Introduction

Children's exposure to environmental toxicants is a current public health concern (Olden and Guthrie, 2000). Children may be more susceptible to the effects of these exposures, as they have higher rates of metabolism, less mature immune systems, and different patterns of activity and behavior than adults (NRC, 1993; Faustman et al., 2000). The Food Quality Protection Act of 1996 included requirements that pesticide tolerances on produce take into account aggregate exposures and cumulative risks to children, necessitating consideration of all exposure pathways, and exposure to chemicals with a common mechanism of toxicity.

Diet is one potentially significant source of pesticide exposure considered in aggregate and cumulative risk models (Berry, 1997; Thomas et al., 1997; ILSI, 1999; Akland et al., 2000). Children eat more food per body mass and tend to eat different foods than adults (NRC, 1993). These foods include fresh produce, juice and processed foods, including fruit juices and baby food, which may contain higher levels of pesticide residues than foods commonly consumed by adults (NRC, 1993). An understanding of children's dietary exposure to pesticides is central to the assessment of aggregate exposures and cumulative risks, but this component has not been well characterized.

One method of estimating exposures to a population from food ingestion is to combine information on dietary composition with data on residue concentrations in and on the food consumed (Whitmore et al., 1994; MacIntosh et al., 1996; Tomerlin et al., 1997). The federal government maintains programs that provide information on pesticide residue levels on crops, and which are intended to survey and enforce tolerances on imported and domestic foods (USFDA, 1996; USDA, 1997). Residue data, however, are often limited by inconsistencies in methods used for sampling and analysis, reflecting both the different criteria developed for specific sampling purposes, as well as individual laboratory capabilities (NRC, 1993). Additionally, large monitoring programs cannot accurately assess exposure from food supplied outside of the production and retail distribution system, such as food grown in a home garden (Berry, 1997). These estimates are also limited by incomplete information on individual diets and in particular on diets that vary from the norm (Blair, 1989). Furthermore, available dietary consumption information is often outdated and may not reflect current trends in children's dietary habits (Hubal et al., 2000). Thus, extrapolation of residue and consumption data to population subgroups such as infants and children may be subject to major sources of error.

Personal monitoring is another option for estimating exposures. This can be achieved through the collection of duplicate diet samples, where study participants prepare and collect duplicate portions of all foods and beverages consumed during the monitoring period. These portions are composited and analyzed to calculate individual exposure. Duplicate diet sampling allows calculation of the quantity of contaminants actually ingested by an individual (WHO, 1985), accounts for the effects of food production, storage, and preparation (Thomas et al., 1997; Tomerlin et al., 1997; Akland et al., 2000), and can characterize the diets of specific subpopulations that do not adhere to population norms (Hubal et al., 2000). Studies investigating total individual exposures to pesticides via multiple pathways often employ personal monitoring including duplicate diet sampling (Melnyk et'al., 1997; Gordon et al., 1999; Quackenboss et al., 2000; MacIntosh et al., 2001), and the World Health Organization recommends this method for measuring dietary exposure to contaminants within restricted subpopulations (WHO, 1985).

This study employs duplicate diet sampling strategy to characterize dietary exposures to 15 organophosphorous (OP) pesticides of preschool children in Washington State. No previous duplicate diet sampling studies have focused on children in this age range: the Agricultural Health Study pilot project included several young children (Melnyk et al., 1997); the Minnesota Children's Pesticide Exposure Study design includes collection of duplicate diets, but results from this study have not yet been reported (Quackenboss et al., 2000). In each of these studies only a limited number of OP pesticides have been targeted for analysis.

OP pesticides share structural characteristics, modes of pesticidal action of toxicity, and toxic effects (ILSI, 1999), and these characteristics require consideration of their cumulative effects. OP pesticides were selected for this study because of their acute toxicity, presence in multiple environmental media, and widespread use both residentially and agriculturally (WHO, 1985; Eskanazi et al., 1999). This work is part of a larger study designed to assess the relative importance of multiple exposure pathways for children by comparing OP pesticide residue levels found in environmental and dietary samples with urinary biomarkers of exposure (Kedan, 1999).

Methodology

Study Population

Potential participants for this study included families with children aged 2 to 5 years. Subjects were recruited from a pool of families already enrolled in larger pesticide exposure studies (Lu et al., 2001; Koch et al., submitted). These families were originally recruited from two different geographic locations in Washington State. Chelan and Douglas counties in Central Washington are rural and agricultural, and include a substantial portion of the state's orchard industry. The Seattle metropolitan area includes the city of Seattle and surrounding suburbs in King and Snohomish counties. Families were contacted through women, infants and children (WIC) clinics in both areas and from a private pediatric clinic in the Seattle metropolitan area. The complete sampling strategies for these larger studies are reported elsewhere (Lu et al., 2001; Koch et al., submitted).

Children with high potential OP pesticide exposure based on combined urinary dialkylphosphate (DAP) levels measured in the previous studies were targeted for participation in the current study. This strategy was used to increase the potential for detecting OP pesticide residues within the children's homes and diet, and is a more efficient use of limited resources. This strategy has been used in other studies for similar reasons (Quackenboss et al., 2000).

Parents of children were initially contacted by telephone beginning in May 1998. The objectives of the study, as well as a description of the field sampling procedures, were explained at this time. The full study protocol included 24-h air sampling, soil, house dust, and drinking water sampling, and 24-h urine sampling, in addition to the 24-h duplicate diet sampling reported here. Thirteen families, six from Chelan and Douglas counties and seven from the Seattle metropolitan area, agreed to participate.

Sample Collection and Analysis

The first sampling took place in the summer (June through August) of 1998, followed by a second sampling in the fall (October) of 1998, with the exception of one home, which was sampled during the first week of December. Parents were asked to provide duplicate portions of all food and beverages consumed by the child for an entire day (24-h). Parents were given plastic plates and cups, Ziploc™ bags, and aluminum foil to facilitate food collection and storage, and were instructed to cover and refrigerate samples until collection by field staff. Food items were frozen at -20°C and transported to the University of Washington laboratory where they were stored for 2 months. The samples were later divided into four categories: I — fresh fruits and vegetables (unprocessed), II — fruit juices and beverages, III — dairy products, and IV — processed foods. Each sample was then weighed, transferred to a glass jar, and stored at -20°C. Samples were shipped on dry ice overnight to the Washington State University (WSU) Food and Environmental Quality Laboratory (FEQL) in Richland, Washington for analysis.

Dietary samples were analyzed for the 15 OP pesticide compounds shown in Table 1. Solid food samples were thawed slightly, homogenized using a food chopper (model RS16V, Robot Coupe, Jackson, MS), and refrozen. Liquid samples were thoroughly mixed by shaking. Analysis of dietary samples was conducted on 20 g of produce or processed foods, 300 ml of beverages, or 100 ml of dairy products. Sample preparation was adapted from the Association of Analytical Chemists (AOAC) method 970.52, "Organochlorine and Organophosphorous Pesticide Residues." In brief, all samples were extracted in acetonitrile and washed with 30% aqueous sodium chloride and hexane. Deionized water was added and the OP pesticides were partitioned into methylene chloride. Samples were concentrated (Rotovapor model R-114, Buchi Laboratoriums Technik, Flawil, Switzerland) and brought to 2 ml in 1:1 hexane:acetone. Samples were analyzed on a Varian Star 3400 gas chromatograph (Varian Instruments, Sugarland, TX) with a pulsed flame photometric detector and a DB-1 column (J&W Scientific, Folsom, CA).

Table 1 - Limits of quantitation (LOQ) (ng/g) and analyte mean recoveries^a (%).

Full table (61K)

Drinking water was not collected as a part of beverage sampling. A sample of tap water was collected from each home and analyzed for five OP pesticides (azinphosmethyl, chlorpyrifos, diazinon, dichlorivos, and phosmet). No detectable levels of these pesticides were found in any of the samples, and these findings are not discussed further in this paper.

Quality Control and Quality Assurance

Performance of the analytical procedure was examined by extracting samples fortified with known amounts of the selected OP pesticides. Three replicates at each of three levels were fortified and analyzed along with one matrix blank for each of the four matrices. Fortified samples were prepared by adding the appropriate volume of each standard solution directly to a representative matrix. For the dairy group, whole milk was used as the matrix. The beverage group was represented by a mixture of Tree Top® apple juice, Welches® grape juice and Tropicana® orange juice (with pulp). Equal portions of cored apples and peeled bananas represented fresh fruits and vegetables. Oreo® cookies, Kellogg's® Sugar Pops, Diane's® corn tortillas, Oscar Mayer® bologna, Frito's® corn chips, Orowheat® oatnut bread and Kraft® Macaroni and Cheese comprised the matrix for the processed foods group.

Chromatographic standards at five concentrations were prepared and quantitation of peak areas of at least three solutions containing known concentrations of the OP pesticide analytes were measured at the beginning and end of each sample set. Check standards, which are calibration standards with concentrations in the interior of the calibration curve, were run intermittently with each batch of samples. Each batch of samples also contained one fortification sample to verify method performance during the extraction set. None of the data were corrected for recovery efficiency. The lowest standard concentration regularly used represented the limit of quantitation (LOQ) for most compounds in all matrices except milk. Table 1 shows the LOQs and analyte recoveries for OP pesticide residue levels by food category. Azinphosmethyl was not analyzed in dairy products due to the presence of interfering peaks and column fouling.

Data Analysis

Summary statistics were used to characterize dietary exposure. In order to evaluate cumulative risk from OP pesticide exposure, the toxicity equivalency factor (TEF) approach was used. The TEF represents the toxic potency of an individual chemical relative to the potency of a reference chemical, which typically has a well-characterized toxicity (NRC, 1993; ILSI, 1999). The TEF is derived by comparing benchmarks such as no-observed-effect levels (NOELs) or lowest-observed-adverse-effect levels (LOAELs) for chemicals shown to cause the same critical effects, act on the same molecular target, and act by the same biochemical mechanism (NRC, 1993; ILSI, 1999). This approach normalizes and sums the doses of multiple chemicals according to the formulawhere dose_i is the estimated dose of each OP pesticide, TEF_i is the toxicity equivalency factor relative to a reference chemical, and dose_TEQ is the total equivalency dose, expressed as g/kg/day. This analysis assumes that the amount of pesticide ingested is fully absorbed across the gastrointestinal tract.

Results

For the rural population, 10 families were contacted out of 42 families enrolled in a longitudinal biomonitoring study in Central Washington (Koch et al., submitted). Six of the 10 families contacted were ultimately enrolled in this study. Of the four families contacted but not enrolled, three expressed interest in participating in the study but were not available during the proposed sampling period because of work obligations and one family was not interested in participating. All rural population families were recruited from a WIC clinic. For the suburban population, 13 families were contacted out of 96 families who were enrolled in a biomonitoring study in the greater Seattle area (Lu et al., 2001). Seven of the 13 families contacted were ultimately enrolled in this study. Of the six families who did not enroll, three expressed interest but were unavailable during the proposed sampling period, and three were not interested due to the time commitment involved. Five of the seven suburban families enrolled were from suburbs northeast of Seattle and were originally recruited from a private pediatric office, and two were from suburbs south of Seattle and were originally recruited from a WIC clinic.

Selected demographic information regarding the children enrolled in this study is presented in Table 2. The average child age was 3.9 years and the average child weight was 16.8 kg. Ten girls and three boys were enrolled. All of the rural families contained at least one adult who worked in an agriculture-related job, whereas none of the adults in the suburban families were involved in agricultural work.

Table 2 - Age, weight, and gender of children.

Full table (30K)

A total of 88 individual diet samples were collected from the four food categories combined. Of the 15 targeted OP pesticides, 6 were detected: azinphosmethyl, chlorpyrifos, malathion, methidathion, methyl parathion, and phosmet. Sixteen samples (18%) contained detectable levels of at least one OP pesticide, and two samples (2%) contained two OP pesticides. The latter two samples belonged to the fresh fruits and vegetables category. Table 3 summarizes pesticide detections by food category, and provides the net weight of the samples analyzed and the full contents of each sample. Dairy product samples (category III) are not included since no pesticide determinations were made in this category. Sample size varied by food category because not every duplicate diet contained food samples from all four categories. Table 4 summarizes pesticide detections by child.

Table 3 - Pesticide concentration, food mass, and contents of samples containing quantifiable concentrations of pesticides by food category.

Full table (79K)

Table 4 - Pesticide detections by child. Category III, dairy, had no detections in any of the samples. Pesticide concentrations in samples with detectable levels are in parentheses (ng/g).

Full table (66K)

Fresh fruits and vegetables (category I) had more frequent pesticide determinations than the other categories. Azinphosmethyl was detected most often, and was found in four of the 17 fresh fruit and vegetable samples (24%). The highest single determination occurred in a sample of 10 cherry tomatoes containing 350 ng/g of chlorpyrifos and 30 ng/g of azinphosmethyl. Methyl parathion and phosmet were each detected in one category I sample. Beverage samples (category II), and particularly those including apple juice, demonstrated frequent azinphosmethyl detections. Four of 21 beverage samples (19%) contained azinphosmethyl, and of samples including apple juice, four of nine (44%) contained azinphosmethyl. Methidathion was found in two beverage samples, each containing an orange drink. No detections occurred in any of the 24 dairy samples in category III, although azinphosmethyl was not measured in this category due to analytical difficulties. Malathion was the only OP pesticide detected in processed foods, appearing in 4 of the 26 samples (15%).

In order to estimate the upper bounds of the cumulative risk from dietary OP pesticide exposure for these subjects, the total daily OP pesticide dose was estimated for the subject with the highest dietary exposure during the sampling period. Subject S1 consumed 114 g of cherry tomatoes contaminated with 30 ng/g of azinphosmethyl and 350 ng/g chlorpyrifos (see Tables 3 and 4). Chlorpyrifos was selected as the reference chemical, and the TEF for azinphosmethyl was calculated by taking the ratio of the LOAEL for azinphosmethyl and chlorpyrifos (NRC, 1993). The LOAEL was 1.0 mg/kg/day for each chemical and therefore the TEF was equal to 1. The mass of azinphosmethyl consumed was converted to chlorpyrifos equivalents by multiplying by this TEF and then adding the mass of chlorpyrifos consumed. The total mass of chlorpyrifos equivalents consumed was 43.3 g, and this value was divided by the subject's body weight (see Table 2) to yield a total dietary dose of OP pesticides of 2.5 g/kg/day. The subject with the second highest dietary exposure during the sampling period was child R4, whose total dietary dose was calculated to be 0.24 g/kg/day.

Discussion

This study documents several OP pesticide residues in 24-h duplicate diets collected in the summer and fall from 13 children aged 2 to 5 years living in rural and suburban households. While pesticide concentrations are monitored annually in many foods by the United States Food and Drug Administration (USFDA) and the United States Department of Agriculture (USDA), this study is among the first to specifically target the unique dietary exposures to children.

This study included several limitations. Recoveries for several OP pesticides in fortified samples were confounded by the presence of analytes in the fortified matrix. In order to create realistic matrices for the fortification samples, food items similar to those found in the actual samples were purchased and combined. The background levels in these samples ranged from 0.6 ng/g for diazinon in processed foods to 42 ng/g for azinphosmethyl in fresh fruits and vegetables. These background levels prevented consistent fortification recovery results at low concentrations, and therefore the reported levels of quantitation are likely higher than they would be otherwise. Organic foodstuffs might have been a more reasonable choice in creating blank matrices.

Azinphosmethyl was not analyzed in dairy samples due to the presence of interfering peaks as well as column fouling. However, azinphosmethyl was not detected in any dairy samples in the 1997 USDA Pesticide Diet Program study (USDA, 1997).

The current study was also limited by a small sample size and cannot be generalized to the overall population, or even to the populations from which the samples were drawn. It should be emphasized that the children identified for this study exhibited relatively high OP pesticide metabolite levels among the larger study populations. Finally, while this study included repeated measures on each child, it was limited in its ability to capture data on the variability of the children's diets over time.

The concentrations of pesticides detected in this study were below federal tolerance levels. However, current tolerance-setting procedures do not consider exposure to multiple compounds with common mechanisms of toxicity, such as the OP pesticides. Also, these pesticide tolerances are intended for raw produce, and washing or cooking of produce is expected to decrease pesticide levels (Olden and Guthrie, 2000; Schattenberg et al., 1996). It is also possible that foods containing OP residues less than the limit of detection were combined with foods with detectable residues, and therefore the pesticide concentration in the composite sample may be diluted and thus may underestimate true exposure.

In conducting duplicate diet studies, it is important to divide food into categories that are both meaningful in terms of consumption patterns and that allow for reasonable analytical recoveries. The division of food samples in this study into four distinct categories was based on moisture content, fat content, and likelihood of OP residue presence. Homogeneity of composited samples in terms of fat, moisture, ash, or protein reportedly improves analytical recoveries (Thomas et al., 1997; Melnyk et al., 1997; Quackenboss et al., 2000), and combining foods based on likelihood of analyte presence reduces underestimation based on sample dilution. These criteria were therefore used to determine food categories for this study.

Although 15 OP pesticides were targeted in this study, only 6 were detected in any of the food samples. Azinphosmethyl was the most frequently detected pesticide, and was most commonly found in apples or apple juice. This corresponds with the fact that azinphosmethyl is commonly used to control the codling moth in apple orchards. Of the food samples in which pesticide residues were detected, 78% contained fresh produce or fruit juice. The sample containing the highest level of OP pesticides was a sample of cherry tomatoes containing 30 ng/g azinphosmethyl and 350 ng/g chlorpyrifos. This sample was consumed by a child from a suburban home in the fall. When asked about indoor and outdoor OP pesticide use during the fall sampling period, the parents of this child did not report any use. This, coupled with the fact that azinphosmethyl is only registered for agricultural uses, suggests that agricultural application, as opposed to residential contamination, was the source of the OP pesticides found in this sample.

A higher frequency of OP pesticide determinations was seen in the diet of suburban children during the summer. This may have been a result of differences in the contents of the dietary samples submitted by parents. Fifty percent of rural children in the summer did not eat any fresh fruits or vegetables, compared to one out of seven (14%) of the suburban children. Beverage samples were submitted by all but one suburban family over both sampling periods, whereas half of the rural families did not submit beverage samples during one or the other sampling period, and one rural family did not submit beverage samples during either period. It is not clear whether these children did not consume beverages or whether beverages were consumed, but duplicates were not submitted.

This study is among the first to target children's dietary exposures to pesticides using duplicate diet methodology. The Agricultural Health Study (AHS) intends to collect 24-h duplicate diet samples from farm children in Iowa and North Carolina, and an AHS pilot study conducted in 1994 collected six 24-h duplicate diet samples from entire farming households (Melnyk et al., 1997). Two of the households also provided separate 24-h duplicate diet samples from a designated child. Of the pesticides investigated in the current study, the AHS pilot study also targeted chlorpyrifos, diazinon, malathion, phorate, and terbufos. The investigators found no quantifiable concentrations of diazinon, phorate, or terbufos. Malathion was detected in the "solid foods" category, which included processed foods, in five of the six farms, and in one of the child food samples (2.7 ng/g). Chlorpyrifos was found in low or trace amounts in the household diet samples from three of the six farms, and in one of the child food samples (1.3 ng/g). These levels are lower than those detectable concentrations found in the current study, where chlorpyrifos levels range from 12 to 350 ng/g and malathion levels range from 4.3 to 21 ng/g. The AHS also reported that locally produced food contained measurable amounts of pesticides, supporting the use of personal monitoring as a tool to estimate dietary exposure.

A study for the National Human Exposure Assessment Survey (NHEXAS) in Maryland investigated temporal variation in human exposure to pesticides through dietary ingestion using duplicate diet sampling of 75 individuals older than 10 years (MacIntosh et al., 2001). These researchers separated the duplicate diets into two categories, solid foods and beverages, and analyzed the samples for 10 pesticides, including two OP pesticides, malathion and chlorpyrifos. All malathion and chlorpyrifos concentrations below the 95th percentile reported for solid food samples were lower than the limit of detection of the current study. For comparison purposes, pesticide concentrations in the solid food samples in this study were calculated by combining samples in categories I, III, and IV, except dairy samples composed entirely of milk. The 95th percentile and the maximum values for malathion concentration in solid food samples were higher in the current study than in the NHEXAS study (6.9 vs. 5.9 g/kg and 21.1 vs. 16.5 g/kg, respectively). The 95th percentile for chlorpyrifos reported in the NHEXAS study (2.9 g/kg) was below the limit of detection for the current study. Both the 99th percentile and the maximum value for chlorpyrifos were higher in the current study than in the NHEXAS study (60.4 vs. 7.7 g/kg and 79.5 vs. 24.3 g/kg, respectively.) These findings lend support to the theory that young children's exposure to pesticides through their diet typically exceeds that of older children and adults.

This sampling strategy focused on short, distinct time periods and therefore represents acute exposures. The acute population-adjusted daily reference dose (aPAD) for chlorpyrifos exposure is 1.7 g/kg/day, and this safety benchmark was exceeded for one child whose daily dose of chlorpyrifos equivalents was estimated at 2.5 g/kg/day (EPA, 1999a; EPA, 2000). All other subjects had acute exposures at both sampling events below the aPAD, with the next highest individual exposure reaching 0.24 g/kg/day. However, dietary exposure represents only one component of aggregate exposure. OP pesticide exposures from additional pathways such as inhalation, dermal absorption, and ingestion of nondietary contamination (e.g., house dust and yard soil) must also be considered. Additionally, duplicate diet sampling does not necessarily account for food contamination occurring after food preparation. It is possible that while children are handling their food, pesticide transfer to food from contaminated surfaces could occur (Akland et al., 2000). For these reasons, the doses presented here may be underestimates of the children's actual total doses.

Conclusions

This study demonstrates the feasibility of using duplicate diet samples to monitor children's dietary exposures to OP pesticides. It also shows the high variability in children's individual dietary exposures, and suggests that in order to assess aggregate exposure, personal monitoring is necessary. This work also lends support to the theory that due to dietary differences, young children may experience higher levels of pesticide exposures through their diets than older children and adults. None of the OP pesticide detections were above the legal tolerances for residues on produce, however apples and apple juice were found to have higher rates of OP pesticide detections, particularly azinphosmethyl, than other foodstuffs. Although tolerances were not exceeded, one subject had an estimated daily dose greater than the aPAD. Additionally, aggregate exposure from multiple pathways was not investigated in this segment of the study, and must be considered in order to fully characterize exposure and to determine whether such exposures surpass regulatory guidance values.

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Acknowledgements

This work was supported by the U.S. Environmental Protection Agency (U.S. EPA) through the Science to Achieve Results (STAR) program (Grant No. R819186-01) and by Cooperative Agreement U07/CCU012926-04 (Pacific Northwest Agricultural Safety and Health Center) from the National Institute for Occupational Health/Centers for Disease Control and Prevention (NIOSH/CDC). Contents are solely the responsibility of the authors and do not necessarily represent the official view of the U.S. EPA or NIOSH/CDC. The chemical analyses for this study were conducted by the Washington State University Food and Environmental Quality Laboratory. Special thanks to Dr. Carol Weisskopf for her efforts. The authors also thank Dr. Chris Saint, the EPA Project Officer, and the families who participated in this study.