WATER RESOURCES RESEARCH, VOL. 40, W04207, doi:10.1029/2003WR002445, 2004

Nonideal transport of solute and colloidal tracers through reactive
zeolite//iron pellets
Pengfei Zhang
Department of Earth and Atmospheric Sciences, City College of New York, New York, USA

Jirka Šimůnek
Department of Environmental Sciences, University of California, Riverside, Riverside, California, USA

Robert S. Bowman
Department of Earth and Environmental Science, New Mexico Institute of Mining and Technology, Socorro, New Mexico,
USA
Received 1 July 2003; revised 14 February 2004; accepted 23 February 2004; published 17 April 2004.

[1] Solute and colloidal tracer tests were conducted in laboratory columns to examine thehydraulic properties of a foamed zeolite/iron pellet material that was developed for in situ
remediation of contaminated groundwater. The colloidal tracer (1 mm polystyrene
microspheres) moved through the columns much faster than the nonreactive solute tracer
tritiated water, reflecting the interpellet preferential flow paths in the packed material.
Flow interruption experiments with tritium and bromide showed concentration rebound ofboth tracers after the interruption (during elution), indicating the existence of nonadvective
zones inside the pellets. Inverse modeling of microsphere data using a physicalnonequilibrium transport model yielded immobile water content (qim) equivalent to theintrapellet porosity (0.40), suggesting that the microspheres were excluded from the small
intrapellet pores and could only move through the large interpellet pore spaces. Inverse
modeling of tritium data using physical nonequilibrium and dual-permeability dualporosity models yielded qim values of 0.1–0.2, confirming the existence of nonadvective
zones inside the pellets as suggested by the flow interruption experiments. The dualpermeability dual-porosity model also indicated that 6–11% of the total porosity waspreferential flow porosity, consistent with the observation of enhanced microspheretransport with respect to tritiated water. Forward modeling with the dual-permeability
dual-porosity model suggested that the immobile water in the pellets would not
significantly affect the removal efficiency of contaminants subject to sorption andreduction. In contrast, the preferential flow porosity would drastically lower the
INDEX TERMS: 1829 Hydrology: Groundwater hydrology; 1832
contaminant removal efficiency.
Hydrology: Groundwater transport; 1055 Geochemistry: Organic geochemistry; KEYWORDS: colloid transport,
dual-permeability dual-porosity model, nonideal solute transport, perchloroethylene reduction, preferential
flow, zeolite/iron pellets
Citation: Zhang, P., J. Šimůnek, and R. S. Bowman (2004), Nonideal transport of solute and colloidal tracers through reactive
zeolite/iron pellets, Water Resour. Res., 40, W04207, doi:10.1029/2003WR002445.

1. Introduction[2] Zero-valent iron (ZVI) has been used extensively as areactive material in subsurface permeable reactive barriers(PRBs) to remove a wide variety of contaminants [Puls etal., 1998; Reeter et al., 1998; Puls et al., 1999a, 1999b;
Vogan et al., 1999; Morrison et al., 2001; Gillham et al.,
2002; Kjeldsen and Locht, 2002]. Since reaction rates areoften directly related to the surface area of the ZVI[Gotpagar et al., 1997; Wang and Zhang, 1997; Cao etal., 1999; Ponder et al., 2000], fine-grained ZVI with large
surface to volume ratio (e.g., ZVI powders) may be used toenhance reaction rates and contaminant removal efficiencies.
Copyright 2004 by the American Geophysical Union.
0043-1397/04/2003WR002445$09.00

However, the use of fine-grained ZVI (or any fine-grained
reactive materials) will reduce the hydraulic conductivity of
PRBs [Grant et al., 1987], diminishing the ability of the
PRBs to effectively capture contaminant plumes. Therefore
efforts have been made to increase the hydraulic conductivityof ZVI PRBs by mixing granular ZVI with coarse sand[O’Hannesin and Gillham, 1998; Puls et al., 1999b] or bymaking foamed aluminosilicate-bound ZVI pellets [Naftz etal., 2000]. Recently, a reactive pellet material was developedby coating surfactant-modified zeolite (SMZ) and ZVI powders onto a highly porous glass foam substrate [Zhang et al.,2002]. This novel process allowed the use of fine-grained
reactive materials to produce pellets with high reactivity,
strong mechanical strength, and superior hydraulic conductivity (9.7 cm s1) [Zhang et al., 2002]. The 10 cm s1
hydraulic conductivity is 2– 3 orders of magnitude greater

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Table 1. Particle Size Distribution of Zeolite and Zero-Valent Iron
(ZVI) Powders Used to Manufacture the Reactive Zeolite/ZVI
Pellets
Zeolite
Sieve
Range

ZVI

Size Range,
Zeolitemm
Retained, %40 – 70 212 – 425
70 – 100 150 – 212
100 – 140 102 – 150
140 – 200 74 – 102
200 – 350 42 – 74
>350
<42
Total

30.4
14.6
14.5
7.7
8.4
23.7
99.3

Sieve
Range

Size Range, ZVI Retained,mm%

70 – 78 177 – 212
80 – 100 150 – 177
100 – 120 125 – 150
120 – 200 74 – 125
>200
<74
Total

7
14
20
37
22
100

than that of granular ZVI, granular ZVI mixed withcoarse sand, or foamed aluminosilicate-bound ZVI pellets[O’Hannesin and Gillham, 1998; Puls et al., 1999a, 1999b;
Naftz et al., 2000]. While highly permeable, SMZ/ZVI pellets(2.5-cm cubes), and other pelletized reactive materials, havesome potential drawbacks: (1) Preferential flow paths may
exist between the pellets due to their large sizes; and (2) nonadvective zones may be present within pellet coatings (e.g,the fine-grained SMZ and ZVI powders used here). The
presence of preferential flow paths and nonadvective zones
may limit the mass transfer of contaminants to the reactive
sites of pellets, leading to decreased contaminant removal
efficiencies. Understanding the hydraulic properties of the
SMZ/ZVI pellets can lead to optimized production processes
for these and other composites, and to improved modeling ofreactive solute transport through PRBs formed of pelletizedmaterials.
[3] Colloidal-sized particles have been observed to travel
faster than dissolved solutes in porous media containing
macropores or fractures (preferential flow paths) [Champand Shroeter, 1988; Harvey et al., 1989; McKay et al.,
1993; Vilks and Bachinski, 1996; Grolimund et al., 1998;
Pang et al., 1998; Deborde et al., 1999; Zhang et al., 2001a;
Scheibe and Wood, 2003]. The enhanced transport ofcolloids is often attributed to exclusion processes. Becauseof their large sizes, colloids are prevented from enteringsome convective pores of the same scale as the colloids
and are rerouted to the macropores or fractures (aprocess known as macroscopic ‘‘pore exclusion’’ [Ginn,
2002]). Since velocity is greater in the macropores or
fractures, colloids travel faster on average than nonreactive
solutes. Therefore comparison of breakthrough curves(BTCs) of colloidal particles versus BTCs of a nonreactive
solute tracer can indicate the presence or absence ofpreferential flow paths. Enhanced transport of colloidal
particles may also arise from microscopic (pore-scale)exclusion processes. For example, within a pore throat,
colloids are prevented from approaching pore walls due torepulsive interaction forces (charge exclusion) and/or large
colloid sizes (chromatographic effect). Since colloids do
not experience the low velocities near the pore walls, themean velocity of colloids is increased relative to nonreactive solute tracers. While microscopic exclusion processes
are important in coarse-grained media where the colloids are
far smaller than almost all pores [Harter et al., 2000],
macroscopic ‘‘pore exclusion’’ plays a significant role in

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media containing aggregates and preferential flow paths forenhanced colloid transport.
[4] Flow interruption during the transport of a nonreactive solute is a sensitive means of determining the presenceof physical nonequilibrium, or diffusive mass transfer
between mobile (e.g., macropores or fractures) and immobile (e.g., fine matrix) zones [Brusseau et al., 1989; Hu andBrusseau, 1995; Reedy et al., 1996; Brusseau et al., 1997].
During tracer injection (i.e., on the ascending limb of a
BTC), solute concentrations within macropores or fractures
are greater than those within the fine matrix. During flowinterruption the solute diffuses from the macropores or
fractures into the fine matrix, resulting in a decrease ineffluent concentration after the flow interruption [Hu andBrusseau, 1995; Reedy et al., 1996]. Conversely, during
tracer elution (i.e., on the descending limb of a BTC), soluteconcentrations within the macropores or fractures are lower
than those within the fine matrix. During flow interruption
the solute diffuses back from the fine matrix to the macropores or fractures, leading to an increase in effluent concentration after the flow interruption [Reedy et al., 1996;
Brusseau et al., 1997]. Therefore, for a nonreactive tracer, aconcentration perturbation upon imposition of flow interruption is indicative of the presence of physical nonequilibrium, or nonadvective zones.
[5] The objectives of this study were to (1) verify theexistence of preferential flow paths and nonadvective zones
in the packed reactive zeolite/ZVI pellets; (2) quantify thefraction of preferential flow porosity and immobile watercontent; and (3) determine the impacts of preferential flow
paths and nonadvective zones on contaminant reduction.
Transport experiments with a colloidal tracer (1 mm polystyrene microspheres) and a nonreactive tracer (tritiatedwater) were carried out to determine the presence ofpreferential flow paths in the packed pellets. Flow interruption experiments with the tritiated water and bromide wereconducted to determine the presence of nonadvective zones
inside the pellets. Microsphere and tritium breakthroughdata were inversely modeled using physical nonequilibriumand dual-permeability dual-porosity transport models tofurther quantify the fraction of preferential flow porosity
and immobile water content. Forward modeling using thedual-permeability dual-porosity model was carried out todetermine the effects of variable preferential flow porosity
and immobile water content on the reduction of contaminants subject to sorption and reduction.

2. Materials and Methods
2.1. Pellet Preparation[6] The pellet-production process involved coating zeolite powder (St. Cloud Mine, Winston, New Mexico) andZVI powder (Peerless Metal Powders and Abrasives,
Detroit, Michigan) onto a highly porous glass foam substrate (Cercona of America, Inc., Dayton, Ohio). The
particle size distribution of the zeolite and ZVI powders ispresented in Table 1. Foam slabs 22.9 cm square and 2.5 cm
thick were slotted (2.5-cm slots, 1.25 cm deep, running
perpendicular to one another on opposite sides of the slab)
for easy breaking into 2.5-cm cubes following coating withzeolite and ZVI. To coat the glass foam substrate, ahomogeneous slurry of zeolite powder (14.5% by weight),

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Figure 1. Crushed zeolite/zero-valent iron (ZVI) pellets and original 2.5-cm cubes.
ZVI powder (43.5% by weight), and water (42.0% by
weight) was prepared in a large mixing bath. The foamslabs were dipped into the slurry for about 5 s, removed
from the bath, and dried at room temperature for 4 days.
The dipping process allowed the slurry to flow into thefoam substrate without significant clogging of the pore
structure [Bowman et al., 2002]. When dry, the zeolite/ironmixture adhered tightly to the silica foam substrate. The
dried slabs were then broken into 2.5-cm cubes (Figure 1)
for a pilot-scale PRB test [Bowman et al., 2002]. For
laboratory column experiments, zeolite/ZVI cubes were
further crushed to smaller size pellets (0.5 – 1.0 cm in one
dimension, Figure 1) using a hammer and a screwdriver.
During the crushing process, about 50% of the zeolite/ZVI
coating was lost, making the final composition of thecrushed pellets approximately 12.5% zeolite, 37.5% iron,and 50% silica foam substrate by weight. Readers arereferred to Bowman et al. [2002] and Zhang et al. [2002]
for more details of the preparation of the reactive zeolite/
ZVI pellets, and the performance of the pellets for chemically reducing perchloroethylene (PCE) and chromate inbench- and pilot-scale tests.
2.2. Transport Experiments
TM[7] Four CHROMAFLEX glass chromatograph columns (Kontes, Vineland, New Jersey), 30 cm long by
4.8-cm diameter, were packed with the crushed zeolite/ZVI
pellets. The columns were purged with CO2 for 1.5 hours
to promote water saturation, then fed with degassed
synthetic groundwater (0.52 mM NaHCO 3 , 0.08 mM
MgSO 4, 0.09 mM CaCl 2, 0.01 mM KNO 3 , 0.12 mM
CaCO3, and 0.02 mM Mg(NO3)2) at a linear velocity of
0.5 m d 1 . This synthetic groundwater was used as
the aqueous matrix for all subsequent experiments. The
0.5 m d1 velocity used here is typical of groundwatervelocities and was identical to the groundwater velocity inthe pilot-scale PRB filled with the reactive zeolite/ZVI
pellets [Bowman et al., 2002]. All injections were conducted in an upward flow mode with a PHD 2000
programmable multichannel syringe pump (Harvard Apparatus, Holliston, Massachusetts). The syringe pump wasused in order to (1) minimize pulses in flow; (2) eliminate
the crushing of soft colloids (e.g., polystyrene microspheres) by the squeezing motion of peristaltic pumps;(3) avoid soft tubing (e.g., silicone, Tygon1, or Viton1
tubing often required in peristaltic pumps) that would reactwith organic solvents (e.g., PCE) and interfere with our
subsequent study of reductive dechlorination of PCE by
the reactive pellets; and (4) avoid changes in flow rate due
to stretching of the soft tubing over time. The weight ofeach column was determined approximately every porevolume (PV) until there was no weight difference between
two consecutive measurements. At that time a column wasconsidered fully saturated and the porosity of the packed
pellets was determined by the difference between the saturated weight and the dry weight of the column. The bulk
density of the packed pellets was calculated based on the dry
mass of the packed pellets and the volume of the column.
[8] Upon saturation, two columns were modified with thecationic surfactant hexadecyltrimethylammonium chloride(Aldrich Chemical Company, Milwaukee, Wisconsin) forthe purpose of enhancing contaminant removal [Bowman etal., 2002; Zhang et al., 2002]. The surfactant modification
was not expected to change the physical properties (e.g.,porosity and hydraulic conductivity) of the pellets [Zhang etal., 2002], but was expected to alter the surface chemistry of
the pellets by reversing zeolite’s surface charge from net
negative to net positive via a two-step process: (1) theformation of a surfactant monolayer with positively charged
surfactant head groups adsorbed on the surface (via electrostatic interactions) and the hydrophobic tails orientatedtoward solution; and (2) the formation of a second surfactant layer (via hydrophobic interactions among the surfactant tails) with the positively charged surfactant head groups
orientated toward solution [Li and Bowman, 1998]. As aresult, anions such as bromide (a solute commonly considered as a nonreactive tracer) would be adsorbed [Li andBowman, 1997] and retarded during transport through thesurfactant-modified pellets.
[9] A 3-PV pulse of tritiated water (11,800 counts min1mL1) was injected into the columns together with KBr(100 mg L1 as Br) at a linear velocity of 0.5 m d1,
followed by 7 PV of tracer-free synthetic groundwater. Flowrates were verified from the cumulative volume of effluent
over time. Effluent samples were collected in glass scintillation vials using Retriever II fraction collectors (ISCO Inc.,
Lincoln, Nebraska). Flow was stopped at the second PV(during tracer injection) and fourth PV (during tracer

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Table 2. Properties of the Packed Columns and Conditions of theTransport Experimentsv, m d1
Column Length, cm
1
2
3
4

30.0
30.0
30.0
30.0

q
0.813
0.810
0.808
0.814

Input Pulse(PV)rb, g cm3 Solute Colloid Solute Colloid
0.666
0.668
0.663
0.670

0.482
0.492
0.501
0.500

0.479
0.489
0.502
0.505

2.96
2.96
3.01
3.03

2.95
2.97
3.04
3.07

elution) of the experiment for 29 hours (2 PV) each time toallow possible diffusive mass transfer of solutes into or out
of nonadvective domains in the packed pellets. A separatetransport experiment with carboxylate-modified polystyrene
microspheres (1.0 mm diameter, 5.0  106 spheres mL1,
Molecular Probes, Eugene, Oregon) was also conducted in asimilar manner (i.e., 3-PV pulse of injection followed by
7 PV of elution, at a linear velocity of 0.5 m d1) except that
there was no flow interruption. The microsphere transport
experiment was separated from the solute tracer test to avoid
radioactive tritium contamination of the flow cytometerused for microsphere analysis. The properties of the packed
columns and conditions of the transport experiments are
summarized in Table 2.
2.3. Methods of Analysis[10] Tritium was analyzed by liquid scintillation counting(Beckman LS 6500, Palo Alto, California) after mixing
1 mL of effluent sample with 5 mL of Ecolite+ scintillation
cocktail (ICN Biomedical, Costa Mesa, California). Br
concentrations were measured using an ion specific electrode (Orion, Beverly, Massachusetts) and a model F 45 pH
meter (Beckman Instruments, Inc., Fullerton, California),
after adjusting the ionic strength of the effluent sample withionic strength adjustor (Orion). The electrode was calibrated
daily using Br standard solutions (Orion).
[11] The microsphere concentrations were determinedusing flow cytometry (FACScan Analyzer, Becton Dickenson Immunocytometry Systems, San Jose, California).
The flow cytometry method is easy and fast (30 – 40 samplesh1), and has been used by many researchers for enumerating
bacterial cells and microspheres [Stewart and Steinkamp,
1982; Molema et al., 1998; Fuller et al., 2000; DeFlaun et al.,2001]. The yellow-green fluorescent microspheres were
excited with an argon laser at a wavelength of 488 nm, andthe emitted fluorescence was detected at 515– 545 nm. Three
milliliters of effluent sample was transferred to a polystyrene
tube and sonicated for about 30 s to disperse any coagulated
colloids prior to analysis. Samples were withdrawn by
the flow cytometer at 12 mL min1 and the cumulative
counts over 1 min were recorded. Solutions withknown concentrations of the microspheres (5.0  102 up to
5.0  106 spheres mL1) were analyzed daily to prepare
calibration curves for converting raw counts to microsphereconcentrations in spheres per milliliter. The detection
limit for this method was about 5.0  102 spheres mL1,
and the linear response range was between 5.0  102 and5.0  106 spheres mL1.
[12] Hydraulic conductivity of the pellets was measured
using a constant head method [Freeze and Cherry, 1979].
Sixteen well-shaped cubes were used to estimate the internal

W04207porosity (intrapellet porosity). The internal porosity of thepellets was determined as the difference between the total
cube volume (calculated from the dimensions) and thevolume of solids (determined from the volume of water
displaced by the cubes after saturation). It was assumed thatthe internal porosity did not change significantly during thecrushing process.
2.4. Transport Modeling[13] The packed reactive zeolite/ZVI pellets have large
interpellet pore spaces (up to several millimeters in dimension), small intrapellet pores (5 – 20 mm), and possible
nonadvective zones within the coating (due to the fine sizeof the zeolite/ZVI powders). Therefore a dual-permeability
dual-porosity conceptual model modified from Šimůnek et
al. [2003] was used to describe the movement of water andsolutes in the packed pellets. This model assumes that theporous medium consists of two interacting domains: amobile fracture domain, and a matrix domain containing
both mobile and immobile regions. For reactive solutetransport, the flow equations for the fracture (subscript f ),the mobile region of the matrix (subscript m), and theimmobile region of the matrix (subscript im) are as follows,
respectively:@ qf þ f rb Kd Cf@Cf@qf Cf Gs@
qf Df¼
  f rb Kd ms Cf

@x
@t
@x
@x
w
ð1Þ


@ ½qm þ ð1  f Þerb Kd Cm
@
@Cm
qm Dm
¼
@x
@t
@x
@qm CmGsþ wðCm  Cim Þ

@x
1w
ð2Þ
 ð1  f Þerb Kd ms Cm@ ½qim þ ð1  f Þð1  eÞrb Kd Cim¼ wðCm  Cim Þ@t ð1  f Þð1  eÞrb Kd ms Cim
ð3Þ

where t is time, x is the travel distance, rb is the bulk
density, Kd is the sorption constant, ms is the solid phasefirst-order decay constant, qf is the fracture (preferentialflow) porosity, qm is the porosity in mobile regions of thematrix, qim is the porosity in immobile regions of the matrix,
Df is the dispersion coefficient in the fractures, Dm is thedispersion coefficient in mobile regions of the matrix, Cf isthe solute concentration in the fractures, Cm is the soluteconcentration in mobile regions of the matrix, Cim is thesolute concentration in immobile regions of the matrix, qf is
the flux in the fractures, qm is the flux in mobile regions of
the matrix, w is the ratio of the fracture domain to total soilvolume (equal to the ratio of fracture porosity qf and total
porosity q when porosity in each overlapping region isassumed to be the same), w is the mass transfer coefficient
between mobile and immobile water in the matrix, Gs is thesolute mass transfer rate between the fractures and themobile water in the matrix, f is the fraction of sorption sites
associated with the fractures, (1  f ) is the fraction ofsorption sites associated with the matrix, e is the fraction ofsorption sites associated with mobile water in the matrix,and (1  e) is the fraction of sorption sites associated with

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Table 3. Parameters for Optimization During Inverse ModelingWith Various Transport Models
TracerConceptual Model

Optimized
Parameter

Microspheres

equilibrium
nonequilibrium

aL, ka, kd
aL, qim, ka, kd

Tritium in allcolumns, Br
in columns 1 and 2

nonequilibrium
dual-permeability
dual-porosityaL, qim, w
qm, w, qim, as, w, aL,f

nonequilibrium

qim, w, Kd

Br in columns 3 and 4

immobile water in the matrix. This model assumes that the
reduction of contaminants only occurs on the surface ofreactive materials and the reduction rate coefficient is thesame for all domains. The dispersion coefficient for a
specific domain (Df or Dm) is expressed as the product ofdispersivity (aL,f or aL,m) and pore water velocity in thatdomain. The solute mass transfer rate Gs is expressed as[Gerke and van Genuchten, 1996]
Gs ¼ as ð1  wÞ Cf  Cm

ð4Þ

where as is the first-order solute mass transfer coefficient
between the fracture and the mobile water in the matrix,
expressed as
as ¼

b
Da
d2

ð5Þ

where b is the shape factor (15 for spheres), d is theeffective ‘‘diffusion’’ path length (half the aggregate widthor half the fracture spacing), and Da is the effective
diffusion coefficient representing the diffusion properties ofthe fracture-matrix interface as well as other parameters[Gerke and van Genuchten, 1996].
[14] If the fracture porosity (qf) is zero, then the dualpermeability dual-porosity model is reduced to a dualporosity model (also called physical nonequilibrium model,
or mobile-immobile (MIM) model). If the immobile watercontent (qim) is zero, the dual-porosity model is further
reduced to the so-called equilibrium model. For nonreactive
solute tracers the reduction rate coefficient (ms) and thesorption constant (Kd) are zero, and the terms associatedwith these parameters can be omitted.
[15] Colloidal particles undergo attachment and detachment during transport through porous media [Harvey andGarabedian, 1991; Hornberger et al., 1992; McCaulou etal., 1994; Ryan and Elimelech, 1996; Hendry et al., 1997,
1999; Zhang et al., 2001b]. In addition, colloids are
excluded from some small pores due to the large sizes
and low diffusion constants of the colloids. A physicalnonequilibrium conceptual model (MIM model) was used
to describe the transport of the microspheres through thepacked reactive zeolite/ZVI pellets. This model divides theporous medium into two regions (as opposed to the three
regions in the solute transport model): a mobile region(accessible to the microspheres), and an immobile region(inaccessible to the microspheres due to macroscopic ‘‘pore
exclusion’’). This model accounts for colloid attachment
and detachment and assumes that there is no mass transfer

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between mobile and immobile zones (i.e., the microspheresare completely excluded from the immobile zones). The
governing equations are as follows:@C rb @S
@2C@Cþ
¼D 2 v@t qm @t
@x
@x

ð6Þ

rb @S
r
¼ ka C  kd b S
qm @t
qm

ð7Þ

where C is the colloid concentration in aqueous phase, S
is the colloid concentration on solid phase, D is thedispersion coefficient, v is the pore water velocity, ka is
the colloid attachment rate coefficient, kd is the detachment rate coefficient, and qm is the ‘‘mobile’’ watercontent, or to be more accurate, the porosity accessible tocolloids. The physical nonequilibrium model is reduced tothe equilibrium model if qm is replaced by the total
porosity.
[16] The modified HYDRUS-1D computer code [Šimůnek
et al., 1998] was used to implement the transport models
described above. In HYDRUS-1D, parameter optimizationis achieved by using the Levenberg-Marquardt nonlinear
optimization method (a weighted least squares approach)[Šimůnek et al., 1998]. The parameters for optimization invarious transport models are summarized in Table 3. Since
dispersivity (aL) covaries with sorption constant (Kd), the aL
values for bromide in columns 3 and 4 (subject to adsorption) were fixed in the nonequilibrium model, using the aL
values derived from tritium data of these columns. For thedual-permeability dual-porosity model the effective ‘‘diffusion’’ path length (d) was set to 0.4 cm (half the pellet size),
and the dispersivity in mobile regions of the matrix (aL,m)
was fixed at 1.0 cm, a value appropriate for columns
packed with unconsolidated geological materials [Freeze
and Cherry, 1979]. Inverse modeling with the dual-permeability dual-porosity model was not attempted for bromide
data from columns 3 and 4, since fractions of adsorption
sites associated with different domains could not be independently determined. Multiple inverse simulations withdifferent initial parameter estimates were carried out toensure the uniqueness of optimized parameter values. Flowinterruption was implemented in HYDRUS-1D by changing the boundary conditions from finite flux to zero flux. In
addition to optimized parameter values, HYDRUS-1D also
provided statistical information such as the 95% confidence
interval for each fitted parameter and the goodness of fit(r2) of the simulations to the observed data [Šimůnek et al.,1998].
[17 ] Forward modeling using the dual-permeability
dual-porosity model with various fractions of preferential
flow porosity (w) and immobile water contents (qim) was
carried out to examine the effects of w and qim on
contaminant reduction. The sorption constant (K d =1.0 L kg1) and reduction rate coefficient (ms = 0.5 h1)used in the model are those appropriate for PCE sorption/
reduction by the SMZ/ZVI pellets [Zhang et al., 2002];
other transport parameter values (L = 30 cm, q = 0.81, rb =0.67 g cm3, d = 0.4 cm, v = 0.5 m h1, aL,m = 1 cm,
aL,f = 5 cm, as = 2.4  104 h1, and w = 3.7  103 h1)were determined for this study (see section 3). It is assumed

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Figure 2. Breakthrough curves for the bromide, tritium, and microsphere tracers. The microspheres
broke through the columns much faster than the nonreactive solute tracers. Bromide was retarded withrespect to the tritium tracer in the surfactant-treated columns 3 and 4. Flow interruptions occurred at the
2 and 4 pore volume (PV) (indicated by the arrows) for the solute tracers. A solute concentration reboundwas evident following the 4-PV interruption.
that every 1% of the preferential flow porosity carries 2.9%of the total flux (see discussion in section 3.5.2) but only
contains 0.1% of the total reactive sites (i.e., reactive sites
are mainly associated with the matrix porosity). It is further
assumed that the reactive sites in the matrix are evenly
distributed between the mobile and immobile regions. A
constant concentration solute input was used for eachsimulation, and the effluent C/Co (relative concentration)
at steady state (or plateau of a BTC) was plotted against
w or qim.

3. Results and Discussion
3.1. Pellet Properties[18] The columns packed with the crushed pellets had avery high porosity (0.81) and a very low bulk density(0.66 –0.67 g cm3; Table 2). The internal porosity (intrapellet porosity) of the pellets was 0.40, approximately 50%of the total porosity. The hydraulic conductivity of thecrushed pellets was 9.7 cm s1; the original larger pellets(2.5-cm cubes) would have an even higher hydraulicconductivity. The pellets also showed excellent mechanicalstrength, with no physical deterioration evident aftermonths of operation in a pilot-scale PRB [Bowman et al.,2002].
3.2. Breakthrough Curves[19] All tritium BTCs exhibited asymmetric shapes withearly breakthrough and extended tailing (Figure 2), indicating nonideal transport [Hu and Brusseau, 1995]. Close
examination of the tritium BTCs revealed a small peak orshoulder at about 0.5 PV prior to full breakthrough(Figure 2). A similar early peak was observed when nitrate
was transported through a silica sand column with asimulated fracture in the center; the peak was attributed to
the rapid transport of a small fraction of the nitrate throughthe fracture [Li and Ghodrati, 1997]. Therefore the small
early peak observed in our study may indicate the presenceof preferential flow in the column packed with the reactive
zeolite/ZVI pellets.
[20] Bromide BTCs from columns 1 and 2 (without
surfactant modification) were almost identical to the tritium
BTCs from those columns (Figures 2a and 2b). For thesecolumns no adsorption of bromide occurred and the bromide tracer behaved similar to the nonreactive tritium tracer.
Bromide in columns 3 and 4 (with surfactant modification),
however, was significantly retarded with respect to the

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Table 4. Percent Mass Recovery of the Solute and Colloidal
Tracers After Transport Through the Columns Packed With theReactive Zeolite/ZVI Pellets
ColumnTritium

BromideMicrosphere1, no surfactant
2, no surfactant
3, surfactant
4, surfactant

101.2
99.5
101.2
98.9

100.2
98.8
92.8
91.2

55.9
57.4
56.2
57.9

tritium tracer (Figures 2c and 2d). The surfactant-modified
pellets are expected to possess positive surface charges andreversibly adsorb Br [Li and Bowman, 1997], leading to
the retardation of the bromide tracer.
[21] The microspheres appeared in the effluent muchearlier than the nonreactive tritium tracer in all four columns, and the BTCs also exhibited asymmetric shapes andextended tailing (Figure 2). As mentioned in section 1,
colloidal particles are restricted to fractures and macroporesdue to their large sizes, and travel faster on average thansolute tracers in porous media containing preferential flow
paths. Therefore the earlier breakthrough of the microspheres with respect to tritium is indicative of the presenceof preferential flow paths, consistent with the observation of
the early tritium peak.
3.3. Flow Interruption[22] The flow interruption during tracer elution (at 4 PV)
resulted in concentration rebound for both the tritium andbromide tracers in all columns (Figure 2). The concentration
rebound of the nonreactive tracers (i.e., tritium in allcolumns and Br in columns 1 and 2) is a clear indicationof diffusive mass transfer of the tracers between advective
zones and immobile zones in the pellets [Reedy et al., 1996;
Brusseau et al., 1997]. The immobile zones likely existed
within the coating, due to the fine size of the zeolite andZVI powders used (Table 1). In contrast to the flowinterruption at 4 PV, the flow interruption at 2 PV (during
tracer injection) did not cause a noticeable concentration
decrease (Figure 2). At 2 PV, the tritium concentration (inall four columns) and the bromide concentration (in columns 1 and 2) were almost 100% of the input concentration(Figure 2), and the lack of effluent concentration change
implies near-equilibrium conditions [Brusseau et al., 1989].
3.4. Mass Recovery[23] Solute and colloid mass recoveries for the fourcolumns (determined by trapezoidal integration of the areas
under the BTCs) are shown in Table 4. Full mass recovery
was achieved for the tritium tracer in all four columns, andfor the bromide tracer in Columns 1 and 2 (Table 4). The
91– 93% mass recovery of bromide in columns 3 and 4(Table 4) was due to termination of the experiments prior tofull bromide elution, when the effluent bromide concentrations were still around 6% of the input concentration(Figures 2c and 2d). The 56– 58% mass recovery of microspheres (Table 4) was due to the irreversible attachment of afraction of the microspheres onto the pellets.
3.5. Inverse Modeling Results[24] Inverse modeling of the microsphere data yielded thesame unique values of optimized parameters for both theequilibrium and the nonequilibrium models. However, for

Figure 3. Best fit simulations of the microsphere data(column 3) using the equilibrium and physical nonequilibrium models. The nonequilibrium model provided asignificantly improved fit.
the solute tracers, simulations with the physical nonequilibrium model converged to three different solutions witheither (1) an extremely small immobile water content0), (2) a very large qim value (close to the total(qim
porosity of 0.8) with a large mass transfer coefficient, or (3) aqim value around 0.15. The first two solutions are essentiallythe equilibrium solutions, and our system was clearly not an
equilibrium one (see discussion in sections 3.2 and 3.3). The
third solution reflects the properties of the pellets and will be
discussed further in section 3.5.2. Inverse modeling of thesolute data with the dual-permeability dual-porosity modelyielded unique values for five (qm, w, qim, as, and w) out of
the six parameters optimized. The sixth parameter (aL,f)varied significantly, and some intermediate values will bepresented in section 3.5.2.
3.5.1. Colloidal Tracer[25] The equilibrium model gave reasonable fits to theobserved microsphere BTCs (see an example of column 3
data in Figure 3), but yielded extremely high dispersivity
values (average 1.3  103 cm, or 43 times the length of thecolumn; data not shown). The unrealistically high dispersivity values imply that the equilibrium model attempts tolump all processes unaccounted for in the conceptual model
into the dispersivity. Therefore the equilibrium model is notadequate in describing the transport of microspheres
through the packed zeolite/ZVI pellets. The physical non-Table 5. Optimized Parameter Values for the Microspheres Using
the Physical Nonequilibrium Modela
ColumnaL, cmqimka, h1kd, h1

r2

1
2
3
4
Average

29 ± 38
25 ± 25
29 ± 2430 ± 3228

0.45 ± 0.10
0.40 ± 0.10
0.35 ± 0.08
0.40 ± 0.10
0.40

0.13 ± 0.04
0.10 ± 0.03
0.10 ± 0.03
0.11 ± 0.03
0.11

0.003 ± 0.001
0.002 ± 0.002
0.002 ± 0.001
0.002 ± 0.002
0.002

0.96
0.96
0.97
0.96

a
The 95% confidence limits are indicated by the number following theplus/minus sign.

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Figure 4. Best fit simulations of the tritium data(column 3) using the nonequilibrium and dual-permeability
dual-porosity models. The dual-permeability dual-porosity
model fit the ascending limb and plateau of the breakthrough curve (BTC) better than the nonequilibrium model
but fit the descending limb and tail less satisfactorily.

equilibrium model significantly improved the fit (Figure 3)
and yielded much lower dispersivity values (average 28 cm,
Table 5). Furthermore, the physical nonequilibrium modelgave qim (porosity inaccessible to the microspheres) values
similar to the independently measured intrapellet porosity of
0.40 (Table 5), indicating that the microspheres were
excluded from the intrapellet pores and could only transport
through the large interpellet pore spaces. This exclusion of
microspheres from the intrapellet pores was further supported by the fact that the microspheres transported
through the packed pellets identically despite the differencein pellet surface chemistry (see microsphere BTCs in
Figures 2a –2d). Surfactant-modification of the zeolite/ZVI
pellets (columns 3 and 4) reversed the surface charge of thepellets from net negative to net positive [Li and Bowman,
1998], as reflected in the bromide retardation in thesecolumns. The charge reversal should also have resulted insignificant electrostatic attractions for the microspheres(slightly negative) at close range. The lack of increased
microsphere removal with the surfactant-modified pellets(columns 3 and 4) suggested that the majority of the
microspheres were not in close contact with the pellet
surfaces, consistent with the characteristics of transport
through large interpellet pore spaces. The removal of afraction (42 – 44%) of the microspheres was likely due tothe capture of the microspheres by external asperities of thepellets (Figure 1). The apparent velocity of the microspheres
was about 1.6 times the apparent velocity of the conservative
tritium tracer, based on the physical nonequilibrium model.
3.5.2. Solute Tracers[26] The simulated BTCs using the physical nonequilibrium model showed good agreement with the observed
solute BTCs (see an example of column 3 tritium data in
Figure 4). The immobile water content (qim) derived fromthe nonequilibrium model varied from 0.14 to 0.19 with anaverage of 0.15 (Table 6). These qim values implied that 35–
48% of the intrapellet porosity contained immobile water,

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confirming the presence of nonadvective zones inside thepellets as indicated by the flow interruption experiments.
The mass transfer coefficients varied from 0.06 to 0.15 h1(Table 6), within the range observed (using similar MIM
models) for tritium and bromide in synthetic aggregatedporous media [Hu and Brusseau, 1995] and field soils[Reedy et al., 1996; Casey et al., 1997].
[27] Conceptually, the dual-permeability dual-porosity
model describes the hydraulic properties of the packed
pellets better than the physical nonequilibrium model (seesection 2.4). However, the more complex dual-permeability
dual-porosity model only improved the fit marginally compared with the nonequilibrium model (see simulated BTCs
in Figure 4 and r2 values in Tables 6 and 7). The more
complex model predicted the ascending limb and theplateau of the BTC better than the nonequilibrium model
but fit the descending limb of the BTC less satisfactorily(Figure 4). Nevertheless, the dual-permeability dual-porosity model yielded physically meaningful qim and w (fractionof preferential flow porosity) values (Table 7). The qim
values derived from the dual-permeability dual-porosity
model (0.08– 0.12, with an average of 0.10; Table 7) wereless than those derived from the nonequilibrium model but
were still significant. The w values (6 – 11% of the total
porosity, average 8%; Table 7) were less than the interpellet
porosity (50% of the total porosity), indicating that only afraction of the interpellet porosity acted as preferential flow
paths. The average flux in the matrix was 1.28 cm h1(Table 7), or 77% of the total flux (1.67 cm h1). The
remaining 23% of the total flux was in the 8% preferential
flow porosity, implying that every 1% of the preferential
flow porosity carried 2.9% of the total flux.
3.6. Impacts of Preferential Flow and Immobile Wateron Contaminant Reduction[28] The predicted relationships between steady state
PCE breakthrough concentration and fraction of preferential
flow porosity (w) are depicted in Figure 5. For a qim of
0.001, the steady state C/Co increased linearly from 0.0072
to 0.71 (2 orders of magnitude) as the w values increased
from 1% to 30% (Figure 5). It is worth noting that thesteady state C/Co increased by almost tenfold (from 0.0072
to 0.070) as w increased from 1% to 5%, demonstrating that

Table 6. Optimized Parameter Values for the Solute Tracers Using
the Physical Nonequilibrium Modela
ColumnaL, cm1
2
3
4

11.2 ± 22.3
6.7 ± 10.3
8.1 ± 6.9
12.3 ± 16.4

1
2
3
4
Average

9.8 ± 16.6
7.5 ± 10.9
8.1b
12.3b
9.3

Kd, L kg1

r2

Tritium
0.19 ± 0.01 0.15 ± 1.75
0.17 ± 0.01 0.13 ± 1.07
0.14 ± 0.01 0.15 ± 1.25
0.14 ± 0.01 0.10 ± 1.11

...
...
...
...

0.91
0.94
0.95
0.94

Bromide0.15 ± 0.01 0.13 ± 1.67
0.14 ± 0.01 0.08 ± 0.72
0.15 ± 0.01 0.10 ± 0.83
0.15 ± 0.01 0.06 ± 0.36
0.15
0.11

...
...
1.30 ± 0.29
1.27 ± 0.31
1.29

0.91
0.94
0.89
0.86

qim

w, h1a
The 95% confidence limits are indicated by the number following theplus/minus sign.
b
Fixed and not included in the average.

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Table 7. Optimized Parameter Values for the Nonreactive Solute Tracers Using the Dual-Permeability Dual-Porosity Modela
Columnqm, cm h1

w (=qf / q)

qim

1
2
3
4

1.27 ± 0.16
1.29 ± 0.07
1.28 ± 0.09
1.25 ± 0.16

0.06 ± 0.04
0.11 ± 0.07
0.09 ± 0.07
0.07 ± 0.04

0.10 ± 0.10
0.11 ± 0.07
0.11 ± 0.03
0.08 ± 0.07

1
2
Average

1.34 ± 0.21
1.27 ± 0.12
1.28

0.08 ± 0.10
0.07 ± 0.04
0.08

0.12 ± 0.10
0.10 ± 0.07
0.10

as (104 h1)w (103 h1)

aL,f, cmr2

2.7 ± 5.0
1.2 ± 3.5
1.0 ± 1.0
3.1 ± 3.7

5.2 ± 13.1
3.3 ± 5.0
2.3 ± 2.9
2.2 ± 4.7

1.9 ± 70
13 ± 30
2.8 ± 81.9 ± 36

0.93
0.95
0.96
0.95

1.4 ± 2.9
5.0 ± 3.7
2.4

5.0 ± 9.7
3.9 ± 7.43.7

4.6 ± 405.8 ± 19
5.0

0.94
0.95

Tritium

Bromidea

The 95% confidence limits are indicated by the number following the plus/minus sign.

even a small preferential flow porosity (e.g., 5%) would
drastically lower contaminant removal efficiency (inversely
related to the steady state breakthrough concentration). It
appears that PCE removal efficiency would not be sensitive
to qim under the transport conditions examined here, as only
minor changes in C/Co are predicted as qim increased from0.001 to 0.2 (Figure 5). The minimal impact of qim on PCE
removal efficiency may be due to the relative fast masstransfer between the mobile and immobile regions of thematrix (i.e., w = 3.7  103 h1).
[29] Linear relationships between steady state PCE breakthrough concentration and immobile water content (qim)were also predicted (Figure 6). The slope of steady state
C/Co vs. qim (0.10, Figure 6) was much less than the slopeof C/Co versus w (2.5, Figure 5), indicating a much lower
dependence of contaminant removal efficiency on qim thanon w under the transport conditions examined. As qim
increased from 0.01 (insignificant immobile water content)
to 0.1 (as observed in the SMZ/ZVI pellets used here), theFigure 5. Predicted relationships between steady state
perchloroethylene (PCE) breakthrough concentration (C/Co)
and fraction of preferential flow porosity (w). Straight lines
are best linear fits. Parameter values used in the dualpermeability and dual-porosity model are L = 30 cm, q =0.81, rb = 0.67 g cm3, d = 0.4 cm, v = 0.5 m h1, aL,m = 1
cm, aL,f = 5 cm, as = 2.4  104 h1, w = 3.7  103 h1,
Kd = 1.0 L kg1, and ms = 0.5 h1.

steady state PCE breakthrough concentration (C/Co) increased from 0.0072 to 0.0115 at w = 0.001 and from0.205 to 0.211 at w = 0.1. Such minor increases in C/Co dueto the presence of immobile water (as a result of using finegrained reactive materials) would be easily compensated by
the gain in surface areas and reduction rates (e.g., a tenfold
decrease in particle diameter would lead to a 100-foldincrease in surface area). Therefore it would still be advantageous to use fine-grained materials in reactive pellets.
However, efforts should be made to minimize preferential
flow porosity (as observed in pilot- and field-scale PRBs
with pelletized reactive materials [Zhang et al., 2001a,2003] by manufacturing smaller pellets or other means,since even a small fraction of preferential flow porosity may
significantly decrease contaminant removal efficiency.

4. Summary and Conclusions[30] The hydraulic properties of a foamed zeolite/ZVI
pellet material were studied by conducting solute andcolloidal tracer tests. Preferential flow paths existed between the pellets, as indicated by the enhanced transport of

Figure 6. Predicted relationships between steady state
PCE breakthrough concentration (C/Co) and immobile
water content (qim). Straight lines are best linear fits.
Parameter values used in the dual-permeability dualporosity model are the same as in Figure 5.

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microspheres relative to tritium and the considerable preferential flow porosity determined by fitting the dual-permeability dual-porosity model. Nonadvective zones existed
inside the pellets, as indicated by the rebound of soluteconcentration during flow interruption and the significant
amount of immobile water content determined by fitting thenonequilibrium and dual-permeability dual-porosity models. Quantitatively, the packed pellets had a total porosity of
0.81, of which 50% was large interpellet porosity (0.41) andthe remaining was small intrapellet porosity (0.40). About
12– 22% of the large interpellet porosity was preferential
flow porosity, and about 19– 28% of the small intrapellet
porosity contained immobile water (based on the dualpermeability dual-porosity model). Forward modeling withthe dual-permeability dual-porosity model indicated that theimmobile water in the pellets would not significantly affectcontaminant removal efficiency. In contrast, the preferential
flow porosity would drastically lower contaminant removal
efficiency.

Notation
C
Co
Cf
Cm
Cim
d
D
Da
Df
Dm
e
f
ka
kd
Kd
L
qf
qm
S
t
v
w
x
aL
aL,f
aL,m
as
b
ms
q
qf
qm
qim
rb

concentration in aqueous phase, M L3.
input concentration, M L3.
solute concentration in fractures, M L3.
solute concentration in mobile
regions of matrix, M L3.
solute concentration in immobile
regions of matrix, M L3.
effective ‘‘diffusion’’ path length (half theaggregate width or half the fracture spacing), L.
dispersion coefficient, L2 T1.
effective diffusion coefficient, L2 T1.
dispersion coefficient in fractures, L2 T1.
dispersion coefficient in mobile regions ofmatrix, L2 T1.
fraction of sorption sites associated withmobile water in matrix.
fraction of sorption sites associated with fractures.
colloid attachment rate coefficient, T1.
colloid detachment rate coefficient, T1.
sorption constant, L3 M1.
column length, L.
flux in fractures, L T1.
flux in mobile regions of matrix, L T1.
concentration on solid phase, M L3.
time, T.
pore water velocity, L T1.
ratio of fracture domain to total soil volume.
travel distance, L.
dispersivity, L.
dispersivity in fractures, L.
dispersivity in mobile regions of matrix, L.
first-order solute mass transfer coefficient between
fractures and mobile water in matrix, T1.
shape factor.
solid phase first-order decay constant, T1.
total porosity, L3 L3.
fracture (preferential flow) porosity, L3 L3.
porosity in mobile regions of matrix, or porosity
accessible to colloids, L3 L3.
porosity in immobile regions of matrix, L3 L3.
bulk density, M L3.

w
Gs

W04207mass transfer coefficient between
mobile and immobile water in matrix, T1.
solute mass transfer rate between fractures andmobile water in matrix, M L3 T1.

[31] Acknowledgments. Funding for this research was provided by
the U.S. Department of Energy’s National Energy Technology Laboratory
under contract DE-AR21-95MC32108. The zeolite/ZVI pellets were prepared by Douglas Wolf of the University of Dayton Research Institute. Xian
Tao assisted in sample collection and analysis.

References
Bowman, R. S., P. Zhang, X. Tao, R. L. Johnson, and D. Wolf (2002),
Surface-altered zeolites as permeable barriers for in situ treatment ofcontaminated groundwater, Phase IIB topical report to the U.S. Dep. ofEnergy, Pittsburgh, Pa.
Brusseau, M. L., P. S. C. Rao, R. E. Jessup, and J. M. Davidson (1989),
Flow interruption: A method for investigating sorption nonequilibrium,
J. Contam. Hydrol., 4(3), 223 – 240.
Brusseau, M. L., Q. Hu, and R. Srivastava (1997), Using flow interruption
to identity factors causing nonideal contaminant transport, J. Contam.
Hydrol., 24(3 – 4), 205.
Cao, J., L. Wei, Q. Huang, L. Wang, and S. Han (1999), Reducing degradation of azo dye by zero-valent iron in aqueous solution, Chemosphere,
38(3), 565 – 571.
Casey, F. X. M., R. Horton, S. D. Logsdon, and D. B. Jaynes (1997),
Immobile water content and mass exchange coefficient of a field soil,
Soil Sci. Soc. Am. J., 61(4), 1030 – 1036.
Champ, D. R., and J. Shroeter (1988), Bacterial transport in fractured
rock—A field scale tracer test at the Chalk River Nuclear Laboratories,
Water Sci. Technol., 20(11 – 12), 81 – 87.
Deborde, D. C., W. W. Woessner, Q. T. Kiley, and P. Ball (1999), Rapid
transport of viruses in a floodplain aquifer, Water Res., 33(10), 2229 –2238.
DeFlaun, M. F., M. E. Fuller, P. Zhang, W. P. Johnson, B. J. Mailloux, W. E.
Holben, W. P. Kovacik, D. L. Balkwill, and T. C. Onstott (2001), Comparison of methods for monitoring bacterial transport in the subsurface,
J. Microbiol. Methods, 47(2), 219 – 231.
Freeze, R. A., and J. A. Cherry (1979), Groundwater, Prentice-Hall, Upper
Saddle River, N. J.
Fuller, M. E., S. H. Streger, R. K. Rothmel, B. J. Mailloux, J. A. Hall, T. C.
Onstott, J. K. Fredrickson, D. L. Balkwill, and M. F. DeFlaun (2000),
Development of a vital fluorescent staining method for monitoring bacterial transport in subsurface environments, Appl. Environ. Microbiol.,66(10), 4486 – 4496.
Gerke, H. H., and M. T. van Genuchten (1996), Macroscopic representationof structural geometry for simulating water and solute movement in dualporosity flow models, Adv. Water Resour., 29, 1225 – 1238.
Gillham, R. W., K. Ritter, Y. Zhang, and M. S. Odziemkowski (2002),
Factors in the long-term performance of granular iron PRBs, IAHS Publ.,
275, 421 – 426.
Ginn, T. R. (2002), A travel time approach to exclusion on transport inporous media, Water Resour. Res., 38(4), 1041, doi:10.1029/
2001WR000865.
Gotpagar, J., E. Grulke, T. Tsang, and D. Bhattacharyya (1997), Reductive
dehalogenation of trichloroethylene using zero-valent iron, Environ.
Prog., 16(2), 137 – 143.
Grant, D. C., M. C. Skriba, and A. K. Saha (1987), Removal of radioactive
contaminants from West Valley waste streams using natural zeolite,
Environ. Prog., 6(2), 104 – 109.
Grolimund, D., M. Elimelech, M. Borkovec, K. Barmettler, R. Kretzschmar,and H. Sticher (1998), Transport of in situ mobilized colloidal particles inpacked soil columns, Environ. Sci. Technol., 32(22), 3562 – 3569.
Harter, T., S. Wagner, and E. R. Atwill (2000), Colloid transport and filtration of Cryptosporidium parvum in sandy soils and aquifer sediments,
Environ. Sci. Technol., 34(1), 62 – 70.
Harvey, R. W., and S. P. Garabedian (1991), Use of colloid filtration theory
in modeling movement of bacteria through a contaminated sandy aquifer,
Environ. Sci. Technol., 25(1), 178 – 185.
Harvey, R. W., L. H. George, R. L. Smith, and D. R. LeBlanc (1989),
Transport of microspheres and indigenous bacteria through a sandy
aquifer: Results of natural- and forced-gradient tracer experiments,
Environ. Sci. Technol., 23(1), 51 – 56.

10 of 11

W04207

ZHANG ET AL.: NONIDEAL TRANSPORT OF SOLUTE AND COLLOIDAL TRACERS

Hendry, M. J., J. R. Lawrence, and P. Maloszewski (1997), Role of sorption
in the transport of klebsiella oxytoca through saturated silica sand,
Ground Water, 35(4), 574 – 584.
Hendry, M. J., J. R. Lawrence, and P. Maloszewski (1999), Effects ofvelocity on the transport of two bacteria through saturated sand, Ground
Water, 37(1), 103 – 112.
Hornberger, G. M., A. L. Mills, and J. S. Herman (1992), Bacterial transport in porous media: Evaluation of a model using laboratory observations, Water Resour. Res., 28(3), 915 – 938.
Hu, Q., and M. L. Brusseau (1995), Effect of solute size on transport instructured porous media, Water Resour. Res., 31(7), 1637 – 1646.
Kjeldsen, P., and T. Locht (2002), Removal of chromate in a permeable
reactive barrier using zero-valent iron, IAHS Publ., 275, 409 – 414.
Li, Y., and M. Ghodrati (1997), Preferential transport of solute through soilcolumns containing constructed macropores, Soil Sci. Soc. Am. J., 61(5),1308 – 1317.
Li, Z., and R. S. Bowman (1997), Counterion effects on the sorption of
cationic surfactant and chromate on natural clinoptilolite, Environ. Sci.
Technol., 31(8), 2407 – 2412.
Li, Z., and R. S. Bowman (1998), Sorption of perchloroethylene by surfactant-modified zeolite as controlled by surfactant loading, Environ. Sci.
Technol., 32(15), 2278 – 2282.
McCaulou, D. R., R. C. Bales, and H. F. McCarthy (1994), Use of shortpulse experiments to study bacteria transport through porous media,
J. Contam. Hydrol., 15(1 – 2), 1 – 14.
McKay, L. D., J. A. Cherry, R. C. Bales, M. T. Yahya, and C. P. Gerba(1993), Field example of bacteriophage as tracers of fracture flow,
Environ. Sci. Technol., 27(6), 1075 – 1079.
Molema, G., G. Mesander, B. Kroesen, W. Helfrich, D. Meijer, andL. de Leij (1998), Analysis of in vitro lymphocyte adhesion andtransendothelial migration by fluorescent-beads-based flow cytometriccell counting, Cytometry, 32, 37 – 43.
Morrison, S. J., D. R. Metzler, and C. E. Carpenter (2001), Uranium precipitation in a permeable reactive barrier by progressive irreversible dissolution of zero-valent iron, Environ. Sci. Technol., 35(2), 385 – 390.
Naftz, D. L., C. C. Fuller, J. A. Davis, S. J. Morrison, G. W. Freethey, E. M.
Feltcorn, R. G. Wilhelm, M. J. Piana, R. C. Rowland, and J. E. Blue
(2000), Field demonstration of permeable reactive barriers to remove
dissolved uranium from groundwater, Fry Canyon, Utah, September
1997 through September 1998, Rep. EPA 402-C-00-001A, U.S. Environ.
Prot. Agency, Washington, D. C.
O’Hannesin, S. F., and R. W. Gillham (1998), Long-term performance of anin situ ‘‘iron wall’’ for remediation of VOCs, Ground Water, 36(1), 164 –170.
Pang, L., M. Close, and M. Noonan (1998), Rhodamine WT and bacillus
subtilis transport through an alluvial gravel aquifer, Ground Water, 36(1),112 – 122.
Ponder, S. M., J. G. Darab, and T. E. Mallouk (2000), Remediation of Cr(VI) and Pb (II) aqueous solutions using supported, nanoscale zero-valentiron, Environ. Sci. Technol., 34(12), 2564 – 2569.
Puls, R. W., D. W. Blowes, and R. W. Gillham (1998), Emplacement
verification and long-term performance monitoring of a permeable reactive barrier at the USCG Support Center, Elizabeth City, North Carolina,
IAHS Publ., 250, 459 – 466.
Puls, R. W., D. W. Blowes, and R. W. Gillham (1999a), Long-term performance monitoring for a permeable reactive barrier at the U.S. Coast
Guard Support Center, Elizabeth City, North Carolina, J. Hazard. Mater.,
68(1), 109 – 124.
Puls, R. W., C. J. Paul, and R. M. Powell (1999b), Application of in situ
permeable reactive (zero-valent iron) barrier technology for the remediation of chromate-contaminated groundwater: A field test, Appl. Geochem., 14(8), 989 – 1000.

W04207

Reedy, O. C., P. M. Jardine, G. V. Wilson, and H. M. Selim (1996), Quantifying the diffusive mass transfer of nonreactive solutes in columns offractured saprolite using flow interruption, Soil Sci. Soc. Am. J., 60(5),1376 – 1384.
Reeter, C., A. Gavaskar, N. Gupta, and B. Sass (1998), Permeable reactive
wall remediation of chlorinated hydrocarbons in groundwater, NASA
Moffett Field, Mountain View, California, in Proceedings of the 2nd
International Water Resources Engineering Conference, Aug 3 – 7,
1998, pp. 153 – 158, Am. Soc. Civ. Eng., Reston, Va.
Ryan, J. N., and M. Elimelech (1996), Colloid mobilization and transport ingroundwater, Colloids Surf. A, 107, 1 – 56.
Scheibe, T. D., and B. D. Wood (2003), A particle-based model of size oranion exclusion with application to microbial transport in porous media,
Water Resour. Res., 39(4), 1080, doi:10.1029/2001WR001223.
Šimůnek, J., M. Sejna, and M. T. van Genuchten (1998), The HYDRUS-1D
software package for simulating the one-dimensional movement of water,
heat, and multiple solutes in variably-saturated media, Version 2.0, Rep.
IGWMC-TPS-70, Int. Ground Water Model. Cent., Colo. School ofMines, Golden, Colo.
Šimůnek, J., N. J. Jarvis, A. Gardenas, and M. T. Van Genuchten (2003),
Review and comparison of models for describing non-equilibrium andpreferential flow and transport in the vadose zone, J. Hydrol., 272(1 – 4),
14 – 35.
Stewart, C., and J. Steinkamp (1982), Quantification of cell concentration
using the flow cytometer, Cytometry, 2, 238 – 243.
Vilks, P., and D. B. Bachinski (1996), Colloid and suspended particle
migration experiments in a granite fracture, J. Contam. Hydrol., 21(1 – 4),
269 – 279.
Vogan, J. L., R. M. Focht, D. K. Clark, and S. L. Graham (1999), Performance evaluation of a permeable reactive barrier for remediation ofdissolved chlorinated solvents in groundwater, J. Hazard. Mater.,
68(1), 97 – 108.
Wang, C.-B., and W.-X. Zhang (1997), Synthesizing nanoscale iron particles for rapid and complete dechlorination of TCE and PCBs, Environ.
Sci. Technol., 31(7), 2154 – 2156.
Zhang, P., W. P. Johnson, M. J. Piana, C. C. Fuller, and D. L. Naftz (2001a),
Potential artifacts in interpretation of differential breakthrough of colloids
and dissolved tracers in the context of transport in a zero-valent ironpermeable reactive barrier, Ground Water, 39(6), 831 – 840.
Zhang, P., W. P. Johnson, T. D. Scheibe, K.-H. Choi, F. C. Dobbs, and B. J.
Mailloux (2001b), Extended tailing of bacteria following breakthrough at
the Narrow Channel focus area, Oyster, Virginia, Water Resour. Res.,
37(11), 2687 – 2698.
Zhang, P., X. Tao, Z. Li, and R. S. Bowman (2002), Enhanced perchloroethylene reduction in column systems using surfactant-modified zeolite/
zero-valent iron pellets, Environ. Sci. Technol., 36(16), 3597 – 3603.
Zhang, P., R. S. Bowman, T. L. Johnson, and R. L. Johnson (2003), Influence of preferential flow on perchloroethylene reduction in a surfactantmodified zeolite/zero-valent iron permeable reactive barrier, paper
presented at the 2003 Geological Society of America Annual Meeting,Seattle, Wash., 2 – 5 Nov.




R. S. Bowman, Department of Earth and Environmental Science, New
Mexico Institute of Mining and Technology, 801 Leroy Place, Socorro, NM
87801, USA.
J. Šimůnek, Department of Environmental Sciences, University ofCalifornia, Riverside, 900 University Avenue, Riverside, CA 92521, USA.
P. Zhang, Department of Earth and Atmospheric Sciences, City Collegeof New York, Convent Avenue at 138th Street, New York, NY 10031,
USA. (pzhang@sci.ccny.cuny.edu)

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