Model-based predictions of soil and dust ingestion rates for U.S. adults using the stochastic human exposure and dose simulation soil and dust model.

BACKGROUND: Adults can be exposed to chemicals through incidental ingestion of soil and dust, either through hobbies, occupations, or behaviors that increase contact with soil or dust (e.g., cleaning or renovating). However, few data describing these ingestion rates are available. OBJECTIVE: Our objective was to use the Stochastic Human Exposure and Dose Simulation Soil and Dust (SHEDS-Soil/Dust) model to estimate distributions of soil and dust ingestion rates for adults (≥21 years old) with varying degrees of soil and dust contact. METHODS: We parameterized SHEDS-Soil/Dust to estimate soil and dust ingestion rates for several categories of adults: adults in the general population; adults with moderate (higher) soil exposure (represented by hobbyists, such as gardeners, with increased soil contact); adults with high soil exposure (represented by occupationally exposed individuals, such as landscapers); and individuals who have high dust exposure (e.g., are in contact with very dusty indoor environments). RESULTS: Total soil plus dust ingestion for adults in the general population was 7 mg/day. Hobbyists or adults with moderate soil exposure averaged 33 mg/day and occupationally exposed individuals averaged 123 mg/day. Total soil plus dust ingestion for adults in the high dust exposure scenario was 25 mg/day. Results were driven by time spent in contact with soil and, thus, warmer seasons (e.g., summer) were associated with higher ingestion rates than colder seasons (e.g., winter). SIGNIFICANCE: These results provide modeled estimates of soil and dust ingestion rates for adults for use in decision making using real-world exposure considerations. These modeled estimates suggest that soil and dust ingestion is a potential concern for adults who spend a higher amount of time interacting with either soil or dusty environments. IMPACT STATEMENT: The parameterization of real-world scenarios within the application of SHEDS-Soil/Dust model to predict soil and dust ingestion rates for adults provides estimates of soil and dust ingestion rates useful for refining population-based risk assessments. These data illuminate drivers of exposure useful for both risk management decisions and designing future studies to improve existing tracer methodologies.

Model based prediction of age-specific soil and dust ingestion rates for children.

BACKGROUND: Soil and dust ingestion can be a primary route of environmental exposures. Studies have shown that young children are more vulnerable to incidental soil and dust ingestion. However, available data to develop soil and dust ingestion rates for some child-specific age groups are either lacking or uncertain. OBJECTIVE: Our objective was to use the Stochastic Human Exposure and Dose Simulation Soil and Dust (SHEDS-Soil/Dust) model to estimate distributions of soil and dust ingestion rates for ten age ranges from infancy to late adolescents (birth to 21 years). METHODS: We developed approaches for modeling age groups previously not studied, including a new exposure scenario for infants to capture exposures to indoor dust via pacifier use and accounting for use of blankets that act as a barrier to soil and dust exposure. RESULTS: Overall mean soil and dust ingestion rates ranged from ~35 mg/day (infants, 0-<6 m) to ~60 mg/day (toddlers and young children, 6m-<11 yr) and were considerably lower (about 20 mg/day) for teenagers and late adolescents (16-<21 y). The pacifier use scenario contributed about 20 mg/day to the median dust ingestion rate for young infants. Except for the infant age groups, seasonal analysis showed that the modeled estimates of average summer mean daily total soil plus dust ingestion rates were about 50% higher than the values predicted for the winter months. Pacifier use factors and carpet dust loading values were drivers of exposure for infants and younger children. For older children, influential variables included carpet dust loading, soil adherence, and factors that capture the frequency and intensity of hand-to-mouth behaviors. SIGNIFICANCE: These results provide modeled estimates of children's soil and dust ingestion rates for use in decision making using real-world exposure considerations. IMPACT STATEMENT: The parameterization of scenarios to capture infant soil and dust ingestion and the application of SHEDS-Soil/Dust to a broader age range of children provides additional estimates of soil and dust ingestion rates that are useful in refining population-based risk assessments. These data illuminate drivers of exposure that are useful to both risk management applications and for designing future studies that improve upon existing tracer methodologies.

A sensitivity analysis of a human exposure model using the Sobol method.

The Air Pollutants Exposure Model (APEX) is a stochastic population-based inhalation exposure model which (along with its earlier version called pNEM) has been used by the U.S. Environmental Protection Agency (EPA) for over 30 years for assessment of human exposure to airborne pollutants. This study describes the application of a variance decomposition-based sensitivity analysis using the Sobol method to elucidate the key APEX inputs and processes that affect variability in exposure and dose for the simulated population. Understanding APEX's sensitivities to these inputs helps not only the model user but also the EPA in prioritizing limited resources towards data-collection and analysis efforts for the most influential variables, in order to maintain the quality and defensibility of the simulation results. This analysis examines exposure to ozone of children ages 5-18 years. The results show that selection of activity diaries and microenvironmental parameters (including air-exchange rate and decay rate) are the most influential to estimated exposure and dose, their aggregate main-effect indices (MEIs) equaling 0.818 (out of a maximum of 1.0) for daily-average ozone exposure and 0.469 for daily-average inhaled ozone dose. The modeled person's home location, sampled from national Census data, has a modest influence on exposure (MEI = 0.079 for daily averages), while age, sex, and body mass, also sampled from Census and other survey data, have modest influences on inhaled dose (aggregate MEI = 0.307). The sensitivity analysis also plays a quality-assurance role by evaluating the sensitivities against our knowledge of the physical properties of the model.

The Residential Population Generator (RPGen): Parameterization of Residential, Demographic, and Physiological Data to Model Intraindividual Exposure, Dose, and Risk.

Exposure to chemicals is influenced by associations between the individual's location and activities as well as demographic and physiological characteristics. Currently, many exposure models simulate individuals by drawing distributions from population-level data or use exposure factors for single individuals. The Residential Population Generator (RPGen) binds US surveys of individuals and households and combines the population with physiological characteristics to create a synthetic population. In general, the model must be supported by internal consistency; i.e., values that could have come from a single individual. In addition, intraindividual variation must be representative of the variation present in the modeled population. This is performed by linking individuals and similar households across income, location, family type, and house type. Physiological data are generated by linking census data to National Health and Nutrition Examination Survey data with a model of interindividual variation of parameters used in toxicokinetic modeling. The final modeled population data parameters include characteristics of the individual's community (region, state, urban or rural), residence (size of property, size of home, number of rooms), demographics (age, ethnicity, income, gender), and physiology (body weight, skin surface area, breathing rate, cardiac output, blood volume, and volumes for body compartments and organs). RPGen output is used to support user-developed chemical exposure models that estimate intraindividual exposure in a desired population. By creating profiles and characteristics that determine exposure, synthetic populations produced by RPGen increases the ability of modelers to identify subgroups potentially vulnerable to chemical exposures. To demonstrate application, RPGen is used to estimate exposure to Toluene in an exposure modeling case example.

Evaluating potential human health risks from modeled inhalation exposures to volatile organic compounds emitted from oil and gas operations.

Some states and localities restrict siting of new oil and gas (O&G) wells relative to public areas. Colorado includes a 500-foot exception zone for building units, but it is unclear if that sufficiently protects public health from air emissions from O&G operations. To support reviews of setback requirements, this research examines potential health risks from volatile organic compounds (VOCs) released during O&G operations.We used stochastic dispersion modeling with published emissions for 47 VOCs (collected on-site during tracer experiments) to estimate outdoor air concentrations within 2,000 feet of hypothetical individual O&G facilities in Colorado. We estimated distributions of incremental acute, subchronic, and chronic inhalation non-cancer hazard quotients (HQs) and hazard indices (HIs), and inhalation lifetime cancer risks for benzene, by coupling modeled concentrations with microenvironmental penetration factors, human-activity diaries, and health-criteria levels.Estimated exposures to most VOCs were below health criteria at 500-2,000 feet. HQs were < 1 for 43 VOCs at 500 feet from facilities, with lowest values for chronic exposures during O&G production. Hazard estimates were highest for acute exposures during O&G development, with maximum acute HQs and HIs > 1 at most distances from facilities, particularly for exposures to benzene, 2- and 3-ethyltoluene, and toluene, and for hematological, neurotoxicity, and respiratory effects. Maximum acute HQs and HIs were > 10 for highest-exposed individuals 500 feet from eight of nine modeled facilities during O&G development (and 2,000 feet from one facility during O&G flowback); hematologic toxicity associated with benzene exposure was the critical toxic effect. Estimated cancer risks from benzene exposure were < 1.0 × 10-5 at 500 feet and beyond.Implications: Our stochastic use of emissions data from O&G facilities, along with activity-pattern exposure modeling, provides new information on potential public-health impacts due to emissions from O&G operations. The results will help in evaluating the adequacy of O&G setback distances. For an assessment of human-health risks from exposures to air emissions near individual O&G sites, we have utilized a unique dataset of tracer-derived emissions of VOCs detected at such sites in two regions of intense oil-and-gas development in Colorado. We have coupled these emission stochastically with local meteorological data and population and time-activity data to estimate the potential for acute, subchronic, and chronic exposures above health-criteria levels due to air emissions near individual sites. These results, along with other pertinent health and exposure data, can be used to inform setback distances to protect public health.

Statistical properties of longitudinal time-activity data for use in human exposure modeling.

Understanding the longitudinal properties of the time spent in different locations and activities is important in characterizing human exposure to pollutants. The results of a four-season longitudinal time-activity diary study in eight working adults are presented, with the goal of improving the parameterization of human activity algorithms in EPA's exposure modeling efforts. Despite the longitudinal, multi-season nature of the study, participant non-compliance with the protocol over time did not play a major role in data collection. The diversity (D)--a ranked intraclass correlation coefficient (ICC)-- and lag-one autocorrelation (A) statistics of study participants are presented for time spent in outdoor, motor vehicle, residential, and other-indoor locations. Day-type (workday versus non-workday, and weekday versus weekend), season, temperature, and gender differences in the time spent in selected locations and activities are described, and D & A statistics are presented. The overall D and ICC values ranged from approximately 0.08-0.26, while the mean population rank A values ranged from approximately 0.19-0.36. These statistics indicate that intra-individual variability exceeds explained inter-individual variability, and low day-to-day correlations among locations. Most exposure models do not address these behavioral characteristics, and thus underestimate population exposure distributions and subsequent health risks associated with environmental exposures.

Comparison of four probabilistic models (CARES(®), Calendex™, ConsExpo, and SHEDS) to estimate aggregate residential exposures to pesticides.

Two deterministic models (US EPA's Office of Pesticide Programs Residential Standard Operating Procedures (OPP Residential SOPs) and Draft Protocol for Measuring Children's Non-Occupational Exposure to Pesticides by all Relevant Pathways (Draft Protocol)) and four probabilistic models (CARES(®), Calendex™, ConsExpo, and SHEDS) were used to estimate aggregate residential exposures to pesticides. The route-specific exposure estimates for young children (2-5 years) generated by each model were compared to evaluate data inputs, algorithms, and underlying assumptions. Three indoor exposure scenarios were considered: crack and crevice, fogger, and flying insect killer. Dermal exposure estimates from the OPP Residential SOPs and the Draft Protocol were 4.75 and 2.37 mg/kg/day (crack and crevice scenario) and 0.73 and 0.36 mg/kg/day (fogger), respectively. The dermal exposure estimates (99th percentile) for the crack and crevice scenario were 16.52, 12.82, 3.57, and 3.30 mg/kg/day for CARES, Calendex, SHEDS, and ConsExpo, respectively. Dermal exposure estimates for the fogger scenario from CARES and Calendex (1.50 and 1.47 mg/kg/day, respectively) were slightly higher than those from SHEDS and ConsExpo (0.74 and 0.55 mg/kg/day, respectively). The ConsExpo derived non-dietary ingestion estimates (99th percentile) under these two scenarios were higher than those from SHEDS, CARES, and Calendex. All models produced extremely low exposure estimates for the flying insect killer scenario. Using similar data inputs, the model estimates by route for these scenarios were consistent and comparable. Most of the models predicted exposures within a factor of 5 at the 50th and 99th percentiles. The differences identified are explained by activity assumptions, input distributions, and exposure algorithms.

Quantifying children's aggregate (dietary and residential) exposure and dose to permethrin: application and evaluation of EPA's probabilistic SHEDS-Multimedia model.

Reliable, evaluated human exposure and dose models are important for understanding the health risks from chemicals. A case study focusing on permethrin was conducted because of this insecticide's widespread use and potential health effects. SHEDS-Multimedia was applied to estimate US population permethrin exposures for 3- to 5-year-old children from residential, dietary, and combined exposure routes, using available dietary consumption data, food residue data, residential concentrations, and exposure factors. Sensitivity and uncertainty analyses were conducted to identify key factors, pathways, and research needs. Model evaluation was conducted using duplicate diet data and biomonitoring data from multiple field studies, and comparison to other models. Key exposure variables were consumption of spinach, lettuce, and cabbage; surface-to-skin transfer efficiency; hand mouthing frequency; fraction of hand mouthed; saliva removal efficiency; fraction of house treated; and usage frequency. For children in households using residential permethrin, the non-dietary exposure route was most important, and when all households were included, dietary exposure dominated. SHEDS-Multimedia model estimates compared well to real-world measurements data; this exposure assessment tool can enhance human health risk assessments and inform children's health research. The case study provides insights into children's aggregate exposures to permethrin and lays the foundation for a future cumulative pyrethroid pesticides risk assessment.

Modeled estimates of soil and dust ingestion rates for children.

Daily soil/dust ingestion rates typically used in exposure and risk assessments are based on tracer element studies, which have a number of limitations and do not separate contributions from soil and dust. This article presents an alternate approach of modeling soil and dust ingestion via hand and object mouthing of children, using EPA's SHEDS model. Results for children 3 to <6 years old show that mean and 95th percentile total ingestion of soil and dust values are 68 and 224 mg/day, respectively; mean from soil ingestion, hand-to-mouth dust ingestion, and object-to-mouth dust ingestion are 41 mg/day, 20 mg/day, and 7 mg/day, respectively. In general, hand-to-mouth soil ingestion was the most important pathway, followed by hand-to-mouth dust ingestion, then object-to-mouth dust ingestion. The variability results are most sensitive to inputs on surface loadings, soil-skin adherence, hand mouthing frequency, and hand washing frequency. The predicted total soil and dust ingestion fits a lognormal distribution with geometric mean = 35.7 and geometric standard deviation = 3.3. There are two uncertainty distributions, one below the 20th percentile and the other above. Modeled uncertainties ranged within a factor of 3-30. Mean modeled estimates for soil and dust ingestion are consistent with past information but lower than the central values recommended in the 2008 EPA Child-Specific Exposure Factors Handbook. This new modeling approach, which predicts soil and dust ingestion by pathway, source type, population group, geographic location, and other factors, offers a better characterization of exposures relevant to health risk assessments as compared to using a single value.

A new method of longitudinal diary assembly for human exposure modeling.

Human exposure time-series modeling requires longitudinal time-activity diaries to evaluate the sequence of concentrations encountered, and hence, pollutant exposure for the simulated individuals. However, most of the available data on human activities are from cross-sectional surveys that typically sample 1 day per person. A procedure is needed for combining cross-sectional activity data into multiple-day (longitudinal) sequences that can capture day-to-day variability in human exposures. Properly accounting for intra- and interindividual variability in these sequences can have a significant effect on exposure estimates and on the resulting health risk assessments. This paper describes a new method of developing such longitudinal sequences, based on ranking 1-day activity diaries with respect to a user-chosen key variable. Two statistics, "D" and "A", are targeted. The D statistic reflects the relative importance of within- and between-person variance with respect to the key variable. The A statistic quantifies the day-to-day (lag-one) autocorrelation. The user selects appropriate target values for both D and A. The new method then stochastically assembles longitudinal diaries that collectively meet these targets. On the basis of numerous simulations, the D and A targets are closely attained for exposure analysis periods >30 days in duration, and reasonably well for shorter simulation periods. Longitudinal diary data from a field study suggest that D and A are stable over time, and perhaps over cohorts as well. The new method can be used with any cohort definitions and diary pool assignments, making it easily adaptable to most exposure models. Implementation of the new method in its basic form is described, and various extensions beyond the basic form are discussed.

Modeling energy expenditure and oxygen consumption in human exposure models: accounting for fatigue and EPOC.

Human exposure and dose models often require a quantification of oxygen consumption for a simulated individual. Oxygen consumption is dependent on the modeled individual's physical activity level as described in an activity diary. Activity level is quantified via standardized values of metabolic equivalents of work (METS) for the activity being performed and converted into activity-specific oxygen consumption estimates. However, oxygen consumption remains elevated after a moderate- or high-intensity activity is completed. This effect, which is termed excess post-exercise oxygen consumption (EPOC), requires upward adjustment of the METS estimates that follow high-energy expenditure events, to model subsequent increased ventilation and intake dose rates. In addition, since an individual's capacity for work decreases during extended activity, methods are also required to adjust downward those METS estimates that exceed physiologically realistic limits over time. A unified method for simultaneously performing these adjustments is developed. The method simulates a cumulative oxygen deficit for each individual and uses it to impose appropriate time-dependent reductions in the METS time series and additions for EPOC. The relationships between the oxygen deficit and METS limits are nonlinear and are derived from published data on work capacity and oxygen consumption. These modifications result in improved modeling of ventilation patterns, and should improve intake dose estimates associated with exposure to airborne environmental contaminants.

A probabilistic arsenic exposure assessment for children who contact CCA-treated playsets and decks, Part 1: Model methodology, variability results, and model evaluation.

Concerns have been raised regarding the safety of young children who may contact arsenic residues while playing on and around chromated copper arsenate (CCA)-treated wood playsets and decks. Although CCA registrants voluntarily canceled the production of treated wood for residential use in 2003, the potential for exposure from existing structures and surrounding soil still poses concerns. The EPA's Office of Research and Development developed and applied the probabilistic Stochastic Human Exposure and Dose Simulation model for wood preservatives (SHEDS-Wood) to estimate children's absorbed dose of arsenic from CCA. Skin contact with, and nondietary ingestion of, arsenic in soil and wood residues were considered for the population of children in the United States who frequently contact CCA-treated wood playsets and decks. Model analyses were conducted to assess the range in population estimates and the impact of potential mitigation strategies such as the use of sealants and hand washing after play events. The results show predicted central values for lifetime annual average daily dose values for arsenic ranging from 10(-6) to 10(-5) mg/kg/day, with predicted 95th percentiles on the order of 10(-5) mg/kg/day. There were several orders of magnitude between lower and upper percentiles. Residue ingestion via hand-to-mouth contact was determined to be the most significant exposure route for most scenarios. Results of several alternative scenarios were similar to baseline results, except for the scenario with greatly reduced residue concentrations through hypothetical wood sealant applications; in this scenario, exposures were lower, and the soil ingestion route dominated. SHEDS-Wood estimates are typically consistent with, or within the range of, other CCA exposure models.

A probabilistic arsenic exposure assessment for children who contact chromated copper arsenate (CCA)-treated playsets and decks, Part 2: Sensitivity and uncertainty analyses.

A probabilistic model (SHEDS-Wood) was developed to examine children's exposure and dose to chromated copper arsenate (CCA)-treated wood, as described in Part 1 of this two-part article. This Part 2 article discusses sensitivity and uncertainty analyses conducted to assess the key model inputs and areas of needed research for children's exposure to CCA-treated playsets and decks. The following types of analyses were conducted: (1) sensitivity analyses using a percentile scaling approach and multiple stepwise regression; and (2) uncertainty analyses using the bootstrap and two-stage Monte Carlo techniques. The five most important variables, based on both sensitivity and uncertainty analyses, were: wood surface residue-to-skin transfer efficiency; wood surface residue levels; fraction of hand surface area mouthed per mouthing event; average fraction of nonresidential outdoor time a child plays on/around CCA-treated public playsets; and frequency of hand washing. In general, there was a factor of 8 for the 5th and 95th percentiles and a factor of 4 for the 50th percentile in the uncertainty of predicted population dose estimates due to parameter uncertainty. Data were available for most of the key model inputs identified with sensitivity and uncertainty analyses; however, there were few or no data for some key inputs. To evaluate and improve the accuracy of model results, future measurement studies should obtain longitudinal time-activity diary information on children, spatial and temporal measurements of residue and soil concentrations on or near CCA-treated playsets and decks, and key exposure factors. Future studies should also address other sources of uncertainty in addition to parameter uncertainty, such as scenario and model uncertainty.