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Exposure Research

Exposure Research: Chemical Safety

Methods, Models, Tools and Databases


  • Physiological and Anatomical Visual Analytics (PAVA)
    In order to better address issues regarding chemical safety, EPA scientists have developed a new web-based modeling tool, known as PAVA (Physiological and Anatomical Visual Analytics), that lets users import and combine results from multiple computer models and transforms them into animated visualizations. The absorption, distribution, metabolism, and excretion results are automated and then rendered in a way that lets scientists see changes in chemical concentrations in specific tissues over time - from chemical to chemical, scenario to scenario, and model to model.
  • SHEDS-Multimedia
    EPA's Stochastic Human Exposure and Dose Simulation model (known as SHEDS) allows scientists to estimate total exposures and risks people face from chemicals encountered in everyday activities. SHEDS can estimate the range of total chemical exposures in a population from different exposure pathways (inhalation, skin contact, dietary and non-dietary ingestion) over different time periods, given a set of demographic characteristics. The model enhances estimates of exposure in many different contexts, and has been used to inform EPA human health risk assessments and risk management decisions.
  • Dietary Exposure Potential Model (DEPM)
    Dietary models can be used for identifying the importance of diet relative to other exposure pathways and indicating the potential for high exposure of certain populations. Existing consumption and contaminant residue databases, normally developed for purposes such as nutrition and regulatory monitoring, contain information to characterize dietary intake of environmental chemicals. A model and database system, termed the Dietary Exposure Potential Model (DEPM), correlates extant food information in a format for estimating dietary exposure.
  • SPARC Performs Automated Reasoning in Chemistry (SPARC)
    EPA ecosystems researchers have developed a predictive modeling system known as SPARC (SPARC Performs Automated Reasoning in Chemistry) for estimating chemical reactivity parameters and physical properties for a wide range of organic molecules. This information is needed to be able to predict the fate and transport of pollutants in the environment. SPARC is being designed to incorporate multiple mathematical approaches to estimate important chemical reactions and behavior. It will then interface directly with air, water, and land models to provide scientists with data that can inform risk assessments and help prioritize toxicity-testing requirements for regulated chemicals.
  • Supercomputer for Model Uncertainty and Sensitivity Evaluation (SuperMUSE)
    EPA’s Supercomputer for Model Uncertainty and Sensitivity Evaluation, or SuperMUSE, enhances quality assurance in environmental models and applications. Uncertainty analysis (UA) and sensitivity analysis (SA) remain critical, though often overlooked steps in the development and evaluation of computer models. As a result of the SuperMUSE hardware and software technology, EPA can now better investigate new and existing UA and SA methods. EPA can also more easily achieve UA/SA of complex, Windows-based environmental models, allowing scientists to conduct analyses that have, to date, been impractical to consider.
  • Exposure Analysis Modeling System (EXAMS)
    EXAMS is a modeling system that supports development of aquatic ecosystem models for rapid evaluation of the fate, transport, and exposure concentrations of synthetic organic chemicals like pesticides, industrial materials, and leachates from disposal sites. The system is able to generate and summarize data critical for ecological risk assessments. Much of the data required for EXAMS to function has been collected historically. This allows data needs to be met for a certain projects without intensive field sampling.
  • EPA's Exposure Related Dose Estimating Model (ERDEM)
    ERDEM is a physiologically-based pharmacokinetic (PBPK) and pharmacodynamic (PD) modeling system, developed by EPA scientists to predict how chemicals move through and concentrate in human tissues and body fluids. With ERDEM, scientists are able to examine how chemical exposures impact organs and tissues in the human body and determine how long they will take to be naturally processed or expressed. The ERDEM framework provides the flexibility for scientists to use either existing models or build new PBPK and PBPK/PD models to address specific science questions.
  • Air Pollution Exposure Model for Individuals (EMI)
    Air pollution health studies help scientists understand potential health risks people face from air pollution exposure. However, because of the cost and participant burden associated with indoor and personal air monitoring, health studies often estimate exposures using outdoor ambient measurements from central site air monitors. Unfortunately, these ambient concentration levels do not necessarily reflect personal exposures since indoor air pollutant levels can differ from ambient levels. This potential exposure error can increase the uncertainty of air pollution health risks estimated in health studies. To reduce this potential error, EPA scientists have developed an exposure model for individuals (EMI) who are participating in air pollution health studies.



  • Expocast Database
    EPA's ExpoCast research project advances characterization of the exposure required to translate findings in computational toxicology to support risk assessment.
  • PPCP (Reference Databases)
    Published Literature Relevant to the Issues Surrounding PPCPs as Environmental Contaminants
  • Meteorological Data
    These meteorological data files contain measurements taken at 237 weather stations located throughout the United States for a period extending from 1961 to 1990.
  • Consolidated Human Activity Database (CHAD)
    EPA scientists have compiled detailed data on human behavior from 19 separate studies into EPA's Consolidated Human Activity Database (CHAD). The database includes a total of more than 30,000 individual study days of detailed human behavior, with each day broken down into individual hours and activity types. The data also include demographic information which allows researchers to examine specific groups within the general population and how their unique behavior patterns influence their exposures to chemicals. Scientists at EPA, other government agencies, academia, and the private sector routinely use CHAD data in human exposure and health studies, and in models used for exposure and risk assessments that protect human health.
  • Chlordane Pesticide Dataset
    EPA scientists have developed a dataset that compiles chlordane measurements from published literature in one place. The dataset provides researchers with a useful resource that taps into peer reviewed published results, summarizing and organizing the data into a user friendly tool. The dataset compiles about 2,400 enantiomer-specific measurements for five pairs of chlordane enantiomers. It consolidates information that may be useful for scientists interested in studying trends, estimating exposure and toxicity of mixtures, developing methods, and modeling enantiomers.
  • Consolidated Pesticide Information Dataset (CPI)
    EPA scientists have developed a dataset of basic information on approximately 1,700 pesticides. The dataset was gathered from multiple sources and is in spreadsheet format. It contains a total of twenty fields, including chemical names, identification numbers, structures, and pesticide use class — such as insecticide, herbicide, and fungicide. The CPI dataset will serve as a valuable tool for those interested in pesticide mixtures, green or sustainable pesticides, development of methods and models, and other areas of pesticide research.

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