Collecting Foliar Pesticide Residues Related to Potential Airborne Exposure of Workers William J. Popendorf, Robert C. Spear,* and Sieve Selvin Department of Biomedical and Environmental Health Sciences, School of Public Health, University of California, Berkeley, Calif,
~
.A technique is presented for collecting foliar dust samples for pesticide residue analysis. T h e proposed procedure is intended t o collect t h a t fraction of t h e foliar residue t h a t can become airborne due to t h e activity of workers engaged in harvesting or thinning crops. T h e foliar d u s t levels measured by this procedure were experimentally shown to be highly correlated with airborne dust concentrations. T h e variability of replicated measurements of foliar dust levels using this technique was experimentally estimated. Dust level d a t a collected over a five-month period using t h e new procedure in t h e Central Valley of California show increasing levels on citrus foliage over the spring and summer months. Both t h e route and the extent of exposure of agricultural workers to organophosphate pesticide residues apparently vary with certain environmental and physical conditions. Although vapors ( I , 2 ) and direct contact with moisture ( 3 ) may be important exposure vehicles in some circumstances, pesticide contaminated foliar dust appears to be more widely implicated as central to the residue intoxication hazard (4-7). As a result. a means of collecting foliar residue samples was sought which would be highly correlated with t h e potential airborne exposure of workers t o these pesticide-laden dusts. A vacuuming procedure has been developed and is described in this paper together with results of experimental comparisons with the “dislodgeable” residue technique of Gunther et al. (8)
in t h e gap are equivalent t o natural winds of 10 times t h e speed in t h e gap, i.e., -170 mph. T h e air flow as it approaches the gap over a rough leaf surface is not “ideal,” b u t Blasius’ boundary layer equations predict a boundary layer thickness of 300 p , t h e velocity decreasing smoothly below free stream conditions in this region. Fluctuations in surface contours, particle position, size, and shape make a detailed investigation of particle adhesion and removal very complex. T h e depth of the dust deposit and accumulations of t h e pesticide in natural leaf cusps a t the time of application are two important factors affecting not only the available fraction of t h e surface deposit but also the residue chemical composition. T h a t these aerodynamic removal forces would ideally match those during picking would be fortuitous, a t best. However, it was suspected t h a t t h e airborne particulate residue availability would be more closely assayed by this method than by washing procedures. Experimental Procedures
Design Considerations
T o estimate the variability of t h e proposed vacuum procedure, t o evaluate its potential as a predictor of airborne dust levels during picking, and to contrast the measurements with Gunther’s dislodgeable residue procedure, a set of field experiments was undertaken. In these experiments attention was focused on the physical availability of t h e dust, and no chemical analyses were performed. However, organophosphate vapor retention characteristics of t h e membrane filter are reported as very good ( 1 0 ) . T h e possible loss of pesticide due t o vaporization and t h e resulting significance of differences in measured residue concentra-
T h e basic requirements imposed on the sample collection method are that: ( a ) t h e particulate removal principle be sensitive to the comparative “availability” of the surface residue to be dislodged and aerosolized during picking operations; ( b ) the method be applicable t o any crop with foliage; (c) t h e method result in readily interpretable and reproducible quantities-i.e., pg of pesticide residue per cm2 of leaf area: and ( d ) the sampling unit be portable and preferably operable by one person. Initial investigations indicated t h a t a vacuuming technique might meet these requirements. T h e current version of t h e vacuum assembly, Figure I,employs a modified commercial crevice tool fitted with spacing skids, joined to a bell mouth which, in turn, is coupled t o modified hi-vol air sampling accessories. Suction is provided by a L a m b twostage direct air-flow vacuum motor operating a t a vacuum of 73 & 3 in. of water (9).T h e dust available to the moving air stream is collected on a preueighed 90-mm membrane filter positioned a t the open end of the bell mouth. T h e flow characteristics a t the nozzle gap were selected largely from judgment and limited prototype experience. Average velocities in t h e 2.3 m m (0.090 in.) gap are 7.5 f 1 m/sec (1500 ft/min). T h e constrained flow through the gap produces boundary layer conditions quite dissimilar to natural wind over the leaf surface. With t h e gap height as t h e characteristic dimension, t h e calculated Reynolds number equals 1100. An equal velocity free flow across a flat 50-mm leaf would have a n average Reynolds number of 12,500. These assumptions indicate that boundary layer conditions
Dimenions are in c m (inches in parentheses). Materials are aluminum with plastic sleeve, steel skids, and neoprene seals
Figure 1.
Vacuum nozzle and filter assembly
Volume 9, Number 6, June 1975 583
tions between the vacuuming technique and the dislodgeable residue technique of Gunther await future investigation. Six representative orange groves were selected in three citrus-growing regions of California: three in t h e S a n Joaquin Valley, two in southern California, and one on the coast. None of t h e fields had been sprayed with pesticides for a t least 30 days. Within each grove, a rectangular block of six trees was chosen a t random. Eight sampling points on each tree were evenly spaced a t 45' intervals and a t a height of 5-6 ft, similar to the sampling procedure outlined by Gunther e t al. (8). Each of the replicated samples consisted of 48 whole leaves, one from each sampling point on each tree. Five to 10 min were required to vacuum each sample. T h e sampling procedure consists of collecting a number of whole leaves. each leaf being carefully clipped a t the stem, placed on a covered board and vacuumed on both sides, top side first. After vacuuming, the leaves are mounted flat on a sheet of paper and spray painted t o produce individual shadows subsequently sized to give leaf area. T h e filter is carefully removed from t h e holder assembly, desiccated, and postweighed to determine collected dust weight.
Table I. Tabulated Sample Values (Rank Ordered) and Their Basic Statistics in Each Grove Grove n u m b e r ,
jn
2
1
/ =
4
3
5
6
A. Vacuum Samples, p G Dust/Cm? Leaf 30.15 32.0< 25.2? 55.4? 6 7 . P 34.7" 33.6b 2 9 . P 57,2r 7 6 . V 36.8" 34.4c 29.gh 61.2h 76.gd 38.1b 3 8 . P 30.5< 63.OC 7 9 . V 83.0" 40.@ 41.1b 31.V 78.5" 41.26 4f1.2~ 32.9$ 91.2b
=1 2 3 4 5
6 7
Mean, xi Variance,
J ~ ?
36.9 17.2
37.7 29.0
30.0 7.0
67.7 198.9
76.8 31.8
51.6? 56.9( 59.3,' 65.Lib 72.0b 85.3h 105.f~~ 70.9 357.3
B. Leaf Punch Samples, pG Dust/Cm2 Leaf
/=1 2 3 4
Mean, x i Variance, z,?
278 285 290 304 289 121
289 289 297 313 297 128
206 213 232 240 223 253
420 422 473 480 449 1036
352 357 422 438 392 1947
282 289 293 327 298 401
C. Personal Air Samples, M g Dust/M' A i r j=1
2
Mean, p L
31.1 28.4 29.7
23.9 25.7 24.8
38.0 76.2 57.1
15.0 8.3 11.7
... ...
...
61.2 64.4 62.8
Replicate n u m b e r . b V a c u u m s a m p l e s t a k e n b y i n e x p e r i e n c e d s a m p l e r . Vacu,um s a m p l e s t a k e n b y experienced s a m p l e r . d V a c u u m s a m p l e s t a k e n jointly. ,I
T h e magnitude of available dust is thus directly expressible as pg of dust per cm2 of leaf surface. Subsequently, the filters can be extracted and chemically analyzed for the pesticide in question, the residue value given as ppm of dust or ng/cm2 of leaf surface. Four leaf punch samples were taken in t h e same pattern as vacuum samples, each comprising 48 3-cm disks. These samples were then washed as described by Westlake e t al. (6) t o remove surface detritus which was then collected on desiccated Whatman GF/C glass fiber filters ( I I ) . To generate an aerosol, a simulated picking exercise was conducted for approximately 30 min on trees in each grove. No data were collected in grove No. 5 due to high wind conditions. For this experiment, two workers wearing membrane filter personal air samplers stood side-by-side about 1 ft from the tree's outer foliage and uniformly stroked the fruit-bearing branches in a manner designed to simulate t h e disturbance of foliage which would take place in an actual picking situation. T h e workers moved completely around each tree, remaining a t each "picking" station for 10 sec t o allow for aerosol dissipation before changing their positions. Uniformity between trees and groves was stressed more than the recreation of the exact motions of a picker. Filter flow rates (3.0 f 0.25 l/min) and dust gravimetric analyses were determined in accordance with standard practice (12). Results and Discussion Table I contains the ordered replicated values of vacuum samples, leaf punch samples, and personal air samples from each grove. T o assess the variability of the vacuum procedure, the data from Section A of Table I were subjected to an analysis of variance (unbalanced, twofold nested classification based on components of variance model). T h e components considered were variability between groves (a;), between individuals taking the samples and residual variability (&). Table I1 gives the results of this analysis which indicate t h a t the variability between the six groves as well as between t h e experienced and inexperienced samplers was significant a t the 1% level. T h a t is, the vacuum procedure results indicate t h a t the six groves in fact had different levels of vacuumable foliar residues. Further, there are independent detectable differences in t h e measurements t h a t can be attributed to t h e fact t h a t different persons conducted the sampling procedure (i.e., significant variability among samplers). In particular, 77.9% of the total variation in the data of Table I Section A can be attributed to the differences among groves, 10.6% to differences among samplers, and 11.4% to residual variation. T h e residual variance was further partitioned into components attributable to the experienced sampler (A), t h e inexperienced sampler (B), and both working together (C).
(a,),
~
Table II. Analysis of Variance for Vacuum Samples S o u r c e of variation
Total Groves, G Samplers residual Samplers, 5 Residual, R Sampler A Sampler B Sampler C
+
'2
A p p r o x i m a t e F value. Fs,?&(1%)
584
S u m of squares
Degrees of f r e e d o m
Mean squares
16,029.4 12,497.1 3,532 .O 1,702.8 1,829.5 210.6 1,491.6 127.3
35 5 30 6 24 9 11 4
2499.4 117.7 283.8 76.2 23.4 135.6 31.8
= 3.90. F5.x
Environmental Science & Technology
(1%) = 3.67.
E s t i m a t e of mean squares
F
statistic
Table 111. Estimated Standard Errors of the Mean Assuming One Sampler and Several Samplers No. of observations in the samDle
Single sampler,:
x n
n n n n
= l =3 =5 = 10
Several samplers(>
10.85 6.27 4.85
8.73
5.04 3.90 2.76
3.43
Table IV. Product Moment Correlation Coefficients Relating Sampling Methods Methods
Vacuum vs. punch Punch vs. air sample Vacuum vs. air sample
Correlation coefficient
p-Value
n
0.750 0.687 0.985
