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Published in J. Environ. Qual. 32:2207-2215 (2003).
© ASA, CSSA, SSSA
677 S. Segoe Rd., Madison, WI 53711 USA

TECHNICAL REPORTS

Organic Compounds in the Environment

Modeling of Sorption and Biodegradation of Parathion and Its Metabolite Paraoxon in Soil

K. Saffih-Hdadi*,a, L. Brucklera and E. Barriusob

a Site Agroparc, Institut National de la Recherche Agronomique, CSE-sol, 84914 Avignon cedex 09, France
b Institut National Agronomique de Paris-Grignon, EGC-sol, BP 01, 78850 Thiverval-Grignon, France

* Corresponding author (hdadi{at}avignon.inra.fr).

Received for publication November 4, 2002.

   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 THEORY
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
To investigate the distribution of parathion [O,O-diethyl O-(4-nitrophenyl) phosphorothioate] and its highly toxic metabolite paraoxon [O,O-diethyl O-(4-nitrophenyl)phosphate] between the soluble and sorbed pools in the soil, batch experiments were conducted to evaluate the rate of adsorption and desorption of 14C-labeled parathion and paraoxon in soil. The mineralization and degradation of these products were also investigated during a 56-d experiment under controlled laboratory conditions. Adsorption patterns indicated initial fast adsorption reactions occurring within 4 h for both parathion and paraoxon. We also observed the formation of nonextractable residues. The paraoxon was more intensively degraded than the parathion, and production of p-nitrophenol and other metabolites was observed. A kinetic model was developed to describe the sorption and biodegradation rates of parathion, taking into account the production, retention, and biodegradation of paraoxon, the main metabolite of parathion. After fitting the parameters of the model we made a simulation of the kinetics of the appearance and disappearance of paraoxon. From the simulation we predicted a quantity of metabolite in the liquid phase amounting to 1% of the quantity of parathion initially applied. This is in agreement with the experimental data.

Abbreviations: HPLC, high performance liquid chromatography • LSC, liquid scintillation counting


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
THEORY
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
ORGANOPHOSPHATES are a group of highly toxic compounds that are used extensively as agricultural and domestic pesticides (Costa, 1988). The toxicity of the organophosphate insecticide parathion (Fig. 1a) for nontarget organisms has been the subject of extensive research (Bauer and Römbke, 1997; Chang et al., 1997). The most widely used test methods for assessing the toxicity of chemical compounds and effluents use pure cultures. However, there can be major differences between effects in a pure culture and in soil. During the last few decades, tests have been developed that use soil (Houx and Aben, 1993; Ronday et al., 1997) rather than artificial substrates such as water, filter paper, or pure sand. Testing toxicological effects in soil conditions is necessary because by taking into account the chemical and biological transformations of pesticides in soils it is possible to show that competitive sorption may result in either reduced or increased toxicity compared with the parent compounds in solution (Guilhermino et al., 1996). Houx and Aben (1993), for example, found that pesticides were 100- to 1000-times more toxic to nematodes in aqueous solution than in soil.



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  		Fig. 1. Structural formulae of chemicals used in sorption and degradation studies.

 
Under aerobic conditions, parathion is activated by oxidative desulfuration (Costa, 1988) to the oxygen analog paraoxon (Fig. 1b), which has a potent anticholinesterase effect (Eto, 1974) and is more toxic than the parent chemical (Guilhermino et al., 1996). In this work, therefore, we studied the evolution of both parathion and paraoxon. To analyze the environmental effects of pesticides in soils, (i) the main processes (sorption, biodegradation) leading to a pesticide concentration in the liquid phase must be considered, and (ii) the pesticide and its main metabolites must be simultaneously taken into account, especially in cases where the biodegradation products are more toxic than the parent chemical.

For a general analysis of pesticide dissipation in soil, models are valuable tools for explaining the behavior of chemicals in the complex environment of a soil. Numerous models have been proposed to describe the sorption–desorption and biodegradation of pesticides in soil. In several previous studies sorption has been treated as a rapid equilibrium, single-valued, reversible process (Weber et al., 1992; Mandal and Adhikari, 1995), but in other studies kinetic models have been developed (Wu and Gschwend, 1986; Brusseau et al., 1991; Ma and Selim, 1994; Shelton and Doherty, 1997). These models describe sorption in terms of a two-site model characterized by fast and slow binding sites. There are also some models that combine equilibrium with kinetic hypotheses (McCall and Agin, 1985; Xue and Selim, 1995; Xue et al., 1997).

Among these approaches, dynamic models including a two-site representation of the sorption process are probably the most realistic when compared with independent experimental data describing the dynamics of pesticides in soil (McCall and Agin, 1985; Wu and Gschwend, 1986; Brusseau et al., 1991; Ma and Selim, 1994; Xue and Selim, 1995; Ma et al., 1996; Shelton and Doherty, 1997; Xue et al., 1997). Modeling biodegradation kinetics in the soil system is a difficult exercise, owing to the biological, physical, and chemical complexity of the soil environment. Many published papers describing the degradation of pesticides include models of biodegradation (Soulas, 1997). There are two main types of degradation models. The formulation of the first is based on equations of chemical kinetics where only pesticide concentration limits degradation (Hill et al., 1955; Zimdhal et al., 1970), while the second type consists of biological models that take into account the dynamics of microbial populations (Simkins and Alexander, 1984; Schmidt et al., 1985; Soulas and Lagacherie, 1990; Shelton and Doherty, 1997). In cases where adapted biodegradation occurs, the linkage between the growth of the adapted biomass and the availability of the pesticide in the soil must be taken into account. However, none of the models found in the literature simultaneously take the dissipation of the parent compound in the soil into account and describe the fate and partitioning of its metabolites.

The present work has two main objectives: (i) to quantitatively assess the adsorption, desorption, and degradation of parathion and paraoxon in soil by laboratory experiments, and (ii) to model this process with a kinetic model that takes into consideration the production of metabolite and can describe the distribution and biodegradation of the pesticide and its biodegradation products in soil.


   THEORY
TOP
 ABSTRACT
 INTRODUCTION
 THEORY
METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Basic Model and Equations
Interactions between pesticide and soil are analyzed as a dynamic process where t (s) is time. For clarity, quantities of pesticide or metabolite are expressed in kilograms. Taking into account the mass of soil (kilograms) and the volume of water mixed with the soil (cubic meters), concentrations referring to the mass of soil (kilogram per kilogram) or volume of water (kilogram per cubic meter) can then easily be derived.

Three components in the soil are taken into account (Fig. 2 , Table 1): (i) a liquid phase in which the pesticide S1(t) (kg) is regarded as a solute component (soluble phase), (ii) a first solid phase in which the pesticide S2(t) (kg) is rapidly and weakly sorbed to the soil (weakly sorbed pesticide), and (iii) a second solid phase which is connected to the first one, and to which the pesticide S3(t) (kg) is more slowly but more strongly sorbed (bounded residue phase). Pesticide allocation between the three phases is possible through forward kinetic coefficients (respectively, k1 [s-1] for sorption between the soluble phase and the weakly sorbed phase, and k3 [s-1] for sorption between the weakly sorbed phase and the bounded residue phase) and backward coefficients (respectively, k2 [s-1] for desorption between the weakly sorbed phase and the soluble phase, and k4 [s-1] for desorption between the bounded residue phase and the weakly sorbed phase). Thus, the model assumes that the bounded residue phase is produced via the weakly sorbed phase and not directly from the soluble phase. This hypothesis was made by taking into account several experimental data and/or theoretical considerations indicating that the processes by which the pesticide diffuses through the soil micropores, combined with its progressive sorption to organic and mineral compounds in the soil during diffusion, are in good agreement with this framework. The model can be used to simulate various cases: one can assume, for example, that k3 = k4 = 0 corresponds to the case where no bounded residues are produced, or that k4 = 0 corresponds to the case where the formation of bounded residues is irreversible. Moreover, the model assumes that biodegradation of the pesticide occurs in the soluble phase (Ogram et al., 1985).



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  		Fig. 2. Schematic representation of the model (S for the pesticide and M for the metabolite).

 

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  		Table 1. Basic equations of the model.{dagger}

 
Two main degradation pathways by the microbial biomass are taken into account (Table 1): (i) Biodegradation by a cometabolism mechanism, involving the total biomass B(t) of the soil which dissipates the pesticide present in the soluble phase in the same way as other organic compounds. In this case (Table 1), biodegradation of S1(t) depends on the mass of pesticide in the soluble phase S1(t) (kg), on the ratio B(t)/B0, where B0 (kg) is the total initial biomass at the start of the experiment (input of the pesticide into the soil) and on a constant {alpha}1 (s-1), which provides the mean rate of biodegradation. (ii) Adapted biodegradation, assuming that only a part of the total biomass BS1(t) contributes to biodegradation of the pesticide and that the growth of this adapted microbial community depends on the concentration S1 of the substrate in the soluble phase, as follows:

[1]

Here, BS1 (kg) is the adapted biomass in the soil sample at a time t, S1(t) is the mass of pesticide in the soluble phase in kilograms, µS1 is the specific growth rate of the biomass per second, KS1 is the half-saturation constant in kilograms, and mBS1 is the mortality rate per second of the adapted biomass. Eq. [1] describes the growth and disappearance of the adapted biomass BS1 as a function of substrate concentration S1(t) and mortality rate (Monod, 1949).

In this case (Table 1), the biodegradation function of S1(t) has some parameters in common with the biomass growth equation (Eq. [1]), but biomass yield YS1 is added (kilograms biomass per kilograms pesticide). In the model, the two biodegradation pathways (adapted and cometabolic) can be activated independently (only one pathway is considered), or simultaneously (the two pathways are taken into account together). Biodegradation of the pesticide may be complete, leading directly to CO2 production through total mineralization of the pesticide, or incomplete, leading to the production of a metabolite (Table 1) at rate fpc (between 0 and 1), or fpa (between 0 and 1) for cometabolism and adapted biodegradation, respectively. When a metabolite is produced in the liquid phase, the sorption and biodegradation pathways of the metabolite are described by the same general pattern as for the pesticide (Fig. 2, Table 1): The metabolite can be allocated between the three phases through forward kinetic coefficients km1 for sorption between the soluble phase M1 (t) (kg) and the weakly sorbed phase M2(t) (kg), and km3 (s-1) for sorption between the weakly sorbed phase and the bounded residue phase M3(t) (kg), respectively, and backward kinetic coefficients for desorption between the weakly sorbed phase and the soluble phase, and km4 (s-1) for desorption between the bounded residue phase and the weakly sorbed phase, respectively.

Similarly, two pathways of degradation by the microbial biomass are taken into account: cometabolism and adapted biodegradation. When adapted biodegradation is considered, the growth of the specific biomass is described by:

[2]
which is formally similar to Eq. [1] except that M refers to the metabolite instead of the pesticide. However, since only one metabolite is taken into account here, biodegradation of the metabolite, if it occurs, is assumed to be complete biodegradation leading to CO2 production.

Numerical Procedures and Parameter Estimation
Equations [3] to [8] (Table 1) were numerically solved after linearization with a varying time step of {Delta}t (s). Considering biodegradation involving only a cometabolism mechanism for both the pesticide and the metabolite, Eq. [3] to [8] are linear with constant coefficients and we chose an explicit numerical scheme. Considering adapted biodegradation for the pesticide and/or the metabolite, Eq. [1] to [8] are linked in a nonlinear way because BS1 and/or BM1 depend on S1(t) and/or M1(t), and vice versa, S1(t) and/or M1(t) depend on BS1 and/or BM1 as described in Eq. [3], [6], [1], and [2], respectively. In this case we chose a fully implicit iterative scheme to solve simultaneously Eq. [1] to [8]. The quantities S1(t), S2(t), S3(t), and/or M1(t), M2(t), M3(t), as well as the biodegradation characteristics, are used as the descriptive variables with an automatically varying time step which is optimized to ensure a relative cumulative mass balance of <10-3. The model starts with the observed initial situation [i.e., pesticide concentration S1(t), S2(t), S3(t) at t = 0]. The model can be used in a direct way (simulation procedure) or in an inverse way (parameter estimation), according to the user's choice. For estimating the parameters, the conceptual model corresponds to a nonlinear least-square minimization problem: to find the parameters that minimize the differences between measured and calculated concentrations S1(t), S2(t), S3(t), and/or M1(t), M2(t), M3(t), and/or [CO2] production when biodegradation is involved and the data available. Initial biomass (B0) was determined on the basis of microbial population densities measured in soil (400 mg kg-1 soil). The term B(t) was assumed to be constant, so it's equal to B0 (no organic matter was added, a short time of incubation). The Gauss–Marquardt algorithm (Marquardt, 1963; Bard, 1974) was used to solve this least-squares optimization problem. Iterations were stopped when either the relative difference of the sums of squares or the relative variation of the estimated parameter values was <10-4 between two successive iterations (both criteria were examined simultaneously). The program is written in standard Fortran 77 and runs on a minicomputer (Sun, Unix) in a few seconds to simulate parameters and in a few minutes to estimate parameters for an experiment of several days.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 THEORY
 METHODS
RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Chemicals
Carbon 14-U-labeled parathion (specific activity, 930 MBq mmol-1; radiopurity > 98%) and 14C-U-labeled paraoxon (specific activity, 1118 MBq mmol-1; radiopurity > 98%) were supplied by Dislab (Saulx Les Chartreuses, France). Unlabeled parathion and paraoxon (analytical standards) were supplied by Sigma-Aldrich (Lyon, France). For the sorption studies, MilliQ (Millipore, Billerica, MA, USA) water solutions of both 14C-labeled chemicals were prepared in 0.01 M CaCl2 by isotopic dilution, giving a final concentration of 1 mg L-1 for both the parathion and the paraoxon. For the degradation studies, water solutions were prepared at 10 and 2 mg L-1, respectively, for parathion and paraoxon, with radioactivity levels of 8482 and 8097 Bq mL-1, respectively.

Soil
The soil (Typic Eutrochrept) was sampled in the surface layer (0–20 cm) of a bare experimental plot located at Caumont (France). Soil samples were air dried and passed through a 4-mm sieve. Soil had a pH in water of 8.3 and a water field capacity of 18% (w/w), with (g kg-1 of dry soil): 342 of clay, 465 of silt, 193 of sand, 351 of lime (total), and 15.5 of organic C. A part of the soil sample (sieved at 5 mm) was not dried and was kept at 4°C for the degradation studies.

Sorption Experiments
A classical batch procedure was used to obtain parathion and paraoxon sorption kinetics at 22 ± 1°C. Ten-milliliter aliquots of 14C water solutions, at 1 mg L-1 for both the parathion and the paraoxon, were added to 3 g of air dried soil in 20-mL glass centrifuge tubes with Teflon caps. Different tubes were prepared to allow sorption measurements after increasing shaking times: 1, 2, 4, 8, 16, 24, and 48 h. For each shaking time, the samples were centrifuged at 8000 g for 15 min and the parathion or paraoxon concentration in the solution was calculated from the supernatant radioactivity as measured by liquid scintillation counting (LSC) in a Packard Tri-Carb 2100 TR liquid scintillation analyzer (Packard, Meriden, CT, USA) with UltimaGold XR (Packard) as liquid scintillate. The amount of chemical sorbed was determined by the difference between the initial and equilibrium solution concentrations. The whole experiment was duplicated.

Incubation Experiments
Parathion and paraoxon water solutions were applied to 10-g oven-dry equivalents of moist soil and placed in 0.5-L glass incubation flasks. The soil water content was adjusted to 85% of the soil water field capacity (18% w/w). Vials containing 5 mL of 1 M NaOH were placed in each incubation flask to trap CO2, and the flasks were then hermetically sealed and incubated in the dark at 28 ± 1°C in a thermostatic chamber. The NaOH traps were periodically removed for analysis and replaced. The soil moisture content was adjusted every week by weighing. Samples were taken on 10 dates (Days 2, 4, 7, 14, 21, 28, 35, 42, 49, and 56 of incubation) with three replicates per date. To assess the abiotic degradation of parathion and paraoxon, the same experiments were conducted with sterilized soil. Sterilized soil was obtained by treating first soil samples by gamma rays at a maximum dose of 59 kGy.

Analysis
The evolved 14CO2 trapped in NaOH was directly measured by LSC with UltimaGold XR (Packard) as liquid scintillate. During incubation, extractable residues were analyzed after extracting 10 g of soil in a 150 mL-glass centrifuge bottle with a Teflon cap. This sample was first extracted in 30 mL of 0.01 M CaCl2 water solution. After 24 h of shaking and centrifugation (15 min at 8000 g), the radioactivity in the extract was measured by LSC. The soil pellet was then extracted three times in succession with 30 mL of methanol by the same procedure as for the water extraction, and the radioactivity in the methanol extracts was measured. The soil containing the nonextractable 14C residues was air dried and finely ground, and its radioactivity measured after combustion of triplicate 200-mg aliquots with a Sample Oxidizer 307 (Packard). All extractions were made on six dates (few minutes after the beginning of the incubation and Days 7, 14, 28, 49, and 56 of incubation).

High performance liquid chromatography (HPLC) analysis of the 14C residues in the water and methanol extracts was performed for each incubation time. The extracts from the three replicates were pooled. The water extracts were concentrated by solid phase extraction with Env+ cartridge (200 mg; Isolute, Glamorgan, UK), previously activated with methanol and MilliQ water, and eluted with 10 mL of methanol. The methanol extracts were concentrated by evaporation to near dryness under vacuum. The residues were dissolved in 2 mL of the solvent used for the HPLC analysis, and filtered through a Cameo 13N syringe nylon filter (0.45 µm; MSI, Westboro, MA, USA). The HPLC analysis was performed with a Waters appliance (600E Multisolvent Delivery System, 717 Autosampler and a Novapak C18 column of 5 µm and 4.6 x 250 mm; Waters-Millipore, Milford, MA, USA) equipped with a photo diode array detector (Waters 996) coupled on line with a radioactivity continuous flow detector (Packard-Radiomatic Flo-one A550). The mobile phase was methanol:water (50:50) at 1 mL min-1, and the injected volume was 200 µL. Under these conditions, retention times were 35 min for parathion and 16 min for paraoxon.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 THEORY
 METHODS
 RESULTS
DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Adsorption
Parathion and paraoxon adsorption results as a function of time are shown in Fig. 3 . The adsorption patterns indicate initial fast adsorption reactions occurring within 4 h. Parathion adsorption was almost complete (88% of added 14C) and equilibrium was reached within 4 h. For paraoxon, 34% of added carbon 14 was adsorbed without clearly reaching equilibrium, probably because of its rapid degradation.



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  		Fig. 3. Sorbed parathion and paraoxon vs. time. Error bars are also presented but not always visible when they are small.

 
Mineralization
The kinetic pattern of pesticide breakdown was estimated by the evolution of 14CO2. The data (Fig. 4a) show that degradation of parathion and paraoxon is principally of biological origin, since <3% of abiotic degradation was observed for either parathion or paraoxon in sterilized soil samples between the beginning and Day 7 of incubation. After 7 d of incubation, the soil was colonized by microbial biomass again, and the biological mineralization of parathion and paraoxon started. This biological mineralization is due to the emergence of bacterial spores which are capable of surviving unfavorable conditions and of emerging under more favorable laboratory conditions. In fact, a high number of microorganisms were found in the sterilized samples on the last day of incubation (results of counting microbial biomass not shown here). For nonsterilized samples, the first observation for both parathion and paraoxon (Fig. 4b) was an immediate release of 14CO2 that was intense from the first days of incubation. Microbial biomass certainly began instantaneous activity under the incubation conditions applied, indicating that the predominant biodegradation pathway was possibly cometabolism. The second observation was that a plateau was reached toward the end of incubation, probably because the availability of parathion and paraoxon in the soil had lessened owing to sorption and biodegradation. In 56 d of incubation, around 54% of the initial applied radioactivity was released for both parathion and paraoxon. Paraoxon was observed to biodegrade faster than parathion.



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  		Fig. 4. (a) Cumulative rate of total 14CO2 released from sterilized soil samples (quantities are expressed as percentages of the initial applied radioactivity). (b) Cumulative rate of total 14CO2 released from nonsterilized soil samples (quantities are expressed as percentages of the initial applied radioactivity).

 
Sorption and Biodegradation
The evolution of the distribution of parathion and paraoxon across time between the soluble (water extractable fraction), sorbed (methanol extractable fraction), and unavailable sorbed (nonextractable fraction) pools was investigated. The soluble pool comprises the most easily desorbable residues and hence the most readily available fraction, the sorbed pool comprises residues that are more difficult to desorb and are therefore less readily available, and the unavailable sorbed pool defines resistant or very slowly available residues. Figure 5a and 5b summarize the mineralization and partitioning of parathion and paraoxon vs. time. The water-extractable fraction decreased as residence time in the soil increased, from 14% of the recovered radioactivity after a few minutes of incubation to 2% after 56 d for parathion and from 51 to 1% for paraoxon. Simultaneously, the methanol-extractable fraction decreased from 88 to 8% for parathion, and from 17 to 2% for paraoxon. A nonextractable residue fraction formed rapidly after parathion application, amounting to 5% of the recovered residues after a few minutes, increasing continuously and at 56 d amounting to 38% for parathion and 32% for paraoxon.



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  		Fig. 5. Mass balances for parathion (a) and paraoxon (b) vs. time (quantities are expressed as percentages of initial applied radioactivity).

 
Biodegradation Products
The 14C metabolites of parathion and paraoxon were detected in both water and methanol extracts of soil samples. In water, quantities extracted were very small, so we were interested essentially in analysis the results of methanol extracts. The HPLC results show that the paraoxon disappears rapidly (Fig. 6b) . This can be explained by the instability of its structure; it is quickly converted to another metabolite, p-nitrophenol, which appears a few minutes after the start of incubation. Other metabolites were detected but have not been identified; they are probably products of the biodegradation of p-nitrophenol. Parathion, on the other hand, persists in the soil until Day 28 of incubation (Fig. 6a); a large amount of p-nitrophenol was also detected from the start of incubation, but no paraoxon was detected. We assume that paraoxon is the first metabolite to appear, but is quickly transformed into p-nitrophenol. We conclude that, in the presence of parathion, there is a continual production of paraoxon that is quickly degraded into p-nitrophenol and other metabolites. As a consequence, we assume there is a transient presence of the paraoxon in the soil as long as parathion is present.



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  		Fig. 6. Kinetics of appearance and disappearance of (a) parathion, (b) paraoxon, and their degradation products.

 
Parameter Estimation for Paraoxon
With the results of experimental characterization of paraoxon (sorption and biodegradation), the model was first used to estimate simultaneously parameters related to its sorption and biodegradation (km1, km2, km3, km4, and {alpha}m1). Carbon dioxide production vs. time, as well as paraoxon concentrations in the soluble, sorbed, and unavailable sorbed phases were used as the objective function to minimize the least squares between measured and simulated data. The type of biodegradation involved was assumed to be cometabolism, since estimations based on adapted biodegradation had yielded no satisfactory results. This was in agreement with the observed rapid biodegradation of paraoxon (Fig. 4b). Many correlations (r > 0.50) were observed: (i) between km1 and km2 (r = 0.69), because these two parameters are both responsible in determining the instantaneous soil solution concentration; (ii) between km2 and km3 (r = 0.78), this is consistent with the fact that decreasing the concentration of the first solid phase S2(t) results in both higher km2 and/or km3; (iii) between {alpha}m1 and km2 (r = -0.99), because the soluble phase S1(t) decreases when both km2 decreases and/or {alpha}m1 increases (and vice versa); and (iv) between {alpha}m1 and km1 (r = -0.65), and {alpha}m1 and km3 (r = 0.74), respectively. These correlations are the consequences of both previous correlations between first {alpha}m1 and km2, and second between km2 and km1, and km2 and km3, respectively, as indicated before. Considering these correlations, we concluded that is was impossible to estimate independently the forward and backward sorption parameters. Consequently, we tested in a second step the ability of model to estimate only three parameters (km1, km3, and {alpha}m1) when km2 and km4 are fixed at their optimal values (2.23 x 10-4 s-1 and 1.46 x 10-7 s-1, respectively). Table 2 gives the matrix of correlations, the mean values, and confidence intervals of the estimated parameters. Small correlations were found, except between {alpha}m1 and km1 or km3, but this is probably a basic property of the model. The confidence intervals were always positive and relatively small. As expected, the km1 and km2 coefficients were greater than the km3 and km4 coefficients, showing that the proposed model, which takes into account both slow and rapid kinetics for sorption and desorption, was well fitted. Moreover, the coefficients for sorption (km1 and km3) were always greater than coefficients for desorption (km2 and km4), indicating that sorption was easier than desorption. Figure 7 gives a synopsis of the fit of the simulated curves to the experimental data points for the evolution across time of soluble, sorbed, unavailable sorbed, and total (soluble plus sorbed plus unavailable sorbed) paraoxon. The symbols represent the data at dates (few minutes after the beginning of the incubation and Days 7, 14, 28, 49, and 56 of incubation), while the lines represent model fitting. Soluble paraoxon decreases and sorbed paraoxon increases rapidly, and then both soluble and weakly sorbed paraoxon decrease simultaneously due to the biodegradation process and the formation of strongly adsorbed residues. The simulated curves are in good agreement with the experimental data. Assuming that the results of the parameter estimations for the paraoxon in the soil were now established, we then estimated the parameters of parathion sorption and biodegradation taking the complete model into account (Eq. [1] to [8]).


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  		Table 2. Matrix correlation, confidence intervals, and mean values of paraoxon parameters estimated by the model.

 


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  		Fig. 7. Measured (symbols) and predicted (continuous lines) kinetics for paraoxon. Experimental points located on the concentration axis correspond to observations made at t = 0 and 0.083 d.

 
Parameter Estimation for Parathion
We assume that parathion is transformed into paraoxon during the biodegradation process (then fpc = 1) and that the paraoxon is then sorbed and degraded to CO2. Parameters of sorption for paraoxon were fixed to their estimated values as provided in the previous section. As before, in a first step, sorption and biodegradation parameters for parathion (k1, k2, k3, k4, {alpha}1) and the mean rate of biodegradation of paraoxon {alpha}m1 were simultaneously estimated with the model. In this case also, CO2 production vs. time and parathion concentrations in the soluble, sorbed, and unavailable sorbed phases were used as the objective function to minimize the least squares between measured and simulated data. As before, biodegradation by cometabolism was assumed since no satisfying results were found with estimations based on adapted degradation. As expected, the results exhibited high correlations between forward and backward sorption parameters, and biodegradation and sorption parameters as it was explained in the previous case study for paraoxon. Consequently we estimated four parameters (k1, k3, {alpha}1, and {alpha}m1) when k2 and k4 were fixed to their optimal values (2.28 x 10-4 s-1 and 1.90 x 10-7 s-1, respectively). The {alpha}m1 was again estimated because change in rate of paraoxon biodegradation is possible in these conditions (low concentration of paraoxon, competition between biodegradation of parathion and paraoxon). The matrix correlation, mean values, and confidence intervals of the parameters estimated by the model are presented in Table 3. The confidence intervals were positive and relatively small for sorption coefficients. The confidence interval for biodegradation of paraoxon was large, but includes the mean value (0.14 x 10-4) of {alpha}m1, as estimated from previous paraoxon biodegradation estimation. There was no correlation between parameters, except between k1 and {alpha}1, but, as before, it is probably a basic property of the model. As expected, k1 and k2 are greater than the k3 and k4 coefficients, the coefficients of sorption greater than the coefficients of desorption, and {alpha}m1 > {alpha}1. Figure 8a shows the fitness of the simulated curves to experimental data points for the disappearance of soluble, sorbed, unavailable sorbed, and total (soluble plus sorbed plus unavailable sorbed) parathion. The results show a rapid decrease in the soluble phase during the rapid adsorption process at the beginning of the experiment. There is then an increase in the unavailable phase followed by a decrease because the k4 coefficient was not set at zero. In fact, the question of the reversibility of the S3 compartment is not settled: more data across longer times are needed to draw any clear conclusion. Figure 8b shows the partitioning of the paraoxon produced by parathion biodegradation into soluble, sorbed, and unavailable sorbed fractions, and total paraoxon. The model predicts that metabolite will be produced at 1% the concentration of the initial quantity of parathion applied and simulates a high rate of paraoxon biodegradation, as expected from the experimental results.


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  		Table 3. Matrix correlation, confidence intervals, and mean values of parathion parameters estimated by the model.

 


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  		Fig. 8. (a) Measured (symbols) and predicted (continuous lines) kinetics for parathion. Experimental points located on the concentration axis correspond to observations made at t = 0 and 0.083 d. (b) Predicted kinetics for metabolite of parathion.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 THEORY
 METHODS
 RESULTS
 DISCUSSION
CONCLUSIONS
 REFERENCES
 
The model can predict the kinetics of the production, adsorption-desorption, and biodegradation of both the parent chemical and a metabolite. As far as we know, no model in the literature takes this linkage into account. From a toxicological point of view, the concentrations of paraoxon in the liquid and solid soil phases given by the model may be quite different from the doses arbitrarily adopted for toxicological tests. However, some comments or/and limitations of the proposed model may be listed:

(i) In the light of experimental and theoretical arguments, the biodegradation of both compounds is assumed to be cometabolic. Experimental results exhibited a rapid mineralization of both compounds from the early days of incubation. No latency period was observed, which suggests we are probably looking at degradation by total soil biomass, hence a cometabolism mechanism. The results of the model confirm this hypothesis, since it was impossible to provide a satisfactory fitting for any other type of biodegradation. However, other authors indicate that a specific biomass degrading parathion may exist (Sethunathan and Yoshida, 1973; Barles et al., 1979; Rosenberg and Alexander, 1979; Lal, 1982; Itoh, 1991). Applying repeated treatments of parathion and paraoxon to samples is an experiment that could give more explanations to the type of biodegradation process.

(ii) The model considers one metabolite only. In fact, parathion may be transformed by reduction into aminoparathion which is hydrolyzed to 4-aminophenol, or by oxidative desulfuration to paraoxon which is also hydrolyzed to 4-nitrophenol (Roberts and Hutson, 1999). As a consequence, what we call metabolite in the model may be regarded as a mixture of different products. This will depend on the metabolic pathway of the parathion, according to the type of soil and therefore the type of microbial biomass. Paraoxon being the most toxic of these products, from a toxicological point of view the predicted concentrations of the metabolite in the model can probably be regarded as the maximum concentration of paraoxon in the soluble phase, since paraoxon is probably mixed with other products. This analysis is in agreement with our experiments, in which no paraoxon was found in water and methanol extracts from soil treated with parathion, although p-nitrophenol was found. Probably, in the light of the model results and the biodegradation pathways of parathion proposed, the quantity of paraoxon produced is too small to be measured by HPLC technique and/or is rapidly transformed into other metabolites (Lichtenstein and Schulz, 1964). Finally, from a theoretical standpoint, the proposed model could be generalized, taking into account a number of successive metabolites. Such a model would be useful if all its parameters could be successively or simultaneously estimated, but from a technical point of view it is not easy to detect the multiple products and transformation pathways involved.


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 THEORY
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
REFERENCES
 
We used experimental and simulation approaches to investigate the kinetics of sorption and degradation of parathion and its toxic metabolite paraoxon in soil. The data showed that parathion and paraoxon degradation was of biological origin, that the adsorption of these chemicals was rapid and reversible, and that nonextractable residues were formed. The parathion persisted in the soil until Day 28 of incubation. In soils treated by paraoxon, a large amount of p-nitrophenol was rapidly found, and 4 h after the start of incubation the paraoxon had disappeared. In soils treated with parathion, paraoxon was not detected in the soluble phase but there was production of p-nitrophenol. We conclude that in the presence of the parathion, there was probably a continuous production of paraoxon and other metabolites that were quickly degraded into p-nitrophenol and/or other metabolites. This resulted in the transient presence of paraoxon in the soil.

The model simulated the kinetics of sorption and biodegradation of parathion, and predicted a maximum transient production of paraoxon at a concentration of 1% of the quantity of parathion initially applied. Consequently, in view of their adverse effects on soil organisms, when evaluating the risk of parathion and paraoxon in soil, it is probably important to take into account the physicochemical and biological processes and soil parameters that affect the dissipation of this pesticide.


   ACKNOWLEDGMENTS
 
The authors sincerely thank Dr. Marie-Paule Charnay (UMR INRA-INAPG Environnement et Grandes Cultures) for helpful discussions and Mrs. Valérie Bergheaud (UMR INRA-INAPG Environnement et Grandes Cultures) for her technical assistance.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 THEORY
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 


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K. Saffih-Hdadi, L. Bruckler, F. Lafolie, and E. Barriuso
A Model for Linking the Effects of Parathion in Soil to its Degradation and Bioavailability Kinetics
J. Environ. Qual., January 5, 2006; 35(1): 253 - 267.
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