Pesticide exposure scenarios

Brouwer, D.H. and van Hemmen, J.J. (1994) Fitting personal protective equipment (PPE) to the hazard selection of PPE for various pesticide exposure scenarios in greenhouses, in Book of Abstracts of the American Industrial Hygiene Conference Exposition, American Industrial Hygiene Association, Anaheim, CA. 

For the purposes of these field studies, a test system is defined as a specific tract of land managed in part through use of pesticides. Test systems are normally limited to one crop or land use type and may include row crops, grains, fruits or golf courses. The tract of land, of course, has associated biota that are present naturally or as part of the management practices. These biota are also part of the test system and are normally described as test species or species of interest. Selection of test systems is critical to evaluate wildlife exposure scenarios in a sufficient number of sites within appropriate geographic regions. 

Appendix B provides brief case studies of two pesticides, glyphosate and permethrin, for which a pre-existing risk assessment can help to put biomonitoring results into perspective. In both cases, the Environmental Protection Agency (EPA) has evaluated risks for a wide array of exposure scenarios as part of the reregistration process, and there are biomonitoring data whose interpretation could benefit from these risk assessments. 

Microcosm and mesocosm studies can be directly designed for the purpose of EQS derivation (e.g., the exposure scenario, communities to be monitored, etc.). Guidance for design and conduction of microcosm and mesocosm studies can be found in the references given for pesticide risk assessment, but OECD has recently published a guideline for a lentic field test that is not focused on pesticides alone (OECD 2006b). 

The composition and size of the patches used in dermal exposure studies are important considerations and should be based on the characteristics of the pesticide and the exposure scenario. For sprays, the use of papermaking pulp or... 

For purposes of screening-level assessments, while multiple applications may exist for a given pesticide, e.g. indoor residential exposures may originate from pesticide applications to ornamental and landscape plantings, typically a worst-case or high-end exposure scenario is selected for the initial assessment. For example, turf grass broadcast application is the use pattern that often serves to provide the worst-case estimates of human contact and exposure for most outdoor uses of a pesticide. 

The typical end-use product and application method chosen as representative of the extreme-case exposure scenario must be used to attain the highest permissible rate allowed by label directions. Sampling for indoor residues should consider all potential sites where appreciable residues are expected and are accessible. For instance, dermal contact may come from exposure to the pesticide as a residue on carpets, vinyl tile, upholstery and counter tops, while airborne residue (vapor- or particle-phase) may provide the source of inhalation exposure. The measurements taken are linked specifically to the method of application. 

Application of in vivo and in vitro dermal absorption study results to a risk assessment for dermal exposure to a pesticide requires professional judgment as it is rare for a dermal absorption study to mimic the exposure scenario exactly. Some specific challenges are presented below. As noted in the previons section, achieving a harmonized approach on these challenges, and other technical issnes related to application of dermal absorption stndies to risk assessment, wonld facilitate work-sharing and joint-review endeavonrs. 

Although a comprehensive survey of occupational and residential exposure data requirements across all regulatory jurisdictions is beyond the scope of this chapter, it is evident that the majority of jurisdictions require an assessment of exposure for conventional pesticides if the proposed use pattern indicates potential handler exposure (either occupational and residential) or agricultural re-entry exposure. The extent to which exposure assessments are required/conducted for residential re-entry scenarios or bystanders, however, is less uniform. Although most jurisdictions require/conduct assessments for residential re-entry scenarios (e.g. treated turf, carpeL etc.), exposure assessment methodology for other types of bystander exposure scenarios (e.g. residential exposure in agricultural areas, direct exposure to drift, exposures from the use of pesticides in schools, daycare centers and other public places) is less mature and requires further collaboration. 

Approaches for aggregating exposure for simple scenarios have been proposed in the literature (Shurdut et al., 1998 Zartarian et al., 2000). The USEPA s National Exposure Research Laboratory has developed the Stochastic Human Exposure and Dose Simulation (SHEDS) model for pesticides, which can be characterized as a first-generation aggregation model and the developers conclude that to refine and evaluate the model for use as a regulatory decision-making tool for residential scenarios, more robust data sets are needed for human activity patterns, surface residues for the most relevant snrface types, and cohort-specific exposure factors (Zartarian et al, 2000). The SHEDS framework was used by the USEPA to conduct a probabilistic exposure assessment for the specific exposure scenario of children contacting chromated copper arsenate (CCA)-treated playsets and decks (Zartarian et al, 2003). 

Methodological guidance for conduct of exposure studies to measure other exposure scenarios (e.g. residential exposure in agricultural areas, direct exposure to drift, and exposures from the use of pesticides in schools, daycare centers and other public places). 

Some possible exposure scenarios exist, however. When the pilot is also the mixer/loader of the pesticides, the pesticide exposure from that activity may be continued in the cockpit through contaminated PPE. The pilot also may be exposed to pesticides when climbing into or out of the cockpit of a contaminated aircraft. Finally, in some situations, a pilot making a sharp turn or flying in strong wind may fly through the pesticide swath just released from the aircraft. If the cockpit is not enclosed or if its air is not filtered, the resultant pesticide exposure could be significant. 

Define the potential threat in terms of the toxicity of the pesticide to be handled and the conditions under which it will be handled (i.e., the exposure scenario). 

As outlined below, many factors combine to define the exposure scenario. In turn, the scenario combined with the toxicity of the pesticide determines the level of protection that should be used. 

Insecticides were developed to kill insect pests that are themselves proven to be hazardous to human health. Pesticides have generally been useful to the human race. Insecticides have forever banished the threat of locust plagues that swept nineteenth century America. Also, they have been a primary reason why American agriculture is dominant in the world, and they allow us to debate what to do with our grain surpluses rather than argue over how to increase agricultural productivity to feed our people. Significant occupational exposures to these compounds continue to exist around the world, and this exposure scenario may result in toxicity when inadequate safety precautions are applied. 

It may also be important to decide whether the model output will be used to identify exposure conditions that are protective (e.g., if the model output gives a result less than X, there are unlikely to be impacts on field populations at exposures equal to or less than the one modeled) or whether the results are intended to be used in a more predictive manner (e.g., the model might be used to predict the time necessary for full population recovery following a defined pesticide application scenario). Whether the model result should be predictive, or merely protective, is likely to place different constraints on the model s complexity. Regardless of what types of models are to be used, careful attention has to be paid to adequately testing, verifying, and validating them (see Chapter 9). 

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