Pesticides on Household Surfaces May Influence ... - ACS Publications

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Pesticides on Household Surfaces May Influence Dietary Intake of Children Lisa Jo Melnyk,†,* Margie Z. Byron,‡ G. Gordon Brown,§ C. Andrew Clayton,|| and Larry C. Michael§ †

U.S. Environmental Protection Agency Office of Research and Development National Exposure Research Laboratory 26 West Martin Luther King Drive Cincinnati, Ohio 45268, United States ‡ Rho, Inc. 6330 Quadrangle Drive Suite 500 Chapel Hill, North Carolina 27517, United States § RTI International 3040 Cornwallis Road Research Triangle Park, North Carolina 27709, United States

bS Supporting Information ABSTRACT: The physical and chemical environment influences children’s exposures to pesticides in and around the home. Children’s activities, which increase their potential for exposure especially during eating, have been captured in the Children’s Dietary Intake Model (CDIM). In addition to the chemical exposure associated with the food itself, this model incorporates excess dietary exposures due to handling of food during consumption. To stochastically evaluate CDIM, distributions of measured, and in some cases estimated, model factors were determined from measurements of permethrin, chlorpyrifos, and diazinon derived from assembled databases and laboratory experiments. Using the distributions of these factors, Monte Carlo simulations were performed to obtain distributions of total dietary intake of pesticides. To target the sources of pesticide contamination that were influencing total dietary intake, each factor was evaluated. We found pesticide surface concentration to be highly influential. By excluding surface concentration, we were also able to determine the influence of the other factors based on the F-statistic. Transfer efficiencies, followed by pesticide residue in consumed foods and amount of food consumed, were the next most influential factors within the model. With these distributions for model inputs, CDIM has the potential to more accurately predict total dietary intake of a contaminant by a child.

’ INTRODUCTION Pesticides are detected in most occupied homes in the United States as determined by the American Healthy Homes Survey.1 Most nonpersistent pesticides used indoors, including the pyrethroid pesticides, adsorb onto surfaces when a pesticide formulation is applied or onto particulates on surfaces. Following application, distribution of pesticides throughout the home may occur through suspension and resuspension of airborne particles,2 or pesticides may migrate indoors from outside applications by physical relocation through human activity.3 Whichever mechanism is occurring, pesticide residues are available within a home to provide potential exposures to occupants.4
6 Children in the home are of particular interest.7 These highly pesticide-susceptible family members spend most of their time indoors between birth and 3 years.8 Children’s activities have been studied and shown to increase their potential for exposure to pesticides on surfaces. Movements near surfaces (e.g., crawling, hand-to-mouth activities, object-to-mouth activities) have all been shown to increase a child’s potential for exposure to pesticides within a home.8
10 These same activities have also been shown to increase children’s dietary intake of pesticides;11
15 indeed, for total pesticide intake by all exposure routes, the dietary contribution may actually dominate.16,17 Using the breadth of dietary pesticide r 2011 American Chemical Society

exposure research already conducted as a motivation, a simple algebraic model was conceived to capture total dietary exposures of children. This deterministic model, the Children’s Dietary Intake Model (CDIM), focuses on predicting pesticide intakes of children by incorporating activities that influence dietary exposure into the traditional calculations.11,18 The model includes not only intrinsic pesticide residue contamination of foods consumed, but also factors to describe surface-to-food and surfaceto-hand-to-food contamination mechanisms. These include the transfer efficiencies between surfaces and hands and between foods and surfaces. Excess dietary activities (i.e., consuming foods that have contacted contaminated surfaces) contributed significantly to the overall exposure.11,18 Understanding the model’s individual factors and the influence of each could identify sources of children’s excess dietary exposures and lead to an improvement in the predictability of these exposures. In light of the model development performed to date, a sensitivity analysis was needed to identify the model factors with the greatest influence on total pesticide intake of children. This information would guide not only the nature of further research Received: December 15, 2010 Accepted: April 11, 2011 Revised: April 5, 2011 Published: April 25, 2011 4594

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Environmental Science & Technology (e.g., improved and expanded measurements of pesticide concentrations on surfaces, if surface concentration was found to be highly influential to total intake), but also might suggest children’s behaviors that contribute to pesticide exposure in the home. Our study was conducted to further evaluate each factor within a probabilistic CDIM to facilitate estimation of the relative influence on total dietary intake of young children (i.e., 1 otherwise); • TSH = surface-to-hand transfer efficiency (dimensionless); • ASH = surface-to-hand contact frequency (dimensionless);

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• THF = hand-to-food transfer efficiency (dimensionless); • AHF = hand-to-food transfer frequency (dimensionless); and • FH = total food surface area in contact with contaminated portion of hand (cm2); this is the product of the portion of food surface area in contact with the contaminated portion of the hand (PH) and the total food surface area (Q). For convenience, the expression for CDIM can be represented as the sum of three terms (eq 2): I ¼ Term 1 þ Term 2 þ Term 3

ð2Þ

where • Term 1 = R 3 FT = contaminant intake associated with the original pesticide residue on/in the single piece of food prior to handling by the child; • Term 2 = [CS 3 FS 3 TSF 3 ASF] = surface-to-food contaminant intake due to the single piece of food being in contact with a contaminated surface (e.g., a hardwood floor); and • Term 3 = [CS 3 FS 3 TSH 3 ASH][THF 3 AHF 3 FH] = surface-to-hand-to-food contaminant intake due to the child touching contaminated surfaces (e.g., a hardwood floor) and then handling a single piece of food. Thus, Terms 2 and 3 represent excess dietary pesticide contributions due to the child’s eating behaviors and the influence of contaminated surfaces. Because the model only estimates the intake of a pesticide for a “piece” of food (i.e., contamination is independent across pieces of food), the individual intakes can be summed across all pieces of food for a given food item (e.g., apple slices are summed to yield the intake for the entire apple) and, if possible, across all food items consumed within a given time period, to yield the total pesticide intake for a period of time (e.g., 24 h).

’ DATA ACQUISITION AND ASSEMBLY Determining the relative influence of the model factors on pesticide intake involved the following procedural steps for each model factor: • Identify and assemble the data from all relevant sources. • Determine the distribution type and distribution parameters. • Run the Monte Carlo simulations using the distribution parameters. • Perform the sensitivity analysis to determine the relative influence of each factor. These steps are discussed in detail below. Identification of Relevant Data Sources. To provide distributions of the model factors required for sensitivity analyses, we conducted broad investigations of existing databases, published literature, and to a very large extent, information from personal contacts with researchers in this area. Personal research contacts provided very valuable leads to identify sources of data which otherwise would have been missed due to their lower visibility. Ultimately, the compiled data were essential to inform the model factors for creating data distributions. Where data for such distributions were simply unavailable, data distributions were synthesized or otherwise derived from comparable data. After examination, much data could not be used, principally because it was not generated by observational studies and was therefore incompatible with this model. 4595

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Environmental Science & Technology A significant quantity of pesticide residue data, along with food consumption information, has already been compiled into national databases and has been modeled with the Stochastic Human Exposure and Dose Simulation (SHEDS) modeling system.19,20 These modeled results comprise Term 1 of CDIM and were used in our evaluation. Terms 2 and 3 were populated with data from three studies: • Dietary Intake of Small Children 18 [factors: TSF, CS, TSH, ASF, ASH]; • EPA In-House Study 13,14 [factors: CS, TSH]; and • Micro-Activity Pattern Study 11,21 [factors: AHF, ASH, ASF]. Our desire was to focus on children’s dietary exposures to pesticides in their home and, as such, the three studies cited above were conducted in typical residential environments. These particular studies that included data to address factors as listed above were chosen based on three factors: (1) study designs and emphases consistent with typical dietary exposures ; (2) availability of data across multiple factors for a given pesticide; (3) availability of food diary information required to compute estimates of total pesticide intake. Table S1 of the Supporting Information (SI) provides an overview of the three studies identified above, as well as the numerous studies whose data were considered for this evaluation. While the total numbers of participants were fairly small, multiple sampling events were conducted for each study subject. Furthermore, all of the data from the selected studies represented typical residential dietary exposures of children, a critically important criterion in minimizing the tendency of the sensitivity analysis to overemphasize the influence of specific model factors. Some of the model factors (e.g., surface-to-hand and hand-to-food contacts) are highly influenced by children’s age. The children in these three studies ranged from 1 to 4 years old. Although there might be some variation in eating behaviors across this age range, that variation was assumed to be typical of this population and thus important to capture. Additional data from other studies were included 13,14,17 only as necessary to provide sufficient information for factors not represented by the three studies above. In these cases, data (e.g., various transfer efficiencies) were collected in a laboratory or field study to provide values for input parameters that met the criteria as listed above. Incorporation of SHEDS Data. Data sets that informed the SHEDS model were obtained to support the creation of the distributions for the factors in Term 1 of CDIM. These data included the U.S. Department of Agriculture (USDA)
sponsored pesticide analysis results 22 for specific food items and the food consumption data by age and gender. Using the food diary data from our current study, (which informed Terms 2 and 3 as described below), two data “bridges” were created: (1) between the food items in the USDA pesticide residue database and the food consumption database, and (2) between the food items in the food consumption database and the food diary data for the selected studies. Because no de facto relationship existed among these food descriptors, judgments of comparability had to be made based on common sense and personal experience with children’s diets. For example, “baby lasagna” in the food diary was linked to “ravioli, cheese-filled, with tomato sauce, baby food, toddler” in the pesticide residue database used in SHEDS. With the exception of the diaries contained in the Dietary Intake of Small Children Study, food consumption data recorded in the food diaries were, typically, in units such as “2 small full bowls.” No

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reliable conversion was available to express this quantity in the mass units required by the SHEDS data. Thus, the USDA database was queried for each of the food codes assigned to the diary food items. The mean amount of food consumed for each food code was extracted for children aged one to four. For consistency, this value, and its distribution, was employed for the quantity of food consumed by each of the study participants, in lieu of the ambiguous food quantity recorded in some of the diaries. Construction of Analysis-Level Data Sets. Individual SAS (SAS Institute, Cary NC) data sets were created for each of the data sources, typically from an Excel file (Microsoft Corp., Seattle, WA) for each sample matrix and pesticide. Prior to converting the files to SAS data sets, the data (i.e., surface concentration loadings and transfer efficiencies) were thoroughly investigated for data points flagged as nondetects, below detectable limit, below quantitation limit, etc. Measurements from each data source were used to determine the shape and factor estimates for the distributions to be used in the Monte Carlo analyses. These measurements were used without modification except for imputation of half the method detection limit for values censored by the contributing study as nondetects.23 Alternatively, half the lowest reported value by pesticide and food was used. In some cases, a zero was reported as the loading or transfer value in the original data. In those instances, the zero was retained. Relevant variables were extracted from each study and standardized to create a single SAS data set. Of particular importance were those variables that described the surface type (e.g., floor, counter top), pesticide class (e.g., pyrethroids, carbamates, organophosphate), food type, and specific pesticide. Measurement units for each variable were verified to ensure consistency. Where needed, measurements were standardized to ensure compatibility between all factors and terms in the model. The “standardized” data sets were assembled to create analysis-level data sets for transfer, loading, and contact for the transfer coefficients, pesticide surface loadings, and contact frequency data, respectively. Descriptive labels were associated with each variable to provide documentation of the contents. Parametric Distributions of Raw Data. Data files were combined to yield individual data sets for contacts, surface loadings, and transfer efficiencies. Categorical variables (e.g., surface type) were created for selected variables to provide consistent values across the study data files. Preliminary frequency examinations of the analysis-level files revealed that the greatest amount of data available was for the following pesticides: cis-permethrin, transpermethrin, chlorpyrifos, and diazinon. Consequently, analyses were performed exclusively on these four pesticides, for two surface types (porous [carpet] and nonporous [vinyl and wood flooring]), for the following 17 food categories: • American cheese

• graham cracker

• wheat cracker

• apple

• ham

• low moisture/low fat 14

• bologna, with fat

• pancake

• high moisture/low fat 14

• bologna, without fat

• sugar cookie

• low moisture/high fat 14

• bread

• tortilla

• high moisture/high fat 14

• fruit roll-up

• watermelon

For the majority of model factors, distribution shape and associated factor estimates were determined for each model factor and combination of food type, surface type, and pesticide using the SAS Univariate Procedure by fitting various standard statistical distributions (e.g., normal, log-normal, beta, exponential uniform) 4596

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Environmental Science & Technology to the data and determining the best fit based on likelihood estimates. As before, maximum likelihood methods were used to determine the best fitting distribution. Where data for each combination of food type, surface type, and pesticide was insufficient to allow assignment of distributions, model factors were determined across the surface types and/or food categories. For example, THF and AHF were examined across food categories, while TSF and ASF were examined across surface types and food categories. Similarly, in cases where the available data for cis-permethrin and trans-permethrin were not sufficient for fitting a distribution, the available data for the model factor across all available pesticide types were used as a surrogate to obtain the distribution shape and factor estimates. On the basis of the assumption that the physicochemical properties of pesticides were relatively similar, it seemed reasonable that transfer efficiencies would be less dependent on the specific pesticide than on surface or food type, so the data were pooled across available pesticides when additional observations were needed to adequately fit a distribution to the transfer efficiency data. The contact frequency data were in the form of counts, so those model factors were fit to a Poisson distribution. No data were available for the factors: Q, PH, and PS (subfactors of FH and FS). The ranges of the transfer efficiency factors were used to determine possible ranges of values by food category for THF and the uniform distribution was employed. PH and PS were assigned to uniform distributions with values ranging from 0 to 1. Values of Q were also assigned to uniform distributions but surface area estimates for each food type were used to assign the value ranges. Pesticide transfers from multiple surface types, multiple pieces of foods, and multiple consumed food items were considered to be independent, thereby allowing summation of intakes. However, this assumption was not valid for numbers of contacts for surface-food, surface-hand, or hand-food. However, if each contact between a pesticide-contaminated surface and a single piece of food was assumed to be independent from all other contacts with that same piece of food, multiple contacts would result in a multiplicative effect on the total amount of pesticide transferred. For example, if the transfer efficiency between the surface and the food was 80%, four contacts between the food and the surface would yield an effective transfer efficiency of 320%. Since it seems reasonable that the adsorptive characteristics of both the food and the surface will change once the contact has occurred and will, therefore, affect subsequent contacts, it seems incorrect to assume that the transfer efficiency will remain constant for each of a series of contacts. To accommodate this expected variability, a geometric progression was instituted which would bound the total transfer efficiency for a single piece of food between 0 and 200%. Using the previous example, the first encounter between the surface and piece of food would be 80%, the second would be 40%, the third would be 20%, the fourth would be 10%, and so on. Under this regimen, the greatest the total transfer efficiency could ever be is 200%. A value of 200% would take into account that each contact could be, at least in part, with a new, previously uncontacted region of the surface. It assumes that each contact is neither in exactly the same surface location (total surface depletion after many contacts) or is in a totally fresh, previously uncontacted location. Given the lack of highly detailed child-specific information available about the handling of each piece of food, we believe that this approach led to a reasonable approximation of reality.

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Monte Carlo Simulations. Monte Carlo simulations were used to estimate distributions of total dietary intake of pesticides commonly found in fruits and vegetables. The mean value for each food item in CDIM Term 1 was computed directly from the SHEDS model for children in each of three age classes: • Infant ( 0; • Total Intake, given that Term 3 > 0; • Total Intake, given that Term 2 > 0 and Term 3 > 0; • Total Intake, given that Term 2 > 0 and Term 3 = 0; • Total Intake, given that Term 2 = 0 and Term 3 > 0; and • Total Intake, given that Term 2 = 0 and Term 3 = 0. The pesticide intake values generated for each of the above simulations for each age-class  pesticide  food  surface combination were evaluated by calculating summary statistics, including the sample sizes, means, standard deviations, coefficients of variation, and selected percentiles. For any of the model factors, if insufficient data or information in the literature existed to specify a sampling distribution, then a range of realistic values was used for that variable(s) (i.e., surface areas for typical apples). One approach employed was to assume a uniform distribution over this range or to simply perform the simulations at the minimum, midpoint and maximum of the range of values. Sensitivity Analysis. With the summary statistics resulting from the Monte Carlo simulation in hand, an appropriate sensitivity analysis could be implemented. The sensitivity analysis approach focused on a comparison of means using a simple ratio of variances and a graphic display. This was accomplished by setting each model factor to a low value (fifth percentile), a mean value, and a high value (95th percentile of the probability distribution). A Monte Carlo simulation was then performed at each of these three fixed points, calculating a mean and standard error and then plotting the intake distributions side by side. This provided a visual method of detecting which factor had the greatest effect on the intake. The statistic calculated to assess the sensitivity of the factors to the intake model was the ratio of the variation (model mean square) in pesticide intake attributed to a particular model factor when it was fixed at low, medium, and high values to the variation (mean square error) in pesticide intake that was not attributed to the particular model factor. This ratio is known to have an F-distribution and is identical to the F-test statistic (F-statistic) from ANOVA (analysis of variance). In the case of the sensitivity analysis for the intake model, the F-statistic was a measure of the contribution of each model factor toward the estimates of pesticide intake. The F-statistic was selected as the basis for measuring the effect of a model factor based on classical analysis of variance theory. For the simulation performed here, it was straightforward and had the desired properties in examining the ratio of variability. Because each 4597

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Environmental Science & Technology Monte Carlo iteration was independent of all other iterations, the final simulated observations could be treated as a simple random sample, making ANOVA methods applicable. If a model exhibited high sensitivity to a particular factor, (e.g., surface loading), then as the magnitude of the factor was varied, the total estimated pesticide intake, I, would vary by a large amount. Conversely, if a model exhibited very low sensitivity to a particular factor, (e.g., the number of hand-food contacts), as that factor was varied, the total estimated pesticide intake would exhibit less variation. The F-statistic, based on ANOVA as described above, was calculated and used as a measure of the impact that a given model factor had on the intake. This was performed by modeling the intake from the simulation experiment as a function of the predetermined levels of input factor. A large F-statistic implied that the difference in the mean intakes at the given levels was varying significantly compared to the variability of intake at a given level of the factor. In other words, the factor with the biggest impact on intake would produce the largest F-statistic. The Monte Carlo simulation was performed for each combination of the 4 pesticides, 17 foods, and 2 surface types, resulting in 136 combinations. For each combination, the F-statistic from each factor was ranked from largest to smallest. For purposes of this simulation study, factors with an F-statistic larger than 4.0 were considered as having a measurable (i.e., significant) impact on intake. F-statistics that were less than 4.0 indicated that the factor had, at best, only a marginal impact on intake. The threshold selection of 4.0 was statistically justified. If a factor had no impact on intake and standard assumptions of homogeneity, normality, etc. were valid, then the probability of seeing a value greater than 4.0 would be approximately 0.045 (i.e., approximately equivalent to the commonly used 0.05 significance level in statistical analyses). It is important to note that the F-statistic was used solely to rank the results and not in performing formal hypothesis tests. Ideally, the simulations would have first generated a shape, then generated factor estimates, then generated a value for each factor. For all of the simulations presented in this work, only the best-fitting distribution for each model factor was used. For this reason, the variance estimates described above may be biased low, which would tend to increase the magnitude of the F-statistic produced in the simulation study. Conversely, the limited data available to create distributions for some factors might have impacted the sensitivity analyses. For example, many of the model factors were bounded by 0 and 1. When data were limited, the distribution tended toward a uniform distribution with a range from 0 to 1. This would likely have two effects: (1) to overstate the sensitivity of this factor as it increases the probability of obtaining a value in the tails of the distribution for that factor, and (2) to obscure the factor’s true contribution to the final intake. A factor that exhibits high sensitivity for this model implies that the intake distribution varies widely when the model factor changes. However, depending on the distributional assignment, a paucity of data for a given model factor might cause the estimated sensitivity to be biased high in some cases and low in others. For this reason, extreme care and significant thought was applied to the exploration of the available data for each factor, to minimize any effects to the sensitivity analysis associated with limited data.

’ RESULTS AND DISCUSSION Sensitivity analyses were performed on all 4 pesticides, 2 surface types, and 17 food categories. Means and 95% confidence limits of total dietary intake for each model factor at low, mean, and high values of the distribution were plotted. A subset of the

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distributions of the F-statistics is summarized in Figures 1 and 2. Each graph in these figures indicated the range (shown by a bar) and mean (shown with a hash mark) of the F-statistic for each factor. Figure 1 includes all factors. Figure 2 omits CS because its influence was so great (i.e., had the largest range) that it compressed the vertical scale and obscured comparisons of the relative influence of the other factors. The F-statistic for each model factor, average of the four pesticides, the four general food categories (low moisture/ low fat, high moisture/low fat, low moisture/high fat, and high moisture/high fat 14), and the porous and nonporous surface types are presented in Tables S2 and S3 of the SI. The F-statistic was evaluated to determine influence of a single model factor on the total intake. Surface concentration (CS) was highly influential in every combination of pesticide, surface, and food category with an F-statistic range of 221.9 to 1,858 (Tables S2 and S3 of the SI). It appeared to be somewhat more important for the organophosphate pesticides than for the two pyrethroid pesticides (data not shown—pesticides were combined for presentation). The importance of the factor CS may be attributable to the appreciable variation in the data, a finding one might expect in residential measurements. This was also the most logical conclusion because excess dietary exposure would be negligible if no pesticide residues were detected on surfaces. Reviewing the F-statistic for the influence of the other factors (Tables S2 and S3 of the SI) revealed that the following were impacting sensitivity to total intake: • TSH, surface-to-hand transfer coefficient (averaged F-statistic range: 4.6
42.7); • THF, hand-to-food transfer coefficient (2.4
38.4); • R, the pesticide concentration in the food (0.7
117.0); • FT, the amount of food consumed (0.7
336.5); • Q and PH, food surface area and proportion of that area contacted by the hand (1.9
20.1); • PS, the proportion of the food surface contacting the surface (1.1
5.7); and • TSF, surface-to-food transfer (0.6
11.2). Influence of R and FT on intake, as evidenced by the range of F-statistic, was highly dependent on fat and moisture content of foods. The range of averaged F-statistics for all of the transfer factors was generally quite large, suggesting a significant variation in influence across specific values of pesticide, surface type, or food category. In general, the averaged F-statistic range for activityrelated factors AHF (number of hand-food contacts, 0.5
3.4), ASH (number of surface-hand contacts, 0.4
4.5), and ASF (number of surface-food contacts, 0.7
1.9), and SHR (handsurface wiping factor, 0.5
1.9) exhibited very little influence, as evidenced by the low F-statistics. This suggests that differences in “table manners” between children may not be particularly important in their overall pesticide exposure from foods. Care must be exercised to avoid over-interpreting the effect of these factors, because data were somewhat limited. Comparison of F-statistics for the porous and nonporous surfaces revealed higher levels for the transfers and activities associated with hands on porous surfaces than for hands on nonporous surfaces. The porous surface used in the simulations was carpet. Transfers and activities from surfaces to foods had lower influence from porous surfaces. This finding was supported by the observations and data from studies focusing on transfer of pesticides from carpet to foods.13,14 Because the data cited were also used in the calculation of the F-statistic, the model shows evidence of consistency with the experimental data. 4598

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Figure 1. Ranges of F-statistic values for pesticides, surface types, and food categories for each model factor.

Pesticide intakes for individual foods recorded in the diaries of the selected studies were computed using the mean value for each factor. The means were subsequently summed across all pieces of food comprising a given item. Food amounts consumed were derived from the USDA database provided in the SHEDS model. The number of pieces of food comprising an item was based on thoughtful consideration of eating habits of young children. Table S4 of the SI shows the intakes of each pesticide across all pieces of food. The selected foods were segregated by eating event or meal. Fruits and vegetables were separated from the others. Substantial variation of the intake values was observed for the different pesticides, with

Figure 2. Ranges of F-statistic values for pesticides, surface types, and food categories for each model factor, excluding surface concentration.

diazinon exhibiting values >10 μg for 6 out of 17 foods. Careful examination of values calculated for each model factor showed that food surface area (Q), by virtue of its absolute magnitude, had a profound influence on the overall pesticide intake. Because factor Q was purely an estimate of what the food surface area for a single piece of food was thought to be, a small difference in the estimate could have a large impact on the overall pesticide intake. 4599

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Environmental Science & Technology The simulations and resulting data indicate an agreement with previous work that excess dietary exposure can be a potential source of increased pesticide intake.11
15 Sensitivity analyses indicated that R and FT became more influential as transfers decreased. The model evaluation again clearly demonstrated that contamination of food can occur during consumption by children. The specific studies which contributed data for the model factors were limited in size and may not represent all U.S. children, but they were typical of unintentional exposure situations. Verification of representativeness was not the intent of this evaluation and should not be assumed. Future evaluation of dietary exposures of children to pesticides should consider potential sources from behaviors and activities. Even with the limitations, the model used in this study has shown that this potential exists and should be considered in the protection of food sources from chemical contaminations. Two of the pesticides included in the evaluation, diazinon and chlorpyrifos, have been banned for residential use, but offer a wealth of data to allow the benefits of an extended dietary exposure model to be realized. In summary, the CDIM predicts relative influence of children’s activities to total dietary intake, allowing researchers to identify potential sources of increased exposure, which, in turn, will facilitate mitigation and/or education to decrease the impact. The SI contains tables that provide greater detail about the various studies considered for the evaluation, F-statistic data for all of the factors, and estimated intakes using newly formed distributions for CDIM.

’ ASSOCIATED CONTENT

bS

Supporting Information. Tables of data referred to within the text. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Phone: 513-569-7494. Fax: 513-569-7757. E-mail: Melnyk.lisa@ epa.gov. )

Notes

Formerly of RTI International.

’ DISCLOSURE The U.S. Environmental Protection Agency, through its Office of Research and Development, funded and managed the research described here under contract EP-D-04
068 to Battelle, subcontracted to RTI International. It has been subjected to Agency review and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. ’ REFERENCES (1) Stout, D. M., II; Bradham, K. D.; Egeghy, P. P.; Jones, P. A.; Croghan, C. W.; Ashley, P. A.; Pinzer, E.; Friedman, W.; Brinkman, M. C.; Nishioka, M. G.; Cox, D. C. American healthy homes survey: A national study of residential pesticides measured from floor wipes. Environ. Sci. Technol. 2009, 43, 4294–4300. (2) Stout, D. M., II; Mason, M. A. The distribution of chlorpyrifos following a crack and crevice type application in the us epa indoor air quality research house. Atmos. Environ. 2003, 37, 5539–5549.

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