illite spatial distribution patterns dictate cr(vi...

Illite Spatial Distribution Patterns Dictate Cr(VI) SorptionMacrocapacity and MacrokineticsLi Wang† and Li Li*,†,‡,§

†John and Willie Leone Family Department of Energy and Mineral Engineering, ‡Earth and Environmental Systems Institute (EESI),and §EMS Energy Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, United States

*S Supporting Information

ABSTRACT: This work examines the largely unexplored role of illite spatialdistribution patterns in dictating the sorption of Cr(VI), a ubiquitously occurringcontaminant. Flow-through experiments were carried out at 0.6, 3.0, and 15.0 m/dayusing columns packed with the same illite and quartz mass however with different spatialpatterns and permeability contrasts. Column-scale sorption macrocapacity andmacrorates were found to decrease with transport connectivity, a quantitative measureof heterogeneity characteristics. At 0.6 and 3.0 m/day, well-connected low permeabilityillite zones oriented in the flow-parallel direction lead to diffusion-controlled masstransport limitation for accessing sorption sites. This results in up to 1.4 order ofmagnitude lower macrocapacity and macrorates compared to those in minimallyconnected columns with well-mixed illite and quartz. At 15.0 m/day, effects of spatialheterogeneities are less significant (up to a factor of 2.8) owing to the close to chemical kinetics-controlled condition. Althoughthe column-scale macrocapacity can reach full sorption capacity under low flow conditions, the macrorates are 10−1 to 10−3 of themicrorates measured in well-mixed reactors. Insights gained here bridge gaps between laboratory observations and fieldapplications and advance predictive understanding of reactive transport processes in the naturally heterogeneous subsurface.

■ INTRODUCTION

Chromium is a pervasive contaminant due to its extensiveindustrial usage and natural occurrence. Anionic Cr(VI) ishighly mobile and poses significant environmental risks.1−3

Cr(VI) sorption on clays is important in controlling itstransport and fate in natural environments.4−6 Extensive studieson Cr(VI) sorption have documented its strong dependence onsolution pH, metal concentration, ion strength, column length,and flow rate.1,7−11 These studies used well-mixed batchreactors and homogeneously packed columns, primarily aimingat understanding controls on Cr(VI) sorption and developinghigh performance adsobents using chemical modifications.Large discrepancies have been documented for sorption

dynamics in well-mixed batch reactors and those in the naturalsubsurface.10,12−16 Sorption occurs fast in well-mixed batchreactors, often reaching equilibrium within seconds tohours.9,17,18 Sorption “microkinetics” or microrates in thesesystems have been observed to undergo multiple stages, withearly fast sorption reflecting chemical kinetics followed by laterslower rates limited by intragrain diffusion.1,19−23 Sorption“macrokinetics” or macrorates observed in natural systems atlarge scales, however, have been documented to be muchslower with temporal scales between days and weeks.12,24−26

Stochastic analysis indicated that macrorates depend on flowand mass transport limitation imposed by natural systemcharacteristics.27−30

The natural subsurface typically exhibits spatial variations inphysical and geochemical properties at multiple scales.Reactivity variations at micrometer scales arise from different

mineral types and pore structures.31,32 Soils are characterized bydifferent horizons with distinct properties.33 Low-permeabilityclay lenses commonly occur in soils, aquifers, and rocks.34,35

Different lithofacies are characterized by orders of magnitudedifference in permeability and (bio)geochemical properties,leading to uneven distribution of water, flow, and chem-icals.25,36−39 In this work, we define “spatial distributionpattern”, or spatial heterogeneities, as the spatial variations inboth physical (porosity and permeability) and geochemicalproperties (sorption capacity). Characteristics of spatialheterogeneities have been quantified by a variety of differentmeasures, including the closely related correlation length andconnectivity.40,41 Connectivity has been used to quantifychanneling, preferential flow paths, and early solute arrival asflow bypasses low-permeability zones.42−45

The role of physical heterogeneity in controlling flow andsolute transport has been extensively studied in the pastdecades with the development of stochastic theory,46,47

continuous random walk (CTRW),48 fractional advective-dispersion equations (fADE),49 multirate model,50 mobile-immobile models,51,52 and dual-porosity models,53 amongothers.54 The impacts of chemical heterogeneities, especiallythe spatial distribution patterns of mineral type and abundance,however, have received much less attention until re-

Received: July 11, 2014Revised: November 25, 2014Accepted: January 5, 2015Published: January 5, 2015

Article

pubs.acs.org/est

© 2015 American Chemical Society 1374 DOI: 10.1021/es503230fEnviron. Sci. Technol. 2015, 49, 1374−1383

pubs.acs.org/est

http://dx.doi.org/10.1021/es503230f

cently.15,55−61 Spatial heterogeneities, especially low perme-ability clay zones, can impose a much longer diffusion lengththan the intragrain diffusion length observed under well-mixedconditions.26,29 Most existing work on heterogeneity effects onsorption used numerical experiments and stochastic analysis toexplore the extent of difference caused by chemicalheterogeneities. Stochastic analysis indicated the emergenceof macrokinetics in heterogeneous porous media under thelocal equilibrium assumption.27,28,62

A few recent experimental studies highlighted the importanceof spatial heterogeneities.63,64 For example, Liu et al. (2014)39

found that the distribution patterns of sediment grains leadingto preferential flow paths had much lower rates of U(VI)desorption than those with relatively homogeneous flow. Byand large, however, there is a significant lack of experimentaldata that document the role of spatial distribution patterns incontrolling sorption processes.15,26,65,66 As a result, an accurateprediction of reactive transport, natural attenuation, andremediation in heterogeneous natural subsurface is challenging.The objective here is 2-fold. One is to systematicallyunderstand how, to what extent, and under what conditionsspatial heterogeneities regulate Cr(VI) sorption macrocapacityand macrokinetics at the column scale; the other is to identifykey measures of spatial patterns that govern Cr(VI) sorption inheterogeneous media. Among various types of clays, illiteubiquitously occurs in the natural subsurface and was chosen asthe model clay.67,68 Illite is a 2:1 layer mineral containing one[Al2(OH)4]

2+ octahedral layer sandwiched between two(Si2O5)2 tetrahedral sheets and has a limited tendency forinterlayer swelling.68−70 It is important to note that althoughhexavalent chromium and illite are chosen as the modelcontaminant and clay here, the insights and principles learnedhere are applicable to sorption processes in general.

■ METHODOLOGYColumn Spatial Patterns and Property Measurement.

Columns with the same total mass of illite (9.4 ± 0.2 g, 10%volume fraction of the total solid volume) and quartz werepacked in different spatial patterns (Figure 1). The point ofzero charge for illite and quartz are approximately 5.6 and 2.8,respectively.71,72 Under the inlet pH of 4.0 in this work, illite isthe primary sorbing mineral while sorption on quartz isnegligible. The C and PC sets differ in their permeability ratiosof the illite and quartz zones κratio,Ill/Qtz and thereforepermeability variance by having different grain sizes (Table1). Both sets have M, FT1, and FP columns. The PC setincludes an additional FT2 column. The spatial patterns differin zonation orientations relative to main flow direction andcorrelation length. The FP columns have the longestlongitudinal relative correlation length λL of 1.0 calculated asthe characteristic length of the illite zone divided by the columnlength. In contrast, those of M columns are in the order of 10−3,calculated as the grain size (hundreds of microns) divided bythe column length. FT1 and FT2 have the same λL of 0.13,however differ in transverse relative correlation length λT (1.0and 0.5, respectively). A total of 21 columns were packed forthree flow velocities (0.6, 3.0, and 15.0 m/day). Measuredproperties, including surface area, porosity, permeability, anddispersivity are listed in Table 1. Measurement details aredocumented in the Supporting Information.Tracer Test for Dispersivity and Connectivity. Tracer

experiments were conducted using an inlet bromide concen-tration of 5.0 mg/L. Effluent samples were collected and

measured every 0.3 residence times for 3.5 residence times. Theone-dimensional advection-dispersion equation was solvedusing the reactive transport code CrunchFlow75 to quantifythe dispersivity assuming homogeneous columns:

∂∂

=∂∂

−∂∂

Ct

DC

xv

Cx

BrL

Br Br(1)

where CBr is the bromide concentration (mg/L), v is theaverage flow velocity (m/s), t is time (seconds), and DL is thelongitudinal dispersion coefficients (m2/s):

α= * +D D vL L (2)

ϕ* =D Dn0 (3)

where D* is the effective diffusion coefficient in porous media(m2/s), αL is the longitudinal dispersivity (m), ϕ is theporosity, n is the cementation factor with a value of 1.0, and D0is the molecular diffusion coefficient in water (1.0 × 10−9 m2/s).76 The αL values were obtained by reproducing bromidebreakthrough data and residual minimization using averageporosity and permeability, and therefore are effective values atthe column scale.Transport connectivity (CT95%) was calculated as the ratio of

t95% and τ, with the former being the time when the effluentbromide reaches 95% of the inlet concentration and the latterbeing the residence time calculated as the total pore volume ofthe column divided by overall flow rates. It compares the timefor 95% arrival to the average residence time and is aquantitative measure of spatial heterogeneities.41,45

Figure 1. Two dimensional cross-sectional schematics of the 7columns with different spatial distribution patterns of illite and quartz.Each set has the same total mass of illite and quartz. The illite-over-sand permeability ratio, κratio,Ill/Qtz, differ in the C and PC sets. The Ccolumns have illite and quartz grains with the same size range of 210−300 μm to minimize the effects of physical heterogeneity to the extentpossible and to primarily focus on the effects of chemicalheterogeneity. The PC columns have smaller illite grains (75−150μm) compared to that of quartz (350−420 μm) and therefore thecombined effects of physical and chemical heterogeneities. Within eachcolumn set, the mixed column (M) has illite uniformly distributedwithin the quartz matrix; the flow-transverse 1 zone column (FT1) hasillite in one horizontal layer in the direction perpendicular to the mainflow; the flow-parallel column (FP) has illite in one cylindrical zone inthe direction parallel to the main flow. The PC set has an additionalflow-transverse two-zone column (FT2) where illite is horizontallydistributed in two zones in the middle of the column, with one zone inthe upper left and the other in the lower right. Pictures of the columnsare shown in Figure S1 in the Supporting Information.

Environmental Science & Technology Article

DOI: 10.1021/es503230fEnviron. Sci. Technol. 2015, 49, 1374−1383

1375


Sorption Experiments in Batch Reactors for Micro-capacity and Microkinetics. Cr(VI) sorption in batchreactors were carried out to quantify the intrinsic microcapacityand microkinetics under well-mixed conditions. A Cr(VI)solution (1.5 mg/L) was prepared using K2CrO4 and abackground water with 0.01 mol/L NaCl to maintain relativelyconstant ionic strength.77,78 The pH was adjusted to 4.0 usingHCl. Illite grains (2.0 g) and 250 mL of Cr(VI) solution weremixed in a 500-ml conical flask and agitated on anelectromagnetic stirrer at 25 °C. Samples were taken out andfiltered periodically and measured using UV−vis spectroscopy(Thermo Fisher Scientific 333183) at 540 nm based on Cr(VI)reactions with diphenyl carbazide.11 Values of pH weremeasured immediately after sample collection. The dataindicated an equilibrium time of around 60 min, as will bediscussed later.Flowthrough Sorption Experiments for Macrocapac-

ity and Macrokinetics. All experiments were carried outusing a peristaltic pump (Cole Parmer 7535-08) with theinfluent solution (5.0 mg/L Cr(VI), 0.01 mol/L NaCl) flowingupward. The sorption reactions are listed in Table S1 in theSupporting Information. Before the experiment, the columnswere flushed at a high flow velocity of 20.0 m/day forapproximately 40 pore volumes to remove impurities and toensure a relatively similar starting point across columns.Effluent samples were taken every 2 pore volumes for a totalof 50 pore volumes. The three flow velocities, 0.6, 3.0, and 15.0m/day, are within the typical range of groundwater flowvelocity79 and correspond to residence times of 98.1, 19.6, and3.9 min, respectively. Note that they are either higher or lowerthan the equilibrium time of approximately 60 min determinedin batch reactors.

Quantification of Microcapacity and Microkinetics.Sorbed concentration Csb (mg/g illite) was calculated asfollows:

=−

CC C

MV

( )sb

0(4)

where C0 is the initial aqueous concentration (mg/L), C is theaqueous concentration during the experiment, V is the solutionvolume (L), and M is the total illite mass (g). The Csb,eq is thesorbed concentration at equilibrium, which defines the intrinsicsorption microcapacity in batch reactors. As will be discussedlater, Cr(VI) sorption exhibits two sorption stages with earlierhigh rates and later slower rates. The microkinetic ratesconstants kb1 and kb2 (mg/g/s) were obtained from the earlyand late slope of the cumulative sorbed Cr(VI) curves beforereaching the equilibrium concentration, respectively.

Quantification of Macrocapacity and Macrokinetics.For column experiments, the sorbed Cr(VI) concentration Csc(mg/g illite) was calculated as follows:

∫=

−⎡⎣⎢

⎤⎦⎥

CQ C t

M

dt C

CC

Csc

0 Cr,0i Br

Br,0

Cr

Cr,0

(5)

where Q is the flow rate (L/s); CBr and CBr,0 are the modeledeffluent and inlet Br concentrations (mg/L); CCr and CCr,0 arethe measured Cr(VI) effluent and inlet concentrations (mg/L).According to equation 5, Csc was obtained by integrating thedifference between the normalized bromide and Cr(VI)concentrations. That is because Cr(VI) concentration dependson both transport and sorption, while Br concentration onlyreflects transport. The difference between the two therefore

Table 1. Physical and Geochemical Properties of the Columns

aσln K2: natural logarithm permeability variance, a measure of the extent of permeability variation.73 bThe dash “−” indicates not applicable. cSSA:

measured BET Specific surface area. dTSA: total surface area. eλL: longitudinal correlation length, calculated as the ratio of illite zone thickness alongthe flow direction over the column length.74 fλT: transverse correlation length, calculated as the ratio of the illite zone thickness perpendicular to theflow direction over the column diameter. gkeff (average ± STD): effective permeability, calculated using Darcy’s law based on measured flow ratesand pressure gradient. hϕave (average ± STD): average porosity, calculated as the ratio of the water volume used during packing over the totalcolumn volume. The keff and ϕave were measured three times. iαL: longitudinal dispersivity.

jDL is the longitudinal dispersion coefficients at 0.6 m/day. kCT95% (transport connectivity) was calculated at three flow velocities; their average values and standard deviations are shown in the table.



1376


quantifies the sorbed mass. The Csc,m is the maximum sorbedconcentration in columns and therefore defines macrocapacity.Similar to sorption in batch reactors, sorption in columns alsoexhibit two distinct stages. The macrokinetic rate constants kc1and kc2 were similarly obtained from the early and later slope ofthe cumulative Csc evolution curves, respectively, as will bediscussed later in the Results and Discussion section.Comparison between Micro- and Macro-character-

istics. The ratio of the macrocapacity Csc,m over themicrocapacity Csb,eq, γc, is essentially the proportion ofaccessible sorption sites in columns. Similarly, the ratios ofthe macrorates from the columns and microrates from thebatch reactors, γk, quantify the rate difference between the twosystems:

γ γ= =C

C k,

kkc

sc,m

sb,eq

c

b (6)

Effects of Spatial Patterns. Comparison between zonationcolumns (FP, FT1, and FT2) and their corresponding mixedcolumn (M) quantifies the effects of illite distribution patternson sorption macrocapacity and macrokinetics. Here βc and βkare defined as follows:

β β= =C

C

k

k,c

sc,m,Z

sc,m,Mk

c,Z

c,M (7)

where Csc,m,Z and kc,Z are macrocapacity and macrorates ofzonation columns; Csc,m,M and kc,M are those for theircorresponding mixed columns. Large deviation of β valuesfrom one means large differences between zonation and mixedcolumns and therefore indicates the significance of spatialheterogeneities.

■ RESULTS AND DISCUSSION

Column Properties. The two column sets differ in bothphysical and geochemical properties, as shown in Table 1. Thesmaller grain illite in the PC set has smaller specific and totalsurface area, possibly due to aggregate formation within finematerials (<125 μm), as discussed in literature.80,81 Measuredeffective permeability of the C columns vary in the range of(8.61−11.80) × 10−13 m2, and those of the PC columns have alarger range of (1.32−10.68) × 10−13 m2. The κratio,Ilt/Qtz valuesfor the C and PC sets are 0.67 and 0.01, corresponding topermeability variance σln K

2 values of 0.03 (±0.01) and 1.85(±0.74), respectively. To put these numbers in perspective, theBordon site (Borden, Ontario) is known as relativelyhomogeneous with a low σln K

2 around 0.2082 and theMacrodispersion Experiment (MADE) site is considered highlyheterogeneous with a σln K

2 around 4.5.83 Therefore, the Ccolumns are essentially physically homogeneous while the PCcolumns are intermediately heterogeneous.

Dispersivity and Connectivity. For both column sets, thebromide breakthrough curves (BTCs) of the M and FT1columns overlap (Figure 2), indicating similar dispersivecharacteristics. The FPC column has an earlier breakthroughand a slightly longer tail (Figure 2A). The differences amongthe C columns are relatively small, with the outlet quicklyreaching 0.99C0 within 1.5 pore volumes (Figure 2A). The FPPC, and FT2PC columns, however, show much longer tails.Most of bromide broke through between 0.6 and 1.2 porevolumes, reaching about 0.80C0. After that, slow breakthroughcontinued with the outlet approaching 0.95C0 at about 2.5 porevolumes. These highly asymmetric BTCs and long tails arecommonly observed in porous media with large permeabilityvariances.45,66,84,85 As expected, the ADE equation reproduced

Figure 2. (A and B) Experimental and modeling output of the bromide breakthrough curves for the C columns (A, gray) and PC columns (B, black)at 0.6 m/day. Although not presented here, the Br breakthrough curves at 3.0 and 15.0 m/day are similar to curves shown here. Note that the PCcolumn has much longer tails than the C columns, which is characteristic of heterogeneous porous media with zones of distinct properties. (C) 1 −CBr/CBr,0 for C and PC columns in the logarithm scale. This curve allows better visualization of the breakthrough tails. Note that the effluentconcentrations drop to below 10−2C0 in the C columns within 1.5 pore volumes, while the PC columns show much longer tails, especially FP andFT2. (D) Longitudinal dispersivity αL positively correlates to the mean of CT95% values at three flow velocities for each column. The FPPC andFT2PC columns have much larger connectivity than other columns.



1377


the data well for all M and FT1 columns, however not for theFPPC, and FT2PC columns.Higher permeability contrast and long connected low

permeability zones lead to high connectivity. These mediatherefore have early breakthrough from short, highly permeablepaths and long tails from long, low permeability flow paths,generating wider BTCs and larger dispersivity. The oppositeoccurs in the less connected M and FT1 columns. The αL

values are much larger for FT2PC and FPPC and positivelycorrelate to connectivity (Figure 2D).84

Microcapacity and Microkinetics. During the batchexperiments, Cr(VI) aqueous concentrations decrease quicklyin the first 20 min and gradually reach a plateau at around 60min (Figure 3). The pH increases over time and reachesapproximately 4.4 and 4.2 for C and PC grains, respectively.The microcapacities are 0.103 and 0.056 mg/g, respectively,which are in the range of illite sorption capacity.5,87,88 Thekinetics indicates early fast, almost instantaneous sorption atexternal surface sites characterized by kb1 values at 1.26 × 10−4

and 4.8 × 10−5 mg/g/s for C and PC grains, respectively. Thesecond-stage microrates kb2 are 7.44 × 10−6 mg/g/s and 9.2 ×10−6 mg/g/s for the C and PC sets. The multistage sorptionbehavior has been discussed extensively.1,19−22 The early stagekinetics reflect the chemical kinetics without mass transportlimitation while the later rates are limited by intragrain diffusionat the grain scale of hundreds of micrometers.Macrocapacity and Macrokinetics: Effects of Spatial

Patterns and Permeability Variance. At 0.6 m/day, Cr(VI)breaks through much earlier from the PC columns than from

the C columns (Figure 4A), with the earliest from FPPC.Correspondingly, this column has the least sorbed Cr(VI) andthe lowest rate (Figure 4B), and effluent pH closest to the inletpH (Figure 4C) among all columns. Similar to observations inbatch reactors, sorption is fast early on and is slow later in allcolumns (Figure 4A and B). For MPC, Cr(VI) does not breakthrough until after 5 pore volumes. After that, Cr(VI) breaksthrough quickly, reaching about 80%C0 around 13 porevolumes, beyond which slow sorption continues. FT2PC fallsbetween these two extremes (M and FP). The C columnsbehave similarly to the PC columns, except that Cr(VI) breaksthrough much later than their corresponding PC columns. Thisis due to its smaller permeability contrast and larger total illitesurface area that is almost 2 times that of the PC set. The FT1columns in both sets behave similarly to the M columns asshown in Figure S2 in the Supporting Information.Although the kinetics are quite different, the macrocapacities

Csc,m for all C columns are close to the microcapacity (0.103mg/g). The macrocapacities of the PC columns are much lowerthan the microcapacity (0.056 mg/g) except that for MPC. ThepH increases in all columns (Figure 4C) because Cr(VI)sorption consumes H+, as shown in the reaction network(Table S1).89−91 The outlet pH of MC is higher than FPCbecause of its higher sorption rates early on, while thedifference becomes negligible at later times due to similar Csc,m.The outlet pH of MPC is much higher than that of other PCcolumns because of its higher macrocapacity.The fact that FT1 and M are similar and are different from

the FP columns indicates the importance of the zonation

Figure 3. Evolution of (A) Cr(VI) aqueous concentration normalized by initial concentration C0 (1.5 mg/L) and pH (insert) in batch experiments;(B) sorbed Cr(VI) concentration (mg/g illite). The illite specific surface area is 15.36 m2/g in the C set, almost 2 times that of the 8.67 m2/g in thePC set, leading to almost doubled microcapacity of the C set illite than that of the PC set. Note that the sorption occurs fast within the first 20 min,corresponding to the chemical kinetics for sorption on easily accessible, external sites (kb1). The later slow sorption reflects a rate limited by masstransport through intragrain diffusion (kb2). Values of kb1 and kb2 were obtained by drawing tangent lines and quantifying the slopes of Csb curve(panel B) in early and late stages before reaching equilibrium.

Figure 4. Temporal evolution of (A) Cr(VI) breakthrough curves normalized by inlet concentration CCr,0; (B) sorbed Cr(VI) concentration (mg/gillite), and (C) effluent pH for the C (gray) and PC columns (black). All data are for 0.6 m/day with a residence time of 98.1 min. Note that the totalillite surface area was 144.4 m2 in the C set, almost 2 times that of the 80.9 m2 in the PC set. The FT1 column data is not shown here because italmost overlaps with the M column. Data from all columns are shown in the Supporting Information.



1378


Figure 5. Evolving Cr(VI) effluent normalized concentrations (top) and cumulative sorption mass (bottom) at 3.0 (left) and 15.0 m/day (right).The residence times are 19.63 and 3.93 min, respectively.

Figure 6. Cr(VI) sorption parameters as a function of the transport connectivity (CT95%) under different flow conditions: (A) total sorbed massCsc,m; (B) early sorption rate kc1; and (C) late sorption rate kc2. For comparison the two microkinetic rate constants kb1 and kb2 are also shown herefor C (gray) and PC (black) as horizontal lines. (D−F) γ values comparing macro- to microvalues. Macrocapacities are within an order of magnitudelower than the microcapacities; macrorates are 1 to 3 orders of magnitude lower than the microrates. (G−I) β values comparing zonation columns totheir corresponding mixed columns. The impacts of spatial heterogeneities are within a factor of 2.0 under high flow condition (15 m/day) and canreach close to 2 orders of magnitude under low flow conditions (0.6 and 3.0 m/day). The dashed lines here are to help visually connect the symbolsof the same flow condition; however, they do not necessarily suggest a linear relationship between the variables.



1379


orientation. This is similar to observations for magnesitedissolution.92 In FT1, the Cr(VI)-containing inlet solution canflow through the low permeability illite zone and therefore allsurface sites are accessible. In FPC with κIll/Qtz being 0.67, flowvelocity in the illite zone is about 67% of that in the sand zone,still high enough to reach full sorption microcapacity. In FPPC,however, flow in the illite zone is only about a negligible 1% ofthe sand zone. As such, the sorption sites can only be accessedthrough diffusion, with the diffusion length being the radius ofthe illite zone (r = 0.45 cm). To access all surface sites in FPPC,the time needs to be higher than t = r2/D* = 888.2 min with adiffusion coefficient D* of 3.8 × 10−10 m2/s. At 0.6 m/day theresidence time is 98 min, about 1 order of magnitude lowerthan t. As such, only about 17% of the surface sites areaccessible. The fact that FT1PC is very different from FT2PCindicates that both correlation lengths λT and λL are importantin determining the sorption behavior. In FT2PC, flow can gothrough the connecting point and bypass the two illite zones,leading to similar sorption dynamics as in FPPC.The Role of Flow Conditions. The BTCs and sorbed

chromium evolution at 3.0 m/day are similar to those at 0.6 m/day for all columns, with M having the highest while FP has thelowest rates and capacity within each set (Figure 5). At 15.0 m/day, however, the macrocapacity for all columns are muchlower. The residence time at 3.0 m/day is 19.6 min. Batchresults indicate that more than 80% sorption occurs on theeasily accessible sites within the first 20 min, a time muchshorter than the equilibrium time of 60 min. As such, in Ccolumns, Cr(VI) can still access most of the sorption sites,leading to similar macrocapacity values to microcapacity. At15.0 m/day, the residence time of 3.9 min is much shorter thanthe 20 min that is needed to access most of the external sites.As such, C(VI) can only access a relatively small portion of thesorption sites in all columns. At 15.0 m/day, the differenceacross columns within each set is much smaller, indicating theless significant role of spatial heterogeneities under high flowconditions. Although macrocapacity decreases with flowvelocity, the early rates kc1 of MC increase from 1.54 × 10−6

at 0.6 m/day to 6.06 × 10−6 at 3.0 m/day, and to 7.63 × 10−6

mg/g/s at 15.0 m/day.Connectivity Controls. Figure 6A−C show the depend-

ence of macrocapacity and macrokinetics on the transportconnectivity CT95%. The C columns have low connectivity andhigh macrocapacity and macrokinetics. In the well-connectedFT2PC and FPPC, a large proportion of inaccessible sites leadsto much lower capacity and rates. Macrocapacity values are thesame as the microcapacity (γc = 1) values only in lowconnectivity columns at 0.6 and 3.0 m/day (Figure 6D−F),where the residence times are longer or similar to the sorptionequilibrium time (60 min). Under all other conditions,macrocapacities are lower than microcapacities, with the lowestbeing 11% of the microcapacity in FPPC at 3.0 m/d. Note thatin this column with the largest connectivity, γc values almostoverlap at different flow velocities (Figure 6D), indicatingstrong diffusion-control imposed by the low permeability illitezone, instead of advection-control.As discussed in the literature,1,20−22 the microkinetic rates kb1

and kb2 reflect the chemical kinetic rates and intragrain diffusionrates, respectively. The kb1 from the C set is larger than thatfrom the PC set (Figure 6B). The kb2 in the PC set is slightlyhigher than that in the C set (Figure 6C), likely due to itssmaller grain size and therefore shorter intragrain diffusionlength. All macrokinetic rates are lower than the microkinetic

rates. At 15.0 m/day, macrorates are highest among the threevelocities and are within the same order of magnitude as themicrokinetic rates, indicating close to chemical-kinetic controlat this flow velocity. This explains the lower dependence onspatial patterns. Interestingly, at this high flow velocity, the γk1values (kc1 relative to kb1) increase slightly with connectivity,which is the opposite of all other parameters (Figure 6E). Thisis likely due to the difference in local flow velocities in differentcolumns. Although under the same 15.0 m/day, the largepermeability contrast in FPPC and FT2PC diverts the majorityof the flow to the sand zone, leading to much higher flow ratesat the sand−illite interface and higher sorption rates. Incontrast, in other columns, flow velocities are averaged outbecause of the same permeability in the direction transverse tothe main flow. The γk2 values are very close to 1 in lowconnectivity M columns because illite are homogeneouslydistributed and the diffusion length is defined by the grain sizes,similar as in batch reactors. The γk2 values decrease withincreasing connectivity because of the longer diffusion length inthe FPPC and FT2PC columns.At 0.6 m/day, both kc1 and kc2 are much smaller and are far

from the kinetic-controlled regime. Mass transport limitssorption, which explains the consistent and pronounced ratedependence on connectivity. It is important to note that evenin the homogeneous M column without spatial heterogeneities,rates are 1.1%−2.3% of the microrates, although its macro-capacity is the same as the microcapacity. This indicates thatthe rate discrepancy across scales is not merely due toheterogeneities, but also because of the flow and transportprocesses that are not represented under well-mixed conditions.Measurements under well-mixed systems essentially quantifythe reaction potential and cannot be directly extrapolated tonatural conditions where reactions occur simultaneously withflow and transport.The significance of spatial heterogeneities (Figure 6G−I),

quantified by the deviation of β values from 1.0, increases withincreasing connectivity at each flow velocity. In this column at0.6 m/day, the macrocapacity is 16% of the MPC column, andthe kc1 and kc2 values are 7% and 9% of the rates in the Mcolumns. At 15.0 m/day, the deviation from 1.0 is muchsmaller.The data here indicates that chemical heterogeneity alone

causes much less effects than the coupled chemical and physicalheterogeneities. Among the three C columns, although illitespatial patterns are the same as the PC columns, the capacityand rates are very similar. Only in PC columns where the illitepermeability is 2 orders of magnitude lower than that of quartz,the effects of spatial patterns are significant. The general trendof macrokinetic rates increasing with increasing velocity anddecreasing connectivity confirms conclusions from earlierstochastic analyses on single sorbing solutes.27,28 Theseanalyses show that the time scale of sorption, which is inverseof sorption rates, increases with correlation length (positivelycorrelates to connectivity) and decreases with flow velocity.7,8

Environmental Implications. This work presents the firststudy that systematically examines the role of illite spatialpatterns in determining Cr(VI) sorption. Connectivity exerts asignificant control on sorption macrocapacity and macro-kinetics, especially at low flow velocities relevant to ground-water. Highly connected low permeability illite zones in flow-parallel orientation lead to approximately an order ofmagnitude lower capacity than those in minimally connectedhomogeneous columns. The column-scale macrocapacity can



1380


reach the intrinsic microcapacity in low connectivity columnsunder low flow conditions where the residence time iscomparable to or longer than the intrinsic sorption equilibriumtime. The macrokinetic rates are 1−3 orders of magnitudelower than the microkinetic rates. Macrocapacity and macro-kinetic rates are in general lowest with highest connectivity.Liu et al. (2014)39 made similar observations that preferential

flow path results in minimum uranium desorption. Li et al.(2014)92,93 found that layered structures of magnesite lead tolower dissolution rates by a factor of 2, with the maximumheterogeneity impacts at 18.0 m/day. Interestingly, here thespatial patterns have the most significant impacts under lowflow conditions. This is likely because reactions with differentrates have different “critical” flow regimes where the effects ofspatial heterogeneities are important.93 For Cr(VI) sorption, at15.0 m/day, the sorption rates are close to the kinetic-controlled regime where transport does not affect sorption,leading to the least difference among columns. With magnesitedissolution, even in the high flow velocity of 18.0 m/day, it isstill influenced by mass transport and therefore spatialheterogeneities play a significant role.Low permeability clay lenses, such as those in FPPC

columns, commonly exist in the natural subsurface. This mayexplain the strikingly longer time needed for groundwaterremediation than the prediction based on laboratory measure-ment.94,95 In these systems, macrocapacity can only reach arelatively small portion (11%) of the overall microcapacityregardless of the flow condition. This implies that althoughnatural systems can have high sorbing potential, the lowpermeability prevents the contaminant−clay interaction. As aresult, the contaminant may end up remaining mobile.

■ ASSOCIATED CONTENT*S Supporting InformationDetails of the materials and methods section are provided inthe Supporting Information. This material is available free ofcharge via the Internet at http://pubs.acs.org. All data in thiswork are available for public access at http://www.earthchem.org/a with the DOI number 10.1594/IEDA/100509.

■ AUTHOR INFORMATIONCorresponding Author*Phone: 814-867-3547; fax: 814-865-3248; e-mail: [email protected] authors declare no competing financial interest.

■ ACKNOWLEDGMENTSThis work is supported by the Office of Sciences SubsurfaceBiogeochemical Research (SBR) Program under Project No.DE-FOA-0000311. We appreciate the support of the MaterialCharacterization Laboratory (MRL) at Penn State in assistingwith chemical analysis. We acknowledge the associate editor Dr.Daniel Giammar and three anonymous reviewers for theirthorough, insightful, and constructive comments that havesignificantly improved the paper.

■ REFERENCES(1) Mohan, D.; Pittman, C. U. Review: Activated carbons and lowcost adsorbents for remediation of tri- and hexavalent chromium fromwater. J. Hazard. Mater. 2006, 137 (2), 762−811.(2) Richard, F. C.; Bourg, A. C. M. Aqueous geochemistry ofchromium: A review. Water Res. 1991, 25 (7), 807−816.

(3) Owlad, M.; Aroua, M. K.; Daud, W. A. W.; Baroutian, S. Removalof hexavalent chromium-contaminated water and wastewater: Areview. Water Air Soil Pollut. 2009, 200 (1−4), 59−77.(4) Lu, H. J.; Luan, M. T.; Zhang, J. L. Transport of Cr(VI) throughclay liners containing activated carbon or acid-activated bentonite.Appl. Clay Sci. 2010, 50 (1), 99−105.(5) Khan, A. A.; Muthukrishnan, M.; Guha, B. K. Sorption andtransport modeling of hexavalent chromium on soil media. J. Hazard.Mater. 2010, 174 (1−3), 444−454.(6) Fonseca, B.; Maio, H.; Quintelas, C.; Teixeira, A.; Tavares, T.Retention of Cr(VI) and Pb(II) on a loamy sand soil. Kinetics,equilibria and breakthrough. Chem. Eng. J. 2009, 152 (1), 212−219.(7) Deng, L.; Shi, Z.; Li, B.; Yang, L. F.; Luo, L.; Yang, X. Z.Adsorption of Cr(VI) and phosphate on Mg−Al hydrotalcitesupported kaolin clay prepared by ultrasound-assisted coprecipitationmethod using batch and fixed-bed systems. Ind. Eng. Chem. Res. 2014,53 (18), 7746−7757.(8) Setshedi, K. Z.; Bhaumik, M.; Onyango, M. S.; Maity, A.Breakthrough studies for Cr(VI) sorption from aqueous solution usingexfoliated polypyrrole-organically modified montmorillonite claynanocomposite. J. Ind. Eng. Chem. 2014, 20 (4), 2208−2216.(9) Guerra, D. L.; Oliveira, H. C. P.; da Costa, P. C. C.; Viana, R. R.;Airoldi, C. Adsorption of chromium(VI) ions on Brazilian smectite:Effect of contact time, pH, concentration, and calorimetricinvestigation. Catena 2010, 82 (1), 35−44.(10) Krishna, B. S.; Murty, D. S. R.; Prakash, B. S. J.Thermodynamics of chromium(VI) anionic species sorption ontosurfactant-modified montmorillonite clay. J. Colloid Interface Sci. 2000,229 (1), 230−236.(11) Li, W.; Tang, Y. K.; Zeng, Y. T.; Tong, Z. F.; Liang, D. W.; Cui,W. W. Adsorption behavior of Cr(VI) ions on tannin-immobilizedactivated clay. Chem. Eng. J. 2012, 193, 88−95.(12) Fox, P. M.; Davis, J. A.; Hay, M. B.; Conrad, M. E.; Campbell, K.M.; Williams, K. H.; Long, P. E. Rate-limited U(VI) desorption duringa small-scale tracer test in a heterogeneous uranium-contaminatedaquifer. Water Resour. Res. 2012, 48, W05512.(13) Smith, J. A.; Sahoo, D.; McLellan, H. M.; Imbrigiotta, T. E.Surfactant-enhanced remediation of a trichloroethene-contaminatedaquifer. 1. Transport of Triton X-100. Environ. Sci. Technol. 1997, 31(12), 3565−3572.(14) Vereecken, H.; Vanderborght, J.; Kasteel, R.; Spiteller, M.;Schaf̈fer, A.; Close, M. Do lab-derived distribution coefficient values ofpesticides match distribution coefficient values determined fromcolumn and field-scale experiments? J. Environ. Qual. 2011, 40 (3),879−898.(15) Miller, A. W.; Rodriguez, D. R.; Honeyman, B. D. Upscalingsorption/desorption processes in reactive transport models to describemetal/radionuclide transport: A critical review. Environ. Sci. Technol.2010, 44 (21), 7996−8007.(16) Chai, J. C.; Onitsuk, K.; Hayashi, S. Cr(VI) concentration frombatch contact/tank leaching and column percolation test using fly ashwith additives. J. Hazard. Mater. 2009, 166 (1), 67−73.(17) Comans, R. N. J.; Hockley, D. E. Kinetics of cesium sorption onillite. Geochim. Cosmochim. Acta 1992, 56 (3), 1157−1164.(18) Bhattacharyya, K. G.; Sen Gupta, S. Adsorption of chromium-(VI) from water by clays. Ind. Eng. Chem. Res. 2006, 45 (21), 7232−7240.(19) Shang, J. Y.; Liu, C. X.; Wang, Z. M.; Zachara, J. M. Effect ofgrain size on uranium(VI) surface complexation kinetics andadsorption additivity. Environ. Sci. Technol. 2011, 45 (14), 6025−6031.(20) Liu, W. F.; Zhang, J. A.; Zhang, C. L.; Wang, Y. F.; Li, Y.Adsorptive removal of Cr(VI) by Fe-modified activated carbonprepared from Trapa natans husk. Chem. Eng. J. 2010, 162 (2),677−684.(21) Abdelwahab, O.; El Sikaily, A.; Khaled, A.; El Nemr, A. Mass-transfer processes of chromium(VI) adsorption onto guava seeds.Chem. Ecol. 2007, 23 (1), 73−85.



1381

http://pubs.acs.org

http://www.earthchem.org/a

http://www.earthchem.org/a

mailto:[email protected]

mailto:[email protected]


(22) Khezami, L.; Capart, R. Removal of chromium(VI) fromaqueous solution by activated carbons: Kinetic and equilibrium studies.J. Hazard. Mater. 2005, 123 (1−3), 223−231.(23) Liu, C.; Zachara, J. M.; Qafoku, N. P.; Wang, Z., Scale-dependent desorption of uranium from contaminated subsurfacesediments. Water Resour. Res. 2008, 44, (8).(24) Curtis, G. P.; Davis, J. A.; Naftz, D. L., Simulation of reactivetransport of uranium(VI) in groundwater with variable chemicalconditions. Water Resour. Res. 2006, 42, (4).(25) Bao, C.; Wu, H.; Li, L.; Newcomer, D.; Long, P. E.; Williams, K.H. Uranium bioreduction rates across scales: biogeochemical hotmoments and hot spots during a biostimulation experiment at Rifle,Colorado. Environ. Sci. Technol. 2014, 48 (17), 10116−10127.(26) Friedly, J. C.; Davis, J. A.; Kent, D. B. Modeling hexavalentchromium reduction in groundwater in field-scale transport andlaboratory batch experiments. Water Resour. Res. 1995, 31 (11), 2783−2794.(27) Espinoza, C.; Valocchi, A. J. Stochastic analysis of one-dimensional transport of kinetically adsorbing solutes in chemicallyheterogeneous aquifers. Water Resour. Res. 1997, 33 (11), 2429−2445.(28) MirallesWilhelm, F.; Gelhar, L. W. Stochastic analysis ofsorption macrokinetics in heterogeneous aquifers. Water Resour. Res.1996, 32 (6), 1541−1549.(29) Seeboonruang, U.; Ginn, T. R. Upscaling heterogeneity inaquifer reactivity via exposure-time concept: Forward model. J.Contam. Hydrol. 2006, 84 (3−4), 127−154.(30) Seeboonruang, U.; Ginn, T. R. Upscaling heterogeneity inaquifer reactivity via the exposure-time concept: Inverse model. J.Contam. Hydrol. 2006, 84 (3−4), 155−177.(31) Landrot, G.; Ajo-Franklin, J. B.; Yang, L.; Cabrini, S.; Steefel, C.I. Measurement of accessible reactive surface area in a sandstone, withapplication to CO2 mineralization. Chem. Geol. 2012, 318, 113−125.(32) Peters, C. A. Accessibilities of reactive minerals in consolidatedsedimentary rock: An imaging study of three sandstones. Chem. Geol.2009, 265 (1−2), 198−208.(33) Jin, L.; Andrews, D. M.; Holmes, G. H.; Lin, H.; Brantley, S. L.Opening the “Black Box”: Water chemistry reveals hydrologicalcontrols on weathering in the Susquehanna Shale Hills critical zoneobservatory. Vadose Zone J. 2011, 10 (3), 928−942.(34) Barber, L. B.; Thurman, E. M.; Runnells, D. D. Geochemicalheterogeneity in a sand and gravel aquiferEffect of sedimentmineralogy and particle-size on the sorption of chlorobenzenes. J.Contam. Hydrol. 1992, 9 (1−2), 35−54.(35) Campbell, K. M.; Kukkadapu, R. K.; Qafoku, N. P.; Peacock, A.D.; Lesher, E.; Williams, K. H.; Bargar, J. R.; Wilkins, M. J.; Figueroa,L.; Ranville, J.; Davis, J. A.; Long, P. E. Geochemical, mineralogical andmicrobiological characteristics of sediment from a naturally reducedzone in a uranium-contaminated aquifer. Appl. Geochem. 2012, 27 (8),1499−1511.(36) Tompson, A. F. B.; Schafer, A. L.; Smith, R. W. Impacts ofphysical and chemical heterogeneity on cocontaminant transport in asandy porous medium. Water Resour. Res. 1996, 32 (4), 801−818.(37) Li, L.; Steefel, C. I.; Kowalsky, M. B.; Englert, A.; Hubbard, S. S.Effects of physical and geochemical heterogeneities on mineraltransformation and biomass accumulation during biostimulationexperiments at Rifle, Colorado. J. Contam. Hydrol. 2010, 112 (1−4),45−63.(38) Miller, A. W.; Rodriguez, D. R.; Honeyman, B. D. Simplifiedbehaviors from increased heterogeneity: I. 2-D uranium transportexperiments at the decimeter scale. J. Contam. Hydrol. 2013, 148 (39−50), 39−50.(39) Liu, C.; Shang, J.; Shan, H.; Zachara, J. M. Effect of subgridheterogeneity on scaling geochemical and biogeochemical reactions: Acase of U(VI) desorption. Environ. Sci. Technol. 2014, 48 (3), 1745−1752.(40) Moser, K. W.; Georgiadis, J. G. Extraction and validation ofcorrelation lengths from interstitial velocity fields using diffusion-weighted MRI. Magn. Reson. Imaging 2004, 22 (2), 257−268.

(41) Renard, P.; Allard, D. Connectivity metrics for subsurface flowand transport. Adv. Water Resour. 2013, 51, 168−196.(42) Le Goc, R.; de Dreuzy, J. R.; Davy, P. Statistical characteristics offlow as indicators of channeling in heterogeneous porous and fracturedmedia. Adv. Water Resour. 2010, 33 (3), 257−269.(43) LaBolle, E. M.; Fogg, G. E. Role of molecular diffusion incontaminant migration and recovery in an alluvial aquifer system.Transport Porous Med. 2001, 42 (1−2), 155−179.(44) Park, C. H.; Beyer, C.; Bauer, S.; Kolditz, O. A study ofpreferential flow in heterogeneous media using random walk particletracking. Geosci. J. 2008, 12, 285−297.(45) Bianchi, M.; Zheng, C. M.; Wilson, C.; Tick, G. R.; Liu, G. S.;Gorelick, S. M. Spatial connectivity in a highly heterogeneous aquifer:From cores to preferential flow paths. Water Resour. Res. 2011, 47,W05524.(46) Gelhar, L. W. Stochastic subsurface hydrology from theory toapplications. Water Resour. Res. 1986, 22 (9), S135−S145.(47) Dagan, G. Transport in heterogeneous porous formationsSpatial moments, ergodicity, and effective dispersion. Water Resour.Res. 1990, 26 (6), 1281−1290.(48) Berkowitz, B.; Cortis, A.; Dentz, M.; Scher, H. Modeling non-Fickian transport in geological formations as a continuous timerandom walk. Rev. Geophys. 2006, 44 (2), RG2003.(49) Benson, D. A.; Wheatcraft, S. W.; Meerschaert, M. M.Application of a fractional advection−dispersion equation. WaterResour. Res. 2000, 36 (6), 1403−1412.(50) Haggerty, R.; Gorelick, S. M. Multiple-rate mass transfer formodeling diffusion and surface-reations in media with pore-scaleheterogeneity. Water Resour. Res. 1995, 31, 2383−2400.(51) Simunek, J.; van Genuchten, M. T. Modeling nonequilibriumflow and transport processes using HYDRUS. Vadose Zone J. 2008, 7(2), 782−797.(52) Vangenuchten, M. T.; Wierenga, P. J. Mass-transfer studies insorbing porous-media. 1. Analytical solutions. Soil Sci. Soc. Am. J. 1976,40 (4), 473−480.(53) Larsson, M. H.; Jarvis, N. J. Evaluation of a dual-porosity modelto predict field-scale solute transport in a macroporous soil. J. Hydrol.1999, 215 (1−4), 153−171.(54) Heidari, P.; Li, L. Solute transport in low-heterogeneitysandboxes: The role of correlation length and permeability variance.Water Resour. Res. 2014, n/a−n/a DOI: 10.1002/2013WR014654.(55) Semra, S.; Sardin, M.; Simonnot, M. O. Effect of chemicalheterogeneity on adsorbed solute dispersion at column scale. AIChE J.2008, 54 (4), 950−956.(56) Li, L.; Peters, C. A.; Celia, M. A. Upscaling geochemical reactionrates using pore-scale network modeling. Adv. Water Resour. 2006, 29,1351−1370.(57) Brusseau, M. L. Transport of Reactive Contaminants inHeterogeneous Porous-Media. Rev. Geophys. 1994, 32 (3), 285−313.(58) Li, L.; Peters, C. A.; Celia, M. A. Applicability of averagedconcentrations in determining reaction rates in heterogeneous porousmedia. Am. J. Sci. 2007, 307, 1146−1166.(59) Zhang, X. X.; Crawford, J. W.; Young, L. M. Does pore watervelocity affect the reaction rates of adsorptive solute transport in soils?Demonstration with pore-scale modelling. Adv. Water Resour. 2008, 31(3), 425−437.(60) Massabo, M.; Bellin, A.; Valocchi, A. J., Spatial moments analysisof kinetically sorbing solutes in aquifer with bimodal permeabilitydistribution. Water Resour. Res. 2008, 44, (9).(61) Tompson, A. F. B.; Jackson, K. J. Reactive transport inheterogeneous systems: An overview. In Reactive Transport in PorousMedia; Lichtner, P. C., Steefel, C. I., Oelkers, E. H., Eds.; MineralogicalSociety of America: Washington, DC, 1996; Vol. 34, pp 269−310.(62) Espinoza, C.; Valocchi, A. J. Temporal moments analysis oftransport in chemically heterogeneous porous media. J. Hydrol. Eng.1998, 3 (4), 276−284.(63) Miller, A. W.; Rodriguez, D. R.; Honeyman, B. D. Simplifiedbehaviors from increased heterogeneity: I. 2-D Uranium transport



1382


experiments at the decimeter scale. J. Contam. Hydrol. 2013, 148, 39−50.(64) Miller, A. W.; Rodriguez, D. R.; Honeyman, B. D. Simplifiedbehaviors from increased heterogeneity: II. 3-D Uranium transport atthe decimeter scale and intertank comparisons. J. Contam. Hydrol.2013, 148, 51−66.(65) Seuntjens, P.; Mallants, D.; Simunek, J.; Patyn, J.; Jacques, D.Sensitivity analysis of physical and chemical properties affecting field-scale cadmium transport in a heterogeneous soil profile. J. Hydrol.2002, 264 (1−4), 185−200.(66) Miller, A. W.; Rodriguez, D. R.; Honeyman, B. D. Simplifiedbehaviors from increased heterogeneity: II. 3-D Uranium transport atthe decimeter scale and intertank comparisons. J. Contam. Hydrol.2013, 148 (51−66), 51−66.(67) Bibi, I.; Singh, B.; Silvester, E. Dissolution of illite in saline-acidicsolutions at 25 degrees C. Geochim. Cosmochim. Acta 2011, 75, 3237−3249.(68) Gu, X.; Evans, L. J. Modelling the adsorption of Cd(II), Cu(II),Ni(II), Pb(II), and Zn(II) onto Fithian illite. J. Colloid Interface Sci.2007, 307 (2), 317−325.(69) Konan, K. L.; Peyratout, C.; Smith, A.; Bonnet, J. P.; Magnoux,P.; Ayrault, P. Surface modifications of illite in concentrated limesolutions investigated by pyridine adsorption. J. Colloid Interface Sci.2012, 382, 17−21.(70) Missana, T.; Alonso, U.; Garcia-Gutierrez, M. Experimentalstudy and modelling of selenite sorption onto illite and smectite clays.J. Colloid Interface Sci. 2009, 334 (2), 132−138.(71) Gu, B. H.; Doner, H. E. Adsorption of hydroxy-al polycationsand destabilization of illite and montmorillonite suspensions. ClaysClay Miner. 1990, 38 (5), 493−500.(72) Churchill, H.; Teng, H.; Hazen, R. M. Correlation of pH-dependent surface interaction forces to amino acid adsorption:Implications for the origin of life. Am. Mineral. 2004, 89 (7), 1048−1055.(73) Gelhar, L. W. Stochastic Subsurface Hydrology; Prentice Hall:Englewood Cliffs, NJ, 1993; 390 pp.(74) Webster, R.; Oliver M. A. Geostatistics for EnvironmentalScientists; John Wiley: West Sussex, U.K., 2007.(75) Steefel, C. I.; Lasaga, A. C. A coupled model for transport ofmultiple chemical species and kinetic precipitation/dissolutionreactions with application to reactive flow in single phase hydro-thermal systems. Am. J. Sci. 1994, 294, 529−592.(76) Li, Y. H.; Gregory, S. Diffusion of ions in sea-water and in deep-sea sediments. Geochim. Cosmochim. Acta 1974, 38 (5), 703−714.(77) Lim, T. T.; Tay, J. H.; Teh, C. I. Sorption and speciation ofheavy metals from incinerator fly ash in a marine clay. J. Environ. Eng.-ASCE 1997, 123 (11), 1107−1115.(78) Amaya, T.; Suzuki, K.; Oda, C.; Yoshikawa, H.; Yui, M.; Gray,W. J.; Triay, I. R. Adsorption of Sn(IV) on goethite in 0.01M NaClsolution at ambient temperature. Mater. Res. Soc. Proc. 1997, 465,819−826.(79) Newell, C. J.; Hopkins, L. P.; Bedient, P. B. A hydrogeologicdatabase for groundwater modeling. Ground Water 1990, 28 (5), 703−714.(80) Horowitz, A. J.; Elrick, K. A. The relation of stream sedimentsurface area, grain size and composition to trace element chemistry.Appl. Geochem. 1987, 2, 437−451.(81) Zhang, Y.; Dou, X. M.; Zhao, B.; Yang, M.; Takayama, T.; Kato,S. Removal of arsenic by a granular Fe−Ce oxide adsorbent:Fabrication conditions and performance. Chem. Eng. J. 2010, 162(1), 164−170.(82) Sudicky, E. A. A natural gradient experiment on solute transportin a sand aquifer: Spatial variability of hydraulic conductivity and itsrole in the dispersion process.Water Resour. Res. 1986, 22 (13), 2069−2086.(83) Boggs, J. M.; Young, S. C.; Beard, L. M.; Gelhar, L. W.; Rehfeldt,K. R.; Adams, E. E. Field study of dispersion in a heterogeneousaquifer: 1. Overview and site description. Water Resour. Res. 1992, 28(12), 3281−3291.

(84) Zinn, B.; Meigs, L. C.; Harvey, C. F.; Haggerty, R.; Peplinski, W.J.; Von Schwerin, C. F. Experimental visualization of solute transportand mass transfer processes in two-dimensional conductivity fieldswith connected regions of high conductivity. Environ. Sci. Technol.2004, 38 (14), 3916−3926.(85) Haggerty, R.; McKenna, S. A.; Meigs, L. C. On the late-timebehavior of tracer test breakthrough curves. Water Resour. Res. 2000,36 (12), 3467−3479.(86) Knopman, D. S.; Voss, C. I.; Garabedian, S. P. Sampling designfor groundwater solute transportTests of methods and analysis ofCape Cod tracer test data. Water Resour. Res. 1991, 27, 925−949.(87) Pandey, S.; Mishra, S. B. Organic−inorganic hybrid of chitosan/organoclay bionanocomposites for hexavalent chromium uptake. J.Colloid Interface Sci. 2011, 361 (2), 509−520.(88) Ward, N. E., Adsorption of hexavalent chromium on kaoliniteand illite. Master Dissertation, The University of Arizona, Tucson,AZ,1990.(89) Zachara, J. M.; Cowan, C. E.; Schmidt, R. L.; Ainsworth, C. C.Chromate adsorption by kaolinite. Clays Clay Miner. 1988, 36 (4),317−326.(90) Ainsworth, C. C.; Girvin, D. C.; Zachara, J. M.; Smith, S. C.Chromate adsorption on goethite-effects of aluminum substitution.Soil Sci. Soc. Am. J. 1989, 53 (2), 411−418.(91) Zachara, J. M.; Ainsworth, C. C.; Cowan, C. E.; Resch, C. T.Adsorption of chromate by subsurface soil horizons. Soil Sci. Soc. Am. J.1989, 53 (2), 418−428.(92) Li, L.; Salehikhoo, F.; Brantley, S. L.; Heidari, P. Spatial zonationlimits magnesite dissolution in porous media. Geochim. Cosmochim.Acta 2014, 126, 555−573.(93) Salehikhoo, F.; Li, L.; Brantley, S. L. Magnesite dissolution ratesat different spatial scales: The role of mineral spatial distribution andflow velocity. Geochim. Cosmochim. Acta 2013, 108, 91−106.(94) Zachara, J. M.; Long, P. E.; Bargar, J.; Davis, J. A.; Fox, P.;Fredrickson, J. K.; Freshley, M. D.; Konopka, A. E.; Liu, C.; McKinley,J. P.; Rockhold, M. L.; Williams, K. H.; Yabusaki, S. B. Persistence ofuranium groundwater plumes: Contrasting mechanisms at two DOEsites in the groundwater−river interaction zone. J. Contam. Hydrol.2013, 147, 45−72.(95) Carroll, K. C.; Truex, M. J.; Brusseau, M. L.; Parker, K. R.;Mackley, R. D.; Rohay, V. J. Characterization of persistent volatilecontaminant sources in the Vadose Zone. Ground Water Monit. Rem.2013, 33 (2), 68−84.



1383


illite spatial distribution patterns dictate cr(vi...

Documents