2 4 6 8 10 12 14 16 18 20 22 24 26 28

x (meters)

2

4

6

8 (

ete

s) Clay

Sand

Solvents and Fuel in Soil and Groundwater: When the Usual Hydrogeologist’s Toolbox is Insufficient

Walt W. McNab, Jr., Ph.D., P.G. Roux Associates, Inc., Oakland, CA

Why should you care about solvents and fuels?

2

Solvent and fuels = majority of groundwater contamination

Significant release volumes pose an enormous groundwater contamination potential

Neither mixes well with water

Sparingly soluble liquids represent long-term sources

Quantifying the movement of solvents and/or fuels coexisting with groundwater in an aquifer is problematic

One gallon of TCE can contaminate a 30-ft-thick aquifer across 119 acres (based on MCL of 5 parts per billion)

Solv

ent

What does “sparingly soluble” imply?

3

Both phases may require evaluation at some sites

Dissolved phase • Where “contamination” is defined

• Solubilities of common solvents and fuel constituents ~100s mg/L

• Dissolved contaminants move with groundwater

• Basic hydrogeology

Separate phase solvent of fuel • Ultimate source of contamination

• Where the large bulk of the mass resides

• Separate-phase liquids do not move with groundwater

• Beyond basic hydrogeology

Contamination = exceedance of drinking water standards by dissolved benzene, TCE, etc. at part-per-billion concentrations

Most of the chemical mass resides here, slowly dissolving into water; chronic source

Conventional Environmental Forensics Approaches Address the Dissolved Phase

When was it released?

Where was it released?

Where did it go?

Darcy’s law is often key to informing groundwater plume forensics

xhKv

∆∆

=φ

Pore velocity

Hydraulic conductivity

Hydraulic head gradient

Porosity

xh

∆∆ can be estimated from a

groundwater elevation map

K can be estimated from pumping tests (or aquifer texture)

v is used to trace path of dissolved contaminants back to source over time

C0

−−

+×

−−

+×

+−

×

+−=

xZz

xZz

xYy

xYy

Rvt

vR

vtx

vRxC

tzyxC

zzyy

x

x

x

x

αααα

α

αλαλ

α

2erf

2erf

22/erf

22/erf

2

41

erfc4

112

exp8

),,,( 0

Y

v

Solvent or fuel leak from a pipeline

Well

C(x, y, z, t)

For simple questions, textbook equations can sometimes suffice

e.g., what is the expected concentration far downgradient of source?

Easy to implement

Spreadsheet model

Typical hydrogeologist’s toolbox

Conventional Environmental Forensics Approaches Address the Dissolved Phase

Fuels, Solvents, and Environmental Forensics: the Conventional Approach Fails When the Fluids Don’t Mix

“How long did fuel spill take to reach groundwater?”

Definitions: • LNAPL = light non-

aqueous phase liquid (gasoline, bunker fuel)

• DNAPL = dense non-aqueous phase liquid (PCE, TCE, manufactured gas plant residual)

When forensic questions require an understanding of NAPL behavior, special approaches are required

“Did the spilled solvent move to this location from elsewhere?”

LNAPL

DNAPL

Unsaturated Zone

Saturated Zone

Groundwater flow

Clay

Plume

Plume

Multiple fluids share the pore spaces Water

NAPL: solvent or fuel

± Gas/air (unsaturated zone)

Fluid movement impacted by relative permeability Each fluid has a separate,

saturation-dependent effective permeability

Equations developed from oil industry experience

Mineral grain

Water NAPL

Fuels, Solvents, and Environmental Forensics: Why do NAPLs Behave Differently in an Aquifer?

NAPL and water blobs become discontinuous and cannot move easily when both are present

These are petroleum engineering concepts

0.00.10.20.30.40.50.60.70.80.91.0

0.0 0.2 0.4 0.6 0.8 1.0

Rela

tive

Perm

eabi

lity

NAPL Saturation

Water

NAPL

Low saturation = low mobility

Fuels, Solvents, and Environmental Forensics: Additional Reasons for NAPL Behavior

Trichloroethylene + water Oil + water

Density differences Lighter than water (LNAPL)

Tends to reside above water table

Denser than water (DNAPL) Tends to sink through aquifer to

confining layer

Viscosity differences “Runny” liquids

Water

Fresh, neat TCE or PCE

“Gooey” liquids

Heavy fuel oil

Manufactured gas plant residue

Two liquids with different viscosities

Fuels, Solvents, and Environmental Forensics: Re-visiting the Conventional Approach

xhKv

∆∆

=φPore velocity now separate

pore velocities, one for each fluid

Hydraulic conductivity now a complex function of density, viscosity, and relative permeability, for each fluid

Hydraulic gradient replaced by pressure gradients, one for each fluid, that depend on fluid saturations

X Simpler models no longer suffice

More sophisticated numerical models that solve the relevant equations are warranted

X

Tools to Quantitatively Address NAPL Behavior in Soils and Groundwater Added physics requires solving large

numbers of complex equations Numerical models = solvers for large sets of

complex equations

Simplified numerical models “Bare-bones” numerical models that consider only

a portion of the relevant physics

Simpler to work with and justified for limited data

Complex numerical models Sophisticated, multi-physics numerical simulators

(e.g., UTCHEM, TOUGH, oil reservoir simulators, etc.)

Multi-phase flow

Dependence of density and viscosity on conditions

Temperature effects

Geochemistry, biodegradation, surfactants

Oil industry pedigree

Vetted in the scientific literature

What types of data can inform numerical models for NAPLs?

Parameter values for models Soil properties

Intrinsic permeability

Relative permeability curves

Can be measured or assumed (based on texture)

NAPL characteristics Density

Viscosity

Boundary conditions, initial conditions, release history Postulate these to match the data

Data to validate model output Remedial investigation data

Soil, soil vapor, and aqueous concentrations

Physical descriptions M

odel

set

up

Mod

el te

stin

g

The Decision to Apply a Model

When is quantitative analysis of NAPL behavior warranted?

How quickly did NAPL travel from Point A to Point B?

Is NAPL expected to move under (fill-in-the-blank) conditions?

Three example applications:

1. DNAPL poured into a water-filled sandbox (demo)

2. NAPL migration to a nearby extraction well

3. A litigation problem involving vertical transport of LNAPL

Questions

Example #1 – DNAPL in a Sandbox Experiment

Demo simulations with UTCHEM simulator Modified lab-scale example

test problem supplied with code

Converted LNAPL example to DNAPL

Refined grid

Added heterogeneity

Tested effects of different DNAPL fluid properties

DNAPL source (short-duration)

Three scenarios: 1. Baseline 2. Reduced density contrast 3. Reduced density contrast + high DNAPL viscosity

3 ft. 1

ft.

Highly permeable sand

Low-permeability sand

Example #1 – DNAPL in a Sandbox Experiment

Example #1 – DNAPL in a Sandbox Experiment

Example #1 – Extension to a More Complex System

Highly heterogeneous geologic material

Impact of DNAPL physical characteristics on vertical movement NAPL #1 = manufactured

gas plant (MGP) residual

NAPL #2 = tetrachloroethylene (PCE)

Approach can be used to estimate vertical migration rate for variety of similar situations

100 m

Example #2 – NAPL Migration Toward an Extraction Well

NAPL source area (saturation = 0.5)

Sand

Clay

• What if the problem definition is more complex?

• A numerical model provides needed the flexibility

Question: will NAPL be encountered in the extraction well?

C0

Y

v

Solvent or fuel leak from a pipeline

Well

C(x, y, z, t)

Simple textbook model can’t handle migrating NAPL

Numerical multiphase flow model explicitly handles NAPL migration

Ambient groundwater flow paths (ignores effect of NAPL)

Example #2 – NAPL Migration Toward an Extraction Well

Numerical multi-phase flow model

Intrinsic permeability and relative permeability parameters vary in space

Relative permeability for both NAPL and water change dynamically

NAPL viscosity = 3 x water viscosity

Model run represents approximately 14 years

t = +50 days NAPL saturation = ~0.07 – ~0.13

Some spreading evident, stemming from initial condition

Example #2 – NAPL Migration Toward an Extraction Well

t = +100 days

More spreading, but rate has already slowed

Example #2 – NAPL Migration Toward an Extraction Well

t = +1,000 days

Spreading rate has slowed further

Example #2 – NAPL Migration Toward an Extraction Well

t = +5,000 days NAPL saturation = ~0.04 – ~0.05

Mineral grain

Water NAPL

Little additional spreading between 2.7 and 13.7 years

Very low NAPL saturation discontinuous blobs essentially no mobility

Answer: Little NAPL advancement toward extraction well can be expected.

Example #2 – NAPL Migration Toward an Extraction Well

Question: will NAPL be encountered in the extraction well?

Example #3 – LNAPL Infiltration from Sudden and Accidental Release Events

Setting: LNAPL releases at a large industrial facility

Litigation problem: associating groundwater impacts with specific release events

Can LNAPL from these events migrate through the thick unsaturated zone?

No way to quantify, except by modeling A complex numerical model for NAPL

percolation from two separate releases

?

1. Release size constrained (reports) 2. Subsequent migration depth not

constrained.

Separate phase

Dissolved phase

Water table

Example #3 – LNAPL Infiltration from Sudden and Accidental Release Events

Numerical modeling used to explore percolation scenarios, but…

…there is a paucity of data:

Hydraulic conductivity

Relative permeability parameters

Ambient precipitation recharge

Modeling with ranges of reasonable parameter values is a good approach when data are lacking

The coexistence of three phases (NAPL + water + air) complicates percolation rate assessment

Example #3 – LNAPL Infiltration from Sudden and Accidental Release Events

0

10

20

30

40

50

60

70

80

90

100

0.00 0.05 0.10 0.15 0.20

z

LNAPL Saturation

Scenario #1

Scenario #2

Scenario #3

Run large number of trials (e.g., n = 1,000) with simplified model

Each scenario based on random selections of parameter values

Intrinsic and relative permeability soil boring logs and textbook values

Precipitation recharge local rainfall, consideration of ground surface

Evaluate parameter sensitivity; forecast groundwater impact likelihood

• Each scenario represents a unique set of posited parameters

• Simplified physics (SciLab script) to expedite large number of simulations

Example #3 – When do unknown parameters (hydraulic conductivity, rainfall percolation) matter?

05

10152025303540

0.000 0.010 0.020 0.030 0.040Dep

th o

f NA

PL

Fron

t

NAPL Residual Saturation

05

10152025303540

0.0E+00 1.0E-09 2.0E-09 3.0E-09 4.0E-09 5.0E-09Dep

th o

f NA

PL

Fron

t

Water Recharge Rate (m/sec)

05

10152025303540

0.0E+00 2.0E-13 4.0E-13 6.0E-13 8.0E-13 1.0E-12 1.2E-12Dep

th o

f NA

PL

Fron

t

Absolute Permeability (m2)

05

10152025303540

1.50 1.70 1.90 2.10 2.30 2.50Dep

th o

f NA

PL

Fron

t

Van Genuchten "n" Parameter

Sensitive model parameter

Less-sensitive model parameter

Relative permeability curve shape factor

Example #3 – What is the likelihood of NAPL reaching groundwater?

0

20

40

60

80

100

120

140

160

180

200

5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0 27.5 30.0 32.5 35.0 37.5 40.0

Num

ber

(sce

nari

os)

Depth of NAPL Front at +50 Years

Approximate depth to groundwater

~4% of scenarios

Example #3 – Do field data confirm model predictions?

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

0% 5% 10% 15% 20%

z

LNAPL Saturation

0%

5%

10%

15%

20%

25%

30%

5.5 ft 10.5 ft

Abu

ndan

ce

Sample

TPH_C07

TPH_C08

TPH_C09-C10

TPH_C11-C12

TPH_C13-C14

TPH_C15-C16

TPH_C17-C18

TPH_C19-C20

TPH_C21-C22

TPH_C23-C24

0%5%

10%15%20%25%30%35%

60.5 ft 70.5 ft

Abu

ndan

ce

Sample

TPH_C07

TPH_C08

TPH_C09-C10

TPH_C11-C12

TPH_C13-C14

TPH_C15-C16

TPH_C17-C18

TPH_C19-C20

TPH_C21-C22

TPH_C23-C24

Fingerprinting analysis indicates pulses from separate releases have advanced and then stopped at multiple locations, consistent with model

Summary

Both LNAPLs and DNAPLs pose special challenges for environmental forensics

Large, residual source of contamination

Complex subsurface physics answers are not always obvious

Numerical models can provide useful insights into behavior of NAPLs that cannot be obtained otherwise

Pose the questions to be addressed by the model in a manner that matches the quantity/quality of available data