Ability of the MACRO Model to Predict Long-Term Leaching of

Mar 25, 2009 - Sciences, Department of Agroecology and Environment,. Aarhus University, P.O. Box 50, DK-8830 Tjele, Denmark,. Department of Clinical ...
0 downloads 0 Views 242KB Size
Download PDF
Environ. Sci. Technol. 2009, 43, 3221–3226

Ability of the MACRO Model to Predict Long-Term Leaching of Metribuzin and Diketometribuzin A N N E T T E E . R O S E N B O M , * ,† JEANNE KJÆR,† TRINE HENRIKSEN,‡ MARLENE ULLUM,¶ AND PREBEN OLSEN§ Geological Survey of Denmark and Greenland, Øster Voldgade 10, DK-1350 Copenhagen, Denmark, Faculty of Agricultural Sciences, Department of Agroecology and Environment, Aarhus University, P.O. Box 50, DK-8830 Tjele, Denmark, Department of Clinical Pharmacology, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen Ø, Denmark, and COWI A/S, Parallelvej 2, DK-2800 Kongens Lyngby, Denmark

Received September 29, 2008. Revised manuscript received January 30, 2009. Accepted March 3, 2009.

In a regulatory context, numerical models are increasingly employed to quantify leaching of pesticides and their metabolites. Although the ability of these models to accurately simulate leaching of pesticides has been evaluated, little is known about their ability to accurately simulate long-term leaching of metabolites. A Danish study on the dissipation and sorption of metribuzin, involving both monitoring and batch experiments, concluded that desorption and degradation of metribuzin and leaching of its primary metabolite diketometribuzin continued for 5-6 years after application, posing a risk of groundwater contamination. That study provided a unique opportunity for evaluating the ability of the numerical model MACRO to accurately simulate long-term leaching of metribuzin and diketometribuzin. When calibrated and validated with respect to water and bromide balances and applied assuming equilibrium sorption and first-order degradation kinetics as recommended in the European Union pesticide authorization procedure, MACRO was unable to accurately simulate the long-term fate of metribuzin and diketometribuzin; the concentrations in the soil were underestimated by many orders of magnitude. By introducing alternative kinetics (a two-site approach), we captured the observed leaching scenario, thus underlining the necessity of accounting for the long-term sorption and dissipation characteristics when using models to predict the risk of groundwater contamination.

Introduction The pesticide authorization procedure in the European Union (EU) (1) relies on the use of well-documented mathematical models such as MACRO (2), applied in accordance with the FOCUS (FOrum for the Coordination of pesticide fate models and their USe) guidelines (3) to predict leaching of pesticides and their metabolites through the vadoze zone. The simulated yearly average pesticide-metabolite concentration in leachate * Corresponding author phone: +45 3814 2934; fax: +45 38142050; e-mail: [email protected]. † Geological Survey of Denmark and Greenland. ‡ Rigshospitalet. § Aarhus University. ¶ COWI A/S. 10.1021/es802752x CCC: $40.75

Published on Web 03/25/2009

 2009 American Chemical Society

of 1 m below ground surface is used as the reference when determining whether an active substance can be approved for use at the member state level. As pesticides and metabolites are increasingly detected in groundwater (4), it is imperative to evaluate the ability of these environmental fate models to predict the long-term leaching of pesticides and especially of their metabolites. However, such studies have not hitherto been reported in the literature (5). The ability of these models to predict such long-term leaching scenarios is difficult to evaluate because of a lack of field-scale leaching studies encompassing an appropriate time span (6), a lack of site specific coherent information on climate, subsoil (e.g., hydrology, geology), and substance properties (sorption and degradation) (7), and the general inadequate use of the model process descriptions due to the incorporation of questionable data on sorption and dissipation kinetics obtained by using disturbed soil rather than naturally structured soil in the field (8, 9). The reason such data are questionable is that soil aggregates, micropores, macropores, and the solution/solid ratio differ markedly among soils under natural and disturbed conditions (10). The leaching evaluation procedure used for regulatory purposes in Europe (3) relies on first-order degradation kinetics and equilibrium sorption, and the models are generally unable to use parameters derived from more advanced approaches (11) that take into account the fact that true equilibrium may never be reached in soil because of dissipation processes such as leaching (including diffusion) and chemical, physical, or biological transformation (8). However, different options do exist for a few of the models. Models such as MACRO and PEARL contain an option to simulate long-term sorption kinetics, using a two-site approach. With this approach, substances in the liquid phase and sorbed to sorption sites in instantaneous equilibrium are degraded according to first-order kinetics, while pesticides sorbed to nonequilibrium sorption sites (only applicable to the micropore domain of MACRO) can be protected from degradation, if desired (2, 3). The objective of this study was to evaluate the ability of the one-dimensional, dual-permeability model MACRO (2) to predict long-term leaching of the herbicide metribuzin and one of its primary metabolites diketometribuzin (DK), of which the latter threatens the application of groundwater to be used for drinking water (12). The study is based on in situ leaching data and other data gathered by our research group over a period of many years at a sandy field research site, which were previously utilized in other studies (12-16). Given evidence of preferential flow and bromide transport occurring at this site (14), we chose the FOCUS-listed MACRO model. We combined our field scale data on leaching of metribuzin and diketometribuzin (12) with data on sorption and dissipation characteristics (13) as input to a MACRO model setup that had been calibrated and validated using seven years of water balance and bromide leaching data (14). This ensured the necessary scale and time span for determining whether modeling of leaching using the standard first-order kinetic parameters, following the FOCUS guidelines (3), accurately predicts long-term leaching of the pesticide and its metabolite or whether alternative kinetics such as the two-site approach is more appropriate.

Field Research Site Studies The metribuzin and diketometribuzin data used in the present modeling study are those previously reported by Kjær et al. (12) obtained at a sandy field research site in Tylstrup, VOL. 43, NO. 9, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

3221

TABLE 1. Equilibrium and Nonequilibrium Dissipation and Sorption Parameters for Metribuzin and Diketometribuzin (DK)a input parameter

horizon

metribuzin

equilibrium parameters DT50 (days) A 32 (92, 187) B n.d. (227, 500) C1 n.d. >500 C2 >500 (227, 500) Kd (L/kg) A 0.94 (0.3, 2.2) B/C1 n.d. (0.01, 1.5) C2 0.001 (0.001, 0.1) A 0.56 Freundlich coefficient (-) B/C1 n.d C2 0.56 nonequilibrium parameters kinetic sorption rate All 1.04 coefficient Rk (1/day) fraction of sorption sites available for kinetic All 0.95 sorption, fne (-)

DK 29 n.d. n.d. >500 0.72 n.d. 0.001 0.58 n.d 0.58 1.01 0.95

a Parameters were estimated using disturbed soil samples from the horizons A (0-0.32 m bgs), B (0.33-0.75 m bgs), C1 (0.76-1.00 m bgs), and C2 (1.01-5.00 m bgs), obtained from both the Tylstrup field site and similar geological settings close to the Tylstrup site (Supporting Information). Estimates for similar geological settings are given as numbers in brackets, which indicate the range of estimates obtained. The nonequilibrium parameter Rk derives from the findings of Ladlie et al. (18), while parameter fne derives from the calibration process.

Denmark. The data on degradation and sorption of metribuzin and diketometribuzin are derived from batch experiments, conducted using soil samples from the Tylstrup site (Table 1) previously reported by Henriksen et al. (13). The water balance and bromide data used to calibrate the MACRO model were obtained at the Tylstrup site as previously reported by Barlebo et al. (14). The characteristics of the field site and agricultural management are in the Supporting Information, together with a brief summary of the monitoring data (12, 14) and degradation and sorption data (13). Model Setup. The modeling study was performed using the one-dimensional MACRO model version 5.1 (2), which (i) is one of the four FOCUS-listed models, (ii) can account for the observed preferential flow and transport, and (iii) provides the options necessary for evaluating long-term leaching of pesticides and their metabolites. This dual-permeability model of water flow and solute transport in structured soils (two separate flow regions: micropores, here 98%, and macropores, here 2%) considers nonsteady water, heat, and solute fluxes for a variably saturated layered soil profile (Richards’ equation for micropores, gravity flow for macropores). We used an existing, comprehensive model setup based on direct measurements of structural and hydraulic parameters (retention, saturated hydraulic conductivity, etc.) that encompassed four horizons (A, B, C1, and C2) and incorporated site-specific agricultural management and climate history for the period 1989-2007 (including crops and irrigation, but excluding different tillage operations). Given the unknown representation of the direct measurement of the field heterogeneity, we imposed some uncertainty on the model setup and its solution. The model was calibrated and “validated” against soil-water saturation, measured by means of time domain reflectometry (TDR), water table depth measured in piezometers, and bromide concentration in water sampled from suction cups using monitoring data obtained during the periods May 1999-June 2004 (calibration) and July 2004-July 2007 (validation) (14, 15). The latter type of monitoring data collected from 1 and 2 m depths at two locations on site showed indications of preferential flow (Figure S1 of the Supporting Information) 3222

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 43, NO. 9, 2009

by spatial differences in the bromide arrival time at the two depths and double-peaked breakthroughs. We tried to reduce the discrepancies between measured and simulated data by applying the MACRO linked inverse modeling program SUFI (sequential uncertainty fitting) (17), but without success. The design of a multiobjective approach is, therefore, subject to future research. In MACRO, solute transport in micropores is described by the convection-dispersion equation, while that in macropores is described by mass flow. The version of MACRO used here provides the options necessary for evaluating longterm leaching of pesticides and their metabolites. The model is able to simulate the parent pesticide plus one metabolite, following a two-step procedure. The first step is to generate a metabolite file containing the degraded mass of the parent compound, as a function of both depth and time, which is accomplished by running a leaching simulation with the parent compound. In the second step, this file is used as the input data file for a leaching simulation with the metabolite. This requires information on the fraction of the parent compound that is degraded to the metabolite. On the basis of the confidential information supplied by the Danish Environmental Protection Agency, we set the fraction to 0.2 for metribuzin and diketometribuzin. To allow for alternative degradation kinetics to the first-order kinetics recommended in the EU pesticide authorization procedure (1,3), we incorporated a two-site approach into MACRO. However, this was only applicable to the micropore domain. Two types of model scenarios, including all the metribuzin applications in the 1990s, were thus evaluated using the metribuzin leaching data from the Tylstrup field research site. One-Site Approach. The one-site approach is presently used in the EU pesticide authorization procedure. It is assumed that all sorption sites are in equilibrium and that degradation follows first-order kinetics. Sorption and degradation are specified by a sorption coefficient Kd and a halflife DT50, respectively (Table 2, metribuzin scenarios M1-M6 and diketometribuzin scenarios DK1-DK5). Two-Site Approach. In the in two-site approach, the presence of two types of sorption sites is assumed: (I) A fraction that undergoes equilibrium sorption combined with first-order degradation specified by a sorption coefficient Kd and a half-life DT50, respectively. (II) A fraction that undergoes nonequilibrium sorption excluding and/or including protection of the site from a first-order degradation (Table 2, metribuzin scenarios M7-M9 and diketometribuzin scenarios DK6-DK8). Fraction II needs to be specified by the following parameters: (i) the fraction of sorption sites undergoing kinetic sorption (fne), (ii) a first-order mass transfer coefficient (Rk), and (iii) an indication of whether or not the sites should be protected from first-order degradation. Input parameter fne was obtained during the calibration process, while parameter Rk was obtained from the findings of Ladlie et al. (18) on a Hillsdale sandy clay loam at pH 4.6, which is similar to the pH of the Tylstrup field site. Because no estimates of Kd and DT50 were available for horizons B and C1 at the Tylstrup site (Table 1), we followed the recommendations of the FOCUS group (19) for dealing with such situations. FOCUS Standard. When no estimates were available from a similar geological soil setting, we multiplied the degradation rate for horizon A by default factors to yield degradation rates for horizons B and C1. Kd was kept constant with depth (Table 2, metribuzin scenarios M1 and M7 and diketometribuzin scenarios DK1 and DK6). FOCUS Extra. When estimates from a soil horizon in a similar geological setting and with similar climatic conditions to those at Tylstrup (10) were available, these were used. FOCUS extra was, therefore, only directly applicable to

TABLE 2. MACRO Parameter Settings for Various Metribuzin and Diketometribuzin Leaching Scenariosa leaching scenario

M1, M2, M3, M4, M5, M6,

FOCUS standard FOCUS extra maximum DT50 optimal fit maximum Kd setting minimum Kd setting

M7, FOCUS standard M8, FOCUS extra M9, optimal fit DK1, DK2, DK3, DK4, DK5,

FOCUS FOCUS FOCUS FOCUS FOCUS

standard extra extra extra extra

DK6, FOCUS standard DK7, FOCUS extra DK8, optimal fit

DT50for horizons A, B, C1 and C2. (days)

Kd for horizons A, B, C1 and C2. (L/kg)

metribuzin, one-site approach (32, 79, 107, 500) (0.94, 0.94, 0.94, 0.001) (32; 500, 500, 500) (0.94, 0.94, 0.94, 0.001) (187, 500, 500, 500) (0.94, 0.94, 0.94, 0.001) (275, 500, 500, 500) (0.94, 0.94, 0.94, 0.001) (32, 500, 500, 500) (2.2, 1.5, 1.5, 0.1) (32,500, 500, 500) (0.3, 0.01, 0.01, 0.1) metribuzin, two-site approach (32, 79, 107, 500) (0.94, 0.94, 0.94, 0.001) (32, 500, 500, 500) (0.94, 0.94, 0.94, 0.001) (32, 32, 32, 500) (0.94, 0.94, 0.94, 0.001) diketometribuzin, one-site approach (29, 72, 97, 500) (0.72, 0.72, 0.72, 0.001) (29, 500, 500, 500) (0.72, 0.72, 0.72, 0.001) (29, 500, 500, 500) (0.72, 0.72, 0.72, 0.001) (29, 500, 500, 500) (0.72, 0.72, 0.72, 0.001) (29, 500, 500, 500) (0.72, 0.72, 0.72, 0.001) diketometribuzin, two-site approach (29, 72, 97, 500) (0.72, 0.72, 0.72, 0.001) (29, 500, 500, 500) (0.72, 0.72, 0.72, 0.001) (29, 29, 40, 500) (0.72, 0.72, 0.72, 0.001)

rk (1/day)

fne (-)

parent scenario

-

-

-

1.04 1.04 1.04

0.95 0.95 0.95

-

-

-

M1 M2 M3 M4 M9

1.01 1.01 1.01

0.95 0.95 0.95

M7 M9 M9

a The leaching scenarios were tested using a one-site or a two-site approach. The numbers in bold are estimates introduced following the FOCUS guidelines (3) and are not based directly on the field-scale leaching study performed at Tylstrup (12).

metribuzin (Table 2, metribuzin scenarios M2, M3, M5, M6, and M8; see Supporting Information for details) but, because of the lack of appropriate estimates of Kd and DT50, not to diketometribuzin. We thus assumed DT50 to be 500 days for horizons B and C1 (i.e., the same DT50 as used in the FOCUS extra setup for metribuzin) and ran this setup as an undocumented FOCUS extra setup for diketometribuzin. As the MACRO model was unable to accurately simulate measured leaching of metribuzin and diketometribuzin when following these recommendations, we changed DT50 until an optimal fit was obtained between simulated and measured leaching (i.e., the scenarios designated optimal fit in Table 2). Here the model efficiency, R2, was used to evaluate the model performance. To achieve the optimal fit, our changing of the Kd depth distribution did not give the right effect; therefore, Kd was kept constant.

Results Leaching of Metribuzin: One-Site Approach. The leaching simulations performed using the one-site approach and following the FOCUS standard or FOCUS extra recommendations (3) for estimating DT50 and Kd for horizons B and C1 (Table 2, scenarios M1 and M2) yielded similar concentrations (Figure 1a,b; scenarios M1 and M2). With both scenarios, the simulated metribuzin concentration in the soil of 0.05-0.20 m bgs was underestimated by a factor of 103 and could not account for the observed long-term fate of metribuzin residues in the soil at this depth (Figure 1a). The simulated porewater concentration of metribuzin (1 m bgs) was far below the detection limit of metribuzin in water samples and, thereby, in line with the monitoring data (Figure 1b). To elucidate the sensitivity of the model setup to the choice of DT50 and Kd and hereby the effect of the parameters on the model’s ability to capture the observed metribuzin concentration in the soil (0.05-0.20 m bgs), we included the estimate range of these parameters obtained from similar geological settings. DT50 was increased to the highest of these estimates (Table 2, scenario M3), and Kd was either increased to the highest of the estimates or reduced to the lowest of the estimates (Table 2, scenarios M5 and M6, respectively).

This revealed that the one-site approach failed to accurately simulate metribuzin dynamics within the root zone. Thus, when DT50 was increased, the simulated soil concentration of 0.05-0.20 m bgs was less than one tenth of the measured soil concentration, while the porewater concentration (1 m bgs) was below the LOD (Figure 1, scenario M3). Changing the Kd setting also did not result in an optimal fit. Thus, when Kd was reduced (Figure 1, scenario M6), the concentration in the soil (0.05-0.20 m bgs) was elevated for the first 1.5 years, whereafter it decreased, eventually underestimating the observed soil concentration by a factor up to 104. Moreover, the simulated porewater concentration (1 m bgs) peaked at a level exceeding the LOD during the first 1.5 years in conflict with the monitoring data, which showed that metribuzin was not detected (1 m bgs). When Kd was increased (Table 2, scenario M5), the porewater concentration (1 m bgs) was in line with the measurements, but the soil concentration (0.05-0.20 m bgs) was underestimated by a factor of approximately 106 (Figure 1, scenario M5). It was only possible to accurately simulate the observed soil concentration by setting DT50 for horizon A to 275 days, which is a value far beyond the estimates reported for similar geological settings (19) and greater than the DT50 of 187 days measured in batch experiments on disturbed soil samples from a similar geological setting. This optimal fit scenario accurately simulated the measured soil concentration of metribuzin at 0.05-0.20 m bgs and yielded a porewater concentration of 1 m bgs that was below the LOD (Table 2 and Figure 1, scenario M4). Leaching of Metribuzin: Two-Site Approach. When Rk was set to 1.04, fne was set to 0.95, and DT50 was estimated for horizons B and C1 in accordance with the FOCUS standard recommendations (Table 2, scenario M7), the simulated metribuzin concentration in the soil (0.05-0.20 m bgs) accurately reflected the observed long-term fate of the metribuzin residue at that depth (Figure 1c). In contrast, the simulated porewater concentration of metribuzin (1 m bgs) was generally above the LOD (Figure 1d). The same pattern of simulated leaching was obtained when DT50 was set to the highest value recorded at a similar geological setting (Table 2, scenario M8). In order to accurately simulate the measured VOL. 43, NO. 9, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

3223

FIGURE 1. Measured (red b or 4) and simulated (solid lines) concentration of metribuzin (mg/L) in the soil (a and c, 0.05-0.20 m bgs) and porewater (b and d, 1 m bgs). Simulations are based on the one-site (a and b) and two-site (c and d) approaches. Red 4 indicate that the metribuzin concentration is below the LOD (2 µg/kg for soil and 0.01 µg/L for water). concentration in the soil (0.05-0.20 m bgs), fne needed to be set to 0.95 or more, and these sorption sites needed to be protected from degradation. Given the high fraction of sorption sites undergoing nonequilibrium sorption, while concomitantly being protected from degradation, the DT50 of the equilibrium sites present in horizons B and C1 had to be low, similar to the estimated DT50 of horizon A (Table 2 and Figure 2, scenario M9). This optimum fit scenario yielded a simulated pore water concentration of 1 m bgs less than the LOD 3.5 years after application, which is in agreement with the measured concentration. Leaching of Diketometribuzin: One-Site Approach. The model was unable to accurately simulate measured leaching of diketometribuzin, when using the one-site approach. Thus, although the simulated diketometribuzin concentration in the soil (0.05-0.20 m bgs) was below the LOD and hence in line with the measured concentration (Figure 2a) when DT50 for both metribuzin and diketometribuzin was estimated for horizons B and C1 in accordance with the FOCUS standard recommendations (Table 2, scenario DK1), long-term leaching of diketometribuzin to 1 m bgs was underestimated by a factor of 1014 (Figure 2b). As no estimates of DT50 and Kd for diketometribuzin were available from other, similar geological settings, it was not possible to estimate DT50 and Kd for horizons B and C1 using true FOCUS extra recommendations. Setting the DT50 to 500 days for horizons B and C1 as in metribuzin scenarios M2-M4 and basing the diketometribuzin scenarios on the outcome of these metribuzin scenarios (Table 2, scenarios DK2-DK4), we increased the simulated diketometribuzin concentration (1 m bgs) slightly, although the concentration remained far from the measured concentration (Figure 2b). One-site simulations of diketometribuzin leaching based on the optimal one-site or two-site metribuzin scenarios (M4 and M9, respectively) 3224

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 43, NO. 9, 2009

also failed to accurately reflect the measured porewater concentration of 1 m bgs (Figure 2a,b; scenarios DK4 and DK5), which was underestimated by a factor of about 1014. Leaching of Diketometribuzin: Two-Site Approach. Simulation accuracy (in the form of the ability of the model to capture the observed leaching scenario) was considerably improved when using the two-site approach. Three diketometribuzin leaching scenarios were evaluated based on the two-site metribuzin FOCUS standard (M7) and optimal fit scenarios (M9). Scenario DK6: FOCUS Standard. The first scenario was the two-site approach based on scenario M7, using the FOCUS standard recommendations for estimating DT50 and Kd for horizons B and C1 (Table 2, scenario DK6). Scenario DK7: FOCUS Extra. The second scenario was the two-site approach based on the optimal fit metribuzin scenario M9, with DT50 for horizon B and C1 set to 500 days (Table 2, scenario DK7). Scenario DK8 - Optimal fit. The third scenario was the two-site approach based on the optimal fit metribuzin scenario M9, with DT50 for horizons B and C1 set to 29 days based on experience with metribuzin scenario M9 (Table 2, scenario DK8). With all three of these two-site leaching scenarios, the simulated concentration of diketometribuzin in the soil (0.05-0.20 m bgs) was below the LOD 3.5 years after the metribuzin application, which is in agreement with the measured concentration (Figure 2c). In contrast, the measured porewater concentration of diketometribuzin (1 m bgs) was underestimated by a factor of 10-1018 in scenario DK6 and overestimated by a factor up to 102 in scenario DK7 (Figure 2d). As with metribuzin leaching, we obtained the optimal fit with the measured diketometribuzin leaching (R2 ) 0.24) by reducing DT50 for horizons B to the same value

FIGURE 2. Measured (red O or 2) and simulated (solid lines) concentration of diketometribuzin (mg/L) in the soil (a and c, 0.05-0.20 m bgs) and the porewater (b and d, 1 m bgs). Simulations are based on one-site (a and b) and two-site (c and d) approaches. Red O indicate the diketometribuzin concentration is below the LOD (20 µg/kg for soil and 0.02 µg/L for water). as for horizon A and having DT50 ) 40 days for the C horizon (Figure 2c,d; scenario DK8).

Discussion The fate of pesticide metabolites in the environment is attracting increasing attention in relation to the authorization and use of pesticides. The ability of the environmental fate models recommended for use under the EU pesticide authorization procedure to accurately predict long-term parent-daughter leaching has not hitherto been evaluated. Our findings indicate that the prediction of long-term leaching using the environmental fate model MACRO, which incorporates dissipation and sorption kinetics obtained in batch experiments on disturbed soil samples, is subject to considerable uncertainty. The main conclusions are listed below: • The modeling practice recommended under the EU pesticide authorization procedure (3) based on the onesite approach and first-order kinetics fails to accurately simulate measured leaching of metribuzin and diketometribuzin. With this approach, the concentration of diketometribuzin in porewater (1 m bgs) was underestimated by a factor of about 1014. As leaching of diketometribuzin poses a threat to groundwater, use of the one-site approach and the inherent assumption of equilibrium in the soil system leaves the regulatory authorities with an inadequate basis for authorizing the pesticide. • The ability of model scenarios based on the one-site approach to predict long-term leaching of metribuzin was highly sensitive to the choice of DT50 and to a lesser degree of Kd. Therefore, emphasis needs to be placed

on describing the dissipation of metribuzin within the soil profile. • Although the one-site approach was able to accurately simulate the measured metribuzin concentration of 0.05-0.2 m bgs and 1 m bgs by not strictly following the recommendations in the EU pesticide authorization procedure, it failed to accurately simulate leaching of diketometribuzin. • Using the two-site approach, and assuming that no degradation of metribuzin or diketometribuzin takes place within the kinetic pool, MACRO was able to accurately simulate the measured long-term leaching of metribuzin and diketometribuzin. • Incorporating the depth distribution of DT50 in the model scenario as recommended by the FOCUS guidelines failed to yield an optimal fit to measured leaching, as did the use of DT50 estimates from similar geological settings. An optimal fit was only obtained by assuming that the DT50 determined for horizon A by means of batch experiments on disturbed soil was also representative for the B and C1 horizons. However, experimental data verifying that this depth distribution of dissipation is realistic are lacking. In order to be able to simulate the measured long-term parent-daughter leaching pattern, the model scenarios had to include a high fraction of nonequilibrium sorption sites without degradation and a low fraction of equilibrium sites with a relatively high degradation (DT50 < 30 days) in the upper 1 m of the soil profile. However, no experimental data verifying that this is realistic is available. This raises two general issues. How can such parameters best be extrapolated VOL. 43, NO. 9, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

3225

from the numerous fate studies available? Can the model scenarios be adequately parametrized using data obtained from disturbed soil via batch experiments? Studies of metribuzin sorption and dissipation kinetics indicate that these processes in the soil involve kinetics other than first-order kinetics (13). Deviations from firstorder kinetics were detected for metribuzin and diketometribuzin but were not directly applicable for use in the two-site approach. Further knowledge and useable routines are therefore vital in order to enable the findings from the numerous sorption and dissipation experiments to be extrapolated as directly usable input data for the two-site approach that is incorporated in MACRO (2, 20). The data of Henriksen et al. (13) were obtained on disturbed soils via batch experiments, and the DT50 and Kd estimates for the horizons proved to be of questionable value for use in simulating long-term leaching of metribuzin and diketometribuzin, which seems to follow multiphasic reactions under partly nonequilibrium conditions (10). The inappropriateness of these DT50 and Kd estimates in the present leaching scenarios is probably attributable to the fact that disturbing the soil changes its structure and thereby its physical, geochemical, biological, and hydraulic properties and, hence, the equilibrium and nonequilibrium setting of the soil (7, 8, 10, 21). Moreover, the time span of their experiments may have been too short as the solution-soil mixtures were only allowed to equilibrate for up to a maximum 98 h, which may be insufficient for describing the long-term fate of metribuzin under partly nonequilibrium conditions (10). Implication. Our results suggest that the long-term leaching of pesticide metabolites such as diketometribuzin into the groundwater is likely to be underestimated by the modeling practice recommended under the EU pesticide authorization procedure, which is based solely on first-order kinetics. In order to protect the groundwater from this type of metabolite leaching, this study indicates an urgent need for addressing long-term sorption and dissipation characteristics in the EU pesticide authorization procedure.

Acknowledgments This study was funded by the Danish Pesticide Leaching Assessment Programme. We thank the many people whose work within the programme made the present study possible, including Bo Lindhardt, Christian Abildtrup, Henrik Vosgerau, Henning Hougaard, Søren B. Torp and Finn Plauborg (establishment of field sites and monitoring design) and Carsten Kamper, David Croft, Søren H. Jepsen, Birgit Sørensen, Carl H. Hansen, and Lasse Gudmundsson (ongoing field monitoring and data preparation). We are grateful to Steen Marcher and Christian Deibjerg Hansen for competent guidance on EU pesticide risk assessment procedures, to Ole Andersen and Nis Hansen for analysing the soil and porewater concentrations of metribuzin and diketometribuzin, and to David Barry for linguistics assistance.

Supporting Information Available Figure S1 presents simulated and measured bromide concentrations at 1 and 2 m depths. A paragraph discusses field research site studies. Table S1 presents equilibrium and nonequilibrium dissipation and sorption parameters for metribuzin and diketometribuzin (DK), estimated on disturbed soil samples from horizons A, B, C1, and C2 at the Tylstrup field research site and similar geological soil settings close to the Tylstrup site. This material is available free of charge via the Internet at http://pubs.acs.org.

Literature Cited (1) Council Directive of 15 July 1991 Concerning the Placing of Plant Protection Products on the Market; European Economic Community Directive 91/414/EEC; Official Journal L 230; 1991.

3226

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 43, NO. 9, 2009

(2) Larsbo, M.; Roulier, S.; Stenemo, F.; Kasteel, R.; Jarvis, N. An improved dual-permeability model of water flow and solute transport in the vadose zone. Vadose Zone J. 2005, 4 (2), 398–406. (3) Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration; European Communities Document Reference Sanco/10058/2005 version 2.0; Report of the FOCUS Work Group on Degradation Kinetics; FOCUS, 2008; http://focus.jrc. ec.europa.eu/dk/. (4) Kolpin, D. K.; Schnoebelen, D. J.; Thurman, E. M. Degradates provide insight to spatial and temporal trends of herbicides in ground water. Ground Water 2004, 42 (4), 601–608. (5) Saffih-Hdadi, K.; Bruckler, L.; Lafolie, F.; Barriuso, E. A model for linking the effects of parathion in soil to its degradation and bioavailability kinetics. J. Environ. Qual. 2006, 35 (1), 253–267. (6) Fox, G. A.; Sabbagh, G. J.; Malone, R. W.; Rojas, K. Modeling parent and metabolite fate and transport in subsurface drained fields with directly connected macropores. J. Am. Water Resour. Assoc. 2007, 43 (6), 1359–1372. (7) Wauchope, R. D.; Yeh, S.; Linders, J. B. H. J.; Kloskowski, R.; Tanaka, K.; Rubin, B.; Katayama, A.; Kordel, W.; Gerstl, Z.; Lane, M.; Unsworth, J. B. Pesticide soil sorption parameters: theory, measurement, uses, limitations and reliability. Pest Manage. Sci. 2002, 58 (5), 419–445. (8) Walker, A.; Turner, I. J.; Cullington, J. E.; Welch, S. J. Aspects of the adsorption and degradation of isoproturon in a heavy clay soil. Soil Use Manage. 1999, 15 (1), 9–13. (9) Beulke, S.; Brown, C. D. Evaluation of methods to derive pesticide degradation parameters for regulatory modelling. Biol. Fertil. Soils. 2001, 33 (6), 558–564. (10) Smith, M. C.; Shaw, D. R.; Massey, J. H.; Boyette, M.; Kingery, W. Using nonequilibrium thin-disc and batch equilibrium techniques to evaluate herbicide sorption 1. J. Environ. Qual. 2003, 32 (4), 1393–1404. (11) Mamy, L.; Barriuso, E. Desorption and time-dependent sorption of herbicides in soils 1. Eur. J. Soil Sci. 2007, 58 (1), 174–187. (12) Kjaer, J.; Olsen, P.; Henriksen, T.; Ullum, M. Leaching of metribuzin metabolites and the associated contamination of a sandy Danish aquifer. Environ. Sci. Technol. 2005, 39 (21), 8374– 8381. (13) Henriksen, T.; Svensmark, B.; Juhler, R. K. Degradation and sorption of metribuzin and primary metabolites in a sandy soil. J. Environ. Qual. 2004, 33 (2), 619–627. (14) Barlebo, H. C.; Rosenbom, A. E.; Kjær, J. Evaluation of Pesticide Scenarios for the Registration Procedure, Environmental Project No. 1178; Danish Environmental Protection Agency: Copenhagen, Denmark, 2007. (15) Kjær, J.; Rosenbom, A. E.; Olsen, P.; Juhler, R. K.; Plauborg, F.; Grant, R.; Nygaard, P.; Gudmundsson, L.; Bru ¨sch, W. The Danish Pesticide Leaching Assessment Programme: Monitoring Results, May 1999-June 2007; Geological Survey of Denmark and Greenland: Copenhagen, Denmark, 2008. (16) Lindhardt, B.; Abildtrup, C.; Vosgerau, H.; Olsen, P.; Torp, S.; Iversen, B. V.; Jørgensen, O. J.; Plauborg, F.; Rasmussen, P.; Gravesen, P. The Danish Pesticide Leaching Assessment Programme: Site Characterization and Monitoring Design; Geological Survey of Denmark and Greenland: Copenhagen, Denmark, 2001. (17) Abbaspour, K. C.; vanGenuchten, M. T.; Schulin, R; Schlappi, E. A sequential uncertainty domain inverse procedure for estimating subsurface flow and transport parameters. Water Resour. Res. 1997, 33 (8), 1879–1892. (18) Ladlie, J. S.; Meggitt, W. F.; Penner, D. Effect of soil pH on microbial-degradation, adsorption, and mobility of metribuzin. Weed Sci. 1976, 24 (5), 477–481. (19) FOCUS Groundwater Scenarios in the EU Review of Active Substances; Report of the FOCUS Groundwater Scenarios Workgroup, European Community Document Reference Sanco/ 321/2000 rev.2; FOCUS, 2000. (20) Rice, P. J.; Rice, P. J.; Arthur, E. L.; Barefoot, A. C. Advances in pesticide environmental fate and exposure assessments. J. Agric. Food Chem. 2007, 55 (14), 5367–5376. (21) Dousset, S.; Thevenot, M.; Pot, V.; Simunek, J.; Andreux, F. Evaluating equilibrium and non-equilibrium transport of bromide and isoproturon in disturbed and undisturbed soil columns. J. Contam. Hydrol. 2007, 94 (3-4), 261–276.

ES802752X