&EPA
United States
Environmental Protection
Agency
National Risk Management
Research Laboratory
Ada, OK 74820
Research and Development
EPA/600/S-99/001
February 1999
ENVIRONMENTAL
RESEARCH BRIEF
Colloid Mobilization and Transport in Contaminant Plumes:
Field Experiments, Laboratory Experiments, and Modeling
Joseph N. Ryan1, Rebecca A. Ard1, Robin D. Magelky1,
Menachem Elimelech2, Ning Sun2, and Ne-Zheng Sun2
Abstract
The major hypothesis driving this research, that the
transport of colloids in a contaminant plume is limited by the
advance of the chemical agent causing colloid mobilization,
was tested by (1) examining the dependence of colloid
transport and mobilization on chemical perturbations, (2)
assessing the relative transport of mobilized colloids and
the chemicals that caused their mobilization, and (3)
developing a colloid transport model that would begin to
describe these effects. Through field tests, laboratory
experiments, and model development, we made significant
advances toward the testing of the hypothesis. The field
tests, conducted in the uncontaminated and contaminated
zones of a ferric oxyhydroxide-coated quartz sand aquifer,
showed in almost all cases that colloids will not advance
ahead of the plume that caused their mobilization. The
laboratory experiments showed chemical perturbations that
cause increasingly repulsive conditions produced more
extensive and more rapid colloid release. In both the field
and laboratory experiments, good correlations wereobserved
between the surface properties of the colloid and aquifer
grains and their transport and mobilization behavior. The
colloid transport model was developed to describe colloid
' Department of Civil, Environmental, and Architectural Engineering,
University of Colorado, Boulder.
2 Department of Civil and Environmental Engineering University of
California, Los Angeles.
transport in physically and geochemically heterogeneous
porous media similar to that encountered at the field site.
The model results showed that physical and geochemical
heterogeneities could resultin preferential flow of colloids in
layered porous media, while random distributions of the two
heterogeneities, especially the physical heterogeneity, lead
to a random behavior of colloid transport.
Introduction
Colloids have been implicated in the enhanced transport of
radionuclides and metals in recent field studies and laboratory
experiments [Buddemeierand Hunt, 1988; McCarthy and Zachara,
1989; Dunnivant et al., 1992; Puls and Powell, 1992; Grolimund
etal., 1996]. Unfortunately, these studies have rarely delved into
the genesis, nature, and abundance of the colloids responsible
for the enhanced transport. We are relatively certain of the
processes governing the association of these contaminants with
colloids, but we have little knowledge of the potential for colloid
mobilization and subsequent transport in a given aquifer.
Colloid mobilization is caused by chemical and physical
perturbations to aquifer geochemistry and hydraulics [McCarthy
and Degueldre, 1993; Ryan and Elimelech, 1996]. Chemical
perturbations of the type occurring in contaminant plumes are
capable of mobilizing large quantities of colloids. In particular, the
mobilizing effect of organic compounds like surfactants and
reductants is well known [Ryan and Gschwend, 1994; Allred and
Brown, 1994]. Colloid mobilization by physical perturbations is
generally limited to fracture flow and increases in groundwater
flow velocity induced by pumping [Degueldre etal., 1989; Pulset
al., 1992; Backhus et al., 1993].
-------
In contaminant plumes, colloids are mobilized and
transported with the groundwater. If the advance of the
contaminant plume is retarded, the colloids will attempt to move
ahead of the plume. When the colloids re-enter the pristine
groundwater, they will be redeposited. As the plume catches up,
the colloids will be remobilized and the cycle will begin again.
These simultaneous mobilization and deposition processes have
been observed in natural analogs to contaminant plumes like
organic matter-rich water infiltrating from a swamp [Ryan and
Gschwend, 1990] or fresh water advancing into salt water in a
coastal aquifer [Goldenberg et al., 1983]. The major hypothesis
of this research was that the transport of the colloids in a
contaminant plume is limited by the advance of the chemical
agent causing colloid mobilization. To design experiments to test
this hypothesis, we set three overall objectives for the project:
(1) examine the dependence of colloid transport and mobilization
on chemical perturbations, (2) assess the relative transport of
mobilized colloids and the chemicalsthat caused their mobilization,
and (3) develop a colloid transport model that would begin to
describe these effects. These objectives were met and the
hypothesis was tested by field experiments atthe U.S. Geological
Survey Cape Cod aquifer site, laboratory mobilization experiments,
and the development of a colloid transport model emphasizing
the dynamics of colloid transport and the effects of heterogeneity.
Field Experiments
Purpose
Two field experiments were conducted during the summers
of 1996 and 1997 at the U.S. Geological Survey's Toxic
Substances Hydrology Research Site on Cape Cod,
Massachusetts. To address two of the overall objectives of the
project, to examine the dependence of colloid transport and
mobilization on chemical perturbations and to assess the relative
transport of mobilized colloids and the chemicals that caused
their mobilization, the following tests were conducted in the field:
• assessment of the effects of chemical perturbations (elevated
pH, surfactant concentration, and reductant concentration
and decreased ionic strength) on the mobilization of natural
colloids, synthetic colloids, and viruses
• measurement of the rate of deposition of synthetic colloids
and viruses
• determination of the relative rates of migration of a plume of
a colloid-mobilizing agent and the mobilized colloids
In addition, we demonstrated the use of silica-coated metal oxide
tracer colloids in a field experiment unrelated to the original
overall goals of the project.
These experiments were conducted in the field (ratherthan
in the laboratory )forthree major reasons. First, we expect that the
extent of colloid mobilization from a sedimentwould increase with
disturbances to the sediment like sampling, repacking into
columns, and drying. Measurement of the extent of colloid
mobilization in situ should provide a more realistic estimate of the
effect of chemical perturbations on colloid mobilization. Second,
the Cape Cod site provided a unique opportunity to observe
colloid mobilization and transport in a relatively homogeneous
aquifer with a clear geochemical difference imposed by a plume
of secondarily-treated sewage; reproducing such a system in the
laboratory would be very difficult. Third, we hypothesized that
detection of the relative rate of migration of a colloid-mobilizing
agent (e.g., elevated pH, surfactant) and the mobilized colloids
would require monitoring of transport of a distance of a few
meters. The Cape Cod site provided well-instrumented arrays of
multi-level samplers for injection and monitoring to test this
hypothesis.
Site Description
The virus and silica colloid injections were conducted in the
surficial aquifer at the U.S. Geological Survey's Cape Cod Toxic
Waste Research Site nearthe Massachusetts Military Reservation
on Cape Cod, Massachusetts. The aquiferwas contaminated by
disposal of secondary sewage effluent onto rapid infiltration sand
beds for over 50 years [LeBlanc, 1984], creating a contaminant
plume characterized by low dissolved oxygen concentrations
and elevated pH, specific conductivity, and organic carbon
concentrations (Table 1). Previously, the site has been used to
study the transport of groundwater tracers, metals, nutrients,
detergents, microspheres, bacteria, protozoa, and viruses [Barber
et al., 1988; Harvey et al., 1989; 1995; Harvey and Garabedian,
1991; Smith etal., 1991; Hessetal., 1992; Kentetal., 1994; Bales
etal., 1995; Pieperetal., 1997]. The aquiferconsists of Pleistocene
glacial outwash deposits characterized by interbedded lenses of
sand and gravel. The grains (average diameter 0.6 mm) consist
mainly of quartz coated by ferric oxyhydroxides [Coston et al.,
1995]. The effective porosity is 0.39 and the average hydraulic
conductivity is 110 m d~1. The water table depth is between 6 m
and 7 m below the surface near the study site [LeBlanc et al.,
1991].
Groundwater at the site is monitored by approximately
1,000 multi-level samplers (MLSs), each consisting of a bundle
of polyethylene tubes threaded through a polyvinylchloride pipe
to 15 depths below the watertable at 25 cm depth intervals. In the
direction of groundwater flow, these MLSs are spaced at 1m to
2 m distances. For our studies, we used three small arrays of
MLSs near the up-gradient end of the site (Figure 1).
Materials and Methods
The following section highlights important aspects of the
materials used and methods followed during the field experiments.
Details are provided in Ard [1997] and Magelky [1998].
Injections. Two major sets of field experiments were
conducted during the summers of 1996 [Ard, 1997] and 1997
[Magelky, 1998]. Each set of experiments consisted of(1) natural
colloid mobilization by chemical perturbations, (2) syntheticcolloid
deposition under ambient conditions, and (3) synthetic colloid
mobilization by chemical perturbations. Injections of 100L of
amended groundwater were made into the uncontaminated and
contaminated zones of the aquifer using four arrays of multi-level
samplers (MLSs). Each MLS array consisted of an injection MLS
and four to six monitoring MLSs at down-gradient intervals of
approximately 1 m. In 1996, three identical virus and colloid
injections were followed by three different chemical perturbation
injections (Table 2). In 1997, a variety of colloid and chemical
perturbation injections were made (Table 3).
Sampling and Field Analysis. During both field experiments,
six depths in each of the MLSs were sampled immediately before
and after the injections to measure background and initial
concentrations (C0) for each constituent. After the injections,
samples were collected daily (1996) or twice daily (1997). In the
field, sample pH, specific conductance, dissolved oxygen, and
ferrous iron were measured. The pH meter and electrode were
calibrated with pH 4, 7, and 10 buffers at the groundwater
temperature and calibrations varying by more than 5% from the
proper Nernst response were redone. The specific conductance
electrode and meter were calibrated with solutions of known
conductance atthe groundwatertemperature. Calibrations varying
by more than 5% from the known conductances were redone.
The dissolved oxygen measurements, made by Rhodazine D™
(0 to 1 mg L1) and indigo carmine (1 to 12 mg L1) test kits
(CHEMetrics, Inc.), were checked against solutions purged by
-------
Table 1. Chemistry of the groundwater in the unconfined glacial outwash aquifer approximately 150 m downstream of the sewage
infiltration beds at the Cape Cod site.
constituent
unit
uncontaminated contaminated
zone zone
reference
pH
specific conductance
ionic strength
temperature
dissolved oxygen
dissolved organic carbon
MBASa
Na+
K+
Mg2+
Ca2+
NH4+
Mn (dissolved)
Fe (dissolved)
cr
NO3
HCCV
SO42"
total PO43"
sediment clay content
sediment Fe(III)b
sediment phospliate
sediment foc
H-S cm"1
mM
°C
mgL'1
mgL-1
mgL-1
U.M
uM
|JM
uM
U.M
uM
uM
U.M
U.M
|JM
uM
U.M
wt%
uinol g'1
u.mol g'1
5.4 to 5.6
30 to 40
0.5
15.5
4.5 to 6.5
0.4 to 1.0
0.05
250
21
37
28
<1
0.64
0.05
230
<10
28
85
0.74
0.33±0.02
3.6±0.3
0.61±0.09
0.0001
5.8 to 6.0
250 to 330
4.0
15.0
0 to 0.5
2.0 to 4.4
0.10
1,900
200
130
210
<1
15
0.16
760
300
640
360
12
0.35±0.06
4.7±1.4
0.58 to 1.5
0.01
this study
this study
this study
this study
this study
this study
this study
this study
LeBlanc et al. [1991]
LeBlanc et al. [1991]
this study
LeBlanc et al. [1991]
LeBlanc et al. [1991]
this study
LeBlanc et al. [1991]
LeBlanc et al. [1991]
LeBlanc et al. [1991]
LeBlanc et al. [1991]
LeBlanc et al. [1991]
this study
this study
Walter etal. [1996]
Scholl and Harvey
[1992]
a MBAS, methylene blue active substances (detergents, surfactants).
b Sediment Fe(lll) is ferric iron extracted by Ti(lll)-citrate-EDTA-bicarbonate [Ryan and Gschwend, 1991].
nitrogen and saturated by air at the groundwater temperature.
Ferrous iron measurements, made by a 1,10-phenanthroline 0.1
to 10 mg Latest kit (CHEMetrics, Inc.), were checked against 0.1
and 1.0 mg L1 FeCI2 solutions prepared in the field laboratory.
Injection Constituents. Bromide, added as NaBr, was used
as a conservative tracer and measured by ion-specific electrode.
The bromide electrode was calibrated by 0.01, 0.1 and 1.0 mM
sodium bromide solutions before and after each set of
measurements. Calibration curves showing 5% variation from
the proper Nernst response were redone. Sodium hydroxide was
added after dissolution in 1 Lofdeionizedwater(MilliporeMilli-Q)
to elevate pH. Sodium dodecylbenzenesulfonate (NaDBS)
dissolved in high purity water was added to elevate the surfactant
concentration and measured with a methylene blue active
substances (MBAS) test kit (Hach Co.) with a detection limit of
0.1 mg L1.TheMBAStestkitwastestedagainstNaDBSsolutions
of known concentration, /.-ascorbic acid dissolved in deionized
water was added to elevate the reductant concentration. Ascorbic
acid concentrations were measured by UV spectrophotometry at
264 nm checked against standards made in uncontaminated and
contaminated groundwater. To reduce the ionic strength of the
groundwater, injections of deionized water were made and
tracked by specific conductance.
Silica colloids (Nissan Chemical Industries, MP-1040) of
107±21 nm diameter were used as the synthetic colloid during
the 1996 experiments. Silica colloid concentration was measured
by UV spectrophotometry at 340 nm (with a detection limit of
about C C0"1=0.001) and turbidity (with a detection limit of about
C C0"1=0.01). The spectrophotometer and turbidity meter were
calibrated using silica colloid suspensions in the uncontaminated
and contaminated groundwaters. The silica colloids were chosen
to mimic the negative surface charge found on most natural
colloids and to provide a uniform colloid size distribution forwhich
to assess the collision efficiency.
To improve the detection of the colloids injected during our
1997 field experiment, two "tracer" colloids composed of metal
oxides (zirconia, ZrO2; titania, TiO2) coated by silica were
developed [Ryan, Magelky, and Elimelech, in preparation]. The
-------
5m
0
2-
4-
6
Im
2-
4-
6
;m
0
2-
4-
6-
;m
MLS 3-11
Injection
V
5]
MLS 3-1 2
Injection
V
§
_
3A-11
1
5^
3 A- 12
5s,
|
_
1
1
4-11
1
55,
4-12
_
4A-11
5s,
s
§
4A-12
|
=51
_
5-n
51
|
5-n
§
1
_
c
Sj
1
5A-n
51
s
»A-12
(
=51
J=l
_
6
S
§
12
A
L
2
C
7
6-11
Array 1
?. Uncontaminated
Zone
s Contaminated
Zone
Array 2
Uncontaminated
Zone
Contaminated
MLS 4- 15
Injection
_3__
^
*a
_P
4A-15
5s
5^
=!)
§
5-15
^
=5]
=a
^
^
5A-15
*%!
*5,
^
=4)
§
_
6-15
Array:
j Uncontaminated
* Zone
S Contaminated
" Zone
(iroundwaler How
Figure 1. Sketch of multilevel sampler (MLS) arrays used in the field experiments at the Cape Cod site. Array numbering shown
for 1996 experiments. For 1997 experiments, Array 1 is headed by MLS 3-11, Array 2 is headed by MLS 4-11, Array 3
is headed by MLS 3-12, and Array 4 is headed by MLS 4-15. Sampling ports are separated by 25 cm depths. Depths
to the water table and the boundary between the Uncontaminated and contaminated groundwater are shown.
zirconia particles were purchased from Aldrich Chemical Co. in
an acetic acid solution. Thetitania particles were synthesized by
the hydrolysis of titanium(IV)tetraethoxylate in a mixture of water
inethanol(1%v/v)and hydroxypropyl cellulose. The zirconia and
titania particles were coated with silica by hydrolysis of tetramethyl
orthosilicate (Table 4). The concentrations of the silica-coated
zirconia and titania particles were measured by inductively-
coupled plasma-atomicemission spectrophotometryforzirconium
and titanium, providing a much improved detection limit of
C C0~1 < 10"5 owing to the very low background concentrations of
zirconium and titanium. A particle nebulization efficiency factor
was established by comparing instrument response to the pure
zirconia and titania particles to acidified solutions of Zr and Ti
ions. The silica coatings on these particles mimic the negative
charge found on most natural particles (as did the pure silica
particles) and the relatively uniform size distributions foster
accurate collision efficiency calculations and modeling.
The virus used in the injections is the bacteriophage PRD1
[Olsen et al., 1974]. The PRD1 were radiolabeled with
32P-phosphate[Lovelandetal., 1996]. PRD1, which was isolated
from municipal sewage, has a negative surface charge and size
typical of many pathogenic waterborne viruses. Virus
concentrations were measured by liquid scintillation counting
and checked by standards of known 32P activity.
Colloid Characterization. The abundance of natural colloids
was measured by turbidity. Turbidity was converted to a colloid
concentration by measuring the mass of colloids in samples of
known turbidity. The natural colloids were also characterized by
-------
Table 2.
Table 3.
Summer 1996 injection schedule. Constituents of natural colloid mobilization and silica colloid and bacteriophage
PRD1 deposition and recovery experiments. C0 is the concentration measured in samples withdrawn immediately
after injection. , . . , , . , ,
J uncontarmnated contaminated
injectate C0 C0
injection array constituents concentration 6.4 m depth 8.7m depth
natural colloid
mobilization
synthetic colloid
deposition
synthetic colloid
recovery
1
2
•i
j
1
2
3
1
2
3
NaOH
NaBr
NaDBS3
NaCl
ascorbic acid
NaBr
PRD1
SiO2 conoids
NaBr
PRD1
SiO2 conoids
NaBr
PRD1
SiO2 conoids
NaBr
NaOH
NaBr
NaDBS
NaBr
ascorbic acid
NaBr
pH12.5
l.SOmM
0.57mM
2.56mM
1.82mM
l.SOmM
2.6xl06cpmL-1
500 mg L-1
l.SOmM
2.1xl06cpmL-1
500 mg L-1
l.SOmM
2.2xl06cpmL-1
500 mg L-1
l.SOmM
pH12.5
l.SOmM
57 mM
l.SOmM
1.82mM
l.SOmM
pH12.3
1.18mM
0.34mM
2. 13 mM
0.63mM
1.33mM
390 mgL-1
0.79mM
400 mg L-1
l.OSmM
LSxlO'^cpmL1
210 mgL-1
0.90mM
pH11.7
0.77mM
52 mM
1.36mM
1.76mM
0.86mM
pH11.9
1.46mM
0.66 mM
2.47 mM
1.73 mM
1.56mM
1.9xl06cpmL-1
470 mg L-1
0.90mM
l.SxlO^cpmL-1
480 mg L-1
1.12mM
310 mgL-1
1.02 mM
pHll.8
1.04mM
52 mM
1.07 mM
2.09 mM
l.llmM
3 NaDBS is sodium dodecylbenzene sulfonate, an anionic surfactant.
Summer 1997 injection schedule. Constituents of natural colloid mobilization and silica-coated zirconia and titania
colloid deposition and recovery experiments. C0 is the concentration measured in samples withdrawn immediately
after injection. Silica-coated zirconia and titania colloids are shown as Si/ZrO2 and Si/TiO2, respectively.
uncontaminated contaminated
injectate Co C0
injection array constituents concentration3 6.4m depth 8.7m depth
natural colloid
mobilization
synthetic colloid
deposition
1st synthetic
colloid recovery
2IKl synthetic
colloid recovery
1
3
4
1
2
3
4
I
3
3
NaOH
NaBr
deionized water11
NaDBS'
NaBr
Si/ZrO,
NaBr
Si/ZrO2
NaBr
Si/ZrO,
NaBr
Si/ZrO2
SL'TiO,
NaDBS
NaBr
NaOH
NaBr
deionized water
NaOH
NaBr
pHll
l.OOmM
29 mM
l.OOmM
24 9/230 ppm Zr
l.OOmM
26 1/275 ppm Zr
1.00 mM
246/234 ppm Zr
1.00 mM
61/124 ppm Zr
93/140 ppm Ti
72 uM
l.OOmM
pHll
1.00 mM
pHIO
1.00 mM
pH 10.3
0.75 mM
43 mM
0.34 mM
190 ppm
l.llmM
191 ppm
0.99 mM
283 ppm
0.92 mM
62.7 ppm
41.0 ppm
72 uM
1.04 mM
pH 10.8
0.87mM
pH9.7
1.09 mM
pIilO.8
0.72 mM
llmM
0.75 mM
51. 9 ppm
l.llmM
159 ppm
0.69 mM
240 ppm
0.94 mM
33.0ppm
41.1 ppm
72 uM
1.11 mM
pH 1 1 .2
0.81 mM
pH9.7
l.OlmM
" Injectate concentration in uncontaminated and contaminated zones shown as "uncont/cont" where the concentrations were
different in the two injectates.
" Millipore Milli-Cf-'-prepared water used as deionized water
c NaDBS is sodium dodecylbenzene sulfonate, an anionic surfactant.
-------
Table 4. Size of titania and zirconia particles before and after coating with silica. Size measured by dynamic light scattering
reported as the mean diameter ± one standard deviation for a Gaussian distribution of particle sizes.
particle
zirconia
titania
uncoated
diameter
(nm)
1 10 ±34
1030±140
silica-coated
diameter
(nm)
130 ±49
1080±160
silica coat ing
thickness3
(nm)
10
50
3 Coating thickness estimated as one-half the difference between the
silica-coated and uncoated diameters.
SEM and energy-dispersive x-ray spectroscopy (EDX) after
being trapped on filters, mounted on aluminum stubs, and gold-
or carbon-coated. The electrophoretic mobility of the colloids and
virus were determined by microelectrophoresis (Brookhaven,
model ZetaPlus) in uncontaminated and contaminated
groundwater and groundwaters amended by the chemical
perturbations. The consistency of microelectrophoresis
measurements was checked by measuring the zeta potential of
polystyrene latex microspheres. During the elevated pH
mobilization experiments in 1997, dissolved organic carbon was
measured in 0.45|um-filtered samples by combustion on a platinum
catalyst (Shimadzu TOC-5000). The organic carbon analyzer
response was calibrated with potassium biphthalate solutions.
Total organic carbon concentration was also measured by
absorbance of light at 254 nm in unfiltered samples.
Aquifer Sediments. Cores were obtained from the
uncontaminated and contaminated zones of the aquifer about
5 m east of the injection wells used in the field experiments. The
sediments were impregnated with epoxy resin and thin-sectioned
for electron microprobe (JEOL, model 8600, 15 kV accelerating
voltage) and SEM (backscatter electron detection) analysis. The
area of surfaces covered by ferric oxyhydroxide coatings were
estimated by observing the fraction of surface coatings for 400
grains in the uncontaminated and contaminated zone sediments.
The mineralogy of the fine particles of the sediments was
determined by powder x-ray diffraction (XRD). Total Fe, Al, and
Mn were measured by digestion in concentrated HF/HNO3
solutions. Free Fe, Al, Mn were measured by Ti(lll)-citrate-EDTA-
bicarbonate extraction [Ryan and Gschwend, 1991]. The total
digestions and free element extractions were checked by
measuring the Fe, Al, and Mn content of pure oxides of these
elements. The streaming potentials of aquifer grains were
measured in the laboratory of Dr. Philip R. Johnson at Notre
Dame University.
Data Quality Assurance. The data collected in the field were
subject to careful quality assurance procedures outlined in the
Quality Assurance Project Plan for Colloid Mobilization and
Transport in Contaminant Plumes, a report prepared by the
project investigators at the beginning of the project. All of the
data presented in this report met the data quality assurance
criteria.
Specific quality assurance procedures have been included
with the details of analysis above. In addition to these specific
procedures, many general quality assurance procedures were
followed. Analytical methods used to characterize the sediment
and groundwater samples were tested using standards and
spiked samples. Selected samples were measured on alternate
instruments available in nearby laboratories (particle size, zeta
potential) for analyses that cannot be absolutely calibrated or
tested foraccuracy. The influence of contamination from reagents
and laboratory environment was assessed using reagent blanks
and method blanks. Instrumentvariability was tested with internal
standards. Measurements were checked by mass balance. During
fieldwork, field blanks were included in the samples to assess
contamination of samples by sample containers, sampling
handling, storage, and shipping. Preliminary studies were carried
out to determine the primary sources of error in analytical
procedures. In both the laboratory and the field, replicate samples
were tested at various times to assess the effect of sample aging
and storage on results.
Calculation of Attenuation, Collision Efficiency, Recovery,
and Interaction Forces. The relative breakthrough (RB, %) of an
injected constituent is a ratio of the time-integrated mass of the
constituent relative to that of the conservative tracer. Collision
efficiencies (a) were calculated with longitudinal dispersion for
pulse inputs of PRD1 and silica colloids [Harvey and Garabedian,
1991], In the collision efficiency calculations, the single collector
efficiencies [Yaoetal., 1971; Rajagopalan and Tien, 1976] were
calculated using only the convective-diffusion and sedimentation
contributions. An average grain diameter of 0.6 mm, a porosity of
0.39, and fluid velocities of 0.7 m d~1 forthe uncontaminated zone
and 0.4 m d*1 for the contaminated zone were used in these
calculations. Fractional recoveries of virus and silica colloids
following the chemical perturbations were estimated as the
quantity of virus or colloid recovered during the second injection
divided by the quantity of virus or colloid immobilized during the
first injection [Pieper et al., 1997].
Results
Colloid Characterization. The mobilized natural colloids
ranged in size from <0.1 to 10|am, were platy in shape, and were
composed primarily of silica and aluminum with traces of
phosphorus and iron. No significant differences were observed
for colloids mobilized in the uncontaminated or contaminated
zones orfor colloids mobilized by different chemical perturbations.
Mineral identification by XRD revealed quartz, feldspar, kaolinite,
illite/muscovite, and smectite, but no crystalline ferric
oxyhydroxides. Microelectrophoresis analysis revealed a pHpzc
of approximately 2.2±0.3. Above pH 9.5, the zeta potentials of
the colloids reached a plateau of about -45 mV.
The silica and silica-coated colloid surfaces were negatively
charged from about pH 3 to the ambient pH of the uncontaminated
groundwater (Tables 5 and 6). The silica and silica-coated
colloids were slightly more negatively charged in the contaminated
groundwater than in the uncontaminated groundwater.
The PRD1 surface was negatively charged from pH 3.2 to
the ambient pH of the uncontaminated groundwater (Table 7).
PRD1 was slightly more negatively charged in the contaminated
groundwaterthan in the uncontaminated groundwater. The PRD1
-------
Table 5.
Silica-coated zirconia colloid zeta potentials under ambient and perturbed conditions.
sample
silica-coated ZrO2 colloids
uncontam groundwater
LSmMNaBr
silica-coated ZrO2 colloids
uncontam groundwater
silica-coated ZrO2 colloids
con tarn groundwater
LSmMNaBr
silica-coated ZrO2 colloids
con tarn groundwater
conditions
pH 3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
ambient
1 mM NaBr
72 uM NaDBS
49 mM NaDBS
1 mM AscAc
pHlO.O
pH 1 1.0
pH 3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
ambient
1 mM NaBr
72 uM NaDBS
49 mM NaDBS
1 mM AscAc
pHlO.O
pH 1 1.0
zeta potential
(mV)
-16±4
-18±3
-19±4
-21±2
-20±2
-25±7
-21±2
-23±2
-35±5
-21±2
-23±1
-22±2
-40±2
-24±3
-23±2
-35±4
-20±4
-20±2
-21±2
-19±1
-22±2
-23±2
-21±3
-24±2
-26±2
-20±1
-24±2
-22±1
-40±2
-25±4
-24±1
-26±2
Table 6. Silica-coated titania zeta potentials under ambient and perturbed conditions.
zeta potential
sample conditions (mV)
titania (TiO2) colloids
l.SmMNaBr
silica-coated TiO2 colloids
l.SmMNaBr
pH 3.2
4.0
4.5
6.1
9.6
10.0
pH 3.1
3.8
4.1
5.9
7.6
9.5
11.2
9±2
4±3
-8±1
-2 3 ±2
-28±5
-26±2
1+2
-7±3
-13+2
-21+2
-22±4
-27±4
-25±2
-------
Table 7. Bacteriophage PRD1 zeta potentials under ambient and perturbed conditions.
sample
PRD1
uncontam groundwater
l.SmMNaBr
conditions
pH 3.2
4.1
5.2
6.1
zeta potential
(mV)
-8±3
-11±3
-17±2
-25±4
PRD1
contam groundwater
l.SmMNaBr
pH
6.4
-27±4
zeta potentials are slightly more negative than those measured
for PRD1 in a calcium phosphate solution [Bales et al., 1991].
Sediment Characterization. XRD revealed that the
sediments were composed mostly of quartz, feldspars, and
traces of clay minerals (kaolinite, illite/muscovite, smectite) and
iron oxyhydroxides. Electron microprobe analysis of sediment
thin sections from both the uncontaminated and contaminated
zones revealed scattered coatings on the quartz grains that
contained iron oxyhydroxide and clay minerals in both the
uncontaminated and contaminated zones (Figure 2). The coating
coverage was estimated at 3.0±10.0% of the uncontaminated
grain surfaces and 3.5±11.1 % of the contaminated grain surfaces.
Most of the grains were uncoated and a small fraction of the
grains were coated an average of about 50%. This small amount
of surface coverage agrees well with the relatively low sediment
Fe(lll) concentrations measured by reductive extraction in the
aquifer sediments (Table 1). The zeta potential of contaminated
sediments in the contaminated groundwater is significantly more
negative than that of the uncontaminated sediments in the
uncontaminated groundwater (Table 8). The natural colloids
mobilized fromthe uncontaminated sediments were characterized
by a pH^ of about 2.3 and the chemical perturbations increased
their negative charge (Table 9).
Natural Colloid Mobilization by Chemical Perturbations.
Over the two field seasons, two injections of elevated pH
groundwater were performed at approximate injection pH values
of 11 (1997) and 12.5 (1996). The amount of colloids mobilized
increased with increasing pH (Table 10). Colloid mobilization in
the uncontaminated zone always exceeded colloid mobilization
in the contaminated zone. The pH increase was buffered to a
much greater extent in the contaminated zone. The appearance
of the elevated pH plume and the mobilized colloids lagged the
tracer breakthrough in these experiments. Details of the effect of
pH on natural colloid mobilization will be presented in Ryan, Ard,
Magekly, and Elimelech [in preparation].
During the 1996 experiment, the lower concentration of
NaDBS (0.57 mM, 200 mg L1), about half the critical micelle
concentration, caused greater mobilization of natural colloids in
the uncontaminated zone. Owing to the presence of bubbles at
the high NaDBS concentration (50 mM, 1 %), we were not able to
accurately measure turbidity during the 1997 experiment. At the
lower concentration, a measurable amount of the NaDBS was
attenuated, presumably by sorption to the aquifer sediments, but
the overall transport of the NaDBS plume was not significantly
retarded; thus, colloid mobilization coincided or slightly lagged
the passage of the tracer and NaDBS pulses.
During the 1996 experiment, an increase in the ascorbic
acid concentration produced much greater colloid mobilization in
the uncontaminated zonethan in the contaminated zone. Ascorbic
acid was attenuated to C C0~1 levels of 0.05 and 0.5 in the
uncontaminated and contaminated zones of the aquifer,
respectively, but no significant retardation was observed. The
mobilized colloids appeared at the same time or slightly behind
the tracer and ascorbic acid pulses. Iron(ll) concentrations well
above the field detection limit of 1.8 |aM were detected only in the
contaminated zone.
During the 1997 experiment, a decrease in the ionic strength
of the groundwater produced a small amount of colloid mobilization
with slightly greater mobilization occurring in the uncontaminated
zone. The deionized water "plume" moved through the array at
the same rate as the bromide tracer moved through the array in
later experiments and the mobilized colloids always appeared at
the same time or slightly after the depression of specific
conductance caused by the deionized water pulse.
Synthetic Colloid and Virus Deposition. During the 1996
experiments, silica colloids and bacteriophage PRD1 displayed
measurable breakthroughs over the first meter of transport
(Figure 3). Clear breakthrough curves were not detected further
down-gradient [Ard, 1997]. The silica colloids and PRD1 were
less attenuated in the contaminated zone than in the
uncontaminated zone (Table 11). Collision efficiencies calculated
with and without dispersion varied by up to a factor of 3.4 with the
collision efficiency calculated with dispersion always the larger
value. Details of these experiments are presented in Ryan, et al.,
[1998].
During the 1997 experiments, the transport of the silica-
coated zirconia displayed similar trends in the same arrays -
relative breakthroughs were higher and collision efficiencies
were lower in the contaminated zone of the aquifer. Again, clear
breakthroughs were not evident beyondthel mtransportdistance
[Magelky, 1998]. In a special experiment conducted in 1997, both
the silica-coated zirconia and titania particles were injected into
array 4 following the injection of 29 mM (1%) NaDBS to mobilize
natural colloids. Under these conditions, transport through the
uncontaminated and contaminated zones was similar. The silica-
coated zirconia colloids displayed collision efficiencies that were
substantially lowerthan those of the silica-coated titania colloids.
Synthetic Colloid and Virus Mobilization. The amount of
colloids and viruses mobilized by pH elevation increased as the
pH of the injection was increased from 10 to 12.5 (Table 12).
Generally, the pH increase was more effective at mobilizing
colloids in the uncontaminated zone (Figure 4). The migration of
the NaOH plume lagged significantly behind the tracer, but during
the pH 11 injection in 1997, some of the mobilized colloids
appeared slightly ahead of the pH increase at the 1 m transport
distance (Figure 5). Dissolved organic carbon mobilized by this
pH increase also appeared slightly ahead of the pH increase.
Details of this work will be provided in Ryan, et al. [in preparation].
-------
Core 13
(a)
FeOOH
0001 15.8KU
X55 100HID
(b)
300 -
250
en 200
•£3
8 15°
100 -
(c)
02468
energy (keV)
Figure 2. Electron microprobe images of resin-impregnated thin section of contaminated zone sediments of the Cape Cod glacial
outwash aquifer showing AI-, Si- and Fe-rich coating on quartz grain at two different scales: (a) scale bar, 100|am;
magnification, 55 times; (b) scale bar, 10 jam, magnification 500 times. Dark grains are quartz; bright rims contain Al, Si,
and Fe. EDX scan of coating (c).
-------
Table 8.
Table 9.
Aquifer sediment zeta potentials under ambient and perturbed conditions.
zeta potential
sample
uncontam sediment in
uncontam groundwater
contam sediment in
contam groundwater
conditions
ambient, pH 5. 8
1 mM NaBr
72 uM NaDBS
49 mM NaDBS
pHlO.O
pHll.O
ambient, pH 6. 2
1 mM NaBr
72 uM NaDBS
49 mM NaDBS
pHlO.O
pH 1 1.0
(mV)
-22±1
-23±1
-3 Oil
-43±1
-3 Oil
-31±2
-27±1
-27±1
-24±1
-40±1
-31±1
-35±1
Natural colloid zeta potentials under ambient and perturbed conditions.
sample
natural colloids mobilized
from uncontam sediments
suspended in deionized water
natural colloids mobilized
from uncontam sediments
pH5.8
conditions
pH 2.3
2.9
3.7
5.6
6.2
7.8
8.8
10.1
11.4
12.0
NaDBS 0
0.57 mM
2.9 mM
14mM
29 mM
zeta potential
(mV)
0±8
-4±2
-18±5
-26±3
-33±4
-37±3
-44±2
-43±4
-42±7
-45±8
-28±6
-27±2
-33±5
-45±6
-46±4
During the 1996 experiment, NaDBS was more effective at
mobilizing colloids and viruses in the contaminated zone than in
the uncontaminated zone. A comparable experiment was not
carried out during the 1997 field season. SEM examination of
filtered particles revealed at least 90% silica colloids; therefore,
no correction for mobilization of natural colloids was applied. The
migration of the surfactant closely matched the tracer migration.
The mobilized colloids and viruses appeared at the first MLS
concurrently or slightly behind the surfactant peak concentration.
During the 1996 experiment, ascorbic acid was more
effective at mobilizing the silica colloids than the viruses. A
comparable experiment was not conducted during the 1997 field
season. SEM examination of filtered particles revealed at least
90% silica colloids; therefore, no correction for mobilization of
natural colloids was applied. The transport of ascorbic acid was
attenuated, especially in the uncontaminated zone, but no
significant retardation of ascorbic acid occurred. The mobilized
colloids and viruses appeared at about the same time or slightly
after the appearance of the ascorbic acid peaks at the first MLS.
The ascorbic acid injection produced a significant increase in the
Fe(ll) concentration in the contaminated zone, but no significant
increase above the detection limit in the uncontaminated zone.
Dissolved oxygen concentrations were slightly depressed in the
uncontaminated zone during the ascorbic acid injection, but not
significantly different in the contaminated zone.
During the 1997 experiments, a decrease in ionic strength
brought about by an injection of 100 L of deionized water mobilized
more colloids in the uncontaminated zone of the aquifer. The
mobilized colloids appeared at the 1 m transport distance at the
same time or slightly behind the deionized water pulse.
Discussion
Silica and Silica-Coated Colloid, PRD1, and Aquifer Grain
Zeta Potentials During Injections. The negative zeta potentials of
the silica and silica-coated colloids reflect the predominance of
deprotonated surface hydroxyls at pH values above the pH
10
-------
Table 10. Amount of natural colloids mobilized by chemical perturbation injections.
mass of colloids mobilized3
experiment conditions aquifer zone (mg)
11
elevated pH
(NaOH) 12.5
0.57mM
^vat^d surfactant
NaDBS 29mM
elevated reductant !-° lllM
AscAc
decreased
-------
Table 11. Summary of relative breakthroughs (RB) and collision efficiencies (a) calculated with and without dispersion for silica
and silica-coated colloids and PRD1.
colloid
PRD1
silica
silica-coated ZrO2
silica-coated ZrO2 after 1%
NaDBS
silica-coated TiO2 after 1%
NaDBS
aquifer
zone
uncontam
contain
uncontam
contam
uncontam
contam
uncontam
contam
uncontam
contam
replicate
arrays
3
3
3
3
3
3
1
1
1
1
RB
(%)
2.5±1.7
4.3±1.4
13±4
37±16
1.3±1.5
34±42
78
76
11
12
a
with dispersion
0.032±0.016
0.016±0.005
0.023±0.009
0.0056±0.0034
0.039±0.013
0.012±0.008
0.0021
0.0017
0.0077
0.0090
Table 12. Recovery of silica and silica-coated colloids and PRD1 over the first meter of transport. Two pairs of recoveries are
listed for the silica colloids at an elevated pH of 12.5 - the first pair is for silica colloids measured by UV absorbance
and the second pair is for silica colloids measured as 0.6 times the sample turbidity to account for natural colloids.
chemical
perturbation conditions
elevated pH 1 0.0
NaOH
11.0
12.5
elevated 0. 57 mM
surfactant
NaDBS
elevated 1 .0 mM
reductant
AscAc
decreased <5 u.S cm1
ionic strength
deionized water
colloid
silica-coated ZrO2
silica-coated ZrO2
PRD1
silica (UV)
silica (0.6turb)
PRD1
silica (UV)
PRD1
silica (UV)
silica-coated ZrO2
aquifer zone
uncontam
contam
uncontam
contam
uncontam
contam
uncontam
contam
uncontam
contam
uncontam
contam
uncontam
contam
uncontam
contam
uncontam
contam
uncontam
contam
recovery
(%) '
0.00
0.10
1.5
0.70
100
78
240
120
103
37
0.5
49
3.5
15
3.8
24
51
27
0.50
0.10
12
-------
z
o
N
Q
W
H
<
Z
H
z
o
u
z
400 -
200 -
0
200 -
100 -
PRD1
HH
HH
O
p
b
-o— silica (UV)
—B— silica (0.6 turb)
-ffl—rn--m_
Z
O
N
Q
M
H
<
Z
H
Z
O
u
Figure 4.
J 400 -
^ 200 H
& 0
HH
HH
Ol
PRD1
^=*
- silica (UV)
silica (0.6 turb)
»"" •--•
^mm.S'Sm~~Sm
10 -I
8 -
6 -
PH
AAAAAAAA
bromide
0
4 6 8 10 12 14 16
time (d)
Mobilization of silica-coated zirconia colloids after deposition by elevation of pH (pH 11) at 1 m transport distance during
1997 field experiments. Silica-coated zirconia colloids measured as Zr by ICP-AES. Bromide tracer breakthrough
presented as normalized concentration.
13
-------
E
OH
P
8
7
6
5
40
30
20
10
0
J2 4000
OH
3 2000
JH
I
O
o
Q
2.0 -I
1.5 -
1.0 -
0.5 -
0.0
1 o 1.0 -
O
O 0.5 -
'& 0.0 -
0
6 8 10 12 14 16 18
time (d)
Figure 5. Mobilization of silica-coated zirconia particles by an increase in pH in the uncontaminated zone during the 1997 field
experiments. The breakthrough of zirconium, turbidity, and dissolved organic carbon (DOC) slightly precede the advance
of the pH increase.
14
-------
value of about 2 to 2.5 reported for silica [Parks, 1965]. The
similarity between the pHpzc and the zeta potentials of the silica
and silica-coated colloids indicate thatthe silica coating thickness
of 10 to 60 nm was sufficient to mask the underlying surface
properties of zirconia and titania.
The pHpzc for PRD1 in the uncontaminated groundwater is
less than 3.2, similarto a pH value of less than 3.9 in a calcium
phosphate solution. On the basis of studies by Penrod et al.
[1996] relating the pHpzc values of bacteriophage MS2 and
lambda to the composition of their protein capsids, these low
pHpzc values for PRD1 indicate that deprotonatedcarboxyl groups
in amino acids dominate the surface speciation of the PRD1
protein capsid.
In the contaminated groundwater, the silica and silica-
coated colloids and PRD1 were slightly more negative than in the
uncontaminated groundwater. For PRD1, the change in zeta
potential may simply be caused by the higher pH of the
contaminated groundwater, although changes in virus surface
charge may also be attributed to surfactants [Small and Moore,
1987] and fulvic acid [Bixby and O'Brien, 1979]. The silica
colloids, however, appear to have reached a constant zeta
potential at about pH 4.4, so the more negative zeta potential
measured in the contaminated groundwater cannot be attributed
to the higher pH of the contaminated groundwater. The zeta
potential of the silica colloids must have been made more
negative by adsorption of anions; e.g., organic matter and
phosphate. While extensive adsorption of organic matter to silica
at pH near 6.0 is unlikely owing to electrostatic repulsion [Davis,
1982], calcium and magnesium in the contaminated groundwater
may enhance organic matter adsorption by reducing the negative
charge of the organic matter.
The measuredzeta potentials ofthe aquifergrains represents
the net charge of a heterogeneous surface made up of the
underlying quartz grains, ferric oxyhydroxide and clay mineral
coatings, and adsorbed organic matter and phosphate. At the
ambient pH of the groundwater, quartz is negatively charged,
ferric oxyhydroxide is positively charged, and clay minerals are
negatively charged overall with positively charged edges [Parks,
1967]. Adsorption of organic matter and phosphate to the positively
charged surfaces reverses the surface charge [Liang and Morgan,
1990]. The net negative surface potential ofthe uncontaminated
sediment suggests that the ferric oxyhydroxide coating on the
quartz grains is patchy, as suggested by thin sections of Cape
Cod aquifer grains examined by Coston et al. [1995] and this
study (Figure 2). We attribute the more negative zeta potential of
the contaminated sediment to the much higher organic matter
content ofthe contaminated sediments (Table 1). The phosphate
content ofthe contaminated sediments is also elevated, but only
by a maximum factor of about 2.5.
Silica and Silica-Coated Colloid and PRD1 Deposition
Behavior. Most studies of virus attachment to mineral grains
concludethatelectrostaticforces dominate the interaction between
virus and grain surfaces [Gerba, 1984; Murray and Parks, 1980;
Loveland etal., 1996; Penrod etal., 1996; Redman etal., 1997],
If electrostatic forces dominated colloid and virus deposition in
these experiments, the zeta potential data should provide a
qualitative explanation for the observed deposition behavior.
Both the silica and silica-coated colloids and viruses were
transported through the contaminated zone more readily than
through the uncontaminated zone. The greater abundance of
organic matter in the contaminated sediments appears to have
masked the ferric oxyhydroxide coatings, giving the contaminated
sediments a greater negative zeta potential than the
uncontaminated sediments. Consequently, when the negatively
charged colloids and viruses interact with the contaminated
sediments, they experience greater repulsion, resulting in collision
efficiencies lower than those measured in the uncontaminated
zone.
The zeta potential data and the ferric oxyhydroxide coatings
detected in the thin sections indicate that ferric oxyhydroxides in
the Cape Cod sediments enhance colloid and virus attachment
and organic matter in the sewage plume inhibits colloid and virus
attachment. The presence of positively charged oxides limits the
transport of viruses and bacteria because these "biocolloids" are
typically negatively charged at the ambient pH of most waters and
readily attach to positively charged surfaces [Murray and Parks,
1980; Moore etal., 1981; Farrah and Preston, 1991; Scholl and
Harvey, 1992; Mills et al., 1994; Loveland et al., 1996]. For
mineral colloids, Johnson et al. [1996] showed that the transport
of silica colloids in a porous media consisting of mixtures of clean
and iron oxide-coated quartz sand depends strongly on the
amount of iron oxide coating. Organic matter of natural and
anthropogenic (e.g., sewage, surfactants) origin hinders virus
attachment to mineral surfaces [Charney etal., 1962; Burge and
Enkiri, 1978; Sobseyetal., 1980; Gerba etal., 1981; Moore etal.,
1981 ;1982; Atherton and Bell, 1983; Fuhsetal.,1985], presumably
by adsorbing to and masking virus attachment sites. Similarly,
the transport of ferric oxide colloids is enhanced by natural
organic matter in quartz sands [Amirbahman and Olson 1993;
Kretzschmar et al., 1995].
The differences between silica and silica-coated colloid and
virus deposition are more subtle and not statistically significant,
but a qualitative explanation of their deposition behavior based
on the presence of ferric oxyhydroxide coatings and measured
zeta potentials fits the trend of the mean collision efficiencies
measured in the experiments. In both groundwaters, the silica
and silica-coated colloids possess slightly greater negative zeta
potentials, resulting in lower overall collision efficiencies. The
close dependence ofthe measured collision efficiencies on the
zeta potentials ofthe PRD1, silica colloids, and aquifer grains
reinforces the conclusion that electrostatic forces dominate the
transport behavior of viruses in porous media.
In one 1997 experiment, the transport of silica-coated
zirconia and titania colloids was measured in a simultaneous
injection with dodecylbenzene sulfonate. The collision efficiency
calculated for the silica-coated titania particles was about 4-5
times greater than that calculated for the silica-coated zirconia
particles (Table 11), indicating greater repulsion between colloid
and grain for the silica-coated titania particles. This result
qualitatively agrees with the zeta potentials measured for the two
colloids - the silica-coated titania colloids have greater negative
zeta potentials than the silica-coated zirconia colloids near the
ambient pH ofthe groundwater (Tables 5,6).
Grain Surface Heterogeneity and Collision Efficiencies.
The aquifer grain surfaces are primarily made up of patchy
coatings of ferric oxyhydroxides on the underlying quartz grains.
At the ambient pH ofthe groundwaters, the negatively charged
PRD1 should be collected by the ferric oxyhydroxide coating and
repelled by the exposed quartz [Loveland et al., 1996]. The
measured collision efficiencies should reflect the summed
contribution of ferric oxyhydroxide and quartz surfaces [Song et
al., 1994; Johnson etal., 1996]. Using PRD1 collision efficiencies
oiaFe0x =1 forferric oxyhydroxide (owing to electrostatic attraction)
and a^z=6x10'3 for quartz surfaces [Bales et al., 1991], we can
15
-------
estimate the fraction of the surface area coated by ferric
oxyhydroxide, fFe0x, using the following equation:
^measured JFeOx ^FeOx ~"~ Jqtz
(1)
This equation gives estimates of fFeOx = 0.026 for the
uncontaminated zone and fFe0x = 0.010 for the contaminated
zone. The uncontaminated zone fFe0x estimate is in close
agreement with the 3.0% surface coverage detected by electron
microprobe in the thin sections, butthe contaminated zone fFe0x
estimate is lower than the 3.5% surface coverage detected. No
significant difference between the surface coverage in the
uncontaminated and contaminated zones was observed in the
thin sections; therefore, the lower contaminated zone fFe0x
estimate is attributable to the masking of ferric oxyhydroxide
coatings by organic matter and phosphate adsorption.
Colloid and Virus Mobilization by Elevated pH. The NaOH
injection was designed to reverse the charge on the ferric
oxyhydroxide coatings by raising pH above the pHpzc of ferric
oxyhydroxides [Parks, 1967]. The amount of colloid and PRD1
mobilization increased with increasing pH. Although the precision
of the silica colloid recovery measurement is clouded by the
presence of the natural colloids, it appears that similar amounts
of PRD1 and silica colloids were released and that release
occurred more readily in the uncontaminated zone.
In the uncontaminated zone, the lack of buffering in the
groundwater and sediments resulted in a greater increase in pH
and greater release of PRD1 and silica colloids than in the
contaminated zone. The pH in the uncontaminated zone peaked
at nearly 10 at the 1-m distance (injection pH 11.7), while the pH
in the contaminated zone peaked at only 8.5 at the 1-m distance
(injection pH 11.5) (Figure 4). An increase of pH to 10 is sufficient
to reverse the surface charge of any ferric oxyhydroxide, but an
increase to pH 8.5 may not exceed the pHpzc of some ferric
oxyhydroxides. The greater release of colloids and viruses in the
uncontaminated zone must be attributed to the increase in pH
well in excess of the pHpzc of the ferric oxyhydroxide coatings.
In the contaminated zone of the Cape Cod aquifer, Bales et
al. [1995] found that injection of a pH 8.3 solution containing an
unspecified concentration of phosphate for buffering effectively
re-mobilized PRD1 (a fractional recovery was not measured).
This injection caused the detachment of PRD1 even when the pH
increase down-gradient of the injection was only slightly above
the ambient pH. It is likely that the phosphate augmented the
charge reversal caused by the pH increase by adsorbing to the
ferric oxyhydroxide coatings.
Colloid and Virus Mobilization by Surfactant Addition. NaDBS
was added to alter the ferric oxyhydroxide surface charge and
promote release. NaDBS is an anionic surfactant that readily
adsorbs to positively charged oxide surfaces and reverses surface
charge by hemimicelle formation [Dick et al., 1971]. Similar
surfactants have been shown to mobilize natural colloids [Ryan
and Gschwend, 1994] and cause permeability reduction through
colloid mobilization [Allred and Brown, 1994]. The high NaDBS
injection concentration made it difficult to determine with any
precision the amount of NaDBS lost to aquifer sediments by
adsorption. Only a small fraction of the NaDBS injected would be
required to saturate the ferric oxyhydroxide surfaces with adsorbed
NaDBS [Pieper et al., 1997]; however, NaDBS was much less
effective at mobilizing PRD1 and silica colloids than the increase
in pH. Similarly, Bales et al. [1991 ] showed that 1 % Tween 80 and
2.5% beef extract solutions were only marginally effective at
mobilizing PRD1 and MS2, another bacteriophage, relative to an
increase in pH to 8 in a sodium phosphate solution. Bales et al.
[1991] speculated that the high ionic strength of the surfactant
and beef extract solutions inhibited virus detachment. Ouraddition
of NaDBS to the Cape Cod groundwater caused an increase in
ionic strength (0.57 mM) less than that caused by the sodium
bromide tracer (about 1.5 mM).
NaDBS was much more effective at mobilizing PRD1 and
silica colloids in the contaminated zone. In a previous experiment,
Pieper et al. [1997] similarly observed that a 25 mg L1 mixture of
linear alkylbenzenesulfonat.es (LAS) recovered 87% of the injected
PRD1 in the contaminated zone and only 2% in the uncontaminated
zone. The abundance of organic matter in the contaminated zone
must reduce the amount of surfactant needed to reverse the
charge of ferric oxyhydroxide surfaces. In this experiment, the
higher concentration of NaDBS (0.57 mM; 200 mg L1) did not
improve recovery. The results suggest that PRD1 and silica
colloids are more strongly bound in the uncontaminated zone.
Colloid and Virus Mobilization by Reductant Addition.
Ascorbic acid was added to remove the ferric oxyhydroxide
coatings by reductive dissolution, resulting in the release of
PRD1 and silica colloids attached to the coatings. Ryan and
Gschwend [1994] observed that reductive dissolution by ascorbate
mobilized natural colloids from a similar ferric oxyhydroxide-
coated quartz sand as long as increases in the ascorbate
concentration did not raise ionic strength to a level too high to
inhibit release. Inthisexperiment, ascorbicacidaddition promoted
PRD1 and silica colloid release that was somewhat comparable
to the surfactant addition, but less than that caused by the pH
increase. The amount of ascorbic acid injected in this experiment
was similar to the amount promoting the maximum colloid release
in the experiments of Ryan and Gschwend [1994].
The amount of PRD1 and silica colloid release varied
inconsistently in these experiments. Ascorbic acid appeared to
be effectively dissolving ferric oxyhydroxides in the contaminated
zone because the Fe(ll) concentration increased as the ascorbic
acid broke through; however, Fe(ll) release continued near the
peak level of Fe(ll) release for 15 days beyond the ascorbic acid
breakthrough. In contrast, very little ascorbic acid broke through
and very little Fe(ll) was released in the uncontaminated zone.
Some ascorbic acid may have been oxidized by oxygen, although
no significant changes in the oxygen content were observed.
Released Fe(ll) may have been re-adsorbed by aquifer grains or
by released colloids, in which case the Fe(ll) would promote
destabilization and deposition. Based on these results, it is
difficult to assess the dependence of PRD1 and silica colloid
recovery by ascorbic acid addition.
Colloid Mobilization by Ionic Strength Decrease. Decreases
in groundwater ionic strength have frequently caused substantial
colloid mobilization and permeability reduction during artificial
recharge [Nightingale and Bianchi, 1977] and secondary oil
recovery [Khilar and Fogler, 1984]. When low ionic strength water
replaces high ionic strength water in aquifers with substantial clay
contents, an expansion of double layers leads to an increase in
electrostatic repulsion between colloids and grains and colloid
mobilization. In the ferric oxyhydroxide-coated sands at Cape
Cod, however, we hypothesized that a decrease in ionic strength
would not cause substantial colloid mobilization because most of
the colloid-grain interaction is attractive (negatively charged
colloids, positively charged grain coatings). A decrease in ionic
16
-------
strength and expansion of double layers would only serve to
strengthen this attractive interaction. As hypothesized, the
decrease in ionic strength caused minimal mobilization of the
natural colloids and the silica-coated zirconia colloids. There was
one exception - when the deionized water pulse followed the
0.1 M calcium chloride pulse, a substantial recovery of the silica-
coated zirconia was observed. In this extreme case, we surmise
that colloids were mobilized where they were bound to bare
quartz surfaces. No mobilization occurred without the calcium
chloride pulse preceding the deionized water pulse because the
contrast in ionic strength was not sufficient.
Laboratory Experiments
Purpose
To address one of the overall objectives of the project, to
examine the dependence of colloid transport and mobilization on
chemical perturbations, a set of laboratory experiments were
conducted to assess the rate of natural colloid mobilization from
the ferric oxyhydroxide-coated quartz sands from Cape Cod
under more controlled conditions. These laboratory experiments
included the following tests:
• measurement of colloid mobilization in the laboratory from
undisturbed, oriented sediment samples;
• quantitation of the rate of colloid mobilization under controlled
conditions; and,
• examination of a range of chemical perturbations wider than
possible in the field to understand the dependence of the
amount and rate of colloid mobilization on the chemical
conditions.
To perform these experiments, we developed a special column
packing technique to load the Cape Cod sediments into a column
with a minimum of disturbance to the grain-grain arrangement.
We then subjected these packed columns to a wide range of
chemical perturbations and measured colloid mobilization using
flow-through meters and a data acquisition system.
Materials and Methods
Sediment Characterization. Experiments were conducted
on uncontaminated and contaminated sediment samples from
the U.S. Geological Survey's Cape Cod field site. To confirm the
location of the sewage plume in the sediment cores, pore waters
were displaced with nitrogen pressure from the cores and
measured pH, specific conductance, and dissolved oxygen.
Owing to some unavoidable atmospheric contact, specific
conductance was the best measure of contamination. Cores with
a specific conductance of less than 100 S cm"1 were identified as
uncontaminated and cores with a specific conductance of greater
than 300 S cnr1 were identified as contaminated. Cores of
intermediate specific conductance were not used in the laboratory
experiments.
Column Packing and Setup. To prepare undisturbed,
oriented sediment columns, a stainless steel column (5.0 cm
length, 2.5 cm diameter) was gently rotated into a sediment core
through a hole drilled horizontally in the acrylic core sleeve. From
a hole drilled on the opposite side of the core, the sediments were
lightly pressed into the column using a plunger of 2.5 cm diameter
[Ard, 1997]. The sediment was secured in the column by threaded
stainless steel caps, Teflon® washers, a 0.2 |am stainless steel frit
on the influent end (bottom), and a 105 jam polypropylene mesh
on the effluent end (top). The dry weight of sediment packed into
the column averaged 38.6±2.0 g. The average column porosity
was 0.41±0.02 assuming a sediment grain density equal to that
of quartz (2.65 g cm"3). The average column pore volume,
9.0±0.5 mL, was measured bythe decrease in mass afterremoving
water from a saturated column and drying at 105°C overnight.
Influent solutions were pumped through the column at a
flow rate of 0.15 mL min"1 (representing an interstitial groundwater
velocity of 0.7 md"1) using syringe pumps to ensure constant
surge-free flow. The column effluent flowed through (1) micro-
cells measuring specific conductance and pH (volumes of 17 and
11,uL, respectively) and (2) a turbidity cell (volume about 2 mL) to
a fraction collector. A data acquisition system was used to record
data at 1 min intervals.
Column Procedures. After packing each column, the
sediment was flushed with a high ionic strength solution
(0.5 M NaCI) to remove colloids loosened by column packing
until a low background turbidity (roughly 5 NTU) was observed
(typically 5 pore volumes). Next, a low ionic strength solution
(5.0x10"4M NaCI) representative of the ionic strength of the
uncontaminated groundwater, was run through the column until
a low baseline turbidity (roughly 5 NTU) and specific conductance
was observed (typically 25 pore volumes).
The sediments were subjected to a series of chemical
perturbationsthatenvelopedthosetested inthefield experiments.
In some experiments, a single sediment column was subjected to
a sequence of chemical perturbations (Table 13). In these
experiments, each influent solution was separated by a
"groundwaterflush"of5.0x10"4 M NaCI run until the turbidity in the
effluent returned to the background level. In other experiments,
a series of individual columns were subjected to a series of
chemical perturbations (Table 14). The surfactant concentration
perturbations ranged from one-tenth the value of the critical
micelle concentration (CMC) for sodium dodecylbenzene-
sulfonate, 1.2 mM[MukerjeeandMysels, 1971], to approximately
ten times the CMC in a 5% solution.
Colloid Characterization. The elemental composition,
morphology, mineralogy, and zeta potential of the natural colloids
were characterized as described in the field experiments. The
total mass of mobilized colloids was determined by integrating
the colloid concentration in the column effluent over the time of
release and multiplying this by the flow rate. This method assumes
deposition of mobilized colloids is negligible.
Results
Colloid Characterization. The elemental and mineral
composition, morphology, and zeta potentials of the mobilized
colloids closely matched those of the colloids mobilized in the
field experiments. No significant differences were observed
between colloids mobilized by different water chemistries.
Sequential Perturbation Experiments. The sequential pH
increase experiments revealed that very little colloid mobilization
occurred until the influent pH solution reached pH 9.5 and 10.5.
Only the two highest NaDBS concentrations caused significant
colloid release. The ascorbic acid injections caused the most
release during the intervening groundwater flushes.
At the highest colloid mobilization rates by all three
perturbations, it became apparentthatthe sequential perturbations
were removing too many colloids for the later perturbations to
accurately measure the rate and amount of colloid mobilization.
We were able to compare the total amount of colloids mobilized
to assess the contrast between the uncontaminated and
contaminated sediments (Table 15). During pH increase
sequence, about twice as many colloids were mobilized from the
contaminated sediment as in the uncontaminated sediment (in
contrast to the field experiments). During the NaDBS increase
sequence, about 7 times as many colloids were mobilized in the
contaminated sediment as in the uncontaminated sediment (in
agreement with the field experiments). During the ascorbic acid
17
-------
Table 13. Influent solutions for the sequential column colloid mobilization experiments on uncontaminated and contaminated
sediments. Each of these influent solutions was followed by flushing by a "groundwater" solution of 5x10~4M NaCI
(pH 6 and conductivity 70 uS crrr1).
perturbation constituents
unit
sequence of perturbations
elevated pH
(NaOH)
elevated
surfactant
(NaDBS)
elevated
reductant
(AscAc)
pH
NaBr
conductivity
NaDBS
NaBr
conductivity
AscAc
NaBr
conductivity
mM
mS crrr1
mM
mM
mS crrr1
M
mM
mS cnr1
5.5
1.5
0.20
0.12
1.5
0.20
0.0001
1.5
0.20
7.5
1.5
0.20
0.60
1.5
0.20
0.001
1.5
0.25
9.5
1.5
0.20
1.2
1.5
0.35
0.01
1.5
0.80
10.5
1.5
0.25
29
1.5
1.5
0.1
1.5
6.0
11.5
1.5
1.0
140
1.5
6.5
12.5
1.5
10
Table 14. Influent solutions for the individual column colloid mobilization experiments on uncontaminated sediment.
perturbation
elevated pH
(NaOH)
constituents
pH
NaBr
conductivity
unit
mM
mS cm"1
sequence of perturbations
10.5
1.5
0.25
11.5
1.5
1.0
12.5
1.5
10
13.1
1.5
80
Table 15. Total mass of natural colloids mobilized from the uncontaminated and contaminated Cape Cod sediments by
sequential chemical perturbations. The mass of colloids mobilized in the pH and NaDBS experiments was calculated
as the sum of colloids mobilized only during the chemical perturbation steps. For the AscAc experiment, the mass of
colloids mobilized also included the colloids mobilized during the intervening groundwater flushing steps.
mass of colloids mobilized
perturbation aquifer zone (mg)
elevated pH
NaOH
elevated surfactant
NaDBS
elevated reductant
AscAc
uncontam
contam
uncontam
contam
uncontam
contam
3.4
7.2
0.14
1.0
0.47
0.89
18
-------
increase sequence, about twice as many colloids were mobilized
in the contaminated sediment as in the uncontaminated sediment
(in agreement with the field experiments).
IndividualpH Increase Experiments. The amount of colloids
mobilized from the individual columns increased with increasing
pH up to pH 12.5, and then decreased substantially at pH 13.1
(Table 16; Figure 6). The pH 10.5 influent produced a long, slow
colloid release that lasted about 120 pore volumes before returning
to the background turbidity level. The pH 11.5 influent produced
greater and more rapid colloid release than pH 10.5; only about
25 pore volumes were required for return to background turbidity.
Colloid release at pH 12.5 influent was even greater and more
rapid than the lower pH influents. The pH 13.1 influent produced
the most rapid colloid release, but the total mass of colloids
released was small. Details of this experiment will be described
in Ryan, Ard, Magelky, and Elimelech [in preparation].
Discussion
Colloid-Grain Morphology and Composition. The sediments
at the Cape Cod site are primarily composed of iron oxyhydroxide-
coated quartz [Coston et al., 1995]. At the ambient pH of the
groundwater (5.4 to 6.0), quartz is negatively charged (pH^ of
approximately 2.0 [Parks, 1967]). The colloids, mainly clay
particles, also possess net negative charge at this pH value. Any
ferric oxyhydroxides present in the coatings, however, are
positively charged at the ambient pH (e.g., hematite, pH^ 6.7;
goethite, pHpzc8.5; lepidocrocite, pH^ 7.4 [Parks, 1967]). On the
basis of these surface properties, it is likely that the ferric
oxyhydroxide coatings act as a cementing layer between the
grains and colloids. Ryan and Gschwend [1990] made the same
argument for ferric oxyhydroxide-coated quartz sands in two
Atlantic Coastal Plain sediments.
Effect of pH on Colloid Mobilization. Substantial colloid
mobilization began to occur at pH 9.5, but not at pH 7.5. This
threshold appears to represent the pH at which the surface
charge of the ferric oxyhydroxide cement was reversed from
positive to negative. At pH 9.5, all of the mineral components of
the system become negatively charged and colloid release
occurs. Ryan and Gschwend [1994] observed colloid release
threshold at a similar pH value during colloid release experiments
in a ferric oxyhydroxide-coated quartz sand from the Cohansey
Formation in New Jersey.
As the influent pH was increased to 10.5,11.5, and 12.5, the
rate and total mass of colloid release increased. The increase in
pH increased the negative zeta potentials on the colloid and grain
surfaces. The increased negative zeta potentials caused greater
colloid-grain repulsion. Relating the rate and amount of colloid
release to the amount of repulsion is challenging for natural
systems, but we have observed a clear relationship here between
the rate and extent of colloid mobilization and the colloid and
grain surface charge.
Increasing the influent pH to 13.1, however, substantially
decreased the amount of colloid release, while the rate of release
remained rapid. Similar colloid release behavior has been
observed by Kolakowski and Matijevic [1979] in a model system
of chromium hydroxide colloids and glass beads. During the
transition from ambient pH to 13.1, the colloid and grain surfaces
became increasingly repulsive; however, as the column effluent
pH reached 13.1, the ionic strength exceeded 0.1 M. The high
ionic strength compresses the repulsive double layers and shields
the colloid and grain from repulsion; hence, colloid release was
shut down. The initial pulse of colloid release was still rapid
because the pH in the column was rapidly increasing to
13.1 through the pH range that promoted colloid release. At some
pH value between 12.5 and 13.1, the colloid and grain surfaces
have reached their maximum negative surface charge. The
additional increase in pH is only raising ionic strength, not making
the colloid-grain interactions more repulsive. Inhibition of colloid
release by elevated ionic strength was also observed in the field.
Although a large release of colloids was measured at the 1 m
transport distance during the pH 12.5 injection, little release was
observed at the injection point itself.
In contrast with the sequential pH increase experiments,
the release of colloids in the uncontaminated zone was much
greater than that in the contaminated zone during the individual
column experiments. The individual column experiment results
agree with the field experiments. Greater release in the
uncontaminated zone can be attributed to its lower buffering
capacity.
Sediment Disturbance and Orientation. Employing
undisturbed and oriented samples forcolumn experiments makes
this laboratory work more applicable to in situ systems. Using
disturbed Cape Cod sediments, Roy and Dzombak [1996]
measured greater amounts of colloid release under similar
chemical conditions. Disturbing the sediments through column
packing can loosen attached colloids and destroy sediment
arrangements that may have an effect on colloid mobilization. If
repacking is avoided and columns are sampled without
disturbance but the original orientation of the sediments is
Table 16. Results of the elevated pH colloid mobilization experiments for uncontaminated sediment in the individual column
experiments.
pH
10.5
11.5
12.5
13.1
peak colloid
concentration
(mg I/')
14.5
138
196
314
time of
peak release
(pore volume)
26
3.3
2.6
1.4
time to return
to background
(pore volume)
94
20
11
4.3
mass of colloids released
(mg)
4.4
6.1
8.4
0.78
19
-------
13 -
12 -
11 -
10 -
9 -
8 -
7 -
6 -
(a)
PH
conductivity
40 60 80
pore volume
co
,§
|-
I
I
120
40 -
0 -
13-1
12 -
11 -
(b)
0 5 10 15
pore volume
r5
- 4
hi 1
- 0
(c)
(d)
200 -
'-> 150 -
en
E,
-S 100 -
'o
8
50 -
0 -
13 -
12 -
11 -
10 -
I
Q.
9 -
8 -
7 -
«
1
•
*
•1
* \
" ^^kA
• pH
* conductivity
»
JC
j
- 10
-8 'P
o
-6 I
>,
-1-'
-4 -S
o
3
•D
-* §
n
0 10 20 30 40 50 60
pore volume
350
300
250
200
13
12
11
10
4 6
pore volume
ro
t
-20
Figure 6. Natural colloid mobilization from oriented, undisturbed columns of uncontaminated sediment by pH elevation: (a) pH 10.5
influent, (b) pH 11.5 influent, (c) pH 12.5 influent, and (d) pH 13.1 influent.
20
-------
changed, colloid mobilization results could still be very different
from those witnessed in an in situ experiment. Sediment grains
arrange themselves relative to the flow of groundwater around
them, if the sediment orientation is ignored then an influent would
be approaching the sediments at a different angle and towards a
different sediment face than had previously been exposed to
approaching groundwater. Though repacked and disoriented
columns can help define colloid mobilization and transporttrends,
undisturbed and oriented columns will provide more applicable
information.
Modeling
Purpose
To address the third of the major objectives of this project,
the development of a colloid transport model that would describe
the colloid transport in a contaminant plume, we undertook the
modeling of colloid transport in a geochemically and physically
heterogeneous porous medium similar to that found at the U.S.
Geological Survey Cape Cod field site. As a first step toward
modeling the effects of a contaminant plume on colloid transport,
this model simulates the effect of heterogeneity and the dynamics
of colloid deposition. The original objectives of developing a
colloid transport model capable of simulating all of the processes
playing a role in the transport of colloid in a contaminant plume
were not met during this research period. Ongoing model
development is still striving towards those original objectives.
Field and laboratory investigations on the transport of
colloidal particles in aqueous porous media have demonstrated
that advection, hydrodynamic dispersion, particle deposition,
and particle release are the primary mechanisms controlling
colloid transport in porous media [Harvey et al., 1989; Tobiason,
1989; Elimelech and O'Melia, 1990; Elimelech, 1991; Lindqvist
and Enfield, 1992; Higgo et al., 1993; Harvey et al., 1995;
Kretzschmar et al., 1995; Penrod et al., 1996; Harmand et al.,
1996; Pieper et al., 1997]. Various models involving these four
main colloid transport mechanisms have been developed based
on colloid mass balance overa representative elementary volume
(REV) of a porous medium. Among these four main colloid
transport mechanisms, colloid deposition has been investigated
more extensively.
The kinetics of particle deposition have been derived
theoretically and measured experimentally. The theory of
Derjaguin-Landau-Verwey-Overbeek(DLVO) was generally used
to describe the surface-surface interactions to predict the particle
deposition or release rates [Elimelech and O'Melia, 1990].
However, theoretical predictions are generally several orders of
magnitude greater than experimental observations under
unfavorable deposition conditions (i.e., when repulsive double
layer interactions predominate). Various attempts have been
made to explain this discrepancy, including attachment in
secondary energy minima [McDowell-Boyer, 1992; Stumm and
Morgan, 1995], interfacial dynamics of double layer interaction
[Elimelech and O'Melia, 1990], and surface non-idealities [Song
et al., 1994]. Among these explanations, geochemical
heterogeneity (surface charge heterogeneity) is considered to be
the most probable cause of the anomalous colloid deposition
rates observed in porous media [Song et al., 1994].
The geochemical heterogeneity of granular porous media
was modeled either as random microscopic sites or as patches
[Song et al., 1994]. Patchwise charge heterogeneities are
ubiquitous in subsurface environments due to geochemical
variabilities inherent in mineral grains. Johnson et al. [1996]
adopted the patch model to describe the geochemical
heterogeneity of porous media. They assumed a constant
geochemical heterogeneity for the entire porous medium and
incorporated it into a colloid transport model.
The dynamics of colloid deposition have also been studied.
A linear Langmuirian blocking function was proposed by Privman
et al. [1991]. Song and Elimelech [1994] extended the model by
including the non-uniform deposition resulting from spherical
collector geometry and surface heterogeneities. The nonlinear
random sequential adsorption (RSA) model was employed by
Johnson and Elimelech [1995] to describe the dynamics of
blocking in colloid deposition. More recently, Johnson et al.
[1996] have incorporated the RSA model for dynamic blocking
process and the patchwise model for geochemical heterogeneity
into a colloid transport model. They found good agreement
between model predictions and the experimental measurements.
There are relatively fewertheoretical formulations for colloid
release, advection, and hydrodynamic dispersion. The colloid
release is usually described as a kinetic process (first-order
mechanism) instead of a dynamic process in colloid transport
equation [Chrysikopoulos et al., 1990]. When the hydrodynamic
chromatography effect is not obvious in colloid transport, the
difference between the colloid advection velocity and the solute
advection velocity (or colloid and solute dispersion coefficient) is
usually ignored, and the hydrodynamic dispersion linearly depends
on the advection velocity for both colloid and solute transport.
Most of the developed models only describe colloid transport
in physically homogeneous porous media. However, porous
media in subsurface environments are physically heterogeneous
[LeBlanc, 1984; Hess et al., 1992]. Only Abdel-Salam and
Chrysikopoulos [1995] modeled colloid transport in a fractured-
rocks matrix using lognormally distributed fracture aperture. The
physical heterogeneity was described as a random field. Saiers
et al. [1994] carried out experiments of colloid transport in a
structured-heterogeneous porous medium. In these experiments,
the column was packed with different sand layers, which were
parallel to the flow direction; each layer was packed
homogeneously. This physical heterogeneity can be viewed as
layered distributed. The results of both studies showed that
physical heterogeneity of porous media can affect colloid transport
significantly. These researchers focused on colloid advection
and hydrodynamic dispersion to explain the consequences of the
heterogeneous flow field on colloid transport. Although the flow
velocity may also affect the kinetics of particle deposition or
release, such effects were not considered in the above studies.
The previous modeling of colloid transport in geochemically
heterogeneous porous media only dealt with the porous medium
with a constant geochemical heterogeneity. However, a spatially
distributed geochemical heterogeneity is very likely to exist in a
porous medium due to the inherent variability of minerals. Thus,
it is important to study the role of a spatial distribution of
geochemical heterogeneity in colloid transport. Since geochemical
heterogeneity has only recently been introduced to colloid transport
studies, there have been no attempts to incorporate a distribution
of geochemical heterogeneity into colloid transport modeling.
We developed a two-dimensional model forcolloid transport
in physically and geochemically heterogeneous porous media
[Sun, Elimelech, Sun, and Ryan, submitted]. In this model, a
patchwise geochemical heterogeneity and dynamic aspects of
particle deposition and release are included in the governing
equations for colloid transport, which are coupled with the flow
equation.
Model Development
We consider a confined aquifer, where the fluid follows a
steady laminar motion and the suspended colloidal particles
21
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travel at the fluid velocity. The colloidal particles are assumed to
be Brownian (i.e., less than about 1|am) and monodisperse. The
steady state flow field is derived from the transient flow equation
and incorporated into the colloid transport equation. Spatial
distributions of the physical and geochemical heterogeneities of
the subsurface porous medium are rigorously incorporated in the
model.
Flow Field. The transient flow equation for a fluid in a
confined subsurface porous medium, such as a confined
groundwater aquifer, is usually written as
where h is the hydraulic head, t is the time, St is the specific
storage, K is the hydraulic conductivity, and O is the pumping or
recharge rate. Under natural gradient flow conditions, colloid
advection can be described by the steady-state flow equation.
The spatially distributed hydraulic heads are used to calculate the
velocity field by applying Darcy's law
q=-K-W?
(3)
where h is the hydraulic head gradient and q is Darcy's velocity.
The average pore velocity (V), which is used in the colloid
transport equation, is the ratio of Darcy's velocity to porosity.
Physical Heterogeneity of Subsurface Porous Media. The
spatial variation of hydraulic conductivity is the principal cause of
heterogeneous flow field that further influences colloid transport
and the resulting particle concentration in the porous medium.
Two types of physical heterogeneity are investigated, namely,
layered heterogeneity and random heterogeneity.
In a layered, physically heterogeneous subsurface porous
medium, the porous medium is made up of several homogeneous
layers. Thus, while each layer is homogeneous (i.e., with constant
hydraulic conductivity), the entire system is heterogeneous.
Porous media with large blocks of macropores or fractures may
be described as layered heterogeneous.
Substantial progress has been made in the pasttwo decades
to understand the random physical heterogeneity of groundwater
aquifers. Evidence from field-scale hydraulic conductivity
measurements indicates that the spatial distribution of hydraulic
conductivity is lognormal [e.g., Freeze, 1975; Hoeksema and
Kitanidis, 1984; Sudicky, 1986; Hess, 1989]. It was also found
that there exists a non-Gaussian behavior of the log-transformed
hydraulic conductivity at relatively small scales, and thatthis non-
Gaussian behavior shifts to Gaussian behavior as the length
scale increases [e.g., Painter, 1996; 1997; Liu and Molz, 1997].
Because of lack of knowledge on this transition length scale and
the fact that lognormally distributed hydraulic conductivity has
generally been used by numerous hydrologists [e.g., Gelhar et
al., 1979; Gelhar and Axness, 1983; Dagan, 1984; Bellin etal.,
1992], a lognormal distribution is adopted here to describe the
random spatial variation of hydraulic conductivity.
Let Y=lnK, with a constant mean myand variance (TY .The
covariance function of Y is assumed to have an isotropic
exponential form,
Cy(r)=or2exp -
(4)
where rY is the planar distance vector between two positions in
the heterogeneous domain and /yisthe integral scale of V. Using
statistical properties of the spatial distribution, the random field of
hydraulic conductivity can be generated by the turning band
method [Mantoglou and Wilson, 1982; Tompson et al., 1989].
Geochemical Heterogeneity of Subsurface Porous Media.
Coatings and patches of oxyhydroxides (iron and aluminum) on
subsurface mineral grains are the main source of geochemical
heterogeneity in groundwater aquifers [Coston etal., 1995; Ryan
et al., 1999]. These coatings on mineral grain surfaces provide
favorable sites (area) for colloid deposition. Here, we adopt the
patch model [Song et al., 1994] to describe the geochemical
heterogeneity of subsurface porous media. The model is
characterized by the heterogeneity parameter, A,, which is defined
as the ratio of the surface area favorable for colloid deposition to
the total interstitial surface area over a REV of a porous medium.
The surface area favorable for deposition is usually characterized
by a surface charge opposite that of the colloids. Because most
colloids are negatively charged, the favorable deposition areas
are patches of positively charged minerals (e.g., iron and aluminum
oxides, clay edges). Note that colloid deposition or release can
occur on both the favorable and unfavorable fractions, albeit at
much different rates.
Because the chemical composition of subsurface minerals
and solution chemistry vary spatially in subsurface aquatic
environments, the geochemical heterogeneity, defined over a
REV, may vary significantly throughout the subsurface porous
medium. The geochemical heterogeneity of a porous medium
can be assumed to be constant over the entire porous medium,
or to have different values at different locations in the porous
medium. Accordingly, two spatial variations of geochemical
heterogeneity are considered: layered geochemical heterogeneity
and random geochemical heterogeneity.
Compared to layered geochemical heterogeneity, detailed
statistical information on the chemical properties ofthe subsurface
porous medium is needed to model random geochemical
heterogeneity. To date, there are no reported studies on the
random field of geochemical heterogeneity of subsurface porous
media in relevance to colloid transport. Several studies on solute
transport in heterogeneous porous media have described the
variation of solute sorption coefficients by a normal distribution
[e.g., Black and Freyberg, 1987; Chrysikopoulos et al., 1990;
Bosma and van derZee, 1993]. Although solute transport behavior
is quite different than colloidal transport behavior, we adopt a
similar approach and describe the random field of geochemical
heterogeneity as normally distributed with a constant mean E(X.)
and a variance <7\. The turning band method is used to construct
the two-dimensional random field of normally distributed
geochemical heterogeneity, with a first-order exponential
autocorrelation function:
(5)
where L is the integral scale of
The average value of the geochemical heterogeneity
parameter in groundwater aquifers is usually thought to be small,
on the order of a few percent [Heron etal., 1994; Kretzschmaret
al., 1995; Coston et al., 1995; Ryan et al., 1999]. With a mean
value of only a few percent, normal distribution cannot cover a
wide range of geochemical heterogeneity. Thus, in addition to
normal distribution, a lognormal distribution will be used when
significant variations of geochemical heterogeneity, with a small
mean value of only a few percent, are desired.
22
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Colloid Transport Equation. The colloid transport equation
can be derived from mass balance of colloids over a REV of a
porous medium. There are three main mechanisms controlling
colloid transport: hydrodynamic dispersion, advection, and the
colloid exchange between the stationary solid matrix and the
mobile colloidal phase through colloid deposition and release.
These mechanisms can be described by the generalized
advection-dispersion equation [e.g., CorapciogluandKim, 1995]:
(6)
where C is the mass concentration of colloids in the aqueous
phase, S is the ratio of the colloid mass captured by the solid
matrix to the total mass of solid matrix, D is the particle
hydrodynamic dispersion coefficient, Vis the particle velocity, e
is the porosity of the porous medium, and pb is the bulk density of
the porous medium. Because the average pore radius in sandy
aquifers is quite large compared to the size of Brownian
(submicrometer-size) colloidal particles, size exclusion effects
are not considered. Thus, the particle velocity and interstitial fluid
velocity are assumed to be equal. Similarto the relations developed
for solute dispersion, the particle dispersion coefficient is linearly
dependent on the interstitial velocity:
— VV
Dij=aLV8ij+(aL-aT)^I+DdT8ij
(7)
where V. is the component of the spatially distributed interstitial
velocity along direction /, Dd is the colloid diffusion coefficient
obtained from the Stokes-Einstein equation, aL and oc^are the
longitudinal and transverse dispersivities, respectively, and 7 is
the porous medium tortuosity.
To appropriately describe the dynamic aspects of colloid
deposition or release, the mass transport equation should be
expressed in terms of colloid number concentration rather than
mass concentration [Johnson and Elimelech, 1995; Johnson et
al., 1996], that is
(8)
where n is the number concentration of colloids, 8 is the fractional
surface coverage, defined as the total cross section area of
deposited colloids per interstitial surface area of the porous
medium solid matrix,/ is the specific surface area (i.e., interstitial
surface area per porous medium pore volume), and a is the
radius of colloidal particles. It can be readily shown that Eqn. 8 is
equivalent to Eqn. 6.
Colloid Deposition and Release. Using the patchwise
model for geochemical heterogeneity, the particle surface
coverage rate of mineral grains is given by [Johnson etal., 1996]
(9)
When considering the dynamic aspects of particle deposition and
release, the rate equations corresponding to the favorable and
unfavorable surface fractions can be expressed as
Qt
(10a)
(10b)
where the subscripts/and « represent the favorable (A) and
unfavorable (1-X) REV surface fractions, respectively, kdep is the
colloid deposition rate coefficient, k^ is the first order colloid
release rate coefficient, and B(0) and R(9) are the dynamic
blocking and releasefunctions, respectively. The colloid deposition
rate coefficient is related to the single collector efficiency t|
commonly used in filtration theories as [Elimelech et al., 1995]
_riV_ari0V
dep"4e" 4e
(11)
where V is colloid advection velocity, e is the porosity of the
porous medium, a is the collision efficiency, and t|0 is the
favorable single collector removal efficiency.
The dynamic blocking function B(9) describes the probability
of a colloid contacting a portion of collector surface unoccupied
by previously deposited colloids [Song et al., 1994]. It accounts
for the blocking effect of deposited colloids on the particle
deposition rate. Two types of dynamic blocking functions are
generally recognized: Langmuirian dynamic blocking function
and random sequential adsorption (RSA) dynamic blocking
function. Recent experimental investigations have shown that
the RSA model describes the dynamics of particle deposition in
porous media much better than the conventional Langmuirian
model [Johnson and Elimelech, 1995; Johnson et al., 1996].
The general form of the RSA dynamic blocking function is
[Adamczyket al., 1992]
(12)
where emax is the maximum attainable surface coverage, a, and
a2, a3 are coefficients that can be found theoretically (for ideal
particles and collector surfaces) or empirically. The coefficients
used by Johnson and Elimelech [1995] for B(9) will be used in this
colloid transport model as they were found adequate to describe
the dynamics of blocking in flow of monodisperse latex
microspheres in columns packed with spherical and uniform
glass beads [Johnson and Elimelech, 1995]. Because colloid
deposition onto the favorable surface fraction is usually irreversible,
the RSA model can be used to describe the dynamics of particle
deposition onto the favorable surface fraction. A similar dynamic
blocking function was also chosen to describe the blocking of the
unfavorable fraction, although the deposition onto the unfavorable
surface fraction was assumed to be reversible with a non-zero
release rate. This assumption, however, has negligible effect on
the colloid transport behavior since the deposition rate on the
unfavorable surface fraction is much smallerthan on the favorable
fraction, and the maximum surface coverage for the unfavorable
surface fraction is much smaller than the maximum surface
coverage on the favorable surface fraction.
Somewhat analogous to the dynamic blocking function, the
dynamic release function describes the probability of colloid
23
-------
release from porous media surfaces covered by retained colloids.
In principle, this function should depend on the colloid residence
time and the retained colloid concentration [Johnson et al., 1996].
When R(0) = 1, the release terms in Eqn. 10 represent first order
kinetics release mechanism. Because at the present time the
mechanisms of colloid release are poorly understood, only a first
order release rate will be used in this paper.
Correlation between Physical Heterogeneity and Colloid
Deposition Rate. In modeling colloid transport, the variation of
flow field will change the colloid concentration distribution in the
studied domain not only by affecting hydrodynamic dispersion
and advection, but also by influencing the colloid deposition rate.
For Brownian colloids where deposition rate is controlled by a
convective-diffusive mechanism [Elimelech et al., 1995] there
exists a positive relationship between the hydraulic conductivity
and the particle deposition rate. Based on Eqn. 11, the colloid
deposition rate (kdep) is proportional to (r|0V), with t|0 for Brownian
colloids being proportional to V2'3 [Elimelech etal., 1995]. Hence,
combining this relationship with Darcy's law one obtains that the
colloid deposition rate (kdep) is proportional to K"3. A consequence
of this relationship is that a random field of hydraulic conductivity
leads to a random field of colloid deposition rate as well.
We assume that P(x) = In k^x) is normally distributed with
a mean and variance cr2p, and has a similar form of the
covariance function as the hydraulic conductivity field. To describe
the correlation between the random hydraulic conductivity field
and the colloid deposition rate, it is further assumed that
(13)
where coandyare correlation coefficients and Y' is the perturbation
of the hydraulic conductivity field. When co =0 and y>0, Pand Y
are perfectly positively correlated; when o>=0 and y<0, Pand Y
are perfectly negatively correlated; and when co^O, Pand Yare
not perfectly correlated. For Brownian colloids, w=0andy>0; the
value of Y is chosen as 1/3, for the reason discussed above.
Numerical Procedures. In the colloid transport model
presented in the previous section, the transient flow equation is
coupled to the colloid transport equation. Numerical solution can
be obtained with both transient and steady state flow fields using
the multiple cell balance (MCB) method [Sun, 1995]. The flow
region in our model is a vertical rectangular domain, with the
horizontal x axis ranging from 0 to 3 m and the vertical z axis
ranging from 0 and 1 m. The computational domain Q is encircled
by the line boundary F.
Initial and Boundary Conditions. For the flow equation, the
initial and boundary conditions for the flow domain are specified
as follows:
h(3,Z,t) =
for f>0, (3,Z)GT4 (14e)
h(x)=h0 at
for t>0, (0,z)er,
(i4a)
(14b)
dh(x,z,t)
= 0 for f>0, (x,0)er2 (14c)
Z=0
= 0 for t > 0, (x,l) e T3
where F = Fj UT2 LIT, UT4 , and h1 and h2 are fixed values
of hydraulic heads on the boundaries.
The initial and boundary conditions forthe colloid transport
equation are specified as follows. Initially the porous medium has
no deposited colloids (i.e., zero surface coverage, 9= Ou=0). At
the four boundaries (T ), zero dispersive flux boundary conditions
are specified. Furthermore, a given concentration of colloids is
injected into the domain at t>0. The type of colloid injection can
be classified as pulse injection or continuous injection depending
on the duration of the injection. The mode of injection can be
characterized as point injection or line injection based on the
number and locations of injection wells. The injection is set as the
boundary condition forthe colloid concentration.
Multiple Cell Balance Algorithm. Because there are no
analytical solutions forthe flow and colloid transport governing
equations, we adopted the multiple cell balance (MCB) method
[Sun and Yeh, 1983] to solve the two-dimensional transport
model numerically. The MCB method was originally derived for
solving the coupled groundwater flow and solute transport
equations for solutes experiencing equilibrium sorption [Sun,
1995]. The method has never been applied to the more complex
problem of colloid transport. The details on the numerical
formulation and procedures are given in the doctoral dissertation
of Sun [Sun, 1998].
Validation of the Numerical Code. Previous studies
suggested the MCB method provides adequate solution for two-
dimensional non-reacting solute transport problems [Sun, 1 995].
We compared the numerical solution based on our MCB code for
the transport of a tracer in a two-dimensional semi-infinite isotropic
porous medium with the analytical solution provided by Leij and
Dane [1990]. The numerical results closely agreed with the
analytical solution. To validate the MCB code for colloid transport,
the analytical solution derived by Lapidus and Amundson [1952]
forthe one-dimensional solute transport problem with finite rates
of sorption (/cr) and desorption (k2) was compared with our
numerical solution forthe following problem:
=L
dt Ldx2
dt
e dt
(15)
(16)
The initial concentration is set at zero, the concentration at the
inlet boundary is given as a constant C0, and the dispersive flux
of colloid is set at zero to the outlet boundary. The analytical
solution of Lapidus and Amundson [1952] is given by
Vx
where
(14d)
z=l
(17)
(18)
——id
;4Dr err
24
-------
and
4D
(19)
As shown in Figure 7, the numerical results obtained from
the MCB code are in very close agreement with the analytical
solution.
Results and Discussion
The newly developed 2-D colloidal transport model is used
to conduct a numerical investigation of colloidal transport in
physically and geochemically heterogeneous porous media. We
first illustrate the effect of key model parameters on the general
colloid transport behavior. This follows by a systematic
investigation of colloid transport in layered as well as randomly
heterogeneous subsurface porous media.
Influence of Key Model Parameters. The basic values of the
model parameters and the range of their variation during the
numerical investigation are listed in Table 17. The range of
parameter values covers possible scenarios of colloid transport
in sandy aquifers, such as the glacial outwash sandy aquifer in
Cape Cod, Massachusetts, which has been used extensively in
field investigations [LeBlancetal., 1991; Garabedianetal., 1991;
Hess et al., 1992; Gelhar et al., 1992; Sun, 1995; Harvey et al.,
1989; Ryan and Gschwend, 1990; McCarthy and Degueldre,
1993; Johnson etal., 1996; Pieperetal., 1997]. Colloidal particles
are introduced at the boundary x = 0 as a pulse injection with a
duration of 0.5 days. The results (Figure 8) are presented as
relative colloid concentration n/n0 along the flow direction x at a
certain observation time (f = 0.75 d).
Under the examined conditions, an increase in hydraulic
conductivity results in enhanced colloid migration and a wider
spreading of the colloid concentration profiles (Figure 8a). With
a constant hydraulic head gradient, a greater hydraulic conductivity
results in larger flow velocity so that the colloid advection velocity
increases. Because the particle dispersion coefficient is
proportional to the colloid advection velocity, dispersion
(spreading) of colloids also increases asthe hydraulic conductivity
is increased. In addition, the capture of colloids traveling through
the porous medium decreases with increasing flow velocity, thus
resulting in a slightly attenuated particle concentration profile.
Hydrodynamic dispersion is also controlled by the
longitudinal and transverse dispersivities (Eqn. 7). The ratio of
longitudinal dispersivity to transverse dispersivity is typically in
the range of 5 to 20 [Sun, 1995]. We assumed a ratio of 5 and
investigated the effect of varying the longitudinal dispersivity.
Because longitudinal dispersivity is scale dependent, and our
problem is of local scale (ca. 3 m), only a narrow range of values
was selected for the longitudinal dispersivity. The results
(Figure 8b) show that small changes in longitudinal dispersivity
lead to relatively large changes in the colloid concentration
profiles.
To investigate the effect of particle deposition rate, a
constant geochemical heterogeneity (X=0.01) was assumed. We
fixed the favorable particle deposition rate coefficient, and adjusted
the unfavorable deposition rate by choosing different values for
the collision efficiency of the unfavorable surface fraction au. The
results (Figure 8c) show that particle deposition rate can
substantially affectthe colloid concentration profile. Asthe collision
efficiency au increases, the colloid deposition rate on the
unfavorable fraction increases, and less colloids can be detected
in the aqueous phase. The magnitude of the collision efficiency
au reflects the effect of changes in the solution chemical
composition.
By fixing the particle deposition rate coefficients kdg.f and
kdepu, the overall particle deposition rate can be controlled by the
geochemical heterogeneity parameter /L The marked effect of
geochemical heterogeneity on colloidal transport is illustrated in
Figure 8d. An increase in geochemical heterogeneity results in
increased overall colloid deposition rate and reduced concentration
of colloids in bulk solution. For the conditions investigated in
Figure 8d, a substantial geochemical heterogeneity of subsurface
porous media (>10%) may result in nearly complete immobilization
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.5
1.0
1.5
x(m)
2.0
2.5
3.0
Figure 7. Comparison of numerical solutions (symbols) with analytical solutions (lines) for reactive solute transport in isotropic
semi-infinite porous medium. The parameters used for this simulation are VDarc = 1.0 m s~1; aL = 0.05 m, aL: aT = 5:1;
PeL = 1; Courant No. = 0.25-1.0. The observation times from t1 to t6 are 0.5, 1.0, 2.0, 3.0, 4.0, 5.0 d.
25
-------
Table 17. Basic values and ranges for parameters in the colloid transport model.
parameter value range
hydrologic parameters
Vh
transport parameters
lO-2
102
1CH-10-1
10°~103m/dav
Ss
ocL(m)
aL/aT
e
lo-4
0.05
5
0.4
0.01-0. 7m
5-20
0.3-0.5
dc (mm)
f a (m2/m3)
dp (u,m)
pp (g cm3)
Co(mgL-i)
n0 (# m3)
a
b
M%)
Wuc(md-')
kdep,fc(md-1)
kdetu (hr1)
w
0.3
30000
0.3 0.01-1
2.5
10
2.8xl014 IQH-IO15
] Q-3 10"4— 10°
0.0259
0.1 0.1-10
6.5xlO-6
6.5x10-3
o.o io-4~io-2
0.0 0.0
3 Determined from 3(1-e)/(e aj
b Determined from the method of Elimelech and Song [1992].
0 Determined from Eqn. 11.
of colloidal particles as shown by the flat, attenuated colloid
concentration profile.
The colloidal transport model assumes that particle
deposition onto the favorable surface fraction is irreversible;
hence, the colloid release rate from the favorable surface fraction
iszero. This assumption has been confirmed in particle deposition
studies involving oppositely charged particles and collector
surfaces [Elimelech et al., 1995]. On the other hand, in particle
deposition studies involving similarly charged particles and
collector surfaces, a finite rate of colloid release can be detected
[Ryan and Elimelech, 1996]. Hence, we investigated the effect of
colloid release rate from the unfavorable surface fraction on the
colloid concentration profile as shown in Figure 8e. The results
demonstratethat larger release rate coefficients result in increased
colloid concentration in the aqueous phase, whereas smaller
release rate coefficients have no effect on the colloid concentration
profile. Since the colloid release rate depends on the concentration
of deposited particles (first order kinetics), the effect of colloid
release on the colloid concentration profile depends on the
overall colloid deposition rate onto the unfavorable surface
fraction. Figure 8f demonstrates that the model solution is very
sensitive to particle size. Particle size influences colloidaltransport
mainly through its effect on colloid deposition rate. As expected
for deposition of Brownian particles, which is controlled by a
convective-diffusion mechanism, the deposition rate becomes
smaller as particle size increases. Consequently, larger particles
migrate faster in the porous medium and their concentration in
the liquid phase is greater than that of smaller particles.
Colloid Transport in Layered Heterogeneous Porous Media.
The porous medium was divided into three horizontal layers,
parallel to the flow direction. The layers are denoted as layer I (0-
0.3 m), layer II (0.3-0.7 m), and layer III (0.7-1.0 m) from bottom
to top. Layers I and III were assigned the same heterogeneity
parameter values, whereas a different parameter value was
assigned to the middle layer II. The colloid suspension was
assumed to be fed continuously (line injection) into the porous
26
-------
1 '.'I X
/ J\ /\
f / k, / ''
/ / \/
/ / A
' / / \
* P s'&
&& -a- -A
(a)
------ a
"•• '"- "
- (b)
I
- -v - v - v -7-
I
(c)
- -V- -V- -V -V" -7- -V -V- -V- -
(d)
(e)
(f)
Figure 8. The role of model parameters in colloid transport in physically homogeneous porous media. The results are presented
as relative colloid concentration n/ng along the flow direction xat a certain observation time (f = 0.75 d). The effects of
(a) hydraulic conductivity, (b) longitudinal dispersivity, (c) collision efficiency (deposition rates), (d) geochemical
heterogeneity, (e) release rate from unfavorable surface fraction; and (f) particle size on colloid concentration profile
along z = 0.5 m. Parameter values are shown in Table 17.
27
-------
medium at the inlet boundary (x=0), with 11 injection points set at
0.1 mintervalsalongthezdirection. Observations of concentration
profiles over the entire two-dimensional porous medium domain
are presented for f=0.75 d. The physical and geochemical
heterogeneity parameter values used in the numerical
investigation (represented by K and 1, respectively) were
comparable to those reported for the Cape Cod sandy aquifer
[Leblanc et al., 1991].
The effect of layer-distributed physical heterogeneity on
colloid transport is illustrated in Figure 9. The hydraulic conductivity
of the middle layer(layer II) of the porous medium is twice as large
as the hydraulic conductivity in the layers above and below.
Therefore, the fluid flows in the central layer faster than the other
two layers, and most of the colloids migrate with the flow through
the more permeable layer. This example points outthe paramount
importance of preferential flow paths in colloid transport.
Because transverse dispersion reduces the amount of
colloids passing through the preferential flow path, the role of
longitudinal and transverse dispersivities was also investigated.
Two different ratios of longitudinal to the transverse dispersivities
(1 and 10) were studied, as shown in Figure 10. When the
transverse dispersion is relatively large (ccL/ccT=1.0), the extent of
preferential flow in the middle (most permeable) layer of the
porous medium is reduced (Figure 11 a). However, when the
transverse dispersion is relatively small (otL/ocT=10.0), the
preferential transport of colloids in the middle layer of the porous
medium is enhanced (Figure 11 b). The results demonstrate that
hydrodynamic dispersion can influence colloid transport in layered
heterogeneous porous media, but the effect is not strong enough
to explain the preferential transport of colloidal particles.
The effect of a layered geochemical heterogeneity on
colloid transport in physically homogeneous (constant hydraulic
conductivity) subsurface porous medium is shown in Figure 12.
The central layer had a very small value of geochemical
heterogeneity (1=0.001), whereas the upper and lower layers
had much higher values (1=0.025). The results clearly show that
the increased particle deposition rate of colloids onto thefavorable
surface fractions of the more heterogeneous (lower and upper)
layers can result in preferential flow of colloidal particles through
the middle layer, similar to that observed for layered, physically
heterogeneous porous media.
Because subsurface porous media are physically as well as
geochemically heterogeneous, it is of great interest to investigate
the combined effect of layered physical and geochemical
heterogeneity. For the layered physically heterogeneous porous
medium shown in Figure 9, we assumed that the geochemical
heterogeneity is layered distributed as well. Figure 11 a illustrates
the results when the central layer has a larger 1(0.025) compared
to the two side layers (1=0.001). It is interesting to note that for
these conditions the preferential flow path (initially caused by the
physical heterogeneity, Figure 9) disappears due to the
geochemical heterogeneity. On the other hand, the preferential
flow path is enhanced when the middle layer has a smaller
1(0.001) than the two side layers (1=0.025), as shown in
Figure 11b. The results clearly demonstrate that layered
geochemical heterogeneity can significantly alterthe preferential
transport of colloidal particles caused by heterogeneous flow
field. Hence, consideration of physical or geochemical
heterogeneity alone in colloidal transport models may result in
erroneous results.
Colloid Transport in Randomly Heterogeneous Porous
Media. Colloid transport in randomly heterogeneous porous
media is investigated in this section. The numerical investigation
is carried out fora point injection (atx=0.5m, z=0.5m) with a pulse
duration of 0.1 day. Results are presented as snapshots of colloid
concentration in the porous medium at f=0.5 day.
Freeze [1975] pointed out that hydraulic conductivity
variations in aquifers are typically lognormally distributed with a
standard deviation (in log base 10 units) ranging from 0.2 to 2.0.
Since then, several field measurements confirmed this
observation. For instance, it was reported that the mean hydraulic
conductivity ACforthe Borden site is 0.0072 cm/s with the variance
of InK being 0.29 [Sudicky, 1986]. Hufschmied [1986] reported
that the mean value of K\s 0.36 cm/s for the Aefligen site with a
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
1.00
0.50
0.00
0.00
0.50
1.00
1.50
2.00
2.50
3.00
Figure 9. Effect of layered physical heterogeneity of porous media on colloid transport. The hydraulic conductivity of the central
layer is K=100m d"1. The hydraulic conductivities of the upper and lower layers are K = 50 m d"1. The geochemical
heterogeneity for all the layers is the same, 1=0.01; ccL/aT=5. The xandzaxes show distance (m). The bar graph shows
the colloid concentration in the porous medium normalized to the influent colloid concentration at x = 0.
28
-------
1.00
(a)
0.00
0.50
1.00
1.50
2.00
2.50
3.00
1.00
QSO
0«J
H5G
.0.40
0.30
050
O'.IO'
0.00
(b)
o.oa
o.oo
0.50
1.00
1.50
2.00
2.50
3.00
Figure 10. Effect of the ratio of aL to aTon the preferential flow of colloids caused by layered physical heterogeneity of porous media:
(a) 0^/0,.= 1; (b) aL/ccT= 10. The xandy axes show distance (m). The bar graph shows the colloid concentration in the
porous medium normalized to the influent colloid concentration at x = 0.
(a)
2.50
3.00
l.oo
0.98
1.00-
0.50
0.00-
0.00
(b)
0.50
2.00
2.50
3.00
Figure 11. Effect of layered geochemical and physical heterogeneity of porous media on the preferential flow of colloids: (a) central
layer, K= 100m d~1, 1= 0.025; upper and lower layers contain K= 50 md'1, 1=0.001; aL/aT=5; (b) central layer, K= 100
md"1, 1=0.001; upper and lower layers contain K= 50 md~\ 1=0.025; aL/aT=5. The x and y axes show distance (m). The
bar graph shows the colloid concentration in the porous medium normalized to the influent colloid concentration atx = 0.
29
-------
1.00
0.90
0.80
10.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
0.00
0.00
0.50
1.00
1.50
2.00
2.50
3.00
Figure 12. The preferential flow of colloids caused by the layered geochemical heterogeneity in a physically homogeneous porous
medium. The geochemical heterogeneity of the central layer is X=0.001; the geochemical heterogeneity of the upper and
lower layers is 1=0.025. Hydraulic conductivity K= 100 m d~1 for each layer; ocL/aT=5. The x and zaxes show distance
(m). The bar graph shows the colloid concentration in the porous medium normalized to the influent colloid concentration
atx=0.
variance of InK of 2.15. The horizontal (x, y directions) and
vertical (zdirection) correlation scales were reported to be 0.29,
2.8, and 0.12m, respectively, fortheBorden site [Sudicky, 1986];
0.26, 5.1, and 0.26 m, respectively, for the Cape Cod site [Hess,
1989]; and 0.031, 3.0, and 0.91 m, respectively, for the Twin
Lakes site [Moltyaner, 1986].
In our study, the mean value of Kwas set at 0.116 cm s~1
(100 m d~1), the variance of InK was set at 0.24 or 2.4, and the
correlation scale of the vertical porous medium was set as 0.5 m.
The random field of hydraulic conductivity was generated
numerically as outlined earlier in this paper, and was incorporated
into the MCB code of colloid transport. Similar variance values
were used to generate the random field of the particle deposition
rates. The mean values of the deposition rate coefficients were
set at 6.5X10'3 m d~1 for the favorable surface fraction and at
6.5x10"6 m d~1 for the unfavorable surface fraction. Note that the
latter corresponds to 0^=10~3.
Realizations of the random fields of hydraulic conductivity
with two different variance values of lnK(0.24 and 2.4) are shown
in Figure 13. Figure 14 presents the corresponding hydraulic
head distributions and colloid concentration profiles in the
randomly physically heterogeneous porous media; the results for
physically homogeneous porous media are presented as well.
Compared to the physically homogeneous case, random fields of
InK result in obvious irregular hydraulic head distributions
(Figures 14b,c). The irregularity ofthe hydraulic head distributions
increases with the variance of InK A similar trend can be
observed in the colloid concentration profiles. When the variance
of \nK is small, the colloid concentration profile (Figure 15b) is
only slightly different than the homogeneous case. However, a
very irregular shape ofthe concentration profile (Figure 15c)can
be seen when the variance of InK is large. The results clearly
demonstrate that a random physical heterogeneity of porous
media results in a random behavior of colloid transport as well.
Because of lack of field measurements on random
geochemical heterogeneity of subsurface porous media, we
conducted a preliminary numerical investigation on the sensitivity
ofthe colloid transport behavior to the parameters characterizing
the geochemical heterogeneity. Results indicated that the mean
value ofthe geochemical heterogeneity has to be large enough
(X=0.01)to show the effect of its spatial distribution on the colloid
concentration profiles. When the mean value of X is as small as
0.001, which may be a reasonable value for sandy aquifers with
negligible iron oxyhydroxide coatings, the distribution of A,
apparently does not affect the colloid transport behavior, even
when A, is assumed to have a lognormal distribution with a rather
large variance. Therefore, a mean value of X= 0.01 was chosen
to carry out the rest ofthe numerical investigation. This value is
quite reasonable forthe geochemical heterogeneity of subsurface
geological formations [Heron et al., 1994; Kretzschmar et al.,
1995; Coston et al., 1995]. It was also found that a lognormally
distributed field of A, with a relatively small variance does not have
an observable effect on the particle concentration profiles;
therefore, a variance of InX as large as 2.0 was chosen.
RealizationsoftherandomfieldsofXareshown in Figure 16.
Fora normal distribution, the standard deviation was chosen as
large as 0.005. Of the simulated X values, about 2.5% are
negative; these negative values were truncated from the
distribution. Figure 16a shows that the value of A. is mostly
distributed from 0.0 to 0.02. For a lognormal distribution, the
variance of InA. was set as large as 2.0. We truncated about 2 %t
values which are larger than 1.0. The value of Ovaries mainly
between 0.001 to 0.2, and some values of A, even reach 1.0
(Figure 16b).
The colloid concentration profiles for the two different
geochemical heterogeneity fields are compared with the case of
a constant A, in Figure 17. The normally distributed random field
of geochemical heterogeneity apparently does not affect the
colloid concentration profiles (Figure 17b). A lognormal field of
geochemical heterogeneity with a large variance (Figure 17c)
developed only a slight irregularity in the concentration profile.
These results suggest that the effect of a random field of
30
-------
•3600
•3300
•3000
•2700
•2400
•2100
•1800
•1500
1.O
0.5-
O.O
(a)
(b)
2.0
2.5
3.0
x(m)
Figure 13. The realizations of random hydraulic conductivity fields. The scale bar on the left represents hydraulic conductivity (m d~1)
(a) E(K) = 100 m d'1, Var(lnK) = 0.24; (b) E(K) = 100 m d'1, Var(lnK) = 2.4. The xand z axes show distance (m).
13.SSQ
19-9S5
T3.3BB
9.975
0.50 1.00 1.50 2.00 2.50 3.00
(b)
0.00 0.50 1.00 1.50 2.00 2.50 3.00
(c)
0.00 0.50 1.00 1.50 2.00 2.50 3.00
Figure 14. Thehydraulicheaddistributionsinhomogeneousorrandomlyphysicallyheterogeneousporousmedia.(a)E(K) = 100 md'1,
Var(lnK) = 0.0; the porous medium is physically homogeneous; (b) E (K) = 100 m cr1, Var(lnK) = 0.24; (c) E(K) = 100 m
d~1, Var(lnK) = 2.4. The xand zaxes show distance (m).
31
-------
"0.50
(a)
(b)
(c)
Figure 15. The colloid concentration profiles in a homogeneous or randomly physically heterogeneous porous medium for a point
injection at (0.5 m, 0.5 m)with a duration of 0.1 dfora snapshot taken at 0.5 d: (a) E(K) = 100 md'1, Var(lnK) = 0.0; the
porous medium is physically homogeneous; (b) E(K) = 100 m d'1, Var(lnK) = 0.24; (c) E(K) = 100 m d'1, Var(lnK) = 2.4.
The x and z axes show distance (m).
0.95
0.90
0.85 1 00-
0.80
0.75
0.70
0.65
0.60 °-5°-
0.55
0.50
0.45
0-40 n m
i i j_ i i
0.0'
0.00
0.50
1.00
1.50
2.00
2.50
3.00
1.00 1.50 2.00 2.50
3.00
0.04 n
0.03 --
0.03
0.02
0.02 ,
0.01
0.01
0.00 -'
1 37 73 109145181217253289325
(a)
LOOEfOO -
1.00E-01 -:
1.00E-02
1.00E-03
1.00E-04
(b)
Figure 16. The realizations and value distributions of random geochemical heterogeneity fields. The gray bar values show the range
of X. '. (a) normal distribution with E(X) = 0.01 and o(X) = 0.005; (b) lognormal distribution with E(A.) = 0.01, and
Var(lnX) = 2.0. The xand zaxes show distance (m).
32
-------
0*1-
omu o.oo 0.50
1.00 1.50 2.00 2.50 3.00
0.00
,o 0.00
(c)
Figure 17. The effect of random geochemical heterogeneity on colloid transport for a snapshot at t = 0.5 d after a point release at
(0.5 m, 0.5 m): (a) colloid concentration profile in a porous medium with a constant geochemical heterogeneity, ^=0.01;
(b) colloid concentration profile in a normally distributed geochemically heterogeneous porous medium, E(X) = 0.01,
= 0.005; (c) colloid concentration profile in a lognormally distributed geochemically heterogeneous porous medium.
geochemical heterogeneity on colloid transport is not as strong
as the effect of random physical heterogeneity. Hence, the mean
value of geochemical heterogeneity is more important than its
distribution in modeling colloid transport in heterogeneous porous
media.
Summary and Conclusions
The major objectives of this research were to (1) examine
the dependence of colloid transport and mobilization on chemical
perturbations, (2) assess the relative transport of mobilized
colloids and the chemicals that caused their mobilization, and (3)
develop a colloid transport model that would begin to describe
these effects. Through the field tests, laboratory experiments,
and model development designed to meet these objectives, we
made significant advances toward testing the major hypothesis
driving this research, that the transport of colloids mobilized in a
contaminant plume will be limited bythe advance ofthe chemical
agent causing colloid mobilization.
The field tests were conducted in the uncontaminated and
secondary sewage-contaminated zones of the ferric oxyhydroxide-
coated quartz sand aquifer at the U.S. Geological Survey Toxic
Hydrology Research Site on Cape Cod, Massachusetts. These
experiments examined the dependence of colloid transport and
mobilization on chemical perturbations and assessed the relative
transport of mobilized colloids and the chemicals that caused
their mobilization. The transport of mineral (silica and silica-
coated metal oxide) and biological (viruses) colloids were related
to the surface properties ofthe colloids and aquifer grains (as
measured by zeta potential). Excellent agreement was found
between the extent of ferric oxyhydroxide surface coverage
measured by electron microprobe and estimated bythe collision
efficiencies for the viruses. Increases in pH were most effective
in mobilizing colloids (both natural and synthetic) and viruses
because increases in pH above the pH^ were most effective in
reversing the charge ofthe ferric oxyhydroxide coatings. In most
cases, the transport of mobilized colloids was limited by the
advance ofthe colloid-mobilizing agent (e.g., decrease in ionic
strength, anionic surfactant concentration, reductant
concentration). A notable exception occurred when pH was
increased in one field experiment- mobilized colloids appear to
have been transported ahead ofthe hydroxide plume owing to
coating of colloids by natural organic matter. The field research
led to the development of a new class of tracer colloids, silica-
coated metal oxides. The size of the two types of colloids used in
this research, silica-coated zirconia and titania, was controlled by
varying the precipitation conditions.
The laboratory experiments examined the dependence of
colloid transport and mobilization on chemical perturbations
undercontrolled conditions and over a greater range of conditions.
They showed that chemical perturbations that cause increasingly
repulsive conditions produced more extensive and more rapid
colloid release. A series of elevated pH experiments conducted
on individual columns containing oriented, undisturbed sediments
provided excellent data for the assessment of colloid release
rates, a task that will be completed in the future. The laboratory
experiments also showed that an increase of pH to too high a
value produces less repulsion and, hence, less colloid release.
This result duplicated a phenomenon observed in the field in the
injection well ofthe highest pH injection.
The modeling effort aimed at simulating the processes
controlling colloid transport in a contaminant plume focused on
the development of a two-dimensional colloid transport model
that considers the geochemical and physical heterogeneity ofthe
porous medium as well as the dynamic aspects of particle
deposition. While the modeling effort did not achieve the full
objective of simulating the transport of colloids in a contaminant
plume, majoradvanceswere made. Simulationsofcolloidtransport
in layered heterogeneous porous media indicate that both physical
and geochemical heterogeneities play important roles in colloid
33
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transport. Both types of heterogeneities can cause preferential
flow of colloidal particles. The combination of layered physical
heterogeneity and layered geochemical heterogeneity may
enhance or reduce the preferential flow of colloids. Hence,
physical and geochemical heterogeneities should be considered
simultaneously in modeling colloidal transport in layered
heterogeneous porous media. Overall, the numerical investigation
based on the developed 2-D transport model provides a better
understanding of colloid transport in physically and geochemically
heterogeneous subsurface porous media. Experimental data of
colloid transport in different heterogeneous porous media for
laboratory or natural systems are needed to test the model
predictions. Since the proposed model is more sophisticated
than existing models for colloid transport in porous media, the
application of this model will be affected by the availability of the
model parameters. The identification of the model parameters in
this 2-D model is important in applying this model in practice and
will be addressed in future work.
Acknowledgments
We thank the following people for their help during this
project: Bob Puls (U.S. EPA, R.S. Kerr Laboratory) for project
guidance. Ron Harvey and Doug Kent (U.S. Geological Survey)
for discussions on the project direction. Denis LeBlanc, Kathy
Hess, and Tim McCobb (U.S. Geological Survey, Massachusetts
District) for access to the field site and collection of sediment and
groundwater samples. Mike Bonewitz (University of Colorado)
and Jenny Baeseman (University of Wisconsin, Stevens Point)
for field work. Jon Loveland (University of Colorado) for virus
preparation. Dave Metge (U.S. Geological Survey) and Jon
Larson and Dean Abadzic (University of Colorado) for data
analysis. John Drexler (University of Colorado) for scanning
electron microscopy. Phil Johnson (Notre Dame University) for
streaming potential analysis.
QUALITY ASSURANCE STATEMENT
All research projects funded by the U.S. Environmental
Protection Agency that make conclusions or recommendations
based on environmentally related measurements are required to
participate in the Agency Quality Assurance Program. This
project was conducted under an approved Quality Assurance
Program Plan and the procedures therein specified were used.
Information on the plan and documentation ofthe quality assurance
activities and results are available from the Principal Investigator.
DISCLAIMER
The U.S. Environmental Protection Agency through its
office of Research and Development partially funded and
collaborated in the research described here under Cooperative
Agreement No. CR-824593withtheUniversityofColorado. It has
been subjected to the Agency's peer and administrative review
and it has been approved for publication as an EPA document.
Mention oftrade names or commercial products does not constitute
endorsement or recommendation for use.
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