HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Related articles in JEQ 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 (5) Citing Articles via Google Scholar Google Scholar Articles by Wolters, A. Articles by Vereecken, H. Search for Related Content PubMed PubMed Citation Articles by Wolters, A. Articles by Vereecken, H. GeoRef GeoRef Citation Agricola Articles by Wolters, A. Articles by Vereecken, H. Related Collections Pesticides Organic Compounds Other Models Agricultural Pesticides Air Pollution

TECHNICAL REPORTS
Atmospheric Pollutants and Trace Gases

Pesticide Volatilization from Soil

Lysimeter Measurements versus Predictions of European Registration Models

^a Forschungszentrum Jülich GmbH, Institute of Chemistry and Dynamics of the Geosphere IV: Agrosphere, 52425 Jülich, Germany
^b Fraunhofer-Institute for Molecular Biology and Applied Ecology, P.O. Box 1260, 57377 Schmallenberg, Germany

^* Corresponding author (a.wolters{at}fz-juelich.de)

Received for publication August 9, 2002.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
A comparison was drawn between model predictions and experimentally determined volatilization rates to evaluate the volatilization approaches of European registration models. Volatilization rates of pesticides (¹⁴C-labeled parathion-methyl, fenpropimorph, and terbuthylazine and nonlabeled chlorpyrifos) were determined in a wind-tunnel experiment after simultaneous soil surface application on Gleyic Cambisol. Both continuous air sampling, which quantifies volatile losses of ¹⁴C-organic compounds and ¹⁴CO₂ separately, and the detection of soil residues allow for a mass balance of radioactivity of the ¹⁴C-labeled pesticides. Recoveries were found to be >94% of the applied radioactivity. The following descending order of cumulative volatilization was observed: chlorpyrifos > parathion-methyl > terbuthylazine > fenpropimorph. Due to its high air–water partitioning coefficient, nonlabeled chlorpyrifos was found to have the highest cumulative volatilization (44.4%) over the course of the experiment. Volatilization flux rates were measured up to 993 µg m^-2 h^-1 during the first hours after application. Parameterization of the Pesticide Emission Assessment at Regional and Local Scales (PEARL) model and the Pesticide Leaching Model (PELMO) was performed to mirror the experimental boundary conditions. In general, model predictions deviated markedly from measured volatilization rates and showed limitations of current volatilization models, such as the uppermost compartment thickness, making an enormous influence on predicted volatilization losses. Experimental findings revealed soil moisture to be an important factor influencing volatilization from soil, yet its influence was not reflected by the model calculations. Future versions of PEARL and PELMO ought to include improved descriptions of aerodynamic resistances and soil moisture dependent soil–air partitioning coefficients.

Abbreviations: PEARL, Pesticide Emission Assessment at Regional and Local Scales • PELMO, Pesticide Leaching Model

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
POSTAPPLICATION VOLATILIZATION of pesticides from plant and soil surfaces and subsequent atmospheric transport is a primary pathway by which pesticides may be dispersed throughout the general environment (Whang et al., 1993; Taylor and Spencer, 1990) and may lead to contamination by long-range transport and deposition at locations remote from their application (Van den Berg et al., 1999).

Growing concerns over the risks from exposure to airborne pesticides accelerated the development of numerous laboratory and field methods to characterize the most important factors affecting volatilization in recent years (Taylor and Spencer, 1990; Burkhard and Guth, 1981; Stork et al., 1994). Preliminary studies to elucidate the volatilization of soil-surface-applied compounds were performed in small laboratory volatilization chambers whereby transferability to outdoor conditions was limited by the experimental design (e.g., low wind speed–air exchange rates) (Burkhard and Guth, 1981; Farmer et al., 1972). Micrometeorological measurements in the field are very laborious and costly and, like any field experiment, the error margin is comparatively high. Because the use of radiolabeled compounds is prohibited in the field, a complete detection of metabolites or nonextractable residues is impossible for most of the compounds. The complexities of field measurements have made it virtually impossible to obtain a mass balance and to determine quantitatively the amount transferred to the atmosphere under field conditions (Plimmer, 1992). Alternatively, wind-tunnel systems have been developed to approximate field conditions as closely as possible (Stork et al., 1998). The wind tunnel allows for direct measurement of volatilization and biomineralization under field-like conditions, in combination with the advantages of laboratory facilities (e.g., use of radioisotopes) (Stork et al., 1994). Both continuous air sampling, which quantifies volatile organic compounds and ¹⁴CO₂ separately, and the detection of surface-located residues allow for a complete radioactivity and mass balance.

Despite intense research, current knowledge is still insufficient to develop a dynamic, physically based simulation model for predicting the fate of pesticides after application (Van den Berg et al., 1999). Existing approaches and estimations reflect crucial soil processes (e.g., transformation, diffusion, and convection) with a varying degree of accuracy, covering a broad range from empirical and screening models to atmospheric dispersion models (Smit et al., 1997; Jury et al., 1983; Baker et al., 1996; Wang et al., 1997). Precise simulation of volatilization behavior as an integral component of a complete pesticide transport model is of utmost importance, especially as a module for integration into existing predicted environmental concentrations (PEC) models. Assessing the environmental concentrations of pesticides by means of mathematical models is a core action in risk assessment and is used for the registration of new plant protection products. As part of the recent efforts to harmonize registration procedures for pesticides within the European Union, the volatilization modules as part of the models used in the registration processes need to be updated and evaluated.

The PEC model PEARL (Pesticide Emission Assessment at Regional and Local Scales) is used to evaluate the leaching of pesticides to the ground water in support of the European and Dutch pesticide registration procedures (Leistra et al., 2001; Van Dam et al., 1997). It is a one-dimensional, dynamic, multilayer model describing the fate of a pesticide and relevant transformation products in the soil–plant system and includes a module for estimation of volatilization of pesticides from soil. The Pesticide Leaching Model (PELMO) was developed to estimate the leaching potential of pesticides through distinct soil horizons. It is a bucket model based on the PRZM-1 code of the USEPA (Carsel et al., 1984), but was improved with regard to the requirements of the German authorities responsible for the registration of pesticides (Klein, 1995). Processes include estimation of soil temperatures, pesticide degradation, sorption, volatilization, and evapotranspiration.

This paper summarizes results on the volatilization behavior of four pesticides (¹⁴C-labeled parathion-methyl, fenpropimorph, and terbuthylazine and nonlabeled chlorpyrifos) determined in a wind-tunnel experiment of 13 d after simultaneous soil surface application on Gleyic Cambisol. Measured volatilization rates are compared with the output of the European registration models PELMO and PEARL.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Compounds and Experimental Soil
Four pesticides, covering a broad range of physico–chemical properties (Table 1) , were chosen. The pesticide [¹⁴C]parathion-methyl [O,O-dimethyl O-(4-nitrophenyl) phosphorothioate], a nonsystemic insecticide with contact, stomach, and some respiratory action, was obtained from Sigma-Aldrich GmbH, Deisenhofen, Germany. The systemic fungicide [¹⁴C]fenpropimorph and the nonlabeled fenpropimorph formulation Corbel [cis-4-[3-[4-(1,1-dimethylethyl)phenyl]-2-methylpropyl]-2,6-dimethylmorpholine] were supplied by BASF AG, Ludwigshafen, Germany. The pesticide [¹⁴C]terbuthylazine[6-chloro-N-(1,1-dimethylethyl)-N'-ethyl-1,3,5-triazine-2,4-diamine], used as a pre- or postemergence herbicide, has replaced atrazine in many areas where atrazine use has been banned (Gerstl et al., 1997) and was supplied by Syngenta AG, Basel, Switzerland. Chlorpyrifos [O,O-diethyl O-(3,5,6-trichloro-2-pyridinyl) phosphorothioate] is a broad-spectrum organophosphorus insecticide widely used in agricultural and urban pest control (Giddings et al., 1997). The commercial product Dursban 4E (Dow AgroSciences, Indianapolis, IN), representing the most commonly available formulation type of chlorpyrifos (emulsifiable concentrate), was used. The experimental soil is classified as Gleyic Cambisol, and soil properties are listed in Table 2 .

View larger version (33K): [in this window] [in a new window]   Fig. 1. Schematic of the wind tunnel for measuring volatilization of pesticides from the soil surface under field-like conditions. AN, anemometer; C, cooler; CV1-3, converter; F, air filter (including prefilter, fine filter, and activated charcoal filter); GSM, gas stream mixer; H, hygrometer; P, pump/blower; PR, atmospheric pressure (sensors); PY, pyranometer; PUF, polyurethane foam plugs; QUA, quantometer; T, thermo sensors; TDR, time domain reflectometry; XAD, adsorbing resin (Amberlite XAD-7; Sigma-Aldrich GmbH, Deisenhofen, Germany).

View larger version (35K): [in this window] [in a new window]   Fig. 2. Climatic conditions over the course of the wind-tunnel experiment. Irrigation (8 mm) occurred on Day 8 after application.

At the end of the experiment, soil layers up to 10 cm were completely removed and soil samples of deeper layers were taken. Leachate was also pumped off. The wind tunnel was decontaminated with acetone.

Analyses
Foam plugs were extracted separately with methanol using a special squeezing apparatus (Niehaus et al., 1990); extracts were reduced to 5 to 10 mL using a rotary evaporator and further concentrated using a nitrogen gas blowdown. Soil samples, glass fiber filters, and XAD cartridges were extracted with methanol in a Soxhlet apparatus for 16 h. Radioactivity of extracts was determined by liquid scintillation counting (LSC) (TRI-CARB 2500; Canberra Packard). Nonextractable radioactivity in soil material was measured by combustion (TRI-CARB Sample Oxidizer 306; Canberra Packard). The active ingredients of air and soil samples were characterized by radio–high performance liquid chromatography (HPLC) and radio–thin layer chromatography (TLC) in combination with a Bio-Imaging Analyzer (Fujix BAS 100; Fuji, Tokyo, Japan). Nonlabeled chlorpyrifos was quantified with a Hewlett-Packard (Palo Alto, CA) 6890 gas chromatograph equipped with a split inlet, 30-m fused silica DB-1 capillary column with a 0.25-mm i.d. and a 0.25-µm film thickness, and a mass selective detector (MSD). The MSD source (held at 230°C) was operated in positive electron ionization mode, while the mass filter quadrupole (held at 150°C) was operated in selective ion monitoring (SIM) mode. Selected MS fragments (m/z: 197, 199, 314) allowed for the quantification of chlorpyrifos. The injector and gas chromatography (GC)–MSD transfer line were operated at 240 and 270°C, respectively. A Hewlett-Packard 6890 series autoinjector was used to inject 1 µL of sample in splitless mode. The oven temperature started at 90°C, followed by a 3-min hold, and was then programmed at 8°C min^-1 to 270°C and held for 1 min. Throughout the run the carrier gas (helium) was maintained at 2.0 mL min^-1. Pesticide concentrations were calculated by comparing the ratio of peak area response of the analyte over the internal standard (1-methylpyrene) in samples with those of calibration standards.

Volatilization Description of the PEARL Model
In the current version of the PEARL model, the volatilization of the pesticide from soil is described assuming a boundary air layer through which the pesticide has to diffuse before it can escape into the atmosphere (Leistra et al., 2001). The volatilization flux density depends on the concentration gradient of the pesticide across the boundary air layer and is also dependent on the concentration gradient of the pesticide in the top compartment of the soil profile. The following equation is used for determining the volatilization flux:

[1]

where J is the volatilization flux density through the boundary air layer (kg m^-2 d^-1), c_g,l is the concentration in the gas phase at the center of the upper computation layer in soil (kg m^-3), r_a is the resistance for transport through the boundary air layer (d m^-1), and r_s is the resistance for diffusion through the top boundary soil layer (d m^-1). Resistances can be described as:

[2]

[3]

where d_a is the thickness of the boundary air layer (m), D_a is the pesticide diffusion coefficient in air (m² d^-1), z₁ is the thickness of the upper computation layer in soil (m), and D_dif,g is the coefficient for pesticide diffusion in the gas phase (m² d^-1).

The concentration of the pesticide in the gas phase was calculated using the equations describing the partitioning of pesticides between the soil phases. The partitioning between the solid and the liquid phases is described with a Freundlich-type equation. Partitioning between the gas and liquid phases is expressed as:

[4]

where K_H is the nondimensional Henry's law constant, C_G is the pesticide concentration in the gas phase (kg m^-3), and C_L is the pesticide concentration in the liquid phase (kg m^-3). Details on the calculation can be taken from Leistra et al. (2001).

The PEARL model requires information about application, environmental conditions, and pesticide properties summarized in Table 1. The potential evapotranspiration was calculated by the Penman–Monteith method (Monteith, 1965). The PEARL model needs input from a model simulating water flow and heat transport in soil. For this purpose, PEARL is coupled to the hydrological Soil Water Atmosphere Plant Model (SWAP; Van Dam et al., 1997). The upper boundary condition for the soil heat conduction model is the daily average temperature; at the lower boundary of the soil system, the temperature is set at the long-term average temperature of 283 K. Soil horizons were distinguished and a common input file was designed for SWAP and PEARL containing soil properties per horizon of the lysimeter (Table 2). In addition, initial pressure heads were calculated from the measured volumetric water content (Fig. 2) at the beginning of the experiment. PEARL does not allow for direct input of measured soil moisture over the course of the experiment and uses the pressure heads for calculating the time-dependent volumetric water content. For details on the combined computation of SWAP and PEARL refer to Leistra et al. (2001). Soil compartment thickness of the top layer was varied between 0.1 and 1.0 cm to characterize the influence of the computation layers on predicted volatilization rates.

Volatilization Description of PELMO
The current version of PELMO estimates volatilization from soil using a simple volatilization module based on Fick's and Henry's law (Klein, 1995). It is assumed that the concentration of the pesticide in the air above the soil is negligibly low. PELMO considers volatilization from soil water and does not include a description of soil–air partitioning. Volatilization rates are calculated according to the following equation:

[5]

where J is the volatilization rate (g cm^-2 d^-1), D is the diffusion coefficient in air (cm² d^-1), H' is the nondimensional Henry's law constant, d is the air boundary layer (cm), and c_sol is the pesticide concentration in the soil water (g cm^-3).

Pesticide sorption to soil is described with a Freundlich-type equation (Klein, 1995). Application details and pesticide properties can be taken from Table 1; calculations were performed using the standard scenario for PELMO simulations including default values for soil layer thickness (5 cm) and volatilization depth (1 mm; thickness of the soil layer actively involved in the volatilization process). PELMO allows input of the volumetric water content of the soil layers measured in several depths at the beginning of the experiment (Fig. 2). In addition, linear equations based on measurements were included to assess the temperature dependence of Henry's law constants.

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Radioactivity Balance and Soil Residues
Carbon-14 recoveries ranging from 94.4 to 103.9% (Table 3) of the applied radioactivity demonstrate the functionality of the experimental setup and air analysis. System contaminations were very low (0.2% applied radioactivity), which can be attributed to the use of glass as the main construction material and high air exchange rates (Ophoff et al., 1996). In accordance with studies on leaching of the used compounds, no radioactivity was detected in the leachate (Gerstl et al., 1997; Gerstl and Helling, 1984; Ebing et al., 1995).

In total, approximately 10% of the soil-located radioactivity was not extractable and only determinable by combustion and subsequent quantitation of the formed ¹⁴CO₂ by liquid scintillation counting, thus a distinction between contributions of the compounds was not possible. With regard to the measurements of the proportionate radioactivity in the extractable fraction, the moiety of the nonextractable fraction was calculated (Table 3). Due to the short duration of the experiment the different tendencies of the compounds to form bound residues were neglected (Gerstl and Helling, 1984).

Mineralization
The formation of ¹⁴CO₂ was determined as a sum parameter including the mineralization of the ¹⁴C-labeled compounds during the experiment. After 13 d a cumulative mineralization of 0.9% of the applied radioactivity was observed (Table 3). Previous studies revealed that mineralization of ¹⁴C-terbuthylazine to ¹⁴CO₂ is very small throughout most experiments (Gerstl et al., 1997); for example, Langenbach et al. (2001) reported ¹⁴CO₂ production from labeled terbuthylazine ranging between 0.08 and 0.10% over a 2-mo period, thus indicating that mineralization of terbuthylazine contributed very little ¹⁴CO₂ during the wind-tunnel experiment. In contrast, mineralization of fenpropimorph and parathion-methyl may occur, especially in moist soils. On sandy loam and loamy clay, respectively, 3.3 and 1.1% of initially applied fenpropimorph were mineralized within 4 d after soil surface application (Müller et al., 1998). Wind-tunnel studies on the environmental fate of parathion-methyl revealed cumulative mineralization of 2.3% after 19 d (Stork et al., 1998). However, due to the simultaneous application of three ¹⁴C-labeled compounds it was not possible to relate the formation of ¹⁴CO₂ to a single pesticide.

Volatilization Measurements
Cumulative volatilization of 4.3% applied radioactivity (fenpropimorph) and 2.3% applied radioactivity (fenpropimorph acid) within 13 d was observed during the wind-tunnel study (Fig. 3) . Müller et al. (1998) investigated the volatility of fenpropimorph using a laboratory chamber and reported that 11.4% applied radioactivity volatilized from the surface of a loamy sand after 4 d. The main reason for this difference is the use of an irrigation system in the laboratory chamber integrated in the topsoil to adjust the soil moisture to 50% of the maximum water-holding capacity, which resulted in comparatively high soil moisture content (Müller et al., 1998). Therefore, the general tendency of pesticides toward enhanced volatilization under moist conditions (Spencer et al., 1973) may account for increased volatilization rates in the chamber. Volatilization kinetics of fenpropimorph (Fig. 4) confirmed a clear correlation between volatilization rates and soil moisture content in the top layer. Volatilization rates reached a maximum at 24 h after application under moist conditions. During this time the loss kinetics were primarily dictated by volatilization of the pesticide from the liquid phase (Müller et al., 1998). The constant airflow causes drying out of the soil surface between Days 2 and 7. The high K_om value of fenpropimorph (Table 1) results basically in a strong adsorption by soil, lowering the vapor pressure and leading to a decrease of volatilization rates (Spencer et al., 1973). Consequently, irrigation at Day 8 was associated with a slight increase of volatilization followed by an almost uniform decrease during the last days of the experiment. Finally, volatilization rates reached extremely low daily rates, revealing a "phasing out" after 12 d. Increasing soil moisture at Day 8 was connected with a marked increase of the average temperature, suggesting that the simultaneously occurring slow rise of volatilization may be attributed to both effects. However, the sharp temperature increase from Days 9 to 12 combined with decreasing water content and very low volatilization rates indicates that the water content in the top layer was the driving force of fenpropimorph volatilization.

View larger version (32K): [in this window] [in a new window]   Fig. 3. Cumulative volatilization of 14C-labeled pesticides (parathion-methyl, terbuthylazine, fenpropimorph) and metabolites (fenpropimorph acid, desethyl-terbuthylazine) after soil surface application on Gleyic Cambisol determined in polyurethane foam plugs. Net applied radioactivity (AR) = 100%.

View larger version (37K): [in this window] [in a new window]   Fig. 4. Measured volatilization rates after soil surface application on Gleyic Cambisol. Irrigation (8 mm) was given on Day 8 after application. (A) Parathion-methyl. (B) Terbuthylazine. (C) Fenpropimorph.

View larger version (25K): [in this window] [in a new window]   Fig. 5. Measured and calculated volatilization flux rates after soil surface application on Gleyic Cambisol (semilogarithmic plots; compartment thickness: Pesticide Emission Assessment at Regional and Local Scales [PEARL] model calculation = 1 mm, Pesticide Leaching Model [PELMO] calculation = 5 cm). (A) Chlorpyrifos. (B) Fenpropimorph.

Volatilization kinetics of terbuthylazine exhibited an initial increase of volatilization rates during the first 3 d followed by a slight decrease and an increase shortly after irrigation occurred (Fig. 4). At the end of the study, in total, 8.8% of the applied terbuthylazine had volatilized (Table 3). Volatilization rates of terbuthylazine cover a broad range of measurement and are strongly dependent on evaporation of soil water. Water movement downward caused by high irrigation resulted in a rapid displacement of the pesticide from the evaporating surface to deeper layers (Schroll et al., 1999; Lembrich et al., 1999; Langenbach et al., 2001). In accordance with previous studies using test microcosms (Gerstl et al., 1997), cumulative volatilization of terbuthylazine and its main metabolite (desethyl-terbuthylazine) shown in Fig. 3 revealed that more than 90% of the volatilized material was parent compound.

The reasons for the differing behavior of parathion-methyl and terbuthylazine remain speculative and reveal a general problem concerning volatilization studies. Data on pesticide volatilization are extremely heterogeneous and sometimes even contradictory; for example, there are discrepancies in results even from partially standardized laboratory volatilization experiments (Walter, 1998). The comparison of experiments on volatilization is generally associated with uncertainty due to the strong influence of microclimatic conditions and soil conditions.

Model Approaches: Volatilization Rates Calculated by PEARL and PELMO
The measurements for chlorpyrifos and fenpropimorph described above are compared with model predictions (Fig. 5). Immediately after application, both models underestimate the volatilization of chlorpyrifos markedly (Fig. 5A), that is, measured values exceed the PELMO predictions by approximately two orders of magnitude. During the following days both approaches do not reflect the subsequent rapid decline in volatilization rates. Generally, PEARL tends to overestimate volatilization from Day 2 until the end of the study. The main reason for the discrepancy lies in the model's assumption regarding equilibrium phase partitioning, particularly when the soil surface dries out (Baker et al., 1996). PELMO estimates an almost linear increase over the course of the study. This discrepancy with experimental findings is due to the quite simple volatilization module included in PELMO according to Eq. [5]. Due to the reasonably good agreement between calculations and the experimental volatilization rates of chlorpyrifos after Day 5 (Fig. 5A), one might conclude that PELMO generally allows for proper predictions in advanced stages of the studies. Taking into consideration the large differences between measured and computed volatilization of fenpropimorph from Day 5 until the end of the experiment (Fig. 5B), illustrated by a sharp decline of measured volatilization rates in contrast to a linear increase of predicted values, this conclusion appears unsustainable. The reasonable agreement between the PELMO predictions and the mean volatilization rates of chlorpyrifos after Day 5 is obviously accidental and may not be attributed to PELMO's apparent ability to predict volatilization rates correctly. Remarkable differences between experiment and model approaches are observed at the initial stage of the study. These findings are in agreement with previous simulations using the Behavior Assessment Model, which did not correctly reflect the volatilization rates immediately after application (Jury et al., 1983; Wolters et al., 2002). Obviously, the current description of pesticide volatilization is subject to considerable uncertainty, particularly for surface-applied pesticides whose initial volatilization rates are hardly limited by the soil boundary layer. Considering the main route for pesticide volatilization from the soil surface, the relevant processes occurring immediately after spray application must be taken into account. A thin water layer moistens only a fraction of the top millimeter of soil and evaporates rapidly, resulting in an elevated pesticide concentration at the soil surface. Additionally, the formulation additives are present in the top layer in comparable high amounts. This implies that an improved description of film volatilization must include a description of phase partitioning in the top millimeter containing a concentrated pesticide formulation mixture (Boesten, 2000).

To study the influence of intermediate soil moisture variation, irrigation occurring on Day 8 resulted in increased volatilization rates the subsequent day (Fig. 5). This is in accordance with previous studies on soil moisture dependence of volatilization (Spencer et al., 1973). Due to the noticeable increase in calculated volatilization fluxes after irrigation, the PEARL model gives the impression that it has the ability to reflect correctly the influence of soil moisture on volatilization. A direct correlation of calculated volatilization fluxes with calculated water content in the top layer (Fig. 6A) , however, revealed a suppression of volatilization due to irrigation on Day 8. During the following days PEARL calculates increasing flux rates for all pesticides observed, even though the soil dries out. The reason for the calculated increase in volatilization on Day 8 (Fig. 5) is a strong rise in average soil surface temperature (Fig. 2) resulting in higher concentrations of pesticides in the gas phase of the soil and therefore in higher volatilization fluxes. Neither the PEARL model nor PELMO (Fig. 6B) allow for correct estimation of soil moisture dependence on volatilization, illustrating a general limitation of currently available volatilization approaches: existing models implicitly assume partitioning coefficients being independent of water content and consequently calculate lower pesticide vapor pressures at the surface after irrigation (Baker et al., 1996). Developing new model concepts, further progress might be achieved considering the soil moisture dependence of soil–water partitioning coefficients, especially under low-water-content conditions. For this purpose a completely new experimental setup to determine soil–air–water partitioning and its dependence on temperature and soil moisture is currently under construction (German Patent Application no. 101 62 852.8).

View larger version (33K): [in this window] [in a new window]   Fig. 6. Model approaches: volatilization flux and soil moisture calculated by predicted environmental concentrations (PEC) models. (A) Pesticide Emission Assessment at Regional and Local Scales (PEARL) model simulation (compartment thickness = 1 cm). (B) Pesticide Leaching Model (PELMO) simulation (compartment thickness = 5 cm).

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Comprehensive pictures concerning the fate of the applied pesticides were documented by ¹⁴C recoveries ranging from 94.4 to 103.9%. The descending order of cumulative volatilization of the ¹⁴C-labeled compounds (parathion-methyl > terbuthylazine > fenpropimorph) deviated markedly from previous studies illustrating volatilization studies to be generally associated with uncertainty due to the strong influence of microclimatic conditions and soil conditions. Despite its comparatively high organic matter partitioning coefficient, chlorpyrifos showed the highest volatilization rates, suggesting that its volatilization is mainly governed by air–water partitioning.

Neither the PEARL model nor PELMO allow for an adequate prediction of the volatilization rates of pesticides, especially at the initial stage of the experiment, illustrating a general limitation of available models that are not able to handle the nonequilibrium state regarding phase partitioning in a concentrated pesticide mixture at the top layer. Adsorption and its dependence on soil moisture is a crucial factor influencing volatilization from soil surfaces and is not reflected by PEARL and PELMO. Consequently, the models do not describe the soil moisture dependence of film volatilization. The thickness of the upper computation layer in soil was found to influence the predicted volatilization rates significantly. The estimation of suitable and physical-based values to obtain a realistic simulation of the experimental scenario is of utmost importance until future improvements will allow for predictions of volatilization rates independent of the compartment layer thickness.

Additional model development, more specifically a physical-based mechanistic module for volatilization from soil surfaces to be evaluated and validated under field conditions, is required to obtain an accurate description of volatilization. As a starting point for future developments, improved volatilization models should include soil moisture dependent soil–air partitioning coefficients. Furthermore, the simplified concept of stagnant boundary layers should be replaced by a dynamic description of aerodynamic resistances.

	ACKNOWLEDGMENTS

 
Funding was provided by the European Commission within the framework of the APECOP project (Effective Approaches for Assessing the Predicted Environmental Concentrations of Pesticides). The authors wish to thank BASF AG (Ludwigshafen, Germany) and Syngenta AG (Basel, Switzerland) for providing ¹⁴C-labeled compounds.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

• Baker, J.M., W.C. Koskinen, and R.H. Dowdy. 1996. Volatilization of EPTC: Simulation and measurement. J. Environ. Qual. 25:169–177.
• Boesten, J.J.T.I. 2000. From laboratory to field: Uses and limitations of pesticide behaviour models for the soil/plant system. Weed Res. 40:123–138.
• Burkhard, N., and J.A. Guth. 1981. Rate of volatilisation of pesticides from soil surfaces, comparison of calculated results with those determined in a laboratory model system. Pestic. Sci. 12:37–44.
• Carsel, R.F., C.N. Smith, L.A. Mulkey, J.D. Dean, and P. Jowise. 1984. PRZM-1, a model for predicting pesticide and nitrogen fate in the crop root and unsaturated soil zones. User's manual for Release 1. Natl. Exposure Res. Lab., USEPA, Athens, GA.
• Ebing, W., M. Frost, R. Kreuzig, and I. Schuphan. 1995. Investigations on degradation and leaching of fenpropimorph in a lysimeter experiment. Nachrichtenbl. Dtsch. Pflanzenschutzdienstes (Braunschweig) 47:5–9.
• Farmer, W.J., K. Igue, W.F. Spencer, and J.P. Martin. 1972. Volatility of organochlorine insecticides from soil: I. Effect of concentration, temperature, air flow rate, and vapor pressure. Soil Sci. Soc. Am. Proc. 36:443–447.
• Gerstl, Z., and C.S. Helling. 1984. Influence of agronomic amendments on the fate of bound methyl parathion residues in soil. p. 7–16. In Radiotracer studies of bound pesticides residues in soil, plants and food. IAEA-Tecdoc 306. Int. Atomic Energy Agency, Vienna.
• Gerstl, Z., C. Sluszny, A. Alayof, and E.R. Graber. 1997. The fate of terbuthylazine in test microcosms. Sci. Total Environ. 196:119–129.
• Giddings, J.M., R.C. Biever, and K.D. Racke. 1997. Fate of chlorpyrifos in outdoor pond microcosms and effects on growth and survival of bluegill sunfish. Environ. Toxicol. Chem. 16:2353–2362.
• Hornsby, A.G., R.D. Wauchope, and A.E. Herner. 1996. Pesticide properties in the environment. Springer-Verlag, New York.
• Jury, W.A., W.F. Spencer, and W.J. Farmer. 1983. Behavior assessment models for trace organics in soil: I. Model description. J. Environ. Qual. 12:558–564.[Abstract/Free Full Text]
• Klein, M. 1995. PELMO: Pesticide Leaching Model. User's manual. Version 2.01. Fraunhofer-Inst. for Environ. Chem. and Ecotoxicol., Schmallenberg, Germany.
• Langenbach, T., R. Schroll, and I. Scheunert. 2001. Fate of the herbicide ¹⁴C-terbuthylazine in Brazilian soils under various climatic conditions. Chemosphere 45:387–398.[Medline]
• Leistra, M., A.M.A. Van der Linden, J.J.T.I. Boesten, A. Tiktak, and F. van den Berg. 2001. PEARL model for pesticide behaviour and emissions in soil–plant systems. Descriptions of the processes in FOCUS PEARL v 1.1.1. Alterra-rapport 013. Alterra Green World Res., Wageningen, the Netherlands.
• Lembrich, D., F. Beese, and I. Scheunert. 1999. Microcosm studies on the volatility of pesticides from soil surfaces. J. Environ. Qual. 28:721–726.[Abstract/Free Full Text]
• Majewski, M.S., D.E. Glotfelty, and J.N. Seiber. 1989. A comparison of the aerodynamic and the theoretical-profile shape methods for measuring pesticide evaporation from soil. Atmos. Environ. 23:929–938.
• McConnell, L.L., E. Nelson, C.P. Rice, J.E. Baker, W.E. Johnson, J.A. Harman, and K. Bialek. 1997. Chlorpyrifos in the air and surface water of chesapeake bay: Predictions of atmospheric deposition fluxes. Environ. Sci. Technol. 31:1390–1398.
• Monteith, J.L. 1965. Evaporation and the environment. p. 205–234. In G.E. Fogg (ed.) The state and movement of water in living organisms. Cambridge Univ. Press, London.
• Müller, T., N. Staimer, and R. Kubiak. 1998. Influence of soil pH and contents of organic carbon and clay on the volatilization of [¹⁴C]fenpropimorph after application to bare soil. Pestic. Sci. 53:245–251.
• Niehaus, R., B. Scheulen, and H.W. Dürbeck. 1990. Determination of airborne polycyclic aromatic hydrocarbons using a filter/adsorber combination. Sci. Total Environ. 9:163–172.
• Ophoff, H., A. Stork, W. Veerkamp, and F. Führ 1996. Volatilization and mineralization of [3-¹⁴C]fluoranthene after soil incorporation and soil surface application. Int. J. Environ. Anal. Chem. 64:97–109.
• Plimmer, J.R. 1992. Dissipation of pesticides in the environment. p. 79–90. In J.L. Schnoor (ed.) Fate of pesticides and chemicals in the environment. John Wiley & Sons, New York.
• Racke, K.D. 1993. Environmental fate of chlorpyrifos. Rev. Environ. Contam. Toxicol. 131:1–154.[ISI][Medline]
• Rawn, D.F.K., and D.C.G. Muir. 1999. Sources of chlorpyrifos and dacthal to a small canadian prairie watershed. Environ. Sci. Technol. 33:3317–3323.
• Schroll, R., U. Dörfler, and I. Scheunert. 1999. Volatilization and mineralization of ¹⁴C-labelled pesticides on lysimeter surfaces. Chemosphere 39:595–602.
• Seiber, J.N., B.W. Wilson, and M.M. McChesney. 1993. Air and fog deposition residues of four organophosphate insecticides used on dormant orchards in the San Joaquin Valley, California. Environ. Sci. Technol. 27:2236–2243.
• Smit, A., F. van den Berg, and M. Leistra. 1997. Estimation method for the volatilization of pesticides from fallow soil. Environ. Planning Bureau Series 2. DLO Winand Staring Centre, Wageningen, the Netherlands.
• Spencer, W.F., W.J. Farmer, and M.M. Cliath. 1973. Pesticide volatilization as related to water loss from soil. J. Environ. Qual. 2:284–289.
• Stork, A. 1995. Wind tunnel for the measurement of gaseous losses of environmental chemicals from the soil-/plant system under practice-like conditions with direct air analytics using ¹⁴C-labelled chemicals. Ph.D. diss. Univ. of Bonn, Germany.
• Stork, A., H. Ophoff, J.H. Smelt, and F. Führ. 1998. Volatilization of pesticides: Measurements under simulated field conditions. p. 21–39. In F. Führ, R.J. Hance, J.R. Plimmer, and J.O. Nelson (ed.) The lysimeter concept. ACS Symp. Ser. 699. Am. Chem. Soc., Washington, DC.
• Stork, A., R. Witte, and F. Führ. 1994. A wind tunnel for measuring the gaseous losses of environmental chemicals from the soil/plant system under field-like conditions. Environ. Sci. Pollut. Res. Int. 1:234–245.
• Taylor, A.W., and W.F. Spencer. 1990. Volatilization and vapor transport processes. p. 213–269. In H.H. Cheng (ed.) Pesticides in the soil environment: Processes, impacts, and modeling. SSSA, Madison, WI.
• Tomlin, C. (ed.) 2000. The pesticide manual. British Crop Protection Council and The Royal Soc. of Chem., Cambridge.
• Van Dam, J.C., J. Huygen, J.G. Wesseling, R.A. Feddes, P. Kabat, P.E.V. Van Walsum, P. Groenendijk, and C.A. Van Diepen. 1997. SWAP Version 2.0. Theory. Rep. 71. Dep. of Water Resources, Agric. Univ., Wageningen, the Netherlands.
• Van den Berg, F., R. Kubiak, W.G. Benjey, M.S. Majewski, S.R. Yates, G.L. Reeves, J.H. Smelt, and A.M.A. Van der Linden. 1999. Emission of pesticides into the air. Water Air Soil Pollut. 115:195–218.
• Walter, U. 1998. Pesticide volatilization: A comparison of methods for measuring and approaches to fuzzy logic modeling. Ph.D. diss. Humboldt-Univ., Berlin.
• Wang, D., S.R. Yates, and J. Gan. 1997. Organic chemicals in the environment: Temperature effect on methyl bromide volatilization in soil fumigation. J. Environ. Qual. 26:1072–1079.[Abstract/Free Full Text]
• Whang, J.M., C.J. Schomburg, D.E. Glotfelty, and A.W. Taylor. 1993. Volatilization of fonofos, chlorpyrifos, and atrazine from conventional and no-till surface soils in the field. J. Environ. Qual. 22:173–180.[Abstract/Free Full Text]
• Wolters, A., T. Kromer, V. Linnemann, H. Ophoff, A. Stork, and H. Vereecken. 2002. Volatilization of [¹⁴C]fluoranthene and [¹⁴C]diflufenican after soil surface application under field-like conditions: Measurement and comparison with different model approaches. Agronomie (Paris) 22:337–350.
• Wolters, A., T. Kromer, V. Linnemann, A. Schäffer, and H. Vereecken. 2003. A new tool for laboratory studies on volatilization: Extension of applicability of the photovolatility chamber. Environ. Toxicol. Chem. 22:791–797.[Medline]

Related articles in JEQ:

This Issue in Journal of Environmental Quality

JEQ 2003 32: 1167-1172. [Full Text]  

This article has been cited by other articles:

				L. Guzzella, E. Capri, A. Di Corcia, A. Barra Caracciolo, and G. Giuliano Fate of Diuron and Linuron in a Field Lysimeter Experiment J. Environ. Qual., January 5, 2006; 35(1): 312 - 323. [Abstract] [Full Text] [PDF]

				A. Wolters, M. Klein, and H. Vereecken An Improved Description of Pesticide Volatilization: Refinement of the Pesticide Leaching Model (PELMO) J. Environ. Qual., September 1, 2004; 33(5): 1629 - 1637. [Abstract] [Full Text] [PDF]

This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Related articles in JEQ 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 (5) Citing Articles via Google Scholar Google Scholar Articles by Wolters, A. Articles by Vereecken, H. Search for Related Content PubMed PubMed Citation Articles by Wolters, A. Articles by Vereecken, H. GeoRef GeoRef Citation Agricola Articles by Wolters, A. Articles by Vereecken, H. Related Collections Pesticides Organic Compounds Other Models Agricultural Pesticides Air Pollution