&EPA
United States
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/600/R-00/008
January 2000
BIOCHLOR
Natural Attenuation Decision
Support System
User's Manual
1.0
SURFACE
TOP OF
WATER-BEARING
UNIT
BOTTOM OF
WATER-BEARING
UNIT
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EPA/600/R-00/008
January 2000
BIOCHLOR
Natural Attenuation Decision Support System
User's Manual
Version 1.0
by
Carol E. Aziz and Charles J. Newell
Groundwater Services, Inc.
Houston, Texas
James R. Gonzales and Patrick Haas
Technology Transfer Division
Air Force Center for Environmental Excellence
Brooks AFB, San Antonio, Texas
T. Prabhakar Clement and Yunwei Sun
Battelle Pacific Northwest National Laboratory
Richland, Washington
Project Officer
David G. Jewett
Subsurface Protection and Remediation Division
National Risk Management Research Laboratory
Ada, Oklahoma 74820
NATIONAL RISK MANAGEMENT RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OHIO 45268
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NOTICE
The information in this document was developed through a collaboration between the U.S. EPA (Subsurface
Protection and Remediation Division, National Risk Management Research Laboratory, Robert S. Kerr
Environmental Research Center, Ada, Oklahoma [RSKERC]) and the U.S. Air Force (U.S. Air Force Center
for Environmental Excellence, Brooks Air Force Base, Texas). EPA staff contributed conceptual guidance
in the development of the BIOCHLOR mathematical model. To illustrate the appropriate application of
BIOCHLOR, EPA contributed field data generated by EPA staff supported by ManTech Environmental
Research Services Corp, the in-house analytical support contractor at the RSKERC. The computer code for
BIOCHLOR was developed by Groundwater Services, Inc. through a contract with the U.S. Air Force.
Groundwater Services, Inc. also provided field data to illustrate the application of the model.
All data generated by EPA staff or by ManTech Environmental Research Services Corp were collected
following procedures described in the field sampling Quality Assurance Plan for an in-house research
project on natural attenuation, and the analytical Quality Assurance Plan for ManTech Environmental
Research Services Corp. The development of BIOCHLOR and its User's Manual were not funded by the
U.S. EPA and as such are not subject to the Agency's QA requirements.
An extensive investment in site characterization and mathematical modeling is often necessary to establish
the contribution of natural attenuation at a particular site. BIOCHLOR is offered as a screening tool to
determine whether it is appropriate to invest in a full-scale evaluation of natural attenuation at a particular
site. Because BIOCHLOR incorporates a number of simplifying assumptions, it is not a substitute for the
detailed mathematical models that are necessary for making final regulatory decisions at complex sites.
BIOCHLOR and its User's Manual have undergone external and internal peer review conducted by the U.S.
EPA and the U.S. Air Force. However, BIOCHLOR is made available on an as-is basis without guarantee
or warranty of any kind, express or implied. Neither the United States Government (U.S. EPA or U.S. Air
Force), Ground Water Services, Inc., any of the authors nor reviewers accept any liability resulting from the
use of BIOCHLOR or its documentation. Implementation of BIOCHLOR and interpretation of the predictions
of the model are the sole responsibility of the user.
11
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FOREWORD
The U.S. Environmental Protection Agency is charged by Congress with protecting the Nation's land, air,
and water resources. Under a mandate of national environmental laws, the Agency strives to formulate and
implement actions leading to a compatible balance between human activities and the ability of natural
systems to support and nurture life. To meet these mandates, EPA's research program is providing data and
technical support for solving environmental problems today and building a science knowledge base
necessary to manage our ecological resources wisely, understand how pollutants affect our health, and
prevent or reduce environmental risks in the future.
The National Risk Management Research Laboratory is the Agency's center for investigation of technologi-
cal and management approaches for reducing risks from threats to human health and the environment. The
focus of the Laboratory's research program is on methods for the prevention and control of pollution to air,
land, water, and subsurface resources; protection of water quality in public water systems; remediation of
contaminated sites and ground water; and prevention and control of indoor air pollution. The goal of this
research effort is to catalyze development and implementation of innovative, cost-effective environmental
technologies; develop scientific and engineering information needed by EPA to support regulatory and policy
decisions; and provide technical support and information transfer to ensure effective implementation of
environmental regulations and strategies.
This screening tool will allow ground water remediation managers to identify sites where natural attenuation
is most likely to be protective of human health and the environment. It will also allow regulators to carry out
an independent assessment of treatability studies and remedial investigations that propose the use of
natural attenuation.
Clinton W. Hall, Director
Subsurface Protection and Remediation Division
National Risk Management Research Laboratory
in
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Acknowledgments
BIOCHLOR was developed by Drs. Carol Aziz and Charles Newell (Groundwater Services, Inc., Houston,
TX). Customization and testing of the BIOCHLOR solution engine was performed by Drs. Prabhakar
Clement and Yunwei Sun (Battelle Pacific Northwest National Laboratory, Richland, WA).
The authors would like to acknowledge the U.S. Air Force Center for Environmental Excellence (AFCEE) for
supporting the development of BIOCHLOR. We would like to specifically acknowledge Marty Faile and Jim
Gonzales.
We also wish to acknowledge Ann Smith (Radian International, Austin, TX) for contributing to the
development of BIOCHLOR. Special thanks also to Phil deBlanc, Leigh Ita, Ric Bowers, Julia Aziz, Tariq
Khan, and Martha Williams.
The BIOCHLOR software and manual was reviewed by a distinguished review team. We wish to
acknowledge members of the team for their comments and suggestions:
Dr. Harry Beller, Lawrence Livermore National Laboratory, Livermore, CA
Ned Black, U.S. EPA, Region 9, San Francisco, CA
Joan Elliott, U.S. EPA National Risk Management Research Laboratory, Ada, OK
Dr. Rolf Halden, Lawrence Livermore National Laboratory, Livermore, CA
Enamul Hoque, ManTech Environmental Research Services Corp., Ada, OK
Dr. David Jewett, U.S. EPA National Risk Management Research Laboratory, Ada, OK
Dr. Ann Azadpour-Keeley, U.S. EPA National Risk Management Research Laboratory, Ada, OK
Dr. Roger Lee, U.S. Geological Survey, Dallas, TX
Herb Levine, U.S. EPA, Region 9, San Francisco, CA
Dr. Elise Striz, ManTech Environmental Research Services Corp., Ada, OK
Luanne Vanderpool, U.S. EPA Region 5, Chicago, IL
IV
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Contents
Introduction 1
Intended Uses for BIOCHLOR 1
Fundamentals of Natural Attenuation 2
Overview of Natural Attenuation 2
Natural Attenuation Lines of Evidence and the Role of BIOCHLOR 4
BIOCHLOR Concepts 6
BIOCHLOR Model Types 6
BIOCHLOR Data Entry 6
1. Hydrogeologic Data 7
2. Dispersivity 9
3. Adsorption Data 10
4. Biotransformation Data 12
5. General Data 14
6. Source Data 16
7. Field Data for Comparison 18
Analyzing BIOCHLOR Output 19
Centerline Output 19
Array Output 19
Calculating the Mass Balance (Order-of-Magnitude Accuracy) 19
Quick Start 21
Minimum System Requirements 21
Installation and Start-Up 21
BIOCHLOR TROUBLESHOOTING TIPS 21
Spreadsheet-Related Problems 21
Common Error Messages 21
References 23
Appendix A.1 Domenico Single Species Analytical Model 25
Appendix A.2 27
Kinetics of Sequential First Order Decay 27
Chlorinated Ethenes 27
Chlorinated Ethanes 28
Other Chlorinated Compounds 28
1-Zone vs. 2 -Zone Biotransformation 28
How BIOCHLOR Models 2-Zone Biotransformation 30
Appendix A.3 31
BIOCHLOR Solution 31
Governing Equations 31
Analytical Solution Strategy 31
Computational Procedure 33
Appendix A.4 Dispersivity Estimates 36
Appendix A.5 Pump and Treat Comparison 38
Appendix A.6 39
BIOCHLOR Example 40
BIOCHLOR Modeling Summary, 42
Cape Canaveral Air Station, Florida 42
Entering Input 42
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Viewing Output 42
Sensitivity Analysis Examples 46
VI
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Figures
Figure 1. Reductive dechlorination pathways for common chlorinated
aliphatic hydrocarbons (from Vogel and McCarty, 1987) 3
Figure 2. Reductive transformation of chlorinated ethenes 3
Figures. Initial screening process flowchart 5
Figure A.1. Mixed type I/Type III plume conditions 29
Figure A.2. Comparison of solution techniques for BIOCHLOR 1-zone and 2-zone
biotransformation models 30
Figure A.3. Longitudinal dispersivity vs. scale data reported by Gelhar et al. (1992) 37
Figure A.4. Ratio of transverse dispersivity and vertical dispersivity to longitudinal
dispersivity data vs. scale reported by Gelhar et al. (1992) 38
Figure A.5. BIOCHLOR source zone assumption (TCE as example) 41
Figure A.6. BIOCHLOR input screen. Cape Canaveral Air Force Base, Florida 43
Figure A.7. Centerline output. Cape Canaveral Air Force Base, Florida 44
Figure A.8. Individual centerline output for TCE, Cape Canaveral Air Station, Florida 44
Figure A.9. Array concentration output for TCE. Cape Canaveral Air Station, Florida 45
Tables
Table A. 1. 2-Zone Biotransformation Scenarios 28
Table A.2. Modeling Scenario 3 for Chlorinated Ethenes 29
Table A.3. Modeling Scenario 3 for Chlorinated Ethanes 29
Table A.4. Sensitivity Analysis Results - Rate Coefficients 46
Table A.5. Sensitivity Analysis Results- Retardation Factor 46
vn
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Introduction
BIOCHLOR is an easy-to-use screening model that simulates remediation by natural attenuation (RNA) of dissolved
solvents at chlorinated solvent release sites. The software, programmed in the Microsoft® Excel spreadsheet
environment and based on the Domenico analytical solute transport model, has the ability to simulate 1-Dadvection, 3-D
dispersion, linear adsorption, and biotransformation via reductive dechlorination (the dominant biotransformation
process at most chlorinated solvent sites). Reductive dechlorination is assumed to occur under anaerobic conditions
and dissolved solvent degradation is assumed to follow a sequential first-order decay process. BIOCHLOR includes
three different model types:
1. Solute transport without decay,
2. Solute transport with biotransformation modeled as a sequential first-order decay process,
3. Solute transport with biotransformation modeled as a sequential first-order decay process with two different
reaction zones (i.e., each zone has a different set of rate coefficient values).
BIOCHLOR was developed for the Air Force Center for Environmental Excellence (AFCEE) Technology Transfer
Division at Brooks Air Force Base by Groundwater Services, Inc., Houston, Texas. The mathematical technique to solve
the coupled reactive transport equations was developed by researchers at the Battelle Pacific Northwest National
Laboratory.
Intended Uses for BIOCHLOR
BIOCHLOR attempts to answer the following fundamental question regarding RNA:
• How far will a dissolved chlorinated solvent plume extend if no engineered controls or source area
reduction measures are implemented?
BIOCHLOR uses an analytical solute transport model with sequential first-order decay for simulating in-situ
biotransformation (Sun et al., 1999a; Sun and Clement, 1999). The model will predict the maximum extent
of dissolved-phase plume migration, which may then be compared to the distance to potential points of
exposure (e.g., drinking water wells, ground-water discharge areas, or property boundaries). Analytical
ground-water transport models have seen wide application for this purpose (e.g., ASTM, 1995) and
experience has shown such models can produce reliable results when site conditions in the plume area are
relatively uniform.
BIOCHLOR is intended to be used in two ways:
1. As a screening-level model to determine if RNA is feasible at a chlorinated solvent site.
BIOCHLOR is intended to be used as a screening-level model to determine if natural attenuation is
occurring at sufficient rates at a site to warrant a full natural attenuation study. Ideally, site-specific
biotransformation rate constants should be employed, but literature values can be used if measured rate
constants are unavailable. Other useful attributes of BIOCHLOR include the facilitation of site characteriza-
tion data organization and the ability to carry out many simulations in short periods of time. For fuel
hydrocarbon release sites, the BIOSCREEN model (Newell et al., 1996) is more appropriate.
2. As an RNA ground-water model to address selected chlorinated solvent problems
The Technical Protocol for Evaluating Natural Attenuation of Chlorinated Solvents in Ground Water (U.S.
EPA, 1998) describes how ground-water models, in conjunction with other types of analysis, can be used
to evaluate the effectiveness of natural attenuation. BIOCHLOR is an appropriate model at sites where
simplifying assumptions (e.g., uniform ground-water flow, a vertical plane source, first-order decay) can be
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made so that the resulting simulations provide useful information forthe problem being addressed. At other
sites, where these assumptions do not hold, a more sophisticated numerical model such as RT3D
(Clement, 1997) would be appropriate. As with any modeling study, the authors recommend that proper
care be used to select the model that is best suited to 1) the source, hydrogeology, and biotransformation
processes present at the site and, 2) the type of problem being addressed (e.g., screening of alternatives,
providing supporting evidence of natural attenuation, developing detailed design information).
BIOCHLOR has the following limitations:
1. As an analytical model, BIOCHLOR assumes simple ground-water flow conditions.
The model should not be applied where pumping systems create a complicated flow field. In addition, the
model should not be applied where vertical flow gradients affect contaminant transport. (Note that a vertical
distribution of chlorinated solvents throughout the saturated zone does not preclude the use of BIOCHLOR,
as this phenomenon is related to the initial vertical migration of dense non-aqueous phase liquids in source
areas.)
2. As a screening tool, BIOCHLOR assumes uniform hydrogeologic and environmental conditions over the
entire model area.
Being an analytical model, BIOCHLOR assumes constant source, hydrogeological, and biological property
values forthe entire model area and, therefore, simplifies actual site conditions. Forthis reason, the model
should not be applied where extremely detailed, accurate results that closely match site conditions are
required. More comprehensive numerical models should be applied in such cases.
3. BIOCHLOR is primarily designed for simulating the sequential reductive dechlorination of chlorinated
ethanes and ethenes.
The sequential biotransformation feature in BIOCHLOR should not be used for compounds that do not
degrade via sequential first-order kinetics. While the interface is designed for simulating the biotransforma-
tion of chlorinated ethenes (i.e., PCE, TCE, DCE, and vinyl chloride (VC)) and chlorinated ethanes (i.e.,
TCA, DCA, and chloroethane (CA)), the model can be adapted for other sequential decay reactions by
experienced users (see Appendix A.2).
Fundamentals of Natural Attenuation
Overview of Natural Attenuation
"Natural Attenuation" refers to naturally-occurring processes in soil and ground-water environments that act without
human intervention to reduce the mass, toxicity, mobility, volume, or concentration of contaminants in those media.
These in-situ processes include biotransformation, dispersion, dilution, adsorption, volatilization, and chemical or
biological stabilization or destruction of contaminants (U.S.EPA, 1998).
Biotransformation can often be a dominant process in the natural attenuation of chlorinated solvents. At chlorinated
solvent contaminated sites, most of the solvent degradation occurs by reductive dechlorination (U.S.EPA, 1998).
Reductive dechlorination is a microbially-mediated reaction whereby a chlorine atom on the chlorinated solvent is
replaced by a hydrogen atom (Vogel and McCarty, 1987). In many bioremediation processes, an organic contaminant
(such as benzene) acts as an electron donor and another substance (such as oxygen, nitrate, etc.) acts as the electron
acceptor. However, during reductive dechlorination, hydrogen acts as the electron donorand halogenated compounds,
such as chlorinated solvents, act as electron acceptors and thus become reduced, as shown in the following half
reaction:
R-CI + H+ + 2e- > R-H + Cl~
Figure 1 shows the reductive transformation pathways for the common chlorinated aliphatics. More details on the
biotransformation of chlorinated solvents can be found in Appendix A.2.
Reductive dechlorination can be modeled as a sequential first-order decay process. This means that a parent compound
undergoes first-order decay to produce a daughter product and that product undergoes first-order decay and so on.
Generally, the more highly chlorinated the compound, the more rapidly it is reduced by reductive dechlorination (Vogel
and McCarty, 1985; Vogel and McCarty, 1987). Therefore, it is possible for daughter products to increase in
concentration before they decrease as shown in Figure 2. BIOCHLOR accounts for sequential first-order decay of this
nature, and this sets it apart from BIOSCREEN (Newell et al., 1996), which models the biodegradation of fuel
hydrocarbons via first-order decay or electron acceptor-limited (instantaneous reaction) processes.
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1,1,1-
TCA
1,1 -DCA
Chloroethane
h
Ethane
CHgCHg
Major pathway
Minor pathway
PCE = Perchloroethene
TCE = Trichloroethene
DCE = Dichloroethene
TCA = Trichloroethane
DCA = Dichlorethane
Figure 1. Reductive dechlorination pathways for common chlorinated aliphatic hydrocarbons (after Vogel and McCarty,1985; Vogel and
McCarty,1987).
100-
90-
E, *'
0 go
£ * '
fi 30
8 »-
5 10-
0
V
\
\
X- ---,.
PCE
TCE
VG
E1H
/' v>^ ""^---...___ ._-
. ... -^"^v. ill """"""————_.
I !
0 200 400 WO 800 1TO 1200
Distance From Source (ft)
Figure 2. Reductive transformation of chlorinated ethenes.
For biological reductive dechlorination to occur, the following conditions must exist:
1. The subsurface environment must be anaerobic and have a low oxidation-reduction potential (ORP).
2. Chlorinated solvents that are amenable to reductive dechlorination must be present.
3. A population of dechlorinating bacteria must be present.
4. An adequate supply of fermentation substrates to produce dissolved hydrogen must be present.
The environmental chemistry and the ORP of a site play an important role in determining whether reductive
dechlorination will occur. Based on thermodynamic considerations, reductive dechlorination will occur only after both
oxygen and nitrate have been depleted from the aquifer, because oxygen and nitrate are more energetically favorable
electron acceptors than chlorinated solvents when hydrogen is the electron donor (U.S. EPA, 1998).
The role of hydrogen as an electron donor during reductive dechlorination is now widely recognized as a key factor
governing the dechlorination of chlorinated compounds (Gossettand Zinder, 1996; Holligeretal., 1993; Maymo-Gatell et
al., 1997; Hughes etal., 1997; Carrand Hughes, 1998). The hydrogen is produced in the terrestrial subsurface by the
fermentation of a wide variety of organic compounds including anthropogenic compounds such as petroleum hydrocar-
bons and natural organic matter. Hydrogen is then used by the dechlorinating bacteria as an electron donor.
Although BIOCHLOR primarily models the degradation of chlorinated solvents via reductive dechlorination, which occurs
under highly reduced anaerobic conditions, some of the chlorinated solvents may degrade under aerobic conditions.
TCE, c-DCE and VC degrade cometabolically (McCarty and Semprini, 1994) and VC (Hartmans et al., 1985; Hartmans
and de Bont, 1992) and possibly c-DCE (Bradley and Chapelle, 1998) can be directly oxidized to carbon dioxide under
aerobic conditions. PCE has not been found to degrade aerobically (McCarty and Semprini, 1994).
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Natural Attenuation Lines of Evidence and the Role of BIOCHLOR
To support remediation by natural attenuation, it must be scientifically demonstrated that attenuation of the site
contaminants is occurring at rates sufficient to be protective of human health and the environment. According to the
"Technical Protocol For Evaluating Natural Attenuation of Chlorinated Solvents in Ground Water" (U.S. EPA, 1998), three
lines of evidence can be used to support natural attenuation of chlorinated solvents including :
1. Observed reductions in contaminant concentrations along the flow path downgradient from the source of
contamination.
2. Documented loss of contaminant mass at the field scale using:
a) Chemical and geochemical analytical data including decreasing parent compound concentration,
increasing daughter compound concentrations, depletion of electron acceptors and donors, and
increasing metabolic byproduct concentrations; and/or
b) A rigorous estimate of residence time along the flow path to document contaminant mass reduction and
to calculate biological decay rates at the field scale.
3. Laboratory microcosm or field data that support the occurrence of biotransformation and give rates of
biotransformation.
At a minimum, the investigator must obtain the first two lines of evidence or the first and third lines of evidence. The
second or third line of evidence is crucial because it provides biotransformation rate constants. These rate constants can
be used in conjunction with other fate and transport parameters to predict contaminant concentration and to assess risk
at a downgradient point of exposure (U.S. EPA, 1998).
Compared to fuel hydrocarbon plumes, use of natural attenuation as a stand-alone remedy for chlorinated solvent
plumes is appropriate for a much lower percentage of plumes, because of their longer plume lengths. Therefore, it is
particularly important to make an accurate assessment of the potential for natural attenuation prior to investing in a
detailed natural attenuation study. To assist in this endeavor, the natural attenuation screening process is outlined in
Figure 3. The shaded steps indicate the stages where BIOCHLOR plays a role in the screening process.
The first shaded stage (i.e., "Is Biodegradation Occurring?") is the stage where the natural attenuation scoring system
comes into play. The scoring system requires the concentrations of electron acceptors, parent and daughter chlorinated
solvents, methane, TOC, and chloride and ORP, temperature, and pH measurements (U.S. EPA, 1998). These field
data are evaluated and scored for evidence of biotransformation. BIOCHLOR incorporates this scoring system, which
can be accessed from the input page.
If there is evidence of biotransformation, BIOCHLOR may be used subsequently to compare the rate of chlorinated
solvent transport without biotransformation to the rate of attenuation with biotransformation. Being a transient model, the
simulation time can be varied to determine the future extent of contamination. Field-derived biological rate coefficients
should be used if possible, but literature values may be used in the absence of site-specific rate constants or the model
may be calibrated to field data.
The primary purpose of comparing the transport rate to the attenuation rate is to determine if the residence time along the
flow path is adequate to protect human health and the environment (i.e., to estimate if the contaminant degrades to an
acceptable concentration before receptors are exposed). In the case of rate coefficients or any other parameter that is
not known accurately or that varies over the extent of the plume, sensitivity analyses should be conducted. If modeling
shows that the receptors will not be impacted by contaminants at concentrations above regulatory criteria, then the
screening criteria are met, and the investigator can proceed with a full natural attenuation evaluation. Details of a full
natural attenuation evaluation can be found in "Technical Protocol For Evaluating Natural Attenuation of Chlorinated
Solvents in Ground Water" (U.S. EPA, 1998).
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Analyze Available Site Data Along
Core of Plume to Determine if
Biodegradation is Occurring.
-d
Collect More Screening Data.
t
lodegradation
Occurrin
NO or
INSUFFICIENT
DATA
NO
Are
^Sufficient Data^
Available ?
Engineered Remediation
Required. Implement Other
Protocols.
YES
Locate Source(s) and Potentia
Points of Exposure. Estimate
Extent of NAPL, Residual and
Free-Phases.
Determine Groundwater Flow
and Solute Transport Parameters
Along Core of Plume Using
Site-Specific Data; Porosity and
Dispersivity may be Estimated
Estimate Biodegradation Rate
Constant
Compare the Rate of Transport
to the Rate of Attenuation using
Analytical Solute Transport
Model (BIOCHLOR).
Are
Screening Criteri
Met?
Does it
Appear that Natural
Attenuation Alone will
Meet Regulatory
Criteria
Evaluate use of Selected
Additional Remedial Options
along with Natural Attenuation
PROCEED TO FULL
NATURAL ATTENUATION STUDY
,YES
Perform Site Characterization to
Evaluate Natural Attenuation.
^
PROCEED TO FULL
r NATURAL ATTENUA
Figure 3. Initial screening process flow chart.
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BIOCHLOR Concepts
The BIOCHLOR Natural Attenuation software is based on a sequential, first-order, coupled reactive transport model.
The transport problem is analytically solved using the Domenico model (1987) by uncoupling the transport equations
using a novel analytical strategy (Sun etal., 1999a, 1999b; Sun and Clement, 1999) as discussed in Appendix A.3. The
original Domenico model assumes a fully-penetrating vertical plane source oriented perpendicular to ground-water flow
to simulate the release of organics to moving ground water and accounts for the effects of one-dimensional advective
transport, three-dimensional dispersion, linear adsorption, and first-order decay. In BIOCHLOR, the Domenico solution
has been adapted to provide three different model types representing i) transport with no decay, ii) transport with
sequential first-order decay in one zone, and iii) transport with sequential first-order decay in two zones (see Model
Types). Guidelines for selecting key input parameters for the model are outlined in BIOCHLOR Input Parameters. For
help on Output, see BIOCHLOR Output.
BIOCHLOR Model Types
The software allows the user to view results from three different types of ground-water transport models:
1. Solute transport with no decay. This model is appropriate for predicting the movement of conservative
(non-degrading) solutes. The only attenuation mechanisms are dispersion in the longitudinal, transverse,
and vertical directions (if present), and adsorption of contaminants to the soil matrix (if present).
2. Solute transport with sequential first-order decay in one zone. With this model, the reactive transport
of both parent and daughter chlorinated solvents can be modeled. This model accounts for dispersion,
adsorption, advection, and sequential biotransformation. The reductive dechlorination of the parent solvent
to daughter product is assumed to be a first-order process. That is, the solute degradation rate is assumed
to be proportional to the solute concentration. However, the daughter products are also produced by the
first-order degradation of the preceding parent compound. Therefore, the daughter product can simultaneously
undergo both production and degradation. "One zone" means that one set of rate constants is used within
the model area. The model assumes that biotransformation starts immediately downgradient of the source
and that no biotransformation of dissolved constituents in the source area occurs.
The sequential first-order decay model does not directly account for site-specific information such as the
concentration of the electron donor (i.e., hydrogen) orthe number of dechlorinating bacteria; this is implicitly
accounted for in the first-order decay rate coefficient supplied by the user. Ideally, rate coefficients
measured in the field or derived from model calibration to site data should be used. Literature values may
also be employed, but the user must be aware that the literature value may have been measured under
different environmental conditions than those present for the plume being modeled.
3. Solute transport with sequential first-order decay in two zones. This model employs the same
sequential first-order decay kinetics as the preceding model but allows the user to use two different sets of
rate constants within the model area. This may be appropriate for plumes that undergo rapid biotransformation
close to the source where there is an excess of fermentable substrates but negligible biotransformation
further downgradient where fermentable substrates have been depleted or for plumes that go from
anaerobic conditions to aerobic conditions. Aerobic conditions can be considered only in the second zone
and should be modeled only by experienced users as discussed in Appendix A.2.
Note: This two-zone model should be employed only when the plume is at steady-state throughout
the first zone. The plume is at steady-state if plume concentrations (field measurements or model
predictions) are not changing with time. This condition is required to ensure the constant concentration
boundary condition at the boundary between zone 1 and zone 2. Refer to Appendix A.2 fora more detailed
discussion.
BIOCHLOR Data Entry
Three important considerations regarding data input are:
1. To see the example data set in the input screen of the software, click on the "Paste Example Data Set"
button on the lower right portion of the input screen.
2. Because BIOCHLOR is based on the Excel spreadsheet, you must click outside of the cell where you just
entered data or hit "return" before any of the buttons will work.
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3. Parameters used in the model can be entered directly into the white cells or they can be calculated by the
model using data entered in the gray cells (e.g., seepage velocity can be entered directly or calculated using
hydraulic conductivity, gradient, and effective porosity), followed by pressing the "C" button.
NOTE: Although literature values are provided, it is strongly recommended that the user employ measured
hydrogeological and biotransformation values whenever possible. If literature values are used and there is
uncertainty in the value chosen, sensitivity analyses should be conducted to determine the effects of the
uncertainty on model predictions. Examples of a sensitivity analysis can be found in Appendix A.7.
1. Hydrogeologic Data
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Seepage Velocity (Vs)
ft/yr
Actual interstitial ground-water velocity, equaling Darcy velocity divided by
effective porosity. Note that the Domenico model and BIOCHLOR are not
formulated to simulate the effects of chemical diffusion. Therefore, contaminant
transport through very slow hydrogeologic regimes (e.g., clays and slurry walls)
should probably not be modeled using BIOCHLOR unless the effects of chemical
diffusion are proven to be insignificant.
0.5 to 200 ft/yr
Calculated by multiplying hydraulic conductivity by hydraulic gradient and
dividing by effective porosity. It is strongly recommended that actual site data be
used for hydraulic conductivity and hydraulic gradient data parameters; effective
porosity can be estimated.
1) Enter directly or 2) Fill in values for hydraulic conductivity, hydraulic gradient,
and effective porosity as described below and have BIOCHLOR calculate seepage
velocity by pressing the "C" button.
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Hydraulic Conductivity (K)
cm/sec
Horizontal hydraulic conductivity of the saturated porous medium.
-6
Clays: 1 cm/s
Pump tests or slug tests at the site. It is strongly recommended that
data be used for all RNA studies.
Enter directly. If seepage velocity is entered directly, this parameter is
in BIOCHLOR.
actual site
not needed
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Hydraulic Gradient (i)
ft/ft
The slope of the potentiometric surface. In unconfined aquifers, this is equivalent
to the slope of the water table.
0.0001 -0.05 ft/ft
Calculated by constructing potentiometric surface maps using static water level
data from monitoring wells and estimating the slope of the potentiometric surface.
Enter directly. If seepage velocity is entered directly, this parameter is not needed
in BIOCHLOR.
-------
1. Hydrogeologic Data, cont.
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Effective Porosity (n)
unitless
Dimensionless ratio of the volume of interconnected voids to the bulk volume of
the aquifer matrix. Note that "total porosity" is the ratio of all voids (included
non-connected voids) to the bulk volume of the aquifer matrix. Differences
between total and effective porosity reflect lithologic controls on pore structure.
In unconsolidated sediments coarser than silt size, effective porosity can be less
than total porosity by 2-5% (Smith and Wheatcraft, 1993).
Values for Effective Porosity:
Clay 0.01-0.20 Sandstone 0.005-0.10
Silt 0.01-0.30 Unfract. Limestone 0.001-0.05
Fine Sand 0.10-0.30 Fract. Granite 0.00005-0.01
Medium Sand 0.15-0.30
Coarse Sand 0.20 - 0.35
Gravel 0.10-0.35
(From Wiedemeier et al, 1995; originally from Domenico and Schwartz, 1990
and Walton, 1988).
Typically estimated. One commonly used value for silts and sands is an effective
porosity of 0.25. The ASTM RBCA Standard (ASTM, 1995) includes a default
value of 0.38 (to be used primarily for unconsolidated deposits).
Enter directly. Note that if seepage velocity is entered directly, this parameter is
still needed to calculate the retardation factor and plume mass flux.
-------
2. Dispersivity
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Longitudinal Dispersivity (alpha x)
Transverse Dispersivity (alpha y)
Vertical Dispersivity (alpha z)
ft
Dispersion refers to the process whereby a dissolved solvent will be spatially distributed
longitudinally (along the direction of ground-water flow), transversely (perpendicular to
ground-water flow), and vertically (downward) because of mechanical mixing and
chemical diffusion in the aquifer. These processes develop the "plume" shape that is the
spatial distribution of the dissolved solvent mass in the aquifer.
Selection of dispersivity values is a difficult process, given the impracticability of
measuring dispersion in the field. However, simple estimation techniques based on the
length of the plume or distance to the measurement point ("scale") are available from a
compilation of field test data. Researchers indicate that dispersivity values can range over
2-3 orders of magnitude for a given value of plume length or distance to measurement
point (Gelhar et a/., 1992). For more information on dispersivity, see Appendix A. 4.
The user also has the option to enter a fixed diffusivity value or dispersivity relation as a
function of x (distance from the source in ft). BIOCHLOR is programmed with some
commonly used relations based on scale that are representative of typical and low-end
dispersivities. A fixed dispersivity value should be used for 2-zone simulations.
• Longitudinal Dispersivity
The user is given three options:
Option 1 (the default option) allows the user to specify a fixed value for alpha x. One
commonly used relation is to assume that alpha x is 10% of the estimated plume length.
This option is required for conducting 2-zone biotransformation simulations.
Option 2 assumes that alpha x = 0. 1 * x (Pickens and Grisak, 1981)
Option 3 calculates the longitudinal dispersivity using the following correlation:
... f ( X AT'446 (Xu and Eckstein, 1995; Al-Suwaiyan, 1996)
Alpha x - 0 __ _ __ .
3.28 0.28 [I°9^328JJ
• Transverse Dispersivity
The user may choose a ratio of alpha y : alpha x. One commonly used ratio is:
Alpha y: alpha x = 0.10 (Based on high reliability points from
Gelhar et al, 1992)
• Vertical Dispersivity
The user may choose a ratio of alpha z : alpha x. One commonly used ratio is: Alpha z:
alpha x = 0.05 (ASTM, 1995)
Alternatively, alpha z :alpha x can be set to a very low number (e.g., E-99) to yield a
conservative estimate of vertical dispersion. This is the default value used in BIOCHLOR.
Other commonly used relations include:
Alpha x = 0.1 Lp (Pickens and Grisak, 1981)
Alpha y = 0.33 alpha x (ASTM, 1995) (EPA, 1986)
Alpha z = 0.025 alpha x to 0.1 alpha x (EPA, 1986)
Typically estimated using the relations provided above (see Appendix A. 4).
Click on "Change Alpha x Calc. Method" button. Select an option for alpha x. If you select
Option 1, enter a fixed value in the box. Enter ratios for alpha y and alpha z. (Note: If the
"Reset" button is depressed, then the following are the default options and values used by
BIOCHLOR: Option 1 (fixed value) is used to calculate alpha x. The user must input a
value. The alpha y : alpha x ratio is set to 0.1 and the alpha z : alpha x ratio is set to Ix 10"
".)
-------
3. Adsorption Data
Parameter
Retardation Factor (R)
Units
unitless
Description
Adsorption to the soil matrix can reduce the concentration of dissolved contaminants
moving through the ground water. The retardation factor is the ratio of the ground-
water seepage velocity to the rate that organic chemicals migrate in the ground
water. A retardation value of 2 indicates that if the ground-water seepage velocity is
100 ft/yr, then the organic chemicals migrate at approximately 50 ft/yr. The degree
of retardation depends on both aquifer and constituent properties.
Typical Values
1 to 6 (for solvents in typical shallow aquifers)
Source of Data
Usually estimated from soil and chemical data using variables described below (pb =
bulk density, n = effective porosity, Koc = organic carbon-water partition coefficient,
Kd = distribution coefficient, and foc = fraction organic carbon on uncontaminated
soil) with the following expression:
R =1 +
K
where Kd = Koc • foc
When biotransformation rates are insignificant, the retardation factor can be
estimated by comparing the plume length of an adsorbed compound to the plume
length of a conservative (non-adsorbing) compound.
How to Enter Data
l)Enter the retardation factor for each constituent directly. Do NOT press the "C"
button. The worksheet will be updated automatically. OR 2) Fill in the estimated
values for bulk density, partition coefficient, effective porosity, and fraction organic
carbon and calculate the retardation factor by pressing the "C" button.
Common R: BIOCHLOR uses one retardation factor for all the constituents, not
individual retardation factors. Currently, BIOCHLOR calculates the median
retardation factor and uses that value in all calculations. Alternatively, the user can
enter another retardation value in the cell beside Common R. The Common R value
that is chosen should be representative of the retardation factors of the constituents
modeled. In addition, sensitivity analyses should be conducted to evaluate the effect
of the choice of the common retardation factor on the results (see Appendix A.7 for
an example).
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Aquifer Matrix Bulk Density (p t,)
kg/L or g/cm3
Bulk density, in kg/L, of the aquifer matrix (related to porosity
density).
and pure solids
Although this value can be measured in the lab, in most cases estimated values are
used. A value of 1 .7 kg/L is used frequently.
Either from an analysis of soil samples at a geotechnical lab or,
application of estimated values such as 1.7 kg/L.
Enter directly. If the retardation factor is entered directly, this
needed in BIOCHLOR.
more commonly,
parameter is not
10
-------
3. Adsorption Data, cont.
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Organic Carbon Partition Coefficient (Koc)
(mg/kg) / (mg/L) or (L/kg) or (mL/g)
Chemical-specific partition coefficient between soil organic carbon and the aqueous
phase. Larger values indicate greater affinity of contaminants for the organic
carbon fraction of soil.
Perchloroethylene 426 L/kg Trichloroethylene 130 L/kg
Dichloroethylene 125 L/kg Vinyl Chloride 29.6 L/kg
(at 20 °C)
(Note that there is a wide range of reported values and these values are
temperature-dependent)
Chemical reference literature or relations between K and solubility or K and the
octanol-water partition coefficient (Kow).
Enter directly. If the retardation factor is entered directly, this parameter is not
needed in BIOCHLOR.
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Fraction Organic Carbon (foc)
unitless
Fraction of the aquifer soil matrix comprised of natural organic carbon in
uncontaminated areas. More natural organic carbon means more adsorption of
organic constituents on the aquifer matrix.
0.0002-0.02
The fraction organic carbon value should be measured, if possible, by collecting a
sample of aquifer material from an uncontaminated area and performing a
laboratory analysis (e.g., ASTM Method 2974-87 or equivalent). If unknown, a
default value of 0.001 is often used (LaGrega et al., 1994).
Enter directly. If the retardation factor is entered directly, this parameter is not
needed in BIOCHLOR.
11
-------
4. Biotransformation Data
Parameter
First-Order Decay Coefficients (lambda) for Zones 1 and 2
Units
1/yr
Description
Rate coefficient describing first-order decay process for dissolved constituents.
The first-order decay coefficient equals 0.693 divided by the half-life of the
contaminant in ground water. If a dissolved solvent is undergoing first order decay
only, the rate of biotransformation depends on the concentration of the
contaminant and the rate coefficient. In the case of sequential first order decay, the
solvent is assumed to degrade by first order kinetics, but it is also simultaneously
being produced by the first order decay of the preceding compound (see Appendix
A.2).
Considerable care must be exercised in the selection of a first-order decay
coefficient for each constituent to avoid significantly over-predicting or under-
predicting actual decay rates.
For guidance on how to model your site assuming one or two biotransformation
zones, see General Data, Section 5.
0.07 to 1.20 yr"
0.05 to 0.9 yf1
0.18 to 3.3 yr'
0.12 to 2.6 yr
(from
Typical Values
Perchloroethylene
Trichloroethylene
cis-l,2-Dichloroethylene
Vinyl Chloride
z,. \j y i
Wiedemeier etal, 1999)
Note: The equations in BIOCHLOR cannot accept a zero value for any of the rate
coefficients. BIOCHLOR checks entered values and assigns a low value if zero is
entered. Also, no two rate constants in the same zone can be identical.
BIOCHLOR will issue an error message and ask the user to re-enter the rate
coefficients.
Source of Data
Optional methods for selection of appropriate decay coefficients are as follows:
Calibrate to Existing Plume Data: BIOCHLOR can be used to determine first-
order decay coefficients that best match the observed site concentrations. One may
adopt a trial-and-error procedure to derive a best-fit decay coefficient for each
contaminant by varying the decay coefficient until predicted concentrations match
measured concentrations.
Literature Values: Various published references are available listing
biotransformation rate coefficients (e.g., USEPA, 1998; Howard et al., 1991).
Many references report the half-lives; these values can be converted to the first-
order decay coefficients using k = 0.693 / t (see dissolved solvent half-life).
Note: Because the use of literature values may overestimate the amount of
biotransformation occurring, the user should conduct sensitivity analyses to
determine the impact of the chosen rate coefficients on plume lengths (see
Appendix A.7).
Other Methods: The "Technical Protocol for Evaluating Natural Attenuation of
Chlorinated Solvents in Ground Water" (USEPA, 1998) describes other methods
for obtaining rate coefficients, including the use of microcosm data and use of
field-scale tracer data.
How to Enter Data
1) Enter directly or 2) Fill in the estimated half-life values as described below and
have BIOCHLOR calculate the first-order decay coefficients by pressing the "C"
button.
12
-------
4. Biotransformation Data, cont.
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Dissolved Solvent Half-Life (tm)
years
Time, in years, for dissolved plume concentrations to decay by one half as
contaminants migrate through the aquifer. The amount of degradation that occurs is
related to the time the contaminants spend in the aquifer.
Considerable care must be exercised in the selection of a half-life for each
contaminant in order to avoid significantly over-predicting or under-predicting
actual decay rates.
Perchloroethylene 0.58 to 9.9 yr
Trichloroethylene 0.77 to 13.9 yr
cis-l,2-Dichloroethylene 0.21 to 3.9 yr
Vinyl Chloride 0.27 to 5.8 yr
(from Wiedemeier et al., 1999)
Optional methods for selection of appropriate half-lives are the same as for the rate
coefficients
Enter directly in gray cells and press the "C" button. If the first-order decay
coefficient is entered directly, this parameter is not needed in BIOCHLOR.
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Abiotic First Order Rate Coefficient (1lyr)
1 /years
Rate coefficient describing first-order abiotic decay process for chloroethane.
Chloroethane degrades to ethanol under abiotic conditions.
Note: Although 1,1,1-TCA can abiotically decay to 1,1 -DCE via elimination and
to acetic acid as a result of hydrolysis, BIOCHLOR cannot simulate abiotic decay
and chlorinated ethane daughter product generation simultaneously. BIOCHLOR
can be used to simulate the degradation of 1,1,1-TCA alone by setting the initial
daughter product concentrations to zero, the biological rate constants for DCA and
CA to zero, and entering a TCA degradation rate coefficient on the input page.
This rate coefficient represents the sum total of all abiotic and biotic coefficients for
processes observed in the field at your site. BIOCHLOR will generate TCA
predictions, but daughter product predictions should be ignored.
Note that the abiotic rate coefficients for the chlorinated ethenes are very slow
(greater than 106 years half-life, (Jeffers et al., 1989)) and therefore abiotic
degradation can be ignored for PCE, TCE, DCE, and VC.
chloroethane to ethanol 0.37 yr"1 (20°C)
1,1,1-trichloroethane to 1,1 -DCE 0.058-0.32 yr'1 (10-20 °C)
1,1,1-trichloroethane to acetic acid 0.25 to 0.41 yr"1
(from VogelcmdMcCarty, 1987; McCarty, 1996)
Optional methods for selection of appropriate rate coefficients are as follows:
Literature Values: Various published references are available that list rate
coefficients for hydrolysis and other abiotic processes (e.g., Howard et al., 1991).
Press "XA" button. Enter values in the dialog box and press "OK".
13
-------
4. Biotransformation Data, cont.
Parameter
Units
Description
Typical Values
Sources of Data
How to Enter Data
Yield
unitless
Because biotransformation rate expressions are calculated on a molar basis and
BIOCHLOR accepts concentration data on a mass basis (i.e., mg/L), a conversion
factor must be incorporated to account for the amount of mass of daughter product
produced from the degradation of the parent compound. The yield is the ratio of
the daughter product molecular weight to the parent compound molecular weight.
Note: This is NOT the biomass yield.
TCE/PCE 0.795 DCA/TCA 0.742
DCE/TCE 0.737 CA/DCA 0.652
VC/DCE 0.645 ETHA/CA 0.465
ETH/VC 0.450
Values for the chlorinated ethenes and ethanes have been provided. The user only
needs to input yields if working with other substances that decay by sequential first
order decay.
Enter directly.
5. General Data
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Model Area Length and Width (L and W)
ft
Physical dimensions (in feet) of the rectangular area to be modeled. To determine
contaminant concentrations at a particular point along the centerline of the plume (a
common approach for most risk assessments), enter this distance in the "Modeled
Area Length" box and see the results by clicking on the "Run Centerline" button.
If one is interested in more accurate mass calculations, make sure most of the plume
is within the zone delineated by the Modeled Area Length and Width. Find the
mass flux results using the "Run Array" button.
500-3000 ft (length)
250- 1000 ft (width)
Values should be slightly larger than the final plume dimensions or should extend
to the downgradient point of concern (e.g., point of exposure). If only the
centerline output is used, the plume width parameter has no effect on the results.
Enter directly.
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Simulation Time (t)
years
Time (in years) for which concentrations are to be calculated. For steady-state
simulations, enter a large value (i.e., 1000 years would be sufficient for most sites).
1 to 1000 years
To match an existing plume, estimate the time between the original release and the
date the field data were collected. To predict the maximum extent of plume
migration, increase the simulation time until the plume no longer increases in
length.
Enter directly.
14
-------
5. General Data, cont.
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Zone 1 Length and Zone 2 Length
ft
Lengths of first and second biotransformation zones in feet. The zone 1 length is
the same as the model length if the user is modeling the plume as one zone.
Modeling a site using two zones allows the user to specify different first order
decay coefficients for each zone of the aquifer. One biotransformation zone is
appropriate for sites where the environmental conditions (D.O., ORP, hydrogen
concentrations etc.) do not change appreciably over the extent of the plume. For
sites where environmental conditions change significantly over the extent of the
plume, a 2-zone model may be more appropriate. For example, sites with high
levels of fermentable organics (high H ) near the source but not near the plume
front may be best modeled in two zones because the concentration of hydrogen
affects the the rate of reductive dechlorination. The hydrogen concentration, in
turn, affects the first order decay coefficient. Although BIOCHLOR is primarily
designed to model the anaerobic sequential decay of chlorinated solvents and no
degradation zones, aerobic zones can also be modeled by experienced users (see
Appendix A. 2 for instructions).
Note that two-zone biotransformation estimates should only be used when the
plumes in zone 1 are at steady-state (i.e., concentrations not changing with
time). Refer to Appendix A. 2 for a more detailed discussion.
500-3000 ft
If only one biotransformation zone is being modeled, then use the same value as the
model length.
If the plume will be modeled in two zones, delineate the two zones by looking at
field data (e.g., D.O. , fermentable carbon, hydrogen concentrations, etc.) and
determine an appropriate distance from the source.
Enter the value for zone 1 directly. The value for zone 2 will be automatically
calculated by deducting the zone 1 length from the model area length when the "C"
button is pressed. If only one biotransformation zone is being modeled, be sure that
the zone 1 length is the same as the model area length.
15
-------
6. Source Data
Parameter
Source Area Concentrations
Units
mg/L
Description
Aqueous phase concentration of chlorinated solvents in the source area.
The source term corresponds to a vertical source plane, normal to the direction of
ground-water flow, located at the downgradient limit of the area serving as the
principal source of solvent release to the ground water (e.g., affected unsaturated
zone soils, NAPL plume, land disposal unit, spill area etc.). In the absence of such
data, the source term should be located at the point of the maximum measured plume
concentration(s). One "rule of thumb" for inferring the location of DNAPL is to
look for aqueous phase concentrations in excess of 1% of solubility (Pankow and
Cherry, 1995; Cohen and Mercer, 1993). Distance to downgradient points of
exposure should then be measured from this location along the principal direction of
ground-water flow.
Ground-Water
Transport Area:
Lateral transport / attenuation of
constituents in ground-water system
For the single planar option, the maximum source area concentration should be
entered on the input page (or in the dialog box that transfers the data to the input
page). For the spatially-varying option, the user may enter three concentrations.
The maximum concentration in the source area can be used in area 1 and geometric
mean concentrations can be used in areas 2 and 3.
Using a single planar source yields accurate centerline concentration profiles, but
concentrations off the centerline will be overestimated. The use of a spatially
variable source will yield better off-centerline concentration estimates but requires
considerably more computation time. For centerline simulations, the single planar
option is recommended.
Typical Values
0.010 to 120 mg/L
Note: Source area dissolved solvent concentrations should not exceed the aqueous
solubility at a given temperature. The following are the aqueous phase solubilities at
20°C(Mackayefa/., 1993):
PCE 150 mg/L 1,1,1-TCA 4400 mg/L
TCE 1100 mg/L 1,1-DCA 5500 mg/L
cDCE 800 mg/L CA 5710 mg/L
VC 6800 mg/L
Source of Data
Source area monitoring well data
How to Enter Data
Enter directly on input page or press "Source Options" button and follow
instructions.
16
-------
6. Source Data, cont.
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Source Area Width
ft
The Domenico (1987) model assumes a vertical plane source of constant
concentration. The source width is the extent of the source area perpendicular to the
ground-water flow.
120-700 ft
To determine a
direction of grc
typically defin
concentrations c
source area cov(
point in the sour
Single Planar
For a single plar
Spatially- Varyiri
For a spatially
widths and con
diagram below.
source width across the site, draw a line perpendicular to the
und-water flow direction in the source area. The source area is
sd as being the area with contaminated soils having high
)f sorbed organics, free-phase NAPLs, or residual NAPLs. If the
srs a large area, it is best to choose the most downgradient or widest
ce area for determining the source width.
lar source, choose one width.
-+4
\
r^ ) aci
• — « — » — •
g
variable source, BIOCHLOR allows the user to enter up to three
Dentations to define the source area using isopleth data. See the
Y
yv
V.
Enter directly on input page or
instructions.
3
:>^) j n c2
^^ DC3
press "Source Options" button and follow
17
-------
6. Source Data, cont.
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Source Thickness In Saturated Zone (Z)
ft
Thickness of dissolved solvent in the source area
The Domenico (1987) model assumes a vertical plane source of constant
concentration. For many solvent spill sites the thickness of this source area
will be the saturated thickness of the aquifer. As these solvents sink to the
bottom of the aquifer, they leave residual DNAPL behind that act as a source
of ground-water contamination that extends vertically from the water table to
the bottom of the saturated zone.
SURFACE f'^S^^^^i^'T^
\ , '
TOP OF , _^-- — I . ,.-• '"". .M? -^ • —
WATER-BEARING «_ I-" '"" .-* *'"^i \ ^ ^
UNIT , Tfi.-^-C. •.••.• A N. \
1 1 \ ] \, ' f \
Source] 7 1- '-^s^-L-.^, Jl
Thickness t , •'• •' ^s^s;^ """**--«. ~^.
^X ^x
1 \j \. — — • \ —
j N ^--~-_ J
BOTTOM OF I J^fi£>.
WATER-BEARING 1 ^^jgSpppSfe^^
\
N
\
/
X~^
\
\
/
20-50 ft
This value is usually determined by evaluating ground-water data from wells
near the source area screened at different depths. If this type of information
is not available, then the depth of the aquifer can be used as a conservative
estimate.
Enter directly.
7. Field Data for Comparison
Parameter
Units
Description
Typical Values
Source of Data
How to Enter Data
Field Concentrations (and Distances from Source)
mg/L
These parameters are concentrations of dissolved organics in wells near the
centerline of the plume. These data are used to help calibrate the model and are
displayed with model results in the "Run Centerline" option.
0.001 to 50 mg/L
Monitoring wells located near the centerline of the plume.
Enter as many or as few of these points as needed. The data are used only to help
calibrate the model when comparing the results from the centerline option. Enter the
distance from the source that corresponds to the field concentration.
Warning: Do NOT cut and paste field data from one column to another. This can
cause spreadsheet errors. Copy data and then erase unwanted data.
18
-------
Analyzing BIOCHLOR Output
The output shows concentrations along the centerline (for two kinetic models at the same time) or as an array (one
kinetic model at a time). Note that all results are for the time entered in the "Simulation Time" box.
Centerline Output
Centerline output is displayed when the "Run Centerline" button is pressed on the input screen. The centerline output
screen shows the concentration at the top of the saturated zone (z=0) along the centerline of the plume (y=0). The first
screen shows the concentration profiles and field data for all the constituents on one plot as well as a no degradation
curve for the total chlorinated solvents. This information is plotted on a linear plot. The user may view the output on a
semi-log plot by pressing the "Log <—> Linear" button.
On the second output screen, the user can view the no degradation curves and the biotransformation curves for each
constituent one at a time by pressing the buttons to the right. The model predictions are also presented in tabular form
and may be printed out.
After a simulation has been run and the user has returned to the input page, the user may opt to use the "See Output"
button. This button allows the user to go directly to the output without running the model. If the "See Output" button is
pressed prior to running a simulation, output errors may result.
Array Output
The array output is displayed when the "Run Array" button is pressed on the Input screen. Choose the constituent that
you would like to view by selecting it in the upper right hand corner. Then select one of the two model types (No
Degradation or Biotransformation). A 3-D graphic presents the concentration profile on an 11 -point-long by 5-point-wide
grid. To alter the modeled area, adjust the Model Area Length and Width parameters on the input screen.
To see the plume array that exceeds a certain target level (such as an MCL or risk-based cleanup level), enter the target
level in the box and push "Plot Data > Target". Only sections of the plume exceeding the target level will be displayed.
To see all the data again, push "Plot All Data". Note that BIOCHLOR automatically resets this button to "Plot All Data"
when the "Run Array" button is pressed on the input screen. Approximate mass flux data are presented on the array
output screen.
Calculating the Mass Balance (Order-of-Magnitude Accuracy)
Plume Mass (kg)
BIOCHLOR calculates the mass of organics in the plume array for two models:
1) No Degradation and 2) Sequential First Order Decay (Biotransformation/Production)
The mass is calculated by assuming that each point represents a cell equal to the incremental width and length
(except for the first column which is assumed to be half as long as the other columns because the source is assumed
to be in the middle of the cell). The volume of the affected ground water in each cell is calculated by multiplying
the area of each cell by the source depth and by effective porosity (the mass balance calculation assumes 2-D
transport). The mass of organics in each cell is then determined by multiplying the volume of ground water by the
concentration and then by the retardation factor to account for sorbed constituents.
Mass Removed (kg), % Biotransformed, and % Change in Mass Flux
The mass removed is the difference between the mass of contaminant if no biotransformation occurs and the mass of
contaminant if biotransformation/productions occurs. For some daughter products, the mass removed may be
negative as more mass is created than would be present if no biotransformation occurred. The percent biodegraded
is the mass of solvent removed divided by the mass of solvent if no biotransformation occurs. The percent change in
mass flux is the difference in mass flux at the source compared to the mass flux at the boundary of the model area.
19
-------
Current Volume of Ground Water in Plume (ac-ft)
BIOCHLOR counts the number of cells in the 5x10 array with concentration values greater than 0, and multiplies
this by the volume of ground water in each cell (length * width * source thickness * effective porosity).
If the user wishes to estimate the volume of the plume above a certain target level, enter the target level in the
appropriate box and press the appropriate model (No Degradation or Biotransformation) to display the result.
Note that the model does not account for any effects of vertical dispersion.
If BIOCHLOR Says "Can't Calc." for Volume
If the contaminant concentration in the plume at the end of the model length is greater than 0.005 mg/L , then the
model concludes that the model area (see Input Screen, Section 5: General Data) is not sized to capture the entire
plume volume in the 5x10 array and writes "Can't Calc" in the box. The user is encouraged to adjust the modeled
length and width to capture the plume in the 5x10 array.
Flow Rate of Water Through Source Area (ac-ftlyr)
Using the Darcy velocity, the source thickness, and the source width, BIOCHLOR calculates the rate
that clean ground water moves through the source area where it will pick up dissolved solvents. Note
that the ground-water Darcy velocity is equal to the ground-water seepage velocity multiplied by
effective porosity.
20
-------
Quick Start
Minimum System Requirements
The BIOCHLOR model requires a computer system capable of running Microsoft® Excel 7.0 or'97 for Windows. If you
have Excel '97, you are advised to use the Excel '97 version of BIOCHLOR. Operation requires an IBM-compatible PC
equipped with a Pentium or later processor running at a minimum of 150 MHz. A minimum of 32 MB of system memory
(RAM) is strongly recommended.
Installation and Start-Up
The software is installed by copying the BIOCHLOR model file (BIOCHL7.xls or BIOCH97.xls) and the BIOCHLOR help
file (BIOCHLR.hlp) to the same folder on your computer hard drive. To use the software, start Excel and load the
BIOCHLOR model file from the File/Open menu. If you are using Excel '97, you may see a message box that asks you
whether you want to disable or enable the macros. For BIOCHLOR to operate effectively, you must enable the macros.
BIOCHLOR Troubleshooting Tips
Spreadsheet-Related Problems
The buttons won't work: BIOCHLOR is built in the Excel spreadsheet environment, and to enter data one must click
anywhere outside the cell where data was just entered. If you can see the numbers you just entered in the data entry part
of Excel above the spreadsheet, the data have not yet been entered. Click on another cell to enter the data.
#### is displayed in a number box: The cell format is not compatible with the value, (e.g., the number is too big to fit
into the window). Tofixthis, press the "Unprotect Sheet" button. Then, select the cell, pull down the format menu, select
"Cells" and click on the "Number" tab. Change the format of the cell until the value is visible. If the values still cannot be
read, select the format menu, select "Cells" and click on the "Font" tab. Reduce the font size until the value can be read.
#DIV/0! is displayed in a number box: The most common cause of this problem is that some input data are missing.
In some cases, entering a zero in a box will cause this problem. Double check to make certain that data required for your
run have been entered in all of the input cells. Note that for vertical dispersivity, BIOCHLOR will convert a "0" in the data
entry cell to a very low number to avoid #DIV/0! errors.
There once were formulas in some of the boxes on the input screen, but they were accidentally overwritten:
Press the closest "C" button or click on the "Restore Formulas" button on the bottom right-hand side of the input screen.
The graphs seem to move around and change size: This is a feature of Excel. When graph scales are altered to
accommodate different plotted data, the physical size of the graphs will change slightly, sometimes resulting in a graph
that spreads out over the fixed axis legends. You can manually resize the graph to make it look nice again by double-
clicking on the graph and resizing it (refer to the Excel User's Manual).
The source dialog boxes keep closing. If you press "Enter" when inputting data in a dialog box ("pop-up window")
then the dialog box will close. Do not press "Enter" and move to the next cell by using the mouse and clicking. If you do
press "Enter" by accident, simply select your source option again.
The scale on the 3-D graphic on the array page is not even. This is a feature of Excel. There is no way to create an
even scale when using unevenly spaced data in a 3-D graphic.
Common Error Messages
Unable to Load Help File: The most common error message encountered with BIOCHLOR is the message "Unable to
Open Help File" after clicking on a Help button. Depending on the version of Windows you are using, you may get an
Excel Dialog Box, a Windows Dialog Box, or you may see Windows Help load and display the error. This problem is
related to the ease with which the Windows Help Engine can find the data file, BIOCHLR.HLP. Here are some
suggestions (in decreasing order of preference) for helping WinHelp find it:
• If you are asked to find the requested file, do so. The file is called BIOCHLR.HLP, and it was installed in the same
directory/folder as the BIOCHLOR model file (BIOCHL7.xls or BIOCH97.xls).
• Use the File/Open menus from within Excel instead of double-clicking on the filename or Program Manager icon to
open the BIOCHLOR model file. This sets the "current directory" to the directory containing the Excel file you just
opened.
21
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Change the WinHelp call in the VB Module to "hard code" the directory information. That way, the file name and its
full path will be explicitly passed to WinHelp. If you have Excel 7.0, go to Tools and select Options. From Options,
select the View tab and check sheet tabs. You will then see the worksheet tabs. Select the Macro Module tab and
search for the text "Helpfile". Enter the new path. If you have Excel '97, go to the Tools menu and select Macro.
Enter "btnBasic Help_click" for the macro you are searching for. This will take you to all the help files. Enter the new
path.
As a last resort, you can add the BIOCHLOR directory to your path (located in yourAUTOEXEC.BAT file), and this
problem will be cured. You will have to reboot your machine, however, to make this work
22
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References
American Society for Testing and Materials (ASTM), 1995, Standard Guide for Risk-Based Corrective Action Applied at
Petroleum Release Sites, ASTM E-1739-95, Philadelphia, PA.
Al-Suwaiyan, M., 1996, Discussion of "Use of Weighted Least-Squares Method in Evaluation of the Relationship
Between Dispersivity and Field Scale," by M. Xu and Y. Eckstein, Ground Water, 34(4):578.
Bradley, P.M., and F.H. Chapelle, 1998, Effect of Contaminant Concentration on Aerobic Microbial Mineralization of DCE
and VC in Stream-Bed Sediments, Environ. Sci. Technol., 32(5): 553-557.
Carr, C.S. and J.B. Hughes, 1998, Enrichment of High-Rate PCE Dechlorination and Comparative Study of Lactate,
Methanol, and Hydrogen as Electron Donors to Sustain Activity, Environ. Sci. Technol., 32(12): 1817-1824.
Clement, T.P., 1997, RT3D- A Modular Computer Code for Simulating Reactive Multi-Species Transport in 3-
Dimensional Groundwater Aquifers, Battelle Pacific Northwest National Laboratory Research Report, PNNL-SA-
28967.
Cohen, R. M. and J.W. Mercer, 1993, DNAPL Site Evaluation, CRC Press, Boca Raton, FL.
Connor, J.A., C.J. Newell, J.P. Nevin, and H.S. Rifai, 1994, Guidelines for Use of Groundwater Spreadsheet Models in
Risk-Based Corrective Action Design, National Ground Water Association, Proceedings of the Petroleum Hydrocarbons
and Organic Chemicals in Ground Water Conference, Houston, TX, November 1994: 43-55.
Domenico, P.A.A 1987, An Analytical Model for Multidimensional Transport of a Decaying Contaminant Species, J.
Hydrol., 91: 49-58.
Domenico, P.A. and F. W. Schwartz, 1990, Physical and Chemical Hydrogeology, Wiley, New York, NY.
Gelhar, L.W., C. Welty, and K.R. Rehfeldt, 1992, A Critical Review of Data on Field-Scale Dispersion in Aquifers, Water
Resour. Res., 28(7):1955-1974.
Gossett, J.M. and S.H. Zinder, 1996, Microbiological Aspects Relevant To Natural Attenuation of Chlorinated Solvents,
Proceedings of the Symposium on Natural Attenuation of Chlorinated Organics in Ground Water. September 11-13,
1996, Dallas, TX. EPA/540/R-96/509.
Hartmans, S., J.A.M. de Bont, J. Tamper, and K.Ch.A.M Luyben, 1985, Bacterial Degradation of Vinyl Chloride,
Biotechnol. Lett., 7(6):383:388.
Hartmans, S., and J.A.M. de Bont, 1992, Aerobic Vinyl Chloride Metabolism in Mycobacterium aurum Li, Appl. Environ.
Microbiol., 58(4): 1220-1226.
Holliger, C., G. Schraa, A.J. M. Stams, and A.J.B. Zehnder, 1993, A Highly Purified Enrichment Culture Couples the
Reductive Dechlorination of Tetrachloroethene to Growth, Appl. and Environ. Microbiol., 59: 2991-2997.
Howard, P. H., R. S. Boethling, W. F. Jarvis, W. M. Meylan, and E. M. Michalenko, 1991, Handbook of Environmental
Degradation Rates, Lewis Publishers, Inc., Chelsea, Ml.
Hughes, J.B., C. J. Newell, and R. T. Fisher, 1997A Process for In-Situ Biodegradation of Chlorinated Aliphatic
Hydrocarbons by Subsurface Hydrogen Injection. U.S. Patent No. 5,602,296, Issued March 11, 1997.
Jeffers, P.M., L.M. Ward, L.M. Woytowitch, and N.L. Wolfe, 1989, Homogeneous Hydrolysis Rate Constants for Selected
Chlorinated Methanes, Ethanes, Ethenes, and Propanes, Environ. Sci. Technol., 23: 965-969.
LaGrega, M.D., P.L. Buckingham, J.C. Evans, 1994, Hazardous Waste Management, McGraw Hill, New York.
Mackay, D., W.Y. Shiu, and K.C. Ma, 1993, Illustrated Handbook of Physical-Chemical Properties and Environmental
Fate for Organic Chemicals. Vol. III. Volatile Organic Chemicals. Lewis Publishers, Boca Raton, FL.
Martin-Hayden, J. M. and G. A. Robbins, 1997, Plume Distortion and Apparent Attenuation Due to Concentration
Averaging in Monitoring Wells, Ground Water, 35(2): 339-346.
Maymo-Gatell, X., Y. Chien, Y., J. M. Gossett, and S.H. Zinder, 1997, Isolation of a Bacterium That Reductively
Dechlorinates Tetrachloroethene to Ethene, Science, 276:1568-1571.
McCarty, P.L., 1996, Biotic and Abiotic Transformations of Chlorinated Solvents in Groundwater, in Symposium on
Natural Attenuation of Chlorinated Organics in Ground Water, Dallas, TX, Sept. 11-13, 1996.
McCarty, P.L., and L. Semprini, 1994, Groundwater Treatment for Chlorinated Solvents, In: Handbook of Bioremediation,
Lewis Publishers, Boca Raton, FL.
National Research Council, 1994, Alternatives for Ground Water Cleanup, National Academy Press, Washington, D.C.
Newell, C. J., J. Gonzales, and R. K. McLeod, 1996, BIOSCREEN Natural Attenuation Decision Support System., U. S.
Environmental Protection Agency, Center for Subsurface Modeling Support, Ada, OK. EPA/600/R-96/087.
23
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Newell, C. J., R. L. Bowers, and H. S. Rifai, 1994, Impact of Non-Aqueous Phase Liquids (NAPLs) on Groundwater
Remediation, Proceedings of American Chemical Society Symposium on Multimedia Pollutant Transport Models,
Denver, CO, August 1994.
Pankow, J.F. and J.A. Cherry (Eds.), 1996, Dense Chlorinated Solvents and Other DNAPLs in Groundwater, Waterloo
Press, Portland, OR.
Pickens, J.F., and G.E. Grisak, 1981, Scale-Dependent Dispersion in a Stratified Granular Aquifer, Water Resour. Res.,
Powers, S.E, L.M. Abriola and W. J. Weber Jr., 1994, An Experimental Investigation of Nonaqueous Phase Liquid
Dissolution in Saturated Subsurface Systems: Transient Mass Transfer Rates, Water Resour. Res., 30(2): 321-332.
Smith, L. and S.W. Wheatcraft, 1993, "Groundwater Flow" in Handbook of Hydrology, David Maidment, Editor, McGraw-
Hill, New York.
Sun, Y. and T.P. Clement, 1999, A Decomposition Method for Solving Coupled Multi-species Reactive Transport
Problems, Transp. in Porous Media, 37:327-346.
Sun, Y. , J.N. Petersen, and T.P. Clement, 1999a, A New Analytical Solution for Multiple Species Reactive Transport in
Multiple Dimensions, J. Contam. Hydro!., 35(4): 429-440.
Sun Y., J.N. Petersen, T.P. Clement, and R.S. Skeen, 1999b, Development of Analytical Solutions for Multi-Species
Transport Equations with Serial and Parallel Reactions, Water Resour. Res., 35(1): 185-190
U.S. Environmental Protection Agency, 1986, Background Document for the Ground-Water Screening Procedure to
Support 40 CFR Part 269 — Land Disposal. EPA/530-SW-86-047, January 1 986.
U.S. Environmental Protection Agency, 1988, Guidance on Remedial Actions for Contaminated Ground Water at
Superfund Sites, EPA/540/G-88/003, Directive 9283.1-2, Washington, D.C., EPA, Office of Solid Waste and
Emergency Response.
U.S. Environmental Protection Agency, 1998, Technical Protocol for Evaluating Natural Attenuation of Chlorinated
Solvents in Ground Water. EPA/600/R-98/128, September, 1998.
Vogel, T. M. and P.L. McCarty, 1985, Biotransformation of Tetrachloroethylene to Trichloroethylene, Dichloroethylene,
Vinyl Chloride, and Carbon Dioxide under Methanogenic Conditions, Appl. Environ. Microbiol., 49(5): 1080-1083.
Vogel, T.M. and P.L. McCarty, 1987, Abiotic and Biotic Transformations of 1,1,1-Trichloroethane under Methanogenic
Conditions, Environ. Sci. Technol., 21(12): 1208-1213.
Walton, W.C., 1988, Practical Aspects of Groundwater Modeling, National Water Well Assoc., Worthington, Ohio.
Wiedemeier, T. H., Wilson, J. T., Kampbell, D. H, Miller, R. N., and Hansen, J.E., 1995, Technical Protocol for
Implementing Intrinsic Remediation With Long-Term Monitoring for Natural Attenuation of Fuel Contamination
Dissolved in Groundwater (Revision 0), Air Force Center for Environmental Excellence, April, 1 995.
Wiedemeier, T.H., H.S. Rifai, C.J. Newell, and J.W. Wilson, 1 999, Natural Attenuation of Fuels and Chlorinated Solvents,
John Wiley & Sons, New York.
Xu, M. and Y. Eckstein, 1995, Use of Weighted Least-Squares Method in Evaluation of the Relationship Between
Dispersivity and Scale, J. Ground Water, 33(6): 905-908.
24
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Appendix A.1
Domenico Single Species Analytical Model
Domenico (1987) developed a semi-analytical solution for reactive transport with first order decay and a two-dimensional
(i.e., planar) source geometry. BIOCHLOR uses the Domenico solution with Martin-Hayden and Robbins (1997)
improvements and assumes that degradation reactions occur only in the aqueous phase. BIOCHLOR evaluates
centerline concentrations at y=0, z=0 and the 2-D array at z=0. The model equation, boundary conditions, assumptions,
and limitations are discussed below.
Domenico Model with First Order Decay
fx = exp
*er/c
2(axvt
exp
x[l + (l + 4?ia>;/\
2av
*erfc\
(l + 4?iax/vs
-erf
'(y-Y/2)'
/ \0.5
2(ayx)
Definitions
C(x)-
C(x, y, z, t) Concentration at distance x downstream of source and distance y
off centerline of plume at time t (mg/L)
Co Concentration in Source Area at t=0 (mg/L)
x Distance downgradient of source (ft)
y Distance from plume centerline of source (ft)
z Distance from top of saturated zone to measurement point
(assumed to be 0; concentration is always given at top of
saturated zone).
0^ Longitudinal ground-water dispersiviry (ft)
OCy Transverse ground-water dispersivity (ft)
az Vertical ground-water dispersivity (ft)
9e Effective Soil Porosity
'k First-Order Degradation Rate Coefficient(day'l)
vs Seepage Velocity (ft/yr)=Ki/(ee-,
v Chemical Velocity (ft/yr)=v/R
K Hydraulic Conductivity (ft/yr)
R Constituent retardation factor
i Hydraulic Gradient (cm/cm)
Y Source Width (ft)
Z Source Depth (ft)
25
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Note that because biotransformation is assumed to occur only in the aqueous phase, the first order rate constant, X, has
been divided by R. However, R can be canceled out by replacing v (the compound velocity (i.e., vs/R)) in the original
Domenico solution with vs (the seepage velocity).
The Domenico solution was modified for chloroethane (CA) reactive transport to take into consideration both biotic and
abiotic reactions. The first order rate constant for abiotic decay, A,A, is added to the biological rate constant for reductive
dechlorination, X, as shown below. All other terms in the Domenico equation remain the same.
fx = exp
2ax
exp
*erfc
X-A
2(axvt)
0.5
2ax
*erfc
(l + 4(?i + ?iA)ax/vs)'
.0.5
2(axvt)0'5
The initial conditions of the Domenico model are:
1. c(x, y, z, 0) = 0 (Initial concentration = 0 for x, y, z, > 0)
2. c(0, Y, Z, 0) = C0 (Source concentration for each vertical plane source = C0 at time 0)
The key assumptions in the model are:
1. The aquifer and flow field are homogenenous and isotropic.
2. The ground-water velocity is fast enough that molecular diffusion in the dispersion terms can be ignored
(may not be appropriate for simulation of transport through clays).
3. Adsorption is a reversible process represented by a linear isotherm.
The key limitations to the model are:
1. The model should not be applied where pumping systems create a complicated flow field.
2. The model should not be applied where vertical flow gradients affect contaminant transport.
3. The model should not be applied where hydrogeologic conditions change dramatically over the simulation
domain.
The most important modifications to the original Domenico model are:
1. Biotransformation is assumed to occur only in the aqueous phase. The original Domenico model was
derived assuming that biotransformation occurred equally rapidly in the soil and aqueous phases. To make
this adjustment, the rate constants were divided by the retardation factor.
2. To simulate a spatially-varying source, BIOCHLOR superimposes three Domenico models, each with a
different concentration and source width (Connor et al., 1994). The original Domenico model was derived for
a single planar source of constant concentration.
26
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Appendix A.2
Kinetics of Sequential First Order Decay
BIOCHLOR primarily models reductive dechlorination, which is assumed to follow sequential first order kinetics. The
user may model the sequential decay of chlorinated ethenes, such as PCE and TCE, or the decay of chlorinated
ethanes, such as 1,1,1-TCA, as shown below (Vogel and McCarty, 1987):
PPF
*vi
TPF
'VZ
i •*
•V4
Ethene
CH2CH2
Major biotic pathway
Abiotic pathway
PCE = Perchloroethene
TCE = Trichloroethene
DCE = Dichloroethene
TCA = Trichloroethane
DCA = Dichlorethane
Although the chlorinated ethenes primarily degrade biologically, chlorinated ethanes can degrade both biologically and
abiotically. BIOCHLOR allows the user to input both biological and abiotic rate constants for chloroethane. For
chloroethane (CA), abiotic decay to ethanol occurs much more rapidly than biotransformation to ethane. The abiotic
decay of 1,1-DCA is slow relative to biotransformation so its abiotic degradation is ignored in BIOCHLOR. 1,1,1-TCA can
degrade abiotically to both acetic acid (by hydrolysis) and to 1,1-DCE (by elimination)(Vogel and McCarty, 1987).
Abiotic decay of 1,1,1-TCA cannot be modeled using BIOCHLOR if accurate chlorinated ethane daughter product
predictions are required. However, if only TCA predictions are needed, a lumped rate coefficient (sum of abiotic and
biotic first order rate coefficients) can be input to model the degradation of TCA alone.
Chlorinated Ethenes
The reaction rate equations describing the sequential first order decay of the chlorinated ethenes are shown below :
r =-?iC
PCE 1 PCE
r = y ^ C - ^ C
TCE 11 PCE 2 TCE
r =y^C -^C
DCE 2 2 TCE 3 DCE
r = y ^ C - ^ C
VC 33 DCE 4 VC
r = y "k C -"k C
ETH 4 4 VC 5 ETH
where ^,^2, X3, X4, and ^5are the first order biotransformation rate coefficients, yr y2, y3, y4 are the daughtenparent
compound molecular weight ratios, and CPOE, CTOE, CDOE, Cvo and CETH are the aqueous concentration of PCE, TCE,
DCE, vinyl chloride, and ethene, respectively. (Note: BIOCHLOR assumes no degradation of ethene (^5=0) in zone 1.)
From these expressions, it is clear that TCE, DCE, and VC are simultaneously being produced and degraded, which
27
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often results in net accumulation before observed degradation. Furthermore, these reaction expressions cause the
reactive transport equations to be coupled to each other as discussed in more detail in Appendix A. 3.
Chlorinated Ethanes
The following are the rate expressions for the degradation of the chlorinated ethanes.
r =-
TCA
C
5 TCA
-XC
TCA 6 DCA
r = y X C -(X+X)C
CA 66 DCA 7 A CA
where ^5,^6and X7 are the biotransformation rate coefficients, ^Ais the abiotic rate coefficients forchloroethane, y5and y6
CDOA and
COA are the concentration of 1,1,1-
are the daughtenparent compound molecular weight ratios and CTOA,
trichloroethane, 1,1-dichloroethane and chloroethane, respectively.
Because BIOCHLOR is programed in mass units, yield constants (i.e., yr y2,...y6) to account for molecular weight
differences between parent and daughter compounds were incorporated. The constants are necessary because kinetic
expressions are valid on a molar basis only.
Other Chlorinated Compounds
Although BIOCHLOR is programmed to model the reductive dechlorination of chlorinated ethenes and ethanes primarily,
it can also be used to model any chlorinated compound that degrades via sequential first order decay kinetics. To use
BIOCHLOR for compounds other than chlorinated ethenes and ethanes, the user must input the yield constants (the ratio
of daughter product to parent compound molecular weights on the input page). Be aware that output graphs will still
show the chlorinated ethene or ethane labels.
1-Zone vs. 2 -Zone Biotransformation
If the contaminant plumes are at steady-state, BIOCHLOR can be used to model the plume in two zones with a different
set of biotransformation rate coefficients in each zone. BIOCHLOR is primarily designed to handle zones with anaerobic
degradation and no degradation, but it can be manipulated by experienced users to accommodate an aerobic zone in
zone 2 in some cases. BIOCHLOR cannot model aerobic conditions in zone 1 . Table A.1 presents the scenarios that
BIOCHLOR can execute. A "Type I" environment occurs when the primary substrate is anthropogenic carbon (e.g.,
BTEX or landfill leachate) and microbial fermentation of this anthropogenic carbon produces dissolved hydrogen that
drives reductive dechlorination. A "Type II" environment occurs in areas with high concentrations of biologically available
native organic carbon. The microbial utilization of the native organic carbon produces dissolved hydrogen which drives
reductive dechlorination. A Type III environment occurs in areas characterized by low concentrations of both
anthropogenic and natural organic carbon and an oxygen concentration greater than 1 .0 mg/L (USEPA, 1 998). For all
two-zone simulations, a single (fixed) longitudinal dispersivity value must be used for both zones.
Table A.1. 2-Zone Biotransformation Scenarios
Scenario
1
2
3
Zone 1
Type I or II (anaerobic, high
rates)
No Degradation
Type I or II
Zone 2
Type I or II (anaerobic, lower rates or no
degradation)
Type I or II
Type III
Scenario 3 is illustrated in Figure A.1. Here, all the solvents degrade anaerobically in zone 1 but only VC , c-DCE, and
ETH degrade to carbon dioxide under aerobic conditions in zone 2.
In modeling scenario 3 for the chlorinated ethenes, it may be necessary to carry out three separate simulations to
generate concentration profiles for all of the chlorinated solvents and ethene. Multiple simulations are necessary
because the equations programmed in BIOCHLOR incorporate sequential first order kinetics expressions and therefore
link dissolved solvent degradation with daughter product generation. Under aerobic conditions, the solvent is assumed
to degrade directly to carbon dioxide via first order kinetics, and degradation is not linked to daughter product generation.
Input parameters can be manipulated to avoid accounting for daughter product generation. The user should be aware
that BIOCHLOR is primarily designed to display the original anaerobic pathways. The input/output will not
28
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Anaerobic,
High Carbon
Aerobic,
Low Carbon
PCE
TCE
c-DCE
VC
ETH
TCE
c-DCE
VC
ETH
ETH
PCE
TCE
c-DCE
VC
ETH
PCE
TCE
CO2
CO2
CO2
Figure A.1. Mixed type I/Type III plume conditions.
indicate that an aerobic path was used or what the degradation products are. The user should extract only the
pertinent output information using the guidance below.
Table A.2 outlines how to input rate constants for both zones (anaerobic zone 1 and aerobic zone 2) for each simulation.
Rate constants denoted as A,' indicate a rate constant for an aerobic process. Note that the rate of ethene degradation
under anaerobic conditions in zone 1 is assumed to be zero. If only c-DCE degrades under aerobic conditions, then
scenario 3 can be completed in one run. If c-DCE, VC and ETH degrade aerobically, three runs will be required. Run
1 will yield the concentration profiles for PCE, TCE, and c-DCE. (Concentration profiles for VC and ETH must be
ignored). Run 2 will yield the concentration profiles- forVC. (Concentration profiles for all other compounds must be
ignored.) Run 3 will_yield the concentration profile for ethene (again, concentration profiles for all other compounds must
be ignored). The clearest way to present this data is to transfer data from each run to a new Excel spreadsheet and re-
plot.
Table A.2. Modeling scenario 3 for chlorinated ethenes.
Compound Run 1 Run 2 Run 3
PCE
TCE
DCE
VC
ETH
Zone 1
X,
X,
V
A,,
0
Zone 2
0
0
V
0
0
Zone 1
A,,
^
A,
X,
0
Zone 2
0
0
0
X,'
0
Zone 1
A-,
A,
A,
A,,
0
Zone 2
0
0
0
0
X,'
Shaded boxes indicate compounds whose output data should be recorded during each run.
For the chlorinated ethanes, chloroethane is the only solvent that is degraded aerobically so that scenario 3 can be
accomplished with one run as outlined in Table A.3.
Table A.3: Modeling Scenario 3 for Chlorinated Ethanes
Compound
TCA
1,1 -DCA
CA
Run 1
Zone 1
A,
A,,
A,
Zone 2
0
0
A,'
29
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How BIOCHLOR Models 2-Zone Biotransformation
The Domenico solution was developed assuming a constant source concentration and a constant biotransformation rate
coefficient. Simply changing the value of the rate constant at the boundary between zones 1 and 2 yields a large
discontinuity in the concentration profile. Therefore, a new "source" area was defined at the boundary of zones 1 and 2.
The new source was defined using the concentrations in the last cells of the zone 1 array and modeled as a spatially-
variable source. To test the validity of this approach, two simulations were carried out. In the first, the model length was
modeled as one zone of 1200 ft. In the second simulation, the model length was divided into two zones (200 ft for zone
1 and 1000 ft for zone 2) and the biological rate constants that were used in the 1 -zone simulation were used in each
zone of the 2-zone simulation. These simulations were carried out at steady state. These simulations show that this
solution technique yields good concentration estimates when the plume is at steady state (Figure A.2). The steady-state
condition is required to ensure that the concentrations are constant at the boundary between the two zones. The use of
the 2-zone biotransformation model should NOT be used when the plume is not at steady-state throughout
zone 1.
^
Concentration (mg/
100 -
r
0.1 -
0.01 -
0.001 -
0.0001 -
n nnnni -
(
[
! • m O 1-Zone 1
""•ft •2-Zone|
o.
o
*
:
0.
o
1 1 1 1 1 T
) 200 400 600 800 1000 1200
Distance from Source (ft)
Figure A.2. Comparison of solution techniques for BIOCHLOR 1-zone and 2-zone biotransformation models.
30
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Appendix A.3
BIOCHLOR Solution
By T. Prabhakar Clement and Yunwei Sun, Battelle Pacific Northwest National Laboratory, Richland, WA 99345.
Governing Equations
The BIOCHLOR software solves a set of coupled partial differential equations to describe the reactive transport of
chlorinated solvent species, such as PCE, TCE, DCE, VC and ETH, in saturated ground-water systems. The equations
describe one-dimensional advection, three-dimensional dispersion, linear sorption, and sequential, first-order biotrans-
formation. All equations, except the first, are coupled to a parent species equation through the reaction term as shown
below:
*, d2^ d2^ d2^ A,
1 dt x dx2 y dy2 z dz2 s & l l (1)
cb2 d2c2 d2c2 d2c2 dc2
* > *
dc, d2c
dc
where cr c2, c3, c4, and c5are concentrations of PCE, TCE, DCE, VC, and ETH, respectively [mg/L]; Dx, DY, and Dzare
the hydrodynamic dispersion coefficients [ft2/yr]; vs is the seepage velocity [ft/yr]; k is the first-order degradation
coefficient [1/yr]; y is the yield coefficient [a dimensionless value; for example, y1 would represent the mg of TCE
produced per unit mg of PCE destroyed]; and Rr R2, R3, R4, and R5 are respective retardation factors. In BIOCHLOR,
the retardation factor values of different species are averaged to compute an "effective retardation factor, /?', which is in
turn used to compute the effective transport velocity and dispersion coefficients. Also, biotransformation is assumed to
occur only in the aqueous phase (which is a conservative assumption) and hence R is used to divide all the degradation
reaction terms.
Analytical Solution Strategy
The Domenico (1987) solution with some minor improvements suggested by Martin-Hayden and Robbins (1997) was
used as the base solution to solve the three dimensional problem. The solution was directly used to solve the
independent equation 1. However, since equations 2 to 5 are coupled equations, the Domenico solution cannot be used
to solve them. Therefore, in BIOCHLOR a new transformation procedure is used to first uncouple equations 2 to 5 and
recast them in the form of equation 1 (Sun and Clement, 1999; Sun et al. 1999a, Sun et al. 1999b). The transformation
equations used are:
a=c+-C (6)
=c3 + c2
2 - - ,
k2 -k3 (kj-k3)(k2-k3)
31
-------
a, =CA +
v, k,
y2y3k2 k3
y1y2y3k1k2 k3
k3-k4 3 (k2-k4)(k3-k4) 2 (k.-kjik.-kjik.-k,) 1
(8)
=c
|
y3y4k3 k4
~k5 4 (k3-k5)(k4-k5f3 ' (k2-k5)(k3-k5)(k4-k5)'
kkk k
(9)
It can be shown that using transformation equations 6 to 10, the reactive transport equations 2 to 5 can be written in a
transformed "a" domain where the coupled transport equations reduce to a form similar to equation 1. For illustration
purposes, the steps involved in proving the strategy for a one-dimensional, 2-species transport problem are given below.
Consider the following set of one-dimensional fate and transport equations that describe two reacting species that are
coupled by first-order decay reactions:
" d r d r
-*1 T~\ ^ ^ I tVOr 7
-A^T-V^-^C,
dt x dx2 dx
(10)
(11)
t
Since equation 10 is already in the standard form, it can be solved using a standard analytical solution. Based on Sun
et al. (1999a) work, a transformation for the second equation can be written as:
''±^-c.,- 02)
a2=c2
Differentiating equation 12 partially with respect to time we get,
dt dt k}-k2 dt
Substituting (10) and (11) into (13) we get,
(13)
*2 —
dt x dx2
Equation 14 can be rearranged as,
7 7
j - k2
d2c} dc}
D, —T- - v —- - ,
* ^2
3;c2 3*
(14)
dt x dx2
Using (12), equation 15 can be written as:
-v
y1k1 GJ - k2c2
(15)
*2 _
dt x dx2 dx
Combining the last three terms, equation 16 can be simplified to:
(16)
dt
2a, da2
•-v—--k,a
dx2 dx
22
(17)
32
-------
To solve (11), first a standard, one-dimensional solution should be used to solve (17) for computing a2 values and to
solve (10) for computing c1 values (note that c1 is always same as a.,). Then, c2 values can be computed using equation
12 in an inverse mode. This procedure can be repeated for solving any number of coupled reactive species. A more
general analysis of this solution strategy, and a detailed comparison of the analytical results against the numerical results
of the RT3D code are discussed in Sun and Clement (19998).
If retarding species are assumed then an effective retardation factor is used to divide the transport velocity, dispersion
coefficients and degradation rates (since degradation is assumed to occur only in the aqueous phase). It should be
noted that the proposed analytical solution strategy would work only when the constant effective retardation factor is
used to represent the retardation characteristics of all the transported species.
Computational Procedure
In BIOCHLOR, the initial concentration of all the species is assumed to be zero. The boundary conditions at the source
location can be non zero for one or more of the species. The first step involved in applying the solution strategy is to
convert all initial and boundary conditions of all daughter species into the transformed ("a") domain using the
transformation equations 6 to 9. After transforming all initial and boundary conditions, the Domenico solution is used five
times to prepare the solution array "a" (ai values at all nodes for all five species), in the transformed domain. Finally, the
solution arrays are transformed back into the concentration domain ("c" domain) using an inverse form transformation
equations 6 to 9. The FORTRAN code given below shows the implementation procedure:
C Modeling Coupled PCE,TCE,DCE,VC and ETH Transport and Degradation in
C 3-Dimensional Ground-water Aquifers
C This Fortran code was developed by: T.P. Clement & Y. Sun
C Battelle Pacific Northwest National Laboratory.
PARAMETER(nx=60, ny=31, nc=5)
c ny should always be an odd number
REALM k
DIMENSION c(nx,ny,nc),a(nx,ny,nc),k(nc),y(nc),cO(nc),aO(nc)
c Input data for Martin-Hayden and Robbins test problem
c Reference: Vol 35(2), p.339, Groundwater,1997.
dx = 20.0 Idelta x
dy = 20.0 Idelta y
t = 33.0 Itotal simulation time (years)
reta = 5.3 leffective retardation factor
v = 111.7/reta Ivelocity (ft/yr)
ax = 16.4 lalpha x (ft)
ay = 1.64 lalpha y (ft)
az = 0.0 lalpha z
xsdim = 0.0 Isource dimensions
ysdim = 100.0
zsdim = 10.0
c Automatically set source locations
xsloc = 0.0 Isource x location is fixed at the left boundary
ysloc = (((ny-1)/2)+1)*dy !fix source y location at the grid center
c Input reaction parameters
k(1) = 2.0/reta leffective pee decay rate (1/yr)
k(2) = 1.5/reta ! tee decay rate
k(3) = 0.8/reta ! dee decay rate
k(4) = 0.65/reta ! vc decay rate
k(5) = 0.000000001 lethene decay rate
y(1) = 0.79492 ! ytce/pce
y(2) = 0.73744 ! ydce/tce
y(3) = 0.64499 ! yvc/dce
y(4) = 0.4496 !yeth/vc
c Input source concentrations
cO(1) = 0.1 !mg/l source concentration for pee
cO(2) = 15.8 Ifortce
cO(3) = 98.5 Ifor dee
cO(4) = 3.1 Iforvc
cO(5) = 0.03 Ifor eth
c Computing transformation coefficients
p21 =y(irk(1)/(k(1)-k(2))
p32 = y(2)*k(2)/(k(2)-k(3))
33
-------
p31 = y(1)*y(2)*k(1)*k(2)/((k(1)-k(3))*(k(2)-k(3)))
p43 = y(3)*k(3)/(k(3)-k(4))
p42 = y(2)*y(3)*k(2)*k(3)/((k(2)-k(4))*(k(3)-k(4)))
p41 = y(1)*y(2)*y(3)*k(1)*k(2)*k(3)/
$ ((k(1 )-k(4))*(k(2)-k(4))*(k(3)-k(4)))
p54 = y(4)*k(4)/(k(4)-k(5))
p53 = y(3)*y(4)*k(3)*k(4)/((k(3)-k(5))*(k(4)-k(5)))
p52 = y(2)*y(3)*y(4)*k(2)*k(3)*k(4)/
$ ((k(2)-k(5))*(k(3)-k(5))*(k(4)-k(5)))
p51 = y(1)*y(2)*y(3)*y(4)*k(1)*k(2)*k(3)*k(4)/
$ ((k(i )-k(5))*(k(2)-k(5))*(k(3)-k(5))*(k(4)-k(5)))
c Initial concentration is assumed to be zero for all species
c Transform all boundary conditions into "a" domain
aO(1) = cO(1)
aO(2) = cO(2) + p21*cO(1)
aO(3) = cO(3) + p32*cO(2) + p31*cO(1)
aO(4) = cO(4) + p43*cO(3) + p42*cO(2) + p41*cO(1)
aO(5) = cO(5) + p54*cO(4) + p53*cO(3) + p52*cO(2) +p51*cO(1)
c Solve the problem using Domenico solution in the "a" domain
DO ic = 1, nc
CALL Domenico(nx,ny,dx,dy,t,xloc,ysloc,xsdim,ysdim,zsdim,v,
$ ax,ay,az,aO(ic),k(ic),a(1,1,ic))
END DO
c Transforming back into the "c" domain
c Transform Species #1
DO iy=1,ny
DO ix=1,nx
c(ix,iy,1) = a(ix,iy,1)
END DO
END DO
C Transform Species #2
DO iy=1,ny
DO ix=1,nx
c(ix,iy,2) = a(ix,iy,2) - p21*c(ix,iy,1)
END DO
END DO
c Transform Species #3
DO iy=1,ny
DO ix=1,nx
c(ix,iy,3) = a(ix,iy,3) - p32*c(ix,iy,2) - p31*c(ix,iy,1)
END DO
END DO
c Transform Species #4
DO iy=1,ny
DO ix=1,nx
c(ix,iy,4) = a(ix,iy,4) - p43*c(ix,iy,3)
$ - p42*c(ix,iy,2) - p41*c(ix,iy,1)
END DO
END DO
c Transform Species #5
DO iy=1,ny
DO ix=1,nx
c(ix,iy,5) = a(ix,iy,5) - p54*c(ix,iy,4)
$ - p53*c(ix,iy,3) - p52*c(ix,iy,2) - p51*c(ix,iy,1)
END DO
END DO
c Output concentration array
OPEN(10,FILE="conc.out",FORM='FORMATTED',STATUS='UNKNOWN'
DO ic = 1, nc
Write (10,*) "Species* =",ic
DO i = 1, ny
WRITE(10,12)(cG,i,ic),j=1,nx)
ENDDO
ENDDO
12 FORMAT (10e15.6)
34
-------
c Ouput centerline concentrations
OPEN(12,FILE="center.out",FORM='FORMATTED',STATUS='UNKNOWNr
i = (((ny-1)/2)+1) Icenter line location
DOj = 1, nx
WRITE(12,14)j*dx, (c(j,i,ic),ic=1,5)
END DO
14 FORMAT(F10.2,5e15.5)
STOP
END
SUBROUTINE Domenico(nx,ny,dx,dy,t,xsloc,ysloc,xsdim,ysdim,
$ zsdim,v,ax,ay,az,cO,k,c)
USE MSIMSL lusing IMSL subroutine
REALM k
DIMENSION c(nx,ny)
DO j=1,ny
DO i=1,nx
c(i,j)=0.0
ENDDO
ENDDO
c Domenico Anlytical Solution is used as in Martin-Hayden and Robbins paper
c See equations 5 & 1 in GW vol.35(2), 1997, pages p.345 and 340.
cc = SQRT(1.+(4.*k*ax/v))
DO j=1,ny
DO i=1,nx
x=i*dx-xsloc
y=j*dy-ysloc
z= 0.0 !at the water table
hx2=ERFC((x - v*t*cc)/(2*SQRT(ax*v*t)))
IF(hx2.LE. 1.0e-30)THEN
hi =0.0
ELSE
hx1=EXP((x*(1.-cc))/(2.*ax))
M=hx1*hx2
END IF
hx4=ERFC((x + v*t*cc)/(2*SQRT(ax*v*t)))
IF(hx4.LE. 1.0e-30)THEN
h2 = 0.0
ELSE
hx3=EXP((x*(1 .+cc))/(2.*ax))
h2=hx3*hx4
END IF
hx= h1+h2
fy=ERF((y+ysdim/2.0)/(2.0*SQRT(ay*x)))
$ -ERF((y-ysdim/2.0)/(2.0*SQRT(ay*x)))
IF(az. LE .1.0e-30)THEN
fz=2.0
ELSE
fz= ERF((z+zsdim)/(2.0*SQRT(az*x)))
$ -ERF((z-zsdim)/(2.0*SQRT(az*x)))
ENDIF
c(i,j)=(cO/8.0)*hx*fy*fz
ENDDO
ENDDO
RETURN
END
35
-------
Appendix A.4
Dispersivity Estimates
Dispersion refers to the process whereby a dissolved solvent will be spatially distributed longitudinally (along the
direction of ground-water flow), transversely (perpendicular to ground-water flow), and vertically (downward) because of
mechanical mixing and chemical diffusion in the aquifer. These processes develop the "plume" shape that is the spatial
distribution of the dissolved solvent mass in the aquifer.
Selection of dispersivity values is a difficult process, given the impracticability of measuring dispersion in the field.
However, dispersivity data from over 50 sites has been compiled by Gelhar et al. (1992) (see Figures A.3 and A.4). The
empirical data indicates that longitudinal dispersivity, in units of length, is related to scale (distance between source and
measurement point). Gelhar et al. (1992) indicate 1) there is a considerable range of dispersivity values at any given
scale (on the order of 2 - 3 orders of magnitude), 2) suggest using values at the low end of the range of possible
dispersivity values, and 3) caution against using a single relation between scale and dispersivity to estimate dispersivity.
However, most modeling studies do start with such simple relations, and BIOCHLOR is programmed with some
commonly used relations representative of typical and low-end dispersivities.
Note: Based on Gelhar'swork, use of variable dispersivity values should yield a better estimate of concentration at each
distance downgradient of the source. However, when using field data to calibrate the model and estimate rate
coefficients, be aware that the Domenico model assumes constant dispersivity values. The user must choose between
using a variable dispersivity that is likely to be more physically accurate at each point or a fixed dispersivity value that
makes each point mathematically consistent with each other. In general, if the user would like the best estimate of
concentration at each point in a BIOCHLOR simulation, use a variable dispersivity. If the user would like accurate mass
balances between each point, use a fixed dispersivity. Fixed dispersivity values should be used for two-zone
simulations.
BIOCHLOR is programmed with some commonly used relations based on x (distance from the source in ft) that are
representative of typical and low-end dispersivities. The user also has the option to enter fixed diffusivity values.
• Longitudinal Dispersivity
The user is given three options:
Option 1 (the default option) allows the user to specify a fixed value for alpha x. One commonly used
relation is to assume that alpha x is 10% of the estimated plume length.
Option 2 assumes that alpha x = 0.1* x
Option 3 calculates the longitudinal dispersivity using the following correlation:
r (* yi2'446
Alpha x= 3.28-0.82- Iog10 —— (Xu and Eckstein, 1995; Al-Suwaiyan, 1996)
L vJ.zo/J
• Transverse Dispersivity
The user may choose a ratio of alpha y : alpha x. One commonly used ratio is:
Alpha y: alpha x = 0.10 (Based on high reliability points from Gelhar et al., 1992)
• Vertical Dispersivity
The user may choose a ratio of alpha z : alpha x. One commonly used ratio is:
Alpha z: alpha x = 0.05 (ASTM, 1995)
Alternatively, alpha z :alpha x can be set to a very low number (e.g., E-99) to yield a conservative estimate of vertical
dispersion. This is the default value used in BIOCHLOR.
Other commonly used relations include:
Alpha x = 0.1 Lp (Pickens and Grisak, 1981)
36
-------
Alpha y = 0.33 alpha x
Alpha z = 0.05 alpha x
Alpha z = 0.025 alpha xto 0.1 alpha x
(ASTM, 1995) (EPA, 1986)
(ASTM, 1995)
(U.S. EPA, 1986)
The BIOCHLOR input screen includes Excel formulas to estimate dispersivities from scale. BIOCHLOR uses the
modified Xu and Eckstein (1995) algorithm for estimating longitudinal dispersivities because 1) it provides lower range
estimates of dispersivity, especially for large values of x and 2) it was developed after weighing the reliability of the
various field data compiled by Gelhar et al. (1992) (see Figure A.3). BIOCHLOR also employs low-end estimates for
transverse and vertical dispersivity estimates (0.10 * alpha x and 0, respectively) because these relations better fit high
reliability field data reported by Gelhar et al. (see Figure A.4), and Gelhar et al. recommend use of values in the lower
range of the observed data. The user can also enter a fixed longitudinal dispersivity value in the "Change Alpha x Calc."
dialog box on the input screen.
Note that the Domenico model and BIOCHLOR are not formulated to simulate the effects of chemical diffusion.
Therefore, contaminant transport through very slow hydrogeologic regimes (e.g., clays and slurry walls) should probably
not be modeled using BIOCHLOR unless the effects of chemical diffusion are proven to be insignificant.
1Q3
105
w
T5 10°
•o
S
D>
10-
10-2
ID'3
Longitudinal Dispersivity
= 10% of scale
(Pickens and Grisak, 198
Longitudinal Dispersivity
= 0.83 [Log10 (scale)]2.4i4
(Xu and Eckstein, 1995)
RELIABILITY _
=- O
O Low
O Intermediate
QHigh
Data Source: Gelhar et al., 1992 -
i Mini i i iiiMI i i iiiMI i i mini i i iimil i i iimil i i Mini
10'
10°
102 103
Scale (m)
104
105
10s
Figure A.3. Longitudinal dispersivity vs. scale data reported by Gelhar et al. (1992). Data includes Gelhar's reanalysis of several dispersivity
studies. Size of circle represents general reliability of dispersivity estimates. Location of 10% of scale linear relation plotted as dashed
line (Pickens and Grisak, 1981). Xu and Eckstein's regression shown as solid line. Shaded area defines ±1 order of magnitude from
the Xu and Eckstein regression line and represents general range of acceptable values for dispersivity estimates.
37
-------
Data Source: Gelharetai, 1992
Data Source: Gelhar et al, 1992
1C'2
1CT1
10°
101
• Low
O Intermediate
OHIgh
Transverse Q
Dispersivity =
10% of alpha x ° „
, o—o •-
10"' 10° 10' 102 103 104 10*
Scale (m)
_ IU
•5
|«>2
5
D
E
^ 1°
•9
0
3 10«
Q
= 101
11
**
A
A
A -I
•* :
^ 1
A
A Low
A Intermediate
AHIgh !
J J I j j
)-' 10° 10' 102 103 104 10!
Scale (m)
Figure A.4. Ratio of transverse dispersivity and vertical dispersivity to longitudinal dispersivity data vs. scale reported by Gelhar et al. (1992).
Data includes Gelhar's reanalysis of several dispersivity studies. Size of symbol represents general reliability of dispersivity esti-
mates. Location of transverse dispersivity relation used in BIOCHLOR is plotted as dashed line.
Appendix A.5
Pump and Treat Comparison
A useful way to estimate the clean-up time for a contaminated aquifer is to consider the number of pore volumes that
must be pumped from the contaminated zone to achieve clean-up goals. A pump and treat module was added to the
BIOCHLOR array output page to permit users to test the feasibility of pump and treat systems and to compare pump and
treat clean-up times with natural attenuation predictions.
The user is provided with the volume of ground water in the plume (i.e., a pore volume). One pore volume is only a small
fraction of the volume of ground water requiring treatment because dense non-aqueous phase liquids (DNAPLs), such
as solvents, and sorbed constituents act as continuing sources of ground-water contamination. The number of pore
volumes required for clean-up (i.e., the number of times the contaminated region must be flushed) is a function of many
different factors including: the clean-up standard, the initial chemical concentration, the degree of mixing of clean and
contaminated ground water, geologic heterogeneities, the presence and quantity of DNAPL, and sorbed constituents
(NRC, 1994).
In the pump and treat module, the user enters the system pumping rate, and the number of pore volumes treated/
removed in one year is calculated by the program. This value provides the user with an indication of the feasibility of
the pump and treat system. If the extraction rate is less than one pore volume per year, the attainment of clean-up
criteria will likely take decades, even under the most favorable conditions (NRC, 1994).
Another cell asks the user to input the number of pore volumes that must be removed in order to clean up the aquifer.
Using this value and the pumping rate, the time to clean up the contaminated aquifer can be estimated. The number of
pore volumes required to remediate the aquifer is a site-specific and technology-specific value. The document,
"Guidance on Remedial Actions for Contaminated Ground Water at Superfund Sites"(U.S. EPA, 1988), describes two
methods for estimating ground-water clean-up times based on the number of pore volumes: the batch flushing model and
the continuous flushing model. Neither of these methods account for DNAPL and, therefore, underestimate clean-up
times. A third method accounting for DNAPL is reported in Newell et al. (1994) and Wiedemeier et al. (1999).
38
-------
Appendix A.6
BIOCHLOR Example
Example : Cape Canaveral Air Station, Fire Training Area, Florida
Problem: Determine the concentration of TCE discharging into the canal in 1998, given data
collected in 1997.
Given:
Input Data
Fig. A.5 Source Map
BIOCHLOR Modeling Summary
• Fig. A.6 BIOCHLOR Input Data
Results:
Fig. A.7 BIOCHLOR Centerline Output
• Fig. A.8 BIOCHLOR TCE Centerline Output
• Fig. A.9 BIOCHLOR TCE Array Output
39
-------
BIOCHLOR Example
Cape Canaveral Air Station, Florida
DATA TYPE
Hydrogeology
Dispersion
Adsorption
Biotransformation
General
Source Data
Actual Data
OUTPUT
Parameter
• Hydraulic Conductivity:
• Hydraulic Gradient:
• Effective porosity:
• Longitudinal Dispersivity:
• Transverse Dispersivity:
• Vertical Dispersivity:
• Individual Retardation
Factors
Common Retardation Factor
• Aquifer Matrix Bulk Density
• foe:
• Koc:
Biotransformation Rate
Coefficients, (1/yr)
PCE — > TCE
TCE — >c-DCE
c-DCE— >VC
VC > ETH
• Modeled Area Length:
• Modeled Area Width:
• Simulation Time:
• Source Thickness:
• Source Widths (ft)
• Source Concentrations (mg/L)
PCE
TCE
c-DCE
VC
ETH
Distance From Source (ft):
PCE Cone. (mg/L):
TCE Cone. (mg/L)
c-DCE (mg/L)
VC (mg/L)
ETH (mg/L)
Centerline Concentration:
Array Concentration:
Value
1.8xlOz (cm/sec)
0.0012 (ft/ft)
0.2
40
4
0(ft)
PCE:7.1 TCE: 2.9
c-DCE: 2.8 VC: 1.4
ETH: 5.3
2.9
1.6 (kg/L)
0.184%
PCE: 426 (L/kg) TCE: 130
(L/kg)
c-DCE: 125 (L/kg) VC: 29.6 (L/kg)
ETH: 302 (L/kg)
2.0
1.0
0.7
0.4
1085 (ft)
700 (ft)
33 (yrs)
56(ft)
Area 1 Area 2 Area 3
105 175 298
Area 1 Area 2 Area 3
0.056 0.007 0.001
15.8 0.316 0.01
98.5 1.0 0.01
3.080 0.089 0.009
0.030 0.013 0.003
560 650 930 1085
<0.001 ND <0.001 <0.001
0.220 0.0165 0.0243 0.019
3.48 0.776 1.200 0.556
3.080 0.797 2.520 5.024
0.188 ND 0.107
0.150
See Figures A.7, A.8
See Figure A.9
Source of Data
• Slug-tests results
• Static water level
measurements
• Estimated
• Intermediate value for
800-1200 ft. plume (from
Gelharetal. (1992))
• 0.1 x long, dispersivity
• Assume vertical
dispersivity is zero since
depth of source is
approx. depth of aquifer
• Calculated from
R=l+Koc.fc.pb/n
• Median value
• Estimated
• Lab analysis
• Literature correlation
using solubilities at 20 °C
Based on calibration to
field data using a
simulation time of 32
years (field data collected
in 1997). Started with
literature values and then
adjusted model to fit
field data
Based on area of affected
ground-water plume
From 1965 (first release)
to 1998
• Based on geologic logs
and monitoring data (see
figure A. 5 for TCE
Example)
• Modeled source area as
variable source
• Source concentrations are
aqueous concentrations
• Based on 1997 observed
concentrations at site
near centerline of plume
40
-------
CCFTA2-11
WD
Banana
River
Source
Zone Width (ft)
105
175
298
Actual Source Cone.
in1997(mg/L)
15.8
0.316
0.01
How Derived
Maximum concentration
Geometric mean between edge of zone 1 and 2
Geometric mean between edge of zone 2 and 3
NOTE: This method of determining widths is different from the method
used in BIOSCREEN.
LEGEND
® Monitoring point
-^- Monitoring well location
0.003 TCE detected in groundwater sample, mg/L
10 TCE concentration isopleth, mg/L
ND No TCE detected
SCALE (ft.)
^^•^^=
0 150 300
BIOCHLOR SOURCE ZONE
ASSUMPTIONS
(TCE AS EXAMPLE)
CCFTA-2, Cape Canaveral Air Station, Florida
Figure A.5. BIOCHLOR source zone assumptions (TCE as example).
41
-------
BIOCHLOR Modeling Summary,
Cape Canaveral Air Station, Florida
Entering Input
• BIOCHLOR was used to reproduce the movement of the plume from 1965 (the best guess for when the release
occurred) to 1998.
• The hydraulic conductivity, hydraulic gradient, and the effective porosity were entered and the "C" button was
pressed to generate the seepage velocity.
• For longitudinal dispersivity, a fixed dispersivity of 40 ft (Option 1) was chosen. The ratio of lateral dispersivity to
longitudinal dispersivity was set to 0.1 and the vertical dispersivity was set to 0. This last value was chosen because
the depth of the source area is similar to the depth of the saturated zone.
• To determine the retardation factors, the aquifer matrix bulk density, the partition coefficients at 20°C, and the
fraction of organic carbon were input into the gray cells and the "C" button was pushed to yield the retardation factors.
BIOCHLOR uses one retardation factor, not individual retardation factors for each constituent. The default value for
the common retardation factor is the median retardation factor, but the user can over-ride this value. In cases where
the retardation factor varies significantly among the constituents, it is advisable to do a sensitivity analysis to
determine how the choice of the common R affects the model predictions. For this simulation, the median value of
2.85 was chosen.
• For modeling biotransformation, the user has the choice of modeling the plume in one ortwo zones. Modeling in two
zones permits the use of a different set of rate coefficients in each zone, but requires that the plumes be at steady
state (as established from field data). In this example, we will model the plume as one anaerobic zone using one set
of rate coefficients. (Field dissolved oxygen, ORP, and geochemical data were used to establish anaerobic
conditions.) Because field-scale rate coefficients and rate data from microcosms were unavailable, rate coefficients
previously obtained by calibrating the model to 1997 field data were used. Here, the rate coefficients were entered
into the white cells.
• In the General Section, the model area length, width, and simulation time must be entered. The model area length
is the distance from the source to the receptor (the canal, in this case study). A width of 700 ft is chosen to be
significantly larger than the plume width to capture all of the mass discharging into the canal. A simulation time of
33 years was chosen because the simulation is being conducted for 1998, and the solvents were released starting
in 1965.
• Because we are interested in centerline predictions and the mass flux into the canal, the source area will be modeled
as a spatially-variable source. By pressing the "Source Options" button and selecting "Spatially-Variable Source", a
dialog box pops up that allows for the input of source area concentration and width data. To obtain the most
conservative centerline predictions, the maximum concentration in the source area were used for zone 1. The other
two concentrations were obtained by taking the geometric means between adjacent isopleths (see Figure A.5).
Once these data are entered and "OK" is pressed, the data are transferred to the input page and you will see the
layout shown in Figure A.6. Note that any subsequent changes to the source area concentrations can be done
directly on the input page without going through the dialog box. Lastly, the thickness of the source area was
determined by entering the deepest depth where chlorinated solvents were detected in the aqueous phase.
• Finally, there is an input area for field data. In this example, the 1997 field data were used (previously) to determine
the rate constants provided. If one does not have field-scale rate constants or rate data from microcosm studies,
these values become important for calibrating the model.
Viewing Output
• There are two choices for viewing the output. Centerline predictions are shown for all five species in Figure A.6 and
forTCE in Figure A.8. Figure A.7 shows the centerline predictions for each chlorinated solvent and a no degradation
curve for all of the chlorinated solvents added together as well as field data. From this screen, the user can view the
centerline predictions of each constituent individually or go to the array screen. Figure A.8 shows the centerline
prediction forTCE, with and without biotransformation. However, any of the constituents can be viewed by pressing
the buttons to the right of the graph. Here we can see that the TCE concentration discharging into the canal (at
1085ft) is 0.003 mg/L.
42
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• From this screen or from the input screen the array page can be selected. The array output for this problem is
displayed in Figure A.9. This three-dimensional figure shows the longitudinal and lateral extent of contamination.
Again, the user can select the constituent to be viewed and the no degradation or biotransformation prediction. Note
that the scale on the array automatically changes depending on the magnitude of the concentrations. These array
values are maximum values because the array is evaluated at z=0.
Other information that is presented on the output screen includes the mass removed, the percent biotransformed, and
the mass flux. The TCE mass flux discharging to the canal is approximately 83 mg/day.
HQCHLOTt
6.
y. i
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:i.
'I
fe.
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4. -IS! Cud'
f on
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| Fills l£tJi!!:SiOj:J5>fi|
t J
Figure A.6. BIOCHLOR input screen. Cape Canaveral Air Force Base, Florida.
43
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^™y«l»lTlTlTlTlTlUlll,.,
Kstspet fnm SOUTH
Figure A.7. Centerline output. Cape Canaveral Air Force Base, Florida.
TGi
r-r'aiA
EM PCI
Snyrct ill.}
ft
Input
Figure A.8. Individual centerline output for TCE, Cape Canaveral Air Station, Florida.
44
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Otfjrwtiatfi
I
0 BUS I* j ,
j H-
Dislanc* (It, i
1«?
CMtoiii
j
» »•,:/; .;•:->••*!-! -<-?:-.- j -r ••
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to
Figure A.9. Array concentration output for TCE. Cape Canaveral Air Station, Florida.
45
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Sensitivity Analysis Examples
Sensitivity analyses are recommended when literature values are used or if there is uncertainty in an input parameter.
To illustrate the response of the BIOCHLOR model to changes in the input parameters, a sensitivity analysis was
conducted for the first order decay coefficients and also for the common retardation factor.
In the first sensitivity analysis example, the case study (baseline) problem was run with the same input parameters
except that the first order decay coefficients were multiplied by 2. Similarly, another simulation was conducted whereby
the rate coefficients were 0.1 times those used in the baseline example. The centerline concentrations of PCE, TCE and
the daughter products 1085 ft downgradient from the source are shown in Table A.4 for each simulation. In this instance,
the simulated concentrations of PCE and its daughter products increase substantially when the rate coefficient is
decreased by a factor often and doubling the rate coefficient decreases the chlorinated solvent concentrations at the
canal location. In this example, the chlorinated ethene concentrations are very sensitive to the magnitude of the rate
coefficient.
TableA.4. Sensitivity Analysis Results - Rate Coefficients
Constituent
PCE
TCE
DCE
VC
Concentrations
(mgIL)
2X Baseline
0.000
0.000
0.003
0.137
Baseline *
0.000
0.003
0.202
2.039
0.1X Baseline
0.006
2.254
19.443
8.819
baseline
yr\
=1 .00 yr\
=0.70 yr\
=0.40 yr1
In contrast, changes in the retardation factor have nominal effects on the dissolved chlorinated solvent concentrations as
shown in Table A. 5. In this sample case, when the retardation factor is decreased from the baseline value of 2.9,
chlorinated solvent concentrations increase slightly. Also, with an increase in the retardation factor chlorinated solvent
concentrations at the canal location decrease by a small amount. These small variations in the concentrations due to the
changes in the retardation factor can probably be attributed to the plume being near steady-state in this example.
Table A.5. Sensitivity Analysis Results - Retardation Factor
Constituent
PCE
TCE
DCE
VC
Concentrations (mgIL)
R=1.4
0.000
0.003
0.204
2.161
R=2.8 (Baseline )
0.000
0.003
0.202
2.039
R=4.7
0.000
0.003
0.112
0.798
In this example, the BIOCHLOR model is more sensitive to changes in the first-order decay coefficient and less sensitive
to changes in the retardation factor. However, the results of these sensitivity analyses are site-specific and do not apply
to all sites.
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