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SOIL PHYSICS

Modeling Competitive Arsenate-Phosphate Retention and Transport in Soils: A Multi-Component Multi-Reaction Approach

Sturgis Hall, School of Plant, Environmental and Soil Sci., Louisiana State Univ., Baton Rouge, LA 70803

^* Corresponding author (mselim{at}agctr.lsu.edu).

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MODEL FORMULATION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
This study was conducted to investigate the kinetics of arsenate [As(V)]-phosphate [P] competitive retention during transport in soils. Time-dependent batch experiments were performed to describe competitive As(V)-P sorption kinetics in Olivier (fine-silty, mixed, active, thermic Aquic Fraglossudalfs) and Windsor (mixed, mesic Typic Udipsamments) soils. Miscible-displacement experiments were also performed to quantify As(V)-P competition when anion pulses were introduced simultaneously or consecutively into water-saturated soil columns. The results demonstrated that the rates and amounts of As(V) sorption are significantly reduced by increasing addition of P. Due to competitive sorption, the presence of P resulted in increased mobility of As(V) in the soil columns. Flow interruptions indicated the dominance of time-dependent sorption during As(V) and P transport in soils. We extended the equilibrium-kinetic multireaction model (MRM) to simulate competitive retention kinetics of multiple chemical species in soils. Competitive coefficients from Sheindorf–Rebhun–Sheintuch (SRS) equation were adopted to describe the extent of As(V)-P competition. A multi-component multireaction model (MCMRM) was coupled with the advection-dispersion equation (ADE) to describe breakthrough curves (BTCs) of As(V) and P simultaneously. Model predictions of measured BTCs for competitive As(V)-P transport were achieved using rate coefficients based on inverse modeling.

Abbreviations: ADE, advection-dispersion equation • As(V), arsenate • BTC, breakthrough curve • MCMRM, multi-component multi-reaction transport model • MRM, multireaction model • P, phosphate • SRS, Sheindorf–Rebhun–Sheintuch

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MODEL FORMULATION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Several studies indicated that P in soils competes with As(V) for available adsorption sites because of their similar chemical properties. Both As(V) and P are specifically sorbed on mineral surfaces by forming similar types of innersphere surface complexes through ligand exchange. In fact, it has been shown that the presence of P substantially suppressed the sorption of As(V) on minerals as well as soils (Roy et al., 1986a, 1986b; Peryea, 1991; Melamed et al., 1995; Darland and Inskeep, 1997; Jain and Loeppert, 2000; Violante and Pigna, 2002; Williams et al., 2003).

Competitive sorption between P and As(V) generally depends on the surface properties of the adsorbent, concentrations of As and P, pH, sequence of addition, and residence time (Violante and Pigna, 2002). Arsenate and P are specifically adsorbed on a similar set of surface sites, although evidence showed some sites are only available for either As(V) or P. Based on competitive adsorption of anions on goethite and gibbsite, Hingston et al. (1971) proposed two types of adsorption sites on mineral surface; the first type is available for both anions where competition takes place while the second type of adsorption sites is specifically available for either anions. Violante and Pigna (2002) demonstrated that minerals rich in Al have a greater affinity of P than As(V), whereas metal oxides and phyllosilicates rich in Fe were more effective in adsorbing As(V) than P. In general, the adsorption of both P and As(V) on Fe/Al oxides decreases with increasing pH (Manning and Goldberg, 1996). Furthermore, Jain and Loeppert (2000) reported that the effect of P on As(V) adsorption on ferrihydrite was greater at high pHs than at low pHs.

Equilibrium conditions are often assumed when describing ion adsorption in the retention and transport studies. However, time-dependent behavior of As(V) adsorption on minerals and soils were reported by Raven et al.(1998), Darland and Inskeep (1997) and Williams et al. (2003). Due to the heterogeneous nature of adsorption sites and kinetic behavior of sorption, the sequence of addition, that is, addition of P to replace As(V) or vice versa, was shown to influence the competition between the two anions (Liu et al., 2001).

Miscible displacement experiments provided evidence that the presence of P greatly enhanced the movement of As in soils. For example, Melamed et al. (1995) observed that increasing presence of P shifted As(V) BTCs to the left, indicating reduced sorption and thus enhanced mobility of As(V) in soil. Similarly, Darland and Inskeep (1997) found that peak concentration and total recovery of As, from a sand column containing free Fe oxides, increased with increasing P addition. Moreover, they found that a significant fraction of As(V) was retained even though the P loading exceeded the maximum adsorption capacity for P. Williams et al. (2003) compared the effects of pH, pore water velocity, and P additions on As(V) transport through columns of a subsurface soil. They concluded that among the three factors, the addition of P has the greatest impact on the mobility of As(V). Even though significant influence of P on the retention and transport of As(V) are well characterized, a literature search revealed that mathematical models were not developed to simulate the competitive retention kinetics and transport of As(V) and P in porous media.

The objectives of this investigation were (i) to study the kinetics of competitive retention of As(V) and P during transport in saturated soil columns; and (ii) to test the predictive capability of a MRM for describing the time-dependent retention and transport of As(V) and P in soils. Moreover, we extended an existing multireaction kinetic approach to simulate the competitive sorption between As(V) and P during transport in soils. The newly formulated MCMR model accounts for nonlinear equilibrium, reversible kinetic, and irreversible kinetic reactions which represent various As(V) and P retention mechanisms in the soil system.

	MODEL FORMULATION

TOP NOTES ABSTRACT INTRODUCTION MODEL FORMULATION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
The transport of reactive chemical compounds in soils and aquifers is largely dependent on sorption–desorption processes in soils. Competition between various chemical species for the same set of sorption sites is a common phenomenon for heavy metals (Murali and Aylmore, 1983) as well as organic compounds (Xing et al., 1996). Enhanced mobility as a result of sorption competition has been widely observed for several contaminants (e.g., McGinley et al., 1996). Therefore, competitive sorption should be considered for the prediction of contaminants transport in the vadose zone and aquifers.

Geochemical models (e.g., MINTEQ, PHREEQC) have been adopted to simulate the reactions between multiple contaminants during their transport in soils and aquifers (e.g., Manning and Goldberg, 1996, Smith and Jaffe, 1998). Such models require detailed description of chemical and mineral composition of solution and porous media, as well as numerous reaction constants. However, those types of information are either unavailable or unreliable under most circumstances (Nitzsche et al., 2000). In addition, heterogeneity of the natural porous media also impedes the application of chemical reaction based models. More importantly, sorption processes are often regarded as instantaneous (i.e., equilibrium conditions are assumed) in such geochemical models. In fact, numerous studies have demonstrated the lack of reaction equilibrium of contaminants in soils (for a review see Selim, 1992).

Rather than geochemical models, equilibrium sorption reactions are frequently simulated using empirical models. Freundlich equation is a widely used empirical adsorption model that can be expressed as

[1]

where S is the amount of adsorption (mmol kg^–1), C is the solution concentration (mmol L^–1), is the distribution or partitioning coefficient (L kg^–1), and N is a dimensionless reaction order commonly less than one.

The sorption of chemicals by soils and sediments may require weeks to months to reach equilibrium. Therefore, the kinetic (time-dependent) sorption models of the Freundlich type have been developed to simulate the sorption of solutes during their transport in soils and aquifers (Selim, 1992). The reversible nth-order (Freundlich-type) kinetic sorption equation is in the form of

[2]

where k_f and k_b are the forward and backward reaction rate coefficients (h^–1), respectively, b is a nonlinear parameter usually less than 1, t is reaction time (h), is the soil bulk density (g cm^–3), and is the volumetric water content (cm³ cm^–3). Under equilibrium conditions, that is, =0, Eq. [2] yields Freundlich Eq. [1] assuming = and N = b.

The SRS equation has been developed to describe competitive or multicomponent sorption where it is assumed that the single-component sorption follows the Freundlich equation (Sheindorf et al., 1981). The derivation of SRS equation was based on the assumption of an exponential distribution of adsorption energies for each component. A general form of the SRS equation can be written as

[3]

where i, j indicate component i and j, l is the total number of components, _i,j is a dimensionless competition coefficient which describe the inhibition by component j to the adsorption of component i. By definition, _i,j equals 1 when i = j. If there is no competition, i.e., _i,j = 0 for all j i, Eq. [3] yields a single species Freundlich equation for component i. It should also be pointed out that if the single-species sorption isotherm is linear, i.e., , Eq. [3] predicts the absence of competition. Equation [3] was successfully employed by Roy et al. (1986ab) to describe the competitive adsorption isotherms of As(V) and P in several soils.

For time-dependent sorption, we extend Eq. [3] such that for reversible nth-order multi-component kinetic retention, the equation is of the form

[4]

Under equilibrium condition, Eq. [4] yields Eq. [3] assuming and _i=. On the other hand, if there is no competition, that is, _i,j = 0 for all , Eq. [4] yields a single species Nth- order kinetic sorption Eq. [2].

Kinetics of adsorption and desorption have been included in several models for the purpose of describing the time-dependent sorption of chemicals in the soil environment. The MRM approach of Selim and coworkers (Amacher et al., 1988; Selim, 1992) considers several interactions of chemical species with soil matrix surfaces. Specifically, the model assumes that a fraction of the total sorption sites is kinetic in nature whereas the remaining fractions interact rapidly or instantaneously with solute in the soil solution. The model accounts for reversible as well as irreversible sorption of the concurrent and consecutive type (Fig. 1). The model chosen in this analysis can be presented in the following formulation:

[5]

[6]

[7]

[8]

where S_e is the amount retained on equilibrium sites (mmol kg^–1), S₁ is the amount retained on kinetic type sites (mmol kg^–1), S₂ is the amount retained irreversibly by consecutive reaction (mmol kg^–1), S_s is the amount retained irreversibly by concurrent type of reaction (mmol kg^–1), n and m are dimensionless reaction order commonly less than 1, K_e is a dimensionless equilibrium constant, k₁ and k₂ (h^–1) are the forward and backward reaction rates associated with kinetic sites, respectively, k₃ (h^–1) is the irreversible rate coefficient associated with the kinetic sites, and k_s (h^–1) is the irreversible rate coefficient associated with solution. For the case n = m = 1, the reaction equations become linear. In the above equations we assumed n = m since there is no known method for estimating n and/or m independently. The total amount of solute retention on soil is:

[(9)]

View larger version (11K): [in this window] [in a new window]   Fig. 1. A schematic diagram of the multireaction model (MRM) with equilibrium, kinetic, and irreversible adsorption sites. Here C is concentration in solution, Se is the amount sorbed on equilibrium sites, S1 is the amount sorbed on kinetic sites, S2 is the amount retained on consecutive irreversible sites, and Ss is amount retained on concurrent irreversible sites. The parameters Ke, k1, k2, k3, and ks are the respective rates of reactions.

[10]

[11]

[12]

[13]

The notations used above have similar meaning as they are in the MRM except that subscripts i are added to indicate the ith component. The competitive coefficients _i,j from the SRS Eq. [3] is used to describe the competition of component j on component i.

We incorporated Eq. [10–13] into the one-dimensional reactive advective-dispersive transport equation (ADE) under steady water flow (Selim, 1992)

[14]

where x is distance (cm), D is dispersion coefficient (cm² h^–1), v (= q/) is average pore water velocity (cm h^–1), and q is Darcy's water flux density (cm h^–1). The appropriate initial and boundary conditions for a finite soil column are

[15a]

[15b]

[15c]

[15d]

where C_init is the initial solution concentration (mg L^–1), S_init is the initial amount of sorption (mg kg^–1), C_o is the input solute concentration (mg L^–1), T_p is the duration of applied solute pulses, L is the length of column (cm). The dispersion coefficient is further interpreted as the combination of hydrodynamic dispersion, and intraparticle diffusion coefficient D_w (cm² h^–1)

[16]

where (cm) is the longitudinal dispersivity and is the tortuosity factor (Brusseau, 1993; Ma and Selim, 1994). Flow interruption or stop-flow is accounted for in the proposed models by simply assuming = 0 and D = D_w/ during flow interruption. The above Eq. [10–15] were solved numerically using finite difference approximations (Selim et al., 1990). Specifically, the solute transport (Eq. [14]) was simulated using Crank-Nicholson explicit-implicit method. The kinetic retention processes (Eq. [11–14]) were solved with 4-th order Runge-Kutta method. Mass balance at each step of the simulation was used to check the numerical results. The single-species simulation results were further tested with the analytical solution for the two-site non-equilibrium transport model provided by CXTFIT (Toride et al., 1995).

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MODEL FORMULATION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Soils from the Ap horizon (0–10 cm) of Olivier loam, and Windsor sand were used in this study (Table 1). These soil samples were collected from Louisiana (Olivier) and New Hampshire (Windsor). The soils were air-dried and passed through a 2-mm sieve before use.

Competitive transport of As(V) and P in soils was investigated using the miscible displacement technique as described by Selim et al. (1987). Acrylic columns (5-cm in length and of 6.4-cm i.d.) were uniformly packed with air-dry soil and were slowly water-saturated with a background solution of 0.01 M KNO₃ at a low Darcy flux. Input solutions of 0.01 M KNO₃ were applied for several pore volumes using a variable speed piston pump, and the fluxes were adjusted to the desired flow rates. To maintain constant ionic strength, between 10 and 20 pore volumes of 0.01 M KNO₃ were applied to each column before introduction of As(V) or P pulse solutions. Two pulses of 1.33 mM As(V) solution in 0.01 M KNO₃ as background solution were introduced to Column 1 and 4. For Column 2 and 5, an 1.33 mM As(V) input pulse was followed immediately by a 3.23 mM P input pulse, whereas two pulses of mixed solution of 1.33 mM As(V) and 3.23 mM P were supplied to Column 3 and 6. During pulse application, column flow was completely stopped for a duration of 4–6 d to evaluate the influence of kinetic retention on As(V)/P transport. The volume of each As(V)/P pulse along with soil parameters associated with each column are given in Table 2.

View larger version (16K): [in this window] [in a new window]   Fig. 2. Tritium breakthrough curves (BTCs) for Olivier (column 3) and Windsor (column 6) soils. Solid curves depict results from curve-fitting using the advective-dispersive equation (ADE) for non-reactive solutes.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MODEL FORMULATION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

View larger version (16K): [in this window] [in a new window]   Fig. 3. Single component As(V) and P adsorption isotherms for Windsor and Olivier soils after 24 h of reaction time. Solid and dashed curves are simulations using the Freundlich equation (Eq. [1]).

[17a]

[17b]

\

The unknown variables (C_As, C_P, S_As, and S_P) were solved simultaneously from the initial experimental conditions (i.e., initial As(V) and P concnentrations, solid/solution ratios). Specifically, the nonlinear set of equations was solved through iterative improvements, similar to the approach of Barrow et al. (2005). The competitive coefficients _As,P and _P,As given in Table 3 were obtained by fitting the competitive adsorption data to Eq. [17] using nonlinear least square optimization.

Sorption Kinetics
Results from our single component kinetic batch experiments are presented in Fig. 4 and 5 for As(V) and P, respectively. Highly time-dependent retention behavior of the two anions are clearly illustrated by the decreasing concentrations of As(V) or P in soil solution versus reaction time for the two soils. The rate of As(V) or P retention was rapid initially and was followed by slow reactions. The kinetic adsorption data of As(V) and P from our batch study was described using single component MRMs (Eq. [5–8]). Previous studies demonstrated that the use of 24-h Freundlich N in place of MRM n (Eq. [5]) and m (Eq. [6]), that is, N = n = m, where was satisfactory and was thus performed in the simulations presented here (Zhang and Selim, 2006). Specifically, values of n of 0.311 and 0.287 were used in MRM simulation for As(V) adsorption by Olivier and Windsor soils, respectively. For P, the values for n used were 0.461 and 0.486 for Olivier and Windsor soils, respectively (see Table 3). Other parameters (K_e, k₁, k₂, k₃, and k_s) were obtained through Levenberg-Marquardt nonlinear least square optimization of the kinetic batch data to the MRM. The parameters K_e, k₁, k₂, and k₃ are given in Table 4 along with their goodness-of-fit. Excellent fit of the experimental data was achieved for both As(V) and P, as shown by the high coefficients of determination (r²) and the low root mean square errors (RMSE) given in Table 4. MRM simulations with kinetic parameters provided in Table 4 are depicted by the solid and dashed lines in Fig. 4 and 5. It should be emphasized that the MRM was applicable for the entire range of input concentrations of 0.067 to 1.333 mM for As(V) and 0.32 to 3.23 mM for P.

View larger version (24K): [in this window] [in a new window]   Fig. 4. Arsenate concentrations versus reaction time for Olivier and Windsor soils. Symbols are for different initial As(V) concentrations of 0.067, 0.13, 0.27, 0.53, 1.07, and 1.33 mmol L–1. Solid and dashed curves are single component multireaction model (MRM) simulations.

View larger version (19K): [in this window] [in a new window]   Fig. 5. Phosphate concentrations versus reaction time for Olivier and Windsor soils. Symbols are for different initial P concentrations of 0.32, 1.29, and 3.23 mmol L–1. Solid and dashed curves are single component multireaction model (MRM) simulations.

View larger version (23K): [in this window] [in a new window]   Fig. 6. Arsenate concentrations versus reaction time with the presence of various concentrations of phosphate for Olivier and Windsor soils. The initial As(V) concentrations were 0.13 mmol L–1. Symbols are for different initial P concentrations of 0, 0.32, 1.29, and 3.23 mmol L–1. Solid and dashed curves are multi-component multi-reaction model (MCMRM) simulations.

View larger version (21K): [in this window] [in a new window]   Fig. 7. Phosphate concentrations versus reaction time with the presence of various concentrations of arsenate for Olivier and Windsor soils. The initial P concentration was 0.32 mmol L–1. Symbols are for different initial As(V) concentrations of 0, 0.13, and 1.29 mmol L–1. Solid and dashed curves are multi-component multi-reaction model (MCMRM) simulations.

View larger version (23K): [in this window] [in a new window]   Fig. 8. Competitive sorption kinetics of As(V) and P on Windsor soils. Top: The initial As(V) concentrations was 0.13 mmol L–1. Symbols are for different initial P concentrations of 0, 0.32, 1.29, and 3.23 mmol L–1. Bottom: The initial P concentration was 0.32 mmol L–1. Symbols are for different initial As(V) concentrations of 0, 0.13, and 1.33 mmol L–1. Solid and dashed curves are multi-component multi-reaction model (MCMRM) simulations.

View larger version (28K): [in this window] [in a new window]   Fig. 9. Experimental As(V) breakthrough curves (BTCs) in Olivier (Column 1) and Windsor (Column 4) soil without addition of P. Solid curves are single-component multi-reaction model (MRM) predictions using batch kinetic parameters. The dashed curves depict MRM results based on nonlinear optimization. Arrows indicate pore volumes when flow interruptions occurred.

View larger version (26K): [in this window] [in a new window]   Fig. 12. Experimental As(V) and P breakthrough curves (BTCs) in Windsor soil (Column 6). Solid curves are multi-component multi-reaction model (MCMRM) predictions using batch kinetic parameters. The dashed curves are MCMRM simulations using kinetic parameters obtained from single component As(V) transport experiment. Arrows indicate pore volumes when flow interruptions occurred.

View larger version (28K): [in this window] [in a new window]   Fig. 10. Experimental As(V) and P breakthrough curves (BTCs) in Olivier (Column 2) and Windsor (Column 5) soil. Solid curves are multi-component multi-reaction model (MCMRM) predictions using batch kinetic parameters. The dashed curves are MCMRM simulations using kinetic parameters obtained from single component As(V) transport experiment. Arrows indicate pore volumes when flow interruptions occurred.

View larger version (24K): [in this window] [in a new window]   Fig. 11. Experimental As(V) and P breakthrough curves (BTCs) in Olivier soil (Column 3). Solid curves are multi-component multi-reaction model (MCMRM) predictions using batch kinetic parameters. The dashed curves are MCMRM simulations using kinetic parameters obtained from single component As(V) transport experiment. Arrows indicate pore volumes when flow interruptions occurred.

Transport Model
Our modeling efforts were first performed on single component As(V) BTCs using MRM in a fully predictive mode. Specifically, the MRM retention parameters (n, K_e, k₁, k₂, and k₃) from our kinetic batch data (Table 5) were used, coupled with the hydrodynamic dispersion coefficient (D) obtained from tritium BTCs (see Fig. 2 and Table 2). Model predictions are shown as the solid curves in Fig. 9. Consistent with previous studies (Selim, 1992; Selim et al., 1992; Hinz and Selim, 1994), the use of batch model parameters overpredicted concentration maxima (peaks) and underestimated the extent of retardation (BTCs shift to the left). This fully predictive model also underestimated the influence of flow interruption on As(V) transport. In general, the use of batch rate coefficients underestimated the extent of As(V) retention in Olivier and Windsor soils and overestimated the potential As(V) mobility. Failure of batch coefficients in the prediction of column results may be due to several factors including: differences between sorption time used for batch experiments and hydrologic retention time in column experiment; and inherent physical constraints of closed batch systems when compared with continuous flow conditions in column experiments. In addition, we evaluated several MRM formulations in their capability to predict the BTCs and found no significant difference between the MRM formulations. Based on As(V) retention kinetic results, Zhang and Selim (2006) found that several MRM versions fit the data equally well for three different soils.

View this table: [in this window] [in a new window]   Table 5. Parameters and goodness-of-fit of muli-component multi-reaction model (MCMRM) for the simulation of As(V) and P breakthrough curves (BTCs). The values for As-P and P-As as well as Freundlich n for As(V) and P are those given in Table 3 and 4.

The competitive transport of As(V) and P was initially simulated using the MCMRM. The competitive coefficients (_As-P and _P-As) and retention parameters derived from our kinetic batch data (see Table 5) were used in model simulations. Model predictions are shown as the solid curves in Fig. 10, 11, and 12. Consistent with single component MRM simulations, we found the use of batch rate coefficients underestimated As(V) retention and overestimated the extent of As(V) mobility in columns 2, 3, and 5 (see Fig. 10 and 11). However, the model successfully predicted the snow-plow effect [C/C_o > 1.0 for As(V)] of Column 6 (Windsor) shown in Fig. 12. Moreover, the predicted BTCs closely followed measured data except at flow interruptions, where the decrease in concentration was underestimated. The MCMRM predicted P BTCs for Olivier and Windsor columns with moderate success. The model well described peak concentrations and the tailing of the BTCs. However, predicted BTCs for Windsor soils had a much larger retardation than experimentally observed. Moreover, the effect of flow interruptions was underestimated in all model simulations, suggesting that extent of kinetic sorption during transport was underrepresented by model predictions.

To further test the capability of the proposed competitive model in describing As(V) transport in the presence of P, we decided to implement As(V) parameters derived from single component column BTCs (see Table 5). The resulting model predictions are shown by the dashed curves in Fig. 10, 11, and 12. Based on visual observations, as well as r² and RMSE, the use of column-derived kinetic As(V) retention parameters significantly improved the prediction of As(V) BTCs for Column 2, 3, and 5 where P was present (see Fig. 10 and 11). Such improvements were a result of the fact that similar experimental conditions during transport were maintained. For Olivier soil (Column 2 and 3), the competitive model successfully predicted the concentration increase of the sorption side and the extensive tailing of the desorption side in the BTCs. Furthermore, the effect of flow interruption was successfully predicted by the model. Based on these predictions, we conclude that the model provides an overall representation of possible retention mechanisms for Olivier soil during competitive As(V) and P transport.

The use of column kinetic parameters improved model prediction of As(V) BTC for Column 5 with less success (see Fig. 10). There were apparent discrepancies between the predicted and measured adsorption (or front) side and desorption (or release) side of the BTCs. In addition, the use of column rate coefficients resulted in better prediction of the effect of flow interruption but failed to predict the snow-plow effect observed in As(V) BTCs for Column 6 (see Fig. 12). The poor prediction observed in Windsor soil (Columns 5 and 6) was probably due to inaccurate parameter estimation or unaccounted mechanisms in the simulation of kinetic retention during P transport.

The results presented here demonstrate As(V) and P predictions of their retention kinetics in soils based on MCMRM. The MCMRM accounts for short- and long-term mechanisms of As(V) and P sorption in heterogeneous soils. The nonlinear competitive adsorption equations in MCMRM provide useful quantitative predictors of possible competitive retention interactions in soils.

	CONCLUSIONS

TOP NOTES ABSTRACT INTRODUCTION MODEL FORMULATION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
A nonlinear equilibrium-kinetic MRM was extended to describe competitive sorption and transport of multiple species in soils. We evaluated this MCMRM for its prediction capability of As(V)-P retention as well as transport in soils. Results from kinetic batch experiments demonstrated that the rate and amounts of As(V) sorption was significantly reduced when applied P concentrations in solution increased. Competitive retention for As(V) and P over time was successfully predicted using MCMRM where model coefficients were based on single component kinetic batch results.

Results from miscible displacement transport experiments indicated that the presence of P increased mobility of As(V) in all soil columns. The use of batch rate coefficients provided poor prediction of As(V) BTCs from the competitive column transport experiments. The multi-component model adequately predicted the measured BTCs for competitive As(V)-P transport in Olivier and Windsor soil when rate coefficients obtained from inverse modeling of single component As(V) BTCs were used. The competitive approach was also capable of describing flow interruptions as well as the chromatographic effect of As(V) due to competition with P.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MODEL FORMULATION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Contribution from Louisiana State University Agricultural Center as manuscript no. 07-14-073.

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Received for publication December 5, 2006.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION MODEL FORMULATION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

• Amacher, M.C., H.M. Selim, and I.K. Iskandar. 1988. Kinetics of chromium(VI) and cadmium retention in soils: A nonlinear multireaction model. Soil Sci. Soc. Am. J. 52:398–408.[Web of Science]
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