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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.

Archived samples collected from 1995 to 1997 in the National Human Exposure Assessment Survey (NHEXAS) in U.S. Environmental Protection Agency Region 5 (R5) and the Children's Study (CS) in Minnesota were analyzed for total arsenic, arsenate [As(V)], arsenite, dimethyl arsenic acid (DMA), monomethyl arsenic acid (MMA), arsenobetaine (AsB), and arsenocholine. Samples for the CS included drinking water, urine, hair, and dust; both studies included food (duplicate plate, composited 4-day food samples from participants). Except for AsB and As(V), the levels for As species measured in the food and drinking water samples were very low or nonexistent. The analytical methods used for measuring As species were sensitive to < 1 ppb. During the analysis of food and drinking water samples, chromatographic peaks appeared that contained As, but they did not correspond to those being quantified. Thus, in some samples, the sum of the individual As species levels was less than the total As level measured because the unknown forms of As were not quantified. On the other hand, total As was detectable in almost all samples (> 90%) except for hair (47%), indicating that the analytical method was sufficiently sensitive. Population distributions of As concentrations measured in drinking water, food (duplicate plate), dust, urine, and hair were estimated. Exposures to total As in food for children in the CS were about twice as high as in the general R5 population (medians of 17.5 ppb and 7.72 ppb, respectively). In addition, AsB was the most frequently detected form of As in food eaten by the participants, while As(V) was only rarely detected. Thus, the predominant dietary exposure was from an organic form of As. The major form of As in drinking water was As(V). Spearman (rank) correlations and Pearson (log-concentration scale) correlations between the biomarkers (urine, hair) and the other measures (food, drinking water, dust) and urine versus hair were performed. In the NHEXAS CS, total As and AsB in the food eaten were significantly correlated with their levels in urine. Also, levels of As(V) in drinking water correlated with DMA and MMA in urine. Arsenic levels in dust did not show a relationship with urine or hair levels, and no relationship was observed for food, drinking water, and dust with hair. Urine samples were collected on days 3, 5, and 7 of participants' monitoring periods. Total As levels in urine were significantly associated across the three pairwise combinations--i.e., day 3 versus day 5, day 3 versus day 7, and day 5 versus day 7. Because the half-life of As in the body is approximately 3 days, this suggests that some exposure occurred continually from day to day. This trend was also observed for AsB, suggesting that food is primarily responsible for the continual exposure. DMA and MMA in urine were also significantly correlated but not in all combinations.

The Minnesota Children's Pesticide Exposure Study (MNCPES) of the National Human Exposure Assessment Survey (NHEXAS) was conducted in Minnesota to evaluate children's pesticide exposure. This study complements and extends the populations and chemicals included in the NHEXAS Region V study. One of the goals of the study was to test protocols for acquiring exposure measurements and developing databases for use in exposure models and assessments. Analysis of the data quality is one element in assessing the performance of the collection and analysis protocols used in this study. Data quality information must also be available to investigators to guide analysis of the study data. During the planning phase of MNCPES, quality assurance (QA) goals were established for precision, accuracy, and quantification limits. The data quality was assessed against these goals. The assessment is complex. First, data are not available for all analytes and media sampled. In addition, several laboratories were responsible for the analysis of the collected samples. Each laboratory provided data according to their standard operating procedures (SOPs) and protocols. Detection limits were authenticated for each analyte in each sample type. The approach used to calculate detection limits varied across the different analytical methods. The analytical methods for pesticides in air, food, hand rinses, dust wipe and urine were sufficiently sensitive and met the QA goals, with very few exceptions. This was also true for polynuclear aromatic hydrocarbons (PAHs) in air and food. The analytical methods for drinking water and beverages had very low detection limits; however, there were very little measurable data for these samples. The collection and analysis methods for pesticides in surface press samples and soil, and for PAHs in dust wipes were not sufficiently sensitive. Accuracy was assessed primarily as recovery from field controls. The results were good for pesticides and PAHs in air (75-125% recovery). Recovery was lower (<75%) for pesticides in drinking water and beverages. The recovery of pesticides from hand rinses met QA goals (75-100%), but surface press samples showed lower recovery (50-70%). Analysis by gas chromatography-mass spectrometry (GC-MS) did not confirm the presence of atrazine and other pesticides in hand rinse and surface press samples that had been detected by GC-ECD, but instead GC-MS confirmed background interferences. Assessment of the precision of sample collection and analysis is based on the percent relative standard deviation (%RSD) between the results for duplicate samples. Data are available only for pesticides and PAHs in air. Precision was good (<20% RSD) for analytes with measurable data. There were a few analytes with %RSD >20%, but the number of data pairs was very small in these cases. Precision for instrumental analysis of food sample extracts was excellent, with the median %RSD < 20 for all measurable pesticides. The median %RSD for the analysis of replicate aliquots of food from the same sample composite was considerably higher, indicating the potential for inhomogeneity of food homogenates.

The ability of questionnaires to predict children's exposure to pesticides was examined as part of the Minnesota Children's Pesticide Exposure Study (MNCPES). The MNCPES focused on a probability sample of 102 children between the ages of 3 and 13 years living in either urban (Minneapolis and St. Paul, MN) or nonurban (Rice and Goodhue Counties in Minnesota) households. Samples were collected in a variety of relevant media (air, food, beverages, tap water, house dust, soil, urine), and chemical analyses emphasized three organophosphate insecticides (chlorpyrifos, diazinon, malathion) and a herbicide (atrazine). Results indicate that the residential pesticide-use questions and overall screening approach used in the MNCPES were ineffective for identifying and oversampling children/households with higher levels of individual target pesticides.