Environ. Sci. Technol. 1982, 16, 510-514
Estimating Drift and Exposure Due to Aerial Application of Insecticides in Forests Masood Ghasseml, Page Painter, and Michael Powers TRW, Environmental Division, Redondo Beach, California 90278
Norman B. Akesson Agricultural Englneering Department, University of California, Davis, California 95616 Michael Dellarco U S . Environmental Protection Agency, Washington, D.C. 20460
rn The log-normal statistical distribution was fitted to measurements of insecticide deposition downwind from test plots of forest lands following aerial application. The goodness of fit was very satisfactory. Analysis of several field studies of insecticide drift using this curve-fitting method leads to a range of estimates defining the “worst case” and “best case” estimates of deposition. This range can be used to estimate exposure ranges for human, animal, or plant populations downwind. When compared with theoretical models of pesticide drift, the model in this paper has the advantage of mathematical simplicity and does not require the knowledge of parameters that are difficult to obtain. Application of insecticides or herbicides to forests or agricultural crops can be an important factor in increasing crop yields. Such beneficial effects, however, must be weighed against potential deleterious environmental effects of pesticides. While drift losses do occur from all applications, ground or aircraft, concern may be more acute when pesticides are sprayed from aircraft since the height of the spray release increases drift outside the target area. Drifting spray may have both harmful economic effects and harmful health effects. Herbicides have been reported to cause damage to nontarget crops at considerable distances from the point of application ( I ) , and litigation alleging crop damage from drifting herbicides is not uncommon. This paper focuses on the use of insecticides in forestry where the question of harmful effecta on the health of those living near sprayed timberland has been raised (2,3). The paper presents estimates of the extent of drift in forest insecticide applications by using an empirical modeling approach involving curve fitting to data from actual measurements in test plot spray applications. The drift estimates have been translated into the levels of dermal and respiratory exposures for bystanders in the path of the drifting sprays. The exposure data can be used in conjunction with toxicity data for a specific pesticide to assess the likelihood of health risks associated with spraying of the pesticide and to develop additional label information and guidelines for spray applications. Populations at Risk. Individuals exposed to pesticides sprayed over forest lands can conveniently be divided into three (not necessarily distinct) populations: the first consists of those who come into contact with or ingest substances contaminated with pesticide. This type of exposure depends to a great extent on the environmental fate of the pesticide. The second population consists of workers involved in the application of pesticide. The size of this population is very small, approximately 400, based on personal communications in this study with the representatives of companies involved in spraying forests. Of these 400, about half are involved in such spraying on a 510
Envlron. Sci. Technol., Vol. 16, No. 8, 1982
permanent basis and half on an occasional basis. The third population, and the one which is of interest in this paper, consists of those who are inadvertently caught in the path of spray. This population consists of individuals working, residing, or traveling in or near sprayed forestlands. To identify the population at risk of significant exposure to drifting pesticides, it is important to have a method for predicting the extent and dosage level of drift. Mathematical Models for Predicting Drift. One method of estimating drift is to use a mathematical model based on atmospheric physics. Several models of this type have been proposed (4). One of the most rigorous is the model of Dumbauld et al. (5), which generates predictions that are in good agreement with measurements of spray deposition close to a spray swath but that consistently overestimate deposition at large distances by more than 1 order of magnitude. However, there are several limitations to the utility of this and similar models. For example, some of the required input parameters such as standard deviations of wind azimuth and wind elevation angles, depth of surface mixing layer, droplet size distribution and settling velocity, together with the surface reflection coefficient of each droplet size class, are very difficult to estimate or obtain. In addition, such models generally do not consider topography and require the use of digital computers together with programs for solving the very complex equations predicting pesticide drift. Because of these limitations, the available mathematical models are of little practical value in estimating drift under the range of topographical and meteorological conditions encountered. The inadequacy of the models for providing accurate drift predictions is largely due to our inability to describe accurately certain factors that contribute to the statistical distribution in distances traveled by spray particles before they reach the ground. The most important of these factors is atmospheric turbulence, which is most significant in determining the distance drifted for drops less than 100 pm in diameter. Other factors that affect the distribution of particle drift are the initial size distribution of droplets, which is determined largely by the specific nozzle design and operating conditions, and the characteristics of the specific insecticide formulation which affect evaporation, thereby reducing drop size and hence increasing drift (6). This latter factor can be especially important when the vapor pressure of formulation is relatively high and droplet size is relatively small. Predictions Based on Experimental Data. So that more accurate estimates of pesticide drift could be obtained, standard curve-fitting techniques have been used in the present study to find a distribution function in agreement with experimental data. The complex distributing processes are thus described by a single distribution
0013-936X/82/0916-0510$01.25/0
@ 1982 American Chemical Society
~
-~~
Table I. Background for UCD Forest Pesticide Application Case Studies application lorate,a case caWha equipment study tion insecticide a. 1.
a
1
MT
Dylox 4 (trichlorfon)
2.24
2
MT
Orthene 7 5 5 (acephate)
1.12
3
MT
Dylox 4 (trichlorfon)
2.24
4
MT
Orthene 755 (acephate)
1.12
5
NM
Sevin-4-Oil (carbaryl)
2.24
6
NM
Sevin-4-Oil (carbaryl)
2.24
characteristics
Bell 205 A helicopter with Beecomist spinner Bell 205 A helicopter with Beecomist spinner Bell 205 A helicopter with Beecomist spinner Bell 205 A helicopter with Beecomist spinner Aerospatiale Lama helicopter with 8006 fan nozzles 2 Hiller 12E helicopters with 8004 fan nozzles
steep terrain steep canyon, central ridge steep canyon, one side application steep canyon, converging drainage gentle slope, one side application gentle slope
meteorological conditions neutral t o slightly stable inversion/stable inversion/stable inversion/stable inversion/stable unstable
The volumetric application rate was 9.35 L/ha, except in case studies 1 and 3 where it was 4.68 L/ha.
chosen empirically from well-known statistical functions. Under well-defined meteorological conditions and spraying procedures, the fraction of applied dose that drifts downwind a distance of d or greater from the point of application is defied by the function Q(d). The derivative of Q ( d ) is denoted -p(d), where p is a probability density function. The relationship between applied dose, D, distance downwind, d , and width of the target, W, measured in the direction of drift, is
A = D L d + W p ( dx ~)
9oc
0 V
CASESTUDY 1 CASESTUDY 2 CASESTUDY 3 CASESTUDY 4
o..-..-
CASESTUDY 6
o---
A------
60
-......-*. x .-.-.-
CASESTUDY 5
V
(1)
where A is the predicted surface deposit at distance d downwind from the target. Both A and D are expressed in units of mg/m2 in this paper. Since W is not, in many cases, available, W is assumed to be infinite, and the above expression reduces to
A = D L p ( x ) dx
(2)
Data Base. Data used in this paper describe drift of acephate (Orthene), trichlorfon (Dylox), and carbaryl (Sevin) (7) following six different aerial applications over forest lands. All three of these insecticides are applied as finely atomized sprays with similar droplet size distributions so that they have similar drift potentials. The data were obtained by members of the Department of Agricultural Engineering at the University of California, Davis (UCD) in canjunction with the U.S. Forest Service. Their studies are believed to be the most extensive ones available where profiles of pesticide drift were measured over large distances under 'actual conditibns of use in forestry. Conditions of application in these studies are summarized in Table I, which shows that the data encompass a range of meteorological and topographic conditions. Additional information on application and analytical procedures is given in ref 8. Drift was assessed by measuring pesticide deposited on Mylar sheets and by measuring pesticide trapped in Staplex air samplers running at approximately 600 L/min with a sampling efficiency greater than 95% for particles 0.05 pm and larger. Statistical Methods. The first step in the curve-fitting procedure was to divide the measured deposit on Mylar sheets by the dose applied to the target, thereby generating the percentiles (P) in Figure 1. To fit the data to an exponential function, fAe-&, the natural logarithm of P was regressed against d by using standard linear regression. To fit the data to a power function, Ad-m,the logarithm of P was regressed against the logarithm of d. To fit the log-
o.oO06
'
100
I
1Ooo DISTANCE DOWNWIND (m)
10,Ooo
Flgwe 1. Logarithmic-probit plots of Insecticide fallout on Mylar sheets vs. dlstance downwind and lognormal functions fitted to the actual data points (denoted by symbols).
normal distribution, each value of P was converted to a value of 2 defined by L m G ( x )dx = P
(3)
where G ( x ) is the standard normal function, ( 2 ~ ) - ' / ~ exp(-x2/2). These 2 values were regressed against the natural logarithm of d. This defines constants m and b so that the estimated surface dose at distance d is
D I rnln(d)+b m G(x) dx
(4)
The normal distribution was also fitted to the data, but the results were not satisfactory. Goodness of Fit. Goodness of fit for mathematical functions was assessed by computing the mean sum of squares of residuals (9). Since minimization of the percentage errors, rather than absolute errors, is sought, residuals used in these computations were defined as In (y) - In @) (5) Environ. Sci. Technol., Vol. 16, No. 8, 1982
511
Table 11. Mean Sums of Squares of Residuals for Functions Fitted to t h e Data in Figure 1 mathematical function case study
power
exponential
log-normal
1 2 3 4 5 6
1.63 4.87 1.28 0.42 0.07 0.004
1.80 0.57 0.69 0.47 0.24 0.04
1.46 3.19 0.82 0.21 0.07 0.004
9080-
-
0 n----
-
V....”........
70
60 50 40 -
20 -
s W
4
L
2
percent of applied dose per unit area dist, km
best case 0.085 0.015 0.001
where y is the measured deposit and 9 is the deposit estimated by the mathematical model being tested. Mean sums of squares of residuals (MSSR) for the three most satisfactory functions fitted to the data are given in Table 11. In each case study, the MSSR of the log-normal function is smaller than or equal to that of the power function so that the log-normal function gives a better fit. In four of the six case studies, the log-normal function gives a better fit than does the exponential function. Furthermore, the exponential function usually underestimates the deposit at long distances (data not shown). Therefore, the log-normal function is preferable to the exponential function and was used as the basis for drift predictions. Figure 1compares the fitted log-normal functions with the six case studies. Estimates of Human Dermal Exposure. The above model gives a rational basis for estimating exposure to bystanders in the path of drifting fine sprays. Estimates of human dermal exposure can be made from the formula E = 9s (6) where 9 is the deposit estimated from the log-normal distribution (in units of mass/unit area) and S is the surface area of exposed skin. Since exposure to recreational bystanders is a concern, 1 m2 is suggested as a conservative value for assessing levels of dermal exposure. The actual dose absorbed is estimated to be the product ET where T is the efficiency of absorption of the active compound through human skin. Values of Tare available in the literature (10). For pesticides commonly used in forestry, they range from T = 0.06 for 2,4-D to T = 0.74 for carbaryl. Table I11 presents estimates of surface deposits as a function of distance downwind from the target. These estimates are made from the log-normal curves fitted to six case studies as shown in Figure 1. The maximum predicted deposit among the six estimates is considered to represent the “worst case”, and the minimum predicted exposure is considered the “best case”. The values in the table can be used to estimate the human dermal exposure. 512
3 4
CASE STUDY
5
CASESTUDY
6
10-
5-
Lk
2-
..‘
u W
1W n.
0.5
-
worst case
4.5 2 1.7 5 0.38 10 0.0001 0.1 a Values give the percent of the applied dose that is predicted to be deposited at the indicated distance downwind. The best case is the lowest predicted value based o n the six field studies analyzed, and the worst case is the highest predicted value. Thus, the estimated dermal deposit on an individual with l m z of exposed skin who remains 1 km downwind from the target is, in the “best case”, 0.085% of the dose applied t o 1 m z of the target. 1
0
1 2
15-
0
t-
x -- .. .--.-...--
’.
30
U
Table 111. Estimates of Surface Deposits Due to Drifting Spraysa
A-------
CASESTUDY CASESTUDY CASESTUDY CASESTUDY
Envlron. Sci. Technol., Vol. 16, No. 8, 1982
0.1 -
-
0.05
0.01
-
0.0051
100
*
loo0 DISTANCE DOWNWIND (m)
11 xx)
Flgure 2. Logarithmic-probit plots of insecticides collected on air sampler fllters vs. distance downwind and log-normal functions fitted to the actual data points (denoted by symbols).
Table IV. Estimates of Respiratory Exposure to Drifting Spray: “Worst Case” Condition at 1.12 kg/ha Applied Dose est deposit on sampler filtera dist, km % of dose
mg/filter
exposure,b mg
1 3.42 0.0240 0.000 99 0,000 25 2 0.87 0.0060 5 0.65 0.0045 0.000 1 9 10 0.59 0.0039 0.000 1 6 a Based on the “worst case” in Figure 2. Estimated respiratory exposure to an individual breathing at 25 L/ min.
For example, estimates of exposure to a fine aerosol spray applied at 1.12 kg/ha (112 mg/m2) at a distance of 1km from the target are calculated by multiplying the appropriate values in the table by the applied dose and the skin area (1 m2), giving 5.0 mg for the “worst case” and 0.095 mg for the “best case”, assuming that the bystander remains at this distance while the spray drift passes. The second major type of potential exposure to bystanders is respiratory exposure, which depends on the density and size distribution of drifting aerosol spray and on the respiratory rate of the bystander. In the UCD study, spray density was measured with high-volume Staplex air samplers running continuously at approximately 600 L/min as the spray drifted past the sampling station. These samplers trap nearly all of the pesticide particles larger than 0.05 Fm. In Figure 2, the quantity of insecticide collected per unit area of filter surface (expressed as percent of applied dose) is plotted vs. distance downwind for the six cases studied. In comparison with the Mylar sheet deposit data, a greater degree of scatter was observed, but the log-normal distribution still provided the best fit among the various curves tried. By use of the curves shown in Figure 2, air sampler deposits were estimated for various distances downwind. Estimated values for the “worst case” are presented in Table IV, with values expressed both as the percent of applied dose and as mg/fiiter for the case where the application rate was 1.12 kg/ha. The values presented
in Table IV have been used to develop estimates of respiratory exposure (also shown in Table IV) by using the rationale and methodology described below. The respiratory tree of man traps some particles in the size range commonly encountered in insecticide application. Droplets smaller than 50 pm are readily inhaled and impacted onto membranes in the nose and throat (11),and a variable fraction of larger particles in aerosols are inhaled depending on their size and on the respiratory pattern of the individual. Therefore, the estimated deposits in Table IV for the “worstn condition can be used to obtain estimates of the maximum amount of human respiratory exposure; this has been done by multiplying the estimated deposit quantities (mg/filter) by 1/24, which is the ratio of the breathing rate of an individual engaged in moderate exercise (25 L/min (12))to the volumetric sampling rate (600 L/min). Comparison of the estimated maximum respiratory exposure levels (last column in Table IV) with the corresponding estimates for dermal exposure indicates that the exposure levels (not necessarily the amounts absorbed) are 3 orders of magnitude greater for the dermal route. However, absorption of chemicals through membranes in the upper airways can be much greater than absorption through skin so that the actual amount of insecticide absorbed through the respiratory system could be greater than that absorbed through the skin. Furthermore, with strenuous exercise, the ventilation rate of man can increase by a factor of approximately 10 times the rate of 25 L / m h used above, so that the estimated respiratory exposure would increase proportionally during heavy exercise. However, it would still be less than the dermal exposure. Consistency With Results of Other Studies. The results of the present empirical modeling approach have been compared with other published field studies. Figures 4-11 and 4-12 of Dumbauld et al. (5) present the results of two measurements of drift of a fine aerosol spray. The fraction of the dose at d = 0 that reaches 1km is 0.039 and 0.015, respectively, in two separate trials. Both of these values fall within the range given in Table 111. In another study, a deposit of carbaryl approximately equal to 1% of the applied dose was detected 0.8 km from a sprayed tract (13). This again is consistent with Table 111. Utility of the Empirical Modeling Approach. The empirical modeling approach in this paper may be useful in several ways. It provides a method for identifying some human, animal, or plant populations at risk of receiving toxic doses of drifting insecticides. In the case of insecticides that are applied as fine sprays, risk depends on the dose applied to the target area, on the distance downwind, and on the inherent toxicity (and dermal penetration in the case of humans). With chemicals applied as coarser sprays (e.g., median diameter greater than 350 pm), the estimates presented in this paper provide upper limits of exposure. More accurate estimates would require analysis of deposition data for larger particles than those used in the present study. In cases where a pesticide is known to have toxic effects a t concentrations achieved in the target site, the methods of this paper can be used to estimate widths of buffer zones around sensitive populations. Of particular interest in forestry is the toxicity of some insecticides to aquatic organisms at concentrations below 1 ppm. Consider, for example, a pesticide that is toxic to aquatic organisms at 0.1 ppm and is applied a t 2.24 kg/ha (224 mg/m2). In order to avoid toxic effects in water as shallow as 10 cm, the surface deposit would have to be less than 10 mg/m2. By converting this deposit to a percentage of the applied
dose, the “worst case” values of Table I11 can be used to determine a minimum buffer zone of approximately 1km. It should be emphasized that this width may not be adequate in all cases since the “worst case” used in estimating this width is not the worst possible case. However, this width would be adequate in all of the field studies analyzed in this paper. The empirical modeling approach presented here also provides a way to describe each case study of spray drift by a simple mathematical function. By extending this procedure to more case studies, it should be possible to describe the range of expected drift in a way that is more precise than the “worst case-best case” method used here. The statistical description also makes it possible to look for correlations between the parameters of the distribution function fitted to the data and meteorological conditions or other factors. Finally, the method provides a rational basis for estimating the maximal exposure to humans caught in the path of drifting spray. Exposure estimates can be made for both dermal and respiratory routes. Based on the data base used in the present analysis, the respiratory exposure in most cases is predicted to be a small fraction of dermal exposure. However, under certain conditions, particularly under conditions of strenuous exercise, respiratory exposure can be a significant component of the total.
Acknowledgments This paper is based on an element of the data base developed for the “Forest Use Chemicals Project” (FUCP), a multi-contractorlgrantee study sponsored by the Office of Pesticide and Toxic Substances (OPTS) of the U.S. Environmental Protection Agency (EPA) and aimed at developing guidance for the timber production industry and public on comparative risks and benefits of various chemical and nonchemical methods of timber production. The work presented in this paper has not been subjected to EPA review and therefore does not necessarily reflect the views of the Agency, and no official endorsement should be inferred. We thank Janet Auerbach, Chief of the Regulatory Support Branch, Special Pesticide Review Division of OPTS, for overall guidance in the design and conduct of Forest Use Chemicals Project. Gratitude is expressed to Terry L. Lavy of the Department of Agronomy, University of Arkansas, Donald P. Morgan of the College of Medicine, University of Iowa, and John D. Walstad of the Department of Forest Sciences, Oregon State University, for their review of certain of the draft pesticide exposure assessment documents on which this paper is based.
Literature Cited Akesson, N. B.; Yates, W. E.; Christensen, P. “Aerial Dispersion of Pesticide Chemicals of Known Emissions, Particle Size and Weather Conditions”, Proceedings of the 163rd American Chemical Society meeting, Pesticide Division Paper No. 19, Boston, MA, April 1972; American Chemical Society: Washington, D.C., 1972. U.S. EPA, Office of Pesticide Programs, Office of Toxic Substances. “Epidemiologic Studies Program, Report of h e s s m e n t of a Field Investigation of Six-Year Spontaneous Abortion Rates in Three Oregon Acres in Relation to Forest 2,4,5-T Spray Practices”, Human Effects Monitoring Branch, Benefits and Field Studies Division, Washington, D.C., 1979. Bergelin, N.; Hofherr, L. Feasibility Study Report, Forest Herbicide Project, National Forest Products Association, Washington, D.C., 1980. Christensen, L.; Frost, W. A. “Review of Meteorological Parameters Which Affect Aerial Application”, NASA, Environ. Scl. Technol., Vol. 16, No. 8, 1982
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Environ. Sci. Technol. 1982, 16, 514-525
Wallops Flight Center, Wallops Island, VA, 1979. Dumbauld, R. K.; Rafferty, J. E.; Bjorklund, J. R. "Prediction of Spray Behavior Above and Within a Forest Canopy", Special Report, Methods Application Group, Forest Insect and Disease Management, Forest Service, USDA, Davis, CA, December 1977. Warren, L. E. World Agric. Aviation 1976, 3, 23-28. Akesson, N. B.; Yates, W. E. Report to Forest Service on Contract No. P.S.W.-3,21-39S, Douglas-fir Tussock Moth Control Project, 1976-1977, Berkeley, CA. Yates, W. E.; Akesson, N. B.; Cowden, R. E. "Atmospheric Transport of Sprays from Helicopter Applications in Mountainous Terrain", Paper No. 78-1504, American Society of Agricultural Engineers, Chicago, 1978.
(9) Seber, M. W. "Linear Regression Analysis"; Wiley: New York, 1977. (10) Feldmann, R. J.; Maibach, H. I. Toxicol. Appl. Pharmacol. 1974,28, 126-132. (11) Campbell, K. I. Clinical Toricol. 1976, 9, 849-921. (12) Guyton, A. C. "Medical Physiology"; Saunders: Philadelphia, 1979. (13) South Carolina Epidemiologic Studies Center, Medical University of South Carolina. "Measurement of Exposure to the Carbamate Carbaryl", Maine Carbaryl Study, 1979; interim report, 1979.
Received for review September 15, 1981. Revised manuscript received February 15, 1982. Accepted April 9, 1982.
Visibility and Aerosol Composition in Houston, Texas Thomas 0. Drubay," Robert K. Stevens, and Charles W. Lewis
Environmental Sciences Research Laboratory, US. Environmental Protection Agency, Research Triangle Park, North Carolina 2771 1 Don H. Hern
Systems Applications, Inc., San Rafael, California 94903 William J. Courtney, John W. Tesch, and Mark A. Mason
Northrop Services, Inc., Research Triangle Park, North Carolina 27709 Relationships between light extinction coefficients, visual range, and aerosol mass and composition were studied in Houston, TX. Light extinction coefficients, measured with a telephotometer and black-box targets, agreed accurately with sums of light scattering and absorption coefficients. The light-scattering coefficient due to particles in heated air was highly correlated ( R = 0.987) with the mass concentration of fine particles (
