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Aug 17, 2011 - by Combining Chemical Space Visualization with a Process-Based. Diagnostic ... element of chemical risk assessment and management. Mass...
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Assessing Model Uncertainty of Bioaccumulation Models by Combining Chemical Space Visualization with a Process-Based Diagnostic Approach Emma Undeman* and Michael S. McLachlan Department of Applied Environmental Science, Stockholm University, SE-106 91 Stockholm, Sweden

bS Supporting Information ABSTRACT: As models describing human exposure to organic chemicals gain wider use in chemical risk assessment and management, it becomes important to understand their uncertainty. Although evaluation of parameter sensitivity/uncertainty is increasingly common, model uncertainty is rarely assessed. When it is, the assessment is generally limited to a handful of chemicals. In this study, a strategy for more comprehensive model uncertainty assessment was developed. A regulatory model (EUSES) was compared with a research model based on more recent science. Predicted human intake was used as the model end point. Chemical space visualization techniques showed that the extent of disagreement between the models varied strongly with chemical partitioning properties. For each region of disagreement, the primary human exposure vector was determined. The differences between the models’ process algorithms describing these exposure vectors were identified and evaluated. The equilibrium assumption for root crops in EUSES caused overestimations in daily intake of superhydrophobic chemicals (log KOW > 11, log KOA > 10), whereas EUSES’s approach to calculating bioaccumulation in fish prey resulted in underestimations for hydrophobic compounds (log KOW ∼ 6 8). Uptake of hydrophilic chemicals from soil and bioaccumulation of superhydrophobic chemicals in zooplankton were identified as important research areas to enable further reduction of model uncertainty in bioaccumulation models.

’ INTRODUCTION Quantification of environmental exposure is an essential element of chemical risk assessment and management. Mass balance models describing human exposure to chemicals emitted into the environment are recognized as useful tools for this purpose. Several bioaccumulation models have been developed that calculate the environmental exposure of humans to organic contaminants via inhalation, diet, and drinking water (e.g., refs 1 4). Since this type of model is increasingly used for regulatory purposes (e.g., risk assessment for registration of chemicals under the REACH legislation), it is important to be aware of the state of knowledge that is incorporated in the models. For example, many of the algorithms employed in current regulatory models (e.g., European Union System for the Evaluation of Substances, EUSES) are simple empirical regressions that are more than 10 years old (some more than 20 years old)5 that were derived from chemicals with a fairly limited and undefined range of physical chemical properties.6 Considerable progress has been made in bioaccumulation science in recent years; several mechanistic process descriptions have been developed and new algorithms for partitioning processes have been derived. There is hence an ongoing need to improve the regulatory models and bring current science into chemicals management. r 2011 American Chemical Society

Efforts to improve model performance should be guided by an uncertainty assessment. Currently, uncertainty assessments are typically based on parameter sensitivity/uncertainty analysis (e.g., by using Monte Carlo simulations7). However, the uncertainty in model outputs depends also on model uncertainty.8 The model uncertainty is related to alternate modeling strategies (such as description of processes, temporal and spatial resolution, homogeneity assumptions, and inclusion of exposure pathways) and is usually more difficult to assess.9 One option to explore this type of uncertainty is by model comparison (e.g., refs 10, 11), but this is typically done for a limited number of chemicals and scenarios and does not give an overview of how the model uncertainty varies with chemical properties. In this study, a strategy to assess model uncertainty was employed that combines model comparison with chemical space visualization techniques and mechanism-based evaluation to obtain a broad overview and deeper insight into model uncertainty. This was done by comparing the regulatory model that is currently recommended for model-based risk assessments in a Received: June 15, 2011 Accepted: August 17, 2011 Revised: August 15, 2011 Published: August 17, 2011 8429

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Environmental Science & Technology European regulatory context (EUSES) with a research model based on more recent science (a steady state version of ACCHUMAN,4 hereafter referred to as ACC-HUMANsteady). First, the differences in the models’ predictions of human daily intake (kgchemical kgbody weight 1 day 1) for chemicals with a wide range of environmental partitioning properties (as defined by their partition coefficients between air, water, and octanol) were assessed, and domains in the chemical partitioning space with strong deviations between the models were identified. For these domains the dominant exposure pathways in the two models were identified, as well as the pertinent process descriptions. The uncertainty of these process descriptions was then evaluated on the basis of their theoretical justification and empirical evidence (measured data). In this way, possibilities to reduce model uncertainty in EUSES as well as the current knowledge gaps that hinder further reduction of uncertainty in bioaccumulation models are highlighted.

’ METHODS A brief description of EUSES and ACC-HUMANsteady is provided in the following section. The bioaccumulation model in EUSES (which is recommended for environmental exposure assessment in the REACH guidance documents)12 is based on the Technical Guidance Documents13 and is described in detail elsewhere.5 The spreadsheet version 1.24 of EUSES, accessed at The Netherlands Center for Environmental Modeling (http:// cem-nl.eu/eutgd.html) on April 19, 2010, was employed for the model calculations. The ACC-HUMANsteady model is described in detail in the Supporting Information. General Description of EUSES. EUSES accounts for human exposure resulting from intake of fish, beef, dairy products, leafy vegetables, root crops, drinking water, and air (inhalation). For human exposure at the regional level, the required inputs are chemical concentrations in bulk air, surface water (freely dissolved), agricultural bulk soil, and agricultural soil pore water (freely dissolved). The chemical properties that drive the model are the partitioning coefficient between octanol and water (KOW, m3oct m 3wat), the Henry’s Law constant (Pa m3 mol 1), the molecular weight (g mol 1), the energy of vaporization (kJ mol 1, used for temperature correction of the Henry’s law constant), and the mass fraction of chemical that is bound to aerosols. The total daily dose to humans (kgchemical kgbw 1 d 1) is the sum of the contributions from each exposure vector normalized to the body weight. The daily dose from each food item is calculated by multiplying the intake rate of the food item by the estimated wet weight concentration in the food. See also the Supporting Information for a brief description of the calculations for the individual food items. General Description of ACC-HUMANsteady. ACC-HUMANsteady is a steady-state mechanistic mass balance model based on the extended bioaccumulation model ACC-HUMAN.4,14 It is subdivided into an aquatic food web (plankton, benthos, planktivorous fish, piscivorous fish), an agricultural food chain (grass, milk cow, beef cattle), four different types of crops/ cultivated plants (leafy vegetables, root fruits, aerial fruits and tubers), and the human as the top consumer. The benthos and human modules were not employed in this study. A human daily dose may be calculated from the daily intake rates of the different food items and water/inhalation rate of air multiplied by the fresh weight normalized chemical concentrations in the food and water/chemical concentration in air.

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The concentrations in the various organisms are calculated using steady-state mechanistic submodels, one for each organism, except for zooplankton, which are assumed to be in equilibrium with the surrounding water. Dietary uptake, respiration, drinking, ingestion of soil or sediment particles, excretion via feces, metabolism, and growth are the uptake and elimination routes considered for the marine organisms and cattle. For cattle, additional elimination processes are urination, percutaneous excretion, and lactation. For the vegetation, chemical uptake from soil via the transpiration stream and from air via gaseous and particle deposition is considered. Elimination occurs via growth dilution, volatilization from the leaf surface, and biotransformation within the plant tissues. Model Comparison. The model uncertainty was explored by comparing the two models’ predictions of the total human daily intake. The differences between the models, and the model assumptions causing these differences, vary depending on the physical chemical properties of the substance being studied. Hence, a parameter-based sensitivity/uncertainty analysis will give different results for each set of physical chemical properties chosen. To facilitate a model uncertainty assessment that comprehensively explores the chemical partitioning space, a processbased approach was taken instead of a parameter-based approach. More specifically, for each domain of the chemical partitioning space showing marked discrepancies between the two models’ predictions of human daily intake, the dominant exposure routes for humans were identified. This was followed by a comparative assessment of the models’ process descriptions for these dominant exposure vectors. Chemical Properties. The model simulations were conducted for a set of hypothetical perfectly persistent organic chemicals defined by log KOW ranging from 2 to 12, and log octanol air partitioning coefficients (log KOA) ranging from 4 to 12 in steps of 0.5 log units. The molecular weight was set to 100 g/mol for all chemicals. The heats of phase transfer for temperature correction, ΔUAW, ΔUOW, and ΔUOA, were set to 60, 20, and 80 kJ mol 1, respectively. The Henry’s Law constant (H) required in EUSES was calculated from the temperature corrected log KAW, the gas constant (R), and an environmental temperature T of 288 K (H = KAW(288K)  8.314  288). Environmental Concentrations. For each chemical, the unit world model of chemical fate in the environment that is built into ACC-HUMANsteady was employed to calculate the equilibrium concentrations in air (gaseous and aerosol bound), water (dissolved), and soil (bulk soil and pore water), assuming that the fugacity in each compartment was 10 12 Pa. The terrestrial temperature was 288 K, and the marine compartments (water and sediment) had a temperature of 281 K. In addition, the mass fraction of chemical in the air compartment that was sorbed to aerosols was calculated for each chemical. The environmental concentrations and the fraction associated with aerosol predicted in this manner were used as input for both bioaccumulation models. Food intake Rates. The default settings for daily intake rates of food items are different in the two models. Also, ACC-HUMANsteady employs different parametrizations to calculate concentrations in leafy vegetables (lettuce), grain, fruit (apple), tubers (potato), and root vegetables (carrot), whereas EUSES uses one parametrization to estimate the concentrations in the leaves and roots of a generic plant, which are then assumed to be representative of leafy vegetables (including fruits and cereals) and root crops. To facilitate the comparison of the fundamental 8430

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Figure 1. The log quotient of the human total daily dose predicted by ACC-HUMANsteady and by EUSES (panel A) and the maximum contribution of a single exposure vector to the total human daily intake for EUSES (panel B) and ACC-HUMANsteady (panel C). In those regions where one exposure vector accounts for >50% of the daily dose, the vector is labeled. Each parameter is plotted as a function of log KOA and log KOW at 25 °C.

differences in the modeling approaches employed in the two models, the intake rates of food, water and air were harmonized. The intake rates of EUSES were also employed in ACC-HUMANsteady. The intake rates of leafy vegetables/fruits/grain and of roots/tubers used in ACC-HUMANsteady were scaled up to match the corresponding total leafy vegetable and root crop intake in EUSES (see Table S1, Supporting Information).

’ RESULTS AND DISCUSSION The comparison of the two models’ predictions of human environmental exposure is presented in Figure 1. The quotient between the human total daily dose predicted by ACC-HUMANsteady and EUSES is displayed as a function of the partition coefficients for octanol air (KOA) and octanol water (KOW) (i.e., the physical chemical properties that defined the hypothetical chemicals). Large differences between the models were found in four domains of the chemical partitioning space: (I) for superhydrophobic chemicals with log KOW J 11 and log KOA J 10, where EUSES predicted a ∼3 30 times higher daily dose; (II) for hydrophobic chemicals with log KOW ∼ 6.5 9 and log KOA J 6, where ACC-HUMANsteady predicted a 8 140 times higher daily dose; (III) for chemicals with log KOW = 1 4 and log KOA > 7, where ACC-HUMANsteady predicted a 3 12 times higher daily dose; and (IV) for hydrophilic chemicals with log KOW j 1, where ACC-HUMANsteady predicted a 4 7 times higher daily dose (Figure 1). The explanation for these differences is related to the process descriptions for exposure vectors making the major contribution to the total daily intake in the two models. As shown in Figure 1, panels B and C, the two models’ predictions of the dominant exposure vector for each chemical did not agree in many parts of the chemical partitioning space. The differences in the process descriptions causing these discrepancies are discussed in the following.

Superhydrophobic Compounds and Uptake in Root Crops. Starting with the superhydrophobic chemicals in domain

I (log KOW > 11, log KOA > 10, Figure 1A), EUSES predicted that root crops were mainly responsible for the total uptake of these compounds, whereas ACC-HUMANsteady predicted that fish and/or leafy vegetables were the dominant exposure routes (Figure 1B,C). The intake via leafy vegetables was similar in the two models (on average 6, domain II in Figure 1). These deviations were mainly due to differences in intake via fish. The dominant uptake routes predicted by EUSES were (depending on the KOW value) fish, meat, or root vegetables, whereas ACC-HUMANsteady predicted that the uptake originated nearly exclusively from fish. The chemical concentrations in fish with ACC-HUMANsteady increased with increasing KOW and were 2 28 000 times higher than those predicted by EUSES for log KOW = 5 9.5 (Figure S1, Supporting Information). For chemicals with log KOW = 12, the ACC-HUMANsteady predictions were 420 000 times higher, although this was not reflected in a higher total human daily uptake compared to EUSES due to the high uptake via root vegetables predicted by this model (see above). Note that excluding the root crops from the prediction of daily intake would increase the maximum deviations between the models (see Figure S2, Supporting Information). Both models calculate the concentrations of perfectly persistent chemicals in fish, Cfish, from the KOW value, but whereas EUSES employs an empirical KOW-based regression for the bioconcentration factors (BCF) and default biomagnification factors (BMFs), ACC-HUMANsteady uses a mechanistically based mass balance model (see the Supporting Information for a detailed description). To visualize the differences between the two models, the BCFs (defined as Cfish,no ingestion /Cwater,dissolved), BMFs (defined as Cfish/Cprey), and bioaccumulation factors (BAFs, defined as Cfish/Cwater,dissolved), all on a fresh weight basis, were plotted as a function of log KOW (Figure 2). The two models predicted similar uptake via the gills for chemicals with log KOW < 7, as reflected in the similar BCF values

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in this KOW range (Figure 2). At higher KOW, the ACCHUMANsteady BCF curve levels off at a constant value, reflecting a nonequilibrium state in which uptake over the gill membranes is not sufficiently rapid to compensate for the increase in storage capacity of the fish caused by growth (i.e., the dominant elimination process becomes growth dilution, which is independent of KOW, as opposed to gill elimination, which is dependent on KOW). In EUSES, the BCF decreases between log KOW of 6.5 and 10. The BMF was similar in the two models up to a log KOW of 10. Above that ACC-HUMANsteady predicts BMFs below unity, which reflects decreasing gut absorption efficiency with increasing KOW, while the major elimination process in the chemical property space, growth dilution, is independent of KOW. The total accumulation of chemicals from the ambient water (i.e., the BAF), however, was up to 105.6 times higher in ACC-HUMANsteady. The large differences may be partly explained by the up to 100 times lower BCF predicted by EUSES. log BCF log KOW plots for fish and other higher trophic level aquatic organisms showing decreasing BCFs or leveling off BCFs at high KOW have been reported in several publications (e.g., refs 23, 24). Suggested explanations for both observations include limitations in uptake kinetics (i.e., growth dilution, insufficient time to reach equilibrium), sterically hindered membrane permeability for large molecules, and experimental artifacts such as overestimation of the freely dissolved concentrations (third phase effects) and inaccurate KOW values.25,26 In a laboratory study of aquatic worms, it was found that third phase artifacts led to a bell-shaped form for the apparent BCF versus KOW function, whereas when BCF was artifact corrected the function leveled off at high KOW due to growth dilution.27 Although steric hindrance will at some point limit BCF, there is as yet insufficient evidence indicating what properties control this effect and that they are correlated with KOW. This discussion suggests that the EUSES model may have underestimated BCF. However, the issue is of limited relevance, as bioconcentration does not influence bioaccumulation of high KOW chemicals in fish. The critical factor is that the EUSES model uses BCF to estimate the BAF of high KOW chemicals rather than addressing dietary uptake in a mechanistic manner (see below). The major reason for the differences between the two models was that the “prey” in EUSES is a fish with no dietary uptake (the concentration in prey fish is the product of the concentration in water and the BCF, which accounts for gill uptake only), whereas in ACC-HUMANsteady the prey is a fish that feeds on zooplankton (whereby the zooplankton are in equilibrium with the water). Since dietary uptake is much greater than gill uptake for chemicals with log KOW > 5.5, the EUSES model predicts much lower concentrations in prey (note that ACC-HUMANsteady predicted higher concentrations of chemicals with log KOW > 10 in planktivorous fish than piscivorous fish because the concentration in the feed of the latter is, as opposed to the concentration in zooplankton, limited by growth dilution). The key assumption in ACC-HUMANsteady of the existence of a partitioning equilibrium between water and zooplankton for high KOW chemicals is based on field measurements of PCBs in zooplankton displaying a linear relationship between the log zooplankton water bioaccumulation factor and log KOW (log KOW range ∼5.3 7.4).28 However, other publications have reported the absence of a linear correlation between log KOW and plankton BCFs (or BAFs), which is more consistent with the EUSES model.25,29 31 Although these observations could be due to third 8432

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Figure 3. Cow biotransfer factors (BTFs) for persistent chemicals calculated by ACC-HUMANsteady and EUSES, and the linear regression for measured BTF published by Hendriks et al. The BTFs are displayed as a function of log KOW at 25 °C and are defined as the concentration in cow milk (mg kg 1ww) divided by the influx via ingestion (mg d 1) (i.e., grass, soil, and water) on a wet weight basis. Note that the BTFs in ACC-HUMANsteady are also a function of KAW. Data are shown for a constant log KAW of 4.

phase artifacts, as discussed above, nonequilibrium due to kinetic limitations cannot be excluded. In order to determine which fish model is less uncertain, the models were evaluated using measured data. A comparison between the two models’ predictions of concentrations in fish with field data for high KOW chemicals indicates that EUSES consistently underestimates the concentrations in fish, whereas ACC-HUMANsteady’ predictions agree better with the field data (Figure S2, Supporting Information). The geometric mean of the EUSES predicted/observed quotient was 0.59, 0.12, and 0.03 for the three log KOW ranges presented in Figure S2 (Supporting Information) (note the log scale for the y-axis). For ACCHUMANsteady, the corresponding values were 0.84, 0.88, and 4.15. The evaluation shows that the approach employed in the ACC-HUMANsteady model gives good predictions for PCBs, which are among the most bioaccumulative chemicals described in the literature. This indicates that the equilibrium assumption made for the zooplankton is reasonable for chemicals in the studied KOW range. Note, however, that the deviations were greater in the highest KOW range (Figure S2, Supporting Information) and that there were no data for chemicals with log KOW > 8.3 in the evaluation data set. Such superhydrophobic chemicals have not been employed in the training sets for the regressions used in the EUSES and ACC-HUMANsteady models either. This is due to the paucity of bioaccumulation data for very hydrophobic chemicals. The consequence is that the model uncertainty is very high in this portion of the chemical domain. Moderately Hydrophobic Chemicals and Uptake in Cattle. Considerable differences were found in human exposure via meat and dairy products (Figure S1, Supporting Information). These differences influenced the predicted total daily intake in particular for chemicals with intermediate log KOA and log KOW values (domain III in Figure 1), since the main vector of human exposure to these chemicals in ACC-HUMANsteady was dairy products (Figure 1 B). In contrast, EUSES predicted a comparably low intake via dairy products (Figure 1C). Instead, fish and leafy vegetables were the major source of exposure for the chemicals in this partitioning domain. The differences between the two models were caused by different treatments of bioaccumulation in cattle (Figure 3).

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The Travis and Arms model employed in EUSES (see the Supporting Information) is a regression between observed biotransfer factors (BTFs) and log KOW. The BTF for milk/beef is defined as the concentration in milk/beef (mg kg 1ww) divided by the influx via diet (mg d 1). The model has no apparent mechanistic basis. The biotransfer factor for milk increases linearly with KOW in the KOW range 1.5 6.5 (Figure 3). ACCHUMANsteady employs a mechanistically based mass balance model of bioaccumulation in cattle. In contrast to EUSES, it predicts fairly constant BTFs for persistent chemicals in the log KOW range of 3 8, with values that are higher than the largest predicted by EUSES. The BTF values then drop at higher KOW (i.e., where EUSES’s BTFs are at maximum value). This is the result of a decrease in the modeled dietary absorption efficiency in the gastrointestinal tract (GIT) when log KOW exceeds 6.5. The relationship between BTF and KOW in the ACC-HUMANsteady model is consistent with experimental observations for persistent chemicals, while the Travis and Arms model is not.32,33 Hendriks et al. derived empirical relationships between BTF and KOW based on an extensive survey of available measurements.33 Although the absolute value of BTF cannot be compared with those used in ACC-HUMANsteady and EUSES due to different assumptions regarding feeding rate and milk lipid excretion, there is good agreement between the shape of their relationship for persistent chemicals and the ACC-HUMANsteady relationship (Figure 3). Hendriks et al. showed that biotransfer of stable compounds is independent of hydrophobicity for log KOW 3 7 (see Figure 3). They also showed that the BTF of the labile chemicals were orders of magnitude lower than for persistent chemicals in the same KOW range.33 The declining trend in BTF with decreasing KOW manifested in the Travis and Arms regression is likely due to biotransformation of hydrophilic compounds in their data set. Birak et al. reported that 40% of the chemicals in the Travis and Arms data set were metabolized.34 In Hendriks’ data set, labile compounds were overrepresented in the low KOW range and stable chemicals in the higher range. A regression of such a data set without distinguishing between labile and persistent compounds would be expected to show a correlation between biotransfer and KOW, and this is likely what happened in the Travis and Arms work. However, implying that biotransformation is correlated with KOW is not mechanistically correct. Such an algorithm is inappropriate for a screening model, as it would result in serious underestimations of the bioaccumulation of persistent hydrophilic compounds (i.e., the model would not be conservative). Note that the metabolic rate constant is given as model input in ACC-HUMANsteady, enabling this model to capture the lower accumulation of labile compounds in a more mechanistically correct way. Although the metabolic rate constant may not be readily available for a chemical of interest, having it as an explicit input parameter forces the user to confront the uncertainty associated with biotransformation. Hydrophilic Compounds and Translocation in the Plant Xylem. For the persistent hydrophilic compounds (domain IV in Figure 1A), leafy vegetables (mainly lettuce) and drinking water were the dominant exposure routes in ACC-HUMANsteady and EUSES, respectively. Uptake via water was similar in the two models and the deviations in the predicted total daily intake were hence caused by the 6 17 times higher concentrations in leafy vegetables predicted by ACC-HUMANsteady (Figure S1, Supporting Information). 8433

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Figure 4. The transpiration stream concentration factor predicted by the Dettenmaier equation [TSCF = 11/(11+ 2.6log Kow)], the ACCHUMANsteady model (for lettuce), and the EUSES model (i.e., a truncated version of the Briggs regression).

The most important uptake process for hydrophilic compounds in leafy vegetables predicted by ACC-HUMANsteady was translocation from the soil porewater to the leaves. EUSES calculates this transport of chemicals from the soil pore water to the plant leaves using the transpiration stream concentration factor (TSCF), which is the quotient between the chemical concentrations in the transpiration stream (i.e., the xylem sap) and the soil pore water. The TSCF is predicted by a KOW-based regression proposed by Briggs.35 The TSCF value is multiplied by a transpiration rate (m3 d 1) and the soil pore water concentration. In ACC-HUMANsteady, the transpiration stream is assumed to be in equilibrium with the soil pore water, and hence a higher relative uptake (i.e., TSCF) is predicted for hydrophilic compounds compared to EUSES. As illustrated in Figure 4, the TSCFACC-HUMANsteady for lettuce was ∼10 times higher than the TSCFEUSES for leafy vegetables when log KOW < 0. In addition to the differences in TSCF, the default transpiration rates employed in ACC-HUMANsteady were 6 times higher for lettuce and 3 times higher for grass than the corresponding rates coded into EUSES. This difference, however, is related to natural variability (i.e., parameter uncertainty) rather than model uncertainty and is not discussed further in this study. The higher human uptake of hydrophilic chemicals via leafy vegetables predicted by ACC-HUMANsteady was hence primarily caused by higher TSCFs used in this model. Although ACCHUMANsteady predicted in general higher TSCFs than observed in a study of uptake in soybean and tomato plants conducted by Dettenmeier et al., the high TSCFs for the hydrophilic compounds (i.e., log KOW < 1) predicted by ACCHUMANsteady are consistent with the findings that highly water-soluble chemicals are indeed taken up via the transpiration stream (Figure 4)36 and is also in line with a previous model for fruit trees.37 The Dettenmeier publication also refers to a number of other studies confirming plant uptake of hydrophilic compounds, and it identifies possible errors in the Briggs study (i.e., too small volume of transpired water, short exposure period, and questionable plant health conditions). There is, however, no clear mechanistic explanation for the uptake of hydrophilic compounds via the roots. It is generally believed that chemicals must pass through a water-impermeable membrane, the Casparian strip, to enter the xylem.38,39 The low sorption of highly watersoluble chemicals through this membrane would thus explain the limited translocation of these chemicals from roots to leaves observed by Briggs.35 Uptake of water into roots is, however, a complex process and parallel pathways exist for passage of

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water across various tissues.40 Since there are still relatively few measurements of uptake of neutral polar chemicals in plants published, the description of the root uptake of hydrophilic compounds remains uncertain. Model Uncertainty and Future Research Needs. This systematic model comparison shows how chemical space visualization techniques combined with a process-based diagnostic approach may be used to identify possibilities to reduce the model uncertainty of bioaccumulation models in general and the regulatory modeling tool EUSES in particular. By displaying the quotient of the two models’ prediction of the model end point as a function of the physical chemical properties, the domains of the chemical partitioning space with the greatest deviations could easily be distinguished. A limited number of process descriptions responsible for these deviations could then be identified and the uncertainties of these descriptions evaluated on the basis of their theoretical justification and comparisons with measurements. The process descriptions identified were (1) the assumption of equilibrium partitioning between soil pore water and roots in the EUSES model, which resulted in severely overestimated concentrations of superhydrophobic compounds in root crops; (2) the equilibrium approach taken for fish prey in ACC-HUMANsteady, which resulted in concentrations in fish that agree well with observations for chemicals with 6 < log KOW < 8.3, whereas the BCF regression employed in EUSES underestimated the concentrations for chemicals in this log KOW range; (3) the empirical regression for cattle BTFs used in EUSES, which predicts biodilution for moderately hydrophobic compounds, which likely reflects an apparent correlation of biotransformation rate with KOW (as shown by, for example, ref 33); and (4) the predictions of TSCF for hydrophilic compounds. The lack of measurements and a clear mechanistic explanation for the uptake of highly water-soluble chemicals in plants makes the uncertainty of this process description, and hence the model uncertainty in this portion of the chemical partitioning space, particularly high. Similarly, data on uptake of superhydrophobic chemicals (i.e., log KOW > 8) in zooplankton and fish are lacking, which prevents model evaluation in this chemical domain. Research to further reduce the model uncertainty of bioaccumulation models should hence focus on such measurements. There is also a need to study uptake of moderately hydrophobic persistent chemicals in cattle to confirm the absence of a relationship between hydrophobicity and bioaccumulation in the intermediate KOW range (i.e., 3 > log KOW < 6). The findings of this study agree well with other model comparison studies employing EUSES. For example, EUSES’s underestimation of concentrations of hydrophobic chemicals in fish and the erroneous root model have been observed previously, as well as the shortcomings associated with degradable chemicals.22,41,42 Although these studies have successfully highlighted several weaknesses and uncertainties of the EUSES model for a limited number of chemicals, until now these have not been placed in the context of their impact on human exposure, and an in depth assessment of the model assumptions responsible for the discrepancies between the models has been lacking. The model uncertainty analysis made here has several limitations. The dominant vectors of human exposure to a given chemical will depend on the environmental exposure scenario and the composition of the human diet. Here chemical equilibrium between air, water, soil, and sediment was assumed and the “worst case” diet of EUSES was employed. The dominant exposure routes identified in this study were, however, fairly 8434

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Environmental Science & Technology similar to those predicted by a dynamic version of the ACCHUMANsteady model linked to a dynamic environmental fate model when emissions occurred to air and a representative human diet for southern Sweden was used.43 The analysis of model uncertainty could nevertheless be expanded to include different modes of emissions to the physical environment. This would shift the dominant exposure routes in the various domains of chemical space and hence the contribution of the process descriptions to the model uncertainty. Another factor not considered in this study is degradability of chemicals. Degradation in a specific environmental medium or organism may also change the processes controlling human exposure and hence the major contributors to model uncertainty. Finally, we reiterate that model uncertainty is only one component of uncertainty assessment, for which parameter uncertainty must also be considered. The procedure to assess model uncertainty outlined here goes beyond the established strategy to compare several models for a defined chemical and scenario and then to use the divergence in end point prediction as a measure of uncertainty. Instead, we here identify the most important processes for a wide range of chemicals and then evaluate the models’ treatment of these processes based on current science. This is a scientifically sound and efficient way to assess and reduce model uncertainty, and it furthermore allows identification of specific research needs. In this study we compared just two models. The results were particularly insightful because of the considerably different process descriptions employed in the two models. There is little inherent value in using a large number of models in this kind of model uncertainty assessment. Including additional models in the comparison could provide further insight, but for this to go beyond identifying technical implementation errors it would be necessary for the additional models to also employ different process descriptions and/or model structures.

’ ASSOCIATED CONTENT

bS

Supporting Information. A brief description of the EUSES bioaccumulation model, intake rates employed in ACC-HUMANsteady, a comparison of ACC-HUMANsteady/ EUSES daily intake for individual food items, a comparison between ACC-HUMANsteady/EUSES total daily dose without root crops in the diet, an evaluation of the fish models in ACCHUMANsteady and EUSES, and a detailed model description for ACC-HUMANsteady. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]; phone: +46-8-6747517.

’ ACKNOWLEDGMENT We thank Matt MacLeod for helpful advice regarding selection of physical chemical properties data. Funding was provided by the European Union (GOCE-CT-2007-037017) and the European Chemical Industry Council Long-Range Research Initiative. ’ REFERENCES (1) Mckone, T. E. CalTOX, A Multimedia Total Exposure Model for Hazardous-Waste Sites; U.S. Department of Energy, Lawrence Livermore National Laboratory, Government Printing Office: Washington, DC, 1993.

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