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TECHNICAL REPORTS
Atmospheric Pollutants and Trace Gases

Modeling Pesticide Volatilization from Turf

^a Biological and Environ. Eng., Riley-Robb Hall, Cornell Univ., Ithaca, NY 14853
^b Dep. of Entomology, Fernald Hall, Univ. of Massachusetts, Amherst, MA 01003-2410

* Corresponding author (dah13{at}cornell.edu)

Received for publication May 11, 2001.

	ABSTRACT

TOP ABSTRACT INTRODUCTION METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Pesticide volatilization models are typically based on equilibrium partitioning of the chemical into solid, liquid, and gaseous phases in the soil environment. In turf systems direct vaporization from vegetation surfaces is a more likely source, and it is difficult to apply equilibrium methods to plant material due to the uncertainties of solid–liquid–gas partitioning. An alternative approach is to assume that pesticide volatilization is governed by the same processes that affect water evaporation. A model was developed in which evapotranspiration values, as determined by the Penman equation, were adjusted to chemical vaporization using ratios of water and chemical saturated vapor pressures and latent heats of vaporization. The model also assumes first-order degradation of pesticide on turf vegetation over time. The model was tested by comparisons of predictions with measurements of volatilization for eight pesticides measured during 3 to 7 d in 11 field experiments. Measured volatilization fluxes ranged from 0.1 to 22% of applied chemical. Pesticides were divided into two groups based on saturated vapor pressures and organic C partition coefficients. One pesticide was selected from each group to calibrate the model's volatilization constant for the group, and the remaining pesticides were used for model testing. Testing results indicated that the model provides relatively conservative estimates of pesticide volatilization. Predicted mean losses exceeded observations by 20%, and the model explained 67% of the observed variation in volatilization fluxes. The model was most accurate for those chemicals that exhibited the largest volatilization losses.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
CHEMICALS used for control of turfgrass pests may volatilize, resulting in potential health hazards for turf users (Balogh and Anderson, 1992; Clark et al., 2000). Application typically leaves the pesticides exposed to air on grass and thatch surfaces rather than incorporated into soil, and opportunities for gaseous losses may be greater than with comparable applications to agricultural crops. Field measurements have confirmed that such losses do indeed occur, with volatilization amounts ranging from negligible to as much as 54% of applications (Cooper et al., 1990; Murphy et al., 1996a, 1996b; Taylor et al., 1977; Turner et al., 1977). Most losses occur within the first few days of application and follow a diurnal pattern, with largest losses corresponding to high mid-day temperatures and solar radiation. However, further generalizations are difficult. Many of the pesticides that are used on turf may not volatilize significantly, or may only volatilize under certain environmental conditions.

Field measurements of pesticide volatilization are time-consuming, expensive, and are impractical for the full range of chemicals, weather, and site conditions encountered in practice. Environmental and health assessments of pesticide volatilization will likely be based on mathematical modeling. Available volatilization models are typically based on equilibrium partitioning of solid, liquid, and gaseous chemical phases in the soil environment (Carsel et al., 1998; Jury et al., 1990; Wang et al., 2000; Yates, 1993). However, soil may be a minor source of gaseous losses in turf systems due to the dense protective layer of foliage and thatch. Direct vaporization of the chemical from vegetation, which is neglected in most models, is a more likely source. It is difficult to extend the equilibrium modeling approach to plant material due to the uncertainties of solid–liquid–gas partitioning. At least one model, PRZM-3 (Carsel et al., 1998), includes volatilization from foliage, but only as a constant daily sink.

Nonpartitioning approaches to volatilization may be more feasible. Hill and Schaalje (1985) developed a two-compartment model of pesticide dissipation in soil. Losses from compartment one are surface losses, such as volatilization, and redistribution to compartment two, where biochemical degradation takes place. These three transformations are assumed first-order, with empirical rate constants. Weed et al. (1999) created a modified two-compartment model by rationalizing the rate constants for surface losses and redistribution. Surface losses were treated explicitly as volatilization, with rates related to water evaporation, and redistribution was assumed to be a physical movement of the chemical into the soil due to wash off by rain.

Determination of chemical volatilization from water evaporation rates makes sense because of the similarities between the two processes (Taylor and Spencer, 1990). Factors such as air temperature, solar radiation, and wind movement could be expected to have comparable effects on vaporization of both water and chemicals. Moreover, models for estimating evaporation are readily available and evaporation values from such models may be converted to pesticide vaporization by scaling factors that reflect the differences in chemical properties of the water and pesticide. Weed et al. (1999) based their scaling on the ratio of vapor densities, and fitted the resulting relationship to alachlor [2-chloro-N-(2,6-diethylphenyl)-N-(methoxymethyl)acetamide] volatilization from soil.

This paper describes an evaporation-based approach for estimating pesticide volatilization from turfgrass. Since volatilization is from vegetation rather than soil, water losses are based on an evapotranspiration model. Scaling factors are determined from vapor pressures and heats of vaporization. The model was tested by comparison of predictions with field data for eight pesticides.

	METHODS

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Model Development
Volatilization
Volatilization is based on a chemical mass balance for a surface turf vegetation layer consisting of foliage and thatch. The volatilization model is given by

[1]

where, V_t = pesticide vaporized from surface vegetation during hour t (g ha^-1), R_t is the relative volatility of the chemical and water during hour t (mm), C_t = pesticide available for volatilization on vegetation at the beginning of hour t (g ha^-1), and k is a volatilization constant (mm^-1). Relative volatility is given by

[2]

In this equation ET_t = evapotranspiration during hour t (mm); ps_ct, ps_wt = saturated vapor pressures of the chemical and water, respectively, during hour t (kPa); and _ct, _wt = latent heats of vaporization of the chemical and water, respectively, during hour t (J g^-1).

The first ratio in Eq. [2] adjusts the evapotranspiration value for the differences in chemical and water vapor pressures. The second ratio reflects the differences in energy requirements for vaporizing water and the pesticide. The overall pesticide mass balance must account for other losses, such as bio- or photochemical degradation. Assuming these losses are first-order, or exponential, we have

[3]

for which = degradation rate of the pesticide on vegetation surfaces (h^-1). Equation [3] neglects other forms of chemical loss including runoff, leaching, and removal of clippings. As a result, the model may overestimate the pesticide available for leaching.

Evapotranspiration
The Penman equation, as described by Jensen et al. (1990), provides an estimate of evapotranspiration for well-watered short grass:

[4]

where, _t = slope of saturation vapor pressure curve during hour t (kPa °C^-1), _t = psychrometric constant during hour t (kPa °C^-1), Rn_t = net radiant energy available at surface during hour t (kJ m^-2 h^-1), G_t = net sensible heat flux from the surface to soil during hour t (kJ m^-2 h^-1), u_t = mean wind velocity during hour t (m s^-1), and p_wt = actual water vapor pressure during hour t (kPa).

Parameters in Eq. [4] are given in the following equations, as described by Jensen et al. (1990):

[5]

[7]

[8]

[9]

in which T_t = air temperature during hour t (°C), P = mean atmospheric pressure at the site (kPa), and EL = elevation (m).

The net radiant energy term in Eq. [4] is given by incoming solar radiation minus reflected radiation and net thermal radiation. Only the first of these is generally known, and net energy is usually estimated from regression equations. Jensen et al. (1990) provide a number of these equations determined for various covers and locations, including the following that was obtained for grass in Minnesota:

[10]

where Rs_t = solar radiation during hour t (kJ m^-2 h^-1).

Pesticide Relationships
Vapor pressures of volatile pesticides vary significantly with temperatures, as determined from Grain (1982):

where ps_c0 = vapor pressure (kPa) at absolute temperature Ta₀ (K), _c0 = latent heat of vaporization (J/g) at Ta₀, M = molecular weight of the chemical, R = gas constant (8.32 J/mol-K), Z_b = compressibility factor at boiling point (dimensionless), Ta_t = absolute temperature during hour t (K), and m is a constant.

The compressibility factor is assumed to be 0.97 in the examples given in Grain (1982). Heat of vaporization is approximated from the ideal gas law, and for the above dimensions,

[12]

where K_f is a constant, with mean value = 1.06 for a range of organic compounds. The chemical latent heat of vaporization at time t, _ct, is also estimated from Eq. [12], with Ta_t substituted for Ta₀.

The constant m in Eq. [11] is 0.19 for liquid chemicals, and may be either 0.36, 0.8, or 1.19 for solids, depending on boiling point. However, for the range of field temperatures of interest to pesticide volatilization (280–310 K), Eq. [11] is relatively insensitive to m, and a value of m = 0.8 can be used for solid chemicals with minimal loss of accuracy.

Field Studies
The volatilization model was tested using data from field turf experiments conducted at the University of Massachusetts Turfgrass Research Center in South Deerfield, MA. Experimental design and sampling methods were as described by Murphy et al. (1996a)(1996b). The 0.2-ha plots had well-established creeping bentgrass maintained at 13 mm. Thatch thickness ranged from 10 to 15 mm. The soil was a Hadley silt loam (coarse-silty, mixed, superactive, nonacid, mesic Typic Udifluvents). Irrigation was applied as necessary to prevent drought stress. Testing data included the measured concentrations of volatile residues following application of eight pesticides in 11 experiments conducted in the growing seasons of 1995, 1996, and 1997, as described in Table 1. Plots were mown on Monday, Wednesday, and Friday and applications occurred either immediately after mowing or on Tuesday or Thursday. Ethoprop and isofenphos were the most extensively studied chemicals, with applications in 7 and 6, respectively, of the 11 experiments. The remaining pesticides (bendiocarb, carbaryl, chlorpyrifos, diazinon, isazofos, and trichlorfon) were included in four of the experiments.

For the 7-d experiments, an initial sample was taken for 1 h after application, and two or three additional sampling intervals of 2 to 4 h covered the remainder of the first day. On Days 2 and 3, three sampling intervals of 4 h each were carried out on Days 2 and 3, and one sample each was taken on Days 5 and 7. The shorter, 2- and 3-d experiments were similar, but omitted sampling on the latter days. Measured concentrations generally fell to very low levels after the second day following application. The Theoretical–Profile–Shape method (Murphy et al., 1996a, 1996b; Wilson et al., 1982) was used to estimate volatilization mass flux.

All pesticide applications were made as sprays and immediately were watered in by 6.5 mm postapplication irrigation (Murphy et al., 1996a, 1996b). Applications were made to the 0.2-ha circular turfgrass plots in a manner and at a rate of application commonly used for the selected pesticides on golf courses and according to the manufacturer's instructions. Formulated pesticides were applied in 51.1 L (13.5 gal) of water to each plot. Tank mixtures consisted of 333 mL (11.25 fl oz) of diazinon (diazinon, Giba Geigy) [O,O-diethyl O-(2-isopropyl-6-methyl-4-pyrimidinyl) phosphorothioate], 555 mL (18.75 fl oz) of Dursban Pro (chlorpyrifos, Dow AgroSciences) [O,O-diethyl O-(3,5,6-trichloro-2-pyridyl) phosphorothioate], 333 mL (11.25 fl oz) of Mocap (ethoprop, Rhone-Poulenc) (O-ethyl S,S-dipropyl phosphorodithioate), 333 mL (11.25 fl oz) of Oftonol 2 (isofenphos, Bayer) [1-methylethyl 2-((ethoxy((1-methylethyl)amino)phosphinothioyl)oxy) benzoate], 416 mL (14.1 fl oz) of Proxol (trichlorfon, Nor-Am) [dimethyl (2,2,2-trichloro-1-hydroxyethyl)phosphonate], 167 mL (5.6 fl oz) of Sevin (carbaryl, Lesco Inc.) (1-naphthyl-N-methylcarbamate), 167 mL (5.6 fl oz) of Triumph (isazofos, Ciba Geigy) [O,O-diethyl O-(5-chloro-1-(1-methylethyl)-1H-1,2,4-triazol-3-yl) phosphorothioate], and 213 g (0.47 lb) of Turcam (bendiocarb, NOR-AM) (2,2-dimethyl-1,3-benzoldioxol-4-yl methylcarbamate) per 57 L (15 gal) of water.

Sample Collection and Residue Analyses
Volatile pesticide residues were collected onto XAD-4 resin during high-volume air sampling and extracted with acetone for instrumental analysis (Murphy et al., 1996a, 1996b). Extraction efficiencies ranged from 89 to 99% recovery. Chlorpyrifos, diazinon, ethoprop, isofenphos, and trichlorfon were analyzed simultaneously with isazofos using the method described by Murphy et al. (1996a). Bendiocarb and carbaryl were analyzed using the postcolumn derivatization/HPLC method (Environ. Monit. Systems Lab., 1989). Detection limits for all analytes were 0.02 µg/m³.

Model Testing
Properties of the eight pesticides are listed in Table 2. Equations [1] and [2] were used to predict the volatilization mass flux (V_t) for each pesticide during each sampling interval. Remaining pesticide available for the next interval was determined from Eq. [3]. First-order degradation rates () are not generally available for turf vegetation, so the soil half-lives for aerobic biodegradation given in Table 2 were used to determine . There is limited evidence that soil and turf decay rates are comparable (Cisar and Snyder, 1996), but other studies have found substantial differences (Carroll et al., 2000; Horst et al., 1996). Pesticide saturated vapor pressures and heats of vaporization were calculated by Eq. [11] and [12] using vapor pressure data and molecular weights from Table 2.

It was necessary to develop methods to estimate the volatilization constant (k) in Eq. [1]. If vaporization of the chemical is completely analogous to water evaporation, then it would seem that k = 1. However, pesticide volatilization will be inhibited by adsorption by plant material, incorporation into plant tissue, and drying of sprayed chemical. These processes are not modeled explicitly; rather, their effects are approximated by the volatilization constant.

Since each pesticide in the field studies was included in several experiments, a unique volatilization constant could be determined for each pesticide by model calibration. However, this would limit subsequent applications of the model to the eight chemicals measured in the field studies. A more general approach is to group the pesticides into chemicals with similar characteristics, and determine a single rate constant for each group. These constants would presumably be applicable to other chemicals that could be placed in these groups.

Clark et al. (2000) classified pesticides according to health hazards associated with volatilization. The most hazardous were chemicals with vapor pressures exceeding 10^-5 mm Hg (<1.3 x 10^-6 kPa). This vapor pressure was used to divide the eight chemicals in this study into the two groups shown in Table 2. The exception was chlorpyrifos, which at 2.7 x 10^-6 kPa would just exceed the group 2 boundary. However, chlorpyrifos is very strongly adsorbed to organic matter, as indicated by K_oc = 9930 cm³ g^-1, and this property could presumably have effects similar to a low vapor pressure. The volatilization constant k was estimated by selecting one pesticide from each group and adjusting the constant to produce a match of modeled and measured volatilization fluxes. The remaining three pesticides were used to test the model results using the calibrated constant.

Total measured and modeled volatile pesticide losses were compared for each experiment. These totals are the sum of the measured or modeled losses for each sampling interval in the experiment. Since these intervals were not continuous, and regular intervals were sometimes omitted due to equipment or human failures, actual volatile losses may have been larger than the measured values.

	RESULTS AND DISCUSSION

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Model Calibration
Isazofos and trichlorfon were selected as the calibration pesticides for their respective groups because their measured volatilization losses seemed to place them more or less within the middle of their groups. Results of the calibrations are shown in Table 3. The volatilization constant k was adjusted until the mean volatilization fluxes approximately matched the measured values. Somewhat surprisingly, the constant for the less volatile Group 2 pesticide (k = 405 mm^-1) was much larger than the Group 1 chemical (k = 130 mm^-1).

View larger version (19K): [in this window] [in a new window]   Fig. 1. Comparison of model pesticide volatilization estimates with measured values.

Other Losses
The model's tendency to overestimate volatilization may be due to its neglect of pesticide losses due to clipping removal, runoff, and leaching. None of these losses were measured directly, but their approximate magnitudes can estimated. Assuming that the postapplication watering by 6.5 mm of irrigation distributed the chemical uniformly through the foliage and thatch, clippings losses are not likely to be significant because mowing would remove no more than a small fraction of the total vegetation. For example, Cisar and Snyder (1996) found that total removals of sprayed pesticides over several months in grass clippings were generally <1% of applications. However, this conclusion may not be appropriate for granular pesticides. Cisar and Snyder (1996) reported a total clipping removal of nearly 8% of applied granular chlorpyrifos over a 3-mo period.

Estimates of pesticide losses from runoff and leaching are complicated by lack of water balance data. Irrigation applications and rainfall were not recorded and neither runoff nor percolation from the plots were measured. However, runoff would be a relatively rare occurrence from these plots. The Hadley silt loam belongs to soil hydrologic group B, and the associated runoff curve number for heavily thatched short grass is only 55 (Haith, 2001). Leaching of the pesticide from the vegetation to the soil is more likely, and can be estimated from Haith (2001) as:

[13]

where PL_t = pesticide leached from vegetation during hour t (g ha^-1), C_t = available pesticide (g ha^-1) as defined in Eq. [1], K_oc is the organic carbon partition coefficient (cm³ g^-1), OC = organic carbon content of the surface vegetation (kg ha^-1), R_t = rainfall and irrigation during hour t (mm), and Q_t = runoff during hour t (mm).

Equation [13] was used to estimate the pesticide leaching from the 6.5 mm of irrigation immediately following application (R_t = 6.5). Runoff was assumed to be zero, K_oc values were taken from Table 2, and OC was determined from typical values given in Haith (2001) (1120 and 109 kg ha^-1 per mm thickness of thatch and grass, respectively, or OC = 15420 kg/ha). The results of these computations are given in Table 5. It is apparent that leaching losses for at least three of the pesticides (ethoprop, isazofos, trichlorfon) could be significant, particularly if additional water inputs occurred early in any of the experimental periods.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
This research has shown that pesticide volatilization from turf can be reasonably predicted by a model based on estimates of turf evapotranspiration. Evapotranspiration values, as determined by the Penman equation, are adjusted to chemical vaporization using ratios of saturated vapor pressures and latent heats of vaporization. The model also assumes first-order degradation of pesticide on turf vegetation over time. The model was calibrated and tested using field measurements of volatilization fluxes from eight pesticides.

Testing results indicated that the model is a relatively conservative approach for predicting pesticide volatilization. Predicted mean losses exceeding observations by 20%, and the model explained 67% of the observed variation in volatilization fluxes. The model was most accurate for those chemicals that exhibited the largest volatilization losses (ethoprop, diazinon, and chlorpyrifos). Conversely, the model underpredicted losses for two of the three chemical that had relatively small losses (carbaryl and isofenphos).

The determination of volatilization constant based on the two pesticide groups was largely successful, since model predictions approximated observations for all pesticides in a group when the group rate constant was used. It should be reasonable to apply the model, without further calibration, to additional pesticides in these two groups. However, placement of chemicals in the appropriate group may not always be obvious. Based on its 25°C saturated vapor pressure (2.7 x 10^-6 kPa), chlorpyrifos would appear to fit into Group 1. However, if the calibrated rate constant for Group 1 (k = 130 mm^-1) had been used for modeling chlorpyrifos, estimated volatilization would have been only 30% of observed values. Chlorpyrifos was placed in Group 2 based on its high adsorption potential (K_oc = 9930), but the appropriate K_oc limits for Group 2 are unknown. Alternatively, since the vapor pressure limits proposed in Clark et al. (2000) are relatively arbitrary, the group limits could be changed to say, 3 to 4 x 10^-6 kPa, thus placing chlorpyrifos unambiguously in Group 2.

All pesticides in this study were applied as sprays, and the model may not exhibit similar accuracy with granular chemicals, particularly in the absence of postapplication irrigation. In such cases, pesticide may be less available for volatilization, and the model would likely overestimate volatilization losses.

	ACKNOWLEDGMENTS

 
Research described in this paper was supported, in part, by Green Section Research, U.S. Golf Association.

	REFERENCES

TOP ABSTRACT INTRODUCTION METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

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This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (3) Citing Articles via Google Scholar Google Scholar Articles by Haith, D. A. Articles by Walden, R. R. Search for Related Content PubMed PubMed Citation Articles by Haith, D. A. Articles by Walden, R. R. GeoRef GeoRef Citation Agricola Articles by Haith, D. A. Articles by Walden, R. R. Related Collections Turfgrass Turfgrass Pesticides Air Pollution