Addressing the Challenges in Source Zone Characterization
and Remediation: Recent Progress
Linda M. Abriola
University Professor
Director, Tufts Institute of the Environment
REMTEC Technology Summit:
The Future of Remediation Technology
February 27, 2019
However, we have learned a great deal and as we have moved from the
20th to the 21st century, our research questions have evolved….
Dense Nonaqueous Phase Liquid Contaminant Source
Zones
Despite more than 30 years of research and remedial experience, source zones remain a significant remedial and management challenge
Mackay and Cherry, 1989
Source Zone
Research Ques #1: How do DNAPLs migrate and persist at real sites?
(Abriola and Pinder, 1985)
(Dekker and Abriola, 2000)
(Powers et al., 1994)
Importance of Non-Equilibrium mass transfer
Modeling heterogeneity
Research Ques #2: Can we develop innovative technologies to remediate DNAPL contaminated sites?
Groundwater
flow
Lateral flow
and pooling
along low
permeability
layer
Flushing
Solution in
Flushing
Solution
out
Surfactant Enhanced Remediation
(Amos et al., 2009;Chen et al., 2013 )
Aggressive mass removal
Combined remedies
Reductive Dechlorination
Research Question #3: Is aggressive remediation worth the effort? What controls remedial effectiveness?
0
0.2
0.4
0.6
0.8
1
1.2
0 10 20 30 40 50 60 70 80 90 100
Cumulative PCE Mass Recovery (%)
MF
/MF
O
High GTP (1.6:1)
Low GTP 1 (0.26:1)
Low GTP 2 (0.4:1)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
mass reduction
flu
x r
ed
uc
tio
n
LEV_1A
LEV_2A
LEV_1B
LEV_2B
LEV_1C
LEV_2C
HAV_1
HAV_2
High ganglia- to- pool
Ratio simulations
Low ganglia- to- pool
Ratio simulations
(Lemke et al., 2005)
(Suchomel and Pennell, 2006)
TCE Fractional Mass Removed
0.0 0.2 0.4 0.6 0.8 1.0
TC
E F
lux A
ve
rage
d C
on
ce
ntr
atio
n (
mg/L
)
0.1
1
10
100
1000
TCE Flux Averaged Effluent Conc
PPB averaging window TCE conc. prediction
(Christ et al, 2010)
26.0
0
015.1
0
5.0
0 1
1
01112
PF
PF
oo
PF
MMPF
eq
p
xPF
MMPF
eq
o
p
x
eq
Total
out
C
Cf
C
Cf
C
C
Importance of DNAPL Architecture
Simplified screening tools
So, source zone characterization is key – but accounting for process coupling and heterogeneity present formidable challenges
New Research Questions
#4: How can we improve remedial performance predictions in
complex, heterogeneous systems?
#5: How can we incorporate uncertainty in site characterization,
remediation, and risk assessment?
Overview of some recent research
Exploring Coupled Mass Transfer Processes in Heterogeneous Settings: Model Formation
1 2
Lithofacies DescriptionVolumetricProportion
HydraulicConductivity(m/s)
GS-x Well-sorted Gravel 29% 4.50E-06Gcm Poorly-sorted gravel 57% 2.30E-04S-x Pure, well-sorted sand 6% 1.00E-03
bGcm,ICobble-and-boulder-richgravel
6% 1.30E-01
Lithofacies OC (%)Distribution Coefficient, Kd(m3/gr)
Retardation Factor
FreundlichCoefficient, Kf(gr-nfm-3*nf)
FreundlichExponent nf
First Order Kinetic Rates (day-1)
GS-x 0.5 2.00E-06 15.13 5.06E-06 0.80 1.00E-03
Gcm 0.035 1.40E-07 1.99 2.23E-07 0.90 1.00E-01
S-x 0.035 1.40E-07 1.99 2.23E-07 0.90 1.00E-01
bGcm,I 0.035 1.40E-07 1.99 2.23E-07 0.90 1.00E-01
Goals:
Investigate the influence of back
diffusion from low permeability (sorptive)
zones
Explore the relative importance of
desorption and dissolution processes on
mass persistence
(Yang et al., 2018)
Exploring Coupled Mass Transfer Processes in Heterogeneous Settings: Modeling Process
8
1.So
urce
Zon
e G
en
eratio
n
MT3DMS/MODLOW
Modified for NAPL
dissolution and
nonlinear/rate limited
sorption
2. Tran
spo
rt/rem
ed
iation
sim
ulatio
n3
. Po
st Pro
cessin
g
Generate 3-D Permeability FieldMarkov Transitional Probability
Generate Initial Saturation
UTCHEM Simulator
0
2
46
8
05
1015
0
2
4
6
Aqueous Concentration: 108 Years
mg/L
0.001
0.003
0.01
0.03
0.1
0.3
1
3
10
30
100
150
Flow Direction
(Yang et al., 2018)
Initial DNAPL/PCE Distribution (Saturation
Ranges from 1e-5 to 0.9)
Mathematical Formulation
𝜵 𝑲𝑘𝑟𝑤𝜵ℎ = 𝑆𝑠𝑝𝜕ℎ
𝜕𝑡
𝜕
𝜕𝑡𝜙𝑠𝑎𝐶
𝑎 + 𝛁 ∙ 𝐶𝑎𝑞𝑎 − 𝛁 ∙ ෩𝑫ℎ𝑎 ∙ 𝛁𝐶𝑎 = 𝐸𝑎𝑛 + 𝐸𝑎𝑠 + 𝑅𝑎
𝑘𝑟𝑤 ∶ water relative permeability
𝑞𝑎
• Flux Averaged Concentration @ Transect
𝐶𝑓 =σ 𝐶𝑖𝑞𝑥
𝑎/𝐴∆𝑥∆𝑦∆𝑧
σ 𝑞𝑥𝑎/𝐴∆𝑥∆𝑦∆𝑧
• Maximum Concentration @ Transect
• NAPL Mass / Sorbed Mass in Domain
• Persistence: Time to remove 99.99% of NAPL phase mass or to reduce flux averaged concentration or maximum concentration to MCL for PCE (5 ppb) or 1 ppb
𝐸𝑎𝑛 : Mass transfer between aqueous and NAPL phase
𝐸𝑎𝑠 : Mass transfer between aqueous and sorbed phase
𝑅𝑎 : Reactions or external sources/sinks
𝐸𝑎𝑛 = 𝜅𝑎𝑛(𝐶𝑒𝑞𝑎 − 𝐶𝑎) 𝑆ℎ′ =
𝜅𝑎𝑛𝑑502
𝐷𝑚𝑎
𝑆ℎ′ = 𝑓(𝑅𝑒′, 𝑑50, 𝑈𝑖 , 𝑠𝑎)
𝜅𝑎𝑛 : Lumped mass transfer coefficient
𝑆ℎ′: Modified Sherwood number
𝐸𝑎𝑠 = −𝜌𝑏𝜕𝑆
𝜕𝑡
𝑆 = 𝐾𝑑𝐶𝑎
𝑆 = 𝐾𝑓(𝐶𝑎)𝑛𝑓
−𝜌𝑏𝜕𝑆
𝜕𝑡= −𝛽(𝐶𝑎 −
ҧ𝐶
𝐾𝑑)
Powers et al. 1994
Evaluation Metrics
Transport Equation Two-phase Flow
Sorption 4-rate-limited3-nonlinear2-linear1-no
1000
800
600
400
200
0
Pe
rsis
ten
ce
(Y
ea
rs)
Total
DNAPL Removal
post-DNAPL
Evolution of flux averaged concentration at a down gradient transect
Aqueous concentration contour (left column) and DNAPL saturation distribution (right column)
Comparisons of total plume longevity, DNAPL removal time and post-DNAPL plume longevity for the various sorption models (20 realizations)
Research Results
Observations Subsurface heterogeneity has a predominant influence on mass
sequestration and its subsequent release
Dissolution of DNAPL mass controls plume persistence for much of the plume’s life
Local mass transfer behavior is governed by different processes in different parts of the domain
Desorption nonlinearities and rate limitations greatly influence plume persistence at late times
The influence of trace DNAPL in inaccessible zones is often indistinguishable from the influence of other sequestered mass
2-D simulations capture essential characteristics of 3-D scenarios, but often over- or under- estimate source zone plume persistence metrics
Laboratory scale model of a field downhole
test system
Packed with site aquifer materials (silty
sands/clay)
Hydraulic conditions characterized using a
bromide tracer test
Exploring Coupled Processes – TCE Sorption, Diffusion, and Biotransformation in a Heterogeneous Aquifer Cell
L7
L6
L5
L4
L3L2
L1Clay
InletOutlet
Inflow chamber
Outflow chamberWater table
Aquifer cell
model
construction
Flow field
characterization
10mM bromide pulse injection
Reduce flow rate to 0.05 mL/min
Recirculation with Lactate Amendment
5mM Lactate Pulse
10mM Lactate Pulses
•Bioagumentation•Reduce Flow Rateto 0.1 mL/min
Transformation byproducts, cis-DCE, VC, and ethene, were measured throughout
Biotransformation of TCE to cis-DCE was supported by background DOC from site materials
Dehalococcoides transformation of cis-DCE to VC and ethene required lactate addition
Longer residence time led to more complete transformation to ethene
Aquifer Cell Test Conditions
Coupled Process Transport Modeling
𝜵 𝑲𝒌𝒓𝒘𝜵𝒉 = 𝑺𝒔𝒑𝝏𝒉
𝝏𝒕𝝓𝝏
𝝏𝒕𝒔𝒂𝑪𝒊
𝒂 + 𝜵 ∙ 𝝓𝒔𝒂𝑪𝒊𝒂𝒒𝒂 − 𝜵 ∙ 𝝓𝒔𝒂𝑫𝒉𝒊
𝒂 ∙ 𝜵𝑪𝒊𝒂 = 𝑬𝒊
𝒂𝒔 + 𝑬𝒊𝒂𝒏 + 𝑹𝒊
𝒂
𝒌𝒓𝒘 ∶ water relative permeability
𝒒𝒂
MT3DMS MODFLOW
Enhancement of an industry standard simulator to model the coupled transport and bio-dechlorination of multiple
contaminants by multiple microbial populations in a complex DNAPL source zone
𝑹𝒊𝒂 : Bioreaction
𝑹𝑷𝑪𝑬𝒂 = −𝒓𝑷𝑪𝑬
𝒂
𝑹𝑻𝑪𝑬𝒂 = 𝒓𝑷𝑪𝑬
𝒂 − 𝒓𝑻𝑪𝑬𝒂
𝑹𝑫𝑪𝑬𝒂 = 𝒓𝑻𝑪𝑬
𝒂 − 𝒓𝑫𝑪𝑬𝒂
𝑹𝑽𝑪𝒂 = 𝒓𝑫𝑪𝑬
𝒂 − 𝒓𝑽𝑪𝒂
𝑹𝑽𝑪𝒂 = 𝒓𝑽𝑪
𝒂
𝒓𝒊𝒂 = 𝒌𝒊,𝒎𝒂𝒙 (
𝑪𝒊𝒂
𝑲𝒊,𝒉𝒂𝒍𝒇𝑰𝒊 + 𝑪𝒊𝒂)𝑿𝒌(
𝑪𝑯𝒂 − 𝑪𝑯−𝒕𝒉𝒓𝒆𝒔𝒉−𝒌
𝒂
𝑲𝑯,𝒉𝒂𝒍𝒇 + 𝑪𝑯𝒂 − 𝑪𝑯−𝒕𝒉𝒓𝒆𝒔𝒉−𝒌
𝒂)
𝒅𝑿𝟏
𝒅𝒕= 𝒀𝑷𝑪𝑬𝒓𝑷𝑪𝑬
𝒂 + 𝒀𝑻𝑪𝑬𝒓𝑻𝑪𝑬𝒂 − 𝒌𝒃𝑿𝟏
𝒅𝑿𝟐
𝒅𝒕= 𝒀𝑫𝑪𝑬𝒓𝑫𝑪𝑬
𝒂 + 𝒀𝑽𝑪𝒓𝑽𝑪𝒂 − 𝒌𝒃𝑿𝟐
PCE TCEDCE
DCE VCEthene
𝒓𝒊𝒂 : Monod kinetics for
reductive biodechlorination
Growth of each biomass population:
• 3 microbial populations (a fermentor, and two
dechlorinators)
• 7 chemical components (lactate, hydrogen,
PCE, TCE, cis-DCE, VC, and ethene)
• Modified Monod kinetics used to account for
electron donor availability and daughter
product inhibition – rates determined in
microcosm studies
• Microbial populations are attached
Microcosm Modeling
Microbial Transformation Kinetics Model
𝒓𝒊𝒂 = 𝒌𝒊,𝒎𝒂𝒙
𝑪𝒊𝒂
𝑲𝒊,𝒉𝒂𝒍𝒇𝑰𝒊 + 𝑪𝒊𝒂 𝑿𝒌
𝒅𝑿𝟏
𝒅𝒕= 𝒀𝑻𝑪𝑬𝒓𝑻𝑪𝑬
𝒂 − 𝒌𝒃𝑿𝟏
𝒅𝑿𝟐
𝒅𝒕= 𝒀𝑫𝑪𝑬𝒓𝑫𝑪𝑬
𝒂 + 𝒀𝑽𝑪𝒓𝑽𝑪𝒂 − 𝒌𝒃𝑿𝟐
Microcosm reactors prepared
(in triplicate) with site soil and
anoxic site groundwater -
addition of lactate (in excess),
TCE, and KB-1 inoculum
Trial
Maximum Substrate Utilization Rates
TCE to cis-DCE
cis-DCE to VC
VC to ethene
mmol /(mg cell*d)
mmol /(mg cell*d)
mmol /(mg cell*d)
D2K-1 0.319 0.090 0.222
D2K-2 0.356 0.074 0.222
D2K-3 0.315 0.077 0.176
Two microbial populations
Experimental/Model Comparisons – Post Recirculation
Ethene
cis-DCE
VC
Averaged RMSR: 25%
TCE
Effluent VOCs
TCE was transformed to a combination of cis-
DCE, VC, and ethene
Observed aquifer cell microbial transformation
rates were consistent with batch-fitted values,
when permeability variations were
incorporated and inhibition was neglected
• Continuous
Injection:
0.3 mM TCE
• Flow Rate:
0.1 mL/min
• Initial interpolated concentrationslactate
(Yang et al., in preparation)
Model Predictions – Post Recirculation17
Lactate
(a) (b) (c) (d)
(c)
(b)
(a)
(d)
Local heterogeneity in soil properties
influenced the complete dechlorination of TCE
to ethene
The extent of ethene formation was highly
dependent on the availability of electron donor
(not shown) in the lower permeability layers
Inclusion of heterogeneity in numerical
modeling is crucial to predictive accuracy of
reductive dechlorination
Assessing the Influence of Heterogeneity
Modeled using uniform (averaged)
properties (e.g., hydraulic conductivity,
porosity, initial chemical and biomass
concentrations) over entire domain
The uniform model under predicts ethene production, i.e.,
under predicts complete dechlorination of TCE to ethene
Multi-dimensional models with uniform properties or 1-D
models, employing microcosm-measured dechlorination
rates, were unable to accurately predict aquifer cell
performance
Heterogeneous Model
Commerce Street Pilot Scale Down-Hole Treatability Test (Bioaugmentation)
Commerce Street Superfund Site (Williston, VT)
Four Well Centerline Transect
Industrial park—plating rinse water and sludge disposal (1960—1980) Plume concentration levels: TCE ≤ 18 mg/L; cis- DCE) 1.4 - 34 mg/L
Down-Hole Test Configuration
Bioaugmentation, followed by recirculation, then downgradient
pumping to direct flow through treatment area
Flow field Modeling (downgradient pumping)
Pilot (downhole treatability) Test Modeling—Biodechlorination
Injection well
Monitoring well
Comparison of field-measured and model simulated results for chlorinated ethenes and ethene in DHT-1 and DHT-2
Observed transformation rates were not consistent with temperature-adjusted batch-estimated and aquifer-cell validated parameters
Predicted ethene concentrations were substantially higher than those observed in the field test The aquifer cell was modeled with more detailed heterogeneities and fine grid blocks Transformation rates were likely strongly influenced by the presence of low permeability layers in
the treatment zone, which inhibited mixing
Comparison: Microcosm Aquifer Cell Field TestDrivers of Degradation Rates across Modeling Systems
TCE cis-DCE VC
kmax, I 20-23°C
gram/ (gram-cell *d)
43.36 7.81 12.91
Include Inhibition
Conditions Well-mixed, large liquid to solid ratio, donor in excess
TCE cis-DCE VC
kmax, I 20-23°C
gram/ (gram-cell *d)
43.36 7.81 12.91
Neglect Inhibition
ConditionsStratified geophysical properties, small liquid to solid ratio, limited
access to donor, flow-through
TCE cis-DCE VC
kmax, I 17°C
gram/ (gram-cell *d)
29.30 5.14 10.69
Removal of Inhibition
Conditions
Heterogeneous, porous media, 3-D, transverse mixing, interplay of
transport, transformation and microbial processes, spatial
variation of concentration
Unfortunately, fine scale heterogeneity is not quantifiable at real sites and fine scale simulations are often computationally prohibitive
So, simulations with lab-validated models indicate that a detailed knowledge of heterogeneity (physical and chemical), mass distribution, mass transfer rates, and biomass parameters is important for remedial design and assessment
Reducing Computational Burdens:
Upscaled Mass Transfer/Transformation Rates
• Upscaled modeling of sorption and back-diffusion
with multi-rate mass transfer (MRMT)
• Diffusive transfer between mobile and immobile
regions is described in terms of first-order rates
• Fitting of rates to predictions of 3D fine-scale
simulations.
• Implementation of the upscaled model in the field-
scale transport simulator (MT3DMS).
Multi-rate mass
transfer term
Upscaled due to low-permeability
inclusions acting as barriers
First-order rateCapacity ratio
24
Developing Effective Mass Transfer Rate Correlations
Dispersivity and effective permeability fit
to conservative tracer breakthrough
Slice from fine-scale 3-D simulation (1 m3):
Flow direction
BTC from 3D simulation and fitted upscaled model:
25
Fit of mass transfer rates
Fitted rate effective parameters correlated to media properties:
2
1 haSha
D
2
2 haSh
D
huaPe
D
(Elenius and Abriola, submitted)
Characterization Tools: Estimating Source Zone Mass Distributions from Borehole Data
Texture Saturation
Statistical Model
Equi-probable Realizations
ConcentrationTr
ain
ing
Dat
a
Simulate quantities conditioned on boreholes
and texture
Input data:Texture, borehole concentration, and
borehole saturation data
Concentration Saturation
(Arshadi et al., submitted)
Model Training - Feature Extraction
Features:
permeability at pixel i
permeability difference with lower pixel
distance weighted mean saturation
distance weighted mean concentration
Left
ave
ragi
ng
win
do
w Righ
t averaging w
ind
ow
Comparison of ‘Real’ and Average Simulated Mass DistributionsBorehole Random-field Automated Interpolator for NAPL Source-zones (BRAINS)
PCE DNAPL release (80 L) @ t = 1 year
Comparison of “Real” and Average Simulated Mass Distributions
PCE DNAPL release (160 L) @ t = 10 years
Metric Identification – Estimated vs ‘Real’
DNAPL Mass Aqueous Mass DNAPL Pool Fraction
Comparison with Kriging
0 50 100 150 200
True DNAPL mass (kg)
0
50
100
150
200
Pre
dic
ted
DN
AP
L m
ass (
kg
)
y=x
R2=0.42
(a)
0 1 2 3 4
True aqueous and sorbed mass (kg)
0
0.5
1
1.5
2
2.5
3
3.5
4
Pre
dic
ted
aq
ue
ou
s a
nd s
orb
ed
ma
ss (
kg
)
y=x
R2=0.93
(b)
t=1 year
t=3 year
t=6 year
t=10 year
0 0.2 0.4 0.6 0.8 1
True pool fraction (PF)
0
0.2
0.4
0.6
0.8
1
Pre
dic
ted p
oo
l fr
actio
n (
PF
)
y=x
R2=-35.75
(c)
0 5 10 15
True x-center of mass (m)
0
5
10
15
Pre
dic
ted
x-c
ente
r of
mass (
m)
y=x
R2=0.66
(d)
0 2 4 6
True z-center of mass (m)
0
2
4
6
Pre
dic
ted
z-c
en
ter
of m
ass (
m)
y=x
R2=0.64
(e)
0 2 4
True x-spread of mass (m)
0
1
2
3
4
Pre
dic
ted
x-s
pre
ad
of m
ass (
m)
y=x
R2=0.68
(f)
0 1 2
True z-spread of mass (m)
0
0.5
1
1.5
2
Pre
dic
ted
z-s
pre
ad
of m
ass (
m)
y=x
R2=0.35
(g)
0 50 100 150 200
True DNAPL mass (kg)
0
50
100
150
200
Pre
dic
ted
DN
AP
L m
ass (
kg
)
y=x
R2=0.42
(a)
0 1 2 3 4
True aqueous and sorbed mass (kg)
0
0.5
1
1.5
2
2.5
3
3.5
4
Pre
dic
ted
aq
ue
ou
s a
nd s
orb
ed
ma
ss (
kg
)
y=x
R2=0.93
(b)
t=1 year
t=3 year
t=6 year
t=10 year
0 0.2 0.4 0.6 0.8 1
True pool fraction (PF)
0
0.2
0.4
0.6
0.8
1
Pre
dic
ted p
oo
l fr
actio
n (
PF
)y=x
R2=-35.75
(c)
0 5 10 15
True x-center of mass (m)
0
5
10
15
Pre
dic
ted
x-c
ente
r of
mass (
m)
y=x
R2=0.66
(d)
0 2 4 6
True z-center of mass (m)
0
2
4
6
Pre
dic
ted
z-c
en
ter
of m
ass (
m)
y=x
R2=0.64
(e)
0 2 4
True x-spread of mass (m)
0
1
2
3
4
Pre
dic
ted
x-s
pre
ad
of m
ass (
m)
y=x
R2=0.68
(f)
0 1 2
True z-spread of mass (m)
0
0.5
1
1.5
2
Pre
dic
ted
z-s
pre
ad
of m
ass (
m)
y=x
R2=0.35
(g)
‘True’ Distribution BRAINS result Kriging result
Early Time
Late Time
Kriging Performance on Pool Fraction
• Improved noninvasive characterization tools
• Coupling of characterization tools and mathematical models
• Continued development of laboratory- and field-validated upscaledmass transfer and transformation parameters for use in field-scale modeling and risk assessments
• Use of statistical analysis and machine learning techniques to develop ‘libraries’ of screening tools and models for field deployment
• Improved in situ tests for effective parameter estimation
• Protocols for iterative site characterization and plume management
What is still needed
Project ER-2311
Kurt Pennell Brownco-PI Eric Miller, Tufts
co-PI
Maria Elenis, TuftsPost Doc
(now SMHI)
Natalie L. Cápiro, Tuftsco-PI (now Auburn U)
Masoud ArshadiTufts
Post Doc
Lurong Yang Tufts, PhD
John Christ, USAF Academy
co-PI (now S&B Christ,
Consulting)
Tian Tang, TuftsPhD (now Gradient)
Jason HnatkoTufts, PhD
Jack ElseyTufts, PhD
