Modeling Pesticide Volatilization: Testing the Additional Effect of

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Modeling Pesticide Volatilization: Testing the Additional Effect of Gaseous Adsorption on Soil Solid Surfaces Lucas Garcia, Carole Bedos,* Sophie Génermont, Pierre Benoit, Enrique Barriuso, and Pierre Cellier INRA, AgroParisTech, UMR 1091 EGC, Environment and Arable Crops Research Unit, F-78850 Thiverval-Grignon, France S Supporting Information *

ABSTRACT: Pesticide volatilization from bare soil exhibits usually a diurnal cycle with a potentially large decrease when the soil surface dries. We assume here that this decrease may be due to the increase in adsorption of gaseous pesticides to soil under dry conditions. Thus, a precise description of the change with time of water content of the soil surface and of additional process such as gaseous adsorption is required. We used the Volt’Air model: we first extended the van Genuchten curve to drier conditions and then inserted a partitioning coefficient of the pesticide between the air-filled pore space and the soil constituents. This coefficient was calculated by a quantumchemistry-based method with a dependence on the Specific Surface Area of the soil (SSA) and Relative Humidity (RH) of the air-filled pore space. These developments were assessed by comparing with two data sets on volatilization of trifluralin applied to bare soil. The updated Volt’Air model allowed a better description of the volatilization dynamics on a diurnal cycle (increasing efficiency factor from 0.85 to 0.96 and −2.73 to 0.17 and decreasing RMSE from 146 to 78 and 353 to 168 for both scenarios) as well as the effect of a rewetting situation. Recommendations are made for further refining the description of this process together with the soil water conditions.

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water films by van der Waals interactions and electron donor− acceptor interactions.4 This change in the nature of pesticide adsorption in relation to the soil surface humidity is considered as a source of discrepancies between measurements and modeling of pesticide volatilization, with an overestimation by the models of the volatilization fluxes under dry soil conditions.5−8 Thus, models must be improved by taking into account the dynamics of water under drying or rewetting conditions, the adsorption of pesticide from the gas phase to the solid/aqueous phases, and the effect of this dynamic of water on the adsorption. It has been shown that classical water retention and hydraulic conductivity models (e.g., the van Genuchten-Mualem model9,10) can prove unable to describe the water retention and transfers within the soil profile under dry conditions. Part of the problem is that the retention curve used to deduce the specific parameters of the model for the classical domain are usually measured from saturation to, at best, the wilting point of the soil (i.e., for water potential ranging from 0 to −160 m). This lower limit corresponds to a RH of 98.8% at 20 °C,11 which is high considering the domain of RH encountered at the soil surface. An extension of these curves for the dry domain is thus required. Dealing now with the adsorption process, van den Berg et al.5 and Ferrari et al.6 attempted to establish

INTRODUCTION Estimating the volatilization of pesticides from agricultural fields to the atmosphere implies determining the concentration of the compound in gaseous form at the soil surface. This concentration is strongly governed by physicochemical equilibria. The adsorption describes the reversible equilibrium occurring between the pesticide, either in dissolved or gaseous form, and a solid matrix. The classical representation of the adsorption of pesticides in the soil follows a partitioning process, between the aqueous and solid phases, described by the partition coefficient Kd.1 This representation has been found to be well correlated with the amount of solid organic matter for nonionic pesticides under wet conditions.2 However, this may not be valid over the whole domain of naturally observed soil surface humidity conditions, and the extrapolation of this partition coefficient to describe adsorption of pesticide from the gas phase is not obvious. Reviewing the existing knowledge on this question, Taylor and Spencer2 already emphasized that when the water content decreases, the adsorption of semivolatile organic compounds tends to increase. For the range of 35−40 organic compounds studied, the adsorption from the gas phase to the soil solid surfaces represented 10 to 50% of the total adsorption for a Relative Humidity RH of the air-filled pore space of the soil of 90% as shown by Goss et al.3 This proportion was larger when the RH decreased. This behavior is explained by the reduction of the thickness of the interfacial soil-water film remaining in the soil as RH decreased, involving an increased electrostatic influence of the solid surfaces on the pesticides adsorbed on top of the © 2014 American Chemical Society

Received: Revised: Accepted: Published: 4991

October 3, 2012 April 3, 2014 April 4, 2014 April 4, 2014 dx.doi.org/10.1021/es5000879 | Environ. Sci. Technol. 2014, 48, 4991−4998

Environmental Science & Technology

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several weeks at a time-step ranging from a few min to few hours (with a calculated internal time-step for transfer calculation of the order of a few seconds). When used for semicontrolled conditions (like wind-tunnels), the model is forced by measured soil surface temperature and water evaporation, and the friction velocity is fixed to 0.24 m s−1 as calculated from Loubet et al.19 Extrapolation of the Water Retention Model to the Dry Domain. The van Genuchten-Mualem water retention and hydraulic conductivity models20 introduced in Volt’Air by Garcia et al.9 were selected. Following Schneider and Goss,21 we implemented the following equation to cover the dry domain of the water retention curve (below the wilting point) when this part of the retention curve was not experimentally covered by specific measurements

empirical parametrizations of the adsorption equilibrium constant for PEARL (Pesticide Emission Assessment at Regional and Local Scales) and PELMO (PEsticide Leaching MOdel) models respectively, in order to account for this increase in pesticide adsorption on the soil under dry conditions. These parametrizations were also tested in the Volt’Air model.7 Even if improvements could be obtained, it was difficult to perform a satisfactory calibration of these parametrizations. A mechanistic description of the underlying process of the solidgas adsorption such as proposed here is expected to be more efficient. Such parametrization has been tested in a simple volatilization model to describe a data set obtained with a lab system under controlled conditions.12 The objective of this work is to implement a mechanistic description of the adsorption of pesticides in gaseous form in soil in a process based model and assess if and how this new equilibrium can improve the description of pesticide volatilization rates with soil surface conditions. We used the Volt’Air model as its mechanistic approach in describing processes involved in volatilization at the field scale allows implementing the two required developments. First, the “classical” water retention curve in the classical domain of water potential was extended to the “dry” domain (based on data when available or on a new extrapolation equation introduced in the model) to better describe the dry soil surface conditions. Then, the equilibrium sorption constant for trifluralin was estimated as a function of temperature and relative humidity of the air-filled pore of the soil by implementing the experimental and modeling work on the adsorption of organic compounds from the gas phase to soil solid surfaces.3,4,12,13 A sensitivity analysis on the parameters involved in the new scheme of gaseous adsorption and comparisons between the measurements and the model outputs, considering or not the gaseous adsorption, were done using an original field data set of Bedos et al.14 (referred to hereafter as the “Chavenay” data set). Additional soil sampling and hydraulic characterization were specifically carried out for the purpose of this study. An optimization of the parameters was also conducted. The volatilization dynamic of trifluralin simulated by the model was then assessed using a second data set15 obtained with windtunnels, referred to as the “Grignon” data set.

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log10( −h) = Slope ×

slope =

θ + log10( −h)θ= 0 ρb

1 −0.109 × CF − 0.003

(1a)

(1b)

with log10(−h)θ=0 = 6.8 as estimated by Schneider and Goss,21 and h (cm), ρb (kg m−3), θ (m3 m−3), and CF (dimensionless) are respectively the water potential, the soil bulk density, the soil volumetric water content, and the soil clay content. Implementation of the Solid−Gas Adsorption in the Physicochemical Equilibria Scheme of Volt’Air. The physicochemical equilibrium scheme of Jury et al.18 for nonionic pesticides is based on the total concentration of the pesticide (CT, mol m−3), expressed as a function of the concentrations of the pesticide adsorbed to the soil solid phase (CS, mol m−3), in aqueous solution (CL, mol m−3) and in gaseous phase (CG, mol m−3): CT = ρb CS + θCL + aCG

(2)

where

CG = KHCL

(3a)

CS = KdCL

(3b)

a (m3 m−3) is the soil volumetric air content, KH (−) is the Henry’s law constant, and Kd (m3 kg−1) is the partitioning coefficient of the pesticide between the aqueous form and the adsorbed form to soil (given as the product of the fraction of organic carbon, foc (−), and the organic carbon sorption coefficient Koc (m3 kg−1)). Substituting eq 3a and 3b in eq 2 allows calculating all concentrations like for example CG:

MATERIALS AND METHODS

To focus on the developments specifically made for this study, only the modified modules of Volt’Air are detailed in this publication. The remaining modeling approaches are described in previous publications7,9,16 and in the Supporting Information.

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THE VOLT’AIR MODEL Briefly, the Volt’Air model originally built to simulate ammonia emissions from a field spread with slurry has since been extended to mineral nitrogen fertilizer and pesticide applications. It is organized in modules that calculate the energy budget, the vertical transfer of energy (Fourier’s law), water (Richards’ equation), and solutes (Fick’s law) in the soil profile including the diffusion of gaseous compounds in the air-filled pore space. The soil profile is divided into a user-defined number of layers. The transfers of heat and water are not coupled. Pesticide volatilization to the air is calculated by the local advection analytical solution,17 coupled with the physicochemical equilibrium Jury’s model.18 It runs over

CG =

CT ρb (Kd /KH ) + θ /KH + a

(4)

To take into account the solid−gas adsorption of the pesticide, a partitioning coefficient of the pesticide between air and the hydrated soil solid surfaces Kads (m3 kg−1) was added. Now, CS follows:

CS = KdCL + KadsCG

(5)

Equation 4 now becomes CG = 4992

CT ρb (Kd /KH + Kads) + θ /KH + a

(6)

dx.doi.org/10.1021/es5000879 | Environ. Sci. Technol. 2014, 48, 4991−4998


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Table 1. Input Parameters for the Control, Ads_pest, and Ads_pest_opti Simulations*

*a

refers to parameters for which an optimization procedure was done for the Ads-pest_opti simulation. b refers to new parameters added to the Bedos et al. model.7 c calculated by K. Goss for trifluralin. d from Bedos et al.7 e from PPDB database.31

Parameterization of the Partitioning Coefficient Kads. Following Goss et al.3

Kads = SSA Ks , a 2

adsorbing surface in order to establish the free energy of transfer of the molecule between the adsorbed and aqueous phases. The sensitivity of Kads toward RH was estimated from the empirical relationships proposed by Goss.4 For SSA, a relation describing its reduction as the RH increases can be derived from the measurements of Brusseau et al.22 by means of microscopic X-ray tomography

(7)

−1

where SSA (m kg ) is the Specific Surface Area of the soil, and Ks/a (m3 m−2) the surface specific adsorption constant of a given pesticide. Both Ks/a and SSA have to be estimated. Ks,a is a function of temperature and RH. The temperature relation established by Goss4 was introduced in Volt’Air. The integrated form is presented here, with a reference value Kref s/a (m3 m−2) obtained for a reference temperature Tref (K) RH log KsT/,100% a

⎛ θ ⎞ SSA(RH ) = SSA max ⎜1 − ⎟ θsat ⎠ ⎝

with SSAmax (m2 kg−1) representing the maximum SSA which theoretically describes the SSA of the completely dry soil (at RH = 0%), and θsat (m3 m−3) is the soil water content at saturation. SSA max can be estimated by (i) microscopic X-ray tomography (not used here), (ii) the N2−BET method23 (SSAN2BET max ) which measures the adsorption of gaseous nitrogen under a vacuumed atmosphere, and (iii) theoretical calculations using the smooth-sphere geometrical assumption for the shape of soil particles24

⎞ ΔHs / a + RT̅ ⎛ 1 ⎜⎜ − 1 ⎟⎟ + log Ksref, a =− Tref ⎠ 2.303R ⎝ T (8)

T,100%RH Ks/a

3

−2

with (m m ) representing the value of the partitioning constant for a given temperature at 100%RH, ΔHs/a (J mol−1) is the enthalpy of adsorption, R (J mol−1 K−1) is the gas constant, T (K) is the temperature, and T̅ (K) is the average temperature of T and Tref. Due to the role of water films on the interactions between the pesticide and the soil solid surfaces, both Ks/a and SSA had to be considered as a function of RH of the air-filled pore space. Following Goss,13 an exponential expression was adopted to describe Ks/a decrease with increasing RH RH (100 − RH )δRH Ks , a = KsT/,100% 10 a

(10)

geom =6 SSA max

1 − θsat ρb d50

(11)

with d50 being the median diameter of the particles (m). In this geom study, we tested both SSAN2BET max and SSAmax . To estimate the relative humidity of the air-filled pore space within the soil, the Kelvin equation was introduced in Volt’Air

(9)

with δRH representing the variation coefficient of Ks/a with a unit decrease of RH. At the reference state of 100%RH, all hydrophilic surfaces are covered with so many water layers that the adsorption on these hydrated surfaces is identical to the adsorption on a bulk water surface. ΔHs/a and Kref s/a were calculated by Goss using the software COSMOtherm (COSMOlogic GmbH & Co. KG: Leverkusen, Germany, 2008).13 This was done through molecular modeling by calculating the pesticide’s energy potential in different orientations and different distances of its center from the

RH = 100e

M wg h RT

(12)

−1

with Mw (kg mol ) representing the molar mass of water, and g (m s−2) representing the gravitational acceleration. Experimental Data Sets. The “Chavenay” Data Set. We used the first 24 h of the data set fully described in Bedos et al.14 Briefly, the field experiment was conducted in Chavenay near Grignon (48°5′N, 1°5′E), France, over a 5 ha field. The soil was a silt loam (Table 1), with a bulk density of 1300 kg 4993



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m−3 and an organic carbon content of 12.3 g kg−1. At 8:35 UT on the 31st August 2002 (Day Of Year − DOY- 243.39), a commercial solution of trifluralin (Tréflan, DowElanco) was sprayed at a measured application dose of 0.88 kg ha−1 of active ingredient with an incorporation 24 h after the application. The application took place 3 days after a major rain event (11.8 mm) occurring between DOY-237 and 240. The trifluralin volatilization flux was measured by the aerodynamic profile method25 (with 4 heights of measurements for vertical wind speed and air temperature−averaged over 15 min−and pesticide concentrations measured with a sampling duration ranging from 2 (on the first day) to 4 h in the daytime and averaging 10 h in the nighttime). The atmospheric relative humidity and the soil surface temperature were also monitored - averaged over 15 min. The water content using the gravimetric method was measured by sampling the soil surface with a trowel (0−0.5 cm) several times a day or with a metallic ring (0−2.5 cm). In order to improve the description of soil water transfer for the purpose of this study and the pesticide gaseous adsorption, soil was sampled in this field in Summer 2009 for additional measurements. First, to establish experimental water retention curves in the “classical” domain, i.e. water potentials from 0 (saturation) to −160 m (wilting point), the retention curve was obtained on undisturbed soil samples using the press extractor method26 with a Richard’s press. The depths sampled were 0− 2.5, 2.5−5, and 5−7.5 cm. The van Genuchten-Mualem (VG) water retention model was used to fit the data using the RMSE (Root Mean Square Error) and estimate the VG parameters for this soil in the “classical” domain. Then, specific measurements were performed for water potentials from −242 to −11702 m (as extensively described in Schneider and Goss,21 see Chavenay site results). In that domain, water contents are measured as well as the relative humidity RH of the air-filled pore space, varying then from 98.3 to 42.9%. The depth sampled was 0−5 cm. The water potential h was then calculated following the Kelvin equation (eq 12). The two measurement data sets were then combined to fit new VG model parameters for the entire domain. Finally, the SSAmax was measured on this soil samples with the N2-BET method.23 The “Grignon” Data Set. We used the data set fully described in Bedos et al.15 Briefly, an experiment was conducted in Grignon from 09:17 (UT) 23 June (DOY-178) to 15:30 (UT) 3 July 2000 (DOY-185). Three wind tunnels− consisting of a 0.95 m2 experimental surface each, covered by a U-shaped sheet and submitted to a fixed air flow rate corresponding to a realistic wind velocity−were placed on a bare soil field (silty clay loam −Table 1). One hour before applying the pesticide, the soil was moistened with an equivalent of 5 mm of water. A commercial solution of trifluralin (Tréflan, DowElanco) was applied without incorporation over the experimental surfaces at the measured application dose of 1.166 kg ha−1 of active ingredient. Volatilization flux was calculated given the mass balance equation. Air trifluralin concentrations were measured with sampling periods varying from 2.5 h to 2.5 days. Meteorological conditions, e.g. the soil water evaporation, the soil temperature at the surface, were continuously monitored and averaged on 30 min intervals. The soil volumetric water content inside one tunnel was also continuously monitored. Soil water content was estimated at three depths (0−2, 2−5, and 5−10 cm) using the gravimetric method before the experiment, outside the

experimental surfaces, and at the end of the experiment, inside the wind tunnel. Description of the Simulations. Configuration of the Model. Simulations carried out on the “Chavenay” data set are named “Chavenay scenario”. They were parametrized as close as possible to the previous work of Bedos et al.7 except the choice of the van Genuchten-Mualem water retention and hydraulic conductivity models20 instead of the Clapp and Hornberger one27 (Table 1). Simulations were performed from DOY-243.00 to 244.38 with a time step of 15 min and a 7 day warm-up run of the model to initialize the soil water content profile.7 For the “Grignon scenario”, the simulation was performed from DOY-178.33 to 185.62 with a time step of 30 min and using the same physicochemical inputs as in the “Chavenay scenario”. The soil was also divided into 5 layers at the same depths except the first whose depth was fixed at 0.007 m instead of 0.003 m, due to the higher roughness length (estimated to be 0.003 m). As the experimental field was located in the same area as the “Chavenay” data set with a similar soil type, the same SSAmax was used. The experimental water retention curves and hydraulic conductivity were not measured neither in the “classical” nor in the “dry” range. Given the texture, bulk density, and organic carbon fraction (Table S1) the VG parameters were calculated in the “classical” domain from Wösten et al.28 Then, in the dry region (below the wilting point), the model uses the linear extrapolation given eq 1. Sensitivity Analysis on the “Chavenay” Data Set. Sensitivity analyses were conducted on physicochemical properties of the compound, KH, SSAmax, and Kref s/a, the two latest parameters having been estimated with the greatest uncertainty and all other input data were kept unchanged (based on the “Chavenay” data set). The output selected is the instantaneous volatilization flux for the first peak of DOY 243.41. KH ranged from 0.8 to 10 times the initial value. The chosen range of variation of SSAmax, from 13 000 to 0 m2 kg−1, covered the range obtained through direct measurements using the N2−BET method for the soil under consideration and ref ranged from 6.62 × 10−2 through calculations using eq 11. Ks/a 3 −2 to 14.9 m m , which corresponds to the confidence interval ref from the root-mean-square error of 0.63 on log Ks/a observed by Goss.13 Assessment Procedure of the Pesticide Gaseous Adsorption Concept. For each scenario, a Control simulation was run without the gas-phase adsorption. We assumed Bedos et al.7 input data, including the value of KH obtained after optimization on the first peak of volatilization observed for the first sampling period (DOY 243.41). Then, for each scenario, adsorption from the gas phase was taken into account using eqs 2-11 (including SSAgeom max calculated from eq 11) in a simulation named Ads_pest with the same set of parameters as in the Control simulation. Finally, an optimized simulation Ads_pest_opti was run for the “Chavenay scenario” using calibrated values of (i) KH, as its optimization for the configuration without gas phase adsorption might be no longer adapted and (ii) SSAmax as high differences in value of SSAmax can be obtained whether it is directly measured or calculated. We followed a two-step parameter optimization procedure: first, a value of KH was selected for the simulated instantaneous volatilization flux for the first peak (on DOY-243.41) to fit the measurements; second, using this optimized KH, the SSAmax parameter was optimized, this time 4994



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see eq 12) is 0.056 m3 m−3 for the modeled curve using the entire range of measurements, and 0.080 m3 m−3 for the modeled curve using the classical experimental range (Point B). The water potential corresponding to a water content of 0.056 m3 m−3 on the modeled curve using the classical experimental range is −4619 m (i.e., log(h) = 5.7, Point B’), leading to a RH = 72%. Using a retention curve defined in a restrictively humid range of soil water contents to establish hydraulic parameters leads to a 23% difference on RH which may lead to differences in Kads as discussed later. Simulated Soil Surface Temperature and Water Content. The simulated surface temperatures for the “Chavenay scenario” were in fair agreement with the measurements, with a dynamic well described (Figure 2a). The major

for the simulated cumulative loss from application time to DOY-244.38 to fit the measurements. This procedure was repeated to establish KH and SSAmax values that offer a good trade-off between simulations of the instantaneous volatilization flux and the cumulative loss. We applied these values for the “Grignon scenario”. Efficiency Factors (EF) and RMSE (Root Mean Square Error) were calculated following eqs 13a and 13b for the volatilization flux n

EF = 1 −

∑i = 1 (ysim , i − yexp , i )2 n

∑i = 1 (yexp , i − yexp )2

(13a)

n

RMSE =

∑i = 1 (ysim , i − yexp , i )2 n

(13b)

with ysim representing the simulated values, yexp representing the experimental values, and yexp representing the mean of the experimental series. The closer EF to 1, the better the model is for the tested variable. The smaller RMSE (same unit as y), the better the model is for the tested variable.

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RESULTS AND DISCUSSIONS Extension of the Water Retention Curve to the Dry domain. Figure 1 shows for the “Chavenay scenario” (i) the

Figure 1. Measured and modeled retention curves of the soil surface layer (0 to 2.5 cm) obtained with the two methods for the dry and classical domain for the “Chavenay scenario”. van Genuchten parameters obtained for the classical experimental range and for both classical and dry experimental one were respectively: n = 1.230, θsat = 0.408, θr = 0 and α = 1.2, n = 1.311, θsat = 0.397, θr = 0, and α = 0.6. Points A, B, and B’ with the dash lines refer to the examples discussed in the text.

Figure 2. (a) Surface temperature (°C) as measured (each 5 s and averaged over 15 min) and simulated by Volt’Air for the “Chavenay scenario” whatever the simulation. (b) Soil water contents (m3 m−3) of the soil surface layer as measured (several times a day with a trowel in the 0−0.005 m layer) and soil water content of each soil layer as simulated by Volt’Air for the “Chavenay scenario”.

measured soil water contents as a function of the water potentials, obtained either in the “classical” or in the “dry” domain, (ii) the modeled retention curve established by using the measurements in the classical experimental range, and (iii) the modeled curve using both classical and dry experimental ranges of measurements −noted “whole range” (values of the van Genuchten parameters are given in SI Table 1). Comparison of the “classical” modeled curve and the “dry” measurements showed clear discrepancies, supporting the hypothesis that using the usual range of water potential measurements for establishing the retention curve is not sufficient to adequately describe the hydraulic properties of a soil under dry conditions.9,10 For example, the water content equivalent to a water potential of −720 m (Point A on the graph: log(h) = 4.9, corresponding to a RH of 95% at 25 °C,

rain event between DOY-237 and 240 led to relatively wet soil surface conditions before the application and drying conditions afterward. Overall, the simulations described correctly the decreasing trend in soil surface water content observed in the afternoon but did not reproduce the lower surface water contents even with the water retention curve over the “whole” range (Figure 2b). Estimated Parameters. Table 1 presents all parameters involved in the trifluralin adsorption in gaseous phase. Following eq 8, Ks/a decreases with increasing temperature (SI Figure 1a). Indeed, the resulting increase in vapor pressure of the pesticide in the air-filled pore space shifts the equilibrium from the adsorbed to the gaseous phase. Likewise, following eq 4995



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9, Ks/a decreases with increasing RH, due to additional layers of water molecules stacking on the soil solid surfaces, which implies a larger distance and thus a reduction in the intermolecular interactions between the sites at the soil solid surfaces and the pesticide. To illustrate this effect, Ks/a is increased by a factor of 4 when RH drops from 95% down to 72% (SI Figure 1b, Points A and B). However, these values are higher than those given by Schneider and Goss12 established by fitting on experimental data. For the SSAmax parameter, two values - rather low, compared to Schneider and Goss12- were obtained: (i) 1319 m2 kg−1 with N2BET ) and (ii) 278 m2 kg1 the N2−BET method23 (SSAmax following eq 11 (SSAgeom , with d = 9.85 × 10−6 m established max 50 from measurements of the five classical particle size classes). Based in the literature review we have done, eq 10 introduced in Volt’Air is the only one which has been validated admittedly in a limited range of soil water content, (θ/θsat) varying from 1.0 to 0.4.22 It is based on X-ray microtomography imagery measurements. This method, presenting a much lower resolution (about 12 μm) than the N2-BET method, led to an underestimation of the SSAmax by a 102 factor compared to a N2−BET measurement29 but closer values to the calculated N2BET is expected to be more realistic than SSAgeom max . Even if SSAmax SSAgeom , we choose SSAgeom max max to stay under conditions close to those used to establish eq 10. This choice certainly implies an uncertain description of the evolution of SSA with RH in the presupposes. drier domain, as the discrepancy with SSAN2BET max Sensitivity Analysis. The first peak of trifluralin volatilization flux (at 9:45 UT, DOY-243.41) increased with increasing KH. This increase shows as expected, a smaller range of evolution when adsorption from the gaseous phase is taken into account, but it is still significant (Figure 3a). The first peak of

volatilization (at 9:45 UT, DOY-243.41) decreased from 2000 to 98 ng m−2 s−1 over the range of assumed SSAmax values (Figure 3b). This strong influence of SSAmax on volatilization fluxes highlighted the need for a realistic value of SSAmax. ref with a simulated Similar sensitivity is observed for Ks/a volatilization peak ranging from 280 to 1900 ng m−2 s−1 (Figure 3c) over the range of assumed Kref s/a values. Assessment of the Pesticide Gaseous Adsorption Concept. The Control, Ads_pest, and Ads_pest_opti simulation outputs, averaged on each sampling period, were compared to the measurements, in terms of instantaneous trifluralin volatilization flux for the two data sets (Figure 4).

Figure 4. Volatilization flux (ng m2 s−1) for the Control, Ads_pest, and Ads_pest_opti simulations (averaged on the sampling period) and measurements for (a) the “Chavenay scenario” and (b) the “Grignon scenario”. The black arrows in part b indicate the sampling periods when the improvement of the outputs taking into account the adsorption process is the more significant. The gray arrow indicates the period when a rain event occurred.

Focusing first on the “Chavenay scenario”, the optimization performed on SSAmax and KH resulted in an SSAmax of 600 m2 kg−1 (instead of 278 m2 kg−1) and a KH of 8.25 × 10−3 (instead of 1.1 × 10−3). This SSAmax value is more satisfactory as it reduces the underestimation made when choosing SSAgeom max . Likewise, the optimized value for KH is in a better agreement than the original one used for Ads_pest simulation with the range presented in Bedos et al.7 as well as with HenryWin estimation30 (8.67 × 10−3, HenryWin v3.20 March 2011, USEPA). As expected for the first peak of volatilization on DOY 243, the Control simulation gave results (1770 ng m2 s−1) close to measurements (1927 ng m2 s−1) as it has been calibrated on this first peak by Bedos et al.,7 and the

Figure 3. Instantaneous volatilization flux for the first peak of DOY 243.41 (9:45 UT) (ng m−2 s−1) as simulated for the “Chaveney scenario” by Volt’Air taking into account or not the gaseous adsorption process as a function of KH (a), and taking into account the gaseous adsorption process as a function of the SSAmax parameter (b) and the ref Kref s/a parameter (c), based on the assumed ranges of SSAmax and Ks/a used for sensitivity analysis. 4996



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Ads_pest_opti simulation gave results closer to the measurements than the Ads_pest simulation (1878 and 1244 ng m2 s−1 respectively) (Figure 4a). The major improvement was observed on the third sampling period on DOY-243.61: the Ads_pest_opti simulation largely decreased to values closer to the measured ones (619 ng m2 s−1) than the Control simulation (1021 and 1570 ng m2 s−1 respectively). All simulations gave similar results for the first sampling period on DOY-244.31. Simulated cumulative loss for Ads_pest_opti was 0.041 g m−2, which is closer to the measurements (0.031 g m−2) than the Control simulation (0.046 g m−2). The improvement of the simulated volatilization flux depended on sampling period. We therefore looked at the time evolution of the RH of the air-filled pore space of the soil surface layer (eq 12), the SSA, the Kads together with the instantaneous volatilization flux (presented here for each timestep of the model) (Figure 5). The range of RH was relatively small, going from 99.99 to 99.94% during the afternoon. Although the range of variation of RH might seem small, the SSA, and thus Kads, are reacting significantly: Kads varied by a factor 5 and 2 respectively for Ads_pest and Ads_pest_opti on DOY-243. As shown in Figure 5b and c, this variation corresponded to the third sampling period on DOY 243 and explained the improvement between the Control and the Ads_pest_opti description of the volatilization flux, even if not sufficient. The dynamic of the volatilization flux was thus closer to the observed one as illustrated by better EFs (from 0.76 for the Control simulation to 0.93 for the Ads_pest_opti one) and lower RMSE (from 146 for the Control simulation to 78 for the Ads_pest_opti one) (Table 2). Using empirical parametrizations of PEARL and PELMO, Bedos et al.7 found EFs of 0.73 and 0.70, showing thus that the updated model here presented gives better results than the previous version of the model. Dealing now with the “Grignon scenario”, Figure 4b illustrates the results obtained using the same trifluralin physicochemical properties and SSAmax that were used for the “Chavenay scenario”. Outputs were compared with volatilization flux measured in one tunnel; a 25% uncertainty (calculated given the 3 replications15) is reported as error-bars on the graph. Although, quantitatively, the simulated and the measured curves slightly differed, the dynamic of the volatilization rate was clearly better described when the gaseous adsorption was taken into account, as clearly shown by better EFs and lower RMSE (Table 2). Moreover, by the end of the experimental period, a rewetting of the soil occurred15 due to a major rain event of 20 mm on DOY 184, which led to an increase of the volatilization rate. This can be reproduced by the model only when the gaseous adsorption is considered. Such effect was also found by Schneider and Goss.12 During the “Grignon” simulation, the RH of the air-filled pore of the soil surface layer varied between 0.9% and 99.99% (SI Figure 2). This work is original as it is the first time that specifically calculated solid−gas partitioning coefficients are implemented in a mechanistic description of the gaseous adsorption of pesticides on the soil matrix. This new mechanistic equilibrium was implemented in a process-based volatilization model at the field scale, which furthermore includes a more appropriate theoretical description of soil surface under dry conditions in terms of water content. The better description of the dynamic of the volatilization flux -decreasing during drying period and increasing during rewetting period - confirms the hypothesis that gaseous adsorption is one of the key processes involved in pesticide behavior in the soil. However, the precise

Figure 5. (a) Relative humidity RH of the air-filled pore space of the soil (%), (b) SSA (m2 kg−1) and Kads (m3 kg−1) and (c) instantaneous volatilization flux (ng m2 s−1) for the Control, Ads_pest, and Ads_pest_opti simulations for the “Chavenay scenario”. Dashed lines separate the different sampling periods.

Table 2. Statistical inDicators Calculated for Both Scenarios To Assess Model Efficiencya Chavenay

Grignon

scenario

EF

RMSE

Control Ads_pest Ads_pest_opti Control Ads_pest Ads_pest_opti

0.76 0.83 0.93 −2.73 0.2 0.17

146 122 78 353 163 168

a

EF: Efficiency Factor - the closer to 1 the better; if close to zero, that means that the average is as good as the model itself and RMSE: Root Mean Square Error (same unit as the variable) - the lower, the better.

quantification of this effect remains difficult: the optimization step is not easy even if values obtained for SSAmax and KH were more realistic than the ones before the optimization step and 4997



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(10) Ross, P.; Williams, J.; Bristow, K. Equation for extending waterretention curves to dryness. Soil Sci. Soc. Am. J. 1991, 923−927. (11) Rawlins, S.; Campbell, G.; Klute, A. In Physical and mineralogical methods; 1986; pp 597−618. (12) Schneider, M.; Goss, K. U. Volatilization modeling of two herbicides from soil in a wind tunnel experiment under varying humidity conditions. Environ. Sci. Technol. 2012, 12527−12533. (13) Goss, K. U. Predicting adsorption of organic chemicals at the Air-Water interface. J. Phys. Chem. A 2009, 12256−12259. (14) Bedos, C.; Rousseau-Djabri, M. F.; Gabrielle, B.; Flura, D.; Duran, B.; Barriuso, E.; Cellier, P. Measurement of trifluralin volatilization in the field: Relation to soil residue and effect of soil incorporation. Environ. Pollut. 2006, 958−966. (15) Bedos, C.; Rousseau-Djabri, M. F.; Flura, D.; Masson, S.; Barriuso, E.; Cellier, P. Rate of pesticide volatilization from soil: an experimental approach with a wind tunnel system applied to trifluralin. Atmos. Environ. 2002, 5917−5925. (16) Génermont, S.; Cellier, P. A mechanistic model for estimating ammonia volatilization from slurry applied to bare soil. Agric. Forest Meteor. 1997, 145−167. (17) Itier, B.; Perrier, A. Présentation analytique de l’advection. I. Advection liée aux variations horizontales de concentration et de température. Ann. Agron. 1976, 111−140. (18) Jury, W. A.; Spencer, W. F.; Farmer, W. J. Behavior assesment model for trace organics in soil: I. Model description. J. Environ. Quality 1983, 558−564. (19) Loubet, B.; Cellier, P.; Génermont, S.; Flura, D. An evaluation of the wind-tunnel technique for estimating ammonia volatilization from land: Part 2. Influence of the tunnel on transfer processes. J. Agric. Eng. Res. 1999, 83−72. (20) Van Genuchten, M. T. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 1980, 892−898. (21) Schneider, M.; Goss, K. U. Prediction of the water sorption isotherm in air dry soils. Geoderma 2012, 64−69. (22) Brusseau, M. L.; Narter, M.; Schnaar, G.; Marble, J. Measurement and estimation of Organic-Liquid/water interfacial areas for several natural porous media. Environ. Sci. Technol. 2009, 3619−3625. (23) Brunauer, S.; Emmett, P. H.; Teller, E. Adsorption of gases in multimolecular layers. J. Am. Chem. Soc. 1938, 309−319. (24) Costanza-Robinson, M. S.; Harrold, K. H.; Lieb-Lappen, R. M. X-ray microtomography determination of air-water interfacial areawater saturation relationships in sandy porous media. Environ. Sci. Technol. 2008, 2949−2956. (25) Majewski, M. S. Micrometeorological methods for measuring the post-application volatilization of pesticide. Water, Air, Soil Pollut. 1999, 83−113. (26) Klute, A.; Dirksen, C. In Methods of soil analysis. Part 1; Madison, WI, 1986. (27) Clapp, R. B.; Hornberger, G. M. Empirical equations for some soil hydraulic properties. Water Resour. Res. 1978, 601−604. (28) Wösten, J. H. M.; Lilly, A.; Nemes, A.; Le Bas, C. Development and use of a database of hydraulic properties of European soils. Geoderma 1999, 169−185. (29) Brusseau, M. L.; Peng, S.; Schnaar, G.; Murao, A. Measuring airwater interfacial areas with X-ray microtomography and interfacial partionning tracer tests. Environ. Sci. Technol. 2007, 1956−1961. (30) HENRYWIN; USEPA, 2011. (31) PPDB. The FOOTPRINT Pesticide Properties Database. http://sitem.herts.ac.uk/aeru/footprint/es/index2.htm (accessed Mar 14, 2014).

not general as shown when tested on the Grignon scenario. Several factors remain uncertain. Moreover, this assessment is based on one compound, one soil type, and a characterization of soil hydraulic properties carried out several years after the initial experiment. Data sets comprising all the data needed at adapted spatial and temporal scales, e.g. soil water content at the soil surface continuously, pesticides volatilization rates over short periods of time, are required to further analyze the effect of each factor - temperature, soil water content, ... - and to validate this implementation in a more quantitative way, under various conditions. Moreover, progress could be achieved by establishing the relation SSA=f(RH) in ranges actually more relevant to gaseous adsorption of pesticides using a method with a better spatial resolution.

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ASSOCIATED CONTENT

S Supporting Information *

Details on the Volt’Air model together with additional results. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 00 33 (0)1 30 81 55 36. Fax: 00 33 (0)1 30 81 55 63. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Special thanks are directed to I. Cousin (UR INRA Sciences du Sol, Orléans, France) for her invaluable help in the establishment of the SSA measurements as well as M. Schneider and K. Goss (UFZ, Leipzig, Germany) for their calculation of adsorption coefficient and measurements of water retention under dry conditions.

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REFERENCES

(1) Calvet, R.; Barriuso, E.; Bedos, C.; Benoit, P.; Charnay, M. P.; Coquet, Y. Les pesticides dans le sol. Conséquences agronomiques et environnementales; Paris, 2005. (2) Taylor, A. W.; Spencer, W. F. In Pesticides in the Soil Environment; Soil Science Society of America Book Series: Madison, WI, USA, 1990; Vol. no. 2, pp 213−269. (3) Goss, K. U.; Bushmann, J.; Schwarzenbach, R. Adsorption of organic vapors to air-dry soils: model predictions and experimental validation. Environ. Sci. Technol. 2004, 3667−3673. (4) Goss, K. U. The air/surface adsortion equilibrium of organic compounds under ambient conditions. Crit. Rev. Environ. Sci. Technol. 2004, 339−389. (5) Van den Berg, F.; Wolters, A.; Jarvis, N.; Klein, M.; Boesten, J. J. T. I.; Leistra, M.; Linneman, V.; Smelt, J. H.; Vereecken, H. Attilio Amerigo Maria Del Re, E. C., Ed.; 2003; pp 973−983. (6) Ferrari, F.; Klein, M.; Capri, E.; Trevisan, M. Prediction of pesticide volatilization with PELMO 3.31. Chemosphere 2005, 705− 713. (7) Bedos, C.; Génermont, S.; Le Cadre, E.; Garcia, L.; Barriuso, E.; Cellier, P. Modelling pesticide volatilization after soil application using the mechanistic model Volt’Air. Atmos. Environ. 2009, 3630−3669. (8) Reichman, R.; Rolston, D. E.; Yates, S. R.; Skaggs, T. H. Diurnal variation of diazinon volatilization: soil moisture effects. Environ. Sci. Technol. 2011, 2144−2149. (9) Garcia, L.; Bedos, C.; Génermont, S.; Braud, I.; Cellier, P. Assesssing the ability of mechanistic volatilization models to simulate soil surface conditions. A study with the Volt’Air model. Sci. Total Environ. 2011, 3980−3992. 4998


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