1

Welcome to ITRC’s Internet Training

Historical Case Analysis of Chlorinated Volatile Organic Compound Plumes

March 1999

Sponsored by the ITRC, EPA-TIO &

Lawrence Livermore National Laboratory

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Today’s Presenters

Greg Bartow, R.G., CH.g. California RWQCB gwb@rb2.swrcb.ca.gov

Walt McNab Environmental Protection Department,

Lawrence Livermore National Lab mcnab1@llnl.gov

David Rice Environmental Protection Department,

Lawrence Livermore National Lab rice4@llnl.gov

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Presentation Overview

About the ITRC

Description of the methodology and results of a statistical evaluation of hydrologic and contaminant data from chlorinated compound contaminated plumes

Questions and Answers

Wrap-up and Links to additional

information and resources

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Who’s Involved?

STATE-LED INITIATIVE WITH: 38 States (and growing)

Sponsoring State Organizations

Environmental Western Southern States

Council of Governors’ Energy Board

the States Association Public/Tribal Stakeholders

Industry Representatives

DOE US EPA DOD

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Creating Tools and Strategies to Reduce Technical and Regulatory Barriers to the Deployment of Innovative Environmental Technologies

In Situ BioremediationDNAPLs/In Situ Chemical OxidationPermeable Reactive WallsRadionuclides

Unexploded OrdnanceIn Situ BiodenitrificationPhytoremediationVerification Diffusion Sampler

Active ITRC States

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Understand the factors affecting behavior of the CVOC plumes in ground water from a broad, statistically oriented perspective

Enhance your understanding of plume behavior through examination of data from many sites

Allow you to focus on the major factors influencing plume behavior increase the efficiency of planning site investigations and cleanup

Purpose of this Training

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CVOC Historical Case Analysis — Goals

Gather case information from over 200 VOC plumes Nation-wide “plumathon” DOE, DOD, Industry, ITRC States, EPA

Perform analysis that is defensible and peer reviewed Expert Working Task Force Expert Peer Review Panel

Findings and Conclusions based on case analysis Working Task Force prepares Peer Review Panel reviews

Recommendations for Policy Change Interstate Technology and Regulatory Cooperation Task Force

(ITRC) prepares Peer Review Panel reviews

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Working Task Force

Greg Bartow—California RWQCB Jacob Bear—Technion Institute of Technology Mike Brown/Paul Zielinski—DOE Patrick Haas—DOD/USAF Herb Levine—EPA Curt Oldenburg/Tom McKone—LBL Mike Kavanaugh—Industry Bill Mason/Paul Hadley—ITRC Doug Mackay/Christina Hubbard—University of Waterloo Mohammad Kolhadooz—Industry Mike Pound—DOD/USN Dave Rice (Initiative Coordinator)—LLNL Heidi Temko—California SWRCB Cary Tuckfield—Savannah River Technology Center Walt McNab (Data Analysis Team Leader)—LLNL Richard Ragaini (Data Collection Team Leader)—LLNL

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Peer Review Panel

David Ellis–Dupont Lorne Everett–UC Santa Barbara/Geraghty & Miller Marty Faile–USAFCEE William Kastenberg–University of California, Berkeley Perry McCarty–Stanford University Hanadi Rifai–Rice University Lenny Siegel—Pacific Studies Center Todd Wiedemeier–Parson’s Engineering John Wilson–U.S. EPA, ORD

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CVOC Historical Case Analysis — Potential Benefits to Nation

What are the advantages to looking at CVOC plumes nationwide?

Similar sites can share common lessons learned High or Low risk VOC release scenarios can be identified

Help understand where natural attenuation may be applicable

Reduced Cleanup Costs Focus characterization costs on those factors that most

influence plume behavior

Technology Market Identified Analysis of large number of cases identifies technology needs

Defines technology functional requirements

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VOC Historical Case Analysis — Hypothesis & Questions

Hypothesis: Chlorinated solvent cases have natural groupings

Hypothesis: These groupings can identify sites that have common predictable characteristics

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CVOC Historical Case Analysis — Specific Questions

How often is a dense non-aqueous phase liquid (DNAPL) inferred to be present.

Are Plumes with possible DNAPLS longer?

How often is there evidence of transformation processes

Are plumes with CVOC transformations shorter?

Do daughter product plumes behave differently compared to parent CVOC plumes?

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Historical Case Analysis: A New Data Model

Much of our knowledge of plume behavior comes from well-instrumented research sites.

Much of the CVOC groundwater data is collected at poorly-instrumented sites targeted for cleanup.

Historical case analyses offers a means for systematically analyzing these data.

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Project Scope

Collect hydrogeologic and contaminant data from many sites reflecting diverse environmental and release settings.

Estimate representative values for key variables.

Employ statistical methods to assess relationships between dependent and independent variables.

Validate results with probabilistic modeling.

Source termAdvection

Transformation

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Rules, Definitions, and Assumptions

“Plume” defined per CVOC per site.

Minimum site characterization requirements.

Site exclusion criteria.

Daylighting plumes.

Plumes undergoing active pump-and-treat.

Plumes that were highly complex as a result of unusual conditions.

MW-1

MW-2

MW-3

MW-4 MW-6

MW-5

MW-7

Plume length (10 ppb)

(100 ppb)

Length = Distance from location of max. historical concentration to distal 10-ppb contour.

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Definitions of Major Variables

Independent variables

Source strength

Mean groundwater velocity

Reductive dehalogenation category assignment

Dependent variables

Plume length

Change in plume length over time (growth rate)

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Project Data Set

65 sites included in initial study; over 100 in current data set.

Data from a variety of release scenarios and sources:

D.o.D. and D.O.E. facilities

Dry cleaners

Commercial industrial sites

Landfills

CVOC 10 ppb plumes

100 ppb plumes

1000 ppb plumes

TCE 55 37 19 PCE 32 20 8 1,1-DCE 29 17 8 Cis-1,2-DCE 29 17 7 1,1,1-TCA 23 16 9 Vinyl chloride 20 10 4 1,1-DCA 18 10 2 Chloroform 8 1 0 Trans-1,2-DCE 8 0 0 Carbon tetrachloride 7 2 1 1,1,2-TCA 6 0 0 1,2-DCA 6 2 0 Chloroethane 2 1 0 Chloromethane 2 0 0 Methylene chloride 1 1 0 1,1,2,2-TCA 1 0 0 TOTAL 247 134 58

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Plume Length Distributions

0

1000

2000

3000

4000

5000

6000

7000

Plu

me

le

ng

th (

ft)

10th 25th 50th 75th 90th

Percentile

Benzene vs. CVOC plume lengths

Benzene

CVOCs

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Plume Length and Source Strength

10

100

1000

10000

100000

Max. concentration (ppb)

Plu

me

len

gth

(ft

)

R = 0.40, p = 2 x 10-6

100-ppb plumes

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Groundwater Velocity

Mean groundwater velocity, v, estimated from Darcy’s law:

Geometric mean K estimated from site pumping tests and slug tests.

Mean hydraulic gradient from potentiometric surface maps.

Mean porosity assumed to be equal to 0.25.

0

1

2

3

4

5

6

7

8

9

10

-4.5 -3 -1.5 0 1.5 3

Log groundwater velocity (ft/day)

No

. of

sit

es

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Pro

ba

bili

ty d

istr

ibu

tio

n

Observation Probability distribution

50th percentile ~ 0.2 ft/day

10th percentile ~ 0.005 ft/day

90th percentile ~ 6 ft/day

21Log plume length (ft)

Lo

g v

elo

city

(ft

/day

)

Plume Length and Groundwater Velocity

-8

-6

-4

-2

0

2

4

2.5 2.7 2.9 3.1 3.3 3.5 3.7 3.9 4.1 4.3

r = 0.46, p = 0.006R = 0.46, p = 0.006

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Reductive Dehalogenation

0.001

0.01

0.1

1

10

100

1000

Con

c. (

mg/

L)

No RD Weak RD Strong RD

Median geochemical parameter values from 90th

percentile at each site

Total xylenes

Alkalinity

Mn(II)

No reductive dehalogenation group: 23 sites, no daughter products

Weak reductive dehalogenation group: 18 sites, cis-12,-DCE but no vinyl chloride

Strong reductive dehalogenation group: 20 sites, cis-1,2-DCE and vinyl chloride

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Example: Reductive Dehalogenation at Site 41350001

0.1

1

10

100

1000

0.1 1 10 100 1000 100000.01

0.1

1

10

100

1000

VC Benzene Cl-

PCE conc. (ppb)

VC

an

d b

enze

ne

(pp

b)

Cl - (ppm

)

Coincident PCE and vinyl chloride plumes

GW flow direction

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Reductive Dehalogenation: Distributions of Plume Lengths

No RD

Weak RDStrong RD

0

10

20

300%

20%

40%

60%

80%

100%

1 10 100

1000

1000

010

...

ANOVA: No significant differences between distributions

No. of plumes

Logarithm of plume length (ft)

CD

F

Plume length (ft)

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Where is the reductive dehalogenation effect?

Plume length reduction by reductive dehalogenation is subtle compared to groundwater velocity and source strength effects.

Biases in the data collection/analyses processes skew the results between groupings.

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Biases in the Data Set

Site groundwater velocity contrasts:

For Strong-RD group, median groundwater velocity is 0.21 ft/day.

For No-RD group, 9 of 13 sites have mean velocities below the Strong-RD group median.0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1.E+00 1.E+02 1.E+04 1.E+06 1.E+08

No RD Strong RD

Cu

mul

ativ

e di

stri

buti

on

Max. site concentration (ppb)

Strong RD sites have significantly stronger source terms (p = 0.007).

Strong RD sites have significantly stronger source terms (p = 0.007).

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Biases in Data Set (cont’d)

Site screening process may preferentially exclude certain types of sites:

Small source, low velocity, reductive dehalogenation very small plumes not likely to be well-monitored (excluded).

Large source, high velocity, no transformation very large plumes likely to be subject to early remediation (excluded).

Strong RD No RD

Plume length

No.

Strong RD No RD

Plume length

No.

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Biases in the Data Set: Source Strength and Groundwater Velocity

0%10%20%30%40%50%60%70%80%90%

100%

0.1 1 10 100 1000 10000

Maximum conc. (ppb) X mean site groundwater velocity (ft/day)

Cum

ulat

ive

dist

ribu

tion

No RD Strong RD

p = 0.018p = 0.018

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Analysis of Covariance

510 ft1047 ftAdjusted geometric means (ANCOVA)

872 ft876 ftGeometric means of raw plume lengths

Sites with strong evidence of reductive

dehalogenation

Sites with no evidence of reductive

dehalogenation

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QUESTIONS AND ANSWERS

C0 R,

v

Plume length

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Probabilistic Modeling

0

50

100

150

200

250

Fre

qu

en

cy

1 3 5 7 9 11

-Log v (m/sec)

0

50

100

150

200

250

Fre

qu

en

cy

1 3 5 7 9 11

Decay rate (%/yr)

0

50

100

150

200

250

Fre

qu

en

cy

100 400 700 1000

Length (m)

Solute transport

model

Groundwater velocity

Degradation rate

v c D c c S Rc

t

Plume length

Sensitivity?

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Simulation: Overview

C0 R,

v

Plume length

1/2z

1/2z

1/2y

1/2y

1/2

x

1/2x

1/2

x

x

0

x2

Zerf

x2

Zerf

x2

Y/2yerf

x2

Y/2yerf

tRv

2

/v4R1tRv

xerfc

v

4R11

2

xexp

8

Ct)y,C(x,

Monte Carlo analysis with Domenico (1987) model

Analytical solute transport solution used as model of “average” plume behavior.

Monte Carlo techniques used to generate a synthetic plume set.

Probability distributions of input variables developed from project database.

Two synthetic populations - one transforming and one stable - used to assess reductive dehalogenation effects.

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Plume Length as a Function of Source Strength: Simulation vs. Observation

100

1000

10000

100000

10 1000 100000 10000000

Max. concentration (ppb)

Plu

me

leng

th (

ft)

R = 0.36

Simulated Plume Set

100

1000

10000

100000

10 1000 100000 10000000

Max. concentration (ppb)

Plu

me

leng

th (

ft)

R = 0.20

Observed Plume Set (10-ppb plumes)

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Plume Length as a Function of Ground Water Velocity: Simulation vs. Observation

-8

-6

-4

-2

0

2

4

2.0 2.5 3.0 3.5 4.0 4.5

Log plume length (ft)

Log

vel

ocit

y (f

t/da

y)

R = 0.64

Simulated Plume Set

-8

-6

-4

-2

0

2

4

2.0 2.5 3.0 3.5 4.0 4.5

Log plume length (ft)

Log

vel

ocit

y (f

t/da

y)

R = 0.46

Observed Plume Set (10-ppb plumes)

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0%

20%

40%

60%

80%

100%

100 1000 10000

Stable

Transforming

0%

20%

40%

60%

80%

100%

100 1000 10000

No RD

Strong RD

Contaminant Transformation and Plume Length: Simulation vs. Observation

p = 0.51

Simulated Plume LengthsC

um

ula

tive

dis

trib

uti

on

Plume length (ft)

p = 0.91

Observed Plume Lengths

Cu

mu

lati

ve d

istr

ibu

tion

Plume length (ft)

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Analysis of Covariance: Model Output

705 ft991 ftAdjusted geometric means (ANCOVA)

884 ft790 ftGeometric means of raw plume lengths

Transforming plumes

Stable plumes

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Temporal Analysis of CVOC Measurements in Wells

Analyze temporal trends in data to discern natural attenuation effects

Methodology: Rank-based linear regression

with time 5 or more distinct sampling

events R < -0.5 declining trend R > 0.5 increasing trend

36%

50%

14%

Declining No trend Increasing

TCE concentrations in 533 wells from 41 sites

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Temporal Trends

0.912125Vinyl chloride

2.541533TCE

2.6201831,1-DCE

2.1171071,1-DCA

2.7855Chloroform

3.510341,2-DCA

2.12195PCE

3.91174TCE (+ vinyl chloride)

0.7497Carbon tetrachloride

1.21163Cis-1,2-DCE

4.5821Toluene

6.5191341,1,1-TCA

7.0935Benzene

Decline: increaseNo. of sitesNo. of wellsCompound

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Ratio Analysis: 1,1,1-TCA and 1,1-DCE

0%10%20%30%40%50%60%70%80%90%

100%

0.01 0.1 1 10 100

Ratio of 1,1-DCE (ppb) to 1,1,1-TCA (ppb)

Cum

ulat

ive

dist

ribu

tion

TCA source < 500ft 500-1000 ft > 1000 ft

Median ratio at source: 0.25Median ratio at source: 0.25

Predicted ratio at 1000 ft, assuming mean groundwater velocity of 0.6 ft/day, reaction half-life of 2 years, and 0.2 mole DCE produced from each mole of TCA.

Predicted ratio at 1000 ft, assuming mean groundwater velocity of 0.6 ft/day, reaction half-life of 2 years, and 0.2 mole DCE produced from each mole of TCA.

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Principal Component Analysis and Reductive Dehalogenation

0

2

4

6

8

10

<10%

10-2

0%

20-3

0%

30-4

0%

40-5

0%

50-6

0%

60-7

0%

70-8

0%

80-9

0%>90

%

012345678

<10%

10-2

0%

20-3

0%

30-4

0%

40-5

0%

50-6

0%

60-7

0%

70-8

0%

80-9

0%>90

%

Median = 74% Median = 74%

Median = 58% Median = 58% Median = 77% Median = 77%

No.

of

site

sN

o. o

f si

tes Results of PCA

Variance dominated by a single factor - GW flow regime?

Effect of reductive dehalogenation is apparent.

Results are independent of grouping strategy, i.e. no correlation with:

No. of CVOCs

No. of samples

41

Principal Component Analysis and Temporal Trends

R2 = 0.50

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 20 40 60 80

Inferred plume age (yrs)

Fir

st c

ompo

nen

t va

rian

ce

con

trib

uti

on

26 sites26 sites

= site with evidence of reductive dehalogenation

42

Implications

Can historical case data be used to predict plume behavior?

Yes: Signals (i.e., expected patterns of plume behavior) can be detected through site-specific noise (i.e., heterogeneities, different disposal histories).

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What are the key uncertainties associated with evaluating CVOC plume behavior using historical case data and what types of data are needed?

Ranges of groundwater velocities at sites (i.e., multiple pumping tests).

Geochemical indicator data (redox indicators, total soil organic carbon).

Implications

44

How may CVOC historical case analysis be used in CVOC cleanup decision-making?

Reference frame for comparative analyses of plumes at individual sites.

A set of bounds for typical plume behavior - GIS applications?

Prioritization of characterization and remediation.

Actuarial data for insurance on monitored natural attenuation.

Implications

45

Basic CVOC Plume Metrics*

compared to 1995 LLNL LUFT Study

Change in Plume Length, minimum 3 yrs of data. 29% increasing plume length (8%) 16% decreasing plume length (33%) 55% no statistically significant trend (59%)

Median length 1660 ft (130 ft)

90% less than 6300 ft (306 ft)(*Based on a review of 247 CVOC plumes from 65 sites)

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Silicon Valley – About 125 CVOC Plumes including 24 Superfund SitesSan Francisco

San Francisco Bay Area

47

Non-Fuel Program:S.F. Bay Regional Water Quality Control Board

Remedial Actions at Significant Non-Fuel Sites

0

100

200

300

400

500

Nu

mb

er o

f A

ctio

ns

Source Control

Pump&Treat

Other Controls

There are nearly 600 significant non-fuel cases ranging from Superfund to small dry cleaners (not counting about 900 lower-risk sites)

65% have undertaken source control measures. This includes soil excavation and disposal/treatment, soil venting, soil vapor extraction, free product removal

About 36% have active groundwater cleanup in progress. This includes pump and treat systems, sparging, enhanced biodegradation, and innovative methods

About 13% have other engineering controls including capping and containment barriers

48

Overview

Study produces the first ever statistical analysis of data from CVOC sites.

More variability than LUFT sites.

Don’t look for major changes compared to LLNL LUFT Study.

Look for states, rather than authors, to recommend regulatory response.

Follow-up analysis to confirm results will likely be needed to increase acceptance.

49

Potential Regulatory Response #1

Finding: Unlike Lawrence Livermore 1995 LUFT Study, CVOC plumes show wide variability.

Response: Unlikely to see any “global” regulatory changes.

50

Potential Regulatory Response #2 - Plume Length

Finding: Reductive dehalogenation has less impact on plume length than source strength and groundwater velocity.

Potential Regulatory Response: Plumes with lower source strength and groundwater velocity may be better candidates for reductive dehalogenation - monitored natural attenuation remedies.

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Potential Regulatory Response #3 Transformation Processes

Findings - Presence of Vinyl Chloride appears to indicate that reductive dehalogenation may be playing a role in reducing the extent of CVOC plumes.

Presence of cis-1,2 DCE w/out Vinyl Chloride appears to indicate reductive dehalogenation rates that are insufficient to effectively reduce extent of CVOC plumes.

Response: Focus Reductive Dehalogenation - Natural Attenuation remedies on sites with Vinyl Chloride.

52

Best Candidates for Reductive Dehalogenation - Monitored Natural Attenuation remedies appear to be:

Sites with Vinyl Chloride present,

Slow Groundwater Velocity,

Low Maximum Concentration.

53

Other potential regulatory outcomes:

Need greater focus on collecting data on: hydraulic conductivity organic carbon content in soil and groundwater

Initiative sites were heavily weighted in western U.S. thus findings may be easier to accept in the western vs. eastern states.

Findings of CVOC Initiative will likely need further confirmation prior to gaining wide spread acceptance.

54

Limitations

Data set is relatively small and may exhibit pronounced biases.

Findings are general and not necessarily applicable to individual sites.

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Historical Analysis of CVOC Plumes

56

Wrap-up

QUESTIONS AND ANSWERS

57

Thank You!

Links to Additional Resources