Uncertainties in ecological, chemical and physiological parameters of a bioaccumulation model: implications for internal concentrations and tissue based risk quotients

Integrated Assessment of Bioconcentration, Toxicity, and Hazards of Chlorobenzenes in the Aquatic Environment

The evaluation of bioconcentration, toxicity, and hazard (BTH) of persistent lipophilic organic compounds (LOCs) are generally performed as separate rather than integrated assessments. There are adequate data sets in the literature for chlorobenzenes (CBs) consisting of (a) concentrations in aquatic biota (C_B) and water (C_w) in the natural environment, (b) laboratory-derived bioconcentration factors (K_B) and field concentration ratios (CR), the field equivalent factor of K_B, (c) measured internal lethal concentrations (ILC₅₀) and model estimated ILC₅₀ calculated from K_B and lethal concentrations (LC₅₀), and (d) calculated hazard quotients in aquatic biota (HQ_B) and in water (HQ_W). However, there have been no integrated studies of those parameter values based on the respective lipid-based parameters (C_BL, K_BL, CR_L, ILC_50L, HQ_BL) performed. This study utilized the lipid-based parameters for CBs; a group of widely occuring, bioaccumulative, and toxic LOCs, and integrated those parameters into a bioconcentration-toxicity-hazard (BTH_L) index. The values of the parameters were obtained from selected literature with known lipid contents of the aquatic biota. The results showed that the laboratory derived bioconcentration factors, K_BLs, were comparable to the corresponding field based factors, CR_Ls, and the measured internal lethal concentrations, ILC_50L, showed comparable values with the estimated ones. The integrated BTH_L index was less than an order of magnitude or moderately acceptable for the assessment of variability, uncertainty, and predictive power of the index. This integrated assessment can be used to support decision making dealing with CBs in specific and LOCs in general, both in regional and global aquatic environments.

Modeling the impact of biota on polychlorinated biphenyls (PCBs) fate and transport in Lake Ontario using a population-based multi-compartment fugacity approach

Organisms have long been treated as receptors in exposure studies of polychlorinated biphenyls (PCBs) and other persistent organic pollutants (POPs). The influences of environmental pollution on organisms are well recognized. However, the impact of biota on PCB transport in an environmental system has not been considered in sufficient detail. In this study, a population-based multi-compartment fugacity model is developed by reconfiguring the organisms as populated compartments and reconstructing all the exchange processes between the organism compartments and environmental compartments, especially the previously ignored feedback routes from biota to the environment. We evaluate the model performance by simulating the PCB concentration distribution in Lake Ontario using published loading records. The lake system is divided into three environment compartments (air, water, and sediment) and several organism groups according to the dominant local biotic species. The comparison indicates that the simulated results are well-matched by a list of published field measurements from different years. We identify a new process, called Facilitated Biotic Intermedia Transport (FBIT), to describe the enhanced pollution transport that occurs between environmental media and organisms. As the hydrophobicity of PCB congener increases, the organism population exerts greater influence on PCB mass flows. In a high biomass scenario, the model simulation indicates significant FBIT effects and biotic storage effects with hydrophobic PCB congeners, which also lead to significant shifts in systemic contaminant exchange rates between organisms and the environment.

Modelling ecological and human exposure to POPs in Venice lagoon - Part II: Quantitative uncertainty and sensitivity analysis in coupled exposure models

The study is focused on applying uncertainty and sensitivity analysis to support the application and evaluation of large exposure models where a significant number of parameters and complex exposure scenarios might be involved. The recently developed MERLIN-Expo exposure modelling tool was applied to probabilistically assess the ecological and human exposure to PCB 126 and 2,3,7,8-TCDD in the Venice lagoon (Italy). The 'Phytoplankton', 'Aquatic Invertebrate', 'Fish', 'Human intake' and PBPK models available in MERLIN-Expo library were integrated to create a specific food web to dynamically simulate bioaccumulation in various aquatic species and in the human body over individual lifetimes from 1932 until 1998. MERLIN-Expo is a high tier exposure modelling tool allowing propagation of uncertainty on the model predictions through Monte Carlo simulation. Uncertainty in model output can be further apportioned between parameters by applying built-in sensitivity analysis tools. In this study, uncertainty has been extensively addressed in the distribution functions to describe the data input and the effect on model results by applying sensitivity analysis techniques (screening Morris method, regression analysis, and variance-based method EFAST). In the exposure scenario developed for the Lagoon of Venice, the concentrations of 2,3,7,8-TCDD and PCB 126 in human blood turned out to be mainly influenced by a combination of parameters (half-lives of the chemicals, body weight variability, lipid fraction, food assimilation efficiency), physiological processes (uptake/elimination rates), environmental exposure concentrations (sediment, water, food) and eating behaviours (amount of food eaten). In conclusion, this case study demonstrated feasibility of MERLIN-Expo to be successfully employed in integrated, high tier exposure assessment.

Modelling the fate of micropollutants in the marine environment using passive sampling

Polydimethylsiloxane sheets were used to determine freely dissolved concentrations (C(diss)) of polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) in the Belgian coastal zone. Equilibrium models were used to predict the whole water concentrations (C(ww)) of these compounds as well as their concentrations in sediment, suspended particulate matter (SPM) and biota. In general, contaminant concentrations were predicted well for whole water and biota. C(ww) was increasingly underpredicted as K(oc) increased, possibly because of the presence of black carbon. Concentrations in biota were overestimated by the equilibrium approach when logK(ow) exceeded 6.5, suggesting an increasing role of transformation processes. Concentrations of PAHs and PCBs in sediment and SPM were consistently underpredicted although a good correlation between measured and predicted values was observed. This was potentially due to the use of experimental K(oc) values which have been found to underestimate partitioning of hydrophobic substances to sediment in field studies.

Quantifying uncertainty in the trophic magnification factor related to spatial movements of organisms in a food web

Trophic magnification factors (TMFs) provide a method of assessing chemical biomagnification in food webs and are increasingly being used by policy makers to screen emerging chemicals. Recent reviews have encouraged the use of bioaccumulation models as screening tools for assessing TMFs for emerging chemicals of concern. The present study used a food web bioaccumulation model to estimate TMFs for polychlorinated biphenyls (PCBs) in a riverine system. The uncertainty associated with model predicted TMFs was evaluated against realistic ranges for model inputs (water and sediment PCB contamination) and variation in environmental, physiological, and ecological parameters included within the model. Finally, the model was used to explore interactions between spatial heterogeneity in water and sediment contaminant concentrations and theoretical movement profiles of different fish species included in the model. The model predictions of magnitude of TMFs conformed to empirical studies. There were differences in the relationship between the TMF and the octanol-water partitioning coefficient (KOW ) depending on the modeling approach used; a parabolic relationship was predicted under deterministic scenarios, whereas a linear TMF-KOW relationship was predicted when the model was run stochastically. Incorporating spatial movements by fish had a major influence on the magnitude and variation of TMFs. Under conditions where organisms are collected exclusively from clean locations in highly heterogeneous systems, the results showed bias toward higher TMF estimates, for example the TMF for PCB 153 increased from 2.7 to 5.6 when fish movement was included. Small underestimations of TMFs were found where organisms were exclusively sampled in contaminated regions, although the model was found to be more robust to this sampling condition than the former for this system.

Theoretically exploring direct and indirect chemical effects across ecological and exposure scenarios using mechanistic fate and effects modelling

Predicting ecosystem response to chemicals is a complex problem in ecotoxicology and a challenge for risk assessors. The variables potentially influencing chemical fate and exposure define the exposure scenario while the variables determining effects at the ecosystem level define the ecological scenario. In absence of any empirical data, the objective of this paper is to present simulations by a fugacity-based fate model and a differential equation-based ecosystem model to theoretically explore how direct and indirect effects on invertebrate shallow pond communities vary with changing ecological and exposure scenarios. These simulations suggest that direct and indirect effects are larger in mesotrophic systems than in oligotrophic systems. In both trophic states, interaction strength (quantified using grazing rates) was suggested a more important driver for the size and recovery from direct and indirect effects than immigration rate. In general, weak interactions led to smaller direct and indirect effects. For chemicals targeting mesozooplankton only, indirect effects were common in (simple) food-chains but rare in (complex) food-webs. For chemicals directly affecting microzooplankton, the dominant zooplankton group in the modelled community, indirect effects occurred both in food-chains and food-webs. We conclude that the choice of the ecological and exposure scenarios in ecotoxicological modelling efforts needs to be justified because of its influence on the prevalence and magnitude of the predicted effects. Overall, more work needs to be done to empirically test the theoretical expectations formulated here.

Estimating trophic levels and trophic magnification factors using Bayesian inference

Food web biomagnification is increasingly assessed by estimating trophic magnification factors (TMF) where solvent (often lipid) normalized contaminant concentration is regressed onto the trophic level, and TMFs are represented by the slope of the relationship. In TMF regressions, the uncertainty in the contaminant concentrations is appreciated, whereas the trophic levels are assumed independent and not associated with variability or uncertainty pertaining to e.g. quantification. In reality, the trophic levels may vary due to measurement error in stable isotopes of nitrogen (δ(15)N) of each sample, in δ(15)N in selected reference baseline trophic level, and in the enrichment factor of δ(15)N between two trophic levels (ΔN), which are all needed to calculate trophic levels. The present study used a Markov Chain Monte Carlo method, with knowledge about the food web structure, which resulted in a dramatic increase in the precision in the TMF estimates. This also lead to a better understanding of the uncertainties in bioaccumulation measures; instead of using point estimates of TMF, the uncertainty can be quantified (i.e., TMF >1, namely positive biomagnification, with an estimated X % probability).

Integration of a dynamic organism model into the DynA Model: development and application to the case of DDT in Lake Maggiore, Italy

The Single Organism (SO) model was developed to investigate the influence of temporal dynamics of aquatic organism properties on their exposure to organic chemicals in water. SO was then integrated with an existing dynamic surface-water model (DynA), to form the coupled water-bioaccumulation model EcoDynA. In order to evaluate the model performance, the results produced by EcoDynA were compared to the p,p'-DDT concentrations measured in specimens of whitefish of different age and sex caught in Lake Maggiore after the discovery of a DDT spill. The comparison showed a good agreement. Other satisfying results were obtained comparing model results with p,p'-DDT concentration values measured in another species of whitefish which were available in the literature. A preliminary sensitivity analysis confirmed that accounting for dynamics of parameters such as organism lipid fraction and feeding rate is necessary to obtain accurate exposure predictions.

Modeling approaches for characterizing and evaluating environmental exposure to engineered nanomaterials in support of risk-based decision making

As the use of engineered nanomaterials becomes more prevalent, the likelihood of unintended exposure to these materials also increases. Given the current scarcity of experimental data regarding fate, transport, and bioavailability, determining potential environmental exposure to these materials requires an in depth analysis of modeling techniques that can be used in both the near- and long-term. Here, we provide a critical review of traditional and emerging exposure modeling approaches to highlight the challenges that scientists and decision-makers face when developing environmental exposure and risk assessments for nanomaterials. We find that accounting for nanospecific properties, overcoming data gaps, realizing model limitations, and handling uncertainty are key to developing informative and reliable environmental exposure and risk assessments for engineered nanomaterials. We find methods suited to recognizing and addressing significant uncertainty to be most appropriate for near-term environmental exposure modeling, given the current state of information and the current insufficiency of established deterministic models to address environmental exposure to engineered nanomaterials.

Transfer of PCBs from bottom sediment to freshwater river fish: a food-web modelling approach in the Rhône River (France) in support of sediment management

Since 2005, restrictions have been because of fish consumption along the Rhone River because of high polychlorobiphenyl (PCB) concentrations, which have resulted inadverse economic consequences for professional fisheries in affected areas. French environmental authorities have expended considerable efforts to research sediment remediation strategies and development of sediment quality guidelines designed to protect the health of humans consuming Rhône River fish. Here we: (1) develop a bioaccumulation food-web model that describes PCB concentrations in three common freshwater fish species of the Rhône River, using Bayesian inference to estimate the input parameters; (2) test the predictive power of the model in terms of risk assessment for fish consumption; and (3) discuss the use of this approach to develop sediment quality guidelines that protect the health of humans consuming Rhône River fish. The bioaccumulation model predictions are protective for human consumer of fish and are efficient for use in risk assessment. For example, 85% of the predicted values were within a factor of 5 of measured CB153 concentrations in fish. Using sensitivity analyses, the major role played by sediment and diet behaviors on bioaccumulation process is illustrated: the parameters involved in the respiratory process (contamination from water) have little impact on model outputs, whereas the parameters related to diet and digestion processes are the most sensitive. The bioaccumulation model was applied to derive sediment concentrations compatible with safe fish consumption. The resulting PCB sediment thresholds (expressed as the sum of seven PCB indicator congeners) that are protective for the consumption of the fish species ranged from 0.7 to 3 ng/g (dw).

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

In the regulatory context, bioaccumulation assessment is often hampered by substantial data uncertainty as well as by the poorly understood differences often observed between results from laboratory and field bioaccumulation studies. Bioaccumulation is a complex, multifaceted process, which calls for accurate error analysis. Yet, attempts to quantify and compare propagation of error in bioaccumulation metrics across species and chemicals are rare. Here, we quantitatively assessed the combined influence of physicochemical, physiological, ecological, and environmental parameters known to affect bioaccumulation for 4 species and 2 chemicals, to assess whether uncertainty in these factors can explain the observed differences among laboratory and field studies. The organisms evaluated in simulations including mayfly larvae, deposit-feeding polychaetes, yellow perch, and little owl represented a range of ecological conditions and biotransformation capacity. The chemicals, pyrene and the polychlorinated biphenyl congener PCB-153, represented medium and highly hydrophobic chemicals with different susceptibilities to biotransformation. An existing state of the art probabilistic bioaccumulation model was improved by accounting for bioavailability and absorption efficiency limitations, due to the presence of black carbon in sediment, and was used for probabilistic modeling of variability and propagation of error. Results showed that at lower trophic levels (mayfly and polychaete), variability in bioaccumulation was mainly driven by sediment exposure, sediment composition and chemical partitioning to sediment components, which was in turn dominated by the influence of black carbon. At higher trophic levels (yellow perch and the little owl), food web structure (i.e., diet composition and abundance) and chemical concentration in the diet became more important particularly for the most persistent compound, PCB-153. These results suggest that variation in bioaccumulation assessment is reduced most by improved identification of food sources as well as by accounting for the chemical bioavailability in food components. Improvements in the accuracy of aqueous exposure appear to be less relevant when applied to moderate to highly hydrophobic compounds, because this route contributes only marginally to total uptake. The determination of chemical bioavailability and the increase in understanding and qualifying the role of sediment components (black carbon, labile organic matter, and the like) on chemical absorption efficiencies has been identified as a key next steps.

Parameter uncertainty in modeling bioaccumulation factors of fish

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.

Ecotoxicological mechanisms and models in an impact analysis tool for oil spills

In an international collaborative effort, an impact analysis tool is being developed to predict the effect of accidental oil spills on recruitment and production of Atlantic cod (Gadus morhua) in the Barents Sea. The tool consisted of three coupled ecological models that describe (1) plankton biomass dynamics, (2) cod larvae growth, and (3) fish stock dynamics. The discussions from a series of workshops are presented in which variables and parameters of the first two ecological models were listed that may be affected by oil-related compounds. In addition, ecotoxicological algorithms are suggested that may be used to quantify such effects and what the challenges and opportunities are for algorithm parameterization. Based on model exercises described in the literature, survival and individual growth of cod larvae, survival and reproduction of zooplankton, and phytoplankton population growth are denoted as variables and parameters from the ecological models that might be affected in case of an oil spill. Because toxicity databases mostly (67%) contain data for freshwater species in temperate environments, parameterization of the ecotoxicological algorithms describing effects on these endpoints in the subarctic marine environment is not straightforward. Therefore, it is proposed that metadata analyses be used to estimate the sensitivity of subarctic marine species from available databases. To perform such analyses and reduce associated uncertainty and variability, mechanistic models of varying complexity, possibly aided by new experimental data, are proposed. Lastly, examples are given of how seasonality in ecosystems may influence chemical effects, in particular in the subarctic environment. Food availability and length of day were identified as important characteristics as these determine nutritional status and phototoxicity, respectively.