Characterizing dietary pesticide exposures, particularly for infants and children, has become an essential component of cumulative pesticide risk assessment, as mandated by the 1996 FQPA. Several reports (Lu et al. 2006a
; Schettgen et al. 2002
) have clearly demonstrated the significant contribution of dietary intakes to the overall OP and pyrethroid pesticide exposure in children and highlighted the critical need to quantify the health risks associated with the low but chronic daily exposures to those pesticides. As the result of the 1993 NRC report, the U.S. Congress funded the PDP under the USDA to test pesticide residues annually in foods consumed most often by children and, to the extent possible, “as eaten.” Since its inception, PDP has tested > 200,000 food commodity samples for an extensive list of pesticides. When pesticide residues reported in the PDP database are combined with food consumption information, such as those surveyed in the Continuing Survey of Food Intakes by Individuals [now integrated in the National Health and Nutrition Examination Survey (NHANES) conducted by the Centers for Disease Control and Prevention] or the Total Diet Study conducted by the FDA (Egan et al. 2007
), mathematical model simulations can be used to provide a basis for estimating pesticide dietary exposures and risks. In theory, those estimated pesticide intakes would reflect dietary exposures at the population level; however, it is often the consumption of so-called high-risk food commodities containing elevated pesticide residues that shapes the outcome of dietary risk assessment. Therefore, it is imperative to measure pesticide residues in foods actually consumed by children as part of their customary diets. Direct measures remove many of the unknowns and assumptions that must be made when calculating dietary risk assessments from separately collected consumption surveys and pesticide residue databases.
Before further interpreting the results obtained from the CPES, the limitations of this study should be acknowledged. First of all, many of the 24-hr duplicate food samples that we analyzed were composite food samples, so it is not possible to link pesticide residues found in the composite samples to individual food commodities. Second, the reported pesticide residues for those composite food samples may be underestimated because of the nature of compositing multiple food items into one sample. It is likely that we combined foods containing no pesticide residues with foods with pesticide residues, resulting in a diluted composite sample. Third, the 24-hr duplicate food samples collected from CPES-WA had been frozen at −20°C since 2004 before they were analyzed in 2007/2008. Although we anticipate some degradation of pesticides in certain food samples that undoubtedly would underestimate the true pesticide residues, it is difficult to quantify the magnitude. Fourth, the data reported here are limited by the small number of food samples that we collected from a total of 46 children on 5 different days across two seasons, and cannot be generalized to the overall population of children. Finally, although the collection of 24-hr duplicate food samples has been a common research method used to provide surrogate measurements of dietary pesticide exposure (Bradman et al. 2007
; Fenske et al. 2002
; MacIntosh et al. 2001
; Melnyk et al. 1997
), it is not known whether the 24-hr duplicate food samples provide valid estimates of the true levels of pesticide residues in foods actually consumed by children.
Overall, the levels of pesticide residues detected in the CPES food samples were similar to levels reported by the PDP; however, two CPES food samples and one MBA item exceeded reported PDP levels. These two CPES food samples probably represented a large single exposure for each child. The fact that two CPES children had a single exposure exceeding the PDP reported data highlights both the challenge in monitoring and managing dietary pesticide risks and the need to improve the PDP approach to more accurately capture real-world exposures. In fact, our comparison with the PDP data set was limited by the commodities that the PDP included in their reports. In both CPES study years, the PDP data analyzed only approximately one-third of the foods consumed by the CPES children. Although the PDP is the most comprehensive data set representing pesticide food residues in the United States, it does not test all foods, particularly foods that are commonly consumed by children, despite its increased focus on children’s foods in response to the FQPA. Furthermore, many commonly consumed foods are not tested every year by the PDP. These limitations threaten the success of conducting representative dietary pesticide exposure, risk assessments in infants and children (as mandated by FQPA), and trend analysis of dietary exposure risks.
The PDP database provides a basis for estimating pesticide dietary exposures and risks when combined with food consumption information and information on the toxicological potency of individual pesticides. This practice has been adapted by the Environmental Working Group (EWG) in their annual Shopper’s Guide to Pesticides reports (EWG 2009
), in which commonly consumed fruits and vegetables, surveyed by the Total Diet Study, are ranked based on the pesticide residue data published by the PDP. As stated by EWG, the philosophy behind the Shopper’s Guide is to “give consumers the information they need to make choices in order to reduce pesticides in their diets.” Instead of conducting complex dietary pesticide risk assessments, the Shopper’s Guide provides a qualitative comparison on the overall load of pesticides found on commonly eaten fruits and vegetables. We found that 5 and 6 of the top 12 most consumed food commodities by CPES-WA (apple, peach, grape, carrots, and lettuce) and by CPES-GA children (strawberries, apple, pear, carrots, spinach, and peach), respectively, are among the top 12 most contaminated food items ranked by the Shopper’s Guide’s published in 2009. In fact, all of the top 12 most contaminated food items listed in the Shopper’s Guide’s were consumed at different frequencies by the CPES children. Because some of the high-pesticide-risk commodities, such as peaches, grapes, and pears, are usually not available to the consumers year round, seasonal variations in dietary pesticide exposures and risks may not be captured by risk assessors if seasonality is not taken into account when defining common dietary consumption patterns. We found support for this hypothesis in a recent study in which we found that CPES children consumed more seasonal food commodities, such as apples, peaches, nectarines, melon, grapes, pears, and strawberries, than did NHANES children in the same age range (Riederer et al. 2009
). For food without seasonality, such as cow’s milk, apple juice, and orange juice, consumption differences were not apparent. Our comparisons illustrated how food consumption data collected in a cross-sectional manner, such as those of the NHANES, may not adequately capture seasonal variability in children’s dietary habits, and those measurement errors will no doubt be carried forward to the risk assessment steps.
Among the 44 duplicate food samples containing detectable pesticide residues, we found 11 OP and 3 pyrethroid insecticides, and 16 of these duplicate food samples (7%) contained more than one pesticide (OP or pyrethroids). Among the food samples with detectable pesticide residues, we found much higher levels of pyrethroid insecticides than OP pesticides. This finding is consistent with the tolerances for pyrethroid insecticides that are generally higher than for OP insecticides, and with the residues reported by PDP (). In addition, the higher pyrethroid insecticide residues may have resulted from excessive use of pyrethroid insecticides in the field to battle insect resistance problems. OP pesticide residues were detected more often in the CPES-WA food samples, whereas pyrethroid pesticides were detected more often in the CPES-GA food samples. The possible explanations for this finding may stem from contamination of foods resulting from residential use (Melnyk et al. 2009
) of pyrethroid insecticides in the South/Southeast region, regional differences in agricultural practices, or changes in pesticide use over time. If this pattern holds true, it suggests that it is necessary to conduct dietary pesticide exposure and risk assessments based on geographic location. In addition to the seasonal nature of food consumption patterns among CPES children as reported in our recent study (Riederer et al. 2009
), the findings of multiple pesticide residues in single food commodities and the geographical differences in the type of pesticides further demonstrate the complexity of conducting overall dietary pesticide exposure and risk assessments.
Although the pesticide residue levels measured in CPES were all below U.S. EPA tolerance levels (U.S. EPA 2010
), it should be acknowledged that tolerances were established at on a per chemical per crop basis in order to ensure the best practice of pesticide uses in the field. Also, tolerance levels are intended for monitoring residues in raw produce at the farm gate, before washing, shipping, storage, marketing, and food preparation (Olden and Guthrie 2000
). Furthermore, those tolerance levels do not consider exposure to multiple pesticides with common mechanisms of toxicity, such as the OP pesticides. Regardless, the findings from this study could be used for the basis of future regulatory decisions in terms of implementing a routine pesticide residue monitoring program within the PDP focusing on food items that are commonly consumed by children and on pesticides that are routinely detected in those foods. These data, along with seasonal consumption patterns, would be very useful in dietary pesticide risk assessment. In addition, the findings from this study, along with other reports (e.g., EWG’s Shopper’s Guide), could be used by parents and caregivers who want to keep nutritional foods in their children’s diets but avoid the intake of pesticide residues in the high-pesticide-risk items.