26 F i e l d Worker E x p o s u r e : The Usefulness of Estimates Based on Generic Data RICHARD C. HONEYCUTT Downloaded by TUFTS UNIV on July 14, 2016 | http://pubs.acs.org Publication Date: February 25, 1985 | doi: 10.1021/bk-1985-0273.ch026
Agricultural Division, CIBA-GEIGY Corporation, Greensboro, NC 27419 Generic data derived from existing worker exposure data are useful for estimating exposure to new agricultural chemicals. Several methods of using such data in evaluation of the safety of agrichemicals are discussed. Examples of using generic data for predicting mixer-loader and applicator exposure are presented. The evaluation of the safety of pesticides to farm workers is a complex endeavor which integrates several types of data such as metabolism, residue, d i s s i p a t i o n , dermal absorption, f i e l d worker exposure, use pattern data, and mammalian toxicology data. One of the most important elements of such a safety evaluation is the estimation of exposure of pesticides to f i e l d workers. For this discussion, f i e l d workers are defined as mixers, loaders, a p p l i cators, cleanup workers, and flaggers. Davis, Wolfe, Popendorf and others (1-3) have published extensively in this area. With a considerable volume of data on f i e l d worker exposure available, it is reasonable to consider that i t should be possible to summar i z e these data into a generic form and use them to estimate actual worker exposure. Dr. Hackathorn's and Dr. Reinert s papers presented in this symposium series deals extensively with this concept of using generic data for estimating f i e l d worker exposure. Although much data exist to support the concept of using generic f i e l d data to estimate f i e l d worker exposure to p e s t i cides, very l i t t l e has been published to show how such generic data are actually used to estimate worker exposure. It is the objective of this paper to demonstrate how laboratory absorption data, f i e l d exposure data and product use data can be integrated to estimate exposure to pesticides for f i e l d workers. These data when coupled to toxicology data can then be readily used to make risk assessments for these pesticides to workers. Such exposure estimates/risk assessments should be favored over actual f i e l d testing since i t would obviate the need for involving humans in such f i e l d testing programs. The EPA has strongly recommended f
0097-6156/ 85/0273-0369$06.00/0 © 1985 A m e r i c a n C h e m i c a l Society
Honeycutt et al.; Dermal Exposure Related to Pesticide Use ACS Symposium Series; American Chemical Society: Washington, DC, 1985.
DERMAL EXPOSURE RELATED TO PESTICIDE USE
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this approach in i t s March 1982 draft of Subdivision Κ of the Pesticide Assessment Guidelines for reentry protection (4). Methods Obtaining Laboratory and F i e l d Data: The use of the generic approach to estimating f i e l d worker exposure can be separated into two d i s t i n c t methodologies:
Downloaded by TUFTS UNIV on July 14, 2016 | http://pubs.acs.org Publication Date: February 25, 1985 | doi: 10.1021/bk-1985-0273.ch026
1. 2.
Obtaining exposure data in the laboratory and f i e l d . The Generic Approach, i . e . , using these data to extrapolate from one chemical to another.
Laboratory Absorption Studies: Several techniques are available for estimating dermal absorption of pesticides. These techniques are extensively reviewed in an e a r l i e r section of this book. For most pesticides, a reasonable estimate of dermal absorption for humans can be made using laboratory data. These data are used to estimate an average dermal absorption rate from which one can calculate the percentage of active ingredient that i s absorbed i n a typical eight-hour work day. F i e l d Testing: H i s t o r i c a l l y , f i e l d testing has been an important element in determination of exposure levels of pesticides to f i e l d workers. These tests have been essential in establishing what factors are important for determination of f i e l d exposure. We have done several f i e l d tests on worker exposure and have constantly modified our methods to improve the u t i l i t y and sta t i s t i c a l r e l i a b i l i t y of the data and to lay a data base founda tion for developing a generic approach to estimation of exposure levels. Segmenting F i e l d Tests: Attempts should be made to segment a f i e l d worker's d a i l y tasks into four areas - mixer-loader, a p p l i cator, cleanup worker, and flaggers (where appropriate). This technique allows one to determine which task produces the most exposure. These data can then be used to write precautionary labels directed at s p e c i f i c tasks. Of course, t o t a l exposure for workers carrying out combined tasks can be determined by summa tion . Matrices for F i e l d Testing: Patches (4" χ 4" - cellulose) are used for determining dermal exposure. Inside versus outside patches are beneficial to determine penetration of protective clothing. Personal a i r monitors are used for determining respi ratory exposure. Cotton gloves can be used for hand exposure estimation. Rubber gloves worn over cotton gloves can be used to determine effectiveness of protective gloves. Replications : Generally, f i e l d studies w i l l be replicated 2-3 times. That i s , complete studies are done at 2-3 different dates in the crop season. Each new study consists of two replications of each task, i . e . , mixer-loader, applicator, cleanup worker. Each task can be monitored with and without protective clothing.
Honeycutt et al.; Dermal Exposure Related to Pesticide Use ACS Symposium Series; American Chemical Society: Washington, DC, 1985.
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Calculations : Calculations of dermal exposure are made by extrapolation of patch data to exposed areas of the body. A technique described by Davis Q ) i s commonly used. The f i n a l value of exposure i s in mg/hour. Examples w i l l be used later on to show how values of mg/hour exposure are converted to lifetime exposure values. Use Data: Potential use data for a product is c r i t i c a l for determination of potential exposure to f i e l d workers. There are four general steps in acquiring adequate data to estimate potent i a l exposure to a g r i c u l t u r a l workers.
Downloaded by TUFTS UNIV on July 14, 2016 | http://pubs.acs.org Publication Date: February 25, 1985 | doi: 10.1021/bk-1985-0273.ch026
1. 2. 3. 4.
Determine total acres to be treated for a particular pest for the anticipated market area. Determine number of farms in this area. Determine average acres for a " t y p i c a l " farm. Determine how many acres treated v i a commercial application versus individual farmer application.
The Generic Approach: Extrapolation of F i e l d Data from One Chemical to Another: Extrapolation or estimation of exposure of one chemical Y to f i e l d workers using f i e l d data from exposure to chemical X i s a complex task in which several factors, as well as exposure values must be integrated. Study of f i e l d worker pesticide exposure data has shown that several factors influence exposure. These factors are mostly physical in nature. Most of the time the chemical properties of the pesticide do not come into play as determinants that may have an impact on exposure l e v e l s . The physical factors are: 1.
2.
3. 4.
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Application Equipment: Examples of application equipment are: boom sprayers, airplane, a i r blast sprayer, backpack equipment and high pressure guns. Backpack and a i r blast operations appear to give greatest exposure to applicators. Protective Clothing: Examples of protective clothing are: rubber gloves, coveralls, long sleeves, long pants. Hand exposure i s the major route of dermal exposure to mixerloaders. Rubber gloves reduce mixer-loader dermal exposure by as much as 99% i f the f i e l d worker i s careful not to contaminate the inside of the gloves. Formulations: Examples of formulations are: l i q u i d concentrates, wettable powder, prepacks, granules. Use Patterns and Markets: Commercial and individual farm operations should be considered. Commercial operations result in more lifetime exposure to farm workers. When performing a r i s k assessment, commercial versus single farm operations can be estimated separately using potential market data. Weather Factors include rain, wind v e l o c i t y , wind d i r e c t i o n , as well as temperature. For most estimates, assumptions are made that the weather i s sunny and the wind i s _
