1. Josh D. Larsen1
, Christopher Brueck2
, Dorthe Wildenschild2
, and Marcel G. Schaap1
1
Dept. of Soil, Water and Environmental Science, College of Agriculture and Life Sciences, University of Arizona, Tucson.
2
School of Chemical, Biological, and Environmental Engineering (CBEE), Oregon State University, Corvallis, OR.
Soil colloids are biotic or abiotic nano- to micron-scale particles that have
the potential to migrate through the subsurface and pose risks if they are
pathogenic micro-organisms, or facilitate transport of typically immobile
chemicals of emerging concern (e.g. antibiotics, hormones and other
pharmaceuticals used in farm operations). Several fundamental physical-
chemical colloidal interaction mechanisms have been identified at the pore-
scale. However, implementation of colloid transport into practically usable soil
water models suitable for risk-based predictions is still at its infancy because
of the complexities involved. Key questions are whether current column and
field-scale models accurately represent colloid transport mechanisms that
fundamentally occur at the pore-scale, but also whether all pore-scale
mechanisms are relevant. Can valid simplifications be imposed that effectively
enhance a soil water's model practical applicability regarding colloid transport?
The research meets these challenges by resolving colloidal particle
distributions within idealized porous systems, using near real-time 3D
computed x-ray microtomography and pore-scale lattice-Boltzmann modeling
in which we turn on or off the various colloidal interaction mechanisms and
derive column-scale transport relations.
This study was supported by the USDA-NIFA under grant 2014-67019-21718
Colloids are soil particles that have characteristics
of solutes as well as solids. Their generally agreed
size ranges from 1 nm to about 10 mm. Several
processes govern the behavior of colloids in the soil
and their surface physical chemical properties
determine whether colloids attach or detach from
soil particles or air-water interfaces. Under
saturated conditions there are only two attachment
conditions. However, the air-water interfaces
present at unsaturated conditions lead to a more
complex situation.
Modified from DeNovio et. al., 2004
APS Synchrotron. 900 radiographs collected as sample rotates
(In 16 minutes per scan with 1 s exposure). Resolution = 3.2
µm/pixel totally 8.6 GB per XMT scan. X-Ray attenuation
follows Lambert-Beer Law where each material (air, KI-doped
water, Ag-doped colloid, and glass beads) has unique linear
attenuation coefficient, µ.
X-Ray Microtomography (XMT)
Modified from Wildenschild and
Sheppard, 2013
0( ) x
I x I e 

dI
I
dx
 
Image Processing
33.37 keV 33.37 keV 25.61 keV
Dry Scan A Scan B Scan
Experimental Conclusions
1) The initial deposition of hydrophobic colloids in saturated glass bead
columns is heterogeneous.
2) At near residual saturations, hydrophobic colloid clusters are shaped by
the air-water interfaces
3) The total colloid deposition is dependent on drainage flow rate and
moisture content.
4) Vertical profiles reveal that drainage mobilizes colloids.
5) Pore straining is a dominant retention mechanism for aggregated
hydrophobic colloids.
6) Colloid attachment to air-water interfaces and air-water-solid contact
line are equally important at medium to high water contents.
7) The number of colloids trapped by the disconnected water phase is/
directly proportional to the volume of the DisW phase.
Colloid Partitioning
Initial Colloid Deposition
0.5 mL/hr 5.0 mL/hr 50.0 mL/hr
Saturated
Scans
Residual
Saturation
Scans
Step 1, Saturate. 1 M potassium
iodide (KI) solution at 5 mL/hr
Step 2, Deposit Colloids. 3 mL
of 3 mg/mL colloid solution into
columns by draining at 50 mL/hr
(~2 PVs)
Step 3, Desaturation. Three
columns with three different flow
rates (0.5, 5.0, 50.0 ml/hr) to
reach desired saturation. Flow
stopped, pressure equilibrated
and XMT scan conducted.
Step 4, Saturation 2.
Step 4, Saturation N. Repeat
until residual saturation is
reached.
C. Brueck
Experimental Procedure
XMT Scans. At each saturation two XMT scans are conducted (33.37 and 25.63 keV) to permit
observations of solid phase, KI-doped water phase, Ag-coated colloids, and air. Together with a
dry scan (empty pore space) and image processing a segmented 3D volume is reconstructed
that unambiguously identifies the four phases. Colloid (clusters) are green; solids light
blue; air dark blue; water is red. The system was shown to contain several Representative
Elementary Volumes.
System Properties
Solid
Phase
Diameter 0.8 - 1.2 mm
ζ (pH=9.5) w/o KI -43 mV
ζ (pH=9.5) with KI -
Contact Angle ~30°
Colloid
Mean Diameter 10 µm
ζ (pH=9.5) w/o KI -32 mV
ζ (pH=9.5) with KI -
Contact Angle 135°
Fluid
Phase
Ionic Strength 1 M
pH 9.5
Glass Beads
Colloids (Ag-coated Hollow Glass Spheres)
Materials. We used glass beads as porous
medium (size: 0.8 to 1.2 mm) and hydrophobic
Ag-coated neutrally buoyant glass spheres as
colloid models (size: 10 mm). These colloids are
in principle resolvable with the observed XMT
resolution (3.2 mm/pixel). In practice colloid
clusters were observed.
Experimental Analysis
Partitioning. Depending on saturation colloids
(aggregates) partition into different reservoirs.
5.0 ml/hr, S=0.56
5.0 ml/hr, S=0.06 Straining. Individual colloids are preferentially
transported during drainage. Shown is the colloid
cluster size before (S=0.96) and after drainage
(S=0.05). This process happens at all flow rates.
Partitioning. Depending on saturation colloids
(aggregates) partition into different reservoirs.
Partitioning. The amount of
colloids near solid-water
interfaces decreases with
saturation, whereas the
amount near the solid-air-
water contact line increases.
A minor amount of colloid
(aggregates) is located near
air-water interfaces.
Introduction
Solid-water interface
(SWI) attachment
Pore-straining/
Wedging
Air-water interface
(AWI) Attachment
Air-water-solid (AWS)
contact line
attachment
Immobilization in
disconnected wetting
(DisW) phase
Film straining
Saturated Conditions Unsaturated Conditions
Modified from DeNovio et. al., 2004
Lead and Wilkenson, 2007
Background
Colloid Mobility in Soils, Fundamental Pore Scale Mechanisms, Simplifications and Practical Relevance for Risk Analysis
Examples. The three model runs with 200 colloids show favorable
colloid attachment (top) under:
● A high solute concentration (0.1 Molar, top) resulting in little break
through. The model was run for 0.5s (50,000 iterations) at 0.1
Molar solution, followed by 0.5s (50,000) iterations at 0.0001 Mol
solution.
● Low solute concentration (0.0001 Molar, middle) showing
significant and streamlined breakthrough of colloids.
● High concentration (0.1M ) followed by low ionic strength flushing
(e.g. rainwater infiltration). This is a sequential combination of the
solute concentrations of 1) and 2) but it is interesting to note that
there are fewer colloids that breakthrough under this scenario
than when 0.0001 Molar solution is simulated by itself. It seems to
suggest that a number colloids have been strained, and are not
able to be released under current flow conditions.
Pore-scale Modeling
0.1 M
0.0001 M
0.1 → 0.0001 M
Brownian Force
Water flow. The fundamental process that drives the transport of
colloids is the flow of water. Single-phase and multi-phase flow is
simulated with Lattice Boltzmann (LB) models. Volumes of up to 1x108
(single phase) and 20 x 106
(multi-phase) elements can be simulated
in 2D and 3D mode. Shown above are a 1024x1024 synthetic volume
with flow field (left) and a 64x64 volume for model development (right,
used in this poster).
Project Activities
Experimental: Observe simplified multi-
phase colloid systems with x-ray Micro
Tomography (XMT). This will show the
partitioning of colloids into various
reservoirs as a function of water
saturation. This activity is spearheaded by
Oregon State University
Pore-scale Modeling: Carry out individual-
based modeling of colloids at the pore-
scale by developing a model that
accurately implements the fundamental
forces that govern the behavior of colloids.
This activity is being carried out at the
University of Arizona
Grid refinement. LB models produce grids of fluid velocity in x,y (z)
with realistic resolutions in the order of 1-100 m. Even though this
resolution is high, it is not high enough to resolve nm-sized colloids.
We interpolate coarse-scale LB output to fine-scale colloid-scale.
Fluid velocity
Decomposition. In further computations it is important to know how
far a particular location is from a solid phase. For facilitate numerical
efficiency we decompose most force fields into x,y, (z) direction. This
is a pre-computation step that only must be done once per simulation.
Y distance
(normal)
X distance
(transverse)
Governing Equations
Force balance. A change in colloid momentum is the sum of drag,
colloidal and Brownian motion forces.
Model Results
Drag force. Due to fluid flow. ξ = 6π*μ*ac
Correction terms. Correction terms a function of the non-dimensional
gap distance which is normalized by colloid radius (Gao et. al. 2010).
Brownian force. Due to molecular
momentum transfer. ξ = 6π*μ*ac. G(0,1) =
Gausian distribution (updates continuously).
D0
is diffusion coefficient in water.
Decomposition. Drag force in normal (transverse, left) and normal
direction.
Colloidal force. Governed by three
potentals Electric Double Layer (EDL)
Lifschitz-van der Waals (LW)
Lewis Acid-Base (AB)
Background solute
concentration
Transverse componentNormal component