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Journal of Environmental Quality 30:2113-2119 (2001)
© 2001 American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America

TECHNICAL REPORT
Surface Water Quality

Soil Surface Structure Effect on Isoproturon and Diflufenican Loss in Runoff

Véronique Lecomte*,a,c, Enrique Barriusob, Louis-Marie Bressonb, Caroline Kochb and Yves Le Bissonnaisa

a INRA, Avenue de la Pomme de Pin, BP 20619 Ardon, 45166 Olivet Cedex, France
b INRA/INA-PG, BP 01, 78850 Thiverval-Grignon, France
c BRGM, 3 Avenue Claude Guillemin, 45060 Orléans Cedex, France

* Corresponding author (Lecomte-Morel{at}exchange.brgm.fr)

Received for publication October 16, 2000.

   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSION
 REFERENCES
 
Because soil surface structure has a considerable influence on infiltration rate, the sealing process is postulated to have a significant effect on herbicide loss through runoff. We evaluated the effect of degraded soil surface structures on herbicide loss in runoff, and used the experimental data to test the uniform mixing zone concept and two-site sorption kinetics for modeling herbicide transfer to runoff. The experiments were done with simulated rainfall on 10-m2 plots in the field and 0.25-m2 plots in the laboratory after a surface application of 1.5 kg ha-1 of isoproturon [3-(4-isopropylphenyl)-1,1-dimethylurea] and 0.187 kg ha-1 of diflufenican [2',4'-difluoro-2-({alpha},{alpha},{alpha}-trifluoro-m-tolyloxy) nicotinanilide]. Isoproturon (IPU) and diflufenican (DFF) concentrations were very high in the first runoff (up to 60 mg L-1 for IPU and 2 mg L-1 for DFF) when simulated rainfall was applied 24 h after the treatment. The concentrations decreased very rapidly with total rainfall depth. Degradation of the structural state of the soil surface increased the ratio of pesticide loss to application rate from 0.3 to 10% for IPU and from 0.7 to 7.8% for DFF for a runoff depth of less than 1 mm. The structural state of the soil surface influences the rapidity at which runoff begins after the onset of rain, and the runoff coefficient at steady state. Furthermore, the development of a surface seal seems to limit the depth of soil–runoff interaction and thus influences the dynamics of herbicide mobilization. Concentrations of IPU in the runoff were satisfactorily described with a model incorporating a uniform mixing zone and two-site sorption–desorption.

Abbreviations: CI, couche d'interaction (uniform mixing zone) • DFF, diflufenican • IPU, isoproturon


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSION
 REFERENCES
 
THE transport of chemicals beyond a cultivated field by runoff can contribute to the contamination of surface waters. Runoff and erosion and the transfer of pesticides from soil to runoff are the main processes controlling loss of pesticides by surface runoff.

The mobilization of pesticides by runoff depends not only on the physico–chemical properties of the pesticides, but also on the application methods, soil properties, climate, and agricultural practices (Bailey et al., 1974). The mechanisms of pesticide transfer from soil to runoff must be understood if we are to improve the transfer models. Several approaches have been proposed. For example, the empirical approach correlates the pesticide concentration in the top millimeters of the soil to the concentration in the runoff and defines an extraction ratio (Bruce et al., 1975; Leonard et al., 1979). This is the ratio of soil mass to unit volume that participates effectively in the extraction of pesticides by runoff (Leonard and Nowlin, 1980; Leonard and Wauchope, 1980). It has been incorporated into several models such as GLEAMS (Leonard et al., 1987), PRZM (Carsel et al., 1985), PLIERS (Kenimer et al., 1989), and Opus (Smith, 1992). Implicitly, this approach considers that a certain amount of soil is mixed with and extracted by runoff. Taking into account a zone of instantaneous and complete mixing between a surface soil layer and the runoff water assumes that overland flow, infiltration, and soil solution are at the same concentration. Donigian et al. (1977) and Steenhuis and Walter (1980) used this uniform mixing zone concept to define a mass transfer equation that determines transfer by runoff during a rainfall event:

[1]
where {epsilon} is the mixing depth, {theta}s is the volumetric moisture content at saturation, Cs is pesticide concentration in soil solution, i is the infiltration rate, o is the rate of overland flow, and r is the rainfall intensity. So, for constant {epsilon} and {theta}s:

[2]
where Cw is pesticide concentration in runoff. Ingram and Woohliser (1980) measured higher concentrations in the soil solution than in the runoff and suggested that there is only partial mixing in this soil–runoff interaction zone. Ahuja et al. (1981) and Ahuja and Lehman (1983) showed that the degree of interaction is highest at the soil surface, then decreases rapidly with depth. This has been modeled and approximated by a decreasing degree of mixing with depth (Heathman et al., 1985, 1986). Havis et al. (1992) have defined two control volumes, overland flow and the soil mixing zone. To describe transport phenomena, they take into account a film transport coefficient (K) and the difference of chemical solute concentration between mixing zone and overland flow. This approach, based on non-instantaneous transfer between two uniform compartments, allows us to consider also diffusion and convection. Wallach et al. (1989) have defined an effective depth of transfer (EDT), which varies with time. Other approaches consider the soil as a semi-infinite porous media where pesticide concentration depends on soil surface distance (Ahuja, 1990; Wallach and van Genuchten, 1990; Parr et al., 1994).

Within the considered depth, pesticide partitioning between soil solution and soil particles plays a preponderant role in pesticide runoff concentration. The main models are the Freundlich and the linear isotherms. The linear isotherm model assumes that the ratio between adsorbed pesticides concentration (Cs) and the solution concentration (Cw) is a constant:

[3]

The Freundlich isotherm is a nonlinear empirical model:

[4]
where Kf and nf are empirical coefficients dependent on soil and pesticide types. Barriusso et al. (1992) used a two-compartment equilibrium model where the adsorption is linear for the first compartment and sorption–desorption is described by an exponential function for the second. Other approaches have been developed that consider kinetics of sorption–desorption: (i) considering a chemical non-equilibrium, sorption–desorption is controlled by a rate of reaction (Travis and Etnier, 1981; Cameron and Klute, 1977) and (ii) considering a physical non-equilibrium, soil solution is divided into a mobile phase and a immobile phase (van Genuchten and Wagenet, 1989).

The degree of degradation of the soil surface structure, which evolves as the season advances due to climate and agricultural practices, has been identified as a factor influencing soil infiltration capacity (Boiffin, 1984; Papy and Douyer, 1991; Le Bissonnais and Singer, 1993), notably in the northwestern European loam belt where there is extensive erosion (Ouvry, 1992). Soil surface structure has been mentioned as a possible factor in soil–runoff interaction depth, but this has not been specifically studied as far as we know. The aim of our present study was to determine the influence of the soil surface structure on the transfer of pesticides by runoff. Experiments simulating rainfall were done in the field and in the laboratory on plots with surface structures ranging from fragmented to degraded, until a sedimentary seal formed.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSION
 REFERENCES
 
Soil Description
The experiments took place in a small French agricultural region of Normandy. The soil studied is a silt loam soil (Typic Hapludalf) developed on loess formations overlying northwestern Europe's chalky limestone plateau. It has a high silt content (more than 60%), little clay (10 to 15%), and a higher fine sand content near the coast. The soil generally contains little organic matter (less than 1.5%) (Table 1). Because of the weak soil structure, the Normandy soil is particularly prone to runoff and erosion (Fox and Le Bissonnais, 1998). Rain breaks up the clods of earth and disperses the particles. When the surface sealing occurs and the microrelief diminishes with the disappearance of the clods, the soil loses its infiltration capacity and detention storage, so much that the land surface becomes susceptible to runoff (Le Bissonnais et al., 1998).


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  		Table 1. Texture and organic carbon content of the soil studied in the field and laboratory (the small differences observed correspond with the intra-plot variability).

 
Plots and Surface Structure States
Field experiments were carried out on 10-m2 rectangular plots (5 x 2 m) with a slope gradient varying from 3 to 5% and a nondisturbed soil. Laboratory experiments were done in 0.25-m2 plots made in containers filled with the surface horizon taken on the experimental field after sowing. Containers were filled with 40 kg of soil packed in 10-cm-thick layers and the roughness of the soil surface, corresponding to that at the time of sowing, was reproduced with a rake. The containers were tilted to a 5% slope.

Four surface structure states were studied in the field or laboratory experiments (Bresson and Boiffin, 1990):

  1. F0, initial fragmented surface structure (all particles were clearly distinguishable), corresponding here to the seed bed, oriented roughness reached 2 to 5 cm and random roughness was from 1 to 2 cm;
  2. F11, light structural seal, in which the fragments were joined but still recognizable, both oriented and random roughnesses reached 1 to 2 cm;
  3. F12, heavy structural seal, in which the fragments were joined and outlines had disappeared, but there was no sign of dispersion, both oriented and random roughnesses reached 1 to 2 cm;
  4. F2, sedimentary seal, continuous state with depositional seal, both oriented and random roughnesses were less than 1 cm.

Rainfall Simulation
By simulating rainfall we were not dependent on random climate conditions and were able to control the time, duration, and intensity of rainfall. The field rainfall simulator described by Asseline and Valentin (1978) was modified to irrigate the 10-m2 plots with no outside atmospheric interference (wind, rain). A sprinkler nozzle, placed 3.80 m above ground, moved back and forth, watering the plots sown with wheat (Triticum aestivum L.) in the direction of the slope, which was also the direction of the tillage. The actual intensity of the simulated rainfall in the field ranged from 24 to 28 mm h-1.

The drip system of the laboratory simulator watered 1 m2 and measurements were done in the central 0.25 m2 (Le Bissonnais et al., 1995). The intensity of the simulated rainfall in the laboratory ranged from 19 to 24 mm h-1.

During a first rainfall simulation campaign in the field, three surface structures, F0, F11 and F12, were studied. F11 and F12 states were obtained after 1 h and 2 h of rainfall at 40 mm h-1, respectively (1033 and 2067 J m-2). Pesticides were applied 1 d after soil surface structure degradation by rainfall simulation, when free drainage had become negligible. Twenty-four hours after treatment, rainfall was simulated on the three initial structural states, and was stopped when a steady state of runoff had been reached (after 10 to 15 mm rainfall, which correspond to 25 to 35 min). The initial soil moisture content ranged from 0.195 to 0.21 kg kg-1. Duplicate experiments were done for each surface structure.

During a second rainfall simulation campaign in the field, three successive 2-h rainfall events were simulated on a plot with an initial F12 structure obtained under natural rainfall (around 120 mm rainfall, which correspond to 1100 J m-2). The initial soil moisture content was 0.195 kg kg-1. The degradation of the surface state caused by the first simulated rain led to the appearance of signs of dispersion and the plot was classified as F2 after 20 mm of rainfall.

The laboratory experiments were then done on the two extreme states: a reconstituted seed bed (F0) and a well-developed sedimentary seal (F2) showing total continuity of the seal and signs of dispersion, obtained after the first rainfall simulation. The initial soil moisture content was 0.17 kg kg-1. Three replicates were done for each surface structure.

Pesticides
Two herbicides were studied: isoproturon (IPU) and diflufenican (DFF) (Table 2). Both can be associated in commercial products such as Quartz GT (Rhône Poulenc Agrochimie, Strasbourg, France), a concentrated suspension containing 62.5 g L-1 of DFF and 500 g L-1 of IPU that is used in Europe for weed control of soft winter wheat and winter barley (Hordeum vulgare L.) at an application rate of 3 L ha-1. It can be used during the preemergence period or at the three-leaf stage at the end of tillering.


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  		Table 2. Physico–chemical properties of isoproturon (IPU) and diflufenicanil (DFF).

 
The plots were treated with Quartz GT, at the agronomic rate in the field experiments (1.5 kg ha-1 of IPU and 0.1875 kg ha-1 of DFF) and at twice this rate in the laboratory experiments, 24 h before rainfall simulation. The actual rate applied was checked by weighing the hand sprayer.

Runoff samplers were stored at 4°C. When the volumes were sufficient, runoff samplers were divided into two parts and one part was separated into liquid and solid phases by continuous flow centrifugation (40000 rpm). Analysis of untreated runoff, solution, and sediments (>0.1 µm) were done in the INRA Laboratory of Soil Analysis in Arras using high-performance liquid chromatography (HPLC) with a UV detector for IPU and gas chromatography (GC) with electron-capture detector for DFF. Limits of detection reached 0.1 µg L-1 both for IPU and DFF.


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSION
 REFERENCES
 
Influence of Surface Sealing on Runoff
To simplify the presentation of the results concerning runoff and erosion, only the data from the first rainfall simulation campaign in the field are given here in detail. The results are expressed as a runoff coefficient (RC) in order to facilitate comparison of the different simulations in which the rainfall intensity may have varied by a few millimeters:

[5]

In the field, soil surface degradation resulted in an early onset of runoff after only 0.5 mm of total rainfall on the degraded surface structure, compared with 7.5 mm on the initial fragmented surface. The runoff coefficient was, moreover, greater at steady state (40% for the degraded F12 surface structure compared with 15% for the fragmented F0 surface structure; Fig. 1) . When rainfall continued on an F12 surface, the passage to a sedimentary seal (F2) caused a decrease in the infiltration capacity to between 6 and 7 mm h-1. The runoff coefficient was then greater than 70% for 28 mm h-1 of simulated rainfall.



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  		Fig. 1. Influence of soil surface structure on runoff from 10-m2 plots (n = 2).

 
Influence of Surface Sealing on Sediment Concentration and Erosion
For a fragmented stage (F0), the sediment concentration mobilized at the onset of runoff was around 8 g L-1. It decreased slightly during the rainfall event to about 6.5 g L-1 after 15 mm of total rainfall. The sediment concentration was smaller when the surface state was degraded, dropping from 4 to 2.5 g L-1 during the rainfall event for the F12 surface structure (Fig. 2) and not exceeding 2 g L-1 for the F2 surface structure in the field. This result could be due to the development of a surface water layer, which decreases the raindrop impact and the soil particle detachment.



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  		Fig. 2. Influence of soil surface structure on sediment concentrations in runoff from 10-m2 plots (n = 2).

 
Thus, the increase in the depth of runoff water on degraded surfaces did not result in a proportional increase in the mobilization of eroded particles which, on the contrary, were diluted. Soil loss (runoff depth x sediment concentration) was highest on the degraded surfaces in spite of the smaller sediment concentrations due to the high runoff coefficients on the sealed plots (Fig. 3) .



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  		Fig. 3. Influence of soil surface structure on cumulative sediment yield from 10-m2 plots (n = 2).

 
Influence of Surface Sealing on the Transport of Isoproturon and Diflufenican by Runoff
Pesticide concentration in the first runoff increased with surface structure degradation from 1100 µg L-1 for F0, to 20000 µg L-1 for F11 and 60000 µg L-1 for F12 for IPU, and from 300 to 2200 and 2600 µg L-1 for DFF at the same degradation stages (Fig. 4) .



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  		Fig. 4. Isoproturon (IPU) and diflufenican (DFF) concentrations during runoff from 10-m2 plots for the three soil surface structures (n = 2). Note the logarithmic scale on the y axis.

 
These very high initial concentrations were due in part to the short time lapse between treatment and simulated rainfall (24 h). They were, however, very near the solubility threshold for IPU and even exceeded it for DFF due to the very erosive context of the study (weak structure of the loamy soil) and also to the probable presence of crystals of pesticide still on the soil surface. Indeed, the transport of DFF in the adsorbed form (measured Kd = 108 to 153 L kg-1; sediment concentration = 2.5 to 4.5 g L-1) cannot alone explain the concentrations exceeding the solubility threshold in the runoff from the most degraded surfaces (F11 and F12).

The highest initial concentrations measured on the degraded surfaces correspond to the more rapid onset of runoff on sealed soil, whereas the dilution and infiltration of the pesticides could only have been minimal after only 0.5 mm of rainfall. Thereafter, the herbicide concentrations in the runoff decreased very rapidly during the rainfall event.

Overall, exports of IPU and DFF were greater when the first runoff occurred on a degraded surface due to both the greater volume of runoff and the higher peak in initial concentration. For example, for a 0.7-mm depth of runoff water, the ratio of loss to application rate increased from 0.3 to 10% for IPU and from 0.7 to 7.8% for DFF when there was a seal. A 0.7-mm runoff depth corresponded to 13 mm rainfall on fragmentary state and to 4.5 mm rainfall depth on sealed state. Less was lost on the fragmented surfaces due to infiltration of the first millimeters of rainfall, which mobilized the greatest concentrations of pesticides.

Modeling the Herbicide Mobilization
In the field as in the laboratory, the decrease in herbicide concentrations in the runoff as a function of total rainfall after treatment was exponential (Fig. 4 and 5) . Leonard (1990) called this the ideal pesticide runoff behavior.



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  		Fig. 5. Isoproturon (IPU) and diflufenican (DFF) concentrations during runoff from 0.25-m2 plots for two soil surface structures (n = 3). Note the logarithmic scale on the y axis.

 
Depending on the stage of structural degradation of the soil surface, different equations can be used to describe the decrease in herbicide concentration as a function of the increase in total rainfall. The initial concentration was greater when the surface structure was degraded but the concentration then decreased more rapidly (including for DFF in the field, although the logarithmic representation hides this, Fig. 4). The development of the surface seal probably first limited the diffusion in depth of pesticides, then the thickness of the soil–runoff interaction layer. Thus, the dilution in the soil on a sealed surface would concern a thinner layer and the initial concentration would be greater. However, the concentration in the runoff would then decrease more rapidly because the pesticides infiltrate beyond a thinner uniform mixing zone (couche d'interaction, CI) and would more rapidly become unavailable for runoff. The correlation coefficients were significant at the 95% confidence level for the fitting according to exponential laws for the DFF concentration measured under simulated rainfall in the field as a function of total rainfall for the three initial surface structure states (Fisher test). They were significant at the 99% confidence level for all of the other fittings. For all of the rainfall simulations, the fittings of the IPU concentration as a function of the total rainfall were always better than the fittings of the DFF concentration. The analyses of the total DFF concentration were greatly influenced by the concentration of herbicide adsorbed on eroded particles, the concentration of which varies during a rainfall event.

The thickness of CI can be calculated using the equation defined by Steenhuis and Walter (1980):

[6]
where b is the slope of the fitted curve Ln = a + bPc, with Cs as the concentration in solution (µg L-1) and Pc the total rainfall (mm), {theta}s is the water content at saturation (m3 m-3), ma is the soil bulk density (g cm-3), and Kd is the solid–solution partition coefficient (mL g-1). We thus obtained, for the field measurements of IPU, CI thicknesses of 1.6, 0.9, and 0.8 mm for surface structures F0, F11, and F12, respectively. For DFF, it is hypothesized that the herbicide under a microcrystal form would be lost in runoff much greater under fully sealed conditions, compared with unsealed surfaces, because of faster runoff generation on the sealed surfaces. Therefore, the direct herbicide wash-off and the subsequent dissolution in runoff would underestimate the mixing zone depth for the sealed surfaces. Thus, we estimated CI thickness only for IPU, which is more soluble than DFF.

The second, longer, rainfall simulation campaign in the field (3 x 2 h of rainfall) showed that the fitting according to the exponential law did not correctly describe either the very high initial concentrations and their very rapid decrease during the first millimeters of rainfall or the presence of a concentration greater than 1 µg L-1 after 100 to 200 mm of total rainfall (Fig. 6) . The decrease in the IPU concentration as a function of the total rainfall that we observed was of the same order of magnitude as that measured by Klöppel et al. (1994) on a loamy soil, with values greater than 1000 µg L-1 when runoff started and around 10 µg L-1 after 100 mm of rain. But we are moving away from the ideal pesticide runoff behavior described by Leonard (1990). Different physical processes could cause the tailing of herbicide concentration in runoff, such as slow desorption, slow diffusion of herbicides out of aggregates, or diffusion of herbicides into the mixing zone from soil below. Infiltration flux remained superior to 6 mm h-1 even under sealed conditions under our experimental conditions, and diffusion coefficients measured in the literature with marked molecules are about 600 mm2 h-1 (Scott and Phillips, 1972). Therefore, the slow desorption could be the main process that maintains herbicide concentration greater than 1 µg L-1 after 200 mm depth rainfall.



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  		Fig. 6. Measured and simulated isoproturon (IPU) concentrations during runoff for field tests on an F12 soil surface state (n = 6). Note the logarithmic scale on the y axis.

 
The IPU concentration values as a function of the total rainfall after treatment could be correctly expressed with a model incorporating both a uniform mixing zone (CI, in mm) and two-site sorption–desorption kinetics. Thus, as proposed by an option of the RZWQM model (Ma et al., 1996), we distinguished between instant desorption and slow desorption sites within the adsorbed fraction, with f being the proportion of instant desorption sites and k2 the desorption rate on the slow sites (in h-1). The model was fitted to the experimental results measured in the field for an F12 surface state (with a sufficient number of observations). For Kd = 1.2 mL g-1 (measured value) and CI = 0.8 mm (value calculated with the Steenhuis and Walter [1980] method), the best fit was obtained with f = 0.95 and k2 = 1 h-1 (a small proportion of pesticides adsorbed on the slow desorption sites was enough to maintain the concentration level observed after 50 mm of total rainfall).

The model was also fitted with the values measured in the laboratory for surface states F0 and F2 with the same f and k2 values. We obtained the best fit with CI values of 1.2 mm for the F0 surface structure and 0.6 mm for the F2 surface structure. These CI thicknesses are low compared with those found in the literature, which range from 2 to 10 mm (Steenhuis and Walter, 1980; Havis et al., 1992; Zhang et al., 1997a), but consistent with the sealing character of the soil studied. Zhang et al. (1997b) calculated CIs of 3.6 and 3.5 mm for metolachlor and atrazine, respectively, for a sandy soil and 2.4 and 1.8 mm for a more loamy soil.

Thus, for IPU, if we use a different CI value depending on the structural state we can calculate the different dynamics of IPU mobilization by runoff. By integrating two-site sorption–desorption kinetics into the model, we can reproduce the presence of a significant concentration after more than 100 mm of total rainfall. Nevertheless, the model is a simplification and does not take into account all physical processes. The theory for modeling is plausible but field data are insufficient to ensure that the main processes are effectively integrated.

Such a model does not enable us to correctly calculate DFF concentrations because the very high initial concentrations cannot be reproduced with the measured Kd. If the mobilization of pesticide crystals remaining on the soil surface was taken into account, we could reproduce the high initial concentrations observed. But in the absence of measurements, there is little sense in carrying out such a simulation because of the increased number of parameters to be fitted.


   CONCLUSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSION
REFERENCES
 
The degradation state of the soil surface structure has a considerable effect on herbicide loss during rainfall for a loess soil. Due to surface sealing, which leads to an early onset of runoff and increases the runoff volume, the loss coefficients of degraded soil surfaces can be more than 10 times greater than those of fragmented soil surfaces. The IPU loss coefficient was around 10% after 5 mm of rainfall on crusted loess soil and increased little thereafter. Ninety-five percent of the transfer was in the dissolved form. The DFF loss coefficient was around 8% after 5 mm of rainfall on crusted loess soil and reached 20% after 140 mm of rainfall. Twenty-five to fifty percent of the transfer occurred in the adsorbed form on eroded particles.

The evolution of IPU concentrations as a function of total rainfall could be represented by a model taking into account a uniform mixing zone and two-step sorption–desorption kinetics. The development of a sealing surface would limit the depth of soil–runoff interaction. In the case of a uniform and complete mixing zone, the depth of the interaction layer for IPU and the loess soils studied would be about 0.6 mm for a sedimentary seal and 1.2 mm for a fragmented seedbed. If we take into account slow desorption sites, we could correctly simulate runoff contamination after more than 100 mm of total rainfall. The combination of two-site sorption–desorption kinetics with a uniform and complete mixing zone with varying thickness depending on the surface state enabled us to correctly simulate measured IPU concentrations as a function of total rainfall. Other processes could also have caused the tailing of herbicide concentration in runoff, such as slow diffusion. However, the very high initial DFF concentrations, which exceeded the water solubility threshold, could not be simulated by the model with the Kd values and the sediment concentrations measured.


   ACKNOWLEDGMENTS
 
The authors are grateful to P. Skipwith for his English language correction of the manuscript. The redaction of this article was made possible partially due to the support of PNRH national program (RIDES project), Agence de l'Eau Seine-Normandie, and BRGM Research Program (00EAUR03).


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSION
 REFERENCES
 





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