Source-to-Effect Modeling

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The Issue | Science Objectives | Research Highlights | Impact and Outcome

The Issue

Humans are exposed to varying mixtures of chemicals in real-world environments from multiple sources and by multiple pathways and routes. These exposures may result from a single event, or they may accumulate over time if multiple exposure events occur. The traditional approach of assessing the risk from a single chemical and from a single route of exposure does not provide a realistic description of exposures and the cumulative risks that result from real-world exposure scenarios.

Risk assessments at EPA are evolving toward "cumulative assessments" as mandated by new legislation, including the Food Quality Protection Act and the Safe Drinking Water Act. However, there are considerable uncertainties that remain to be addressed associated with assessing aggregate exposures and cumulative risks. Source-to-effect predictive models can be used to reduce these uncertainties in risk assessments by describing the physical, chemical, and biological processes that lead to exposure and the dose of chemical contaminants.

Science Objectives

The goals of this research are to develop, evaluate, and apply modeling tools that quantitatively describe the ways that people are exposed to multiple environmental pollutants, predict the target tissue doses that may result from such exposures, and provide the fundamental science that enlightens risk assessments and policy decisions about the best ways to minimize exposures and protect human health.

This research will address a number of scientific issues, including determining the environmental pollutants or mixtures of pollutants to which people are exposed, the sources of these pollutants, and where and how people are exposed. Modeling work will focus on exposure and dose estimates for various populations of interest, exposure scenarios, important contributing pathways and factors, quantification of uncertainty in model predictions, and comparison of model predictions against real-world measurements data.

Research Goals:

Generate data, methods, techniques, and models, and evaluate these under real-world scenarios that demonstrate the usefulness of EPA's research tools for aggregate exposures and cumulative risk
Produce fundamental science results that will be used to refine Agency guidance for conducting aggregate and cumulative risk assessments
Provide opportunities for stakeholders to collaboratively develop new tools and guidance, and to conduct state-of-the-art cumulative risk assessments
Provide data for developing risk reduction and risk management strategies
Develop new research hypotheses and for identifying and prioritizing future ORD research

Research Highlights

The Stochastic Human Exposure and Dose Simulation model (SHEDS) is a physically based, probabilistic model developed by EPA's Office of Research and Development (ORD) to predict multimedia, multipathway, chemical exposures for user-specified populations and exposure scenarios. It combines information on chemical usage, human activity data, environmental concentrations, and other important exposure factors using probabilistic sampling methods. The current version of SHEDS focuses on aggregate exposures (single chemical, multiple pathways) and has been used to support the Agency's risk assessments for children's exposure to arsenic in treated wood play sets and decks and for human exposure to n-methyl carbamates. The aggregate version of SHEDS currently is being extended to address cumulative (multiple chemicals, multiple pathways) exposures, as well as aggregate exposures. Future research will focus on refining, evaluating, and releasing a user-friendly version of SHEDS to the public.
SHEDS and other exposure models require the use of data on environmental concentrations, human activities, and exposure factors. Research activities are underway to ensure that high-quality databases containing this critical information are updated continually and readily available to risk assessors both within and outside of EPA. For example, the Human Exposure Database System incorporates data from EPA's exposure measurement studies. The Consolidated Human Activity Database contains data describing individuals (sex, age, etc.) and their personal activities. These data are used to update EPA's Exposure Factors Handbooks, providing a compilation of information useful as inputs to the models necessary for conducting probabilistic exposure assessments.
The Exposure Related Dose Estimating Model (ERDEM) is a physiologically based pharmacokinetic modeling system developed by EPA's ORD that describes internal doses resulting from human exposures to single or multiple chemicals through multiple pathways. Recently, a pharmacokinetic and pharmacodynamic model has been added to provide predictions of actual organ level doses for single chemicals and mixtures. ERDEM is fundamental to studies of aggregate exposure and cumulative risk because it allows for the examination of multiple exposure routes (e.g. oral, dermal, respiratory) simultaneously. It has been used to estimate doses resulting from aggregate exposure scenarios to methyl tertiary-butyl ether, trichloroethylene, chlorpyrifos, malathion, and carbaryl.
A coordinated multidisciplinary project is being conducted across EPA's ORD to develop and apply the data and models for assessing cumulative risk to pyrethroids as part of EPA's regulatory assessment process. This project will elaborate the general principles for considering cumulative exposure and for developing cumulative dosimetry and toxicity models. This project also will enable the Agency to conduct a cumulative assessment with state-of-the-art models.

Impact and Outcomes

Human Health Research Contributions: