While only limited data are available to characterize the potential toxicity of over 8 million commercially available chemical substances, there is even less information available on the exposure and use-scenarios that are required to link potential toxicity to human and ecological health outcomes. Recent improvements and advances such as high throughput data gathering, high performance computational capabilities, and predictive chemical inherency methodology make this an opportune time to develop an exposure-based prioritization approach that can systematically utilize and link the asymmetrical bodies of knowledge for hazard and exposure. In response to the US EPA’s need to develop novel approaches and tools for rapidly prioritizing chemicals, a “Challenge” was issued to several exposure model developers to aid the understanding of current systems in a broader sense and to assist the US EPA’s effort to develop an approach comparable to other international efforts. A common set of chemicals were prioritized under each current approach. The results are presented herein along with a comparative analysis of the rankings of the chemicals based on metrics of exposure potential or actual exposure estimates. The analysis illustrates the similarities and differences across the domains of information incorporated in each modeling approach. The overall findings indicate a need to reconcile exposures from diffuse, indirect sources (far-field) with exposures from directly, applied chemicals in consumer products or resulting from the presence of a chemical in a microenvironment like a home or vehicle. Additionally, the exposure scenario, including the mode of entry into the environment (i.e. through air, water or sediment) appears to be an important determinant of the level of agreement between modeling approaches.

Assessments of exposure to indoor air pollutants usually employ spatially well-mixed models which assume homogeneous concentrations throughout a building or room. However, practical experience and experimental data indicate that concentrations are not uniform in rooms containing point sources of emissions; concentrations tend to be greater in close proximity to the source than they are further from it. This phenomenon could account for the observation that "personal air" monitors frequently yield higher concentrations than nearby microenvironmental monitors (i.e., the so-called "personal cloud" effect). In this project, we systematically studied the concentrations of a tracer gas at various distances from its emission source in a controlled-environment, room-size chamber under a variety of ventilation conditions. Measured concentrations in the proximity of the source deviated significantly above the predictions of a conventional well-mixed single-compartment mass balance model. The deviation was found to be a function of distance from the source and total room air flow rate. At typical air flow rates, the average concentration at IMPLICATIONS Assessments of human exposure to indoor air pollutants are frequently performed using indoor air quality models based on mass-balance equations that assume well-mixed conditions, with homogeneous concentrations in an indoor space such as a room or house. However, data exist that suggest that indoor air pollutant concentrations are not spatially uniform, and that concentrations in close proximity to emitting sources are higher than those further from the source, and higher than well-mixed model predictions. The research reported here is an attempt to develop and validate a modeling concept that more accurately simulates exposure concentrations for people in close proximity to indoor emission sources. This "source-proximate effect" model can help exposure assessors better understand and account for the so-called "personal cloud" effect, wherein measured personal exposure concentrations are frequently found to be higher than concurrently measured microenvironmental concentrations. arm's length (approximately 0.4 meters) from the source exceeds the theoretical well-mixed concentration by a ratio of about 2:1. However, this ratio is not constant; the monitored concentration appears to vary randomly from near the theoretical value to several times above it. Concentration data were fitted to a two-compartment model with the source located in a small virtual compartment within the room compartment. These two compartments were linked with a stochastic air transfer rate parameter. The resulting model provides a more realistic simulation of exposure concentrations than does the wellmixed model for assessing exposure to emissions from active sources. Parameter values are presented for using the enhanced model in a variety of typical situations.

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Postapplication exposure assessment related to indoor residential application of pesticide products requires consideration of product use information, application methods, chemical-specific deposition, time-dependent availability and transferability of surface residues, reentry time, and temporal location and macro- and microactivity/behavior patterns ( Baker et al., 2000 ). Children's mouthing behavior results in potential postapplication exposure to available pesticides in treated microenvironments through the nondietary ingestion route, in addition to the dermal or inhalation routes. Children's activities and associated behaviors may result in multiple or repeat contact of dermal areas (clothed and unclothed body areas and hands) with treated surfaces, or surfaces that may have indirect sources of residues. Further, some surfaces contacted may have transferable pesticide residues and others may not. Transfer of residues from the indoor residential environment to the dermal surface (e.g., hands) of an individual has been assumed to be linear as a function of time and number of contacts. However, studies suggest that this transfer process to the hands and other body areas may be rapidly saturable. In the most recent U.S. Environmental Protection Agency (EPA), Office of Pesticide Programs (OPP) "Residential Exposure Assessment Standard Operating Procedures" (U.S. EPA, 2012), the input variable for the number of dermal contacts (with treated surfaces) is an exponent, making the relationship nonlinear. Further, removal processes such as hand washing and transfer to untreated surfaces are important to consider. Predictive algorithms for estimating children's hand-to-mouth-related incidental ingestion exposures post pesticide application have been developed by the EPA/OPP and incorporated into probabilistic models. A review of literature addressing variables used to estimate potential incidental ingestion exposure is presented. Data relevant to input variables for predictive algorithms are discussed, including the results of a multiyear, pesticide transferable residue measurement program conducted by the Non-Dietary Exposure Task Force (NDETF) and the associated distributional characterization for this key variable. Sources of conservative bias in current hand-to-mouth, incidental ingestion exposure estimation and the role of biomonitoring to evaluate predicted exposures are discussed.

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