Environ. Sci. Technol. 2010, 44, 2522–2528
Fungicide Volatilization Measurements: Inverse Modeling, Role of Vapor Pressure, and State of Foliar Residue C A R O L E B E D O S , * ,†,‡ M A R I E - F R A N C E R O U S S E A U - D J A B R I , †,‡ B E N J A M I N L O U B E T , †,‡ B R I G I T T E D U R A N D , †,‡ D O M I N I Q U E F L U R A , †,‡ O L I V I E R B R I A N D , § A N D E N R I Q U E B A R R I U S O †,‡ INRA, UMR 1091, Environnement et Grandes Cultures, F-78850 Thiverval-Grignon, France; AgroParisTech, UMR 1091, Environnement et Grandes Cultures, F-78850 Thiverval-Grignon, France; and Afsset, French Agency For Environmental And Occupational Health Safety, 253 Avenue Du Ge´ne´ral Leclerc, F-94704 Maisons-Alfort, France
Received October 7, 2009. Revised manuscript received December 30, 2009. Accepted February 5, 2010.
Few data sets of pesticide volatilization from plants at the field scale are available. In this work, we report measurements of fenpropidin and chlorothalonil volatilization on a wheat field using the aerodynamic gradient (AG) method and an inverse dispersion modeling approach (using the FIDES model). Other data necessary to run volatilization models are also reported: measured application dose, crop interception, plant foliage residue, upwind concentrations, and meteorological conditions. The comparison of the AG and inverse modeling methods proved the latter to be reliable and hence suitable for estimating volatilization rates with minimized costs. Different diurnal/nocturnal volatilization patterns were observed: fenpropidin volatilization peaked on the application day and then decreased dramatically, while chlorothalonil volatilization remained fairly stable over a week-long period. Cumulated emissions after 31 h reached 3.5 g ha-1 and 5 g ha-1, respectively (0.8% and 0.6% of the theoretical application dose). A larger difference in volatilization rates was expected given differences in vapor pressure, and for fenpropidin, volatilization should have continued given that 80% of the initial amount remained on plant foliage for 6 days. We thus ask if vapor pressure alone can accurately estimate volatilization just after application and then question the state of foliar residue. We identified adsorption, formulation, and extraction techniques as relevant explanations.
Introduction Pesticides, widely used in agriculture to protect crops, are found in soil, air, and water at various concentrations depending on the given compounds, farming practices, and environmental conditions. Their transfer to the atmosphere * Corresponding author phone: 00 33 (0)1 30 81 55 36; fax: 00 33 (0)1 30 81 55 63; e-mail:
[email protected]. † INRA. ‡ AgroParisTech. § Afsset, French Agency For Environmental And Occupational Health Safety. 2522
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can occur during application by spray droplet dispersion or volatilization and after application by volatilization from the soil or the treated crops (1). Higher volatilization rates have been found from plant surfaces than from soil (2), probably due to a greater surface of exchange, a higher exchange rate in the crop canopy (3), and a lower adsorption capacity of leaves (4). The pesticide volatilization rate from plants has been measured under numerous experimental conditions. Laboratory or microagro-ecosystem conditions (e.g., wind-tunnels) are particularly relevant to study the effect of a sole factor on the volatilization process, e.g., the effect of air velocity, application dose, plot size (5), or type of plant (6). At the field scale, micrometeorological methods (7) allow the measurements of volatilization rates under natural conditions without disturbing the system, thus providing actual rates; however, only a few data sets of this type are available: e.g., volatilization rates from potato crop of chlorpyrifos and fenpropimorph (8) or of parathion and chlorothalonil (9) and volatilization rates from winter wheat of pirimicarb, ethofumesate, propachlor, pyrimethanil, and mecoprop-P (10). These measured volatilization rates can be used to test models describing the volatilization process. On the one hand, correlations between volatilization rates and physicochemical properties of the compounds were sought in empirical models (11, 12). The vapor pressure of the compound was found to be a relevant parameter to describe the volatility of pesticides from plants within the first day(s). Jansma and Linders (11) found discrepancies for several compounds leading them to conclude that more data sets were necessary. On the other hand, data sets obtained at the field scale can be used to test mechanistic models in which processes and environmental conditions are fully described: e.g., measured volatilization rates of fenpropimorph applied on a sugar beet crop have been used to test the PEARL model (13) in addition to measured volatilization of parathion and chlorothalonil from a potato crop (14). However, Scholtz et al. (15) noted that no suitable data set was found in the literature for directly evaluating the PEM model estimates of the pesticides volatilization from plants. Knowledge about the volatilization process of pesticides from crops has been recently reviewed by Leistra (16). The literature shows that apart from volatilization, three various competing processes need to be accounted for: wash-off by rainfall, degradation, and penetration into the leaves. The main factors involved in the volatilization process (either directly or indirectly through an effect on competing processes) are as follows: rainfall (amount and delay after the application time (17)), wind speed (5), temperature, solar radiation, relative humidity, physicochemical properties of the compound (vapor pressure and Kow), the nature of the foliar surface, the pesticide distribution on the foliage (17), application technique, and the formulation, the latter having a great impact on how the pesticide penetrates leaves (16). Data sets as complete as possible are therefore needed if we are to better describe and predict pesticide volatilization from plant foliage. To this end, this work presents a data set comprising the volatilization rates measured at the field scale of two fungicides applied on wheat. Fenpropidin and chlorothalonil were chosen because they represent current practices, as well as because of their differentiated vapor pressures (1.7 × 10-2 and 7.6 × 10-5 Pa for fenpropidin and chlorothalonil, respectively). As our main goal here was to provide a data set as complete as possible for testing volatilization models, 10.1021/es9030547
2010 American Chemical Society
Published on Web 03/03/2010
complementary data are also given: measured application dose, upwind pesticide concentrations, leaf temperature, and an estimation of pesticide residue on plant foliage. The aerodynamic gradient method and an inverse modeling approach (FIDES model, refs 18, 19) were used to measure the volatilization rates. Both methods are based on pesticide concentration measured in the air, but, the first method requires concentrations at several heights, whereas the second one requires only the concentration at one height above the soil. Given the time and expense of chemical analysis, the inverse modeling approach may represent a time- and cost-efficient method. We evaluate its efficiency to estimate cumulated volatilizations and diurnal patterns by comparison with the aerodynamic gradient method. We discuss also the relevance of the vapor pressure to accurately estimate pesticide volatilization from plants just after application. Then, we question the state of pesticide residue quantified on plant foliage. We examine discrepancies in light of competing processes and in terms of extraction techniques.
Experimental Section Volatilization Flux. Volatilization rates were estimated using: (1) the aerodynamic profile method, and (2) the inverse modeling approach (using the FIDES model, Flux Interpretation by Dispersion and Exchange over Short-range). The vertical gradient was followed for two days after application. Measurements were then limited to one height for 3 days (at 1.23 m high), after which the first protocol was returned to for one more day. Measured Volatilization Rates with the Aerodynamic Gradient (AG) Method. Volatilization fluxes Fc (ng m2 s-1) were calculated as follows: Fc ) -κ · u* ·
j ∂C ∂[ln(Z) - ΨΗ(Z/L)]
(1)
where Cj is the pesticide concentration (ng m-3), Z ) z - d, with z the height above the ground and d the displacement height (m), κ is the von Ka`rma`n constant (dimensionless, set to 0.41), u* the friction velocity (m s-1), and L the Monin-Obukhov length (m). ΨH (Z/L) is the atmospheric stratification correction function. Here, u* and L were determined from air temperature and wind speed profile (7) at four heights (0.68, 0.88, 1.23, and 1.98 m above the soil). Foliage temperature, net and global radiation, rainfall, air relative humidity, wind speed in three dimensions, u*, and water evaporation were also continuously monitored (see details in Supporting Information). The vertical concentration gradient required in eq 1 was estimated at the same 4 heights. We assume that no chemical reactions disturb the gradient given the period of time considered. As the crop canopy height ranged from 0.48 m (Day of Year, DOY 124) to 0.625 m (DOY 129), the heights of all sensors were adjusted for canopy growth before the last sampling period (0.82, 1.02, 1.42, and 2.12 m above the soil, except the highest height for air sampling fixed at 1.98 m). Upwind concentration was measured at 2.1 m high at 20 m upwind from the southwest point of the field (dominating wind direction during the experiment). Estimated Volatilization Rates by Inverse Modeling. In direct mode, FIDES predicts the atmospheric concentrations and the gaseous deposition downwind of an emission area, given the local meteorological conditions and the source strength. It has been used to estimate local advection fluxes of ammonia downwind of an intensive source (19, 20). In inverse mode, as used here, it predicts the volatilization fluxes given the local meteorological conditions and the concentration at one height above the emitting field. It has been positively evaluated against measured ammonia fluxes with
the AG method and the REA method (18, 19) as well as against pesticide volatilization rate measured with the AG method after application on bare soil (21). FIDES was used to calculate the volatilization fluxes using: (1) the concentration measured at 1.23 m high; (2) the upwind concentration; (3) the roughness length z0 (estimated with the AG method at 94 × 10-3 m, median value); (4) d (varying from 0.34 to 0.41 m); and (5) u* estimated with the AG method. Experimental Setup and Spraying of the Pesticides. The experimental site, located in the Paris Basin (France) (48.5°N/ 1.58°E) is approximately square-shaped with a total area of 20 ha. The soil was a Luvisol over loess, typical of the area. The field is one of the experimental sites of the “EU CarboEurope/NitroEurope” project for which the energy budget is continuously monitored. Wheat (winter wheat Isengrain, 300 plants m-2) was sown on the 28th of October 2005 and was at the 30-31 growth stage at the time of pesticide application. Fenpropidin and chlorothalonil were applied at target doses of 450 g ha-1 and 880 g ha-1 of active ingredient, a.i., respectively. They were provided as a mixture of commercial solution, respectively Gardian (SYNGENTA Agro SAS, emulsifiable concentrate) and Fungistop FL (PHYTEUROP, suspension concentrate) and applied in aqueous solution at 150 L ha-1. Physicochemical characteristics of the a.i. are given in Table S1 (Supporting Information). Application took place between 9h19 UT and 10h23 UT on the morning of May 4, 2006 (DOY 124). The spray solution was sampled from the spraying nozzles and analyzed in the laboratory. Two sampling zones were defined in the field (Figure S1 of the Supporting Information). In each zone, two types of measurements were carried out. First, using respectively 4 and 6 filter-traps (Whatman, GF/B, 90 mm diameter) set in Petri dishes, the doses at the upper part of the foliage and on the soil surface were measured. Second, leaf cuttings were used to quantify pesticides intercepted by the foliage just after the application, and the evolution of pesticide residues on foliage afterward. Air sampling and micrometeorological masts were set up in the center of the field (to optimize fetch conditions) immediately after the end of the application. Measurements of volatilization, pesticide residues on plant foliage, and upwind pesticide air concentration (hereafter noted upwind contamination) were carried out for 6 days after the application, with a frequency summarized in Table S2 (Supporting Information). Pesticide Sampling Procedure. The pesticide concentrations in the air were estimated by pumping ambient air with pumps through stainless steel tubes filled with Tenax TA. The air volume sampled was measured with volumetric controllers (see Supporting Information for details and trapping efficiency control). Leaves were first sampled before and just after spraying. The following samples were taken around 15:00 UT on DOY 124, 125, and 129. For the samples taken just after the application, the leaves were taken at two levels: from the top of the crop (10 leaves) and from the middle of the crop (20 leaves). Latter, samplings were only taken from the top of the crop. All leaves were cut off at the base. Chemical Analysis. Additional details are given in the Supporting Information. Thermal Desorption. Pesticides trapped in Tenax tubes were extracted on a thermal desorption unit (ACEM 900, Dynatherm inc.) and analyzed by GC/MS (VARIAN, 3800/ Saturn 2000). Chlorothalonil was analyzed in the electronic impact mode and fenpropidin by the chemical ionization mode (acetonitril). Quantification was done in the SIM (Selected Ion Monitoring) mode: fenpropidin (ions 274/273) and chlorothalonil (ions 266/268). Successive thermal desorptions were carried out. Results presented hereafter correspond to the mass quantified during the first desorption VOL. 44, NO. 7, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Evolution of the friction velocity u* (m s-1). Rain amount (mm) is given on the second axis. because even if a compound could sometimes be detected during the following desorptions (scarcely for chlorothalonil and for some samples for fenpropidin), the levels were not quantifiable, showing thus a good efficiency of the desorption unit. Solvent Extraction. Extraction of the application solution (two aliquots of 0.25 mL) and filters was performed with a-50 mL extraction solution by shaking for 16 h. The process was slightly different for leaves. Sampled leaves were weighed (Ml in g), rinsed with 10 mL of the extraction solution and shaken by hand for one minute, after which the solution was poured into a test tube with a screw cap and a PTFE seal. The tests tubes were stored at -20 °C to separate water. All other extracts were dried with Na2SO4. For all, two aliquots of 2 mL were put into two brown vials and stored at 4 °C before GC-ECD analyze (Carlo Erba. HRGC 5300 Mega series). Results given hereafter take into account recovery yields estimated with complementary tests (see Supporting Information for details). Calculation of Pesticide Residue on Plant Foliage. To compare application doses measured on filters and on leaves, the pesticide residue on leaves has to be expressed in g per ha of field. This calculation requires taking into account several parameters (like the Leaf Area Index) and is detailed in the Supporting Information.
Results and Discussion Meteorological Conditions. Weather conditions during the two first days were predominantly sunny, without rainfall, with maximum air temperature at 1.5 m above the crop of about 24 °C around noon on DOY 124 and 19 °C on DOY 125. Mean temperature of the foliage, soil surface and air temperatures are shown on Figure S3 of the Supporting Information. Wind speeds at 2 m above the crop were about 2 m s-1 during daytime from the SW (DOY 124) to the N-NW (DOY 125). Friction velocity shows rather low values, especially during the first and second nights after application (Figure 1). Rain events occurred on DOY 126 (3.8 mm), DOY 128 (14.6 mm), and at the end of DOY 129 (1 mm) (Figure 1). The ratio of upwind fetch to maximum measuring height above the evaporating surface is recommended to be at least 100:1 to allow the measurement of the steady state pesticide flux in the internal boundary layer above the field. This constraint almost always held true over the entire period of flux measurements. Pesticide Concentrations. Spray Solution and Application Dose. The expected concentrations for fenpropidin and chlorothalonil in the spray solution were 3 g L-1 and 5.88 g L-1, respectively, which corresponds to a theoretical application dose of 450 and 880 g ha-1, respectively (application volume of 150 L ha-1). Analyses of two samples of the spray solution revealed 2524
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FIGURE 2. Application doses, filter deposition, and residues on the foliage of fenpropidin and chlorothalonil following application (given in g ha-1 together with coefficient of variation an number of samples, C for Chlorothalonil, and F for Fenpropidin). The calculated application dose is calculated based on the measured concentration of the applied solution and the volume of solution applied, i.e., 150 L ha-1. The quantity intercepted by the plant is estimated as the difference between the quantity deposited on filters at the canopy top and that deposited on the soil. The residue on the foliage is estimated by the extraction of sampled leaves. Values are given after taking into account recovery yields of extraction. 2.97 g L-1 (average of 2.9 and 3.1 g L-1) for fenpropidin and 5.70 g L-1 (average of 4.69 and 6.72 g L-1) for chlorothalonil. The difference for chlorothalonil between the two analyses can be explained by the fact that the chlorothalonil initial solution was a suspension concentrate, while fenpropidin was an emulsifiable concentrate. A suspension concentrate is essentially a suspension of particles in liquids (22). So the aliquot may have been heterogeneous. The resulting calculated application doses, estimated to be 446 g ha-1 for fenpropidin and 856 g ha-1 for chlorothalonil, are close to the theoretical ones. The doses quantified with the filters placed at the foliage level indicated 296 g ha-1 (n ) 8, CV ) 32%) for fenpropidin and 728 g ha-1 (n ) 8, CV ) 14%) for chlorothalonil (Figure 2). These results show that 85% of the calculated application dose for chlorothalonil reached the upper part of the crop, suggesting low losses by drift. However, for fenpropidin, only 66% reached the crop, which is in agreement with previous results found for these compounds (23), suggesting that volatilisation from spray droplets could have occurred. This pathway of transfer to the atmosphere can thus be significant and requires further studies. The analysis of filters placed on the soil showed that 163 g ha-1 (n ) 6, CV ) 41%) of fenpropidin and 231 g ha-1 (n ) 6, CV ) 17%) of chlorothalonil reached the soil surface, which represents respectively 37 and 27% of the calculated application dose. Jensen and Spliid (24) found that around 20 and 40% of applied spray reached the soil after spraying on winter wheat (Ritmo) at a growth stage similar to our case. However, these estimations were very much in function of crop growth, spray quality, and formulation. One way to roughly estimate the spray interception by the crop is to calculate the difference between the dosages estimated with the filters at the foliage level and at the soil level. Using this approach, 133 and 496 g ha-1 of fenpropidin and chlorothalonil, respectively, can be said to have been intercepted by the crop, which represents 45 and 68% of the dosage estimated at the foliage level. The common estimation of interception by the crop ranges between 50 and 70% depending on growth stage, whatever the pesticide (25). Here, results are in accordance with this rough estimation for
chlorothalonil whereas the estimation was different for fenpropidin. A higher dissipation of fenpropidin from the upper level than at the soil level could however explain this difference. Another means to carry out a quantitative estimation of the spray interception by the crop is based on pesticide residue measured on leaves. At the upper part of crop, pesticide residues were 86 g ha-1 (n ) 8, CV ) 68%) for fenpropidin and 524 g ha-1 (n ) 8, CV ) 19%) for chlorothalonil. These estimations are close to the calculated ones given the quantification with filters (i.e., 133 and 496 g ha-1), showing a good accordance between both methods. They all show a fast dissipation of fenpropidin after application. At the middle part of the crop, the estimations were the following: 73 g ha-1 (n ) 6, CV ) 47%) for fenpropidin and 579 g ha-1 (n ) 6, CV ) 32%) for chlorothalonil, which are close to pesticide residues found at the upper part. This could mean that the pesticide spray reached rather well the lower canopy level. The behavior of the two compounds at the application can be compared by focusing on the ratio between chlorothalonil and fenpropidin concentrations (C/F) in the various samples (Figure 2). • C/F ) 1.96 in the target spraying solution. • C/F ) 1.92 in the applied solution. • C/F ) 2.46 at the foliage level and C/F ) 1.42 at the soil (filters). As chlorothalonil quantifications are close to expected ones, and chlorothalonil is less volatile than fenpropidin, we can consider that a ratio higher than 1.92 suggests a higher dissipation of fenpropidin than chlorothalonil. Volatilization from spray droplets could explain the higher losses for fenpropidin than for chlorothalonil (23). Willis and McDowell (17) note that rapid losses may occur before samples are recorded, which may lead to an underestimation of the actual application dose. In our case, the delay between application and the recovering of filters was between 20 and 30 min depending on the zone, which may have led evaporation during the time until collection. The lower ratio C/F observed at the soil level than at the canopy level could indicate that fenpropidin at the soil level is protected from dissipation pathways (linked with lower wind speed and lower temperature). An effect of the formulation could also be involved. • C/F ) 6.09 and C/F ) 7.89 for pesticide residues on plant foliage at the upper and middle levels, respectively. However, coefficients of variation for fenpropidin estimations are higher than for chlorothalonil (Figure 2), leading to a great uncertainty on the C/F estimations for residue on plants. Our measurements also show that a part of the applied pesticides reaches the lower part of the crop. These data could be useful to test models based on a description of a “less exposed” compartment as in PEARL (26). However, a more detailed vertical distribution of the pesticide within the crop height would be useful. Atmospheric Concentrations. Just after application, concentrations in the air at 0.68 m above the soil were about 1.33 and 0.28 µg m-3 for fenpropidin and chlorothalonil, respectively. Fenpropidin concentrations decreased rapidly while the concentration in chlorothalonil remained stable despite a slight decline and was still quantifiable six days after application (64 ng m-3 at 0.82 m above the soil). From DOY 126 to DOY 128, air was sampled at only one level (1.23 m) over 12 h sampling periods. Even with a longer sampling period, the fenpropidin concentration could not be quantified, whereas the chlorothalonil level remained quite stable, with most often a slight decrease during night-time and/or rain events (Figure S4 of the Supporting Information). During the period when vertical gradients of concentrations were measured, a typical gradient of emission was observed for both compounds (Figure 3 and Figure S5 of the
FIGURE 3. Vertical profiles of pesticide concentrations in the air above the field on Day 1 (ng m-3). (top) fenpropidin, (bottom) chlorothalonil. Symbols represent measured concentrations and lines represent the interpolation obtained given aerodynamic gradient method calculations.
FIGURE 4. Residues (g ha-1) of fenpropidin and chlorothalonil on the foliage as a function of time. Vertical bars represent standard deviation. Supporting Information). A good correlation was found between measured and interpolated curves for fenpropidin up to the second day (RRMSEsrelative root mean squared errorsfrom 3 to 24% considering sampling periods when concentrations could be quantified for 3 levels at least) and for chlorothalonil all throughout the period (RRMSE from 1% to 23%). In terms of the upwind contamination, the mast was well positioned except on the first night, but the wind was low. No fenpropidin was detected upwind. Chlorothalonil was present up to the second day following application but with lower concentrations (12 to 25 ng m-3) than those observed on the field (Table S3 of the Supporting Information). Additional information provided by surrounding farmers was in agreement with our observations: fenpropidin was not applied in the vicinity while nearby fields were treated with chlorothalonil. Residue on Plant Foliage after Application. Figure 4 shows the pesticide residue on the foliage averaged over the two zones as a function of time. Fenpropidin and chlorothalonil concentrations remained almost constant as also found by Van den Berg et al. (9) for chlorothalonil applied on potato crop. The small increase observed after application for chlorothalonil remains unexplained; however, high VOL. 44, NO. 7, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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concentration difference between two heights is of the order of a few ng m-3 while the difference between concentration at 1.23 m and the background concentration is of the order of tens to a few hundreds ng m-3 (Figure S5 of the Supporting Information). Moreover, the agreement is better for fenpropidin than for chlorothalonil, especially for small fluxes where the inverse modeling tends to give larger volatilization rates than the AG method (Figure S6 of the Supporting Information). This may be due to the background concentration being larger for chlorothalonil than for fenpropidin. In such case, as discussed above, FIDES would be sensitive to this level of concentration (which is taken into account in the calculation). The cumulated volatilization fluxes were estimated to be 2.5 g ha-1 for fenpropidin over 2 days and 17.5 g ha-1 for chlorothalonil over 5 days.
Discussion
FIGURE 5. Flux volatilization (ng m-2 s-1) for fenpropidin (top) and chlorothalonil (bottom) as a function of time as measured with the aerodynamic profile method (lines) and estimated by inverse modeling (FIDES). standard deviation makes difficult to further interpret the observed pesticide behavior. Volatilization Flux. Measured Volatilization Rates with the Aerodynamic Gradient (AG) Method. Figure 5 presents the volatilization fluxes (ng m-2 s-1) for both compounds based on eq 1. The fenpropidin volatilization rate just after application was higher than the one for chlorothalonil. Then, it decreased over time and could no longer be quantified as early as the second day whereas chlorothalonil showed a more uniform volatilization rate, remaining still quantifiable on Day 6. A diurnal cycle is observed in agreement with previous findings (16). Cumulated fluxes after 31 h reached 3.5 g ha-1and 5 g ha-1 for fenpropidin and chlorothalonil, respectively, representing 0.8 and 0.6% of the theoretical application dose. No data could be found in the literature on measurements of volatilization flux for fenpropidin. As for chlorothalonil, our results are in agreement with Van den Berg et al. (9). 0.6% of chlorothalonil was lost during Day 1 and 4.9% after one week in the case of potatoes. Estimated Volatilization Rates by Inverse Modeling. The volatilization rates estimated with the FIDES model compared well for both pesticides with the ones measured with the AG method (Figure 5). Both magnitudes and the diurnal pattern are reasonably well reproduced. The main discrepancies were observed for chlorothalonil when the measured gradient changed between two sampling periods leading to a marked variation of fluxes between the two sampling periods with the AG method (this variation can be decreased on DOY 125 by suppressing the level of concentration at 0.88 m, which is outside of the fitted profile) while the concentration at 1.23 m remained rather stable (DOY 125 at midday, DOY 129 at midday). To understand this discrepancy further, we should note that the AG method requires resolving the concentration difference between two heights while the FIDES method requires resolving the concentration difference between the middle of the emitting field and the background. The 2526
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This experiment provides a data set comprising fenpropidin and chlorothalonil volatilization rates at the field scale together with complementary data necessary to model the behavior of these compounds after application. First, the usefulness of inverse modeling to estimate volatilization flux is discussed. Second, these data raise two major questions. Is vapor pressure the best descriptor of pesticide volatilization from plants just after application? How may we experimentally quantify the fraction of pesticide residue on leaves available for volatilization? Usefulness of Inverse Modeling to Estimate Volatilization Rates. The use of an inverse modeling method to infer the flux from concentration measured at a single height has proved to be useful to evaluate the volatilization of chlorothalonil during 6 days. The comparison with the gradient fluxes however shows a large scatter and a tendency for the inverse model to predict larger fluxes under low flux conditions (essentially at night-time, Figure S6 of the Supporting Information). Comparisons of FIDES with other data sets have shown a general agreement within a 20% range (19). Other inverse modeling studies have shown the capability of such a dispersion model to estimate the fluxes of trace gases (27) even in disturbed flow (28). However, the discrepancy observed for low wind speed and stable conditions is to be expected since both methods are expected to fail due to the non applicability of the turbulent diffusivity hypothesis (29). Flesh et al. (30) have also shown that under such conditions, the inverse modeling technique systematically overestimates the fluxes. Moreover, the fact that, in this study, the measured concentration is an average for the night period may explain part of the discrepancy (because the average concentration gradient is different from the gradient of averaged concentrations), together with background concentrations. Finally, although the method has its bias, it shows that chlorothalonil still volatilizes after 5 days, and the slope of the cumulated flux suggests that it may have remained large for an even longer period. Vapor Pressure: The Best Descriptor of Pesticide Volatilization from Plants Just after Application? Even if the application dose of fenpropidin is lower than chlorothalonil one, then a higher volatilization rate is observed just after application for fenpropidin. This behavior is qualitatively expected due to the higher vapor pressure. However, the difference found (a factor of around 7.6 for the first sampling period) is small when compared with the difference in the vapor pressure of the two compounds (a factor of 224). According to Woodrow’s estimation, the ratio between logarithm of flux of fenpropidin to logarithm of flux of chlorothalonil should be 2.3, which is not reflected by our measurements for which a ratio of 0.9 is found (with fluxes averaged over the first 24 h). Focusing on the first sampling period, the differences between the two compounds are more
marked, this ratio reaching 1.6 but still being smaller than expected. Potential losses before air sampling beginning could explain part of this discrepancy. However, Boehncke et al. (4) found quite similar behavior when comparing, during the first 24 h after application, volatilization rates of three pesticides (mevinphos, lindane, and deltamethrin) applied on various plants. Looking at the assessment of spray and vapor drift close to the area treated with epoxiconazole and fenpropidin, Butler Ellis and Miller (31) found that, even during the first 10 h after application, the large difference in vapor pressure between these two compounds (
