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Volatilization of Parathion and. Chlorothalonil from a Potato Crop. Simulated by the PEARL Model. MINZE LEISTRA* AND. FREDERIK VAN DEN BERG. Alterra ...
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Environ. Sci. Technol. 2007, 41, 2243-2248

Volatilization of Parathion and Chlorothalonil from a Potato Crop Simulated by the PEARL Model MINZE LEISTRA* AND FREDERIK VAN DEN BERG Alterra, Wageningen University and Research Centre, Post Office Box 47, 6700 AA, Wageningen, The Netherlands

The volatilization of pesticides from crop canopies in the field should be modeled within the context of evaluating environmental exposure. A model concept based on diffusion through a laminar air-boundary layer was incorporated into the PEARL model (pesticide emission assessment at regional and local scales) and used to simulate volatilization of the pesticides parathion and chlorothalonil from a potato crop in a field experiment. Rate coefficients for the competing processes of plant penetration, wash off, and phototransformation in the canopy had to be derived from a diversity of literature data. Cumulative volatilization of the moderately volatile parathion (31% of the dosage in 7.6 days) could be simulated after calibrating two input data derived for the related compound parathion-methyl. The less volatile and more slowly transformed chlorothalonil showed 5% volatilization in 7.6 days, which could be explained by the simulation. Simulated behavior of the pesticides in the crop canopy roughly corresponded to published data.

Introduction Many measurements have been made on the occurrence of pesticides in the air and in precipitation (1, 2). Volatilization from sprayed crops is likely to be one of the main sources of pesticides in the atmosphere. Various studies report on the volatilization of pesticides sprayed onto plants in wind tunnel systems. By trapping the vapor from the airflow through the systems, large fractions of the dose of some pesticides were found to volatilize. For example, a considerable percentage of the fungicide fenpropimorph and of the insecticide parathion-methyl volatilized from plants in wind tunnel systems (3). Measuring of the rate and extent of pesticide volatilization from crops in the field is more difficult, because of the open nature of the field systems and the difficulty in quantifying the concurrent processes (using radiolabeled compounds) at the plant surfaces. Registration authorities need to evaluate any risks related to the volatilization of pesticides to man and the environment (e.g., European Union) (4). The FOCUS air working group (Forum for the Coordination of Exposure Models and their Use) (5) elaborated upon recommendations on how to evaluate the risks of pesticide volatilization. The first requirement in such evaluations is to estimate the rate and extent of volatilization of the pesticides from crops, depending on pesticide properties and environmental conditions. Field experiments are so elaborate and expensive that they seem * Corresponding author phone: +31 317 474344; fax. +31 317 419000; [email protected]. 10.1021/es0627242 CCC: $37.00 Published on Web 03/06/2007

 2007 American Chemical Society

only to be feasible for the most critical pesticides and conditions. Unfortunately, tested models for estimating volatilization fluxes and amounts are not readily available. A model for the volatilization of pesticides from plants has been developed (6). Atmospheric resistance to volatilization is described using the concept of a laminar air boundary layer, with diffusion of pesticide vapor from the plant surface to the turbulent air above this layer. The other relevant processes in the canopy, such as plant penetration and phototransformation, are also simulated. This model has been applied to the quantitative analysis of a series of wind tunnel experiments, in which radiolabeled fenpropimorph was sprayed onto plants (6). However, testing this model for field conditions is limited to one occasion for fenpropimorph (7). Detailed reports of field studies are needed in order to test a plant-volatilization model for pesticides. The volatilization of parathion and chlorothalonil from a potato crop was studied in a well-defined field experiment (8). Both pesticides are relevant with respect to emission to the air, as can be illustrated using various studies (see Supporting Information). In this study, the volatilization of parathion and chlorothalonil in the field was simulated with the previously developed model description (6), incorporated in a new research version of the PEARL model (pesticide emission assessment at regional and local scales) (9). The rates of concurrent processes at the plant surface such as penetration into the plants and phototransformation by sunlight (competing with volatilization in the material balance) have to be estimated from literature data. The study should reveal the prospects of such a model for making initial estimates on the rate and extent of volatilization of a pesticide from crops in the field.

Procedures Field Experiment. A brief description of the field experiment is presented here; more details have been reported (8). Parathion and chlorothalonil were sprayed jointly onto a fully grown potato crop (0.5 m high) on a field at the experimental farm De Kandelaar near Biddinghuizen (Province of Flevoland), The Netherlands. The field was situated in an open landscape, with farm buildings at 700 m to the west as the nearest wind obstacles. The field (96 × 260 m; 2.5 ha) was sprayed on 18 August 1993, from 12 h 36 min to 12 h 55 min. The application rates were 1.06 kg parathion per ha and 1.94 kg chlorothalonil per ha, in a spray volume of 245 L ha-1. Samples of the pesticides in air were taken at 0.8, 1.0, 1.3, and 1.5 m above the soil surface (top of the ridges). The pesticide vapors were trapped on XAD-4 adsorbent in glass cartridges, at an airflow rate of 50 mL min-1. Four series of air samples (1 h of sampling each) were taken on the day of application and again on the first day after the day of application. Two series of samples were taken on the second (1 h), third (1 h) and seventh (2 h) days after the day of application. Wind direction was measured using a wind vane, and wind velocity was measured using cup anemometers at four heights above the ground (0.5, 0.8, 1.3, and 2.0 m). The dew point of the air was measured using a chilled mirror hygrometer at 0.8 and 1.3 m above the ground. Air temperature was measured at the same heights, using chromelconstantan thermocouples. Net radiation was measured using a net radiometer. Rainfall was measured using a tipping bucket rain gauge. Data loggers recorded the course with time of the meteorological values. The volatilization fluxes of the pesticides were estimated using the AeroDynamic method (AD method) and the Bowen VOL. 41, NO. 7, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Decline of the deposit and cumulative volatilization, penetration into the plants and phototransformation computed in Run 1 (lines) for parathion sprayed on a potato crop. O is the cumulative volatilization derived from measurements. Application was shortly after noon on the first day. Ratio method (BR method). In the AD method, the rate of volatilization of the pesticide is proportional to the difference in pesticide concentration in air over a certain height interval and the difference in wind speed over the same interval. The atmospheric stability of the surface air layer is taken into account. More details on the AD method have already been given (10, 11), as have details on the application of this method to the present data set (8). The BR method is based on the assumption that the coefficient for the transfer of sensible heat in air is the same as that for the pesticide. The flux density of sensible heat is calculated from measurements on the energy balance at the earth’s surface. The rate of pesticide volatilization is calculated from the sensible heat transfer coefficient and the measured gradient in pesticide concentration in air. More detailed information on the BR method has already been given (11), as have details on the application of this method to the present data set (8). The volatilization fluxes obtained using the AD and BR methods (8) in the measuring periods were averaged. These fluxes were multiplied by representative time periods (see Supporting Information) to obtain the amount of pesticide volatilized in each period. These amounts were summed to obtain the cumulative amount of pesticide volatilized after each day, as shown in Figures 1 and 2 for parathion and in Figure 3 for chlorothalonil. The rate of decline of the pesticides on/in the plant leaves was measured by taking samples of fully exposed leaves at seven time intervals, up to 7 days after application. Parathion and chlorothalonil in the extracts of XAD adsorbent and leaves were analyzed using gas-liquid chromatography (8). Computation Model. The model describes the various processes for the pesticide in the crop canopy (6). Volatilization of the pesticide from the deposit on the plant surface is determined by vapor diffusion through a laminar airboundary layer:

Jvol,pot ) Da

Ca,s - Ca,t dlam

(1)

where Jvol,pot is the potential flux of volatilization from the surface (kg m-2 d-1), Da is the diffusion coefficient of the pesticide in air (m2 d-1), Ca,s is the concentration of the pesticide in the air at the surface (kg m-3), Ca,t is the concentration in the turbulent air just outside the laminar layer (kg m-3), and dlam is the thickness of the laminar air boundary layer (m). 2244

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FIGURE 2. Decline of the deposit and cumulative volatilization, penetration into the plants and phototransformation computed in Run 2 (lines) for parathion sprayed on a potato crop. O is the cumulative volatilization derived from measurements. Application was shortly after noon on the first day.

FIGURE 3. Decline of the deposit and cumulative volatilization, penetration into the plants and phototransformation computed in Run 3 (lines) for chlorothalonil sprayed on a potato crop. O is the cumulative volatilization derived from measurements. Application was shortly after noon on the first day. Pesticide concentration in air is calculated from its vapor pressure using the general gas law. The dependence of vapor pressure on temperature is described by the ClausiusClapeyron (6). The actual rate of pesticide volatilization is taken to be proportional to the decreasing mass of pesticide deposit on the plants:

Jvol,act ) fmasJvol,pot

(2)

where Jvol,act is the actual rate of pesticide volatilization (kg m-2 d-1) and fmas is the factor for the effect of pesticide mass on the plants. The rate of pesticide penetration into the plants is described by

Rpen ) kpenAp

(3)

where: Rpen is the rate of pesticide penetration into the leaves (kg m-2 d-1), kpen is the rate coefficient of penetration (d-1), and Ap is the areic mass of pesticide on the plants (kg m-2). The rate of pesticide transformation on the plant surface by solar radiation is described by first-order kinetics:

Rph ) kphAp

(4)

where Rph is the rate of phototransformation on the leaves (kg m-2 d-1) and kph is the rate coefficient of phototransformation (d-1). The rate coefficient kph is set dependent on the intensity of solar radiation:

kph )

( )

Iact k Iref ph,ref

(5)

where Iact is the actual solar radiation intensity (W m-2), Iref is the reference solar radiation intensity (500 W m-2), and kph,ref is the rate coefficient of phototransformation at reference radiation intensity (d-1). The complete set of equations and further details of the process descriptions have already been given (6). The pesticide-plant process descriptions were programmed in the new research version 2.1.1-C of the PEARL model (9), which originates from the early PEARL model for pesticide behavior in soil-plant systems (12, 13). Details on the application (sprayed dosage, interception by the plants, etc.), on the properties of the pesticide and on the environmental conditions (hourly data) were specified in input files (see Supporting Information). The computations were done on an hourly basis, allowing the variation in environmental conditions within a day to be taken into account. Because the fully grown potato crop completely covered the soil, the “plant only” option could be used, with only computations for the plant canopy. Selection Of Input Data. The computations for both parathion and chlorothalonil were carried out with a thickness of the laminar air-boundary layer dlam ) 0.5 mm. This value is the average of the dlam-values (n ) 7; s.d. ) 0.2) estimated by the most recent and detailed computer simulations (6, 14) of wind tunnel studies on the volatilization of pesticides. The dlam value of 0.7 mm in a field study (7) was of the same order of magnitude. Parathion is a nonsystemic insecticide and acaricide having contact, stomach and some respiratory action (15). Physicochemical characteristics of parathion are given in Table 1. The vapor pressure of parathion was measured (16) using the gas saturation method. The result of 1.29 mPa at 25 °C corresponds to a vapor pressure of 0.63 mPa at 20 °C (Table 1), using the Clausius-Clapeyron equation and taking the enthalpy of vaporization to be 101 kJ/mol (16). The coefficient of diffusion of parathion in air (Table 1) was calculated according to the FSG method, as presented in a handbook (17). In the literature, no data could be found from which the rate of penetration of parathion into plants could be derived directly. In the plant analyses for the field experiment in the course of time (8), the residue left on the plant surface and that penetrated into the plants was not distinguished. Rate coefficients for the penetration of the related compound parathion-methyl into plants were derived from computer simulations (14) of experiments with micro-agroecosystems, in which plants were sprayed with the radio-labeled pesticide. In the present study, the averaged rate coefficient kpen ) 0.66 d-1 (half-life of 1.05 d) obtained for parathion-methyl was used as an initial estimate for the penetration of parathion into the potato plants. The first substantial amount of rain did not fall until 3.9 days after application, so a moderate value of 0.05 per mm (19) was taken for the wash off coefficient of parathion. Parathion in aqueous solution shows some absorption of sunlight in the range just above 290 nm (20, 21), indicating that direct phototransformation of parathion by sunlight can occur. When an aqueous solution of parathion was exposed to simulated sunlight, its half-life was found to be 1.2 days

TABLE 1. Physicochemical Characteristics of Parathion characteristic

value

reference no.

molar mass melting point boiling point vapor pressure

291.3 g mol-1 6.1 °C 150 °C at 80 Pa 0.63 mPa at 20 °C

molar enthalpy of vaporization solubility in water octanol/water partitioning, log(Pow) diffusion coefficient in air

101 kJ mol-1

15 15 15 calculated from ref 16 16

11.0 mg L-1 at 20 °C 3.83

15, 18 15, 18

0.46 m2 d-1

calculated according to ref 17

TABLE 2. Physicochemical Characteristics of Chlorothalonil characteristic

value

reference no.

mol-1

15 15, 23, 25 25 15 vapor pressure calculated from refs 23 and 25 molar enthalpy 95 kJ mol-1 average value for of vaporization many pesticides (26) solubility in water 0.81 mg L-1 at 25 °C 15, 25 octanol/water 2.94 at 25 °C 25 partitioning, log(Pow) diffusion 0.48 m2 d-1 calculated coefficient according in air to ref 17

molar mass melting point boiling point

265.9 g 252 °C >350 °C 350 °C 0.040 mPa at 20 °C

(22). No directly measured values are available for the rate of phototransformation by sunlight of parathion at plant surfaces. Recently, such values were obtained for the closely related compound parathion-methyl, using computer simulation (14) of a study in which it was sprayed onto plants in a micro-agroecosystem. The average value of the rate coefficient kph,ref ) 1.60 d-1 (half-life of 0.433 d) for phototransformation of parathion-methyl on plants at reference sunlight radiation was used as an initial estimate for parathion in the present computations. Chlorothalonil is a nonsystemic foliar fungicide, which provides rather long protective action at the plant surfaces (23). A typical recommended spray interval is 10-14 days (24). Physicochemical characteristics of chlorothalonil are given in Table 2. A vapor pressure of 0.076 mPa at 25 °C was cited for chlorothalonil (23, 25) from a company report; the description of the procedure is not publicly available. This value was translated to the vapor pressure of 0.040 mPa at 20 °C (Table 1), using the Clausius-Clapeyron equation and taking the molar enthalpy of vaporization to be 95 kJ mol-1 (26). No direct measurements could be found on the rate of penetration of chlorothalonil into the plants. At various times after applying of radio-labeled chlorothalonil to apple seedlings in a greenhouse (27), a first fraction of its residue was obtained by rinsing the leaves with acidified acetone for 30 s. After that time, the remaining leaf residue was obtained by blending with acidified acetone. More than 90% of the residue was obtained by rinsing, indicating that chlorothalonil was mainly present on the leaves or in the outer waxy layer of the leaves. By autoradiography, it was confirmed that there was hardly any translocation of chlorothalonil into the leaves. VOL. 41, NO. 7, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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After applying chlorothalonil to greenhouse crops, there was only a slow decline of its dislodgeable residue (28, 29). Based on these experimental results, the rate of penetration of chlorothalonil into the plants was estimated to be rather low, with a rate coefficient kpen ) 0.14 d-1 (half-life ) 5.0 d). Based on the results of wash off experiments (30) for chlorothalonil on potato foliage, the wash off coefficient for the rain starting to fall at 3.9 d after application was taken to be 0.04 mm-1. No direct measurements have been found in the literature on the rate of phototransformation of chlorothalonil at plant surfaces. Chlorothalonil dissolved in water absorbs sunlight in the wavelength range of 300-340 nm (31), so it can be subjected to direct phototransformation. The rate of photolysis of chlorothalonil in water, obtained in laboratory and outdoor experiments, shows a wide range of values (see Supporting Information). In various plant studies, the rate of decline of chlorothalonil on/in plant surfaces was measured as a function of time after spraying. The dissipation of chlorothalonil on/in potato leaves under field conditions was measured (30) by soaking the leaves in acetone (45 min). The averaged half-life of the residue measured in this way was 4.4 days. After spraying chlorothalonil onto a peanut crop (24), leaf samples were taken at different times after application and rinsed with toluene (for 30 min). The halflife of chlorothalonil on/in the leaves measured in this way was found, on average, to be 13.6 days. Cranberry plants were sprayed with chlorothalonil and the leaf samples were then shaken with methanol (45 s) at various times after application (32). After 7 days, an average of 56.1% of the initial content of chlorothalonil was obtained. Based on all these experimental results, the rate of phototransformation of chlorothalonil at plant surfaces was estimated to be rather low, with a rate coefficient kph, ref ) 0.23 d-1 (half-life of 3.0 d) at reference sunlight radiation (sunny day). This corresponds to a half-life of about 9 days in a day-night-cycle period in summer.

Results Computed Behavior of Parathion. In the first computer run (Run 1), the half-life for penetration of parathion into the plants was taken to be equal to that for parathion-methyl (14). Similarly, the half-life of phototransformation of parathion at reference sunlight radiation was set at the value estimated for parathion-methyl (14). The results of Run 1 for parathion are presented in Figure 1. The model does the computations for full 24-h periods, starting at 0 h (midnight). The pesticides were applied shortly after noon on the first day. The computed course of volatilization in time can be compared with the course of volatilization derived from measurements (8), which is also shown in Figure 1. Initially, the computed rate of volatilization is higher than that measured. At about 2.5 d, computed volatilization had almost been completed, whereas measured volatilization showed that it was continuing. Total computed volatilization of parathion at the end (22.6% of the dosage) is lower than that derived from measurements (31.4% of the dosage). The general tendencies are that the rates of the processes in the simulation are too high and that the role of plant penetration and phototransformation in the material balance for parathion is somewhat over-estimated in the simulation. In computer Run 2 for parathion, the penetration and phototransformation parameters were calibrated to achieve a better description of volatilization as measured. The halflife of penetration into the plants (1.68 d) was taken to be 1.6 times that estimated earlier for parathion-methyl. Similarly, the half-life of phototransformation of parathion (0.69 d) was taken to be 1.6 times that estimated for parathionmethyl. The results of Run 2 are presented in Figure 2. Initially, simulated volatilization is somewhat higher than what was 2246

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measured, but simulated total volatilization at the end (31.0% of the dosage) is very close to the volatilization derived from the measurements (31.4%). Both, penetration into the plants (38.7% of the dosage) and phototransformation (28.4% of the dosage) are simulated to be important processes. Total wash off of parathion from the crop (with most of the rain falling around 4.5 d) was simulated to be 1.9% of the dosage. This low percentage is related to the low amount of deposit computed to remain at this time. Both volatilization and phototransformation show daynight cycles (Figure 2); these processes are calculated to proceed mainly in the daytime. At 3.75 d, the deposit of parathion left on the plant leaves was calculated to be 4.1% of the dosage. At this time, the residue measured with the superficial extraction was 2.4% of the dosage (8), indicating that hardly any plant-penetrated parathion was extracted. This may be due to comparatively deep penetration and transformation within the leaves. Although the computation with the present set of input data can explain the volatilisation behavior of parathion, there is no check on the computed contribution of plant penetration, phototransformation and wash off from the plants to the material balance. Computed Behavior of Chlorothalonil. The results of the computer simulation for chlorothalonil (Run 3) are presented in Figure 3. Simulated volatilization in the initial period is somewhat higher than that derived from the measurements. The computed cumulative volatilization at the end (5.2% of the dosage) is close to the volatilization estimated from the measurements (4.9% of the dosage). In the simulation, penetration into the plants is by far the most important process (total of 43.0% of the dosage). Up to about 4.5 d there was little competition by the other decline processes. So substantial plant penetration could occur in spite of the low rate of this process (corresponding half-life of 5 d). The simulated contribution of phototransformation to the material balance (15.9% of the dosage) is comparatively low. Wash off of chlorothalonil, with most of the rain falling around 4.5 d, was computed to be 26.3% of the dosage. The total plant residue (deposit left plus penetrated residue) is simulated to decrease at a low rate (to 52.7% of the dosage at the end), with most of it being penetrated into the plants by then. The decrease pattern of chlorothalonil measured by superficial extraction of the leaves (8) was irrigular, with a tendency toward a slow decrease. Computation using the present set of input data can explain the volatilization behavior of chlorothalonil reasonably well, but there is no check on the computed contribution of each of the concurrent processes to the material balance.

General Discussion Parathion and chlorothalonil show a different volatilization behavior in the course of time. The comparatively high vapor pressure of parathion causes a high initial volatilization rate, which is, however, reduced from the beginning by the competing processes. The comparatively high rates of plant penetration and phototransformation cause a rapid decline in volatilization rate in the first few days after application. Chlorothalonil has a much lower vapor pressure, which results in a lower initial volatilization rate. Because the competing processes occur at a lower rate, volatilization of chlorothalonil continues for a longer time than that of parathion. This illustrates that in addition to vapor pressure, the rate of the competing processes has a considerable effect on the rate and extent of the volatilization of pesticides. For many pesticides, the significance of the processes at the plant surfaces competing with volatilization is not known quantitatively. Direct measurements on the rate of the processes are not usually available. It is then necessary to estimate these rates from a wide variety of literature data. If

such information is conflicting for critical pesticides, special studies on the processes at the plant surfaces are required. Preferably, the processes are studied by spraying radiolabeled pesticide onto plants in micro-agroecosystems and measuring all fate pathways by specific sampling and analyses (3, 33). Even in this type of elaborate experiment, measurements for some of the processes were limited to the final material balance. More frequent and specific measurements are needed (e.g., for the residue on/in the plants) to uncover the dynamics of the processes. In the present option of the PEARL model, volatilization was calculated using the concept of a laminar air-boundary layer on the plant surfaces, through which the pesticide diffuses from the deposit to the turbulent air. The resistance of the atmosphere to volatilization is thus expressed by the thickness of the laminar air-boundary layer, with 0.5 mm as a reasonable initial estimate. Further calibrated values of dlam can only be obtained by the simulation of well-defined volatilization experiments, with due attention to the competing processes. More advanced volatilization modeling uses concepts of atmospheric resistances above the crop canopy (9). The advantage of such concepts is that the effect of the prevailing meteorological conditions on volatilization is taken into account. However, more advanced models require more detailed input data, which are not available for most of the volatilization experiments carried out so far. It is proposed that the present laminar air-boundary layer concept be used as a tool for making initial estimates of the rate and extent of volatilization of pesticides from crops in the field. The future use of more advanced volatilization descriptions is faced with the same problem of obtaining rate coefficients for the processes competing with volatilization.

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Acknowledgments

(17)

(9)

(10)

(11)

(13)

(14)

(15)

(16)

This work was carried out within the framework of Research Program 416, Pesticides and the Environment, of the Dutch Ministry of Agriculture, Nature and Food Quality. (18)

Supporting Information Available Additional details of the study. This material is available free of charge via the Internet at http://pubs.acs.org.

Literature Cited (1) van Dijk, H. F. G.; Guicherit, R. Atmospheric dispersion of current-use pesticides: A review of the evidence from monitoring studies. Water Air Soil Pollut. 1999, 115, 21-70. (2) Dubus, I. G.; Hollis, J. M.; Brown, C. D. Pesticides in rainfall in Europe. Environ. Pollut. 2000, 110, 331-34. (3) Ophoff, H. Verflu ¨ chtigung und Mineralisierung von Fluoranthen, Parathion-methyl und Fenpropimorph unter simulierten Freilandbedingungen. Dissertation, Landwirtschaftlichen Fakulta¨t, University of Bonn, Germany, 1998. (4) EU. Council Directive of 15 July 1991 concerning the placing of plant protection products on the market (91/414/EEC). With amendments. Annexes II and III; European Union: Brussels, 2004; http://europa.eu.int/comm/food/plant/protection/ evaluation/index_en.html. (5) Kubiak, R.; Bu ¨ rkle, W. L.; Cousins, I.; Hourdakis, A.; Jarvis, T.; Jene, B.; Koch, W.; Kreuger, J.; Maier, W. M.; Millet, M.; Reinert, W.; Sweeney, P.; Tournaye, J. C.; van den Berg, F. FOCUSsAir: Remit and First Results. In Proceedings of the XII Symposium Pesticide Chemistry; Del Re, A. A. M., Capri, E., Padovani, L., Trevisan, M., Eds.; La Goliardica Pavese, Pavia, Italy, 2003; pp 473-485. (6) Leistra, M.; Wolters, A. Computations on the volatilisation of the fungicide fenpropimorph from plants in a wind tunnel. Water Air Soil Pollut. 2004, 157, 133-148. (7) Leistra, M.; Smelt, J. H.; van den Berg, F. Measured and computed volatilisation of the fungicide fenpropimorph from a sugar beet crop. Pest Manag. Sci. 2005, 61, 151-158. (8) van den Berg, F.; Bor, G.; Smidt, R. A.; van de Peppel-Groen, A. E.; Smelt, J. H.; Mu ¨ ller, T.; Maurer, T. Volatilization of Parathion

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(33) Stork, A. Windkanalanlage zur Bestimmung gasfo ¨ rmiger Verluste von Umweltchemikalien aus dem System Boden/Pflanze unter praxisgerechten Bedingungen mit direkten lufanalytische Methoden unter Nutzung der 14C-Traceranalytik. Dissertation, Landwirtschaftlichen Fakulta¨t, University of Bonn, Germany, 1995.

Received for review November 14, 2006. Revised manuscript received February 2, 2007. Accepted February 5, 2007. ES0627242