Including Sorption to Black Carbon in Modeling Bioaccumulation of Polycyclic Aromatic Hydrocarbons:  Uncertainty Analysis and Comparison to Field Data

Abstract

Model estimations of bioaccumulation of polycyclic aromatic hydrocarbons (PAHs) have been higher than field or laboratory data. This has been explained by strong sorption to black carbon (BC). In this paper, eight previously published bioaccumulation datasets were reinterpreted in terms of additional BC sorption. Biota--Solids Accumulation Factors (BSAFs) of PAHs typically decreased by 1-2 orders of magnitude and were better in line with field data in marine, fresh water, and terrestrial ecosystems. Probabilistic BC-inclusive modeling showed that if BC content is not accurately known, uncertainty in BSAFs is 2-3 orders of magnitude (90 percentile confidence interval) due to uncertainty in the BC sorption term. When BC contents are measured, the deviation between model estimations and field measurements reduces to about a factor of 3. This implies that including routine measurements of BC contents is crucial in improving risk estimations of PAHs.

Parameter uncertainty in modeling bioaccumulation factors of fish

“…Although several authors [12,14,49] reported the (potential) contribution of the uncertainty in dissolved concentrations to the uncertainty in estimation of internal concentrations, uncertainties in external concentrations are not expected to cause errors in BAF estimates, because the BAFs were expressed in terms of dissolved concentrations in the present study. The uncertainties in species weight cancel out in the calculation of the BAF.…”

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

“…Evidently, this led to a substantial uncertainty in C sed , which eventually affected the uncertainty in the estimated bioaccumulation. BC and its ability to sequester contaminants and influence chemical bioavailability have only recently been considered in benthic invertebrate modeling of hydrophobic organic pollutant bioaccumulation (Moermond et al 2005; Koelmans et al 2006; Hauck et al 2007; Moermond et al 2007). Incorporating these terms into model simulations was difficult due to data gaps on BC distributions in natural environments and chemical partitioning to this and other sediment components as it applies to the specific cases of species and ecosystems being modeled.…”

“…Using a modeling approach, our goal was to prescribe values for different chemical/physical factors (e.g., chemical K OW , chemical partitioning to proximate components of the animal, its diet, and sediment), physiological factors (e.g., organism respiratory ventilation rate, potential for biotransformation, feeding rate, digestive physiology, and so forth), environmental conditions (e.g., contaminant concentration in food items and respired media) and ecological factors (e.g., foraging range, dietary proportions, and so forth) and their associated measures of variation to develop a range of bioaccumulation scenarios encountered in nature. Indeed, modeling approaches have provided a framework for assessing and better understanding the sources of variation in bioaccumulation among various pollutants and taxonomic species (Thomann and Connolly 1984; Gobas and Mackay 1987; Luoma et al 1992; Wang and Fisher 1999; Arnot and Gobas 2004; Luoma and Rainbow 2005; Hauck et al 2007; Moermond et al 2007; Wang and Rainbow 2008; Ciavatta et al 2009; De Laender et al 2010).…”

An evaluation of the ability of chemical measurements to predict polycyclic aromatic hydrocarbon‐contaminated sediment toxicity to Hyalella azteca

“…Black carbon has the potential to bind organic contaminants to a much greater extent than natural organic carbon, which results in less transfer of the PAHs from the sediment to the pore water. Improved predictions of PAH partitioning [13,14,17,20,21] and toxicity to benthic organisms [11] have been reported using this two-phase organic carbon and BC approach.…”