A quantitative source-to-outcome case study to demonstrate the

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Ecotoxicology and Human Environmental Health

A quantitative source-to-outcome case study to demonstrate the integration of human health and ecological endpoints using the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks David E Hines, Rory B. Conolly, and Annie M Jarabek Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b04639 • Publication Date (Web): 22 Aug 2019 Downloaded from pubs.acs.org on August 24, 2019

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Environmental Science & Technology

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A quantitative source-to-outcome case study to demonstrate the integration of human health and ecological endpoints using the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks

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David E. Hines1, Rory B. Conolly*1, and Annie M. Jarabek2

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Affiliations: 1U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Integrated Systems Toxicology Division, Research Triangle Park, North Carolina 27709, United States 2U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Research Triangle Park, North Carolina 27709, United States *Corresponding author; phone: 919-541-3350, email: [email protected] Abstract:

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Exposure to environmental contaminants can lead to adverse outcomes in both human and non-human

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receptors. The Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP) frameworks

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can mechanistically inform cumulative risk assessment for human health and ecological endpoints by

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linking together environmental transport and transformation, external exposure, toxicokinetics, and

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toxicodynamics. This work presents a case study of a hypothetical contaminated site to demonstrate a

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quantitative approach for implementing the AEP framework and linking this framework to AOPs. We

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construct an AEP transport and transformation model, then quantify external exposure pathways for

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humans, fishes, and small herbivorous mammals at the hypothetical site. A Monte Carlo approach was

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used to address parameter variability. Source apportionment was quantified for each species and

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published pharmacokinetic models were used to estimate internal target site exposure from external

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exposures. Published dose-response data for a multi-species AOP network were used to interpret AEP

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results in the context of species-specific effects. This work demonstrates 1) the construction, analysis,

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and application of a quantitative AEP model, 2) the utility of AEPs for organizing mechanistic exposure

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data and highlighting data gaps, and 3) the advantages provided by a source-to-outcome construct for

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leveraging exposure data and to aid transparency regarding assumptions.

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Key Words: Cumulative Risk Assessment, Ecological Network Analysis, Source apportionment, PBPK models, Target Site Exposure, Aggregate Exposure Pathway, Adverse Outcome Pathway 1 ACS Paragon Plus Environment


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Acronyms:

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Absorption, Distribution, Metabolism and Elimination (ADME) Adverse Outcome (AO) Adverse Outcome Pathway (AOP) Aggregate Exposure Pathway (AEP) Cumulative Risk Assessment (CRA) European Union System for the Evaluation of Substances (EUSES) In Vitro-In Vivo Extrapolation (IVIVE) Key Event (KE) Key Exposure State (KES) Modeling ENvironment for TOtal Risk (MENTOR) Molecular Initiating Event (MIE) National Research Council (NRC) Perchlorate anion (ClO4-) Physiologically Based Pharmacokinetic (PBPK) Reference dose (RfD) Sodium-Iodide Symporter (NIS) Target site exposure (TSE) Triiodothyronine (T3) Thyroxin (T4) Thyroid Stimulating Hormone (TSH)

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1. Introduction

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Risk assessors integrate knowledge about exposure and toxicity pathways to evaluate the

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potential risk of adverse outcomes (AOs) from environmental contaminants and other stressors1.

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Mechanistic approaches provide advantages for risk assessors because they describe the causal

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pathways from sources of contamination to AOs and therefore can facilitate a science-based evaluation

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of the current knowledge, data gaps, and uncertainties in risk assessment results2. While human health

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outcomes are often of primary concern, non-human species are also exposed to contaminants and may

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be adversely affected at different exposure levels than humans. The National Research Council (NRC)1

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emphasized the importance of considering both human health and ecological endpoints in cumulative

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risk assessments (CRAs), which evaluate the combined effects of multiple chemicals and non-chemical

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stressors on organisms for the purposes of identifying AO risk3,4. However, the mechanisms resulting in

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AOs vary across organisms and contaminants. Thus, integrating mechanistic data from human health

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and ecological endpoints in support of site-based risk assessments and CRAs is challenging5.

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Exposure science is a critical part of risk assessments that emphasizes the evaluation of

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interactions between relevant physical, chemical or biologic stressors and their receptors6 and is

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evolving rapidly given the emerging technologies that allow measurement in various biological samples

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and mechanistic characterization of the exposome7. Over the past several decades, researchers have

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developed approaches for quantifying and analyzing the mechanisms behind these interactions8,9. For

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example, Georgopoulos and Lioy (1994)10 presented a framework and twelve-step process to guide the

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collection and management of data characterizing exposure and dose in humans. This theoretical

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framework was later adapted for use in computational studies and guided the development of

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implementations such as the Modeling ENvironment for TOtal Risk (MENTOR), a mechanistic source-to-

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dose toolbox for exposure assessment11. Environmental risk assessment case studies have incorporated

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probabilistic approaches for exposure prediction based on distributions of parameters for mechanistic 3 ACS Paragon Plus Environment


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data, but these approaches are rarely implemented in regulatory contexts with multi-media assessment

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tools such as the European Union System for the Evaluation of Substances (EUSES)9. Nevertheless, the

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insight provided by mechanistic exposure assessment tools is critically important for predicting relevant

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AOs from toxicity data12, as the exposure pathways leading to human health and ecological AOs may

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vary across species. Additionally, absorption, distribution, metabolism and elimination (ADME)

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processes differ across toxicants and organisms, leading to variation in the internal concentration, or

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target site exposure (TSE), of a contaminant that results from external exposures13,14,15. Tools such as

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physiologically based pharmacokinetic (PBPK) models can provide quantitative, mechanistic descriptions

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of ADME processes to predict TSEs, and therefore play a key role in associating external exposures with

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internal dose estimates for multiple species in CRAs16,17. Further developing tools to understand and

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analyze the mechanisms behind external exposure pathways and ADME processes is essential if risk

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assessors are to integrate human health and ecological endpoints into CRAs for regulatory

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applications7,18.

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Toxicologists face similar challenges to those faced by exposure scientists regarding

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physiological differences among organisms when integrating human health and ecological endpoints.

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Understanding the mechanistic similarities governing ADME and tissue responses in organisms can,

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however, make quantitative extrapolation and data comparison across species possible17,19,20. The

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Adverse Outcome Pathway (AOP) framework21 organizes knowledge about these mechanisms by linking

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a biological perturbation at the molecular level, termed a Molecular Initiating Event (MIE), to an AO at

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the organism or population level. A TSE describes the concentration of toxicant at the MIE, and the

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mechanisms leading from an MIE to an AO are represented by a series of causal Key Events (KEs) that

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are empirically measurable steps along the AOP22. Quantitative AOP (qAOP) models, which provide

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mathematical descriptions of the relationships among KEs, may aid in reducing uncertainties in cross-

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species extrapolations and facilitate data integration in risk assessments23,24. Additionally, multiple AOPs 4 ACS Paragon Plus Environment

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with shared KEs can be combined into AOP networks of interacting pathways25, facilitating the organization of mechanistic toxicity data across organisms. While AOPs have been proposed for use in toxicity screening and risk assessment26,27,28, they do

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not incorporate exposure data describing the events leading to activation of an MIE. Teeguarden et al.

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(2016)16 introduced the Aggregate Exposure Pathway (AEP) framework, which utilizes existing concepts

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in exposure science to complement the AOP framework by providing an analogous structure for

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organizing exposure data. In the AEP framework, sources of contaminants are linked to TSEs through a

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series of empirically measurable Key Exposure States (KES) that describe the environmental transport

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and transformation of chemicals, as well as ADME interactions within organisms29,30. A variety of model

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types, including transport and transformation models, could be used within this framework to

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quantitatively evaluate the external exposure pathways within AEPs, and PBPK models can be applied to

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estimate TSEs from these aggregate external exposures31. Thus, AEP and AOP models together

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comprehensively cover conceptual site models16,17,32 and AEPs can facilitate quantitative analyses to

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compliment AOPs for site-based or regional CRAs33,34.

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Recently, Hines et al. (2018)35 presented a case study to demonstrate the utility of a joint AEP-

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AOP construct for integrating mechanistic human health and ecological endpoints to inform community-

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based CRAs. That case study used the perchlorate anion (ClO4-) along with the AOP for the inhibition of

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iodide uptake into the thyroid by the sodium-iodide symporter (NIS) as a data-rich example because this

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AOP is highly conserved and acts across vertebrate species36,37. Specifically, Hines et al. (2018)35 showed

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how mechanistic toxicity data for NIS inhibition and subsequent effects could be organized across an

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AOP network to identify similarities and differences in dose response relationships for a diverse set of

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organisms and used a qualitative AEP description to show how this approach could be used to inform a

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site-specific CRA. The current work expands on Hines et al. (2018)35 by developing a transport and

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transformation AEP model for this ClO4- case study; to our knowledge this is the first such quantitative 5 ACS Paragon Plus Environment


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AEP model in the published literature. Drawing on available environmental data for ClO4-, we evaluate

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species-specific exposures at a hypothetical contaminated site through transport and transformation

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network modeling, then link external exposures to MIEs through TSEs using published pharmacokinetic

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models. Network analysis is used to identify the source apportionment of exposure to three

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representative groups of organisms: human, fish, and small herbivorous mammals to provide examples

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of how this source-to-outcome analysis can help identify at-risk species or groups of organisms. Our

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results 1) demonstrate the type of data needed to construct a quantitative AEP model, 2) provide an

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example of how AEPs can organize knowledge to highlight data gaps, 3) illustrate how various exposure

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pathways intersect at the MIE as the critical input to an AOP, 4) show how quantitative AEPs could be

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applied in a risk assessment or regulatory setting, and 5) highlight the benefits of using this approach for

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integrating of human health and ecological endpoints.

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2. Methods

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We construct a quantitative AEP model to demonstrate how this approach enables evaluation of

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species-specific external exposures and source apportionment, then connect this AEP to AOP data from

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Hines et al. (2018) to complete a source-to-outcome analysis. We us a mass balance transport and

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transformation model of ClO4- at a hypothetical site to provide a complete example of the construction,

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data requirements, and applications of this type of model.

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2.1 Hypothetical contaminated site

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We created a hypothetical contaminated site by piecing together data from multiple locations to

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demonstrate the construction and application of the quantitative AEP model with the available data

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(Tables S1 and S2 of the Supporting Information). This hypothetical site receives ClO4- contamination

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from three sources: atmospheric deposition, surface water runoff, and groundwater input, and

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considers six interacting environmental compartments that represent different components of the 6 ACS Paragon Plus Environment

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ecosystem (Figure 1). ClO4- is highly mobile and environmentally stable36, therefore we assumed that it

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can be transported abiotically between these compartments, removed from the system by flowing

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surface water or groundwater, or be absorbed and accumulate in aquatic plants, terrestrial grass, or

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terrestrial shrubs36,38,39. The volumes of biotic and abiotic compartments in the model were held

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constant at 10,000 m3 for surface water, 5,000 m3 for groundwater, 5,000 m3 for soil, 900 m3 for aquatic

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vegetation, 500 m3 for terrestrial grass, and 1,000 m3 for terrestrial shrubs to provide physical

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constraints that represented a contaminated site with a relatively large volume of surface water and

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active biotic components.

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Three types of organisms: humans, fishes, and small herbivorous mammals, were considered to

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interact with the environmental compartments at the hypothetical contaminated site to provide a

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diverse set of example organisms for demonstrating the integration of mechanistic data from multiple

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species into CRAs (Figure 1). The species used were selected based on availability of published ClO4-

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PBPK models and toxicity data for the NIS inhibition AOP. Fishes were represented by zebrafish (Danio

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rerio) and small herbivorous mammals by rats (Rattus sp.) and meadow voles (Microtus sp.). These

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species may or may not be relevant for actual sites but are used in this work as illustrative examples.

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2.2 Quantitative AEP model

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The AEP model was constructed from a set of six differential equations representing the

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environmental compartments and potential exposure routes for the hypothetical contaminated site.

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Transport of ClO4- between compartments was modeled as the product of a transport (or uptake) rate (𝑟

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) and the amount of ClO4- in the donor compartment; equations for each compartment were then

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constructed by combining the appropriate input and output flows. For example, the differential

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equation for the surface water compartment (EQ.1) was constructed as

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𝑑𝑆𝑊 𝑑𝑡

= 𝑆𝑊𝑖𝑛 + 𝐴𝑇𝑀𝑖𝑛 + 𝑟𝐺𝑊,𝑆𝑊𝐺𝑊 + 𝑟𝐴𝑃,𝑆𝑊𝐴𝑃 ― 𝑟𝑆𝑊,𝐺𝑊𝑆𝑊 ― 𝑟𝑆𝑊,𝐴𝑃𝑆𝑊 ― 𝑟𝑆𝑊,𝐵𝑆𝑊 EQ.1 7 ACS Paragon Plus Environment


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where 𝑆𝑊, 𝐺𝑊, and 𝐴𝑃 are the amounts (g) of ClO4- in the surface water, groundwater, and

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aquatic plant compartments that are used to calculate ClO4- concentrations, 𝑟𝐺𝑊,𝑆𝑊 and 𝑟𝐴𝑃,𝑆𝑊 are the

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daily transfer rates (d-1) from groundwater and aquatic plants to surface water, and 𝑟𝑆𝑊,𝐺𝑊, 𝑟𝑆𝑊,𝐴𝑃, and

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𝑟𝑆𝑊,𝐵 are daily transfer rates (d-1) from surface water to groundwater, aquatic plants, and outside of the

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system boundary (𝐵), respectively. ClO4- inputs (g/d) to the surface water compartment from exterior

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surface water contamination (𝑆𝑊𝑖𝑛) and atmospheric deposition (𝐴𝑇𝑀𝑖𝑛) were held constant for this

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hypothetical model. A full list of equations is available in the Supporting Information (Equations, Tables

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S1 and S2). The model was constructed in R Version 3.2.2 using the enaR package Version 3.040.

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The ClO4- uptake rate into the terrestrial grass compartment was taken from experimental

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measurements reported by Susarla et el. (2000)38, while loss from the terrestrial grass compartment was

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estimated based on bioaccumulation levels discussed in EPA (2002)36 and Hatzinger et al. (2015)41. The

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AEP model tracks ClO4- flows among compartments as g ClO4-/d, and the amount in each compartment is

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reported in g ClO4-. Due to the uncertainty inherent in parameterizing model components using

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disparate literature sources, we assigned each parameter a range of plausible values based on the range

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of reported values. A complete list of the parameters and their corresponding ranges is given in Tables

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S1 and S2 of the Supporting Information.

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Variability in model predictions caused by parameter variability was assessed using a Monte

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Carlo approach. In this analysis, parameter values were determined by sampling uniformly from ranges

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given in Tables S1 and S2 to generate 10,000 sets of initial conditions. For each initial condition, the

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model was run to steady state and mass balance was verified for all model components. While we used

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a uniform sampling distribution because we obtained parameters from unrelated studies to define each

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model flow, it is important to note that other sampling distributions (e.g. truncated log-normal) may be

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appropriate for actual sites if data are available. Each set of parameters represented a single ecosystem


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network model42,43 and the ranges in the steady state mass of ClO4- in the compartments are measures

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of the impact of the variability of parameter values for the model predictions.

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2.3 Exposure Scenarios

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We applied three scenarios to the quantitative AEP model at the hypothetical site: mild,

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moderate, and high ClO4- contamination. In the mild scenario, ClO4- was assumed to be present in all

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compartments, but at concentrations below the ranges reported for documented sites36,41 and at least

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an order of magnitude lower than the other hypothetical scenarios. The moderate scenario maintained

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the same transfer rate parameter restrictions as the mild scenario, but increased ClO4- input through all

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contamination sources (surface water, atmospheric deposition, and groundwater) by ten-fold, resulting

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in an approximate increase to environmental concentrations of an order of magnitude. The high

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contamination scenario maintained the conditions of the moderate scenario but included an additional

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ten-fold increase over the moderate scenario in groundwater input (100 times greater than the mild

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scenario) to simulate a ClO4- spill into this compartment. Environmental ClO4- concentrations in the high

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contamination scenario were well above typical environmental concentrations36,41. Monte Carlo analysis

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was conducted for each of the scenarios resulting in a total of 30,000 network model parameterizations

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(10,000 for each scenario).

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2.4 Evaluation of external exposure

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Concentrations of ClO4- were calculated based on compartment volumes and predicted mass for

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each parameterization and are shown in the Supporting Information (Table S3). The resulting

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concentration distributions for each compartment represented the environmental concentrations in

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each scenario. However, the external exposure pathways that are relevant to organisms depend not

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only on environmental concentrations, but also on species-specific behavior44. For this exercise, in lieu

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of species-specific exposure data, we assumed different behavioral regimens for each type of organism 9 ACS Paragon Plus Environment


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examined and used these regimens in conjunction with distributions of environmental concentrations to

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demonstrate how external exposure to each organism can be predicted in each scenario. The drinking

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of well water (groundwater) in pregnant women was considered as the only direct exposure pathway for

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humans in this hypothetical case study to provide a simplified example, but other exposure pathways

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such as food consumption may be relevant for a ClO4- risk assessment. Pregnant women were selected

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as the target population because the fetus is a vulnerable life stage to developmental neurotoxicity

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through NIS inhibition45,46,47,48. In this scenario, we assumed a body mass for a full-term pregnant female

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of 72.3kg49 and a daily water intake of 2L, although other lifestages (such as women of childbearing age)

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could also be considered50. For fishes, the ClO4- exposure was equivalent to the concentration of the

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surface water because ClO4- is highly mobile, stable, and has poor complexing properties35,51. For small

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mammals, we assumed herbivorous behavior, and that ClO4- exposure came from both consumption of

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terrestrial grass as well as drinking of surface water. Additionally, we assumed a total daily grass and

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water intake of 0.005 g/kg, and a body mass for the small mammal of 0.044 kg, which approximates that

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of voles36,52. Voles (Microtus sp.) can rely heavily on moisture content in food for water53, therefore we

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assumed a daily intake that consisted of 0.00475 g terrestrial grass (95%) and 0.00025 g surface water

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(5%).

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After combining distributions of environmental concentrations and exposure-related behaviors

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to calculate external exposures (intake) for each species, we calculated the apportionment of each ClO4-

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source (surface water, atmosphere, and groundwater) to each organism. Ecological Network Analysis, a

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type of network modeling commonly used to track the transport and transformation of matter through

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ecosystem networks54,55,56 facilitated this task. Specifically, we applied a subset of Ecological Network

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Analysis called Environ Analysis, which can partition the flows in a network model to show the

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contribution of each boundary flow (surface water, atmosphere, and groundwater) to each network

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component through both direct and indirect pathways. For example, ClO4- in contaminated 10 ACS Paragon Plus Environment

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groundwater consumed by a human may have entered the ecosystem through groundwater inputs

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(direct exposure pathway) or may have entered through surface water and was later transported to

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groundwater (indirect exposure pathway); Environ Analysis quantifies the contribution of each exposure

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pathway. This type of analysis differs from other network analyses, such as analysis of directed acyclic

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graphs, that have recently been proposed to draw causal inferences for CRA57 because Ecological

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Network Analysis techniques focus specifically on mass-conservative movement of material, including

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cyclic and acyclic flows. Thus, Ecological Network Analysis is amenable to support quantitative analysis

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of exposure pathways54. We analyzed each model parameterization, producing a distribution of source

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apportionment predictions for each organism. A detailed description of the calculations of Environ

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Analysis can be found in Patten (1978)58, Patten and Matis (1982)59, and Fath and Patten (1999)54.

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2.5 Linking external exposure to TSE and MIE

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The external exposures calculated from the AEP network and behavioral assumptions were

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linked to TSEs to evaluate potential activation of MIEs in humans and small herbivorous mammals using

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published PBPK models. For humans, we used a published model for NIS inhibition in a pregnant mother

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and fetus from Lumen et al. (2013)49, which allowed for simultaneous adjustment of ClO4- and iodide

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doses. We selected an iodide intake rate of 75 µg/iodide/d to represent an iodide deficient woman60, as

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iodide deficiency results in more vulnerability to adverse effects from NIS inhibition36,61. PBPK Model

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simulations were run using ClO4- doses corresponding to the 1st percentile, median, and 99th percentile

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of external exposures predicted by the AEP model for humans in each scenario (Table 1), as well as using

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a dose of zero to observe baseline model behavior. The PBPK model calculated the concentration of

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free ClO4- in the serum (as the TSE), as well as the effects on iodide uptake into the thyroid at the NIS

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(the MIE). Percent NIS inhibition was calculated by comparing the iodide uptake rate into the thyroid in

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each simulation to the zero-toxicant simulation. For small herbivorous mammals, we used a published

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rat PBPK model for NIS inhibition from ClO4- from Merrill et al (2003)62 in a similar manner to the model 11 ACS Paragon Plus Environment


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used for humans. Parameters for the rat model were not altered from those used in Merrill et al.

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(2003)62, except to use an iodide dose of 0.033 mg/kg/d, a body weight of 0.044 kg, and a consumption

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rate of 0.005 g/kg/d to represent a small herbivorous mammal based on the meadow vole36,52. As with

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humans, ClO4- doses corresponding to the 1st percentile, median, and 99th percentile exposures

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predicted by the AEP exposure network were tested and compared with a zero-dose simulation to

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estimate percent NIS inhibition. For fishes, which can equilibrate with their aqueous environment51, no

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published dosimetry models were available. Therefore, we assumed that the concentration of ClO4- in

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the surface water was equivalent to the TSE (Dr. John Nichols, personal communication, 14 October

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2016). Percent NIS inhibition was not estimated in fishes due to the lack of a relevant published

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dosimetry model.

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2.6 Linking TSE and MIE to AOP toxicity data

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Hines et al. (2018)35 used the AOP framework to integrate dose-response data for mechanistic

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endpoints leading from NIS inhibition to AOs in multiple species, including those considered in this case

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study. In the 2018 case study, predictions from the Lumen et al. (2013)49 model were used to inform

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dose-response relationships for humans, while data for endpoints characterizing each KE were

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assembled from literature sources to describe dose-response relationships in other species35. These data

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were organized along the NIS inhibition AOP to make comparisons across taxa, and detailed descriptions

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of the KEs, endpoints, and literature sources for this dose-response data are available in Hines et al.

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(2018)35. In the present study, we used the AEP exposure modeling results as inputs to these dose-

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response data to evaluate species-specific effects of NIS inhibitor contamination. We calculated a

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hazard index (𝐻𝐼) for each species according to EPA guidance using the formula

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𝑛

𝐸𝑖

EQ. 2

𝐻𝐼 = ∑𝑖 = 1𝐴𝐿𝑖


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where 𝑖 is each exposure source, 𝐸 is the exposure level, and 𝐴𝐿 is the acceptable limit of

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exposure63. 𝐴𝐿 was set to the lowest reported activation of a KE in a species35 to provide the most

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conservative estimate of 𝐻𝐼, and we used the 1% and 99% dose for the predicted distribution of each

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species in each scenario to provide the range of possible results. An 𝐻𝐼 of 1 or below indicates that

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exposure levels are below the 𝐴𝐿, while a value greater than 1 indicates that exposure levels are above

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the 𝐴𝐿 and may be cause for concern.

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3. Results and discussion

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The ClO4- case study provides an example application of techniques for site-specific evaluation of

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exposure, toxicity, and risk in both human and non-human targets. We present the results of our

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analysis for the hypothetical contaminated site and discuss the utility of a combined AEP-AOP

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framework in future CRA efforts.

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3.1 Species-specific external exposures

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The sets of 10,000 AEP network models provided distributions of predictions for ClO4-

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concentration in each environmental compartment (Table S3) that reflect the variability in the model

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parameters. In each scenario, the ClO4- concentrations in terrestrial grass and shrubs were 50 to 100-

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fold higher than the concentration in groundwater due to bioaccumulation, which is consistent with

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environmental predictions for this contaminant36. Little to no bioaccumulation was predicted for aquatic

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plants, which was also consistent with literature64. The toxicant concentrations of all compartments

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increased across the mild, moderate, and high contamination scenarios, with large increases in

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groundwater and grass concentrations in the high contamination scenario due to the simulated

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groundwater spill (Table S3). When combined with the behavioral assumptions for each species, these

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environmental concentrations provided species-specific distributions of external exposure predictions in

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each scenario (Table 1). Due to the modeled accumulation of ClO4- in terrestrial grass and the high 13 ACS Paragon Plus Environment


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contribution of grass to their diet, doses were two orders of magnitude higher in the small herbivorous

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mammals than in humans or fish. This result is consistent with the fact that ClO4- has been detected in

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the milk of grazing animals such as cows65,66, and supports the notion that contaminated grass can be an

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important pathway for ClO4- exposure in some terrestrial mammals.

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3.2 Source apportionment for exposure pathways

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There was a dominant source contributor to exposure for each species in the mild scenario

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(Figure 2). For humans and small herbivorous mammals, ClO4- inputs from groundwater contamination

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were responsible for 95% to 99% and 91% to 96% of exposure across the set of 10,000 networks,

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respectively, while surface water input contributions ranged from 69% to 98% of exposure to fishes

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(Figure 2A). In the moderate scenario, the contributions from all sources were multiplied by ten.

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Therefore, ClO4- concentrations were higher than those for the mild scenario, but no change was

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observed in the source apportionment due to the linear assumptions of the hypothetical exposure

320

model (Figure 2B). This finding highlights that source apportionment and environmental concentration

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vary independently in this analysis. Although there is a linear increase in environmental concentration

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with no change to source apportionment between the mild and moderate contamination scenarios,

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non-linear relationships in the ADME and dose-response properties of the AOP imply that linear

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increases in external exposure do not necessarily result in linear increases in AO risk for each species;

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and can be further modified by animal behavior (below). The high contamination scenario, which

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simulated a groundwater spill, resulted in changes in both source apportionment and environmental

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concentration; groundwater contribution ranged from 99% to 100% in humans, 95% to 99% in small

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herbivorous mammals, and from 17% to 80% in fishes (Figure 2C). This large range of predicted source

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apportionment values in fishes was a result of a tradeoff between surface water and groundwater

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inputs. Surface water contributions ranged from 19% to 82% across the set of 10,000 networks for this

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scenario and showed a tight negative correlation with groundwater inputs (Spearman’s rho = -1, 14 ACS Paragon Plus Environment

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p

A quantitative source-to-outcome case study to demonstrate the

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