air-water partition coefficient of the chemical.Accumulation of organic chemicals is typically considered to be a function of the chemical affinity for lipid components in tissues. Here, we use a generic description of chemical affinity for neutral and polar lipids and proteins to estimate blood-air partition coefficients (K ba ) and tissue-air partition coefficients (K ta ) for various mammals. This provides a more accurate prediction of blood-air partition coefficients, as proteins make up a large fraction of total blood components.The results show that 75% of the modeled inhalation and exhalation rate constants are within a factor of 2 from independent empirical values for humans, rats and mice, and 87% of the predicted blood-air partition coefficients are within a factor of 5 from empirical data. At steady-state, the bioaccumulation potential of air pollutants is shown to be mainly a function of the tissue-air partition coefficient and the biotransformation capacity of the species and depends weakly on the ventilation rate and the cardiac output of mammals.

Time‐varying effects of aromatic oil constituents on the survival of aquatic species: Deviations between model estimates and observations

“…Second, the typical RMSE was determined by simply averaging the RMSE Cw values, where m denotes the number of experiments. The RMSE summarizes both random error and systematic bias .…”

“…where m denotes the number of experiments. The RMSE summarizes both random error and systematic bias [40].…”

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“…In fact, also for some fish species, while having very low gill uptake of waterborne metals, their dietary uptake becomes the predominant pathway for metal accumulation (Wang, 2011). Among many biological and physico-chemical factors affecting accumulation of metals in aquatic trophic webs, the competition of related metal ions for active transport sites adds variability to the metal absorption (Veltman et al, 2009).…”

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“…1), assuming that the passage through the cytoplasm and the basolateral membrane will not be rate limiting. The removal of the chemical by blood circulation is also neglected, as this step is fast in humans compared to other processes (Veltman et al, 2009). …”