Society For Risk Analysis Annual Meeting 2017

Session Schedule & Abstracts

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Common abbreviations

Symposium: The Life Cycle-Human Exposure Model (LC-HEM) Project: Research on Sentinel and Aggregate Chemical Exposures from Use of Consumer Products

Room: Salon K   1:30 pm–3:00 pm

Chair(s): Paul Price

Sponsored by Exposure Assessment Specialty Group

Recent studies have shown that chemicals in consumer products (e.g., cosmetics, cleaning products, paints, and building materials) are drivers of high exposure levels as measured by the National Health and Nutrition Survey biomonitoring program (Wambaugh et al., 2014). The U.S. EPA established a research project (LC-HEM) to develop tools to improve the science and practice of estimating chemical exposures that occur over the life cycles of consumer products. The project is developing a model of aggregate longitudinal exposures to chemicals, the Human Exposure Model (HEM). HEM determines doses for thousands of chemicals, from the use of 200+ types of products, for durations ranging from one day to one year. HEM is a Person Oriented Model (POM) (Price and Chaisson, 2005). POMs define the characteristics of individuals and then use the characteristics to determine the likelihood that an individual will interact with a source of exposure and the doses that results from the interaction. Data generated by HEM can be used to determine sentinel exposures (upper bound estimates of exposures that result from the use of specific consumer products) and aggregate exposures (doses that result from the concurrent use of multiple consumer products). Fundamental components of HEM include the Resident Population Generator, Agent Based Models of longitudinal patterns of use of consumer products, databases of the compositions of consumer products, and a POM of aggregate exposures. The model components can be used independently and maybe of interest to a wide range of exposure assessors. The session consists of four talks on model components. The talks will present example model outputs and initial findings. Wambaugh, J.F., et al., High-Throughput Heuristics for Prioritizing Human Exposure to Environmental Chemicals. ES&T, 2014. 48(21): p. 12760–7. Price, P. S. et al. A conceptual framework for modeling aggregate and cumulative exposures to chemicals. JESEE, 2005, 15(6), 473-481.

T3-I.1  1:30 pm  Human Exposure Model (HEM): A modular, web-based application to characterize near-field chemical exposures and releases. Dionisio KL*, Isaacs KK, Phillips K, Lyons D, Brandon N, Levasseur J, Hubbard H, Vallero D, Egeghy P, Price PS; Environmental Protection Agency

Abstract: The U.S. EPA’s Chemical Safety and Sustainability research program is developing the Human Exposure Model (HEM) to assess near-field exposures to chemicals that occur in various populations over the life cycle of a consumer product. The model will be implemented as a web-based, modular system that will produce estimates of population distributions of chemical exposure by route and releases to the environment. One intended use is to support human health impact assessments in Life Cycle Impact Assessments (LCIAs). The model determines aggregate doses of thousands of chemicals from the use of over 300 categories of consumer products over one year. Fundamental components of the model included in the beta-HEM release are the Residential-Population Generator (RPGen), Human Behavior, product formulation, and Source-to-Dose modules. The HEM model has been built to integrate the fundamental model components in a manner which facilitates ease-of-use for a user. Aggregated and cleaned data sets from other sources (e.g. census data, product composition information, habits and practices for product use) which are required as model inputs are built into the model itself. The model currently supports investigation of >200 consumer product types (formulations only) including >1,000 unique chemicals. By interacting with a simple web tool and specifying only a population of interest (by age and gender) and chemical(s) or product category(ies) of interest, the user can generate a population distribution of aggregate doses to the chemicals identified from the use of consumer products. A user is then able to compare the contribution to aggregate dose from different product category types, and to see how population distribution of dose varies by age and gender. Future plans include incorporating occupational exposures and exposures from articles (e.g. furniture) in the model, and to expand functionality of the web tool with additional options for interacting with model output.

T3-I.2  1:50 pm  Developing a rich definition of the person/residence to support person-oriented models of consumer product usage. Price PS*, Glen WG, Hubbard HF, Isaacs KK, Dionisio KL; US Environmental Protection Agency

Abstract: Person Oriented Models (POMs) provide a basis for simulating aggregate chemical exposures in a population over time (Price and Chaisson, 2005). POMs assign characteristics to simulated individuals that are used to determine the individual’s probability of interacting with each of multiple sources of exposure and their resulting doses. The characteristics need to be internally consistent (e.g., physiology and exposure-related behaviors are consistent with the age and gender of the individual). We have developed software, the Residence Person Generator (RPGen), to create populations of simulated individuals with 90 characteristics. The interindividual variation in these characteristics are consistent with interindividual variation in the general U.S. population. Individuals are created by sampling data from four national surveys: Public Use Microdata Survey, National Health and Nutrition Examination Survey, American Housing Survey, and the Residential Energy Consumption Survey. The characteristics include: demographic information (e.g., age, gender, and ethnicity); physiology (e.g., weight, height, surface area, blood flows, and tissue compartment volumes); family structure (e.g., ages and genders of others living in the residence); and residential characteristics (e.g., household income, type and size of residence, and presence of a garden, lawn, or pool). RPGen is part of the Human Exposure Model, a POM to characterize aggregate chemical exposures from the use of consumer products. The characteristics are used to determine the likelihood that a person will use a product (e.g., only adults with a young child use a baby shampoo to wash the baby, and only individuals in homes with yards use a lawn fertilizer) and as inputs to exposure scenarios (e.g. body weight, breathing rates, and skin surface areas). Price, P. S. and Chaisson, C. F. 2005. A conceptual framework for modeling aggregate and cumulative exposures to chemicals. JESEE, 15(6), 473-481.

T3-I.3  2:10 pm  Predicting Exposure to Consumer-Products Using Agent-Based Models Embedded with Needs-Based Artificial Intelligence and Empirically -Based Scheduling Models. Brandon NV*, Price PS, Dionisio KL, Isaacs KK; US Environmental Protection Agency

Abstract: Information on human behavior and consumer product use is important for characterizing exposures to chemicals in consumer products and in indoor environments. Traditionally, exposure-assessors have relied on time-use surveys to obtain information on exposure-related behavior. In lieu of using surveys, we create both an agent-based model (ABM) that simulates longitudinal patterns in human behavior and a scheduling model (SM) that simulates longitudinal patterns in consumer product use. By basing our ABM upon a needs-based artificial intelligence (AI) system, we create autonomous agents that mimic human decisions on residential exposure-relevant behaviors. The model predicts behavior patterns for the following actions: sleeping, eating, commuting, and working/schooling. The SM uses these behavior patterns in scheduling the use of 300+ types of consumer products during periods of idle time (i.e. when the agent is not doing the aforementioned behaviors). The ABM uses 4 different types of agents representing the following U.S. demographic groups: working and non-working adults, school-age and pre-school children. The parameters for the ABM are calibrated using survey data from the Consolidated Human Activity Database (CHAD). The SM’s predictions of daily product use are based on both seasonality of product use and also estimates of prevalence, frequency, and duration of product use derived from publicly available data on consumer habits and practices. The combined ABM/SM approach produces product use histories for individuals in the general US population that are consistent with longitudinal predictions of human behavior, reflect demographic information, and are consistent with the day of the week and season of the year. We propose that by simulating both human behavior and product use, this ABM/SM approach may allow exposure-assessors to characterize exposures from use of consumer products quicker and in ways not possible with traditional survey methods.

T3-I.4  2:30 pm  Leveraging Publicly-Available Consumer Product and Chemical Data in Support of Exposure Modeling. Isaacs KK*, Dionisio KL, Phillips KA, Price PS; United States Environmental Protection Agency

Abstract: Near-field contact with chemicals in consumer products has been identified as a significant source of human exposure. To predict such exposures, information about chemical occurrence in consumer products is required, but is often not available. The Chemicals and Products Database (CPDat) has been developed by the U.S. EPA’s Chemical Safety and Sustainability research program in an attempt to fill these data gaps using information from publicly-available sources. CPDat currently includes qualitative and quantitative information on reported product ingredients from Material Safety Data Sheets and published ingredient lists. In addition, the database contains results of new predictive models for chemical function and product chemical weight fractions. The consumer products included in the database have been mapped to over 300 product use categories (PUCs) that allow linkage to consumer use (i.e. habits and practices) data. CPDat has been integrated with the U.S. EPA’s Computational Toxicology Dashboard (, which allows for linking of the specific product information in CPDat (e.g. ingredient, weight fraction, PUC) with chemical-specific information (e.g. structure, property) available through the Dashboard. These data and linkages allow for the parameterization of EPA’s human exposure models for thousands of chemicals in consumer products. Future refinements to CPDat will include addition of data obtained via targeted or non-targeted analyses of consumer products. As the database grows, it will increase our ability to characterize chemicals in consumer products and articles rapidly and defensibly for use in exposure and risk evaluations.

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