Soil Chemistry Effects and Flow Prediction in Electroremediation of Soil

and compared to electroremediation experiments. It is found that a soil chemistry model involving cation exchange and aluminum chemistry must be inclu...
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Environ. Sci. Technol. 1997, 31, 1191-1197

Soil Chemistry Effects and Flow Prediction in Electroremediation of Soil JOHN M. DZENITIS* Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

This work addresses processes occurring during the removal of contaminants from soils using electric fields. Laboratory experiments and mathematical modeling are used to study the changes in the flows of ions and pore liquid during the process; these flows are directly related to the removal of charged and uncharged contaminants by electromigration and electroosmosis, respectively. Soil properties are explored by electrophoresis measurements, acid/ base titrations, and elemental analyses of pore solutions, then incorporated into an electrochemical transport model, and compared to electroremediation experiments. It is found that a soil chemistry model involving cation exchange and aluminum chemistry must be included to describe the process accurately. Varying electroosmotic flow is successfully predicted, but only until the development of a low ionic strength region in the medium. The insight gained allows the mechanisms of electroosmotic flow reversal and cessation to be identified. As importantly, this investigation finds the low ionic strength region to be an undesirable but likely occurrence with or without significant effects from soil chemistry and shows how controlling the system chemistry makes the electroremediation technique more robust in practice.

Introduction Electroremediation is an innovative method for removing contaminants from soil using in situ, low power, dc electric fields. Like other in situ methods such as bioremediation, vapor extraction, and soil flushing, electroremediation has advantages in avoiding high costs and human health risks of excavation. Additionally, electroremediation is well-suited to heavy metal contaminants, unlike bioremediation and vapor extraction, and it is applicable to contaminants in heterogeneous and low-permeability soils, unlike soil flushing. Charged contaminants such as heavy metals in solution are primarily moved by electromigration, and uncharged contaminants such as soluble organic molecules can be moved with the bulk liquid in the presence of charged soil surfaces by electroosmosis (1, 2). Once the contaminants reach the electrode reservoirs, the solutions can be easily pumped out and treated. Laboratory experiments (3-11) and limited field work (12, 13) have proven that it is possible to achieve nearly complete removal of contaminants using electric fields. However, these studies have also shown that the approach can fail when flow of charge (current) or flow of mass (convection) are not maintained. Determining the causes of decrease in these * Present address: Monsanto Company U4E, 800 North Lindbergh Boulevard, Saint Louis, MO 63167; telephone: 314-694-8696; fax: 314694-1531; e-mail address: [email protected].

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flows is thus of great importance. For electromigration, Hicks and Tondorf (10) showed how products of electrode reactions could halt the removal of heavy metal contaminants by affecting the metal speciation. By controlling the cathode’s product, OH-, they achieved removals of over 95%. For electroosmosis, Shapiro and Probstein (6, 8) showed that in some cases convective flow ceased before high removal percentages were reached. They found that by using a basic purge solution to limit the anode’s product, H+, they could promote flow toward the cathode. The specific mechanism for electroosmotic flow cessation has not been as conclusively identified as in the electromigration case, largely because of the complexity of the multispecies electrochemical transport. The most physically realistic model of electroremediation transport was introduced by Shapiro et al. (5) and extended and generalized by Jacobs et al. (14) and Jacobs and Probstein (15). Despite the detail of this model, the electroosmotic flow velocity is based on the measured flow rate, so the model is not predictive in terms of convective velocity. In particular, the causes of varying flow rate and flow cessation, so important to contaminant removal by electroosmosis, cannot be determined. Eykholt was the first to include dependencies required to model changing electroosmotic flow during electroremediation (16-18). His model did show varying flow rate, and he was able to predict a change in the direction of electroosmotic flow when acid was added to the cathode reservoir. However, there was not quantitative agreement between his experiments and numerical simulations. In this paper, we develop the first quantitatively accurate predictions of varying charge and mass flow during electroremediation. Insights into the mechanisms of flow cessation are uncovered in the process.

Experimental Section Electroremediation Experiments. The apparatus used for the one-dimensional electroremediation experiments is shown schematically in Figure 1 and described in detail in ref 19. The soil mixture with length ≈150 mm was contained in a clear PVC tube with an inner diameter of 54 mm. The ends of the soil were held by filter paper against a stainless steel screen that acted as a mechanical support and voltage probe. Electrode reservoirs on either side of the soil contained carbon fiber electrodes (Fiber Materials Inc., Biddeford, MA) across which the voltage was applied. The cathode reservoir (165 mL) was connected to a tank on a scale, and the anode reservoir was part of a gravity-fed recirculation system (total volume of 4500 mL) using a return pump with wetted surfaces of polypropylene. This large volume gave the anode reservoir a high chemical capacitance, useful for controlling the system chemistry as described below. The pressure at the ends of the cell was balanced by adjusting the feed tank height, so all of the measured mass flow resulted from electroosmosis (2). The dc power supply was adjusted throughout the experiment to maintain 15 V across the 150 mm soil length. Measurements of current, applied voltage, effluent mass, applied soil matrix stress, and local voltage and pressure in the soil were made with a digital data acquisition system. The soil used was an acidic form of a nearly pure kaolin clay (Albion Sperse 100, Albion Kaolin Co., Hephzibah, GA). A barely-liquid mixture was made by gradually stirring the dry clay into a 10 mM NaCl solution until a solid:liquid mass ratio of 1:1 was reached. The loading piston was used to gradually consolidate this mixture in the test cell to assure a tight seal with the walls. To investigate the effect of chemical changes on the process, two electroremediation experiments are presented here. In the first experiment (untreated), the main electrode

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FIGURE 1. Apparatus used for electroremediation experiments. Measurements of current i, voltage distribution V, effluent mass m, and soil matrix stress s are indicated. products, H+ and OH-, were not controlled. As the experiment progressed, the pH at the anode reservoir dropped to 2, and the pH at the cathode reservoir increased to 11. In the second experiment (base addition), the anode product H+ was replaced with Na+ by periodically adding concentrated NaOH to the reservoir, which kept the anode pH above 9. This approach is similar to that taken by Shapiro and Probstein (6, 8). The electroosmotic flow rate through the medium can be characterized with an average electroosmotic permeability ka ) u/Ea (m2 V-1 s-1), where u is the volumetric flow per unit area per unit time or average interstitial velocity (m s-1) and Ea is the applied electric field (V m-1). In one dimension, Ea is the voltage drop across the medium divided by its length. The use of ka for flow of mass emphasizes that the flow results solely from the electric field and forms a useful analogue with average conductivity and flow of charge later. The initial conditions are uniform, and if the charged double layer at the solid/liquid interface is thin as compared to the interparticle distance, the electroosmotic permeability is given by

ka ≈ -

ζ µτ2

at t ) 0

(1)

where  is the permittivity of the liquid (6.93 × 10-10 C V-1 m-1 in 298 K water), µ is the viscosity (kg m-1 s-1), τ is the dimensionless porous medium tortuosity, and ζ is the ζ-potential (V), which is identified with the electric potential at the soil/liquid interface (2). The tortuosity is a constant (g1) introduced to account for the indirect path through the porous medium. As the experiment progresses, the average electroosmotic permeability involves a spatial average because conditions in the medium become non-uniform (5, 8). Equation 1 no longer applies, but the experimental definition ka ) u/Ea still holds. The experimental electroosmotic permeability is plotted in Figure 2. The initial behavior of the two experiments was identical, but the flows diverged after 2 days. The experiment with base addition showed increasing flow rate while the untreated experiment’s flow began to cease at 7 days. At this point, the experiment with base addition had displaced over 2.2 pore volumes while the untreated experiment had displaced only 1. The charge flow analogue to the mass flow above is interstitial current density i, which is movement of charge per unit area per unit time (A m-2). As above, the porous medium’s condition can be represented by a single value, in this case the average electrical conductivity σa ) i/Ea (S m-1). Initially the concentrations are uniform and surface conductivity can be neglected, so σa is simply the conductivity

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FIGURE 2. Electroosmotic flow rate in terms of average electroosmotic permeability ka ) u/Ea for experiments with and without base addition at the anode. At 7 days, the untreated experiment has displaced approximately 1 pore volume and shows flow cessation, while the base addition experiment has displaced over 2.2 pore volumes and is increasing. of the pore solution modified by the tortuosity factor:

σa ≈

F2 τ2

∑z ν c

2 j j j

at t ) 0

(2)

where F is Faraday’s constant (96 487 C mol-1), zj is the charge number of the species j, νj is the mobility (mol s kg-1), and cj is the concentration (mol m-3 or mM). When the chemical composition changes, σa becomes a complicated spatial average involving varying concentrations, diffusion, local electric field effects, and the surface conductivity (5). The experimental definition σa ) i/Ea still holds at these later times, and these results are shown in Figure 3. The initial behavior of the two experiments was again identical up to 2 days and then diverged as the conductivity of the base addition experiment began to rise dramatically at about 4 days. Soil Medium Transport Properties. To determine why mass and current flow decreased in the first case and dropped but later increased in the second case, a detailed numerical model was used to track the multispecies transport including electromigration, electroosmotic convection, diffusion, and chemical reactions. In this work, we used a one-dimensional version of the model introduced by Shapiro et al. (5) and extended by Jacobs et al. (14) and Jacobs and Probstein (15).

FIGURE 3. Charge flow rate in terms of average electrical conductivity σa ) i/Ea for experiments with and without base addition at the anode. Both experiments show an initial drop in conductivity, but the base addition experiment’s conductivity begins to rise dramatically at ≈4 days.

FIGURE 4. ζ-potential measurements and empirical fit for varying pH and at three Na+ concentrations. The model assumes a point of zero net charge at pH 6 and linear dependence on pH and the logarithm of ionic strength.

The deviations and details are not reproduced here. The required solvent properties (density, viscosity, permittivity) were taken to be those of pure water, and the solute properties (diffusion coefficient, mobility, chemical equilibrium coefficients) were taken to be those of the infinitely dilute species in water. The porous medium properties (porosity, tortuosity, hydraulic permeability, surface conductivity, surface potential, chemical behavior) depend on the specific soil and local conditions in the medium, so these properties were determined separately. The results are summarized below, and more details can be found in ref 19. A porosity of 0.54 was measured from consolidation tests, and a tortuosity of 1.65 was calculated from eq 2 using an initial conductivity measurement. The hydraulic permeability was measured to be 4 × 10-16 m2 at the initial conditions, and a surface conductivity of 10-3 S m-1 measured by Shapiro (6) for the same clay was used. The ζ-potential of the surface seen in eq 1 is a property depending on complex physicochemical interactions (20). Given the scale of the transport problem and the complex composition of soils, empirical data and major simplifications are required. Microelectrophoresis measurements of the kaolin clay’s ζ-potential were made with a Zeta-Meter 3.0+ (Zeta-Meter Inc., Long Island, NY) in hydrosols of various composition. Since pH, ionic strength I ) 1/2∑z2j cj, and exchangeable cation concentration are key parameters in determining ζ-potential, the solutions were designed to explore these dependencies. The pH was varied with HCl and NaOH because NaCl was used as a background electrolyte in the electroremediation experiments; it can be shown that the electroremediation process effectively forms this acid and base by separating the salt’s ions (19). The initial ionic strength and cation concentration were varied by adding NaCl to each of the solutions so they had one of three Na+ concentrations: 0.1, 10, or 200 mM. Measurements at 1 and 6 days showed that there was generally little change in ζ-potential over this period. The measured ζ-potentials are shown in Figure 4 together with a simple empirical fit of the data. The pH had a great effect on the clay, resulting in ζ-potentials from +10 mV to -45 mV. Sodium concentration had an effect only for pH > 6. In this range, the ionic strength is equal to the sodium concentration, so there was a decrease in ζ-potential magnitude for higher ionic strengths. This behavior is consistent with more extensive work on clays and metal oxides (20, 21). The empirical fit shown in Figure 4 was based on dependencies seen in these works; we assumed a point of zero net charge at pH 6 and linear dependence on pH and log I. The

form can be physically justified, but the number of data points used here is really insufficient for the curve fit. This is certainly an area that could bear further work. The pH dependence is similar to the measurements of Lorentz (22), which had less variation in ionic strength. Eykholt and Daniel (17) used Lorenz’s data to include ζ-potential dependence on pH. Soil Chemical Behavior. The chemical properties of the soil medium were the final part of the system to be characterized. Chemical behavior is important because it determines the species that are present, the electric field distribution (via the conductivity distribution), and the soil surface charge; in other words, chemical interactions determine what is present and how it moves. Despite its importance, the issue of soil chemistry has been avoided in electroremediation work because of its complexity. Here, soil chemistry was tackled in a manner similar to that used for the surface potential above. The key, again, is to realize that electromigration separates the background electrolyte ions and replaces them with electrode products, so that acid/ base (HCl/NaOH) titration is the appropriate way to explore the soil’s chemical behavior. Alkalinity is the concentration of strong base minus the concentration of strong acid in a solution. For a system with only H+, OH-, Na+, and Cl-, the alkalinity is given by Alk ) [Na+] - [Cl-]. In this case, the concentrations of all species are known when alkalinity and ionic strength are given if electroneutrality, ∑zjcj ) 0, and water equilibrium, [H+][OH-] ) Kw, are assumed to always hold (23). The titrations were performed with initial alkalinity -100 e Alki e 100 mM using NaOH for positive Alki and HCl for negative values. Instead of trying to completely cover the two-dimensional alkalinity/ ionic strength space, two extremes were taken in the titrations: (a) Constant initial ionic strength with NaCl added as necessary to make Ii ) 100 mM. (b) Minimum initial ionic strength with no NaCl added so Ii ≈ |Alki|. In making the soil mixtures, a compromise was struck between reproducing the electroremediation test conditions and having a workable mixture. A solid:liquid mass ratio of 1.5:1 was selected for the initial pH experiments because the resulting slurry could still be stirred but was close to the concentration in the electroremediation experiments (2.2:1). The concentration effect was incorporated later. The pH electrode measurements after 24 h are shown in Figure 5 versus initial liquid alkalinity. The pH curve for a solution without weak acid or base is also plotted for reference. The relative flatness of the experimental curves represents a buffering resistance to pH change. The clay shows some

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FIGURE 5. Experimental titration of kaolin clay showing buffering to both acid and base relative to a solution with no weak acid/base behavior. There is little difference between the constant and varying ionic strength paths. buffering of acid and more pronounced buffering of base, which is expected because this particular clay is in acidic (H+) form. The acidic nature of the clay is seen as well as in fact that with no added alkalinity, the initial pH is less than 7. Ionic strength had little effect on the titration curve. This is an unexpected result in some ways because surface chemistry models usually include ionic strength dependence, and simple ion exchange models would predict a dependence on sodium concentration. On the other hand, the relative insensitivity to background electrolyte compared to H+ is common and is the same sort of behavior seen for the surface potential. There is some deviation between the two curves for small positive alkalinities in Figure 5, and those differences are consistent with higher Na+ concentration displacing more H+ from the clay surface. The differences are small on the overall scale, however, and the dependence on ionic strength will be neglected from now on. Since the clay was seen to have a significant chemical effect, another series of titrations was performed together with elemental analyses of the resulting pore solutions. A slightly lower solid:liquid mass ratio (1.2:1) was used to make it easier to obtain liquid for the analysis, and only the constant ionic strength titration was performed. The mixtures were prepared as before, and 24 h later pore liquid samples were separated from the mixtures by centrifuging through tubes with internal membranes (Ultrafree CL 0.45 µm, Millipore Inc., Bedford, MA). The pore solutions were analyzed for Na, Cl, Al, Ca, Fe, K, and Mg using inductively coupled plasma (ICP) spectroscopy in a Perkin-Elmer Plasma 40 (Norwalk, CT). Since Cl cannot be detected with this device, its concentration was determined indirectly by AgCl precipitation. In retrospect, since kaolinite is an aluminosilicate, silicon should have been added to the elements analyzed. The clay behavior can be modeled without silicon, but better results might have been possible had it been included. The results of the elemental analyses are shown as changes in pore liquid concentration multiplied by magnitude of the ion charge in Figure 6. Presenting the data in this way weights the elements according to their charge contribution. This is not strictly true for aluminum, however, since Al(OH)4- will form at high pH. The major trend seen is consumption of Na+ in the positive alkalinity range. This mechanism can explain the buffering to base (NaOH) seen in Figure 5, as will be discussed below. The changes in Cl- and in Na+ near zero alkalinity may not be significant; in this range, [NaCl] ≈ 100 mM and the accuracy of the analysis are probably not better than 10% for Cl- and 5% for Na+.

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FIGURE 6. Clay chemical effect in titrations in terms of measured change in pore liquid concentration. Significant consumption of sodium (a) and release of aluminum (b) are seen. Changes in Al, Ca, Fe, K, and Mg are only seen as releases since none of these species were present in the liquid initially. The greatest effect was from Al release at both negative and positive alkalinities. In addition, Ca and Mg were released in similar amounts in negative alkalinity. There was no change in Fe or K concentration. All of these measured clay element responses are consistent with the pH results; they are changes that buffer the system pH to both base and acid addition. It is clear that the clay’s reactive concentration range (≈100 mM) is significant on the scale of the initial electrolyte concentration (10 mM). What is not clear is how the soil affects electroremediation and how the soil behavior should be incorporated in the transport model for predictive purposes. The simplest means of incorporating soil chemistry would be to use the mineral’s equilibrium equations, but it can be shown that the measured species and concentrations do not correspond to kaolinite equilibrium. This is not surprising given natural impurities and the long time scale of mineral equilibrium. Creating artificial equilibrium models can be successful, however, because the time scale of simple surface reactions [minutes (23)] is short as compared to the time scale of electroremediation. Three different models of the soil behavior were constructed for use in numerical simulations of the electroremediation experiments. These models are briefly introduced below, then included in the electroremediation model, and compared to the data in the section Applications of Models to Experiments. When soil chemistry is ignored here, the species present are H+, OH-, Na+, and Cl-; the chemical reactions are water electrolysis at the electrodes and water equilibrium throughout the liquid. Soil chemistry is included by introducing additional species and chemical equilibrium reactions to the

chemical system. The simplest model of the measured buffering behavior (Figure 5) is an amphiprotic acid/base site XH capable of accepting or donating a proton. The solid then acts as a weak acid and a weak base, buffering the medium to additions of base and acid through the reactions

XH + Na+ + OH- ) X- + Na+ + H2O (3) XH + H+ + Cl- ) XH2+ + ClA partitioning function fit to the measured pH response was used. This was taken to be a function of pH only because the next likely dependence, ionic strength, was seen to have little effect in the titration. The acid/base site model is designed to match the experimental pH response, but it ignores the ion consumption and release seen in Figure 6. Ion exchange behavior is well known in the field of soil chemistry (see, e.g, ref 24) and can be used to reproduce the cation consumption and release seen in the elemental analysis results. Since Na+ was the major participant, a sodium ion exchange site is used in the second soil chemistry model, giving buffering reactions:

XH + Na+ + OH- ) XNa + H2O (4) XNa + H+ + Cl- ) XH + Na+ + ClSince H+ and Na+ are the only cations in this model, Na+ release serves as a substitute for the actual release of Al3+, Ca2+, and Mg2+. Again, the partitioning function was made to fit the data in Figure 5. The third soil chemistry model reproduces the experimental aluminum release by including solid aluminum hydroxide Al(OH)3(s) as a species. This is largely insoluble at zero alkalinity, but dissolves when sufficient amounts of acid or base is added, which approximates the aluminum release seen in Figure 6. The initial amount of Al(OH)3(s) was based on the maximum dissolved concentration, and literature values (23) were used for the solubility product and equilibrium constants of the dominant dissolved species, Al3+ and Al(OH)4-. As above, sodium ion exchange was used to give the Na+ consumption behavior and match the experimental titration pH.

Applications of Models to Experiments Simulations of electroremediation transport were run without soil chemistry and with soil chemistry models based on acid/ base, ion exchange, and ion exchange/aluminum hydroxide reactions. The first set of simulations focused on soil chemistry effects, and the experimental electroosmotic permeability (Figure 2) was used as an input. All other parameters were either determined from independent experiments or calculated in the simulation. Even using the experimental electroosmotic permeability, the transport problem is quite complex: the concentration and electric potential distributions must be found, and the migration, diffusion, and chemical reactions of all species must be determined as they progress in time and space. The soil surface species (XH, XNa, X-, XH2+) and solid aluminum hydroxide are immobile, but their local concentrations change as the transport shifts the mobile species in solution. Comparisons between the simulations and experiments can be made in terms of the medium’s local and average conductivity. Figure 7 shows the average conductivity for experiments and simulations with experimental mass flow as an input. The average conductivity involves a spatial integration of the species’ concentrations throughout the medium (related to eq 2), and the poor agreement in Figure 7a shows that the composition of the pore solution was not properly predicted.

FIGURE 7. Average conductivity for experiments and (a) NaCl simulations (ignoring soil chemistry) and (b) ion exchange/aluminum hydroxide soil model simulations with experimental mass flow as an input. The poor agreement in panel a and the success in panel b shows that the medium chemistry is not properly predicted without including soil effects. Better results were obtained using the acid/base and cation exchange soil models, but close agreement in both conductivity and voltage distribution required the ion exchange/ aluminum hydroxide model (Figure 7b). In the next set of simulations, the single “free” input to the runs abovesthe electroosmotic flow velocityswas no longer specified. Instead, the empirical surface potential model shown in Figure 4 was used in determining the local contributions to the electroosmotic flow. The inputs were all determined from independent experiments, and the evolution in time was determined solely by the numerical model. Figure 8 shows the flow rate as average electroosmotic permeability for experiments and simulations with and without soil chemistry. As expected from the conductivity results, the case ignoring soil chemistry (Figure 8a) could not predict the electroosmotic flow accurately. The ion exchange/ aluminum hydroxide model gave much better results (Figure 8b), properly predicting initially negative flow (towards the anode), early increase, and subsequent plateaus. Once the flow models diverged significantly from the experiments, the species’ distributions became incorrect, and the simulated system became unstable. The instability of the system and the divergence of both experimental and modeled flow around 1.7 days will be discussed below. Because the empirical surface potential model (Figure 4) was a weak link in the analysis, another simulation based on Lorenz’s data (22, 17) was run. This showed a faster initial increase and higher plateau than seen in Figure 8b, but gave a similar overall behavior.

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FIGURE 8. Electroosmotic flow rate in terms of electroosmotic permeability ka ) u/Ea for experiments and (a) NaCl simulations (ignoring soil chemistry) and (b) ion exchange/aluminum hydroxide soil model simulations with no free inputs. A soil chemistry model is necessary for quantitative predictions, and even then experiment and model diverge at ≈1.7 days.

Discussion Quantitative prediction of electric and electroosmotic flow is important in improving understanding of the processes occurring during electroremediation. One phenomenon that can now be better understood is electroosmotic flow initiation. Negative initial flow is not surprising given that (1) the electroosmotic permeability can be negative (ζ > 0) for acidic pH and (2) clays that give an acidic initial pH are often used in laboratory work. The more interesting question is why the flow becomes positive. The answer is that the local electric field becomes large precisely where the local electroosmotic permeability takes on significantly positive values, increasing the flow toward the cathode. This is predicted even in the absence of soil chemistry because the NaOH solutions formed near the cathode have lower electrical conductivity (hence higher field) than the HCl solutions created near the anode. A related but more pronounced effect is seen when the proper soil chemistry model is included; the soil reaction near the cathode (eq 4a) removes charged species and markedly decreases the local conductivity. This makes a small region near the cathode (10-20% of the total medium length) develop a large electric field and provide virtually all of the positive pumping power. Another phenomenon, at least as important as electroosmotic flow initiation, is the electroosmotic flow cessation seen in this and other experimental work. The most prevalent qualitative explanation of flow cessation involves the soil’s point of zero net charge (pH0, where ζ ) 0). However, it is

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unlikely that the pH in the high field region is exactly pH0. Although the model here did not predict flow cessation, the reason for its occurrence is indicated. The fundamental physicochemical effect is the development of large Debye length (thickness of the charged liquid layer adjacent to the charged clay solid) in the low ionic strength region formed near the cathode. An analysis with simplified geometry (25) shows that the electroosmotic permeability (e.g., eq 1) should be multiplied by a factor that decreases as ionic strength decreases; for the smaller ionic strengths in these simulations (I < 0.1 mM), the attenuation factor can be less than 0.4, which would explain discrepancies such as those seen in Figure 8b. Eykholt (16) includes this effect, but does not emphasize the results. Simplified corrections involve assumptions that are not quantitatively applicable to our situation, but the general concept applies. Low ionic strength is related to low conductivity and the high field region, which means that most of the electrical effort is being applied right where it is least effective. Also, there is a positive feedback effect where the low ionic strength causes higher electric field strength, which accelerates the deionization. The resulting attenuation factor could reduce the flow by orders of magnitude. One important conclusion is that significant flow reductions or cessation can occur even in soils that do not show zero ζ-potential behavior. The low ionic strength region explains two types of deviations seen at about 1.7 days in Figure 8b: (1) the experimental cases’ flow rates diverge because the base addition begins to have an effect on the low ionic strength region, and (2) the simulations diverge from the experiments because the model does not take into account the electroosmotic attenuation. The absence of this attenuation also leads to the instability seen in the simulations. There is also a mechanical effect that may play a role in some observations: the high field region creates low pressure and large pressure gradients, which may not be supportable in some apparatuses. Low pressures can cause external leaks, and large pressure gradients can cause “short-circuiting” of the liquid flow along the walls of a test cell. The apparatus and in-cell consolidation used here prevented these effects from being a problem, but they are likely to occur in other setups. Low pressures and high pressure gradients in the field may cause uneven velocity distributions and channeling, which could lead to expending energy to move liquid that is not contaminated. Low pressure could also cause evolution of dissolved gases, breaking the ionic conductance in the region. Electromigration is an important transport mechanism in electroremediation whether electroosmosis is the primary mechanism for contaminant removal or not. The changes in conductivity seen here are driven mainly by electromigration transport, with shifting of the distributions from convective movement. The early drop in overall conductivity seen in Figure 7 is a result of the local conductivity changes discussed above; the drop occurs before there is much convective displacement. The low ionic strength region can also form when there is no flow of the pore liquid at all, with undesirable effects for electromigration transport itself. First, the region is often associated with a jump in pH that can change the sign of the charge of the dominant heavy metal species, leading to focusing of the contaminant within the medium (1, 10). Second, low conductivity means that the overall movement of ions by electromigration is slow, so the remediation process would take a great deal of time. Controlling the system chemistry is the key to avoiding the formation of the low ionic strength region. The region results from reactions that eliminate charge-carrying ions (e.g., H+ and OH-), in interactions either with each other or with the soil surface. By substituting less reactive species at one or both electrodes, this eliminating reaction is avoided. The resulting electric field distribution is more even, and flow

of charge and mass are maintained. If soil chemistry is negligible, the added ions can have an immediate and strong effect, as is seen in Figure 7a. If the soil participates significantly in reactions with the background electrolyte or the added chemicals, the effect of the chemical addition may be delayed, as shown in Figure 7b. Some soil types (e.g., sandy ones) may have little chemical capacitance compared to the background electrolyte and could be ignored in the system’s chemical model; others (e.g., those rich in humates, clays with smaller particle size) may have significantly higher reactive concentrations than those observed here. The specific soil model constructed here will not be applicable for most natural soils, but the framework and procedure used will be useful and should be included as part of site characterization and process design. It is important to first understand how electroremediation could change the chemical composition throughout the medium and then explore the soil response to the expected changes in batch experiments. A simplified soil chemistry model can then be constructed and used to further refine the chemical control and process design.

Acknowledgments The author thanks R. F. Probstein and R. E. Hicks for their guidance during the time this work was performed. Financial support was provided in part by the Office of Science and Technology within the U.S. Department of Energy’s Office of Environmental Restoration and Waste Management under the Contaminant Plumes Containment and Remediation Focus Area, by the U.S. Environmental Protection Agency Northeast Hazardous Substance Research Center at New Jersey Institute of Technology, and by MIT’s John Hennessy Fellowship for Environmental Studies.

Literature Cited (1) Probstein, R. F.; Hicks, R. E. Science 1993, 260, 498-503. (2) Probstein, R. F. Physicochemical Hydrodynamics: An Introduction, 2nd ed.; Wiley: New York, 1994. (3) Runnells, D. D.; Larson, J. L. Ground Water Monit. Rev. 1986, 6(3), 85-91. (4) Renaud, P. C.; Probstein, R. F. PCH, PhysicoChem. Hydrodyn. 1987, 9(1/2), 345-360. (5) Shapiro, A. P.; Renaud, P. C.; Probstein, R. F. PCH, PhysicoChem. Hydrodyn. 1989, 11(5/6), 785-802.

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Received for review August 16, 1996. Revised manuscript received November 22, 1996. Accepted December 6, 1996.X ES960707E X

Abstract published in Advance ACS Abstracts, February 15, 1997.

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