BIOCHLOR
‘BIOCHLOR

   Natural Attenuation
  Decision Support System

Version 2.2

March 2002

  User’s Manual

Developed for the

Technology Transfer Division

Air Force Center for Environmental Excellence

Brooks AFB, Texas

by

Groundwater Services, Inc., Houston, Texas

Project Officer

  David G. Jewett

Subsurface Protection and Remediation Division

National Risk Management Research Laboratory

Ada, Oklahoma 74820


TABLE OF CONTENTS

BIOCHLOR Natural Attenuation Decision Support System

INTRODUCTION

INTENDED USES FOR BIOCHLOR

NEW FEATURES IN VERSION 2.2

BIOCHLOR CONCEPTS

BIOCHLOR Model Types

BIOCHLOR DATA ENTRY

Hydrogeologic Data

Dispersivity

Adsorption Data

Biotransformation and Abiotic Decay Data

Rate Constant Decision Support

General Data

Source Data

Source Decay

Field Data for Comparison

ANALYZING BIOCHLOR OUTPUT

Centerline Output

Centerline Animation

Array Output

Calculating the Mass Balance

REFERENCES

BIOCHLOR TROUBLESHOOTING TIPS

Minimum System Requirements

Spreadsheet-Related Problems

PUMP AND TREAT COMPARISON

2-ZONE MODELING

ACKNOWLEDGMENTS

Note:  This help file does not include a discussion of the theory and mathematics behind the models used by BIOCHLOR or a case study.  To access this information, please refer to the printed manual (Aziz et al., 2000a) and manual addendum (Aziz et al., 2001) provided in Adobe PDF format.


INTRODUCTION

BIOCHLOR is an easy-to-use screening model that simulates natural attenuation (RNA) of dissolved solvents in ground water.  The software, programmed in the Microsoft Excel spreadsheet environment and based on the Domenico analytical solute transport model, has the ability to simulate 1-D advection, 3-D dispersion, linear adsorption, and biotransformation via reductive dechlorination.  Reductive dechlorination occurs 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 2 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.

BIOCHLOR is made available on an as-is basis without guarantee or warranty of any kind, express or implied.  The United State Government, Groundwater Services, Inc., the authors, and reviewers accept no 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.


INTENDED USES FOR BIOCHLOR

BIOCHLOR attempts to answer the following fundamental questions regarding RNA:

1.	How far will a dissolved chlorinated solvent plume extend if no engineered controls or further source zone 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, 1998).   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.

2.	How long will the plume persist?

If the decaying source option is selected, the user can estimate how long the plume will persist.  This calculation assumes first order decay of the source, which approximates source dissolution and degradation processes.  Therefore, the results should be viewed as rough estimates of the time required for the dissipation of the plume.

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 characterization data organization and the ability to carry out many simulations in short periods of time.  For fuel hydrocarbon release sites, the BIOSCREEN model 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 (USEPA, 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 made so that the resulting simulations provide useful information for the 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 zones).

2.	As a screening tool, BIOCHLOR assumes uniform hydrogeologic and environmental conditions over the entire model area.

Being an analytical model, BIOCHLOR assumes constant hydrogeological, and biological property values for the entire model area and, therefore, simplifies actual site conditions.  For this 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 biotransformation of chlorinated ethenes (i.e., PCE, TCE, DCE, and VC) and chlorinated ethanes (i.e., TCA, DCA, and CA), the model can be adapted for other sequential decay reactions by experienced users (Aziz et al., 2000a).


NEW FEATURES IN VERSION 2.0
NEW FEATURES IN VERSION 2.2

Version 2.2 of BIOCHLOR includes the following new features: rate constant decision support, source decay, and animation. Rate constant decision support was incorporated to help users to estimate rate constants required for model simulations.  In addition to simulating a source of constant concentration, BIOCHLOR Version 2.2 models a decaying source, which allows the simulation of both plume expansion and contraction.  Lastly, an animation feature has been incorporated, which permits the visualization of plume behavior.


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 et al., 1999a, 1999b; Sun and Clement, 1998).  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 biological 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 Data Entry.  For help on Output, see Analyzing 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) or the 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.

1.	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 down-gradient where fermentable substrates have been depleted or for plumes that go from anaerobic conditions to aerobic conditions.  Aerobic conditions can only be considered in the second zone and should be modeled only by experienced users as discussed in Appendix A.2 of the BIOCHLOR manual (Aziz et al., 2000a).

Note: This 2-zone model should only be employed 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.


BIOCHLOR DATA ENTRY

Click on a region of the input screen below to jump to a discussion of a specific input data category.  Note important considerations that follow the graphic.

HYDROGEOLOGIC DATA

DISPERSIVITY

ADSORPTION DATA

BIOTRANSFORMATION DATA

GENERAL DATA

SOURCE DATA

FIELD DATA FOR COMPARISON

Four important considerations regarding data input are:

1.	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.

2.	To view the example data set on the input screen of the software, click on the Paste Example Data Set button on the lower right section of the input screen.

3. 	Because BIOCHLOR is based on the Excel spreadsheet, you must click outside of the cell where you just entered data or hit ìreturnî before any of the buttons will work.

4.	If an entered number has too many digits, the user will see #### in the input cell.  Press the Unprotect Sheet button and then go to the Format menu.  Select Cells and lower the font size until the number is visible.

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.


DATA ENTRY:  Hydrogeologic Data

Parameter	Seepage Velocity  (Vs)
Units	ft/yr
Description	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.  Domenico and Schwartz (1990) indicate that chemical diffusion is insignificant for Peclet numbers (seepage velocity times median pore size divided by the bulk diffusion coefficient) > 100.
Typical Values	0.5 to 200 ft/yr
Source of Data	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.
How to Enter Data	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	Hydraulic Conductivity  (K)
Units	cm/sec
Description	Horizontal hydraulic conductivity of the saturated porous medium.
Typical Values	Clays:   <1x10-6 cm/s Silts: 1x10-6  - 1x10-3 cm/s Silty sands:  1x10-5  - 1x10-1 cm/s Clean sands:    1x10-3  - 1  cm/s Gravels: > 1 cm/s
Source of Data	Pump tests or slug tests at the site.  It is strongly recommended that actual site data be used for all RNA studies.
How to Enter Data	Enter directly.  If seepage velocity is entered directly, this parameter is not needed in BIOCHLOR.
Parameter	Hydraulic Gradient  (i)
Units	ft/ft
Description	The slope of the potentiometric surface.  In unconfined aquifers, this is equivalent to the slope of the water table.
Typical Values	0.01- 0.05 ft/ft
Source of Data	Calculated by constructing potentiometric surface maps using static water level data from monitoring wells and estimating the slope of the potentiometric surface.
How to Enter Data	Enter directly.  If seepage velocity is entered directly, this parameter is not needed in BIOCHLOR.
Parameter	Effective Porosity  (n)
Units	Unitless
Description	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, effective porosity can be less than total porosity by 2-5% (e.g. 0.28 vs. 0.30) (Smith and Wheatcraft, 1993).
Typical Values	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, Wilson, et al., 1995; originally from Domenico and Schwartz, 1990 and Walton, 1988).
Source of Data	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).
How to Enter Data	Enter directly.  Note that if seepage velocity is entered directly, this parameter is still needed to calculate the retardation factor and plume mass rate.


DATA ENTRY:  Dispersivity

Parameter	Dispersivity  (Longitudinal, Transverse, and Vertical)
Units	ft
Description	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 al., 1992).
Typical Values	The user has the option to enter fixed dispersivity values or dispersivity relationships as a function of estimated plume length, Lp. BIOCHLOR is programmed with some commonly used relationships that are representative of typical and low-end dispersivities.     Longitudinal Dispersivity The user is given three options: Option 1 (the default option) allows the user to specify a fixed value for alpha x.    Option 2 assumes that alpha x = 0.1 *Lp  Option 3 calculates the longitudinal dispersivity using a modified version of the Xu and Eckstein correlation:  (Xu and Eckstein, 1995; Al-Suwaiyan, 1996)   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 relationships include: Alpha x =   0.1 Lp (Pickens and Grisak, 1981) Alpha y = 0.33 alpha x (ASTM, 1995)(EPA, 1986) Alpha z = 0.05 alpha x (ASTM, 1995)  Alpha z = 0.025 alpha x to 0.1 alpha x (EPA, 1986)
Source of Data	Typically estimated using the relationships provided above (see Appendix A.4).
How to Enter Data	Enter a value for alpha x in the indicated white cell., or click on Calc. Alpha x button to receive assistance in calculating the longitudinal dispersivity.  Enter ratios for alpha y:alpha x and alpha z:alpha x in the indicated white cells. (Note: If the Reset button is depressed, then the following are the default options and values used by BIOCHLOR: The alpha y:alpha x ratio is set to 0.1 and the alpha z:alpha x ratio is set to 1x 10-99.)


DATA ENTRY:  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 (ρb = bulk density, n = effective porosity, Koc = octanol-water partition coefficient, Kd = distribution coefficient, and foc = fraction organic carbon on uncontaminated soil) with the following expression:      where When biotransformation rates are insignificant, the retardation factor can be estimated by comparing the plume length of an adsorbing compound to the plume length of a conservative (non-adsorbing) compound.
How to Enter Data	1) Enter the retardation factor for each constituent directly. Do NOT press the C button.  The worksheet will update automatically. OR  2) Fill in the estimated values for effective porosity, bulk density, partition coefficient, and fraction organic carbon as described below and have BIOCHLOR 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.
Parameter	Soil Bulk Density (ρb)
Units	kg/L or g/cm3
Description	Bulk density, in kg/L, of the aquifer matrix (related to porosity and pure solids density).
Typical Values	Although this value can be measured in the lab, in most cases estimated values are used.  A default value of 1.7 kg/L is used frequently.
Source of Data	Either from an analysis of soil samples at a geotechnical lab or more commonly, application of estimated values such as 1.7 kg/L.
How to Enter Data	Enter directly.  If the retardation factor is entered directly, this parameter is not needed in BIOCHLOR.
Parameter	Organic Carbon Partition Coefficient (Koc)
Units	(mg/kg) / (mg/L) or (L/kg) or (mL/g)
Description	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.
Typical Values	Perchloroethylene    426 L/kg        Trichloroethylene     130  L/kg Dichloroethylene      125 L/kg        Vinyl Chloride          29.6 L/kg       (all at 20 oC) (Note that there is a wide range of reported values and these values are temperature-dependent)
Source of Data	Chemical reference literature or relationships between Koc and solubility or Koc and the octanol-water partition coefficient (Kow).
How to Enter Data	Enter directly.  If the retardation factor is entered directly, this parameter is not needed in BIOCHLOR.
Parameter	Fraction Organic Carbon (foc)
Units	Unitless
Description	Fraction of the aquifer soil matrix comprised of natural organic carbon in uncontaminated areas.  More natural organic carbon means higher adsorption of organic constituents on the aquifer matrix.
Typical Values	0.0002 - 0.02
Source of Data	The fraction organic carbon value should be measured if possible by collecting a sample of aquifer material from an uncontaminated zone 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, 1994).
How to Enter Data	Enter directly.  If the retardation factor is entered directly, this parameter is not needed in BIOCHLOR.


DATA ENTRY:  Biodegradation Data
DATA ENTRY:  Biotransformation Data

Parameter	First-Order Decay Coefficient (lambda) for Zones 1 and 2
Units	1/yr
Description	Rate coefficient describing first-order biological 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 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 decay by first order kinetics, but it is also simultaneously being produced by the first order decay of the preceding compound (see Appendix A.2 of BIOCHLOR manual). 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  Section 5  General.
Typical Values	Perchloroethylene 0.07 to 1.20 yr-1 Trichloroethylene 0.05 to 0.9 yr-1 cis-1,2-Dichloroethylene 0.18 to 3.3 yr-1 Vinyl Chloride 0.12 to 2.6 yr-1 (from Wiedemeier et al., 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 / t1/2 (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. 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 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.
Parameter	Dissolved Solvent Half-Life (t1/2)
Units	Years
Description	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.
Typical Values	Perchloroethylene 0.58  to 9.9  yr Trichloroethylene 0.77  to 13.9 yr cis-1,2-Dichloroethylene 0.21 to 3.9 yr Vinyl Chloride 0.27 to 5.8 yr (from Wiedemeier et al., 1999)
Source of Data	Optional methods for selection of appropriate half-lives are the same as for the rate coefficients.
How to Enter Data	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	Abiotic First Order Rate Coefficients (1/yr)
Units	1/year
Description	Rate coefficient describing first-order abiotic decay process for chloroethane.   Chloroethane degrades under abiotic conditions to ethanol. 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.
Typical Values	chloroethane to ethanol                               0.37  yr-1  (20oC) 1,1,1-trichloroethane to 1,1-DCE 0.058-0.32 yr-1 (10-20 oC) 1,1,1-trichloroethane to acetic acid        0.25 to 0.41  yr-1 (from Vogel and McCarty, 1987b; McCarty, 1996)
Source of Data	Optional methods for selection of appropriate half-lives are as follows: Literature Values: Various published references are available that list rate coefficients for hydrolysis and other abiotic processes (e.g., see Howard et al., 1991).
How to Enter Data	Press A button.  Enter values in the dialog box and press OK.
Parameter	Yield (Y)
Units	Unitless
Description	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.
Typical Values	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
Source of Data	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.
How to Enter Data	Enter directly.


RATE CONSTANT DECISION SUPPORT

Buschek/Alcantar Method

BIOCHLOR Version 2.2 incorporates the Buschek/Alcantar (1995) rate constant estimation method, which automatically provides an approximate calibration of the model to entered site-specific field data.  This feature can be accessed from the Biotransformation section on the input screen, by pressing the λ Help button.  The Buschek/Alcantar approach uses the equations in (1)-(3), which assume 1-D dispersion, steady-state conditions, and biotransformation only in the aqueous phase.  Although this equation has the potential to overestimate biotransformation rate constants by lumping the effects of lateral and vertical dispersion with biotransformation, it quickly yields a reasonable first approximation of the rate constants.  These rate constant estimates can then be manually refined subsequently.  A minimum of three data points is required for rate constant determination.  This approach is most appropriate for parent constituents.  Caution should be exercised in using this approach with daughter constituents, as daughter product generation is not accounted for in this method.

The Bushek/Alcantar method (1995) starts with the 1-D steady-state solution to the advection-dispersion equation. By plotting, the contaminant concentration  (on a logarithmic scale) versus distance downgradient (plotted on a linear scale), the slope (m) of the line is determined by linear regression.  The biotransformation rate constant (λ) is determined using the slope (m) and equation (3). In addition, BIOCHLOR also calculates the r2 for the linear regression.

Equations Used in Buschek/Alcantar (1995) Rate Constant Estimation Method

(1)       (2)       (3)

where:

λ	is the biotrans. rate constant (1/yr)

vs	is the seepage velocity (ft/yr)

αx	is the longitudinal dispersivity (ft)

m	is the slope of the ln conc. vs.

distance (1/ft)

x 	is the distance downgradient (ft)

Co	is the initial concentration (mg/L)

C(x)	is the concentration at distance x (mg/L)

Use of Literature Values

Although it is preferable to use site-specific field data to estimate rate constants, field data are not always available.  In these cases, the user may opt to assume that no biotransformation is occurring (the most conservative option).  Alternately, if there is evidence of biotransformation (by virtue of the production of daughter products), then literature values for the biotransformation rate constant may be used.  As noted previously, it is difficult to select an appropriate biotransformation rate constant because these parameters are site-specific, depending on the number and type of bacteria present, the amount of electron donor, and environmental conditions (including temperature).  Furthermore laboratory-derived rate constants reported in the literature often overestimate the rate of biodegradation.

To provide users with more reasonable literature values, the BIOCHLOR model was calibrated with chlorinated solvent plume data from the BIOCHLOR database (Aziz et al., 2000b) to yield field-derived rate constants, as shown on Table 2.  Most of the sites in the BIOCHLOR database had significant BTEX (electron donor) concentrations; so these rate constants should only be used at sites with evidence of significant electron donor.

Table 2:  Rate Constants Estimated Using BIOCHLOR

BIOCHLOR Version 2.2 provides guidance for the selection of field-derived biotransformation rate constants for chlorinated ethenes and chlorinated ethanes through the λ Help button in the Biotransformation section of the input screen.

The user is first asked whether there is evidence of reductive dechlorination at the site.  If not, biotransformation rate constants of zero should be employed.  If there is evidence of reductive dechlorination, BIOCHLOR queries the user as to the amount of electron donor present at the site.  If the concentration of electron donor is >0.1 mg/L or hydrogen concentrations are present at > 1 nM, then the user is presented with rate constants for the chlorinated constituents that range from the minimum value to the 75th percentile for rate constants determined in the BIOCHLOR database study (Table 2).  For constituents with limited data, the maximum value was used as the upper limit. If low levels of electron donor are present, but there is still evidence of biotransformation, then BIOCHLOR suggests using a rate constant less than the minimum value.


DATA ENTRY:  General Data

Parameter	Model Area Length and Width (L and W)
Units	ft
Description	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 balance results using the Run Array button.
Typical Values	500-3000 ft  (length) 250-1000 ft  (width)
Source of Data	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.
How to Enter Data	Enter directly.
Parameter	Simulation Time (t)
Units	Years
Description	Time 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).
Typical Values	1 to 1000 years
Source of Data	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.
How to Enter Data	Enter directly.
Parameter	Zone 1 Length and Zone 2 Length
Units	ft
Description	Length of first and second biotransformation zones in feet.  This is the same as the model length if the user is modeling the plume as one zone. Modeling the 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 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 H2) near the source but not near the plume front may be best modeled in 2 zones because the concentration of hydrogen affects 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, no degradation and aerobic zones can also be modeled (see 2-zone Biotransformation Modeling, for instructions).  Note that two-zone biotransformation estimates should only be applied when the plume is at steady-state throughout zone 1.
Typical Values	500-3000 ft
Source of Data	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 hydrocarbon concentrations, ORP, geochemical indicators etc.) and determine an appropriate distance from the source.
How to Enter Data	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.


DATA ENTRY:  Source Data

Parameter	Source Thickness In Saturated Zone (Z)
Units	ft
Description	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 contamination that extends vertically from the water table to the bottom of the saturated zone.
Typical Values	20-50 ft
Source of Data	This value is usually determined by evaluating ground-water data from wells near the source zone 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.
How to Enter Data	Enter directly.
Parameter	Source Area Concentrations (C)
Units	mg/L
Description	Aqueous phase concentration of chlorinated solvents in the source zone.   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.   For the single planar option, the maximum source zone concentration should be entered on the input page (or in the dialog box that transfers the data to the input page).  This yields the most conservative centerline concentration profiles.  For the spatially-varying option, the user may enter three concentrations.  The maximum concentration in the source zone may be used in zone 1 and geometric mean concentrations can be used in zones 2 and 3. Using a single planar source yields more 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 oC (Mackay et al., 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.
Parameter	Source Area Width (Y)
Units	ft
Description	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.
Typical Values	120-350 ft
Source of Data	To determine a source width across the site, draw a line perpendicular to the direction of ground-water flow direction in the source area.  The source area is typically defined as being the area with contaminated soils having high concentrations of sorbed organics, free-phase NAPLs, or residual NAPLs.  If the source zone covers a large area, it is best to choose the most downgradient or widest point in the source area for determining the source width. Single Planar For a single planar source, choose one width. Spatially Varying For a spatially variable source, BIOCHLOR allows the user to enter up to three widths and concentrations to define the source area using isopleth data.  See the diagram below.
How to Enter Data	Enter directly on input page or press Source Options button and follow instructions.


DATA ENTRY:  Source Decay
SOURCE DECAY

Dense non-aqueous phase liquids (DNAPLs), such as PCE and TCE, can act as continuing sources of groundwater contamination.  The rate at which constituents in the DNAPL or source dissolve into the groundwater ultimately determines the concentration of dissolved contaminants in the plume and the lifetime of the plume.

In BIOCHLOR Version 2.2, the user has the option of modeling a source with constant concentration over time, as used in the original Domenico model (Domenico, 1987), or one with a decaying concentration over time.  Source decay is modeled as a first order process. This approach captures all processes that can lead to depressed aqueous-phase concentrations in the source zone, including decreased dissolution rate from the DNAPL, biotransformation, or any other degradation processes.

After selecting the “Decaying” source option (accessed through the “Source Options” button on the input screen), enter a source decay rate constant, ks, for all of the constituents.  This value must be previously calculated by plotting temporal aqueous concentrations in a source area well on a semi-log plot and determining the slope as shown in Figure 1.  Note that Excel will calculate a slope in units of 1/days.    This slope must be converted to units of 1/yr as required by Biochlor 2.2.  A new tool, called BIOSOURCE, is currently being developed for the AFCEE by Groundwater Services, Inc. This tool will assist the user in determining source decay constants.  Be aware that the source decay constant, ks, is different from the biotransformation constant, λ.  ks describes how the concentration in a source area well decreases as the DNAPL is depleted of the constituent of concern, whereas λ  is the biotransformation rate constant for a constituent in the plume.

Figure 1: Determination of ks Using Aqueous Concentrations in Source Area Wells

The equations describing groundwater transport and biotransformation with a decaying source are presented in Appendix A.1.  The decaying source feature can only be used with one-zone simulations.  The user is restricted to ks values that are less than 1/R*(λ+Vs/4α) to prevent unstable complex solutions.  A safety factor of 20% is also incorporated.


DATA ENTRY:  Field Data for Comparison

Parameter	Field Data for Comparison
Units	mg/L
Description	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.
Typical Values	0.01 to 50 mg/L
Source of Data	Monitoring wells located near the centerline of the plume.
How to Enter Data	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.


ANALYZING BIOCHLOR OUTPUT:  Centerline

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 the 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.    On the first output screen, you 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 centerline output shows the concentration at the bottom of the saturated zone (z=0) along the centerline of the plume (y=0).  By pressing the To All button, you are taken to a second screen showing the concentration profiles and field data for all the constituents on one plot as well as a no degradation curve for total chlorinated solvents.  This information is plotted on a linear plot.  The user may push the Log>Linear button to see the results on a semi-log plot.  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.


ANALYZING BIOCHLOR OUTPUT:  Centerline Animation

The animation feature allows users to visualize plume movement.  This animation feature can be accessed by pressing the Run Centerline button on the input screen, which directs the user to the Individual constituent page.

The individual constituent screen shows the concentration along the centerline of the plume (y=0).  Clicking on Prepare Animation divides the simulation into 10 separate time periods and shows the movement of the plume based on either the Biotransformation or No Degradation model.  After the first animation is completed, the user can step forward or backward in time by pressing the Next or Prev buttons, or all 10 simulations can be replayed by pressing the Replay button.


ANALYZING BIOCHLOR OUTPUT:  Array

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 rates are presented on the array output screen.

On the array screen, there is also a section for estimating pump and treat clean-up times.  This feature is described in Appendix A.5 of the BIOCHLOR manual (Aziz et al., 2000a).

Calculating the Mass Balance

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 Rate

The mass removed is the difference between the mass of contaminant if no biotransformation occurs and the mass of contaminant if biotransformation/production 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 biotransformed is the mass of solvent removed divided by the mass of solvent if no biotransformation occurs.  The percent change in mass rate is the difference in mass rate at the source compared to the mass rate at the boundary of the model area.

Current Volume of Ground Water in Plume (ac-ft)

BIOCHLOR counts the number of cells in the 5 x 10 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 to display the result (No Degradation or Biotransformation).

Note that the model does not account for the effects of any 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 Zone (ac-ft/yr)

Using the Darcy velocity, the source thickness, and the source width, BIOCHLOR calculates the rate that clean ground water moves through the source zone 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.


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, Groundwater Water, 34(4):578.

Aziz, C.E., C.J. Newell, J.R. Gonzales, P.Haas, T.P. Clement, Y-W. Sun.  2000a.  BIOCHLOR Version 1.0 User’s Manual.  EPA/600/R-00/008 .

Aziz, C.E., A.P. Smith, C.J. Newell, and J.R. Gonzales. 2000b.  BIOCHLOR Chlorinated Plume Database.  Prepared for the Technology Transfer Division, Air Force Center for Environmental Excellence. Brooks AFB, Texas.

Aziz, C.E., C.J. Newell, and J.R. Gonzales. 2001.  BIOCHLOR Version 2.0.  Manual Addendum.  Prepared for the Technology Transfer Division, Air Force Center for Environmental Excellence. Brooks AFB, Texas.

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.

Buschek, T. E., and C.M. Alcantar. 1995. Regression Techniques and Analytical Solutions to Demonstrate Intrinsic Bioremediation. In Intrinsic Bioremediation. R.E. Hinchee, J.T. Wilson and D.C. Downey, Ed. Battelle Press. Columbus , OH.

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, Baton Rouge, 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, Texas, November 1994: 43-55.

Domenico, P.A.,  1987,  An Analytical Model for Multidimensional Transport of a Decaying Contaminant Species, Journal of Hydrology, 91: 49-58.

Domenico, P.A. and F. W. Schwartz, 1990,  Physical and Chemical Hydrogeology, Wiley, New York, NY.

EPA, 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.

Gelhar, L.W., C. Welty, and K.R. Rehfeldt, 1992,  A Critical Review of Data on Field-Scale Dispersion in Aquifers,  Water Resources Research, 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. Letters, 7(6):383:388.

Hartmans, S., and J.A.M. de Bont, 1992,  Aerobic Vinyl Chloride Metabolism in Mycobacterium aurum Li:, Applied Environmental Microbiology,  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,  Applied and Environmental Microbiology,  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, MI.

Hughes, J.B., C. J. Newell, and R. T. Fisher, 1997, 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, Inc., New York, New York.

Mackay, D., W.Y. Shiu, and K.C. Ma, 1993, Illustrated Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals. Volatile Organic Chemicals. Vol. III. Lewis Publishers, Chelsea, MI.

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.

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, Colorado, 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, J. Water Resources Research, 17(4):  1191-1211.

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 Resources Research , 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, Transport in Porous Media Journal, (In Press).

Sun, Y. , J.N. Petersen, and T.P. Clement, 1999a, A New Analytical Solution for Multiple Species Reactive Transport in Multiple Dimensions, Journal of Contaminant Hydrology, 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 Resources Research, 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 1986.

 U.S. Environmental Protection Agency, 1998, Technical Protocol for Evaluation 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,  Applied Environmental Microbiology, 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.

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, 1995.

Wiedemeier, T.H., H.S. Rifai, C.J. Newell, and J.W. Wilson, 1999,  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, Journal of Ground Water, 33(6):  905-908.


BIOCHLOR TROUBLESHOOTING TIPS

Minimum System Requirements

The BIOCHLOR model requires a computer system capable of running Microsoft Excel ’97 or 2000 for Windows.  Operation requires an IBM-compatible PC equipped with a Pentium II or later processor.  A minimum of 64 MB of system memory (RAM) is strongly recommended.

Installation and Start-Up

The software is installed by copying the Excel BIOCHLOR model file and the BIOCHLOR help file (BIOCHLR22.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.  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.

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).  To fix this, 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 then the dialog box will close.  Do not press Enter and move to the next cell by using the mouse and clicking or by using tabs.  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 of creating 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, BIOCHLR22.HLP.  Here are some suggestions (in decreasing order of preference) for helping WinHelp find it:

1.	If you are asked to find the requested file, do so.  The file is called BIOCHLR22.HLP, and it was installed in the same directory/folder as the BIOCHLOR model file.

2.	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.

3.	Change the WinHelp call in the VBA Module to hard code the directory information. That way, the file name and its full path will be explicitly passed to WinHelp.  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.

4.	As a last resort, you can add the BIOCHLOR directory to your path (located in your AUTOEXEC.BAT file), and this problem will be cured.  You will have to reboot your machine, however, to make this work


PUMP AND TREAT
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 clean up the aquifer is a site-specific and technology-specific value.  The document, Guidance on Remedial Actions for Contaminated Ground Water at Superfund Sites(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 accounts for DNAPL and, therefore, underestimates clean-up times.  A third method accounting for DNAPL is reported in Newell et al. (1994) and Wiedemeier et al. (1999).


2-ZONE BIODEGRADATION MODELING
2-Zone MODELING

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, 1998).

Table A.1: 	2-Zone Biotransformation Scenarios

Scenario	Zone 1	Zone 2
1	Type I or II (anaerobic, high rates)	Type 1 or II (anaerobic, lower rates or no  degradation)
2	No Degradation	Type I or II
3	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.

Figure A.1 Mixed Type I/Type III Plume Conditions

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 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 lambda‘ 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 profile for VC.  (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 simplist 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
	Zone 1	Zone 2	Zone 1	Zone 2	Zone 1	Zone 2
PCE-	lambda 1	0	lambda1	0	lambda 1	0
TCE	lambda2	0	lambda2	0	lambda 2	0
DCE	lambda3	lambda3’	lambda3	0	lambda 3	0
VC	lambda4	0	lambda 4	lambda 4’	lambda 4	0
ETH	0	0	0	0	0	lambda 5’

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	Run 1
	Zone 1	Zone 2
TCA	lambda 1	0
1,1-DCA	lambda 2	0
CA	lambda 3	lambda 3’


ACKNOWLEDGMENTS

BIOCHLOR was developed for the Air Force Center for Environmental Excellence, Brooks AFB, San Antonio, Texas by Groundwater Services, Inc.

AFCEE Project Officer:	Mr. Jim Gonzales
BIOCHLOR Version 2.2 Developers: Customization and Testing of BIOCHLOR Version 1.1 Solution Engine:	Carol E. Aziz, Ph.D. and Charles J. Newell, Ph.D., P.E Groundwater Services, Inc. 2211 Norfolk, Ste. 1000  Houston, Texas 77098 phone:  713 522-6300 fax:  713 522-6010   ceaziz@gsi-net. com cjnewell@gsi-net. com    Prabhakar  Clement, Ph.D., P.E. and Yunwei Sun, Ph.D. Battelle Pacific Northwest National Laboratory 902 Battelle Boulevard P.O. Box 999, MSIN:  K2-10 Richland, Washington 99352 phone: (509)375-2359 fax:(509)372-4660
BIOCHLOR Manual and Addendum:	Carol E. Aziz, Ph.D. and Charles J. Newell, Ph.D., P.E. Groundwater Services, Inc.
BIOCHLOR Review Team:	Dr. Jerome Cruz	ManTech Environmental Research Services, Inc., National Risk Management Research Laboratory, Ada, OK
	Dr. Mingyu  Wang	ManTech Environmental Research Services, Inc.,National Risk Management Research Laboratory, Ada, OK
	Dr. David Jewett	Center for Subsurface Modeling Support,National Risk Management Research Laboratory, Ada, OK