Soil Chemistry Effects and FlowPrediction in Electroremediation ofSoilJ O H N M . D Z E N I T I S *

Department of Mechanical Engineering, MassachusettsInstitute of Technology, Cambridge, Massachusetts 02139

This work addresses processes occurring during theremoval of contaminants from soils using electric fields.Laboratory experiments and mathematical modeling are usedto study the changes in the flows of ions and pore liquidduring the process; these flows are directly related to theremoval of charged and uncharged contaminants by elec-tromigration and electroosmosis, respectively. Soil propertiesare 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 foundthat a soil chemistry model involving cation exchange andaluminum chemistry must be included to describe theprocess accurately. Varying electroosmotic flow is suc-cessfully predicted, but only until the development of alow ionic strength region in the medium. The insight gainedallows the mechanisms of electroosmotic flow reversaland cessation to be identified. As importantly, this investigationfinds the low ionic strength region to be an undesirablebut likely occurrence with or without significant effects fromsoil chemistry and shows how controlling the systemchemistry makes the electroremediation technique morerobust in practice.

IntroductionElectroremediation is an innovative method for removingcontaminants from soil using in situ, low power, dc electricfields. Like other in situ methods such as bioremediation,vapor extraction, and soil flushing, electroremediation hasadvantages in avoiding high costs and human health risks ofexcavation. Additionally, electroremediation is well-suitedto heavy metal contaminants, unlike bioremediation andvapor extraction, and it is applicable to contaminants inheterogeneous and low-permeability soils, unlike soil flushing.Charged contaminants such as heavy metals in solution areprimarily moved by electromigration, and uncharged con-taminants such as soluble organic molecules can be movedwith the bulk liquid in the presence of charged soil surfacesby electroosmosis (1, 2). Once the contaminants reach theelectrode reservoirs, the solutions can be easily pumped outand treated.

Laboratory experiments (3-11) and limited field work (12,13) have proven that it is possible to achieve nearly completeremoval of contaminants using electric fields. However, thesestudies have also shown that the approach can fail when flowof charge (current) or flow of mass (convection) are notmaintained. Determining the causes of decrease in these

flows is thus of great importance. For electromigration, Hicksand Tondorf (10) showed how products of electrode reactionscould halt the removal of heavy metal contaminants byaffecting the metal speciation. By controlling the cathode’sproduct, OH-, they achieved removals of over 95%. Forelectroosmosis, Shapiro and Probstein (6, 8) showed that insome cases convective flow ceased before high removalpercentages were reached. They found that by using a basicpurge solution to limit the anode’s product, H+, they couldpromote flow toward the cathode. The specific mechanismfor electroosmotic flow cessation has not been as conclusivelyidentified as in the electromigration case, largely because ofthe complexity of the multispecies electrochemical transport.The most physically realistic model of electroremediationtransport was introduced by Shapiro et al. (5) and extendedand generalized by Jacobs et al. (14) and Jacobs and Probstein(15). Despite the detail of this model, the electroosmoticflow velocity is based on the measured flow rate, so the modelis not predictive in terms of convective velocity. In particular,the causes of varying flow rate and flow cessation, so importantto contaminant removal by electroosmosis, cannot bedetermined. Eykholt was the first to include dependenciesrequired to model changing electroosmotic flow duringelectroremediation (16-18). His model did show varying flowrate, and he was able to predict a change in the direction ofelectroosmotic flow when acid was added to the cathodereservoir. However, there was not quantitative agreementbetween his experiments and numerical simulations. In thispaper, we develop the first quantitatively accurate predictionsof varying charge and mass flow during electroremediation.Insights into the mechanisms of flow cessation are uncoveredin the process.

Experimental SectionElectroremediation Experiments. The apparatus used forthe one-dimensional electroremediation experiments isshown schematically in Figure 1 and described in detail inref 19. The soil mixture with length≈150 mm was containedin a clear PVC tube with an inner diameter of 54 mm. Theends of the soil were held by filter paper against a stainlesssteel screen that acted as a mechanical support and voltageprobe. Electrode reservoirs on either side of the soil containedcarbon 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 anodereservoir was part of a gravity-fed recirculation system (totalvolume of 4500 mL) using a return pump with wetted surfacesof polypropylene. This large volume gave the anode reservoira high chemical capacitance, useful for controlling the systemchemistry as described below. The pressure at the ends ofthe cell was balanced by adjusting the feed tank height, soall of the measured mass flow resulted from electroosmosis(2). The dc power supply was adjusted throughout theexperiment 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 inthe soil were made with a digital data acquisition system.

The soil used was an acidic form of a nearly pure kaolinclay (Albion Sperse 100, Albion Kaolin Co., Hephzibah, GA).A barely-liquid mixture was made by gradually stirring thedry clay into a 10 mM NaCl solution until a solid:liquid massratio of 1:1 was reached. The loading piston was used togradually consolidate this mixture in the test cell to assurea tight seal with the walls.

To investigate the effect of chemical changes on theprocess, two electroremediation experiments are presentedhere. In the first experiment (untreated), the main electrode

* Present address: Monsanto Company U4E, 800 North LindberghBoulevard, Saint Louis, MO 63167; telephone: 314-694-8696; fax: 314-694-1531; e-mail address: jmdzen@ccmail.monsanto.com.

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products, H+ and OH-, were not controlled. As the experi-ment progressed, the pH at the anode reservoir dropped to2, and the pH at the cathode reservoir increased to 11. In thesecond experiment (base addition), the anode product H+

was replaced with Na+ by periodically adding concentratedNaOH 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 becharacterized with an average electroosmotic permeabilityka ) u/Ea (m2 V-1 s-1), where u is the volumetric flow per unitarea per unit time or average interstitial velocity (m s-1) andEa 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 resultssolely from the electric field and forms a useful analoguewith average conductivity and flow of charge later. The initialconditions are uniform, and if the charged double layer atthe solid/liquid interface is thin as compared to the inter-particle distance, the electroosmotic permeability is given by

where ε 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 thedimensionless porous medium tortuosity, and ú is theú-potential (V), which is identified with the electric potentialat the soil/liquid interface (2). The tortuosity is a constant(g1) introduced to account for the indirect path through theporous medium. As the experiment progresses, the averageelectroosmotic permeability involves a spatial average becauseconditions in the medium become non-uniform (5, 8).Equation 1 no longer applies, but the experimental definitionka ) u/Ea still holds. The experimental electroosmoticpermeability is plotted in Figure 2. The initial behavior ofthe two experiments was identical, but the flows divergedafter 2 days. The experiment with base addition showedincreasing flow rate while the untreated experiment’s flowbegan to cease at 7 days. At this point, the experiment withbase addition had displaced over 2.2 pore volumes while theuntreated experiment had displaced only 1.

The charge flow analogue to the mass flow above isinterstitial current density i, which is movement of chargeper unit area per unit time (A m-2). As above, the porousmedium’s condition can be represented by a single value, inthis case the average electrical conductivity σa ) i/Ea (S m-1).Initially the concentrations are uniform and surface con-ductivity can be neglected, so σa is simply the conductivity

of the pore solution modified by the tortuosity factor:

where F is Faraday’s constant (96 487 C mol-1), zj is the chargenumber of the species j, νj is the mobility (mol s kg-1), andcj is the concentration (mol m-3 or mM). When the chemicalcomposition changes, σa becomes a complicated spatialaverage involving varying concentrations, diffusion, localelectric field effects, and the surface conductivity (5). Theexperimental definition σa ) i/Ea still holds at these later times,and these results are shown in Figure 3. The initial behaviorof the two experiments was again identical up to 2 days andthen diverged as the conductivity of the base additionexperiment began to rise dramatically at about 4 days.

Soil Medium Transport Properties. To determine whymass and current flow decreased in the first case and droppedbut later increased in the second case, a detailed numericalmodel was used to track the multispecies transport includingelectromigration, electroosmotic convection, diffusion, andchemical reactions. In this work, we used a one-dimensionalversion of the model introduced by Shapiro et al. (5) andextended by Jacobs et al. (14) and Jacobs and Probstein (15).

FIGURE 1. Apparatus used for electroremediation experiments. Measurements of current i, voltage distribution V, effluent mass m, andsoil matrix stress s are indicated.

ka ≈ - εúµτ2

at t ) 0 (1)

FIGURE 2. Electroosmotic flow rate in terms of average electroos-motic permeability ka ) u/Ea for experiments with and without baseaddition at the anode. At 7 days, the untreated experiment hasdisplaced approximately 1 pore volume and shows flow cessation,while the base addition experiment has displaced over 2.2 porevolumes and is increasing.

σa ≈ F2

τ2∑zj2νjcj at t ) 0 (2)

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The deviations and details are not reproduced here. Therequired solvent properties (density, viscosity, permittivity)were taken to be those of pure water, and the solute properties(diffusion coefficient, mobility, chemical equilibrium coef-ficients) were taken to be those of the infinitely dilute speciesin water. The porous medium properties (porosity, tortuosity,hydraulic permeability, surface conductivity, surface poten-tial, chemical behavior) depend on the specific soil and localconditions in the medium, so these properties were deter-mined separately. The results are summarized below, andmore 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 aninitial conductivity measurement. The hydraulic permeabilitywas measured to be 4× 10-16 m2 at the initial conditions, anda surface conductivity of 10-3 S m-1 measured by Shapiro (6)for the same clay was used.

The ú-potential of the surface seen in eq 1 is a propertydepending on complex physicochemical interactions (20).Given the scale of the transport problem and the complexcomposition of soils, empirical data and major simplificationsare required. Microelectrophoresis measurements of thekaolin clay’s ú-potential were made with a Zeta-Meter 3.0+(Zeta-Meter Inc., Long Island, NY) in hydrosols of variouscomposition. Since pH, ionic strength I ) 1/2∑zj

2cj, andexchangeable cation concentration are key parameters indetermining ú-potential, the solutions were designed toexplore these dependencies. The pH was varied with HCland NaOH because NaCl was used as a background electrolytein the electroremediation experiments; it can be shown thatthe electroremediation process effectively forms this acid andbase by separating the salt’s ions (19). The initial ionicstrength and cation concentration were varied by addingNaCl to each of the solutions so they had one of three Na+

concentrations: 0.1, 10, or 200 mM. Measurements at 1 and6 days showed that there was generally little change inú-potential over this period.

The measured ú-potentials are shown in Figure 4 togetherwith a simple empirical fit of the data. The pH had a greateffect on the clay, resulting in ú-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 sodiumconcentration, so there was a decrease in ú-potential mag-nitude for higher ionic strengths. This behavior is consistentwith more extensive work on clays and metal oxides (20, 21).The empirical fit shown in Figure 4 was based on depend-encies seen in these works; we assumed a point of zero netcharge at pH 6 and linear dependence on pH and log I. The

form can be physically justified, but the number of data pointsused here is really insufficient for the curve fit. This is certainlyan area that could bear further work. The pH dependenceis similar to the measurements of Lorentz (22), which hadless variation in ionic strength. Eykholt and Daniel (17) usedLorenz’s data to include ú-potential dependence on pH.

Soil Chemical Behavior. The chemical properties of thesoil medium were the final part of the system to becharacterized. Chemical behavior is important because itdetermines the species that are present, the electric fielddistribution (via the conductivity distribution), and the soilsurface charge; in other words, chemical interactions deter-mine what is present and how it moves. Despite itsimportance, the issue of soil chemistry has been avoided inelectroremediation work because of its complexity. Here,soil chemistry was tackled in a manner similar to that usedfor the surface potential above. The key, again, is to realizethat electromigration separates the background electrolyteions and replaces them with electrode products, so that acid/base (HCl/NaOH) titration is the appropriate way to explorethe soil’s chemical behavior.

Alkalinity is the concentration of strong base minus theconcentration of strong acid in a solution. For a system withonly H+, OH-, Na+, and Cl-, the alkalinity is given by Alk )[Na+] - [Cl-]. In this case, the concentrations of all speciesare known when alkalinity and ionic strength are given ifelectroneutrality, ∑zjcj ) 0, and water equilibrium, [H+][OH-]) Kw, are assumed to always hold (23). The titrations wereperformed with initial alkalinity -100 e Alki e 100 mM usingNaOH for positive Alki and HCl for negative values. Insteadof 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 asnecessary to make Ii ) 100 mM.

(b) Minimum initial ionic strength with no NaCl added soIi ≈ |Alki|.

In making the soil mixtures, a compromise was struckbetween reproducing the electroremediation test conditionsand having a workable mixture. A solid:liquid mass ratio of1.5:1 was selected for the initial pH experiments because theresulting slurry could still be stirred but was close to theconcentration in the electroremediation experiments (2.2:1).The concentration effect was incorporated later.

The pH electrode measurements after 24 h are shown inFigure 5 versus initial liquid alkalinity. The pH curve for asolution without weak acid or base is also plotted for reference.The relative flatness of the experimental curves represents abuffering resistance to pH change. The clay shows some

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

FIGURE 4. ú-potential measurements and empirical fit for varyingpH and at three Na+ concentrations. The model assumes a pointof zero net charge at pH 6 and linear dependence on pH and thelogarithm of ionic strength.

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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 infact that with no added alkalinity, the initial pH is less than7.

Ionic strength had little effect on the titration curve. Thisis an unexpected result in some ways because surfacechemistry models usually include ionic strength dependence,and simple ion exchange models would predict a dependenceon sodium concentration. On the other hand, the relativeinsensitivity to background electrolyte compared to H+ iscommon and is the same sort of behavior seen for the surfacepotential. There is some deviation between the two curvesfor small positive alkalinities in Figure 5, and those differencesare consistent with higher Na+ concentration displacing moreH+ from the clay surface. The differences are small on theoverall scale, however, and the dependence on ionic strengthwill be neglected from now on.

Since the clay was seen to have a significant chemicaleffect, another series of titrations was performed togetherwith elemental analyses of the resulting pore solutions. Aslightly lower solid:liquid mass ratio (1.2:1) was used to makeit easier to obtain liquid for the analysis, and only the constantionic strength titration was performed. The mixtures wereprepared as before, and 24 h later pore liquid samples wereseparated from the mixtures by centrifuging through tubeswith internal membranes (Ultrafree CL 0.45 µm, MilliporeInc., 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, itsconcentration was determined indirectly by AgCl precipita-tion. In retrospect, since kaolinite is an aluminosilicate, siliconshould have been added to the elements analyzed. The claybehavior can be modeled without silicon, but better resultsmight have been possible had it been included.

The results of the elemental analyses are shown as changesin pore liquid concentration multiplied by magnitude of theion charge in Figure 6. Presenting the data in this way weightsthe elements according to their charge contribution. This isnot strictly true for aluminum, however, since Al(OH)4

- willform at high pH. The major trend seen is consumption ofNa+ in the positive alkalinity range. This mechanism canexplain the buffering to base (NaOH) seen in Figure 5, as willbe discussed below. The changes in Cl- and in Na+ near zeroalkalinity may not be significant; in this range, [NaCl] ≈ 100mM and the accuracy of the analysis are probably not betterthan 10% for Cl- and 5% for Na+.

Changes in Al, Ca, Fe, K, and Mg are only seen as releasessince none of these species were present in the liquid initially.The greatest effect was from Al release at both negativeand positive alkalinities. In addition, Ca and Mg werereleased in similar amounts in negative alkalinity. There wasno change in Fe or K concentration. All of these measuredclay element responses are consistent with the pH results;they are changes that buffer the system pH to both base andacid addition.

It is clear that the clay’s reactive concentration range (≈100mM) is significant on the scale of the initial electrolyteconcentration (10 mM). What is not clear is how the soilaffects electroremediation and how the soil behavior shouldbe incorporated in the transport model for predictivepurposes. The simplest means of incorporating soil chemistrywould be to use the mineral’s equilibrium equations, but itcan be shown that the measured species and concentrationsdo not correspond to kaolinite equilibrium. This is notsurprising given natural impurities and the long time scaleof mineral equilibrium. Creating artificial equilibrium modelscan be successful, however, because the time scale of simplesurface reactions [minutes (23)] is short as compared to thetime scale of electroremediation. Three different models ofthe soil behavior were constructed for use in numericalsimulations of the electroremediation experiments. Thesemodels are briefly introduced below, then included in theelectroremediation model, and compared to the data in thesection Applications of Models to Experiments.

When soil chemistry is ignored here, the species presentare H+, OH-, Na+, and Cl-; the chemical reactions are waterelectrolysis at the electrodes and water equilibrium through-out the liquid. Soil chemistry is included by introducingadditional species and chemical equilibrium reactions to the

FIGURE 5. Experimental titration of kaolin clay showing bufferingto both acid and base relative to a solution with no weak acid/basebehavior. There is little difference between the constant and varyingionic strength paths.

FIGURE 6. Clay chemical effect in titrations in terms of measuredchange in pore liquid concentration. Significant consumption ofsodium (a) and release of aluminum (b) are seen.

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

A partitioning function fit to the measured pH response wasused. This was taken to be a function of pH only because thenext likely dependence, ionic strength, was seen to have littleeffect in the titration.

The acid/base site model is designed to match theexperimental pH response, but it ignores the ion consumptionand release seen in Figure 6. Ion exchange behavior is wellknown in the field of soil chemistry (see, e.g, ref 24) and canbe used to reproduce the cation consumption and releaseseen in the elemental analysis results. Since Na+ was themajor participant, a sodium ion exchange site is used in thesecond soil chemistry model, giving buffering reactions:

Since 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 madeto fit the data in Figure 5.

The third soil chemistry model reproduces the experi-mental aluminum release by including solid aluminumhydroxide Al(OH)3(s) as a species. This is largely insolubleat zero alkalinity, but dissolves when sufficient amounts ofacid or base is added, which approximates the aluminumrelease seen in Figure 6. The initial amount of Al(OH)3(s)was based on the maximum dissolved concentration, andliterature values (23) were used for the solubility product andequilibrium 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 experi-mental titration pH.

Applications of Models to ExperimentsSimulations of electroremediation transport were run withoutsoil chemistry and with soil chemistry models based on acid/base, ion exchange, and ion exchange/aluminum hydroxidereactions. The first set of simulations focused on soilchemistry effects, and the experimental electroosmoticpermeability (Figure 2) was used as an input. All otherparameters were either determined from independent ex-periments or calculated in the simulation. Even using theexperimental electroosmotic permeability, the transportproblem is quite complex: the concentration and electricpotential distributions must be found, and the migration,diffusion, and chemical reactions of all species must bedetermined as they progress in time and space. The soilsurface species (XH, XNa, X-, XH2

+) and solid aluminumhydroxide are immobile, but their local concentrations changeas the transport shifts the mobile species in solution.Comparisons between the simulations and experiments canbe made in terms of the medium’s local and averageconductivity.

Figure 7 shows the average conductivity for experimentsand simulations with experimental mass flow as an input.The average conductivity involves a spatial integration of thespecies’ concentrations throughout the medium (related toeq 2), and the poor agreement in Figure 7a shows that thecomposition of the pore solution was not properly predicted.

Better results were obtained using the acid/base and cationexchange soil models, but close agreement in both conduc-tivity and voltage distribution required the ion exchange/aluminum hydroxide model (Figure 7b).

In the next set of simulations, the single “free” input tothe runs abovesthe electroosmotic flow velocityswas nolonger specified. Instead, the empirical surface potentialmodel shown in Figure 4 was used in determining the localcontributions to the electroosmotic flow. The inputs wereall determined from independent experiments, and theevolution in time was determined solely by the numericalmodel. Figure 8 shows the flow rate as average electroosmoticpermeability for experiments and simulations with andwithout soil chemistry. As expected from the conductivityresults, the case ignoring soil chemistry (Figure 8a) could notpredict the electroosmotic flow accurately. The ion exchange/aluminum hydroxide model gave much better results (Figure8b), properly predicting initially negative flow (towards theanode), early increase, and subsequent plateaus. Once theflow models diverged significantly from the experiments, thespecies’ distributions became incorrect, and the simulatedsystem became unstable. The instability of the system andthe divergence of both experimental and modeled flow around1.7 days will be discussed below.

Because the empirical surface potential model (Figure 4)was a weak link in the analysis, another simulation based onLorenz’s data (22, 17) was run. This showed a faster initialincrease and higher plateau than seen in Figure 8b, but gavea similar overall behavior.

XH + Na+ + OH- ) X- + Na+ + H2O

XH + H+ + Cl- ) XH2+ + Cl-

(3)

XH + Na+ + OH- ) XNa + H2O

XNa + H+ + Cl- ) XH + Na+ + Cl-(4)

FIGURE 7. Average conductivity for experiments and (a) NaClsimulations (ignoring soil chemistry) and (b) ion exchange/aluminumhydroxide soil model simulations with experimental mass flow asan input. The poor agreement in panel a and the success in panelb shows that the medium chemistry is not properly predicted withoutincluding soil effects.

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DiscussionQuantitative prediction of electric and electroosmotic flow isimportant in improving understanding of the processesoccurring during electroremediation. One phenomenon thatcan now be better understood is electroosmotic flow initiation.Negative initial flow is not surprising given that (1) theelectroosmotic permeability can be negative (ú > 0) for acidicpH and (2) clays that give an acidic initial pH are often usedin laboratory work. The more interesting question is why theflow becomes positive. The answer is that the local electricfield becomes large precisely where the local electroosmoticpermeability takes on significantly positive values, increasingthe flow toward the cathode. This is predicted even in theabsence of soil chemistry because the NaOH solutions formednear the cathode have lower electrical conductivity (hencehigher field) than the HCl solutions created near the anode.A related but more pronounced effect is seen when the propersoil chemistry model is included; the soil reaction near thecathode (eq 4a) removes charged species and markedlydecreases the local conductivity. This makes a small regionnear the cathode (10-20% of the total medium length) developa large electric field and provide virtually all of the positivepumping power.

Another phenomenon, at least as important as electroos-motic flow initiation, is the electroosmotic flow cessationseen in this and other experimental work. The most prevalentqualitative explanation of flow cessation involves the soil’spoint of zero net charge (pH0, where ú ) 0). However, it is

unlikely that the pH in the high field region is exactly pH0.Although the model here did not predict flow cessation, thereason for its occurrence is indicated. The fundamentalphysicochemical effect is the development of large Debyelength (thickness of the charged liquid layer adjacent to thecharged clay solid) in the low ionic strength region formednear the cathode. An analysis with simplified geometry (25)shows that the electroosmotic permeability (e.g., eq 1) shouldbe multiplied by a factor that decreases as ionic strengthdecreases; 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 inFigure 8b. Eykholt (16) includes this effect, but does notemphasize the results. Simplified corrections involve as-sumptions that are not quantitatively applicable to oursituation, but the general concept applies. Low ionic strengthis related to low conductivity and the high field region, whichmeans that most of the electrical effort is being applied rightwhere it is least effective. Also, there is a positive feedbackeffect where the low ionic strength causes higher electric fieldstrength, which accelerates the deionization. The resultingattenuation factor could reduce the flow by orders ofmagnitude. One important conclusion is that significant flowreductions or cessation can occur even in soils that do notshow zero ú-potential behavior.

The low ionic strength region explains two types ofdeviations seen at about 1.7 days in Figure 8b: (1) theexperimental cases’ flow rates diverge because the baseaddition begins to have an effect on the low ionic strengthregion, and (2) the simulations diverge from the experimentsbecause the model does not take into account the electroos-motic attenuation. The absence of this attenuation also leadsto the instability seen in the simulations.

There is also a mechanical effect that may play a role insome observations: the high field region creates low pressureand large pressure gradients, which may not be supportablein some apparatuses. Low pressures can cause external leaks,and large pressure gradients can cause “short-circuiting” ofthe liquid flow along the walls of a test cell. The apparatusand in-cell consolidation used here prevented these effectsfrom being a problem, but they are likely to occur in othersetups. Low pressures and high pressure gradients in thefield may cause uneven velocity distributions and channeling,which could lead to expending energy to move liquid that isnot contaminated. Low pressure could also cause evolutionof dissolved gases, breaking the ionic conductance in theregion.

Electromigration is an important transport mechanism inelectroremediation whether electroosmosis is the primarymechanism for contaminant removal or not. The changesin conductivity seen here are driven mainly by electromi-gration transport, with shifting of the distributions fromconvective movement. The early drop in overall conductivityseen in Figure 7 is a result of the local conductivity changesdiscussed above; the drop occurs before there is muchconvective displacement. The low ionic strength region canalso form when there is no flow of the pore liquid at all, withundesirable effects for electromigration transport itself. First,the region is often associated with a jump in pH that canchange the sign of the charge of the dominant heavy metalspecies, leading to focusing of the contaminant within themedium (1, 10). Second, low conductivity means that theoverall movement of ions by electromigration is slow, so theremediation process would take a great deal of time.

Controlling the system chemistry is the key to avoidingthe formation of the low ionic strength region. The regionresults from reactions that eliminate charge-carrying ions(e.g., H+ and OH-), in interactions either with each other orwith the soil surface. By substituting less reactive species atone or both electrodes, this eliminating reaction is avoided.The resulting electric field distribution is more even, and flow

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

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of charge and mass are maintained. If soil chemistry isnegligible, the added ions can have an immediate and strongeffect, as is seen in Figure 7a. If the soil participatessignificantly in reactions with the background electrolyte orthe added chemicals, the effect of the chemical addition maybe delayed, as shown in Figure 7b.

Some soil types (e.g., sandy ones) may have little chemicalcapacitance compared to the background electrolyte andcould be ignored in the system’s chemical model; others (e.g.,those rich in humates, clays with smaller particle size) mayhave significantly higher reactive concentrations than thoseobserved here. The specific soil model constructed here willnot be applicable for most natural soils, but the frameworkand procedure used will be useful and should be included aspart of site characterization and process design. It isimportant to first understand how electroremediation couldchange the chemical composition throughout the mediumand then explore the soil response to the expected changesin batch experiments. A simplified soil chemistry model canthen be constructed and used to further refine the chemicalcontrol and process design.

AcknowledgmentsThe author thanks R. F. Probstein and R. E. Hicks for theirguidance during the time this work was performed. Financialsupport was provided in part by the Office of Science andTechnology within the U.S. Department of Energy’s Office ofEnvironmental Restoration and Waste Management underthe Contaminant Plumes Containment and RemediationFocus Area, by the U.S. Environmental Protection AgencyNortheast Hazardous Substance Research Center at NewJersey Institute of Technology, and by MIT’s John HennessyFellowship for Environmental Studies.

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Received for review August 16, 1996. Revised manuscriptreceived November 22, 1996. Accepted December 6, 1996.X

ES960707E

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

VOL. 31, NO. 4, 1997 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 1197