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NEXT GENERATION MULTIMEDIA/MULTIPATHWAY EXPOSURE MODELING
The primary objective of this research is to produce a documented version of the aggregate SHEDS-Pesticides model for conducting reliable probabilistic population assessments of human exposure and dose to environmental pollutants. SHEDS is being developed to help answer the following questions:
(1) What is the population distribution of exposure for a given cohort for existing scenarios or for proposed exposure reduction scenarios?
(2) What is the intensity, duration, frequency, and timing of exposures from different routes?
(3) What are the most critical media, routes, pathways, and factors contributing to exposures?
(4) What is the uncertainty associated with predictions of exposure for a population?
(5) How do modeled estimates compare to real-world data?
(6) What additional human exposure measurements are needed to reduce uncertainty in population estimates?
The Stochastic Human Exposure and Dose Simulation model for pesticides (SHEDS-Pesticides) supports the efforts of EPA to better understand human exposures and doses to multimedia, multipathway pollutants. It is a physically-based, probabilistic computer model that predicts, for user-specified cohorts, exposures and doses incurred via eating contaminated foods or drinking water, inhaling contaminated air, touching contaminated surface residues, and ingesting residues from hand- or object-to-mouth activities. To do this, it combines information on pesticide usage, human activity data (from time/activity diary surveys and videography studies), environmental residues and concentrations, exposure and dose factors using 1-stage or 2-stage Monte Carlo probabilistic sampling methods. Under this task SHEDS is being developed to address specific applications of interest to the Agency.
The first generation SHEDS-Pesticides model was a 1-stage Monte Carlo model (variability only) focusing on dermal and non-dietary ingestion routes. The next generation is intended to characterize aggregate human exposure and dose to a variety of environmental pollutants, including various pesticides, metals, and Persistent Bioaccumulant Toxins (PBTs). The model is being developed through case studies. The first focused on children's aggregate exposures to the organophosphate pesticide chlorpyrifos via indoor crack and crevice, lawn, and garden treatments. The second focused on children's aggregate exposures to arsenic and chromium from chromated copper arsenate (CCA)-treated playsets and decks, using a scenario-specific version of SHEDS-Pesticides called SHEDS-Wood. The third case study will focus on children's aggregate exposures to pyrethroid pesticides from indoor crack and crevice, lawn, garden, broadcast, fogger, and pet treatments, and will include algorithms for co-occurrence of a single pesticide and multiple application scenarios in space and time.
While SHEDS-Pesticides has been coded as a source-to-dose model, the concentration-to-exposure module has been the primary focus of development, and relatively simple modules are currently incorporated for source-to-concentration and exposure-to-dose estimation. For example, SHEDS currently uses a lumped pharmacokinetic (PK) model to estimate dose rather than a more complete physically-based pharmacokinetic model to estimate dose. Under this and other tasks, the SHEDS concentration-to-exposure module will be interfaced with more sophisticated source-to-concentration (i.e., fate and transport) models and exposure-to-dose models (e.g., ERDEM).
Model inputs and assumptions have been based on available measurements data, and will continue to be refined as new data become available. In conjunction with other tasks, SHEDS will also be evaluated against data from new field measurement studies (e.g., CTEPP, Jacksonville, CHAMACOS) and also against estimates from other aggregate exposure and dose models. The collection of measurements and model refinement and evaluation will be carried out in an iterative manner: each iteration of SHEDS will use the best available data to identify critical pathways of human exposure and dose and the major uncertainties in those pathways, and the model results will be used to guide future measurement studies. The outcome of this research will be a modeling tool for assisting risk assessors and risk managers in regulatory decision-making when human exposure estimates are required beyond the screening level.