Environ. Sci. Technol. 1997, 31, 523-529
Correlation Techniques for Estimating Pesticide Volatilization Flux and Downwind Concentrations JAMES E. WOODROW AND JAMES N. SEIBER* Center for Environmental Sciences and Engineering and Department of Environmental and Resource Sciences, University of Nevada, Reno, Nevada 89557-0187 LYNTON W. BAKER California Air Resources Board, P.O. Box 2815, Sacramento, California 95812
Because of growing concerns over the potential risks from exposure to airborne pesticides that have acute and chronic human and ecological health impacts, information on concentrations in air downwind of emission sources is being increasingly required, especially in populated areas. A simple and cost-effective approach to estimating downwind air concentrations from emissions was developed by relating physicochemical properties of various pesticides and other organics with their published volatilization rates (flux) from treated soil, plant foliage, and water. The resulting set of ln-ln correlations was used to estimate flux for pesticides with known physicochemical properties. These estimated flux values were used as source strengths in the EPA’s SCREEN-2 dispersion model to calculate downwind concentrations near treated fields for time periods soon after application. Using estimated flux values for carbofuran, oxydemeton-methyl, methidathion, azinphos-methyl, and molinate, downwind concentrations were calculated that compared well with concentrations measured near treated fields for these pesticides applied to field crops, orchards, and rice fields. This approach is useful for prioritizing pesticides that pose potential health hazards and for which monitoring should be considered.
Factors that influence pesticide volatilization losses from treated surfaces include vapor pressure, soil adsorption and depth of incorporation, Henry’s law constant, and diffusion coefficients, along with other conditions and properties that control movement away from treated surfaces (e.g., molecular diffusion and eddy dispersion) (3-6). In recent years, many investigators have attempted to correlate pesticide volatilization with these various influencing factors in descriptive equations that can be used to predict losses from treated surfaces. These equations or models range in complexity from the “effusion” type [e.g., Knudsen effusion equation (5)], which assumes that volatilization is occurring from a non-adsorptive surface or from a well-mixed body of water (only pesticide vapor pressure is the determining factor), to more descriptive equations that include all possible factors that together will significantly affect net volatilization (e.g., diffusion coefficients in water and vapor, mass-transfer coefficients, soil adsorption, Henry’s law constant, vapor pressure, wind speed, etc.) (6-11). Pesticide saturation vapor pressure can be regarded as the underlying driving force for volatilization, while other factors that influence volatilization, such as soil adsorption, depth of soil incorporation, or water solubility, can be viewed as operators on the saturation vapor pressure to give a reduced or effective vapor pressure under a particular set of conditions. In line with this thinking and rather than deriving still another model from first principles, we have correlated published flux data for pesticides and other organics with their vapor pressures, modified by a combination of terms, including physicochemical properties, to reflect environmental conditions. In these correlations, we focused on the flux data measured within the first few hours after application because losses are typically the greatest during this time period and would, therefore, provide the greatest exposure for assessment of acute toxicity (exceptions include some soil fumigants that show a delay [e.g., Telone, metham sodium]). The properties or conditions that we expected to be the most significant operators on vapor pressure include soil adsorption coefficient, water solubility, application rate, and depth of soil incorporation. It was our intent to derive three basic correlations for pesticide residues (1) on soil (including the special case of soil incorporation), (2) on plants, and (3) dissolved in water.
Modeling Volatilization Flux Introduction Volatilization of pesticides after application to soil, water, and plant foliage is often a significant pathway for losses to the atmosphere, with subsequent movement to non-target areas (1). There are growing concerns over the potential risks from exposure to airborne pesticides that have immediate and/or long-term human and ecological health impacts. Because of these concerns, information on concentrations in air downwind of emission sources is being increasingly required, especially in populated areas (2). It is common to obtain such information by making measurements of downwind concentration profiles in the field, but this approach can be costly and difficult when it involves a variety of pesticides and application scenarios. A simpler and more cost-effective approach is to estimate air concentrations from emissions used as source strengths in computer-based dispersion models. One approach would be to estimate the emission factor (flux) for any specific situation based upon correlations with a data base derived from the literature. A data base of flux measurements should include the effect of various factors that influence flux of residues from treated soil, foliage, and water.
S0013-936X(96)00357-4 CCC: $14.00
1997 American Chemical Society
Published studies were used to assemble a data base from which to draw compound/flux combinations for development of correlations. Table 1 is a composite for all of the published studies used to derive these correlations for treated soil, plant, and water matrices under both field and laboratory conditions. Included are the form of the applied compound, application rates, test conditions (i.e., wind speed and temperature ranges), the method used to determine flux, and the time of year and locations for field applications. The flux values used in the correlations were the “worst-case” situations, which frequently occurred in the field within a few hours after application. These flux values were correlated with compound vapor pressures, modified by chemical properties appropriate to the treated matrix [e.g., soil adsorption coefficients (Koc) and water solubility (Sw) for treated soil and (Sw) for treated water]. These correlations for treated soil, plant, and water matrices were formulated as ln-ln plots for two reasons: (1) The flux values and compound physicochemical properties ranged over orders of magnitude (e.g., ∼10-5 to ∼100 Pa for vapor pressure and 101-107 µg/m2‚h for volatilization flux), making it difficult to visually display the data and to assess the positions of the data points relative to
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a
Residue analysis of treated matrix.
thiobencarb (23, 25)
prometon (38) dodecane (39) n-octanol (5) tridiphane (40) pendimethalin (42) 2,4-D (isooctyl) (43) toxaphene (34) deltamethrin (45) ethyl-parathion (46) methyl-parathion (46) mevinphos (46) molinate (23, 25, 48)
p,p′-DDT (34)
atrazine (35) dacthal (36, 37)
chlorpyrifos (29) diazinon (29, 44, 46)
fonofos (30) lindane (29) dieldrin (35, 41)
PCNB (35) trifluralin (28, 41)
eptam (EPTC) (33, 47)
Chevron oil (32)
Beacon oil (32)
compound
b
soil glass soil glass soil water soil soil weedy turf soil soil soil weedy turf soil soil dormant peach water soil soil soil soil cotton soil plastic glass giant foxtail turfgrass wheat cotton water water water water water water water water water
surface treated 656 810 20 1040 2.0 3.04 2.5 2.84 2.5 5.3 1.5 2.5 2.5 1.5 1.5 4.5 ns 2.5 7.1 7.0 1.3 1.3 ns 440 ns 0.56 3.4 0.5 3.73 0.01 ns ns ns 3.14 3.36 ns 4.48 ns
application rate (kg/ha) 0.5-1.2/10-40
