A Field-Validated Model for In Situ Transport of Polymer-StabilizednZVI and Implications for Subsurface InjectionMagdalena M. Krol,† Andrew J. Oleniuk,‡ Chris M. Kocur,† Brent E. Sleep,§ Peter Bennett,∥
Zhong Xiong,⊥ and Denis M. O’Carroll†,*†Civil and Environmental Engineering, The University of Western Ontario, London, Ontario, Canada‡Golder Associates Ltd., 33 Alderney Drive, Suite 460, Dartmouth, Nova Scotia, Canada§Civil Engineering, University of Toronto, Toronto, Ontario, Canada∥Haley & Aldrich, 1956 Webster Street, Suite 450, Oakland, California 94612, United States⊥AMEC Environment & Infrastructure, 121 Innovation Drive, Suite 200, Irvine, California 92617, United States
*S Supporting Information
ABSTRACT: Nanoscale zerovalent iron (nZVI) particles have significant potential to remediatecontaminated source zones. However, the transport of these particles through porous media is notwell understood, especially at the field scale. This paper describes the simulation of a field injection ofcarboxylmethyl cellulose (CMC) stabilized nZVI using a 3D compositional simulator, modified toinclude colloidal filtration theory (CFT). The model includes composition dependent viscosity andspatially and temporally variable velocity, appropriate for the simulation of push−pull tests (PPTs)with CMC stabilized nZVI. Using only attachment efficiency as a fitting parameter, model resultswere in good agreement with field observations when spatially variable viscosity effects on collisionefficiency were included in the transport modeling. This implies that CFT-modified transportequations can be used to simulate stabilized nZVI field transport. Model results show that an increasein solution viscosity, resulting from injection of CMC stabilized nZVI suspension, affects nZVImobility by decreasing attachment as well as changing the hydraulics of the system. This effect isespecially noticeable with intermittent pumping during PPTs. Results from this study suggest thatcareful consideration of nZVI suspension formulation is important for optimal delivery of nZVI which can be facilitated with theuse of a compositional simulator.
■ INTRODUCTION
Groundwater pollution by hazardous industrial chemicals, suchas chlorinated solvents, is a serious problem worldwide.Although considerable advances in the understanding of thephenomena governing groundwater remediation have beenmade, most solutions are still not tailored to source zoneremoval. Consequently, there is a need for development andpilot scale testing of new and innovative remediationtechnologies capable of effectively treating source zonecontaminants.Nanotechnology is an emerging industry with promise for
application to groundwater remediation. Of particular interestto the remediation community is nanoscale zerovalent iron(nZVI) that is capable of reducing chlorinated contaminants,polychlorinated biphenyls (PCBs), and immobilizing metals inthe subsurface (e.g., refs 1−11). Similar to microscale ZVI,originally used in permeable reactive barriers (PRB), nZVI actsas a reductant converting chlorinated contaminants to nontoxiccompounds.1,2,6 However, unlike PRBs, where groundwatercontaminants react with the iron as they flow through thebarriers,12−14 nZVI can be directly injected into source zonesreducing contaminant mass or be transported with thegroundwater to reach contaminated zones. Additionally, sincenZVI has a higher specific surface area than microscale ZVI, it
produces higher reaction rates6,15 and has been recentlyexplored by several groups as a novel remediation option(e.g., refs 6,11,16−21)Although numerous laboratory studies have shown the
effectiveness of nZVI to degrade chlorinated compounds (e.g.,refs 1,3,5, and 6), most lab and field applications have sufferedfrom poor nZVI mobility.19,22−25 This is due to nZVI particlesaggregating quickly, resulting in most of the mass beingdeposited near the injection location. Since the efficiency ofnZVI is directly dependent on the ability to get it tocontaminated areas, aggregation can pose a significant obstacleto remediation applications. To overcome this problem, nZVIparticles can be coated with polymers making them more stablein suspension.19,21,26−30 Polymer coatings provide electro-staticand steric repulsion that counteract the magnetic and van derWaals forces between nanoparticles resulting in well dispersedsuspensions.28,31−34 Carboxylmethyl cellulose (CMC) is acommon stabilizer at field installations. Laboratory studieshave shown that CMC-coated nZVI particles are much more
Received: October 11, 2012Revised: February 26, 2013Accepted: May 31, 2013Published: May 31, 2013
mobile in column experiments than bare nZVI.21,27,35 However,since laboratory tests do not capture the complexities andheterogeneities present at real contaminated sites, field scaletesting is required to realistically assess the effectiveness ofthese coated particles.Numerous nZVI field trials have been performed with
varying success (e.g., refs 16,34,36−40). Early studies usingunstabilized nZVI resulted in poor mobility and well cloggingdue to particle aggregation and settling.16,23,37 More recently,Bennett et al.41 performed a series of push−pull tests (PPTs) toassess CMC coated nZVI mobility and reactivity, whereas He etal.26 completed a pilot study consisting of gravity-fed andpressurized injection of nZVI into the subsurface. Both siteswere contaminated with chlorinated compounds and rapiddechlorination was observed upon nZVI injection. However,nZVI mobility varies depending on soil characteristics, testoperations, and injection velocities, which makes nZVItransport in future field studies difficult to predict.The ability to predict the effectiveness of a remediation
strategy is a prerequisite for effective remediation design.Models can be valuable tools for site remediation practitionersin determining appropriate remedial alternatives, helping tominimize time needed for treatability studies and allowing forassessment of various remediation alternatives. To date,modeling of nZVI movement through porous media has beenlimited to simulation of lab scale experiments, using colloidalfiltration theory (CFT).26,43−45 CFT describes the attachmentof colloid-sized particles due to Brownian diffusion, inter-ception, and gravitational sedimentation43,46 and has been usedto quantify column transport parameters (e.g., attachmentefficiency and travel distances).18,26,44,45,47
Currently, no published modeling study has involvedsimulation of field-scale nZVI transport. In this study a three-dimensional numerical model, modified to incorporate nZVItransport described by CFT, was used to predict the movementof CMC coated nZVI particles at the field scale. In addition,since CMC solutions have viscosities higher than water, themodel was used to assess the importance of including variableviscosity when simulating nZVI field injections. The field test ofBennett et al.41 was used to validate the model by simulatingtracer and nZVI transport and recovery. This study representsthe first validation of CFT for modeling field scale transport ofCMC stabilized nZVI (CMC-nZVI). Additionally, the validatedmodel was used to assess nZVI transport and aquifer hydraulicsdue to the injection of a viscous nZVI suspension under a rangeof field relevant conditions.
■ MATERIALS AND METHODS
Field Test. A field scale test of CMC-nZVI injection wascompleted in the spring of 2006 by Bennett et al.41 Site and testdetails are available in Bennett et al.41 while a brief overviewfollows. The site was located near San Francisco Bay, CA andconsisted of a layered alluvial deposit. Silts and clays layeredwith coarse-grained sediments made up the site’s geology, withthe coarse-grained sediments representing the primary water-bearing zones. The field test was performed in an area overlyinga dissolved TCE plume. In this area, three coarse sand zoneswere bounded by less permeable, clayey soils (Figure 1). Thefirst and third layers were characterized as poorly graded sandwhile the middle layer was classified as a silty sand (seeSupporting Information (SI) for MIP log results).48 The firstcoarse-grained layer was confined and located approximately 1
m below ground surface. Groundwater pore velocity rangedfrom 0.3 m/day to 1.5 m/day.41
Three PPTs were performed at different depths, correspond-ing to different water-bearing zones (PPT-1, PPT-2, and PPT-3), as shown in Figure 1. A multilevel injection well wasinstalled for injection and withdrawal of fluid during the tests.For each PPT, CMC-nZVI particles were prepared on site andinjected into the corresponding aquifer, along with aconservative tracer (NaBr).41 Injection and extraction followeda different pumping schedule for each layer (Table 1). Both
PPT-1 and PPT-2 had periods of inactivity (lag period) whereno injection or extraction was performed, while PPT-3 waspumped continuously. Extracted volumes used to comparesimulated and field results were calculated using appropriateextraction test rates and the elapsed time between samplingevents.
Numerical Approach. The field test was simulated usingCompSim, a three-dimensional, three-phase, finite differencemodel capable of predicting subsurface contaminant migra-tion.49 This model has been used in a variety of laboratory andfield scale applications including pump and treat remediation,hot water flooding, steam flooding, and bioremediation.50−53
To simulate nanoparticle transport, equations based on CFTwere incorporated into CompSim using an attachment ratecoefficient (Katt) which serves as a first order removal term inthe transport equation:
Figure 1. Site Layout.
Table 1. Push-Pull Test Injection/Extraction Schedulea41
PPT-1 inject for 0.6 h at 3.4 L/minno injection/extraction for 13.0 hextract for 3.4 h at 3.4 L/min
PPT-2 inject for 1.6 h at 1.2 L/minextract for 1.9 h at 0.7 L/minno injection/extraction for 13.4 h (pump failed)extract for 8.1 h at 0.7 L/min
PPT-3 inject for 2.1 h at 2.6 L/minextract for 6.0 h at 2.1 L/min
aInjection and extraction rates are averages as reported by Bennett etal.41. Each well was fully screened and packed off over each waterbearing layer.41
where C is the aqueous phase nZVI concentration (moles·L−1),D is the hydrodynamic dispersion tensor (m2·s−1), q ⃗ is theDarcy velocity (m·s−1), and n is porosity (−). The attachmentrate coefficient (s−1) represents the process of colloid filtration,and is defined as
αη= −K
nd
v3(1 )
2attc
0 p(2)
where dc is the collector grain size (diameter) (m), vp is thepore water velocity (νp = q ⃗/n) (m·s−1), α is the attachment orsticking efficiency (−) defined as the ratio of immobilizednanoparticles on the collector per nanoparticle collision, and η0is the theoretical collision efficiency or the single collectorcontact efficiency (−) defined as the ratio of particles strikingthe collector to those approaching the collector. η0 wascalculated using dimensionless parameters associated with theeffects of diffusion, interception, and gravity, using therelationships developed by Tufenkji and Elimelech.43 Thisequation does not take into account particle detachment oraggregation. Aggregation is an important mechanism forunstabilized particles28 while detachment has been shown tobe insignificant in similar studies.26,54
Since the three sand layers were separated by lowconductivity clay (Figure 1), they were considered to beindividual water bearing zones and were modeled separatelywith clay bounding each sand layer. For each PPT, thecorresponding simulation domain was divided into threevertical soil layers (clay−sand−clay) with a total of 11 verticalblocks (3 for clay, 5 for sand, 3 for clay), and 41 blocks in bothlateral and transverse directions with a discretization of 0.09756m in the x and y direction. Discretization in the z directionvaried between 0.144 and 0.448 m, depending on the depth ofthe layer. The top and bottom of the domain were assumed tobe no flow boundaries while hydraulic head values wereprescribed (constant head boundaries) at the left and rightboundaries of the modeled domain, to produce the appropriateregional groundwater velocities (ranging from 0.3 to 1.5 m/day).41 The injection/extraction well was modeled as a source/sink. All sand and clay layers were modeled as isotropic andhomogeneous, and dispersivity was used as the fittingparameter for the bromide transport. Model and siteparameters are shown in Table 2 and are consistent withthose quantified in the field.CMC was modeled as a conservative species as it was added
in excess in solution. Therefore, both nZVI and the polymerwere modeled as separate constituents and their movement wassimultaneously simulated according to the transport equation(eq 1). Several viscosity measurements using CMC (90K) atdifferent polymer fractions were performed in the lab (thedetails of which can be found in the SI). The Grunberg andNissan equation55 (eq 3) was used to fit the lab viscosities byminimizing the root-mean-square error (RMSE) betweencalculated and measured viscosities, using the polymer viscosity(μpoly) as the fitting parameter (SI Figure S1). The resultingsolution viscosities are summarized in Table 2.
μ μ μ= +x xln( ) ln( ) ln( )sol poly poly water water (3)
During the PPTs, injection of CMC led to variations insubsurface viscosities from that of water (1 cP). Equation 3 was
used to calculate this spatially and temporally changingviscosity.
■ RESULTS AND DISCUSSIONTracer Test. In the PPTs conducted by Bennett et al.41 the
nZVI and tracer (bromide) concentrations were measured atthe injection/extraction well. The simulated tracer results aswell as the tracer mass recovered at the PPT well werecompared to the field results (Figure 2 and Table 3). All thesimulations were run with two regional groundwater velocities(simulated by imposing a regional hydraulic head gradient withconstant head boundary conditions) representing the estimatedupper and lower bound of the regional groundwater flow (0.3and 1.5 m/d). The recovered mass calculated by the model atthe two regional groundwater flows (Table 3) bracketed themeasured mass recovery for the first two tests. In PPT-1 andPPT-2, the tracer moved with the regional velocity during thelag periods. Thus, during these periods there was continuingtracer transport, mixing, and dilution from regional ground-water flow, resulting in lowering of bromide concentrations atthe PPT well following the onset (PPT-1) or resumption(PPT-2) of extraction. The higher velocity (1.5 m/day)produced a greater reduction in bromide concentration, asexpected. The simulated tracer extraction curves (Figure 2(a),Figure 2(c), and Figure 2(e)) for the two regional groundwatervelocities bracket the measured extraction curves. The highervelocity results fit PPT-1 data better while the PPT-2 data wasbetter matched by the lower regional velocity through themiddle layer. This is consistent with the soil classification of thelayers, where PPT-1 and PPT-3 layers were characterized aspoorly graded sand, and PPT-2 as a silty sand and thereforewith a lower hydraulic conductivity.41,48 The PPT-3 simulationswere the least affected by the regional groundwater velocity dueto the lack of a lag period between injection and extraction(PPT-3 was continuously pumped).Close agreement between CompSim simulations and
observed behavior suggests that conceptual model assumptionsare appropriate: the aquifer soil can be modeled ashomogeneous and isotropic and all sand layers were distinctwater bearing zones with no flow between layers.
nZVI Transport. Similarly to the tracer simulations, nZVItransport was simulated with two different regional ground-water velocities and α was used as the fitting parameter. Thebest fit was determined by minimizing the RMSE between
observed and simulated nZVI concentrations. The nZVIaqueous concentrations for the three tests were reported withrespect to extracted volumes, and are presented in Figure 2(b),(d), and (f), respectively, while Table 3 presents the recoveredmass and α values for all three tests (also presented in SI FigureS2). The nZVI colloid concentration, which is the combinationof an nZVI core and an oxide shell, was assumed to be equal tothe total iron concentration measured in the field. Thecalibrated α values were assumed to be temporally and spatiallyconstant for simulation of field tests that had changinginjection/extraction rates as well as lag periods. In contrast,η0 was a function of the spatially variable viscosities andvelocities, as described by the Tufenkji and Elimelech43 model.The lowest nZVI recovery (observed and modeled)
corresponded to PPT-1 (Table 3) where a significant lag of13 h occurred between injection and extraction (Table 1).During the lag period, nZVI particles moved away from theinjection/extraction well with regional groundwater flow andattached onto the soil grains, leading to low recovery onceextraction started (SI Figure S3). PPT-1 also had the lowesttracer recovery (Table 3) but the tracer recovery for all threetests was still on the same order of magnitude, unlike nZVIrecovery. This implies that under these conditions, withvelocities equal to the regional groundwater velocity, it wasnot just nZVI movement away from the well that led to the lownZVI recovery but also significant nZVI attachment onto thesoil grains during the lag period (SI Figure S3). This wasconfirmed by calculating the amount of nZVI attached duringdifferent phases of the test. According to the model, only 9% ofthe injected nZVI was attached onto soil grains after injectionbut this amount rose to 90% after the lag period, leaving only10% of nZVI in suspension for extraction. This led to low nZVIrecovery for PPT-1. For PPT-2, 39% of the total mass wasattached after injection and 70% after the first extraction (where15% of mass was extracted), leaving only 15% in suspension.During the lag phase, virtually all of the remaining nZVI wasattached leaving no nZVI in suspension when the secondextraction occurred. This is seen in Figure 2(d) where nZVIrecovery drops to zero after the lag period. PPT-3 had 21% ofmass attached after injection, leaving 79% in suspension forextraction, resulting in much higher nZVI recovery for PPT-3as compared to both PPT-1 and 2. In all cases, very little mass(<1%) was left in suspension after the final extraction period(SI Figure S4). This is also shown in SI Figure S3 which depicts
the attached nZVI mass in the middle of the PPT layersfollowing injection and extraction phases.These simulations show that the nZVI mobility is highly
dependent on approach velocity (vp). In the case of PPT-3 andPPT-2 where extraction occurred right after injection, thehigher injection rate (PPT-3), and therefore higher vp resultedin higher recovery, as observed by others.26,45,57−59 When a lagbetween injection and extraction occurred and vp was equal tothe regional groundwater velocity, the resulting nZVI attach-ment was significantly higher as predicted by CFT, leaving littlemass in suspension. This indicates that constant injection ofnZVI may be better suited for nZVI applications where nZVIneeds to travel far enough to reach the contaminant, whereasinjections with lag periods would be beneficial for remediationscenarios where nZVI emplacement into a source zone is thegoal.The fitted α values for PPT-1 were higher than the other two
tests which had lower injection solution viscosities (Table 2 andTable 3). The sensitivity to α values on the resulting nZVIrecovery is shown in Figure 3 (varied ±50% for vp = 1.5 m/day). For PPT-1, an increase in α resulted in a decrease ininitial nZVI concentration (Figure 3(a)). The effect of α onPPT-2 was only apparent during the initial part of the test,before the lag period of 13.4 h, whereas in PPT-3, the initialnZVI concentration was affected (akin to PPT-1) (not shown).Values for α for various lab scale transport studies of polymer
coated nZVI are given in the SI (Table S1). The only directcomparison of attachment efficiencies can be made with He etal.26 and Raychoudhury et al.60 who used the same nZVIsynthesis methods and the same coating (CMC 90K). Thecalibrated range of α in this study was larger than that reportedby He et al.26 However, He et al.26 used a hydrodynamicparticle diameter (dp) of 18 nm when calculating α. Higherparticle diameters have been reported for uncoated nZVIparticles6,11,16 and given that the particles in this study wereCMC modified and some amount of aggregation would beexpected in the field, a higher dp was assumed.
21 Using a largerdp leads to smaller η0 values43 resulting in larger fitted α.Raychoudhury et al.60 reported higher α values than thosereported in this study. However,60 did not account for thesolution viscosity and used a higher pore water velocity thanthe groundwater velocity observed in the field. Both thesefactors would change the α obtained.
Figure 3. Effect of attachment efficiency (α) on nZVI recovery.
Field Implications. Typical CMC solutions consist of apolymer that has a molecular weight of 90 000 g/mol (CMC90K) or 250 000 g/mol (CMC 250K). Due to the highmolecular weight, the viscosity of the nZVI/polymer solutionwill be greater than that of water and therefore as thesuspension is injected, the fluid viscosity surrounding the wellincreases. As greater amounts of nZVI are injected, the zone ofhigher viscosity increases radially outward. The higher viscosityinjection solution can affect both nZVI attachment as well asthe subsurface flow field.An increase in fluid viscosity will decrease η0 leading to a
decrease in attachment rate (SI Figure S5) and consequently, toan increase in nZVI aqueous concentration. This change in η0 isdue to decreases in Brownian diffusion with increasedviscosity.43 The dependency of η0 on viscosity and approachvelocity will result in a non uniform η0 distribution, and therebyattachment that varies radially out from the well. The effect ofviscosity on η0 and corresponding nZVI concentration can beobserved in Figure 2(f) where the nZVI concentration is lowerthan the observed concentration when simulated with auniform viscosity and the same attachment coefficient. Thischange can be observed even when the injection solutionviscosity of PPT-3 was 2 cP. The tracer results (Figure 2(e))show that for this PPT, the flow field is not affected by theinjected solution viscosity due to the constant injection andextraction at the well (no lag period). Therefore, if uniformviscosity is used to model this scenario, a reasonable fit to theobserved data could be obtained by using α as the fittingparameter (Figure 2(f)). However the α value fitted withuniform viscosity would be lower than that with non uniformviscosity.The injection viscosity can also affect the hydraulics of the
system. The effect will depend on the type of injectionperformed as well as the pumping schedule of the test. ForPPT-1, where a significant lag in pumping occurred (Table 1),the tracer concentration could not be correctly predicted ifuniform viscosity was assumed (Figure 2(a)). This is due to thechanged flow field after injection of viscous fluid into thesubsurface. Once pumping stops, the viscous plume is bypassedby the regional groundwater, reducing nZVI and tracertransport and allowing for more nZVI and tracer to beextracted once pumping restarts. If uniform viscosity isassumed, the tracer and nZVI would travel further from thewell resulting in lower extracted concentrations as seen inFigure 2(a). In this case, neither tracer nor nZVI transportcould be accurately predicted using a uniform viscosity, even
with different fitted α values for nZVI (Figure 2(b)). It isanticipated that predictive capabilities of numerical simulatorswould become poorer as suspension viscosity increased, ifuniform viscosity was assumed.The effect of the injection solution viscosity will also differ
depending on the type of field injection performed. Injection offluids into the subsurface whether for pump tests orremediation purposes are typically performed in two ways:constant head (CH) or constant flux (CF) injections.61,62 CFinjections use a constant volumetric flow rate to inject water (orremediant) into the subsurface, while CH injections specify aconstant head or pressure at the well and the fluid is applied tothe subsurface via gravity feed. CF injections were used byBennett et al.41 in this field trial while others have injectednZVI into the subsurface via CH injections.16,36,37,39,42
Subsurface application of nZVI is governed by Darcy’s Law:
ρμ
=qk g h
rdd (4a)
∫ μρ
= −h hq
k grdr
r
I0 (4b)
where q is the Darcy velocity, k is the soil permeability, ρ is thefluid density (assumed to be constant), g is the gravity, μ is thefluid viscosity, hI is the hydraulic head at the inlet (well), r is theradial distance from the well, and hr is the hydraulic head atdistance r.With a CH injection, the change in head (dh, eq 4a) remains
constant with time, however viscosity changes spatially andtherefore the Darcy velocity decreases with time. For the CFcase, Darcy velocity remains constant since the flux iscontrolled by the injection rate. However, the increase ofviscosity will lead to an increase in inlet head with time (eq 4b).To examine the implication of both injection methods on
nZVI transport, the PPT-1 domain was used to simulate theinjection of CMC-nZVI suspension using CH and CF methodsSI Table S2. For CH, a constant hydraulic head was prescribedat the injection well. The value of the hydraulic head waschosen so that the initial injection rate was equivalent to thePPT-1 injection rate at the beginning of the test (204 L/min,41). However, with prolonged CMC injection, significantreduction in Darcy velocity was observed with time. Forexample, the velocity at the well decreased from 8.8 × 10−5 m/sat 10 min to 4.1 × 10−5 m/s at 48 h leading to decreasinginjection rates (Figure 4(a)). This decrease in velocity was only
Figure 4. (a) Decrease in injection velocity during CH injections (b) Resulting hydraulic heads at the injection well during CF injection at differenttimes with injection viscosity = 13 cP (x = 2 represents location of injection/extraction well).
observed in this simulation and not in the field since CFmethods were used in the field test.These reductions in Darcy velocity will result in increased
attachment (Katt) with time and decreased nZVI mobility (eq2). Therefore, if CH injections are used with CMC-nZVIparticles, the decrease in velocity can lead to a decrease in nZVItransport both in terms of nZVI spread and delivery time. Thisis observed in Figure 5(a) and 5(b) where the nZVIconcentration in the middle of the PPT-1 layer is shown withtime for the two injection methods. This velocity effect onnZVI movement was examined by26 who found that reductionsin pore velocity in a sand column decreased the effluentnanoparticle concentration. Consequently, if a CH injection issimulated with a spatially constant viscosity distribution, thechange in velocity would not be captured leading tooverestimation of nZVI mobility.The constant velocity and lowered nZVI attachment are
advantages of using CF to deliver nZVI to the subsurface. UsingCF injection, nZVI travels further from the injection well and agreater fraction remains in suspension (Figure 5(b)). Forexample at 3 h 72% of the nZVI remains in suspension for theCF case whereas 65% remains in suspension for the CH caseafter 5 h (equivalent injected volume). However CF injectionswill increase the hydraulic head at the well with time (Figure4(b). For example, the simulated hydraulic head at the well (hI)rose from 1.3 at 10 min to 1.7 m at 20 h. This type of pressureincrease can lead to daylighting and cracking in the wellseal.62,63 Either of these outcomes will significantly decrease the
spread of nZVI and potentially render the injection wellunusable for future injections. In addition, it is desirable toinject nZVI quickly into the ground to reduce nZVI oxidationin the synthesis vessel.34 Therefore care needs to be taken sothat the injection pressure does not exceed the overburdenpressure at the well when injecting nZVI using CF methods.The application of nZVI stabilized in a higher viscosity
suspension (e.g., CMC 250K) has been investigated by severalauthors21,29,45 as the nZVI will be stable for longer periods oftime in the synthesis vessel and η0 is further reduced, improvingmobility. Given these benefits, higher viscosity nZVIsuspensions are likely to be applied in the field. However,although increasing fluid viscosity reduces η0, for CH injections,nZVI movement can be drastically reduced due to thedecreased velocity, diminishing the effect of reductions in η0as seen in Figure 5(c). Injecting a higher viscosity nZVIsolution with CF conditions (i.e., CMC 250K, measuredviscosity of 72 cP at an initial polymer fraction of 0.8 wt.% 54),would substantially increase the subsurface spread of nZVI(Figure 5(d)) due to the reductions in η0 (SI Figure S5),however the head at the well would significantly increase overthat of the lower viscosity injection. For the case of PPT-1simulated with CMC 250K, the head at the well would increaseto 7 m at 20 h (compared to 1.7 m with CMC 90K). Such ahead increase may not be suitable for shallow injections.Another consideration is the impact of viscosity on nZVIreactivity. Although not investigated in this study, it is possiblethat the contaminants will diffuse more slowly to the nZVI
Figure 5. nZVI concentration with time during CH (a and c) and CF (b and d) injection at different injection viscosities; x = 2 represents location ofinjection well.
particle surfaces if there is a very viscous polymer coating thesurface, decreasing reaction rates.34,64
■ ASSOCIATED CONTENT*S Supporting InformationThe membrane interface probe (MIP) log results, performedby AMEC Geomatrix; Viscosity measurements; Rescaled Figure2; Mass attached in PPTs with distance and time; ; Previousstudies on α values; Effect of fluid viscosity on η0; andSimulation parameters of Figure 5. This material is availablefree of charge via the Internet at http://pubs.acs.org/.
■ ACKNOWLEDGMENTSThis research was supported by the Natural Sciences andEngineering Research Council (NSERC) of Canada through aPost-Graduate Fellowship (PDF) to M.M.K., as well as NSERCand Ontario Graduate scholarships to A.J.O. Additional supporthas been provided by NSERC Strategic and CollaborativeResearch and Development Grants as well as the OntarioCentres of Excellence.
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