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By the 1960s it became recognized that certain organic compounds that had been used in large quantities and dispersed into the environment could persist and accumulate in biota to concentrations that cause adverse impacts. Examples included DDT, toxaphene, and polychlorinated biphenyls (PCBs). This understanding led to efforts to identify substances with high bioaccumulation potential. Early methods relied on laboratory bioconcentration tests that involved uptake and depuration phases in which aquatic organisms (often fish) were exposed to a constant concentration of the substance dissolved in water followed by exposure to clean water. Organisms were periodically sampled over the course of the test and analyzed to determine tissue concentrations of the substance investigated. These data were fitted to a simple model to estimate the uptake and elimination rates and the bioconcentration factor (BCF). The BCF represented the concentration ratio between the aquatic organism and water at steady state. Subsequent efforts led to the development of models to predict BCF from substance properties, most notably the correlations between BCF and log octanol–water partition coefficient (log KOW) [1, 2].

While standardized laboratory bioconcentration tests provided a logical, empirical approach to identify substance-specific bioaccumulation concerns, further progress was gained through the introduction of mechanistic mass balance models that systematically described the various uptake and loss processes. One early example involved the prediction of PCB bioaccumulation in a Lake Michigan food chain [3]. This modeling framework incorporated a number of important concepts. First, 4 trophic levels were simulated (phytoplankton, zooplankton, forage fish, piscivorous fish) using a set of coupled, first-order kinetic equations under steady-state conditions. Second, 2 principal routes were considered for each trophic level beyond phytoplankton: uptake from water and ingestion of prey from the previous trophic level. Thus, unlike previous food-chain models that required prey concentrations as inputs, this model computed the prey concentrations associated with each trophic level based on the dissolved water concentration. Third, the rate of chemical uptake from water and food and role of growth dilution was linked to the bioenergetics of the organism using allometric scaling equations for each trophic level. Lastly, chemical-specific toxicokinetic parameters (e.g., gill elimination rate, assimilation efficiency of contaminant from water and food) needed for model calibration were estimated using results from earlier lab studies.

An important insight gained from model predictions and confirmed by field data compiled from Lake Michigan was that laboratory derived BCFs could significantly underestimate the extent to which PCBs were accumulated in field biota. This observation had significant management implications for derivation of water quality limits and emission reductions needed to achieve tissue concentrations that met regulatory guidelines. Subsequent field studies from Lake Ontario confirmed that lab BCFs underestimated field bioaccumulation factors (BAFs) for PCBs and a range of other organochlorine compounds even if differences in fish lipid content were taken into account [4]. Further work hypothesized that this discrepancy could be accounted for by dietary exposure in the field and subsequent digestion of prey lipid in the gut resulting in an elevated chemical gradient that caused the observed biomagnification—that is, higher lipid normalized concentration or fugacity in predator to that in prey [5]. In fact, for chemicals with log KOW > 5, the dietary route was shown in subsequent modeling efforts to be dominant route of exposure in pelagic food chains [6]. The hypothesis that lipid digestion in the gut accounted for biomagnification was later confirmed in laboratory and field studies [7].

Concerns posed by chemicals that exhibited biomagnification resulted in restrictions in use and emission in the decades that followed. However, due to their hydrophobic and persistent nature, historical releases, and subsequent distribution to particulates and erosion, deposition and settling to aquatic environments resulted in a legacy of sediments contaminated with these substances. Furthermore, field monitoring and multimedia fate modeling highlighted the role that in-place sediment pollution served in contributing to ongoing bioaccumulation and subsequent impairment of intended water body uses. Food-chain models were extended to include a direct pathway from sediments in addition to the pathway from the water column and to simulate time trends in response to changes in exposure. These models showed that the sediment pathway could be very important for food chains involving demersal fish and shellfish [8, 9].

The paper that is the subject of the present essay provided a timely contribution in addressing the interplay of sediment and water column pathways in chemical fate and bioaccumulation [10]. This work expanded on an earlier modeling effort that predicted bioaccumulation of nonmetabolized, nonionic organic chemicals in a linear, pelagic food chain under steady-state conditions using characteristics of the organisms (e.g., weight, lipid content) and substance partitioning properties (e.g., log KOW) as inputs [6]. The updated model incorporated features of the models applied to Kepone and PCBs in the James River, Virginia, USA and New Bedford Harbor, Massachusetts, USA, respectively [8, 9], including an expanded food web (phytoplankton, benthic invertebrates, forage fish and piscivorous fish); multiple exposure routes for benthic invertebrates, including ventilation of sediment porewater and overlying water; and ingestion of sediment organic matter and overlying phytoplankton and, for forage fish, ingestion of both zooplankton and benthic invertebrates. The model was designed to be generic by inclusion of relative feeding preferences that could be adjusted to reflect different assumptions in the diets of benthic invertebrates and forage fish.

Considerable knowledge from earlier work was incorporated into the construction of this model, including the understanding that freely dissolved concentrations in overlying water and sediment porewater dictates bioconcentration and that lipid and organic carbon normalization of contaminant concentrations in biota and sediment, respectively, facilitate analysis and interpretation of field data relative to equilibrium partitioning theory. Two important conclusions from modeling simulations were that the degree of biomagnification in fish depended on 1) the magnitude to which dietary exposure contributes to bioaccumulation and 2) the nature of sediment–water column interaction. The importance of dietary exposure was expressed in terms of a food-chain multiplier that represented the ratio of dietary uptake to overall elimination and was predicted to attain a maximum value for substances with a log KOW of approximately 7.0. The sediment–water column interaction was expressed as the ratio of the substance concentration in sediment organic carbon and dissolved in the overlying water column. This term accounted for partitioning and degradation in water and sediment compartments as well as the mixing and sediment transport interactions between the 2 compartments. As a consequence, it was reasoned that this parameter is both substance- and site-dependent. The coupled pelagic–benthic food-chain model was used to predict bioaccumulation in Lake Ontario using available field data [4]. Model calibration suggested that the observed bioaccumulation in benthic invertebrates reflected a combination of overlying and sediment pore water exposure as well as sediment and phytoplankton ingestion. Similarly, observed concentrations in forage fish were consistent with a mixed diet of zooplankton and benthic invertebrates. It was concluded that simple equilibrium partitioning predictions based on either a water column or sediment concentration were not reliable representations of observed bioaccumulation in the field. Furthermore, addition of a benthic compartment into the model structure introduced a degree of site specificity that was not generically applicable across aquatic food chains.

The Thomann et al. paper [10] helped advance bioaccumulation science on several fronts. First, publication of various algorithms for estimating toxicokinetic and bioenergetic model parameters encouraged the development of needed lab data and critical review to confirm, improve, or propose new predictive relationships [11-15]. Second, this work prompted collection of relevant information in future field monitoring studies (e.g., water and sediment exposure data, organic carbon and lipid measurements, dietary preferences) and guided subsequent analysis and interpretation for gaining mechanistic insights, including evidence for biomagnification and, in the case of metabolizable compounds, trophic dilution. Third, this model inspired further development of bioaccumulation models for different trophic levels and types of food chains under both steady-state and temporal scenarios [16-19] and classes of chemicals including substances susceptible to biotransformation [20-26]. Moreover, food-chain models have become increasingly used in region or site-specific assessments to inform policy and remedial options [27-38] as well as in prioritization and risk assessment of chemicals in commerce [39-42].

While the quantitative role of degradation processes in air, water, soil, and sediment have routinely been incorporated into environmental fate models and standardized tests and estimation methods have been readily available to guide model calibration, estimation and incorporation of biotransformation processes in evaluative models of food-chain bioaccumulation is at present only in an early stage. The recent BCFBAF model included in US Environmental Protection Agency's Estimation Programs Interface Suite (EPI Suite, Ver 4.10) provides an important recent advance for screening the bioaccumulation potential of substances by including biotransformation estimates for fish [43]. Further work is needed, however, to further validate and expand such models to a broader range of chemicals and biota beyond fish. One key limitation in such evaluative model frameworks is that potential metabolism in the gut is ignored. Because dietary exposure dictates the potential for biomagnification and dietary uptake can be significantly reduced for substances that are susceptible to gut biotransformation, further research is needed to incorporate this process in future evaluative models. Fortunately, the recent publication of an updated Organisation for Economic Co-operation and Development laboratory test guideline that includes protocols for aqueous and dietary exposure tests should foster the development of new data that will facilitate further verification, refinement, and expanded domain of bioaccumulation model frameworks [44]. Further standardization and extrapolation of in vitro biotransformation tests for assessing metabolism in tissue and gut are also expected to contribute to improved models and more reliable prediction of a substance's inherent bioaccumulation potential [45].

Progress has also been made in better characterizing sediment and water column interactions. Gobas and Maclean [46] proposed that organic carbon mineralization can produce apparent disequilibrium between contaminants in phytoplankton and suspended solids (present in the water column) and sediments. Model results indicated that the magnitude of the disequilibrium would be most pronounced for lower log KOW chemicals in deep lakes, consistent with empirical data compiled largely for PCBs from 5 of the Great Lakes. Burkhard et al. [47] reported substance class–dependent disequilibrium behavior for PCBs, polychlorinated dioxins and dibenzofurans, and polyaromatic hydrocarbons in southern Lake Michigan (USA) suspended solids and sediments. These authors concluded that complex geosorbents such as black carbon or coal may play a significant role in contributing to deviations from equilibrium assumptions. Given the difficulty of reliably predicting partitioning in sediments containing different contaminants and heterogeneous binding phases, the advent of passive sampling methods have enabled direct and sensitive measurements of freely dissolved concentrations of hydrophobic chemicals in overlying and sediment porewaters [48, 49]. A recent review by van Noort et al. [50] summarizes passive sampling results in delineating if sediments are either a source or sink of contaminants to the water column and resulting implications for sediment risk assessment and remediation. Future application of passive sampling methods should allow improved calibration of site-specific food-chain models [51]. Given the growing interest in amendments for in situ remediation of contaminated sediment, coupling results obtained using passive sampling data before and after treatment, to calibration of bioaccumulation models is a logical next step [52, 53]. A further application of passive sampling methods in future bioaccumulation research is in reliable estimation and prediction of trophic magnification factors [54].

In summary, the paper by Thomann et al. [10] provided key modeling insights that fostered additional advances for improving bioaccumulation prediction. These topics remain highly relevant today and serve as a continued focus of on-going research in bioaccumulation science.

Acknowledgment

  1. Top of page
  2. SUPPLEMENTAL DATA
  3. Acknowledgment
  4. REFERENCES
  5. Supporting Information

This paper is dedicated to R.V. Thomann, who has led many by his consistent example as a model husband, father, scholar, teacher, mentor, and friend.

REFERENCES

  1. Top of page
  2. SUPPLEMENTAL DATA
  3. Acknowledgment
  4. REFERENCES
  5. Supporting Information
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Supporting Information

  1. Top of page
  2. SUPPLEMENTAL DATA
  3. Acknowledgment
  4. REFERENCES
  5. Supporting Information

All Supplemental Data may be found in the online version of this article. See Table S1 for the number of citations and rank of all the “Top 100” papers, which in this essay is reference [10].

FilenameFormatSizeDescription
etc2317-sm-0001-SupTab-S1.pdf45KSupporting Table S1

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