A weight of evidence approach for bioaccumulation assessment.

Bioaccumulation assessments conducted by regulatory agencies worldwide use a variety of methods, types of data, metrics, and categorization criteria. Lines of evidence (LoE) for bioaccumulation assessment can include bioaccumulation metrics such as in vivo bioconcentration factor (BCF) and biomagnification factor (BMF) data measured from standardized laboratory experiments, and field (monitoring) data such as BMFs, bioaccumulation factors (BAFs), and trophic magnification factors (TMFs). In silico predictions from mass-balance models and quantitative structure-activity relationships (QSARs) and a combination of in vitro biotransformation rates and in vitro-in vivo extrapolation (IVIVE) models can also be used. The myriad bioaccumulation metrics and categorization criteria and underlying uncertainty in measured or modeled data can make decision-making challenging. A weight of evidence (WoE) approach is recommended to address uncertainty. The Bioaccumulation Assessment Tool (BAT) guides a user through the process of collecting and generating various LoE required for assessing the bioaccumulation of neutral and ionizable organic chemicals in aquatic (water-respiring) and air-breathing organisms. The BAT includes data evaluation templates (DETs) to critically evaluate the reliability of the LoE used in the assessment. The DETs were developed from standardized testing guidance. The approach used in the BAT is consistent with OECD and SETAC WoE principles and facilitates the implementation of chemical policy objectives in chemical assessment and management. The recommended methods are also iterative and tiered, providing pragmatic methods to reduce unnecessary animal testing. General concepts of the BAT are presented and case study applications of the tool for hexachlorobenzene (HCB) and ß-hexachlorocyclohexane (ß-HCH) are demonstrated. The BAT provides a consistent and transparent WoE framework to address uncertainty in bioaccumulation assessment and is envisaged to evolve with scientific and regulatory developments. Integr Environ Assess Manag 2023;19:1235-1253. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Dermal permeation data and models for the prioritization and screening-level exposure assessment of organic chemicals.

High-throughput screening (HTS) models are being developed and applied to prioritize chemicals for more comprehensive exposure and risk assessment. Dermal pathways are possible exposure routes to humans for thousands of chemicals found in personal care products and the indoor environment. HTS exposure models rely on skin permeability coefficient (KP; cm/h) models for exposure predictions. An initial database of approximately 1000 entries for empirically-based KP data was compiled from the literature and a subset of 480 data points for 245 organic chemicals derived from testing with human skin only and using only water as a vehicle was selected. The selected dataset includes chemicals with log octanol-water partition coefficients (KOW) ranging from -6.8 to 7.6 (median=1.8; 95% of the data range from -2.5 to 4.6) and molecular weight (MW) ranging from 18 to 765g/mol (median=180); only 3% >500g/mol. Approximately 53% of the chemicals in the database have functional groups which are ionizable in the pH range of 6 to 7.4, with 31% being appreciably ionized. The compiled log KP values ranged from -5.8 to 0.1cm/h (median=-2.6). The selected subset of the KP data was then used to evaluate eight representative KP models that can be readily applied for HTS assessments, i.e., parameterized with KOW and MW. The analysis indicates that a version of the SKINPERM model performs the best against the selected dataset. Comparisons of representative KP models against model input parameter property ranges (sensitivity analysis) and against chemical datasets requiring human health assessment were conducted to identify regions of chemical properties that should be tested to address uncertainty in KP models and HTS exposure assessments.

Application of mass balance models and the chemical activity concept to facilitate the use of in vitro toxicity data for risk assessment.

Practical, financial, and ethical considerations related to conducting extensive animal testing have resulted in various initiatives to promote and expand the use of in vitro testing data for chemical evaluations. Nominal concentrations in the aqueous phase corresponding to an effect (or biological activity) are commonly reported and used to characterize toxicity (or biological response). However, the true concentration in the aqueous phase can be substantially different from the nominal. To support in vitro test design and aid the interpretation of in vitro toxicity data, we developed a mass balance model that can be parametrized and applied to represent typical in vitro test systems. The model calculates the mass distribution, freely dissolved concentrations, and cell/tissue concentrations corresponding to the initial nominal concentration and experimental conditions specified by the user. Chemical activity, a metric which can be used to assess the potential for baseline toxicity to occur, is also calculated. The model is first applied to a set of hypothetical chemicals to illustrate the degree to which test conditions (e.g., presence or absence of serum) influence the distribution of the chemical in the test system. The model is then applied to set of 1194 real substances (predominantly from the ToxCast chemical database) to calculate the potential range of concentrations and chemical activities under assumed test conditions. The model demonstrates how both concentrations and chemical activities can vary by orders of magnitude for the same nominal concentration.

Comparative assessment of the global fate and transport pathways of long-chain perfluorocarboxylic acids (PFCAs) and perfluorocarboxylates (PFCs) emitted from direct sources.

A global-scale multispecies mass balance model was used to simulate the long-term fate and transport of perfluorocarboxylic acids (PFCAs) with eight to thirteen carbons (C8-C13) and their conjugate bases, the perfluorocarboxylates (PFCs). The main purpose of this study was to assess the relative long-range transport (LRT) potential of each conjugate pair, collectively termed PFC(A)s, considering emissions from direct sources (i.e., manufacturing and use) only. Overall LRT potential (atmospheric + oceanic) varied as a function of chain length and depended on assumptions regarding pKa and mode of entry. Atmospheric transport makes a relatively higher contribution to overall LRT potential for PFC(A)s with longer chain length, which reflects the increasing trend in the air-water partition coefficient (K(AW)) of the neutral PFCA species with chain length. Model scenarios using estimated direct emissions of the C8, C9, and C11 PFC(A)s indicate that the mass fluxes to the Arctic marine environment associated with oceanic transport are in excess of mass fluxes from indirect sources (i.e., atmospheric transport of precursor substances such as fluorotelomer alcohols and subsequent degradation to PFCAs). Modeled concentrations of C8 and C9 in the abiotic environment are broadly consistent with available monitoring data in surface ocean waters. Furthermore, the modeled concentration ratios of C8 to C9 are reconcilable with the homologue pattern frequently observed in biota, assuming a positive correlation between bioaccumulation potential and chain length. Modeled concentration ratios of C11 to C10 are more difficult to reconcile with monitoring data in both source and remote regions. Our model results for C11 and C10 therefore imply that either (i) indirect sources are dominant or (ii) estimates of direct emission are not accurate for these homologues.

A model assessment of polychlorinated dibenzo-p-dioxin and dibenzofuran sources and fate in the Baltic Sea.

The contamination of the Baltic Sea with polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) has resulted in restrictions on the marketing and consumption of Baltic Sea fish, making this a priority environmental issue in the European Union. To date there is no consensus on the relative importance of different sources of PCDD/Fs to the Baltic Sea, and hence no consensus on how to address this issue. In this work we synthesized the available information to create a PCDD/F budget for the Baltic Sea, focusing on the two largest basins, the Bothnian Sea and the Baltic Proper. The non-steady state multimedia fate and transport model POPCYCLING-Baltic was employed, using recent data for PCDD/F concentrations in air and sediment as boundary conditions. The PCDD/F concentrations in water predicted by the model were in good agreement with recent measurements. The budget demonstrated that atmospheric deposition was the dominant source of PCDD/Fs to the basins as a whole. This conclusion was supported by a statistical comparison of the PCDD/F congener patterns in surface sediments from accumulation bottoms with the patterns in ambient air, bulk atmospheric deposition, and a range of potential industrial sources. Prospective model simulations indicated that the PCDD/F concentrations in the water column will continue to decrease in the coming years due to the slow response of the Baltic Sea system to falling PCDD/F inputs in the last decades, but that the decrease would be more pronounced if ambient air concentrations were to drop further in the future, for instance as a result of reduced emissions. The study illustrates the usefulness of using monitoring data and multimedia models in an integrated fashion to address complex organic contaminant issues.

Model and input uncertainty in multi-media fate modeling: benzo[a]pyrene concentrations in Europe.

This paper evaluates the contribution of (i) uncertainty in substance properties, (ii) lack of spatial variability, (iii) intermodel differences and (iv) neglecting sorption to black carbon (BC) to the uncertainty of Benzo[a]pyrene (BaP) concentrations in European air, soil and fresh water predicted by the multi-media fate model Simplebox. Uncertainty in substance properties was quantified using probabilistic modeling. The influence of spatial variability was quantified by estimating variation in predicted concentrations with three spatially explicit fate models (Impact 2002, EVn BETR and BETR Global). Intermodel differences were quantified by comparing concentration estimates of Simplebox, Impact 2002, EVn BETR and the European part of BETR Global. Finally, predictions of a BC-inclusive version of Simplebox were compared with predictions of a BC-exclusive version. For air concentrations of BaP, the lack of spatial variability in emissions was most influential. For freshwater concentrations of BaP, intermodel differences and lack of spatial variability in dimensions of fresh water bodies were the dominant sources of uncertainty. For soil, all sources of uncertainty were of comparable magnitude. Our results indicate that uncertainty in Simplebox can be as large as three orders of magnitude for BaP concentrations in the environment and would be substantially underestimated by focusing on one source of uncertainty only.

Modeling the effects and uncertainties of contaminated sediment remediation scenarios in a Norwegian fjord by Markov chain Monte Carlo simulation.

Multimedia environmental fate models are useful tools to investigate the long-term impacts of remediation measures designed to alleviate potential ecological and human health concerns in contaminated areas. Estimating and communicating the uncertainties associated with the model simulations is a critical task for demonstrating the transparency and reliability of the results. The Extended Fourier Amplitude Sensitivity Test(Extended FAST) method for sensitivity analysis and Bayesian Markov chain Monte Carlo (MCMC) method for uncertainty analysis and model calibration have several advantages over methods typically applied for multimedia environmental fate models. Most importantly, the simulation results and their uncertainties can be anchored to the available observations and their uncertainties. We apply these techniques for simulating the historical fate of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in the Grenland fjords, Norway, and for predicting the effects of different contaminated sediment remediation (capping) scenarios on the future levels of PCDD/Fs in cod and crab therein. The remediation scenario simulations show that a significant remediation effect can first be seen when significant portions of the contaminated sediment areas are cleaned up, and that increase in capping area leads to both earlier achievement of good fjord status and narrower uncertainty in the predicted timing for this.

Empirical evaluation of spatial and non-spatial European-scale multimedia fate models: results and implications for chemical risk assessment.

Multimedia environmental fate models are commonly-applied tools for assessing the fate and distribution of contaminants in the environment. Owing to the large number of chemicals in use and the paucity of monitoring data, such models are often adopted as part of decision-support systems for chemical risk assessment. The purpose of this study was to evaluate the performance of three multimedia environmental fate models (spatially- and non-spatially-explicit) at a European scale. The assessment was conducted for four polycyclic aromatic hydrocarbons (PAHs) and hexachlorobenzene (HCB) and compared predicted and median observed concentrations using monitoring data collected for air, water, sediments and soils. Model performance in the air compartment was reasonable for all models included in the evaluation exercise as predicted concentrations were typically within a factor of 3 of the median observed concentrations. Furthermore, there was good correspondence between predictions and observations in regions that had elevated median observed concentrations for both spatially-explicit models. On the other hand, all three models consistently underestimated median observed concentrations in sediment and soil by 1-3 orders of magnitude. Although regions with elevated median observed concentrations in these environmental media were broadly identified by the spatially-explicit models, the magnitude of the discrepancy between predicted and median observed concentrations is of concern in the context of chemical risk assessment. These results were discussed in terms of factors influencing model performance such as the steady-state assumption, inaccuracies in emission estimates and the representativeness of monitoring data.