# EPA’s COMPOSITE MODEL FOR LEACHATE MIGRATION WITH TRANSFORMATION PRODUCTS 

# EPACMTP: USER’S GUIDE

Work Assignment Manager Dr. Zubair A. Saleem and Technical Directions: U.S. Environmental Protection Agency Office of Solid Waste Washington, DC 20460
Prepared by: HydroGeoLogic, Inc. 1155 Herndon Parkway, Suite 900 Herndon, VA 20170 Under Contract No. 68-W4-0017



# U.S. Environmental Protection Agency Office of Solid Waste Washington, DC 20460

19971 U.S. Environmental Protection Agency, Industrial Subtitle D Facility Study (Telephone Survey), October 20, 1986; U.S. Environmental Protection Agency, Solid Waste Dis posal in the United States: Report to Congress, Office of Solid Waste and Emergency Res ponse, EPA/530-SW-88-011, October, 1988 i E:\Projects\EPA\R08-99.217.wpd PREFACE This document presents guidance for the use of EPA ’s C omposite M odel for leachate migration with T ransformation P roducts (EPACMTP) computer code. EPACMTP is a simulation model for subsurface fate and transport of contaminants releas ed from land disposal sites. EPACMTP is designed to predict the groundwater exposure in a domestic drinking water receptor well associated with such releases. The model is applied to support development of regulations for management and disposal of hazardous wastes. Simulations are performed using probabilistic input specifica tions based on nationwide data. The model is designed to be used for generic, nationwide asse ssments using Monte Carlo simulation techniques; it is not intended for site-specific appli cations. EPACMTP extends and enhances the modeling approach adopted for the 1990 Toxicity Characteristic (TC) Rule promulgated by the U.S. Environmental Protection Agency in March 1990. For th is rule, the Agency used EPACML (EPA ’s C omposite M odel for L andfills) to estimate the potential human exposure to hazardous chemicals leaching from land disposal facilities. EPACML accounts for first-order decay and linear equilibrium sorption of chemicals, but disregards the formation and transport of transfor mation products. The analytical groundwater transport solution technique employed in EPAC ML further imposes certain restri ctive assumptions; the solution can handle only uniform, uni-directiona l groundwater flow and thereby ignores the effects of groundwater mounding caused by leakage from the waste site on cont aminant migration. To address the limitations of EPACML, the modeling approach has been enhancedand implemented in EPACMTP. The EPACMTP modeling approach incorporates greater flexibility and versatility in the simulation capability; the model explicitly can take into consideration: (i) chai n decay reactions and transport of daughter and grand-daughter products, (ii) the change to groundwater velocity as a result of water-table mounding, (iii) finite source as well as continuous source scenarios, and (iv) metals transport through linking with the MINTEQ metals speciation model. In addition to enhancing the simulation capabilitie s of the EPACMTP computer code, the Agency has also re-evaluated and updated th e data used in the nationwide Mont e Carlo modeling assessment. The Agency has evaluated additional data from surveys of actual waste sites 1 and other pertinent studies. Based on the additional data sources, the Monte Carlo me thodology as well as the frequency distributions of individual model input parameters have been revised and enhan ced. The Monte Carlo methodology implemented in the current version of EPACMTP uses a regional, site-based approach which directly accounts for any dependencies betweenthe geographical location of a waste site and the corresponding regional climatic and hydr ogeological characteristics. In parallel with enhancing th e model computer code and M onte Carlo methodology, the Agency has conducted various verification and field validation studies. Indivi dual components and modules of the computer code have been verified against existing analytical and numerical solutions, as documented in the EPACMTP Background Document. The modeling approach and solution techniques have been reviewed by the Science Advisory Board; their implem entation has been reviewed and tested by the EPA’s Office of Research and Development and outside review ers. The Agency has fiel d-tested the model at a number of actual field sites for whic h adequate source and site charact erization data, as well as plume monitoring data, were available. Th ese sites include the Borden landfill re search site in Ontario, Canada; a pesticide contaminated site on Long Island, New York; a pesticide monito ring field site near Kansas City,ii E:\Projects\EPA\R08-99.217.wpd Kansas; and an electric utility waste disposal site inNew York State. In all these cases, EPACMTP was found to yield reasonably good predictions of actual plume concentrations. The implementation and documentation of the model have beenrevised also in response to cr itical evaluations of the modeling approach by various public interest and industry groups. In response to these comments, the Agency has conducted add itional testing of the model, as well as reconsidered and revised various aspects of the modeling procedures, e.g., options for placement of the receptor well down-gradient from the waste site were revised to remove a bias towards larger waste sites. The present version of the model represents a development effort whic h has spanned more than six years. Although a considerable effort has been expended on developi ng and testing the model, EPACMTP, like any computer model, provides a simplif ied representation of cond itions and processes that may be encountered at actual waste sites. A lthough there is much ongoing research in the area of subsurface fate and transport processes, there is no clear consensus on how to represent all of these processes in a computationally efficient way, partic ularly in the framework of a generic model such as EPACMTP. Furthermore, the lack of detailed site characterization data on, for instance, heterogeneities and fractures on a nationwide basis, inevitably impos es limitations. EPACMTP therefore represents a balance and compromise between the evolving understa nding of subsurface fate and transport processes and the ability to reproduce these processes in a comput ationally efficient model. Therefore, the use of the model for site-specific applications, where he terogeneities and other co mplexities might make it unsuitable, is discouraged.iii E:\Projects\EPA\R08-99.217.wpd ABSTRACT The EPA’s C omposite M odel for leachate migration with T ransformation P roducts (EPACMTP) code, is used by EPA (the Office of Solid Waste) to simulate the fate and transport of contaminants leaching from a land-based waste management unit th rough the underlying unsaturat ed and saturated zones. EPACMTP replaces EPACML as the best available tool to predict potential exposure at a downstream receptor well. EPACMTP offers improvements to EPACML by considering: 1) the formation and transport of transformation products; 2) the imp act of groundwater mounding on groundwater velocity; 3) finite source as well as continuous s ource scenarios; and 4) metals transport. Fate and transport processes simulated by the model include: advection, hydrodynamic dispersion, linear or nonlinear sorption, and chain-decay reactions. In cases where degradation of a waste constituent yields daughter products that are of concern, EP ACMTP accounts for the formation and transport of up to six different daughter products. The composite model consists of a one-dimensional module that simulates infiltration and dissolved constituent tran sport through the unsaturated zone, which is coupled with a three-dimensional saturated zone module. The saturate d zone module consists of a threedimensional groundwater flow and a three-dimensi onal transport sub-module. The saturated zone groundwater flow sub-module accounts for the effects of leakage from the land disposal unit and regional recharge on the magnitude and direction of groundwaterflow. The saturated zone transport sub-module accounts for three-dimensional advection and dispersion, chain decay reactions with up to seven different chemical species (i.e., parent with up to six daughter products), and linear or nonlinear equilibrium sorption. The fate and transport of dissolved waste cons tituents in the subsurface depends on the chemical properties of the contaminants as well as on hydr ogeological and other parameters. The effect of uncertainty or variability in these parameters on r eceptor well exposure estimates can be taken into account using the Monte Carlo simulation technique. The Monte Carlo simulation module used in EPACMTP is based on the Monte Carlo module in EPACML. Th e Monte Carlo Methodology has been enhanced by incorporating a site-based approach which accomm odates dependencies betweenparameters and accounts for regional differences in clim atic and hydrogeol ogic conditions, and which can accommodate finite as well as continuous sources. This document is intended as a user’s guide for EPACMTP; for details of the mathematical techniques employed, the reader is referred to EPACMTP background document for the unsaturated and saturated zone and Monte Carlo modules (U.S. EPA, 1996b). Details on the finite source modeling procedure and background and methodology for the fate and transport asse ssment of metals are presented in separate background documents (U.S. EPA, 1996c and U.S. EPA, 1996d, respectively).iv E:\Projects\EPA\R08-99.217.wpd DISCLAIMER The work presented in this document has b een funded by the United States Environmental Protection Agency. Mention of trade names or co mmercial products does not constitute endorsement or recommendation for use by the Agency.

WARNING EPACMTP was developed for regulator y purposes for implementation on a nationwide basis using the Monte Carlo si mulation procedure, and the code is not suitable for site-specific use. The mode l does not account for site heterogeneities, complicated source conditions, and several other site-specific conditions which may be encountered at actual waste sites.v E:\Projects\EPA\R08-99.217.wpd ACKNOWLEDGMENT A number of individuals have b een involved with the developmen t of the EPACMTP model. Dr. Zubair Saleem of the U.S. EPA, Office of Solid Wa ste, provided overall techni cal coordination and review throughout this work. Drs. Peter Huyakorn and Jan Kool of HydroGeoLogic, Inc. developed the unsaturated zone module used in the composite model. Dr. Edward Sudicky, Robert McLaren and Rene Therrien of the University of Waterloo developed the sa turated zone module. Ne il Blandford of HydroGeoL ogic, Inc. developed the Monte Carlo module of EPACMTP, based on a similar module in EPA ’s C omposite M odel for L andfills (EPACML). Dr. Carlos Marin of Ambiotec Environm ental Consultants provide d overall technical support for the site-based Monte Carlo conceptualization of the model and the finite source methodology. Raj Lingam and Omar Abdi of HydroGeoLogic, have implemented and tested the current version of the EPACMTP model. The fate and transport methodol ogy for metals is under de velopment in cooperation with the EPA ORD Environmental Research Laborator y in Athens, GA. Drs. Nam-Sik Park and Yu-Shu Wu modified and implemented the model for meta ls in cooperation with Jerry Allison of Allison Geoscience and staff at the EPA Environmen tal Research Laboratory in Athens, GA.vi E:\Projects\EPA\R08-99.217.wpd EPACMTP Quick Start Guide This Quick Start section is inte nded to get new users up and running with EPACMTP quickly. It is not a replacement for the complete User ’s Guide. To fully understand th e model capabilities and assumptions, and how to use EPACMTP effectively, all users should consult this EPACMTP User’s Guide, the Background Document (U.S. EPA, 1996a, b, c). The EPACMTP modeling software consists of: EPACMTP: The Monte Carlo fate and transport model POSTCMTP: A menu-driven post-processor for analyzing EPACMTP model results. The Quick Start guide tells you how to install the soft ware and how to use the m odel and the pre- and postprocessors.
Computer Requirements To install and run EPACMTP, you must have an MS -DOS 80386 or better PC with at least eight (8) megabytes of RAM memory. A fast Pentium comput er is highly recommended. About 1.5 megabytes of free disk space is required to install the software. Finite source modeling runs may require up to 20 megabytes of additional disk space for temporary files. In order for EPACMTP to operate correctly, yourCONFIG.SYS file should contain the following commands: FILES = 20 (or more) DEVICE = ANSI.SYS

Installation Procedure The installation is controlled through a utility program, INSTALL.EXE on the distribution disk. To install follow these steps: • Insert the distribution diskette in the appropriate disk drive. This is the source drive (usually a: ) • Make the source drive the current drive, by typing ‘ a: ’ at the DOS prompt • Type ‘ INSTALL a: c: ’ where a: represents the sour ce drive and c: represents the target drive. The installation program will automatically c opy the contents of the a: drive into a directory call “EPACMTP” on you c: drive. • That’s it!vii E:\Projects\EPA\R08-99.217.wpd After installation is complete, some information on EPACMTP will be displayed on-screen. This information can be viewed again later by typing 

### < MODL-INFO ’.

RUNNING EPACMTP To setup an EPACMTP run you must first create an ASC II data input file. Detailed instructions for this are provided in Appendix A. To create a new input file for another problem, you can use a text editor to modify an existing data input file. Sample data files are provided in the EPACMTP direct ory. Consult the file FILES.LST for a list of the file names. To run EPACMTP, simply type 

### < EPACMTP ’ which will bring up the Main Menu screen. From the Main Menu, using the a rrow keys, select ‘Run’ and hit [RETURN] . This will give you the Run Option screen. To run the model, select the (C)o mmand line mode option by pressing the letter C. Then enter the name of the input data file and press [RETURN]

. • You must give complete DOS file names • EPACMTP will use the same name as the data input file, but an extension of


### < .out ’, to create the model output file When EPACMTP is running in Monte Carlo mode, an iteration counter and estimated completion time are displayed on-screen. This will show the progress of the Monte Carlo simulation. Do not be alarmed when a run takes a long time, especially in the case of finite source simulations.

USING THE POST-PROCESSOR EPACMTP generates a number of output file s. The main output file (extension 

### < .out ’) contains a listing of the data input. The Monte Carlo output is stored in files with extension of 

### < .cdf ’ (for continuous source analyses) and 

### < .sat ’. (for finite source analyses). The POSTCM TP post processor allows you to graphically analyze the modeling results contained in these output files.

POSTCMTP Post-Processor POSTCMTP is a menu-driven utility for graphically analyzing EPACMTP Monte Carlo results. It also has the capability of plotting graphs on a wide variety of printers and plotters, or saving a graph on disk in HPGL format for importing into various wo rd processing and/or desktop publishing programs.viii E:\Projects\EPA\R08-99.217.wpd To start POSTCMTP, type 

### < POSTCMTP ’ at the DOS prompt or select ‘Plot’ from the main menu of EPACMTP. The main menu of POSTCMTP presents three primary options: • (C)ontinuous Source Analysis • (F)inite Source Analysis • (I)nitialize Plotting Device The very first time you run POSTCMTP, you will be ta ken automatically into the Initialization menu, so we will discuss that first. Initialize Plotting Device The initialization menu allows you to select the type of printer or plotter and the parallel (LPT:) or serial port (COM:) through which the printer or plotter is c onnected to your PC. If you select a printer, you will also be asked for the desired density of the printe d graph. A higher density will result in better quality, but also slower printing. After you make the initial setup, the information is stored by POSTCMTP. To change any of the settings, you must go through the (I)nitialize menu again. Continuous Source Analysis After you select (C)ontinuous Sourcefrom the main menu, you are prompted for the name of the Monte Carlo output file. Enter the root filename only, without extension ! The main options on the Continuous Source menu are: (G)raph : View the DAF or CDF curve, and optionally send it to a printer or plotter (S)pecify : Enter a probability level and get the corresponding DAF value, and vice versa (N)ew File : Enter a new filename to analyze If you select the (G)raph option, the cumulative probability functi on (CDF) of the normalized receptor well concentration (i.e., the inverse of DAF) will be drawn on-screen. After the plot is completed, a number of additional options appear at the botto m of the screen. These include (H)ard Copy , to send the plot to your printer or plotter, and (S)avewhich allows you to save the plot on disk for later viewing, or in HPGL format. The latter allows you to import the graph directly into reports and documents by importing the HPGL plot file as a graphic into your wordprocessor. Note that printing to a laser printer at high resolution is quite slow; it may take 5 minutes per plot. Finite Source Analysis After you select (F)inite Sourcefrom the main menu, you will first be prompted for the name of the Monte Carlo output file. Enter the f ilename only, without extension !. The main options on the Finite Source menu are:ix E:\Projects\EPA\R08-99.217.wpd (G)raph : View the graph of leachate versus waste concentrations at the selected protection level (S)pecify : Select the desired protection level, or get the maximum leachate concentration for a given waste concentration (N)ew file : Enter a new file name to analyze. If you select the (G)raph option , a plot of leachate versus waste concentration corresponding to the selected protection level will be drawn on-screen. The default protection level is 85%. After the plot is completed, a number of additional options appear at the botto m of the screen. These include (H)ard Copy , to send the plot to your printer or plotter, and (S)avewhich allows you to save the plot on disk for later viewing, or in HPGL format. The latter allows you to import the graph directly into reports and documents by importing the HPGL plot file as a graphic into your wordprocessor. Note that printing to a laser printer at high resolution is quite slow; it may take 5 minutes per plot.
The (S)pecify option allows you to change the default prot ection level used in generating the graph of leachate versus waste concentrations.x E:\Projects\EPA\R08-99.217.wpd TABLE OF CONTENTS Page
1.0 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 1.1 ORGANIZATION OF THE DOCUMENT . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 1.2 EPACMTP CAPABILITIES AND LIMITATIONS . . . . . . . . . . . . . . . . . . . . 1-4 1.3 CODE REQUIREMENTS AND INSTALLATION PROCEDURES . . . . . . . . . 1-8 1.4 EPA CONTACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-9
PART 1 SIMULATION OBJECTIVES
2.0 SOURCE - RELEASE SCENARIOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.1 CONTINUOUS SOURCE SCENARIO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.2 FINITE SOURCE SCENARIO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1
3.0 METHODOLOGY OF SIMULATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 3.1 DETERMINISTIC MODE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 3.1.1 EPACMTP characteristics in deterministic mode . . . . . . . . . . . . . . . . 3-1 3.1.2 Requirements for running EP ACMTP in deterministic mode . . . . . . . . . 3-1 3.2 MONTE CARLO MODE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2 3.2.1 EPACMTP characteristics in Monte Carlo mode . . . . . . . . . . . . . . . . 3-2 3.2.2 Requirements for running EPACMTP in Monte Carlo mode . . . . . . . . . 3-3
PART 2 WASTE AND WASTE UNIT CHARACTERIZATION
4.0 SOURCE-SPECIFIC PARAMETERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.1 WASTE UNIT AREA AND DEPTH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.2 INITIAL LEACHATE CONCENTRATION (CZERO) . . . . . . . . . . . . . . . . . . 4-1 4.3 RECHARGE RATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5 4.4 INFILTRATION RATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5 4.5 SOURCE LEACHING DURATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6 4.6 WASTE DENSITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7 4.7 HAZARDOUS FRACTION OF WASTE . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7 4.8 SOURCE TRANSFORMATION RATES . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7
5.0 CHEMICAL-SPECIFIC PARAMETERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 5.1 ORGANIC CONSTITUENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 5.1.1 Hydrolysis Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 5.1.2 Reference Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4 5.1.3 Normalized Distribution Coefficient . . . . . . . . . . . . . . . . . . . . . . . . 5-4 5.1.4 Constituent Drinking Water Standard . . . . . . . . . . . . . . . . . . . . . . . . 5-4 5.2 METALS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4 5.2.1 Soil and Aquifer pH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4 5.2.2 Fraction Iron-Hydroxide Adsorbent . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 5.2.3 Leachate Organic Matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 5.2.4 Natural Organic Matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5xi E:\Projects\EPA\R08-99.217.wpd PART 3 SUBSURFACE FATE AND TRANSPORT SIMULATION
6.0 UNSATURATED ZONE SIMULATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1 6.1 THICKNESS OF THE UNSATURATED ZONE . . . . . . . . . . . . . . . . . . . . . 6-3 6.2 SOIL CHARACTERISTIC CURVE PARAMETERS . . . . . . . . . . . . . . . . . . 6-3 6.3 LONGITUDINAL DISPERSIVITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3 6.4 FREUNDLICH SORPTION COEFFICIENT (DISTRIBUTION COEFFICIENT) 6-7 6.5 PERCENT ORGANIC MATTER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7 6.6 FREUNDLICH ISOTHERM EXPONENT . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7 6.7 CHEMICAL AND BIOLOGICAL TRANSFORMATION COEFFICIENTS . . . . 6-8
7.0 SATURATED-ZONE SIMULATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1 7.1 PARTICLE DIAMETER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-7 7.2 POROSITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-7 7.3 BULK DENSITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10 7.4 AQUIFER THICKNESS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10 7.5 HYDRAULIC CONDUCTIVITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10 7.6 HYDRAULIC GRADIENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-11 7.7 SEEPAGE VELOCITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-11 7.8 RETARDATION FACTOR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-11 7.9 DISPERSION COEFFICIENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-12 7.10 TEMPERATURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-14 7.11 GROUNDWATER PH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-14 7.12 FRACTIONAL ORGANIC CARBON CONTENT . . . . . . . . . . . . . . . . . . . 7-14 7.13 RECEPTOR WELL LOCATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-14 7.14 CHEMICAL & BIOLOGICAL TRANSFORMATION COEFFICIENTS . . . . . 7-21 7.15 FREUNDLICH ISOTHERM COEFFICIENT . . . . . . . . . . . . . . . . . . . . . . 7-21 7.16 FREUNDLICH ISOTHERM EXPONENT . . . . . . . . . . . . . . . . . . . . . . . . . 7-22 7.17 DIFFUSION COEFFICIENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-22 7.18 ANISOTROPY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-22
PART 4 OUTPUT INTERPRETATION
8.0 RECEPTOR WELL CONCENTRATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1 8.1 DETERMINISTIC MODE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1 8.2 MONTE CARLO MODE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1 8.2.1 Model Output Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-2
APPENDIX A SIMULATION USING EPACMTP: DATA INPUT GUIDE . . . . . . . . . . . . . . A-1 APPENDIX B EXAMPLE OF MONTE CARLO SIMULATION PROBLEM . . . . . . . . . . . . . B-1
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R-1xii E:\Projects\EPA\R08-99.217.wpd LIST OF FIGURES Page Figure 1.1 A Cross-Sectional View of Subsurface Modeled by EPACMTP . . . . . . . . . . . . 1-5 Figure 4.1 Waste Management Scenarios Simulated by EPACMTP . . . . . . . . . . . . . . . . . 4-3 Figure 6.1 Cross-sectional view of the unsaturated zone considered by EPACMTP . . . . . . . 6-2 Figure 7.1 A schematic view of the groundwater system simulated by the saturated zone module of EPACMTP. The shaded area represents the projection of the contaminant source area onto the top of the aquifer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2 Figure 7.2 Groundwater temperature distribution fo r shallow aquifers in the United States. (After Collins, 1925) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-15 Figure 7.3 Schematic plan view showing pro cedure for determining the downstream location of the receptor well: (a) well location dete rmined using radial distance, R, and angle off center, 2 ; and (b) well location generated uniformly within plume limit . . . . 7-19xiii E:\Projects\EPA\R08-99.217.wpd LIST OF TABLES Page Table 1.1 Summary of the Components of EPACMTP Model . . . . . . . . . . . . . . . . . . . . 1-4 Table 3.1 Probability distributions and their a ssociated codes available for use in Monte Carlo module of EPACMTP composite model . . . . . . . . . . . . . . . . . . . . . . . 3-4 Table 4.1 Source-Specific Variables and Default Distribution Types . . . . . . . . . . . . . . . . 4-2 Table 4.2 Cumulative Frequency Distribution of Waste Unit Area for Industrial Subtitle D Waste Management Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-4 Table 4.3 Cumulative probability distribution of waste density . . . . . . . . . . . . . . . . . . . . 4-8 Table 5.1 Chemical-Specific Variables and Default Distribution Types . . . . . . . . . . . . . . 5-2 Table 5.2 Metals-Specific Variable and Default Distribution Types . . . . . . . . . . . . . . . . . 5-6 Table 5.3 Probability distribution of soil and aquifer pH . . . . . . . . . . . . . . . . . . . . . . . . 5-7 Table 6.1 Unsaturated Zone Specific Variables and Default Distribution Types . . . . . . . . . 6-4 Table 6.2 Statistical parameters for soil properties for three soil types (Carsel and Parrish, 1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5 Table 6.3 Compilation of field dispersivity values (Gelhar et al. 1985) . . . . . . . . . . . . . . . 6-6 Table 7.1 Aquifer-Specific Variables and Default Distribution Types . . . . . . . . . . . . . . . . 7-5 Table 7.2 Regional Hydrogeologic Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6 Table 7.3 Empirical distribution of mean particle diameter (based on Shea, 1974) . . . . . . . 7-8 Table 7.4 Ratio between effective and total por osity as a function of particle diameter (after Mcworter and Sunada, 1977) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9 Table 7.5 Probabilistic representation of longitudinal dispersivity . . . . . . . . . . . . . . . . . 7-13 Table 7.6 Cumulative probability of distan ce to nearest receptor well for landfills (from EPA, 1993d) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-18 Table 7.7 Cumulative probability distribution of receptor well depth . . . . . . . . . . . . . . . 7-20 Table 8.1 Monte Carlo output files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-31-1 E:\Projects\EPA\R08-99.217.wpd 1.0 INTRODUCTION

The USEPA Office of Solid Wastes (OSW) has developed a probabilistic (Monte Carlo) groundwater flow and transport mode ling approach to assess potential exposure of groundwater to toxic chemical constituents in wastes that are managed in Subtitle D industrial waste management units under RCRA regulations. The exposure to groundwater is e xpressed as concentrations of potential contaminants at drinking water well (receptor well) located downgradie nt of the waste disposal facility. This modeling methodology has been incorporated into the EPA’ s Composite Model for Leachate Migration with Transformation Products code (EPACMTP; EPA, 1996 a, b,c,d). The OSW of the U.S. EPA has recently applied EPACMTP, as a general fate and transport model, to establish regulatory levels for concentrations of chemicals in the above wastes under the proposedHazardous Waste Identification Rule (HWIR; EPA, 1995). EPACMTP simulates a one-dimensional, vertically downward flows and transport of contaminants in the unsaturated zone underneath a waste dispos al unit, and 3-D or 2-D groundwater flow and contaminant transport in the underlying saturated z one. The model accounts for the following processes affecting contaminant fate and transport: a dvection, hydrodynamic disp ersion, linear or nonlinear equilibrium sorption, chained first-order decay reac tions, and recharge dilution in the saturated zone. EPACMTP incorporates a Monte Carlo module which allo ws for the assessment of uncertainty in receptor well concentrations due to variati ons in the model input parameters. This document is prepared to facilitate the use of EPACMTP as an analytical tool. Section 1.1 provides general background information to direct the user through this document. Section 1.2, outlines the model capabilities and limitations. Practical code re quirements and installation procedures are provided in Section 1.3. To promote further utilization a nd future enhancements of the model, the user is encouraged to direct questions and comments to EPA’ s contact person. This information is provided in Section 1.4. 1.1 ORGANIZATION OF THE DOCUMENT This report is prepared to facilitate the use of EP ACMTP as an analytical tool. It provides the user a sequentially organized instructions to prepare an input file for EPACMTP. It also presents brief discussion on some of the key concepts of the model, particularly, with rega rd to estimation of input parameters as well as model output analysis. Th is document, alone, does not contain the background information that is necessary fo r a thorough knowledge and understanding of theoretical concepts and methodologies of EPACMTP components. Therefore, it is strongly recommended that the user read this and all other technical documentati on of EPACMTP (EPA 1996 b, c, d) to achieve a comprehensive understanding of the model. The rest of this section provides general info rmation to direct the user through this document. Section 1.2, outlines the model capab ilities and limitations which th e user must be familiar with before using the model for any applications. Practical code re quirements and installation procedures are presented in Section 1.3. To promote further u tilization and future enhancem ents of the model, the user is encouraged to direct questions and comme nts to EPA’s contact person. This information is provided in Section 1.4. This report is divided into the following four parts.1-2 E:\Projects\EPA\R08-99.217.wpd Part 1: Simulation Objectives of the Model Part 2: Waste and Waste Management Unit Characterization Part 3: Fate and Transport Simulation in the Subsurface Part 4: Model Output Processing and Analysis These parts represent the sequence of general cons iderations and information gathering steps that are required to develop an input file for the EPACMTP code. The material covered in each part is briefly described in this section. The ma jor components, options and considera tions for each part of the process are summarized in Table 1.1. This table also provi des a reference column thatgives the specific section of this report where the user can get details on specific items. The first part addresses model i nput parameter which determine th e general settings of the model under which fate and transport simulations are perf ormed. Among these parameters are the model’s execution mode (Monte Carlo or de terministic), waste management un it type (e.g. landfill, surface impoundment), source release scenario ( continuous or finite s ource) etc. This group of parameters are identified in the input file by their group code, GP which stands for general parameters. Chapter two focuses on issues related to source release scenar ios and Chapter 3 addresse s other parameters that determine the general setting of the model. Part 2 addresses parameters required to characterize waste management unit characteristics (e.g., area, depth, geographical location and climatic zone) as well as waste characteristics ( e.g., volume, density and concentrations in the waste and leachate). This group of variables are called source-specific parameters and are identified in the i nput file by their code SS. Describe d in Part 2 of the report also are the chemical-specific parameters identified in the input file by the code CS. This group of variables provide constituent specific information such as hydr olysis rates, diffusion coefficient, organic carbon distribution (koc ) etc. Part 3 of the document focuses on model variable s that characterize unsaturated zone (US) and saturated zone (AS) parameters. The letters shown in the brack ets are the identification codes for these two groups of parameters in the input file respectively. Unsaturated zone parameters include depth to water table, saturated hydraulic conductivity , soil moisture characteristic parameters dispersion etc. Examples of the saturated zone parameters include aquifer thickness, hydrau lic conductivity, hydraulic gradient, dispersivity and receptor well location. Part 4 of the report addresses issues regarding m odel output processing and analysis. It describes how the output of the model, receptor well concentr ations, are used to build probability distribution function which defines differe nt levels of protection. Appendix A contains detailed in structions on how to prepare an EPACMTP input file. The instructions are designed to guide the user, step by step , through the process of creating an input data file. The task of input file preparation would even be easier in the near future when a Windows-based EPACMTP preprocessor with graphi cal user interface is available. A DOS-based program is currently available for post processing of mode l results and is also presented in Appendix A. Sample input and output files for a Monte Carlo simulation scenario fo r landfills are presented in appendix B. Appendix C provides frequency distributions fo r key EPACMTP input parameters.1-3 E:\Projects\EPA\R08-99.217.wpd Table 1.1 Summary of the Components of EPACMTP Model

Component Input File Code* Options Consideration (s) Reference Section A. Source-Release Scenario GP • Continuous • Finite • Waste management scenario of concern 2.0
B. Methodology of Simulation GP • Deterministic • Monte Carlo • Single values for specific parameters • Probability distributions for specific parameters 3.0

C. Waste and Waste Management Unit Characteristics SS • Landfill • Surface impoundment • Waste pile • Land application unit • Active life • Dimensions • Waste value and leachate concentration • Climatic/hydrogeologic Characteristics 4.0

D. Contaminant Type CS • Organic • Metal • Transformation Products • Sorption Characteristics (linear/nonlinear) 5.0

E. Unsaturated zone module US Flow • Steady-state (1D) Transport • Steady-state • Transient • Sorption (linear/nonlinear) • Decay • Depth to water-table • Soil characteristics 6.0

F. Saturated zone module AS Flow • Steady-state (2D or 3D) Transport • Steady-state • Transient • Linear sorption • Decay • Aquifer characteristics • Saturated thickness • Ambient groundwater gradient 7.0

G. Receptor Well Location AS • Within plume • Anywhere downgradient • Along plume centerline only • (x) distance from waste unit • (y) distance from plume centerline • (z) well depth 7.13

*See Appendix A1-4 E:\Projects\EPA\R08-99.217.wpd 1.2 EPACMTP CAPABILITIES AND LIMITATIONS The EPA’s Composite Model for Leachate Migra tion with Transformation Products (EPACMTP) comprises four major components: • A module that performs one-dimensional analy tical and numerical solutions for water flow and contaminant transport in the unsaturat ed zone underlying a waste management unit. • A numerical module for steady-state groundwater flow subject to recharge from the unsaturated zone. • A module comprising analytical and numerical solutions for contaminant transport in the saturated zone. • A Monte Carlo module for assessing the effectof uncertainty or variations in model parameters on predicted contaminant concentrations in a receptor well. A cross-sectional view of the subsurface syst em modeled by EPACMTP is shown in Figure 1.1. The subsurface consists of an unsaturated zone underneath a waste unit, and an underlying water table aquifer. Contaminants move ve rtically downward through the unsaturated zone to the watertable. EPACMTP allows for the simulation of flow and trans port in the unsaturated zone and in the saturated zone, separately or combined. To use the model correctly, the user must be aware of the model capabilities and, perhaps more importantly, its limitations. The EPACMTP code is capable of simulating the fate and transport of dissolved contaminants from a point of release at th e base of a waste disposal unit, through the unsaturated zone and underlying groundwater, to a receptor well at an arbitrary downstream location in the aquifer. The code accounts for the major mechanisms affecti ng contaminant migration, including: transport by advection and dispersion, retardati on due to reversible linear or nonlin ear equilibrium adsorption onto the soil and aquifer solid phase, and bio-chemical degrad ation processes. The latter may involve chain decay reactions if the contaminant(s) of concern form a d ecay chain. As is true of any model, EPACMTP is based on a number of simplifying assumptions whic h make the code easier to use and ensure its computational efficiency. These assumptions, how ever, may cause application of the model to be inappropriate in certain situations. Therefore, the user should verify whether all assumptions in EPACMTP are appropriate before using the model fo r any application. The assumptions in EPACMTP are summarized below: 1) Soil and Aquifer Medium Properties It is assumed that the soil and aquifer are uniformporous media, and that flow and transport are described by Darcy’s law and the advection-dispersion equation, respectively. The model does not account for the presence of preferential pathwa ys such as fractures and macro-pores. Although the aquifer properties are assumed to be uniform , the model does allow for anisotropy in the hydraulic conductivity.1-5 E:\Projects\EPA\R08-99.217.wpd Figure 1.1 A Cross-Sectional View of Subsurface Modeled by EPACMTP.1-6 E:\Projects\EPA\R08-99.217.wpd 2) Flow in the Unsaturated Zone Flow in the unsaturated zone is steady-state one -dimensional vertical flow from underneath the source towards the water table. The lower boundary of the unsaturated zone is the water table. The flow in the unsaturated zone is predominatel y gravity-driven, and therefore the vertical flow component accounts for most of fluid flux between the source and the water table. The flow rate is determined by the long-term average infiltrati on rate through the waste management unit. In surface impoundments, it is determined by the average depth of ponding. 3) Flow in the Saturated Zone The saturated zone module of EPACMTP is designe d to simulate flow in an unconfined aquifer with constant saturated thickness. The concept is that of regional flow in the horizontal direction, with vertical disturbance due to recharge and in filtration from the overlying unsaturated zone and waste disposal facility. The lower boundary of th e aquifer is assumed to be impermeable. Flow in the saturated zone is assumed to be steady-state. EPACMTP accounts for different recharge ra tes underneath and outside the source area. Groundwater mounding beneath the source is repres ented in the flow system by increased head values at the top of the aquifer. This approach is reasonable as long as the height of the mound is small relative to the thic kness of the saturated zone. 4) Transport in the Unsaturated Zone Contaminant transport in the unsaturated zone is by advection and dispersion. The unsaturated zone is assumed to be initially contaminant -free and that contaminants migrate vertically downwards from the disposal facility. EPACMT P can simulate both steady-state and transient transport in the unsaturated zone, with single speci es or multiple species chain decay reactions, and linear or nonlinear sorption. 5) Transport in the Saturated Zone Contaminant transport in the saturated zone is due to advection and dispersion. The aquifer is assumed to be initially contaminant-free and c ontaminants enter the aquifer only from the unsaturated zone immediately underneath the wast e disposal facility, which is modeled as a rectangular horizontal plane source. EPACMTP can simulate both steady-state and transient transport three-dimensional transport in the aquife r. For steady-state transport, the contaminant mass flux entering at the water table must be consta nt with time. For the transient case, the flux at the water table can be constant or may vary as a function of time. EPACMTP can consider the transport of a single species or multiple speci es chain decay reactions, and linear sorption.1-7 E:\Projects\EPA\R08-99.217.wpd 6) Contaminant Phases EPACMTP assumes that the dissolved phase is th e only mobile phase, and disregards interphase mass transfer processes other than adsorption onto the solid phase. The model does not account for volatilization in the unsaturated zone; this is a conservative approach for volatile chemicals. The model also does not account for the presence of a NAPL (e.g. oil) and transport in gas phase. When a mobile oil phase is present, significant migration may occur within it, so that EPACMTP may underpredict the movement of hydrophobic chemicals. 7) Chemical Reactions The EPACMTP model represents chemical r eactions by adsorption and decay processes. Adsorption The EPACMTP assumes that sorption of organi c compounds in subsurface is represented linear adsorption isotherms in both the unsaturated and satu rated zones. It is assumed that the adsorption of contaminants onto the soil oraquifer solid phase occurs in stantaneously, and is entirely reversible. The effect of geochemical interactions is especia lly important in the fate and transport analyses of metals. For the simulation of metals, EPAC MTP utilizes sorption isotherms generated by MINTEQA2 (Allison et al., 1 991, a metal specia tion model.) MINTEQA2 generates concentration-dependent effectiv e partition coefficients, for various combinations of geochemical conditions. This procedure is described in the background document for the modeling of metals transport (EPA, 1996d). Transformation reactions EPACMTP accounts for chemical and biological tr ansformation processes. All transformation reactions are represented by first-order decay proce sses. An overall decay rate is specified for the model, so that the model cannot explicitly consider the separate effects of multiple degradation processes such as oxidation, hydrol ysis and biodegradation. The user must determine the overall, effective decay rate when multiple decay processes are to be repres ented. In order to increase its flexibility, EPACMTP has the capability of determ ining the overall decay rate from chemicalspecific hydrolysis constants using soil a nd aquifer temperature and pH values. EPACMTP assumes that the reaction stoichiometr y is prescribed for scenarios involving chain decay reactions. The speciation factors are specified as constants by the users (see the EPACMTP Background Document, EPA, 1996b). In reality, thes e coefficients may change as functions of aquifer conditions (for example, temperature and pH ) and/or concentration levels of other chemical components.1-8 E:\Projects\EPA\R08-99.217.wpd 1.3 CODE REQUIREMENTS AND INSTALLATION PROCEDURE Code Requirements In order to apply the EPACMTP code effectively the user will need: • A thorough understanding of hydrogeological principles, • A thorough understanding of solute transport mechanisms, • An understanding of the principles of Monte Carlo analysis, • An awareness of the code’s capabilities and limitations, • Familiarity with FORTRAN i nput format conventions, and • Familiarity with the editor, operating system, and file handling concepts of the computer system used. The run-time version of EPACMTP requires an80386/486 or Pentium based computer with an MS-DOS operating system. A 80387 math coprocessor is required for 386 systems. The computer should be equipped with at least eight (8) megabytes of random access memory (RAM) and a hard disk. To accommodate temporary files created during a model run, the hard disk should have at least one megabyte of free space for Monte Carlo simulations with the c ontinuous source option. For model runs with the finite source option, up to twenty megabytes of free disk space may be required by the model for temporary files. Users are strongly encouraged to read the entire User’s Guide before applying the model. This manual explains the assumptions and approximations used in the model, knowledge of which is a necessary prerequisite for using the model e ffectively. Instructions for se tting up, executing, and analyzing a problem are provided in appendix A. Installation Procedure The EPACMTP program should be executed from a harddisk. To facilitate installation of the files from the distribution diskette onto a computer hard disk, an installation utility (INSTALL.EXE) is provided which automatically copies the necessary files. To install the software on your hard disk, insert the distribution diskette into your floppy drive (usually “a”) and type: a:\install a: c: where a: is the drive containing the distribution disk and c : is the hard drive. The installation program will create a directory (default is c: \EPACMTP) in which all program files will be installed. In order to run EPACMTP, your CONFIG.SYS file should contain the following lines:1-9 E:\Projects\EPA\R08-99.217.wpd FILES=20 (or greater) DEVICE=ANSI.SYS Consult your MS-DOS manual for mo re information on configuring th e CONFIG.SYS file. Additional information on system configuration will be displayed during the insta llation procedure and is provided in the README.1ST file on the distribution diskette.
1.4 EPA CONTACT For additional information or to submit comments and recommendations, please contact: Dr. Zubair Saleem U.S. Environmental Protection Agency Office of Solid Waste (Mailcode 5307W) 401 M Street, SW Washington, DC 20460 Phone: (703) 308-0467 FAX: (703) 308-0511 E-mail: saleem.zubair@epamail.epa.govPART 1
SIMULATION OBJECTIVES2-1 E:\Projects\EPA\R08-99.217.wpd 2.0 SOURCE - RELEASE SCENARIOS
There are two types of source release scenarios in EPACMTP. They are continuous and finite soures. In a continuous source release scenario, the c ontaminant leaches out at constant prescribed leachate concentration for an indefinite period of time. Inthis case, the receptor well concentration eventually reaches a steady-state value. For th e finite-source scenario, the release of contaminants occurs over a finite period of time, after which the leachate concentrati on becomes zero (i.e. all the waste inside the waste management unit has leached out). In addition to th e concentration, the receptor well concentration for finite source scenario is also a ffected by the duration of the source. The source-release parameter is one of several general parameters (GP) that are designated in the input file. The discussion in this section in restrict ed to the source-release parameter since it is the most significant general parameter in determining the methodology and appr oach of the simulation. The designation of other genera l parameters are addresse d in detail in Appendix A. 2.1 CONTINUOUS SOURCE SCENARIO In the continuous source scenario, the fate and transport model is run in steady-state mode to compute a Dilution-Attenuation Factor (DAF). The DAF is defined as the ratio of source concentration to receptor well concentration. It represents th e combined effects of the site, and hydrogeologic and chemical specific parameters on the receptor con centrations. The model run can be conveniently performed using a source leachate concentration value of one, so that the DAF becomes the inverse of the receptor well concentration value. In order to asse ss the impact of varying site conditions across the nation, the fate and transport mode l can be coupled to a Monte Carlo module to account for the variability of input parameters. In this way, the model is used to calculate a series of DAF values corresponding to a large number of different model i nput parameter combinations, reflec ting the range of parameter values for different possible waste sites. A probability distri bution of DAF values is the result of a Monte Carlo analysis. The resulting DAF values provide a mean s to identify hazardous versus non-hazardous wastes, based on the DAF values for individual toxic constituen ts as well as their Health Based Numbers (HBNs) in the waste. See Appendix B for a sample continuous source problem. 2.2 FINITE SOURCE SCENARIO In the case of a finite source, simulations are performed for transient conditions and the source is assumed to be a pulse of finite duration. The le ngth of the pulse duration, in the case of landfills, is determined by the code based upon the initial amount of contaminant in the landfill, infiltration rate, landfill dimensions, waste and leachate concentration and waste density (EPA ,1996c). For other waste management scenarios (i.e. waste piles, surface impoundments, and land appli cation units), the duration of the leaching period is determined by the waste unit’s life time (the default is 20 years for waste piles and surface impoundments, and 40 years for land application un its). For the finite source case, the model can calculate either the peak receptor well concentration, or it can calculate an average concentration over a specified period of time. The finite source methodology in EPACMTP is discussed in detail in the finite source background document (EPA, 1996c).3-1 E:\Projects\EPA\R08-99.217.wpd 3.0 METHODOLOGY OF SIMULATION

Two modes of simulation, determin istic mode and Monte-Carlo mode, are available to the user for applications with EPACMTP. The selection of one of these modes is dependent on: 1) simulation objectives; and 2) the type of data available to simula te the waste management scenario(s) of concern. The deterministic mode uses single valu es for various source, chemical, and subsurface parameters. This mode is most appropriate for non-probab ilistic applications, and simple se nsitivity analyses. When using a Monte-Carlo procedure, the source, chemical and subsurface parameters ar e represente d by probability distributions reflecting variati ons based on national or regional level. Specific capabilities and requirements of EPACMTP for both of these two simu lation procedures are presented in the following sections. 3.1 DETERMINISTIC MODE In order to help the user to appreciate the di fferences between the deterministic mode and the Monte Carlo procedure, this section describes the capabilities of the EPACMTP in deterministic mode as well as the specific requirements for running the model in de terministic mode. 3.1.1 EPACMTP characteristics in deterministic mode • Flow is steady-state in both the saturated and unsaturated zones. • Transport in the unsaturated and saturated z ones can be either transient or steady-state. • If a finite source is being simulated, both the unsaturated and saturated zone transport modules are implemented in the transient mode. • Each input parameter is assigned a constant va lue or treated as a derived parameter. An example is the calculation of the partition coefficient ( k d ) from the fraction organic carbon ( f

oc ) and organic carbon distribution coefficient ( k oc ). 3.1.2 Requirements for running EPACMTP in deterministic mode • The logical variable, MC, in input group GP01 must be set to F(alse). • If a transient transport in the unsaturated zone is to be simulated, the times at which the concentration profiles in the unsaturated zone are to be written to the output file must be specified by the user. • If transport in the saturated zone is to be simulated, the user must specify the number of observation wells, and their locations relative to the disposal facility. If a transient run is selected, the user must also specify the time values at wh ich the computed receptor well concentrations are to be written to the output file. Details for setting up a deterministic input data file are presented in Appendix A.3-2 E:\Projects\EPA\R08-99.217.wpd 3.2 MONTE CARLO MODE When EPACMTP is used in the Monte Carlo mode , the primary output from the simulation model is a sequence of receptor well concentration values , where each concentration corresponds to the results of one model simulation. For each simulation, a di fferent set of input para meters are generated from appropriate distributions to calculate a receptor we ll concentration. The Monte Carlo output is then analyzed to derive regulatory DAF values (waste piles, surface impoundments, and application units) or regulatory limits on leachate and waste concentrations landfills. For the continuous source scenario, the DAF is obtained by dividing the source leachate concen tration by the receptor well concentration. Each Monte Carlo iteration yields one DAF value for the c onstituent of concern (one DAF value for each of the transformation products, if the constituent degrades to a daughter product.) Ordering the DAF values from low to high directly yields the DAF cumulative probability distribution (CDF). If appropriate, CDF curves representing different regional distribu tions may be combined into a singl e, weighted curve that represents all regions. (See Part 3, Section 2.2 in the Background document for further discussion.) For the finite source scenario, the post-processing step involves determination of the paired values of leachate concentration, C Lin the case of landfills, and total leachable waste concentration, C w , which satisfy a specified protection level. The procedure is described in the fin ite source background document (EPA, 1996c). To facilitate the post-processing analys is of Monte Carlo simulations, a separate postprocessing program (POSTCMTP) is distributed with the EPACMTP code (see Section 8.0). 3.2.1 EPACMTP characteristics in Monte Carlo mode • Flow in the saturated zone is steady-stat e in the unsaturated and saturated zones. • Transport in the unsaturated and saturated z ones can be either transient or steady-state. • If a finite source is being simulated, both the unsaturated and saturated zone transport modules are implemented in the transient mode. • Prespecified input parameters are represente d by probability distributions specified in the input file. It is allowed, however, to specify that a certain parameter is to be treated as a derived variable. An example is the calcu lation of the waste fraction (ratio of waste volume to facility volume). 3.2.2 Requirements for running EPACMTP in Monte Carlo mode • The logical variable, MC, in input group GP01 must be set to T(rue). • Probability distributions for the stochastic orrandom parameters must be specified in the EPACMTP data input file. • If transport in the saturated zone is to be simulated, the user must specify the number of observation wells, and their locations relative to the disposal facility. If the logical variables CHECK and LDRIV (see A ppendix A) are set to TRUE in the Monte Carlo input file, the generated random and derived variables, respectively, are constrained to lie within the upper and lower bounds defined by the user. The randomly generated variables are checked at the end of3-3 E:\Projects\EPA\R08-99.217.wpd subroutine RANGEN, and the derive d random variables are checked at the end of subroutine ASSIGN. If any of the constrained values lie outside the specified bounds, the entire Monte Carlo run (except for the reading of the input files) prior to the point of checking is repeated. Random and derived variables that are generated out of bounds are written to the Monte Carlo output file { OUTPUT }.BND .
Variables that are to be treated as random must be assigned one of thirteen probability distribution types. The distribution types and their corresponding distribution codes are listed in Table 3.1. The default distribution type for each Monte Carlo variable is discussed in the next chapter. For further information on the various distribution types, consult the EPACMTP Background Document (EPA, 1996b). 3.2.3 Regional Site-Based Monte Carlo Approach The implementation of the regional site-based Mont e Carlo approach in EPACMTP allowed the use of individual waste unit characteristics as well as regi onal specific climatic and hydrogeologic conditions in the fate and transport simulations. Prior to this approach, model input parameters, such as waste unit area and depth, infiltration and recharge rates, depth to water table, aquife r thickness, etc., were generated from national distributions in which correlation between the geographical location of the waste site and its climatic (infiltration and rechargerate) and hydrogeologic (unsaturatedand saturated z one parameters) properties were not preserved. The regional site-bas ed approach is discussed in detail in the EPACMTP Background Document (EPA, 1996b); however, an ove rview of the methodology is presented here. The steps involved in the site-based Monte Carlo approach are presented below. Fundamentally, the approach consists of determin ing the probability distribution of DAF based on the existing Subtitle D waste sites, on the assumption that these sites are anadequate representation of the universe of possible waste sites in the U.S. Since the the Subtitle D data set only includes the area, volume, location and relative weight of the facility, other data sources are utilized to determine the additional input parame ters required to estimate the receptor well concentration for each OPPI site. The groundwater parameters are obtained by divi ding the U.S. into the thirteen hydrogeologic environments developed in the HGDB groundwater study (API, 1989). Each groundwater region includes multiple site- specific combinations of hydrogeologi c parameters. In order to assign the groundwater parameters for a given waste site, the hydrogeologic e nvironment is first determ ined from the geographic location of the waste site. One of the availabl e combinations of groundwater parameters for the hydrogeologic environment is then assigned to the wast e site. In the case where the selected groundwater parameter combination has missing values, a joint pr obability distribution is derived for each groundwater region to generate the mi ssing value as a function of the known valu es. Given the location of the waste site, the climatic parameters for th e nearest climatic center are assigned. The remaining parameters needed to determine the receptor well concentration for a given waste site are generated by using national distributions. Given a specific realization of input parameters representing a waste site, and the climatic, hydrogeologic, and receptor well characteristics of that site, the corresponding receptor well concentration can be determined. This process is repeated in an iterative manner to construct a probability distribution of receptor well concentrations. During each iteration, a site is selected at random from the Subtitle D data set. The sites are sampled with replacement, i.e., th e same site may be selected more than once. The3-4 E:\Projects\EPA\R08-99.217.wpd probability of selecting a specific site depends on the re lative weight assigned to th at site in the data set. . The procedure of the Monte Carl o simulation run is summarized below. STEP 1: Select a Waste Site from Subtitle D Database STEP 2: Generate Recharge and In filtration for Selected Waste Site STEP 3: Generate Hydrogeologic Va riables for Selected Waste Site STEP 4: Generate Remaini ng Model Input Parameters for Selected Waste Site STEP 5: Calculate the receptor well concentration value for Selected Waste Site STEP 6 Repeat Steps 1 through 5 for the number of interations required, and estimate the national distribution of receptor well concentrations After Step 5 the receptor well concentration value fo r a specific realization is obtained. The process is repeated to yield concentration values which represent the nationwide distribu tion of drinking water exposure concentrations.3-5 E:\Projects\EPA\R08-99.217.wpd Table 3.1 Probability distributions and their asso ciated codes available for use in Monte Carlo module of EPACMTP composite model.

Distribution Type Distribution Code

1. Constant 2. Normal 3. Lognormal 4. Exponential 5. Uniform 6. Log10 Uniform 7. Empirical 8. Johnson SB 9. Gelhar Empirical a 10. Area Transformation b 11. Vertical Well Position c 12. Site-based d 13. Derived Variable 0 1 2 3 4 5 6 7 8 9 12 99 -1

Note: All statistical parameters (mean, standard de viation, lower and upper bounds) are input in standard arithmetic (lineas) space. Appropriate transforma tions are subsequently performed by the code.

a Gelhar’s distribution applies only to satura ted zone dispersivities (Gelhar et al., 1992).

b This distribution applies to municipal Subtitle D landfill areas only.

c This distribution applies to the vertical position of the receptor well below the water table only; it is used to specify that the receptor well is located at a fixed depth below the water table.

d This distribution applies to parameters that are read dir ectly from an ancillary data file that contains waste locations, volume and area, and corresponding re gional climatic and hydr ogeological parameters for the site-based Monte Carlo analysis.PART 2
WASTE AND WASTE UNIT CHARACTERIZATION4-1 E:\Projects\EPA\R08-99.217.wpd 4.0 SOURCE-SPECIFIC PARAMETERS
A list of the source-specific input parameters is provided in Table 4.1. EPACMTP can perform Monte Carlo or deterministic analyses for four differe nt types of waste management scenarios: landfills, surface impoundments, waste piles, a nd land application units. Figur e 4.1 shows a schematic, crosssectional view of the different types of waste units. In terms of the model analysis, each type of waste management scenario is describe d by a relatively small number of pa rameters. The differences between waste units are represented by different values or fre quency distributions of the source-specific parameters. Source-specific stochastic parameters that may be used by EPACMTP include capacity and dimensions of the waste unit, the leachate concentr ation, infiltration and recharge rates, pulse duration, fraction of hazardous waste in the waste unit, density of the wast e, and concentration of the chemical constituent in the hazardous waste. Data on the area, volume and location of waste units was obtained from the 1986 EPA Survey of Industrial Subtitle D waste facilities in the U.S. This survey is referred to as the OPPI Survey. The derivation of these parameters for each type of waste management unit is described in the EPACMTP background document (U.S. EPA, 1996b). 4.1 WASTE UNIT AREA AND DEPTH The cumulative frequency distributions of the areas of landfill, surface impoundment, waste pile and land application waste units ar e listed in Table 4.2. These frequency distributions are shown graphically in Figures C1 through C15 in Appendix C. 4.2 INITIAL LEACHATE CONCENTRATION (CZERO) The (initial) leachate concentrati on for each species in the contam inant decay chain being modeled must be provided by the user. For continuous source (steady-state) simulations, the source concentration of the parent compound is convenien tly set to 1.0 (mg/L). Initial daughter product concentrations should be set to zero. For finite source simulations, the (in itial) parent leaching concentration should be set to a fixed value. An artificially large value (e.g. C L= 10 6mg/L) is recommended for computational convenience. Initial daughter product leaching concentr ations should again be set to zero. The actual parent leaching concentration that results in the desired level of protection is determ ined as part of the postprocessing analysis conducted after the Monte Carlo simu lation is completed. This procedure is used for organic contaminants and metals with linear sorption behavior. The post processing step takes advantage of the fact that the receptor well concentration for th ese chemicals varies linearly with the source leachate concentration. For details see the fi nite source background document (EPA, 1996c). In the case of metals with nonlinear sorption isotherms, relationship between receptor well concentration and the initial source leachate concentrati on is nonlinear. Therefore, the model has to be run with different initial source leachate concentrations until the desired protection level is achieved. Because of the nonlinearity the simulation time required is significantly longer than the linear case.4-2 E:\Projects\EPA\R08-99.217.wpd Table 4.1 Source-Specific Variables and Default Distribution Types.

Variable Units Distribution Code** Default Distribution

Waste Site Area (Aw) m 2 99 Regional Site-based Waste Site Length (Xw) m -1 Derived from area Waste Site Width (Yw) m -1 Derived from area Leachate Concentration (C L ) mg/l 0 Constant (1 x 10 +6for finite source; 1 for continuous source) Areal Recharge Rate (I R) m/yr 99 Regional Site-based Infiltration (I) m/yr 99 Regional Site-based Ponding Depth* m 99 Regional Site-based Thickness of Liner* m 0 Constant (2 feet) Hydraulic Conductivity of the Liner* m/yr 0 Constant (1 x 10 -7 cm/s) Source Leaching Duration (tp) yr -1 Derived Depth of the Waste Unit (d) m 99 Regional Site-based Fraction of Hazardous Waste in the Waste Disposal Unit (F h) 4 Uniform Density of Hazardous Waste (P hw ) (g/cm 3 ) 6 Empirical

* Required only for surface impoundment if source ponding op tion is used, i.e. infiltration is set to be a derived parameter. ** See Table 3.14-3 E:\Projects\EPA\R08-99.217.wpd Figure 4.1 Waste Management Scenarios Simulated by EPACMTP.4-4 E:\Projects\EPA\R08-99.217.wpd Table 4.2 Cumulative Frequency Distribution of Waste Unit Area for Industrial Subtitle D Waste Management Facilities.

Landfill Surface Impoundment Waste Pile Land Application Unit % Area (m2 ) % Area (m 2 ) % Area (m 2 ) % Area (m 2 )

1 2 5 10 15 25 35 50 65 75 85 90 95 98 99 Min Max 44.5 72.8 141.6 809.4

1214 3,683 8,094 16,188 32,3766 60,705 93,081 141,645 221,371 554,439 1,049,792
40.5 3,116,190 1 2 5 10 15 25 35 50 65 75 85 90 95 98 99 Min Max 24.3 40.5 40.5 80.9 161.9 404.7 849.9 2,024 5,059 8,094 20,235 40,065 81,345 234,726 518,016

13.4 60,705,000 1.8 15.0 25.0 31.4 35.5 40.1 45.0 50.1 60.7 70.5 80.2 85.1 90.0 95.0 99.0 Min Max 20.2 28.3 52.6 93.1 157.8 230.7 364.2 465.4 1,133 2,064 5,200 8,992 18,576 50,264 1,011,750
12.1 2,015,406 1.9 5.1 10.5 20.5 31.3 41.0 50.4 59.5 70.3 80.0 85.1 90.3 95.1 99.1

Min Max 80.9 930.8 3,642 10,117 21,813 40,875 97,128 161,880 273,173 465,405 472,164 105,222 1,942,560 6,879,900

5.1 8.1107

Note: The % column denotes the cumu lative frequency, i.e. P(A # Area (m 2 )).4-5 E:\Projects\EPA\R08-99.217.wpd 4.3 RECHARGE RATES The model requires input of the net rate of ve rtical downward percolation through the unsaturated zone to the water table. The model allows the flow rate through the waste source to be different from the regional average rate. The former is referred to as (s ource) infiltration rate, the la tter is referred to as the recharge rate. The source infiltration rate can be diffe rent from the recharge rate for a variety of reasons, including engineering design of the waste site, topography, land use, and vegetati on. The recharge rate depends on the regional climatic cond itions based on: 1) the proximity of the waste site to one of 97 climatic centers; and 2) the prevalent regional soil type. Details of the development of the current recharge rate database are pres ented below. Variations in soil types for determining regiona l recharge rates have been implem ented in the following manner: The Soil Conservation Service (SCS) has a county-by-county soil mapping databa se. Using this database, the soil classifications in the U.S. were grouped according to the U.S. Department of Agriculture’s definitions of coarse, medium, and fine textures. These th ree categories are represented in EPACMTP by soils equivalent in properties to sandy lo am, silt loam, and silty clay loam for the landfill cover materials. An analysis of the SCS database indicates that coarse grained soils, medium grained soils, and fine grained soils represent 15.4 percent, 56.6 percent, and 28.0 pe rcent, respectively, of the soils that have been mapped thus far. The recharge rate was determin ed separately for each soil type and each of the 97 climatic centers using the HELP model. The recharge rate values for each climatic center are listed in the form of EPACMTP input files at the end of the repor t. Frequency distributions of recharge rates for different waste management units are provided in A ppendix B. The probability of each of the three soil types is assumed to be uniform nationwide. In other words, the soil type and corresponding recharge rate at each modeled waste site is selected in the Monte Ca rlo analysis from the three different available types, with a weighting equal to thei r nationwide relative frequency. 4.4 INFILTRATION RATES Infiltration rates for landfills, waste piles, and land application units were calculated for each of the 97 climatic centers with the HELP model usi ng the procedure and assumptions regarding design characteristics of different types of waste units as described in the EPACMT P background document (EPA, 1996b). In the case of landfills, if the cover type and soil type underneath the unit are the same, the infiltration rate will be the same as the regional recharge rate for that soil type. The infiltration rate values for the landfills, waste piles, and land application units in the modeling database are listed in the form of EPACMTP data input files at the e nd of this report. The frequency distributions are shown graphically in Appendix C. For the surface impoundment scenario, the leachate flux rate is computed as a derived parameter, as part of the unsaturated zone flow module. The algorithm is described in the EPACMTP Background Document (EPA, 1996b). In essence, the leachate flux rate is calculated by applying Darcy’s law as a function of: (i) impoundment depth, ( ii) thickness of a sediment layer at the base of the impoundment, and (iii) hydraulic conductivity of this sedimentlayer and the underlying soil material. For the HWIR analyses (EPA, 1995), impoundment depths were obtained from the Subtitle D survey data. All impoundments were assumed to have a sediment layer of 2 feet thickness with a hydraulic conductivity of 10 -7 cm/s. Although the latter two paramete rs were taken to be constant, each impoundment in the survey has its own operating depth, and consequently the variation in surface impoundment leachate flux rates is proportional to the variation in operating depths of the impoundments4-6 E:\Projects\EPA\R08-99.217.wpd (4.1)

(4.2) in the survey. The frequency distribution of the surface impoundment infiltration rate is shown graphically in Appendix C of this document. 4.5 SOURCE LEACHING DURATION If a finite source simulation is being performed, the duration of the leachate release period must be specified. The duration of the leachate release peri od is determined in different ways for each of the waste management units. Except for landfills, the rel ease period is determined by the operational life (or active life) of the unit with default values as follows: Surface Impoundments - 20 years Waste Pile - 20 years Land Application Unit - 40 years In the case of landfills, the waste is left in place af ter closure of the site, and may continue to leach over a long period of time. The landfill scenar io is therefore best handled as follows: In the Monte Carlo analysis, the pulse duration, t p , is treated as a derived parameter and can be set in one of two ways. Specifically, the am ount of contaminant leached over the time periodt p must be equal to the amount of contaminant initially present inthe landfill. Method 1, Eqn. 4.1 below, assumes that source contamination, C L , is constant until an amount of mass equal to the initial mass is depleted.

where d = Depth of waste unit (m) F

h = Volume fraction of waste unit that contains waste of concern P

hw = Density of the waste (g/cm 3 ) I = Areal infiltration rate (m/y) C

L = Leaching concentration of constituent (mg/L) C

w = Total leachable waste concentration of the constituent (mg/kg) Alternatively, Method 2, E qn. 4.2 below, calculates t pfrom a more rigorous analysis that considers the decrease in the source concentration, C L , due to leaching from the waste. In this case, the leachate concentration decreases exponentially, with (EPA, 1996b):

where C L 0

= Initial leachate concentration (mg/L) t = Time (yrs)4-7 E:\Projects\EPA\R08-99.217.wpd Information on the distributions of waste density ( P hw) and fraction of the waste in the unit (F h ) for landfill disposal units is provided in the following secti ons. During the Monte Carlo analysis, the model simulations are performed for a range of different ratios of C w/C L(or C w/C L 0). The specific values of C w and C L(or C L 0) which correspond to a desire d level of protection, such as the 90th percentile, are determined following the Monte Carlo run as a post-processing step. 4.6 WASTE DENSITY Information on the density of hazardous waste was developed using th e densities of 4 major categories of waste (solvents, paints, petroleum pr oducts, pesticides) and thei r contributions to the composition of hazardous wastes in Subtitle D landfills (Schanz and Salhotra, 1992). The results are expressed as an empirical distribution of waste densities, given in Table 4.3. The distribution shows a relatively narrow range of vari ation, from 0.7 to 2.1 g/cm 3

. 4.7 HAZARDOUS FRACTION OF WASTE Data to determine whether a Subtitle D waste e xhibits hazardous characteristics are limited. By definition, Subtitle D waste is primarily non-hazardous. The fraction of waste contributed to Subtitle D waste facilities that may exhibit hazardous characteris tics is assumed to be randomly distributed and simulated using uniform distribution with lower and upper bounds of 0.036 to 1.0, respectively. The lower bound ensures that the modeled waste unit will always contain a minimum amount of hazardous waste. The lower bound was obtained from an analysis of wa ste composition in municipal landfills (Schanz and Salhotra, 1992). Note that the input values for waste density and fraction of the waste unit that is occupied by the waste, are required only for the landfill scenario. 4.8 SOURCE TRANSFORMATION RATES The model has the capability to account for first-orde r type chain decay reactions inside the waste source. This requires values for the transforma tion rate constants of th e parent compound and any daughter products. By default, however, the source tran sformation rate is set to zero, i.e., it is assumed that the contaminant does not degrade inside the waste unit.4-8 E:\Projects\EPA\R08-99.217.wpd Table 4.3 Cumulative probability distribution of waste density.

Waste Density (g/cm 3 ) Cumulative Probability

0.7 0.9 1.12 1.13 1.28 1.30 1.33 1.34 1.36 1.46 1.50 1.62 1.63 1.64 1.65 2.10 0 0.530 0.550 0.551 0.553 0.640 0.728 0.815 0.826 0.904 0.905 0.906 0.994 0.995 0.996 0.9985-1 E:\Projects\EPA\R08-99.217.wpd (5.1) 5.0 CHEMICAL-SPECIFIC PARAMETERS
Chemical-specific parameters describe the degrad ation, adsorptive and diffusive characteristics of each of the chemical species being simulated. The EPACMTP accounts for pr ocesses such as decay, adsorption, and diffusion. The model handles the chem ical-specific parameters of constituents differently depending on whether the chemical be ing modeled is an organic compound or a metal. Chemical-specific parameters for the two categories ar e described below. Chemical-specific parameters of the two chemical categories are described below. 5.1 ORGANIC CONSTITUENTS For organic constituents, EPACMTP accounts for chemi cal and biological transformations by considering a first order over all degradation ra te which may be available directly or derived from acid, base and neutral chemical-specific hydrolysis rates (i.e. Arrh enius equation). The model also takes into account adsorption behavior of organic constituents by calcula ting a retardation factor based on the organic carbon distribution coefficient ( k oc ) of each constituent and fractional organic carbon in the soil. Other inputs required by the model are reference temperature and c onstituent drinking water standard. In addition, if a finite source analysis is performe d, values are required for the app licable drinking water standard for each constituent, and the exposure averaging period usedin the health-risk calculations (e.g. 70 years for a carcinogenic constituent). By default, averaging is not used and the model computes the peak exposure concentration at a downgradient receptor well. In the event that the byproducts are hazardous and their chemical-specific parameters are known, they can be included in the simulation by specifying them to be part of a decay chain. Note that when a multi-species simulation is desi red, the necessary chemical-specific parameters must be repeated ,for all species in the decay chain, NSPECI times. A list of the chemicalspecific parameters is provided in Table 5.1. 5.1.1 Hydrolysis Rates The transport of organic constituents is influencedin part by hydrolysis. Hydrolysis rate constant for each chemical is obtained from: EPA’s Environmental Research Laboratory, Athe ns, GA. (e.g. EPA, 1993 and Kollig et al., 1990) Acid- and base-catalyzed hydrolysis rate constants are influenced by pH. The pH dependency is included directly in EPACMTP and the rates are adju sted via the independently derived distribution for pH. The acid-catalyzed, neutral and base-catalyzed hydrolysis rates are all influenced by groundwater temperature. This effect is quantified using the Arrhenius equation:5-2 E:\Projects\EPA\R08-99.217.wpd Table 5.1 Chemical-Specific Variable s and Default Distribution Types.

EPACMTP * Parameter Variable Units Distribution Code ** Default Distribution

RLAM 2 Sorbed phase hydrolysis decay rate ( 8 1 ) 1/yr -1 Derived
RLAM 1 Dissolved phase hydrolysis decay rate ( 8 2 ) 1/yr -1 Derived

AHYDR 1 Acid catalyzed hydrolysis rate constant 1/mol/yr 0 Constant
NHYDR Neutral hydrolysis rate constant 1/yr 0 Constant
BHYDR Base catalyzed hydrolysis rate constant 1/mol/yr 0 Constant
RTEMP Reference temperature °C 0 Constant (25°C) KOC Normalized distribution coefficient for organic carbon ml/g 0 Constant

DSTAR Effective molecular diffusion coefficient m2 /yr 0 Constant (0.0 m2 /yr) DWS Drinking water standard (i.e., HBL or MCL) mg/l 0 Constant
CARC Exposure averaging period yr 0 Constant

* See Appendix A, Table A.2 ** See Table 3.15-3 E:\Projects\EPA\R08-99.217.wpd (5.2)

(5.5) where
J = a for acid, bfor base, and n for neutral
T = temperature of the groundwater [°C]
T

r = reference temperature [°C]
Ka

T

, rband Ka T

,b = the second-order acid- and base -catalysis hydrolysis rate at temperature T r and T respectively [l/mole-yr]
Kn

T

r and Kn T = the neutral hydrolysis rate at temperatures T

r and T respectively [1/yr].
R g = universal gas constant [1.987E-3 Kcal/deg-mole]
E

a= Arrhenius activation energy [Kcal/mole]
Note that, using the generic activation energy of 20 Kcal/mole recommended by Wolfe (1985), the factor E a /R ghas a numerical value of 10,000. The acid-catalyzed, base-catalyzed, and neutral hydr olysis rate constants can be combined (Mills et al., 1981) to yield a composite first orde r hydrolysis rate in the dissolved phase:

where 8 1 = decay constant for dissolved phase [1/yr] [H + ] = the hydrogen ion concentration [mole/l] [ OH
- ] = the hydroxyl ion concentration [mole/l] Note that [H + ] and [ OH
- ] can be computed from the pH of the aquifer using [H + ] = 10 -pH(5.3) [ OH
- ] = 10 -(14-pH) (5.4) For the case of sorbed phase hydrolysis, evidence suggests that base-neutralized hydrolysis can be neglected and that the acid-neutralized hydrolysis rate is enhanced by a factor of " . Thus, the effective sorbed phase decay rate can be expressed as:

where K " = acid-catalyzed hydrolysis rate enhancement f actor for sorbed phase with a typical value of 10.0, and 8 2= decay constant for the sorbed phase [1/yr]. K a T , K b T and K a T are chemical-specific constants.5-4 E:\Projects\EPA\R08-99.217.wpd 5.1.2 Reference Temperature The chemical-specific hydrolysis rates are specifi ed for a constant reference temperature, the default value of which is 25°C. 5.1.3 Normalized Distribution Coefficient The normalized distribution coefficient for organic carbon ( k oc ) is used in conjunction with the fractional organic carbon content to obtain the distribution coefficient ( k d ). The k ocis a chemical-specific variable which is input as a constant. The use of k octo estimate sorption is appropriate for organic compounds which tend to sorb preferentially on the natural organic matter in the soil or aquifer. 5.1.4 Constituent Drinking Water Standard Two additional parameters must be specified if a finite source simulation is being performed. These are: (1) the applicable drinking water standa rd for each constituent in the waste, and (2) the exposure averaging period used in the risk calculations for determining the drinki ng water standard. Usual values of the latter parameter are 70 years (lifetime exposure) or 30 years (avera ge residence time). Note that the drinking water standard must be expressed inthe same units used for the leaching concentration, usually mg/L. Both these parameters are entered as constants. By default, the exposure period for averaging the receptor well concentration is set to zero; in other words, the model calculates the peak receptor well concentration. 5.2 METALS The sorption behavior of metals is divided into two groups. Those with linear sorption behavior and those with nonlinear sorption. The sorption char acteristics of the first group is represented by a linear adsorption isotherm coefficient ( k d ) which is a function of the pH in th e unsaturated zone. The relationship with the pH is described in the EPACMTP bac kground document for Metals (EPA, 1996d). Metals that belong to this group are: As, Sb, Cr +6 , Se, and Tl. The sorption behavior of the second group is characterized by nonlinear sorption isotherms. Metals in this group are: Ba2+ , Cd2+ , Be 2+ , Cr 3+ , Ni 2+ , Pb 2+ , Ag 2t , Zn2+ , V 4+ . The sorption isotherms for these metals are calculated by MINTEQA2, a meta l speciation model, and are provided to EPACMTP in tabular form as a sec ondary input file. Th e sorption isotherm data account for the effect of varying geochemical conditions in the soil and in the aquife r on the mobility of metal species by using different effective sorption isotherms whic h depend on a number of environmental master variables. These controlling master variables are listed in Table 5.2. For Monte Carlo analyses modeling, these parameters are treated as random variables. Sorption isotherms data are available for various combination of master variables varying from “low”, “medium”, or “high” va lues. This procedure is discussed in detail in the metals background document (EPA, 1996b). Probability distributions for the master variables are discussed below. 5.2.1 Soil and Aquifer pH The pH distribution was obtained by analysis of nearly 25,000 field measured pH values obtained from EPA’s STORET database. The data are represen ted by an empirical distribution with low and high values of 3.2 and 9.7, respectivel y and a median value of 6.8 (Table 5.3). The 0th and 100th percentile5-5 E:\Projects\EPA\R08-99.217.wpd (5.6) values, determined from national distribution of pH, are lower a nd upper bounds of pH measurements. This distribution was derived from a literature search for reported pH values of uncontaminated groundwater. For modeling purposes it is assumed that the unsaturated zone and the saturated zone have the same pH value. 5.2.2 Fraction Iron-Hydroxide Adsorbent Amorphous iron hydroxides (FeOx) represents one of the dominant adsorbents for metal sorption. Limited available data on iron hydroxidecontent is used to define a uni form distribution which results in a low value of 0.0126 and a high value of 1.115 pe rcent iron hydroxide by weight. The values are obtained from analyses by Loux et al. (1990) on aquife r samples collected by the U.S. EPA Office of Solid Waste (OSW) in Florida, New Jersey, Oregon, Texas, Utah, and Wisconsin. 5.2.3 Leachate Organic Matter The model uses a uniform distribution for the c oncentration of anthropogenic dissolved organic carbon (DOC a ) in the leachate. The distribution has a low value of 0.001173 mg/L, and a high value of 0.00878 mg/L. The mixture of organic compounds usedto represent the leachate-derived anthropogenic dissolved organic carbon is based on analyses of leach ate samples collected by the OSW in Florida, New Jersey, Oregon, Texas, Utah, and Wisconsin (Loux et al., 1990). 5.2.4 Natural Organic Matter The distribution for the unsaturated zone natura l organic matter varies with soil type, and is specified as the percent organic matter. Conversely, the natural organic matter in the saturated zone is specified as the fraction organic carbon ( f

oc ). The saturated zone frac tion organic carbon has a Johnson SB distribution with mean and standard deviation (in arithmetic space) of 4.32 10 -4and 0.0456, respectively, and an upper bound of 0.0638. The pe rcent organic matter and fraction organic carbon can be converted to fraction of organic carbon using:

where the conversion factor of 174 is the reciprocal of the average mass fraction of carbon in organic matter (Enfield et al, 1982).5-6 E:\Projects\EPA\R08-99.217.wpd Table 5.2 Metals-Specific Variable and Default Distribution Types.

Variable Units Distribution Code* Default Distribution

Metal Identification Number --- 0 Constant Soil and aquifer pH 6 Empirical (Table 5.3) Fraction Iron-Hydroxide absorbent Weight fraction 4 Uniform Leachate organic matter content mg/ R 4 Uniform Natural organic matter Weight fraction 7 Johnson SB Fraction organic carbon Weight fraction 7 Johnson SB

* See Table 3.15-7 E:\Projects\EPA\R08-99.217.wpd Table 5.3 Probability distribution of soil and aquifer pH.

Cumulative Probability (percent) pH Value

0.0 1.0 5.0 10.0 25.0 50.0 75.0 90.0 95.0 99.0 100.0 3.20 3.60 4.50 5.20 6.07 6.80 7.40 7.90 8.20 8.95 9.70E:\Projects\EPA\R08-99.217.wpd PART 3
SUBSURFACE FATE AND TRANSPORT SIMULATION6-1 E:\Projects\EPA\R08-99.217.wpd 6.0 UNSATURATED ZONE SIMULATION
A schematic view of the one-dimensional systemsimulated by the unsaturated zone module is provided in Figure 6.1. Details on the mathematical formul ation and solution techniques of the unsaturated zone flow and transport module are provided in the EPACMTP b ackground document (EPA, 1996b). To guide the user in evaluating appropriate fate and tr ansport options to simulate the pr ocesses in the subsurface, a summary of major assumptions is provided here: • Flow of the fluid phase is isothe rmal and is governed by Darcy’s law. • Flow is one-dimensional, vertica lly downward, and at steady-state. • The fluid is slightly compressible and homogeneous. • The dynamics of a second phase (e.g., air or nonaqueous liquid) can be disregarded. The solutions for transient and steady-state tran sport in the unsaturated zone are based on the following assumptions. • The leachate concentration entering the soil is either constant (with a finite or infinite duration) or decreasing with time following a first-order decay process. • Sorption of contaminants onto the soil solid phase is described by a linear or nonlinear (Freundlich) equilib rium isotherm. In order to simulate flow and transport in the unsaturated zone between the base of the disposal facility and water table, th e following data are required: • Thickness of the unsaturated zone (depth to water table from base of disposal unit). • Soil hydraulic parameters. If these paramete rs are not available, but the soil type is known, the parameter values fromthe database of Carsel a nd Parrish (1988) can be used. These data are used for Monte Carlo simulations. • The water flow rate (infiltration rate) th rough the disposal unit. This input can be determined for landfills from th e long term net percolation rate (precipitation minus runoff minus evapotranspiration). For surface impoundments , it may be determined from the depth of water ponding in the impoundment, and thickness and satu rated conductivity of the impoundment liner. The code can derive th e infiltration rate from these parameters. • Soil transport parameters. If not available, the dispersivity can be estimated from the unsaturated zone thickness, and the retarda tion and decay coefficients can be estimated from the soil bulk density, fraction of organic matter and chemical-specific properties. Procedures for calculating these parameters are presented in the next five sections.6-2 E:\Projects\EPA\R08-99.217.wpd Figure 6.1 Cross-sectional view of the uns aturated zone considered by EPACMTP.6-3 E:\Projects\EPA\R08-99.217.wpd (6.1) • The leachate concentration emanating from the base of the waste site. If a finite source is being simulated, the duration of the pulse mu st be specified. It is derived for landfills. • The number of component species and decay r eaction stoichiometry, in the case of chain decay reactions. • Organic carbon partition coefficient (K oc ) for organic chemicals and the effective partition coefficient ( k d ), as a function of concentration, in the case of metals with nonlinear sorption. A list of unsaturated zone parame ters is provided in Table 6.1. 6.1 THICKNESS OF THE UNSATURATED ZONE The unsaturated zone thickness, or depth to the wa ter table for each waste site is obtained from the regional hydrogeologic data base. Based on a site’s location, th e corresponding hydrogeologic region is selected. The unsaturated zone thickness is thenselected randomly from data available for that hydrogeologic region. The frequency distribution of unsaturated zone thickness is in Appendix B. 6.2 SOIL CHARACTERISTIC CURVE PARAMETERS Solution of the unsaturated zone flow requires know ledge of the soil characteristic curves, i.e., the relationship between water saturation and pressure head and betw een hydraulic conductivity and water saturation. The van Genuchten (1980) model is widely used for predic ting soil-water content as a function of pressure head. Probability distributions for the pa rameters of this model we re presented by Carsel and Parrish (1988). The variables analyzed by Carsel and Parrish include saturated hydraulic conductivity (K s ), residual water content ( 2 r), saturated water content ( 2 s ), and two empirical constants ( " and $ ). The statistical parameters presented by Carsel and Parrish (1988) for three soil types used by EPA for Monte Carlo simulations are presented in Table 6.2. The frequency distribution of the saturated hydraulic conductivity, residual water content, a nd van Genuchten soil parameters ( " and $ ) are also presented in Appendix B. 6.3 LONGITUDINAL DISPERSIVITY The longitudinal dispersivity of the soil can be i nput as a distribution or it may be derived. If derived, it is computed as a linear function of the total depth of the unsaturated zone using

where:
D u = total depth of the unsaturated zone [m] "

Lu= longitudinal dispersivity [m] Equation (6.1) is based on a regre ssion analysis of data presented by Gelhar et al. (1985), which are provided in Table 6.3. The correlation coefficient squared (R 2 ) of equation (6.1) is 66 percent.6-4 E:\Projects\EPA\R08-99.217.wpd Table 6.1 Unsaturated Zone Specific Va riables and Default Distribution Types.

Variable Units Distribution Code* Default Distribution

SATK Saturated hydraulic conductivity (K s ) cm/hr 2 Lognormal (Depends on soil type, Table 6.2) ALPHA Moisture retention parameter, " cm -1 2 Lognormal

BETA Moisture retention parameter ( $ ) 7 Johnson SB

WCR Residual water content ( 2

r) 7 Johnson SB

WCS Saturated water content ( 2

s ) 0 Constant for each soil type DSOIL Thickness of unsaturated zone m 99 Regional Site-based
DSPR Dispersivity ( " ) m -1 Derived based on regression analysis of data by Gelhar et al. POM Percent organic matter (% OM) 7 Johnson SB
RHOB Bulk density ( D b ) g/cm 3 0 Constant (1.65) UFCOF Freundlich sorption coefficient (K 1 ) cm 3 /gr -1 Derived from % OM and chemical-specific K oc UFEXP Freundlich exponent ( 0

) 0 Constant (1.0) UCLAM Chemical degradation rate coefficient ( 8 cu ) 1/yr -1 Derived from chemicalspecific hydrolysis constants BIOU Biological degradation rate coefficient ( 8 bu ) 1/yr 0 Constant (0.0)

* See Table 3.16-5 E:\Projects\EPA\R08-99.217.wpd Table 6.2 Statistical parameters for soil properties for three soil types (Carsel and Parrish, 1988). All values are in arithmetic space.

Variable Distribution Type Limits of Variation Mean Standard Deviation Minimum Maximum Soil Type - Silty Clay Loam KS cm/hr 2

r " cm -1 $ % OM D

b 2

s SB NO SB NO SB Constant Constant 0. 0.00 0.00 1.0 0.0

-

- 3.5 0.115 0.15 1.5 8.35

-

- 0.017 0.089

.009 1.236 0.11 1.67 0.43 2.921 0.0094

.097 0.061 5.91

-

- Soil Type - Silt Loam KS cm/hr 2

r "

cm -1 $ % OM D

b 2

s LN SB LN SB SB Constant Constant 0. 0.00 0.00 1.0 0.0

-

- 15.0 0.11 0.15 2.0 8.51

-

- .343 .068 .019 1.409 0.105 1.65 0.45 .989 0.071 0.012 1.629 5.88

-

- Soil Type - Sandy Loam KS cm/hr 2

r "

cm-1 $ % OM D

b 2

s SB SB SB LN SB Constant Constant 0. 0. 0. 1.35 0.0

-

- 30.0 0.11 0.25 3.00 11.0

-

- 2.296 0.065 0.070 1.891 0.074 1.60 0.41 24.65 0.074 0.171 0.155 7.86

-

-

% OM = Percent Organic Matter NO = Normal distribution SB = Log ratio distribution, Y = ln [(x-A)/(B-x)], A < x < B LN = Log normal distribution, Y = ln[x] where Y = normal distributed parameter x = actual data6-6 E:\Projects\EPA\R08-99.217.wpd Table 6.3 Compilation of field dispersivity values (Gelhar et al. 1985).

Author Type of Experiment Vertical Scale of Experiment (m) Longitudinal Dispersivity " L (m)

Yule and Gardner (1978) Laboratory 0.23 0.0022

Hildebrand and Himmelblau (1977) Laboratory 0.79 0.0018 Kirda et al. (1973) Laboratory 0.60 0.004 Gaudet et al. (1977) Laboratory 0.94 0.01 Brissaud et al. (1983) Field 1.00 0.0011, 0.002 Warrick et al. (1971) Field 1.20 0.027

Van de Pol et al. (1977) Field 1.50 0.0941 Biggar and Nielsen (1976) Field 1.83 0.05
Kies (1981) Field 2.00 0.168 Jury et al. (1982) Field 2.00 0.0945 Andersen et al. (1968) Field 20.00 0.70
Oakes (1977) Field 20.00 0.206-7 E:\Projects\EPA\R08-99.217.wpd (6.2) 6.4 FREUNDLICH SORPTION COEFFICIENT (DISTRIBUTION COEFFICIENT) For organic compounds, the user must specify two isotherm parameters: the Freundlich sorption coefficient and the Freundlich exponent. A special case occurs when the isotherm is linear. In this case, the Freundlich exponent is 1.0. Wh en the isotherm is linear, the leading Freundlich coefficient is known as the solid-liquid phase distribution coefficient ( k d ). The distribution coefficient may be specified directly or as a derived parameter. In the latter case, it is computed from the fraction organic carbon and the organic carbon partition coefficient ( k oc ), the latter of which must be given in the chemical-specific input group. For metals, this input parameter is not used, instead the k d is calculated either as a function of pH, or is provided in tabular form as a func tion of the concentration value (EPA, 1996c). 6.5 PERCENT ORGANIC MATTER The soil bulk density and the organic matter conten t for the three soil types are included in Table 6.2 These values are based on the nationwide data base developed by Carsel and Parrish (1988). The percent organic matter is converted by EPACMTP to fractional organic carbon content through the following equation (Enfield et al., 1982):

where: f oc = fractional organic carbon content, %OM = percent organic matter, and 174 = conversion factor. Once the fractional organic carbon content is obtained, the linear distribution coefficient can be found using: k d = k ocf oc(6.3) where: k d = distribution coefficient [cm 3 /g], and k

oc= normalized organic carbon distribution coefficient [cm 3 /g]. Note that equation (6.3) is based on the assu mption that hydrophobic binding dominates the sorption process (Karickhoff, 1985). The frequency distributi on of percent organic matter in the soil is in Appendix C. 6.6 FREUNDLICH ISOTHERM EXPONENT For a nonlinear isotherm, the Freundlich exponent for each chemical species must be provided in addition to the distribution coefficient. The nonlin ear isotherm is currently handled within MINTEQA2 isotherm data. If this parameter is omitted from the data file, it is a ssigned a default value of 1.0, which is equivalent to specifying a linear sorption isotherm. In the case of metal transport simulation, tabulated values of k d versus concentration are read in from an external data file, and the Freundlich isotherm parameters are not used.6-8 E:\Projects\EPA\R08-99.217.wpd (6.4)

(6.5) 6.7 CHEMICAL AND BIOLOGICAL TRANSFORMATION COEFFICIENTS EPACMTP accounts for biochemical transformation processes using a lumped first-order decay coefficient. The overall decay coeffi cient is the sum of the chemical and biological transformation coefficients.

where 8 u= overall decay coefficient (first-order transformation) (1/yr) 8

cu= transformation coefficient due to chemical transformation (1/yr) 8

bu= transformation coefficient due to biological transformation (1/yr) The coefficients 8 cuand 8 buare specified in the EPACMTP data inputfile, either as constants or as a distribution. By default, 8 buis set to zero and 8 cuis a derived parameter, in which case it is calculated from the chemical-specific hydrolysis constants:

where 8 1 = hydrolysis constant for dissolved phase [1/yr], 8

2 = hydrolysis constant for sorbed phase [1/yr], D

b = bulk density of the porous media [g/cm 3 ], and N

= porosity [cm 3 /cm 3 ]. Note that 8 1 and 8 2may be temperature and pH dependent. Th e values of temperature and pH for the unsaturated zone are considered to be the same as those generated for the satura ted zone (see Sections 7.10 and 7.11).7-1 E:\Projects\EPA\R08-99.217.wpd 7.0 SATURATED-ZONE SIMULATION
The aquifer system simulated by the saturated-z one module of EPACMTP is depicted schematically in Figure 7.1. Note that the system is assumed to be symmetric in the horizont al-transverse direction about the plane y = 0; thus only one-half of the system al ong the positive y-axis is simulated by the model. The vertical coordinate (z) is taken to be positive downward with z=0.0 at the water table. The shaded area on top of the aquifer represents the area directly underneath the waste site. Contaminants entering the water table are assumed to be distributed uniformly over this area. Groundwater flow in the saturated zone is simu lated using a steady-state solution for predicting hydraulic head and Darcy velocities in a constant thickness groundwater system subject to infiltration and recharge along the top of the aquifer and a regi onal (ambient) gradient defined by upstream and downstream head boundary conditions. As illustrated in Figure 7.1, EPACMTP simulates a portion of the aquifer with length and width x

Land 1⁄2 yL , respectively. The modeled aquifer region enco mpasses the waste site as well as any specified observation wells. The value of x

Land yLare determined internally by EPACMTP, based upon specified input parameters (disposal unit area, receptor well dist ance, dispersivity) of the simulated problem. The x

Land yLare determined to be large enough that boundary effects will be negligible. The boundary-head values H 1 and H 2 are in turn determined from the regional hydraulic gradient, r , and the selected aquifer length, x L : H 1 = rx L (7.1) H

2= 0 If necessary, the regional gradient is computed fr om the longitudinal satu rated hydraulic conductivity, K x , and the regional ambient groundwat er flow rate per unit area, q : r = q/K x (7.2)
The solution for groundwater flow is based on the following assumptions: • The aquifer is homogeneous. • Groundwater flow is steady. • The saturated thickness of th e aquifer remains constant and mounding is represented by the head distribution on the top boundary. • Flow is isothermal and governed by Darcy's law. • The fluid is slightly compressible and homogeneous.7-2 E:\Projects\EPA\R08-99.217.wpd Figure 7.1 A schematic view of the groundwater sy stem simulated by the saturated zone module of EPACMTP. The shaded area represents th e projection of the contaminant source area onto the top of the aquifer.7-3 E:\Projects\EPA\R08-99.217.wpd In addition to modeling fully three-dimensional (3D) groundwater flow and contaminant fate and transport, EPACMTP offers the option to perform anefficient 2D modeling approximation. When this option is selected, the model ignores either the flow component in the horizontal-transverse (-y) direction, or the flow component in the vertical (-z) direc tion. The appropriate 2D approximation is selected automatically by the code, based on the relative magnit ude of plume movement inthe horizontal-transverse versus the vertical direction. Details of this procedure are provided in th e saturated zone background document (EPA, 1996b). The saturated zone transport module describes th e advective-dispersive transport of dissolved contaminants in a three-dimensional, constant thic kness aquifer. The initial contaminant concentration is set to zero. The concentration gradient along th e downstream boundary is zero , and the lower aquifer boundary is taken to be impermeable . A zero concentration condition is used for the upstream aquifer boundary. Contaminants enter the satu rated zone through a patch source of either constant concentration or constant mass flux on the upper a quifer boundary. The source may be of finite or infinite duration. Recharge of contaminant-free infiltration water o ccurs along the upper aquifer boundary outside the patch source. Transport mechanisms considered are a dvection, dispersion, linear or nonlinear equilibrium adsorption, and first-order decay with daughter produc t formation. As in the unsaturated zone, the saturated zone transport module can simulate multi- species transport involving chained-decay reactions. The saturated zone transport module of EPACMTP can perform either a fully three-dimensional transport simulation, or provide a 2D approximation. The latte r approach ignores advection in either the horizontal transverse (-y) direction, or the vertical (-z) directi on. In the course of a Monte Carlo simulation, the appropriate 2D approximations are selected auto matically for each individual Monte Carlo iteration. The major simplifying assumptions used to simula te contaminant transport in the saturated zone are: • The flow field is at steady state. • The aquifer is homogeneous and initially contaminant free. • Adsorption onto the solid phase is described by a linear or nonlinear equilibrium isotherm. • Chemical and/or biochemical degradation of the contaminant can be described as a firstorder process. • For a multicomponent decay chain, the number of component species (parent and daughters) does not exceed seven. • The mass flux of contaminants through the sour ce is either constant or controlled by firstorder decay, until all mass has b een released from the source. • The chemical is dilute and present inthe aqueous and aquifer solid phases only. EPACMTP variables for saturated z one are listed in Table 7.1. Some of these parameters can be input directly or can be computed by the code using the relationships shown insubsequent sections. If a multi-species simulation is desired, the chemical-dependent parameters (e.g. decay coeffi cient, Freundlich parameters) must be repeated for each of the NSPECI components in the decay chain.7-4 E:\Projects\EPA\R08-99.217.wpd A number of the parameters are characterized by nationwide distributions. However, the key hydrogeological parameters: hydraulic conductivity, hydraulic gradient, sa turated thickness, and depth to water table and groundwater temperature, are define d by regional distributions obtained from the API’s Hydrogeologic Data base for Mode ling (HGDB, Newell et al., 1990). The procedure for determining these parameters is as follows: The HGDB contai ns data on depth to groundw ater, hydraulic conductivity, hydraulic gradient, and saturated th ickness for approximately 400 hazardous waste sites nationwide. Each site is also classified in terms of 12 hydrogeological regions, see Table 7. 2. This classification is based on the USGS groundwater resources classification (USGS, 1984). As a result, data are available for the above mentioned key hydrogeological parameters for each of the 12 hydrogeological environments. Based on the locations of each of the waste mana gement units in the EP ACMTP modeling database (see source-specific parameters), the hydrogeologic envi ronment for each unit was determined using USGS state-by-state water resource maps (USGS, 1984). Note that EPACMTP uses a total 13 hydrogeological environments (Table 7.2). The hydrogeological envir onment no. 13 is assigned to those waste sites for which no specific hydrogeological environment coul d be identified from the state-by-state USGS groundwater maps. The aquifer parame ters for this 13th environment are the mean values from the entire HGDB database. In other words, if a waste site is not classifiable in terms of the hydrogeological environment, it is assigned nationwide average va lues for hydraulic conductivity, depth to groundwater, saturated thickness and hydraulic gradient. During each iteration of the Monte Carlo analysis, a waste unit is first selected at random (e.g., from the OPPI set of waste sites). This also de termines the climatic and hydrogeologic environment and, therefore, the distribution of the key hydrogeologi c parameters. It should be noted that for each hydrogeologic environment, the correlations betweenthe hydrogeologic parameters (which represent measurements taken at the same region) are preserved in the analysis. This is done by selecting the HGDB parameters together. For instance, assume that for hydrogeologic environment A, there are 20 sets of hydrogeologic parameters, i.e. combinations of uns aturated zone depth, hydraulic conductivity, hydraulic gradient, and saturated thickness meas ured at 20 locations that fall in that hydrogeologic environment. The aquifer parameter values used in each model run for waste sites located in hydrogeologic environment A are assigned by randomly selecting one out of the 20 parameter combinations. If data for one or more parameters in the select ed set are missing, they are generated based on the statistical correlations among the parameters for that hydrogeological environment. As a result, regional differences in aquifer characteristics as well as dependencies between hydrogeological parameters are automatically accounted for in th e EPACMTP Monte Carlo analysis.7-5 E:\Projects\EPA\R08-99.217.wpd Table 7.1 Aquifer-Specific Variable s and Default Distribution Types.

Variable Units Distribution Code* Default Distribution Particle diameter (d) (cm) 6 Empirical (Table 6.12) Porosity (N ) -1 Derived from d Bulk density (D b ) g/cm 3 -1 Derived from Aquifer thickness (B) m 99 Regional Site-based Hydraulic conductivity (K) m/y 99 Regional Site-based Hydraulic gradient (S) 99 Regional Site-based Seepage velocity (V) m/y -1 Derived from K, S, N Retardation factor (R s ) -1 Derived from k d , N , and D b Longitudinal dispersivity ( " L ) m 8 Empirical (Table 6.14) Transverse dispersivity ( " T ) m 8 Empirical Vertical dispersivity ( " v ) m 8 Empirical Temperature (T) °C 99 Regional Site-based (Figure 6.2) pH 6 Empirical Fractional organic carbon content (f oc ) 7 Johnson - SB

Radial distance of observation well m 6 Empirical (Table) Angle off-center 4 Uniform (0-90°) from plume centerline Depth of well below water table 4 Uniform (Fraction of saturated zone thickness) Decay coefficient ( 8 u ) 1/y -1 Derived from T, pH, D b , N Leading Freundlich sorption coefficient (k d) cm 3 /gr -1 Derived from koc , f oc Freundlich isotherm exponent ( 0

) 0 Constant (=1.0)

Diffusion coefficient m2 /y 0 Constant (=0.0) Anisotropy ratio (K x /Kz ) 0 Constant (=1.0)
* See Table 3.17-6 E:\Projects\EPA\R08-99.217.wpd Table 7.2 Regional Hydrogeologic Environments.

Region Description

1 2 3 4 5 6 7 8 9 10 11 12 13 Metamorphic and Igneous Bedded Sedimentary Rock Till Over Sedimentary Rock Sand and Gravel Alluvial Basins Valleys and Fans River Valleys and Floodplai ns with Overbank Deposits River Valleys and Floodplains without Overbank Deposit Outwash Till and Till Over Outwash Unconsolidated and Semiconsolidated Shallow Aquifers Coastal Beaches Solution Limestone Others (unclassified Hydrogeological Environments)7-7 E:\Projects\EPA\R08-99.217.wpd (7.3)

(7.4) 7.1 PARTICLE DIAMETER For Monte Carlo analyses, porosity and bulk dens ity are determined from the mean particle diameter. The mean particle diameter distributi on is based on data compiled by Shea (1974). Shea (1974) presents a frequency distribution of particle sizes based on analysis of 11,000 sa mples. These data have been adopted for nationwide modeling purposes, usi ng particle sizes ranging from a lower bound of 4×10 -4 cm to an upper bound of 0.8 cm. The resulting empirical distribution is presented in Table 7.3. The frequency distribution of particle diameterof the aquifer is provided in Appendix C. Alternatively, if the particle diameter is treated as a derived parameter, then its value is calculated using the value of porosity (which may be constant or randomly generated from a probability distribution) using an empirical relationship ba sed on data reporte d by Davis (1969):

where: d = mean particle diameter [cm] N

= porosity [dimensionless] 7.2 POROSITY In the absence of a user-specified distribution for porosity, porosity can be calculated from the particle diameter by rewriting (7.3):

where N and d are are defined in Section 7.1. This approximation yields the total porosity of the aquifer. For contaminant transport assessments, it is more appropriate to use effective porosity, N e , than total porosity. The effective porosity can be significantly smaller than the total porosity, although an exact relationship cannot be established. Mcworter and Sunada (1977) present data on total and effective porosity for a range of aquifer materials. Their data were used to establish ranges for the ratio between effectiv e and total porosity as a function of grain size (Table 7.4). For Monte Carlo assessments, it is assumed that the actual ratio varies uniformly between the upper and lower value for N e /N in each particle-size class. For a given value of the mean aquifer grain size, the total porosity can thus be conve rted into an effective por osity. Overall lower and upper bounds of 0.05 and 0.50 are impos ed on the effective porosity. It should be noted that EPACMTP makes the conversion from total to effective porosity only if porosity is specified as a derived parameter (porosity is calculated from grain size), which is the default option of the model. In all other cases, no conversion is performed and the user must specify the actual porosity data to be used by the model, providing a distribution for either total porosity or effective porosity directly.7-8 E:\Projects\EPA\R08-99.217.wpd Table 7.3 Empirical distribution of mean particle diameter (based on Shea, 1974).

Particle Diameter (cm) Cumulative Probability

3.9 10 -4 7.8 10 -4 1.6 10 -3 3.1 10 -3 6.3 10 -3 1.25 10 -2 2.5 10 -2 5.0 10 -2 1.0 10 -1 2.0 10 -1 4.0 10 -1 8.0 10 -1 0.000 0.038 0.104 0.171 0.262 0.371 0.560 0.792 0.904 0.944 0.976 1.0007-9 E:\Projects\EPA\R08-99.217.wpd Table 7.4 Ratio between effective and total poros ity as a function of particle diameter (after McWorter and Sunada, 1977).

Mean Particle Diameter (cm) N e /N Range

# 6.25 10 -3 0.03 - 0.77 6.25 10 -3 - 2.5 10 -2 0.04 - 0.87 2.5 10 -2- 5.0 10 -2 0.31 - 0.91 5.0 10 -2- 1.0 10 -1 0.58 - 0.94 > 1.0 10 -1 0.52 - 0.957-10 E:\Projects\EPA\R08-99.217.wpd (7.5)

(7.6) 7.3 BULK DENSITY The aquifer bulk density directly influences the re tardation of solutes and is related to aquifer porosity. An exact relationship between porosity, par ticle density, and the bulk density can be derived (Freeze and Cherry, 1979). Assuming the particle density to be 2.65 g/cm 3 , this relationship can be expressed as:

where D bis the bulk density of the soil [g/cm 3 ]. 7.4 AQUIFER THICKNESS The aquifer saturated thickness is assigned as a regional site-bas ed parameter. The frequency distribution of aquifer thic kness is shown in Appendix C.
7.5 HYDRAULIC CONDUCTIVITY By default, the aquifer hydraulic conductivity is a ssigned as a regional site-based parameter using the procedure outlined at the beginning of this sec tion. The frequency distribution of aquifer hydraulic conductivity is given in Appendix C. Alternatively, the hydraulic conductivity can be speci fied as a derived parameter. In this case it is calculated directly from the particle diam eter using the Kozeny-Carman (Bear, 1979) equation

where: K = the hydraulic conductivity [cm/s] D

= the density of water [kg/m 3 ] g = acceleration due to gravity [m/s 2 ] :

= the dynamic viscosity of water [N-s/m 2 ] d

= particle diameter [m] In Equation (7.6) the constant 1.8 incl udes a unit conversion factor to yield K in units of cm/s. Both the density and the dynamic viscosity of water are func tions of temperature and are computed using the regression equations presented in CRC (1981 ). Note that the particle diameter, d , must be in units of meters, as opposed to centimeters as used in equation (7.3).7-11 E:\Projects\EPA\R08-99.217.wpd (7.7)

(7.8) 7.6 HYDRAULIC GRADIENT The hydraulic gradient is assigned as a regional, site-based parameter using the procedure outlined in the beginning of this section. The frequency distribution of the hydraulic gradient is provided in Appendix C. 7.7 SEEPAGE VELOCITY The seepage velocity is related to the aquifer pr operties through Darcy’s law. The regional seepage velocity may be input directly or it may be a derived parameter (default). In the latter case, it is computed as

where v x= regional seepage velocity in x-direction [m/y] K

x= hydraulic conductivity in x-direction [m/y] r = regional hydraulic gradient [m/m] N

e= effective porosity [dimensionless] Default lower and upper bounds for the s eepage velocity are 0.1 and 1.1×10 4m/y, respectively. The range of seepage velocity values is based on survey data reported by Newell et al (1990). 7.8 RETARDATION FACTOR The retardation factor is defined for a linear adsorption isotherm by

where D b= bulk density of the porous media [g/cm 3 ] k

d = distribution coefficient [cm 3 /g] N

= porosity When the sorption isotherm is nonlinear, Ris no longer constant but depends on concentration (see Section 2.0). In this case, Rmust be given as a de rived variable and the k d-concentration relation must be specified by the user in terms of two Freundlich coefficients. For the modeling of metals, a linear sorption isothermis used for the saturated zone. The effective k

d value is determined from the maximum contaminant concentration at the water table and values of the four environmental master variables (pH, iron-oxide , leachate organic matter, and natural organic matter in the aquifer), following the procedure descri bed in the metals bac kground document (EPA, 1996b).7-12 E:\Projects\EPA\R08-99.217.wpd (7.9)

(7.10) 7.9 DISPERSION COEFFICIENTS The spreading and dilution of the contaminant pl ume in the saturated z one is controlled by two mechanisms: advection and dispersion. EPACMT P allows for non-uniform groundwater flow due to vertical recharge and infiltration from the waste s ite. The model simulates actual spreading mechanisms occurring in the field. For non-degrading contamin ants, the dilution caused by dispersive mixing is a controlling factor in determining the concentration observed at a receptor well. Somewhat surprisingly therefore is the fact, that in Monte Carlo analyses, tr ansport is a relatively insensitive to dispersion. The reason for this is as follows: low dispersivities will lead to a compact, concentr ated plume. If the plume is relatively small, the likelihood that the recepto r well will intercept the plume is reduced, but the concentration in the well, if it does, will be high. High dispersivities will lead to a more dilute plume which occupies a greater volume, thereby increasing the likelihood that a receptor well will intercept the plume. Concentrations in the plume however, are likely to be lower than in the first case. In the course of a full Monte Carlo analysis, these eff ects will tend to compensate for each other. The model computes the longitudinal, lateral, a nd vertical dispersion coefficients as the product of the seepage velocity and longitudinal ( " L ), transverse ( " T ) and vertical ( " V ) dispersivities. A literature review indicated the absence of a generally accepted theory to describe dispersivities, although a strong dependence on scale has been noted (EPRI, 1985; Gelhar et al., 1992). In the absence of user-specified values or distributions, the default model distribution is utilized if the distribution type of dispersivities is set equal to 8, which represents a probabilistic formulation for the longitudinal dispersivity as shown in Table 7. 5 [Gelhar, 1986, Personal communication]. Within each of the three classes, the longitudinal dispersivity is assumed to be uniform. Note that the values of longitudinal dispersivity in Table 7.5 are based on a receptor well distance of 152.4 m. For distances other than 152.4 m, the following equation is used:

where x t is the average horizontal travel distance defined as

In other words, the travel distance x t is equal to the distance of the receptor well ( x r) from the downgradient facility boundary, plus one-half of the f acility dimension. The average distance for all of the contaminants to migrate to the e dge of the waste management unit is equal to one half the length of the unit or 1⁄2 x w . The minimum value of " Lis 0.01 m. The input mean value for " T and " V is the ratio of " L to " T and/or " V . Hence, the desired dispersivity is comput ed as the longitudinal dispersivity divided by the user-specified mean valu e. The values assigned to " T and " V are based on the relationship with " L . This option allows the user to define different values for the ratios of the longitudinal to the vertical and/or transverse dispersivities. By default, the following ratios are used: " L / " T = 8 and " L / " V = 160. The frequency distribution of the aquifer longitudi nal dispersivity is provided in Appendix C.7-13 E:\Projects\EPA\R08-99.217.wpd Table 7.5 Probabilistic representation of longitudinal dispersivity.

Cumulative Probability " L(m)*

0.0 0.1 0.7 1.0 0.1 1.0 10.0 100.0

*Assumes x t = 152.4 m (see eq. 7.9)7-14 E:\Projects\EPA\R08-99.217.wpd The relationship in equation (7.9) has been derived based on an informal review of available data. More recently, Gelhar et al. (1992) have compiled a nd documented results from a large number of studies in which dispersivity values have been reported. These studies represent a wide range of spatial scales, from a few meters to over ten-thousa nd meters. The data as presented by Gelhar et al. (1992) show a clear correlation between scale and apparent dispersivity. The relationship (7.9) used in EPACMTP describes the observed data reasonably well. The field data suggest a somewhat steeper slope of the distancedispersivity relation on a log-log scale than is us ed in the modeling analyses. A sensitivity analysis performed using EPACMTP (HydroGeoLogic, 1992) ha s shown that the model results are virtually identical when the slope is varied from 0.5 to 1.5. For this reason the original relationship as shown in (7.9) has been retained. The datapresented by Gelhar et al. (1992) also show that the ratios between longitudinal, and horizontal and ver tical transverse dispersivities used in the nationwide modeling, are consistent with published data.
7.10 TEMPERATURE Groundwater temperature is assigned as a regiona l parameter based on the location of the waste management unit. For each waste site, the assigned temperature is obtained from averaging the upper and lower temperatures within a region as shown in Figure 7.2. 7.11 GROUNDWATER pH A nationwide groundwater pH distribution was de rived from EPA’s STORET data base. The model assumes that the groundwater is sufficiently buffe red such that pH is not influenced by the input of contaminants or changes in temperature. The defau lt distribution is an empirical distribution (Table 5.3) with a median value of 6.8 and lower a nd upper bounds of 3.2 a nd 9.7, respectively. 7.12 FRACTIONAL ORGANIC CARBON CONTENT The organic carbon content, f oc , is used to determine the lin ear distribution coefficient, k d . This approach is valid for organic contaminants c ontaining hydrophobic groups. These chemicals will tend to sorb preferentially on non-polar natural organic compounds in the soil or aquifer. Unfortunately, few if any comprehensive subsurface characterizations of organic carbon content exist. In general, the reported values are low, typically less than 0.01. A low range for f ocwas assumed and the distribution shape was based on the distribution of measured dissolved organic carbon recorded as entries to EPA’s STORET data base. The default distribution fo r fractional organic carbon content is a Johnson SB distribution with a mean and standard deviation in arithmetic space of 4.32×10 -4and 0.0456, respectively and upper and lower limits of 0.064 and 0.0, respectively. In the case of metals, the sorption is controlled by complex geochemical interactions which are simulated using MINTEQA2 (EPA, 1996d). 7.13 RECEPTOR WELL LOCATION A receptor well is a hypothetical drinking water well that is located downgradient of the waste management unit in consideration. It represents the location at which the potential exposure to the groundwater is measured. A receptor well can be located1) anywhere downgrad ient of the waste management unit, 2) within the areal extent of the contaminant plume, and/or 3) only along the contaminant plume centerline. The receptor well depth can be positioned anywhere within the saturated zone. The user specifies the receptor well location by assigning the following:7-15 E:\Projects\EPA\R08-99.217.wpd Figure 7.2 Groundwater temperature distribution for shallow aquifers in the United States. (After Collins, 1925)7-16 E:\Projects\EPA\R08-99.217.wpd (7.11)

(7.12)

(7.13) x, distance from the waste unit y, distance from the plume centerline z, well depth within the saturated zone By default, the y-location of the receptor well is a llowed to be anywhere dow ngradient from the waste source, i.e, the angle off-center varies between 0 and 90 degrees. In a Monte Carlo simulation, the primary output from the model is the exposure concentration at receptor well located down gradient from the waste site, within a one-mile distance from the site. Available studies and surveys suggest that on average, multiple down-gradient wells are present within this one-mile distance. To ensure a degree of conservatis m in the modeling analysis, the model computes the concentration at the nearest down gradient well. Information on the downstream distance to the nearest receptor well can be obtained from the OSW landfill su rvey (EPA, 1993). The data are presented as an empirical distribution in Table 7.6. At most waste s ites included in this survey, the direction of ambient groundwater flow was in most cases not known exactly; it can therefore not be ascer tained that the nearest receptor well is located directly along the plume centerlin e. To reflect uncertainties and variations in the location of the receptor well in relation to the dir ection of ambient groundwater flow, the modeled well is allowed to be positioned at a variable y-distance from the plume centerline. EPACMTP incorporates two options for determining the x- a nd y-coordinates of the receptor well. The first, and default, option i nvolves determining the x- and y- coordinates of the receptor well as a function of the radial distance, R, to the edge of the waste unit, and the angle off-center, 2 , as depicted in Figure 7.3a. In this case the x- and y-coordinate values must be specified as derived variables and are computed as

where R = radial distance between waste unit and well (m), 2 = angle measured counter-clockwise from the plume centerline (degrees) x r, y r = Cartesian coordinates of the receptor well (m) To include all wells located downstream of the waste units, the angle 2 is taken to be uniformly distributed between 0 and 90°. R is determined using the empirical distribution shown in Table 6.15. Optionally, the y-coordinate of the well can be constrained to lie within the areal extent of the main contaminant plume as defined by

where y 0 = half-width of the waste unit in the y-direction (m) x w = length of the waste unit in the x-direction (m) "

T = horizontal transverse dispersivity (m)7-17 E:\Projects\EPA\R08-99.217.wpd This approximation for the lateral extent of the cont aminant plume is based on the assumption that plume spreading in the horizontal-transverse direction is cau sed by dispersive mixing, which results in a Gaussion profile of the plume cross-section. Use of (7.13) implies that 99.7% of the contaminant mass will be present inside the transverse plume limit. The shaded area in Figure 7.3a i ndicates the area occupied by the contaminant plume. The option to force the well location to lie w ithin the areal plume boundaries is selected in EPACMTP by setting the logical control parameter LYCHK = TRUE in group GP02 of the data input file. The second method imbedded in EP ACMTP to determine the recepto r well location is to generate a well position directly so that the well is located uniformly between the plume centerline and the areal plume boundary, for any given x-distance (Figure 7.3b). With this option, a x-distance is generated from the empirical distribution in Table 7.6. Next, the y-coordinate of the well is generated from a uniform distribution with a minimum value of zero, and a maxi mum value given by Equation (7.13). To select this option, the parameter LYCHK should be set to TRUE , the receptor well x-distance should be specified as an empirical parameter with values as given in Table 7.6, and the receptor well y-distance should be given as a uniform distribution. The frequency distributions of radial distance to the well and its off-center angle are provided in Appendix C. Several options are available for specifying the ve rtical position of the well intake point below the water table. The first, and default, option is to model the vertical position of the well as being uniformly distributed between the water table (z=0) and the satu rated aquifer thickness. Th is option is selected by specifying the z-position as a uniform distribution withlower and upper limits of 0.0 and 1.0. EPACMTP will multiply this uniformly generate d value by the saturated zone thickne ss to yield the actual receptor well depth below the water table for each Monte Carlo iteration. In the second option, data on the depth of recep tor wells obtained from Agency surveys, can be used directly in the model as an empirical distributi on. The data values range from 15 ft (4.5 m) to 301 ft (90.9 m). Table 7.7 shows the distribution of well depths and the corresponding cumulative probability values. When the generated value for the vertical position of the receptor well intake point exceeds the saturated thickness of the aquifer (a physically impossible condition), a new well position is generated to ensure that the well depth is always less than the saturated thickness. Conve rsely, the well depth cannot be less than the minimum depth to the saturated zone.7-18 E:\Projects\EPA\R08-99.217.wpd Table 7.6 Cumulative probability of distance to nearest receptor well for landfills (from EPA, 1993d).

Cumulative Probability Distance (R) (m)

0.0 0.03 0.04 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 0.98 1.00 0.6 13.7 19.8 45.7 103.6 152.4 182.9 243.8 304.8 304.8 365.7 396.2 426.7 457.2 609.6 762.0 804.6 868.6 914.4 1158.2 1219.1 1371.5 1523.9 1609.37-19 E:\Projects\EPA\R08-99.217.wpd Figure 7.3 Schematic plan view showing procedur e for determining the downstream location of the receptor well: (a) well location determined us ing radial distance, R, and angle off center 2

; and (b) well location generate d uniformly within plume limit.7-20 E:\Projects\EPA\R08-99.217.wpd Table 7.7 Cumulative probability distribution of receptor well depth.

Depth (m) Cumulative Probability

4.55 4.55 6.06 6.06 9.09 9.09 10.61 10.61 12.12 12.12 15.15 20.61 21.21 21.21 22.73 22.73 24.24 24.24 27.27 27.27 30.30 30.30 39.39 39.39 45.45 45.45 60.61 60.61 71.21 75.76 75.76 90.91 0.000 0.015 0.045 0.060 0.090 0.134 0.149 0.179 0.194 0.209 0.254 0.299 0.313 0.328 0.358 0.373 0.388 0.403 0.433 0.478 0.493 0.597 0.642 0.657 0.672 0.746 0.761 0.881 0.925 0.955 0.970 1.0007-21 E:\Projects\EPA\R08-99.217.wpd (7.14)

(7.15) For both these options, the vertical position of th e observation well can be optionally constrained to lie within the approximate verti cal penetration depth of the contamin ant plume emanating from the waste unit. This is achieved through the Monte Carlo input variable LZCHK. If LZCHK is set to FALSE, the constraint is not enforced. If LZCHK is set to TRUE , the z-coordinate of the receptor well is constrained to lie within the approximate vertical exte nt of the contaminant plume as defined by

where Q 1 - Q 4 = Components of the groundwater flow field (m 2 /yr), see EPA (1996a), Part 2 Figure 2.3 B = Saturated zone thickness (m) x w = Length of source in downstream direction (m) x r = Horizontal distance between source and receptor well (m) "

L = Longitudinal dispersivity (m) "

v = Vertical dispersivity (m) D o = Molecular diffusion coefficient (m2 /y) Finally, the well position may be fixed at a constant depth. In this case a distribution code of 12 (not 0) should be entered for the verti cal well position in the data input file. 7.14 CHEMICAL & BIOLOGICAL TRANSFORMATION COEFFICIENTS The overall decay coefficient or th e sum of the chemical and biological transformation coefficients can be expressed as a distribution or a constant valu e. The saturated zone de rivation of the overall decay is calculated the same way as for the unsaturated zone (see Section 6.7). Although the temperature and pH values are assumed not to vary between the sa turated and unsaturated z ones, the porosity and bulk density values may differ, leading to a difference in the decay coefficients for the two zones. Note that the biological transformation coefficient has a defau lt value of zero and has not been used in any EPA application to date. 7.15 FREUNDLICH ISOTHERM COEFFICIENT When the adsorption isotherm is nonlinear, the us er must specify two isotherm parameters: the Freundlich sorption coefficient and the Freundlich exponent (see next sub-section). A special case occurs when the isotherm is linear. In this case, the Fre undlich exponent has a value of 1.0. When the isotherm is linear, the leading Freundlich coefficient is known as the solid-liquid phase distribution coefficient ( k d ). The distribution coefficient may be specified directly, or as a derived parameter. In the latter case, it is computed from the fraction organic carbon and the or ganic carbon partition coefficient. The following relationship is used:7-22 E:\Projects\EPA\R08-99.217.wpd where: k d = distribution coefficient [cm 3 /g], k

oc= distribution coefficient with respect to organic carbon [cm 3 /g], and f

oc= fraction organic carbon. Note: The above does not apply to simulations of metals transport. In this case, the sorption isotherm is determined according to the setting of the i nput parameter KDEVAL. A detailed discussion of metals modeling is provided in EPA (1996c). 7.16 FREUNDLICH ISOTHERM EXPONENT When a nonlinear adsorption isotherm is used, the Freundlich exponent for each chemical species must be given. No probability distribution data ar e currently available for this parameter. If the Freundlich exponent is omitted from the data file, it is assigned a default value of 1.0. which is equivalent to specifying a linear sorption isotherm. 7.17 DIFFUSION COEFFICIENT The diffusion coefficient is set to a constant va lue of zero by default, but a distribution may be specified if desired. 7.18 ANISOTROPY An anisotropy factor is used by the Monte Carl o module if the horizontal and vertical aquifer hydraulic conductivities are different. The relationship between horizont al and vertical conductivities is:
K

z= K x /A r where: K z= hydraulic conductivity in z direction [m/y], K

x= hydraulic conductivity in x direction [m/y], and A r

= anisotropy ratio = K x /K z. The default value of A r is 1, which indicates an isotropic sy stem. Because anisotropy ratios observed in the field may commonly be on the order of 100:1 oreven larger (Freeze and Cherry, 1979), a uniform distribution of A r with limits of 1-100 may be reasonable. In EPACMTP, the horizontal transverse hydraulic conductivity is always set equal to the horizontal longitudinal conductivity, i.e., K y = K x .PART 4
OUTPUT INTERPRETATION8-1 E:\Projects\EPA\R08-99.217.wpd 8.0 RECEPTOR WELL CONCENTRATIONS The output of any EPACMTP run consists of predic ted concentration levels, either at the water table (output from the unsaturated z one module) and/or at a receptor we ll(s) in the saturated zone (output from the saturated zone module). The methodology (deterministic or Monte Carlo) and application (continuous or finite source) determines the method of processing predicted c oncentration levels for a specific objective. The post-processing of these outputresults is dependent on a number of factors which are outlined and discussed in this section. 8.1 DETERMINISTIC MODE In the deterministic mode, EPACMTP variables are assigned constant values and the model computes receptor well concentration which, along with the source concentration, may be used to compute a Dilution Attenuation Factor (DAF). The DAF is define d as the ratio of source concentration to receptor well concentration. For chemicals with linear adsorption, the model may be run using a source concentration value of one, so that the DAF is just the inverse of the receptor well concentration value. When a deterministic simulation is performed, th e final output is single receptor concentration and generally does not require additional post processing.
8.2 MONTE CARLO MODE When EPACMTP is used in the Monte Carlo mode , the primary output from the simulation model is a sequence of receptor well concentration values, where each concentration corresponds to the result of one model simulation. The Monte Carlo output is th en analyzed to derive regulatory DAF values or regulatory limits on leachate and waste concentrations. Each Monte Carlo iteration yields one DAF value for the constituent of concern (plus one DAF valu e for each of the transformation products, if the constituent degrades to form daughter product). Si nce each Monte Carlo iteration has equal probability, ordering the DAF values from low to high directly yields the DAF cumulative probability distribution (CDF). If appropriate, CDF curves representing differe nt regional distributions may be combined into a single, weighted curve that represents all regions. (See Part 3, Section 2.2 in the EPACMTP background document for further discussion, EPA 1996b.) For a finite source scenario, the post-processing st ep involves determination of the paired values of leachate concentration, C L , and total leachable waste concentration, C w , which satisfy a specified protection level. The procedure is described inthe finite source background document (EPA, 1996b). To facilitate the post-processing analysis of Monte Ca rlo simulations, a separate post-processing program (POSTCMTP) is distributed with the EPACMTP c ode and discussed in S ection A.5 of Appendix A. Several types of output files are generated when the Monte Carlo op tion is used. The name of the primary output file (OUTPUT.OUT) is interactively speci fied by the user. This file consists of two major segments: (1) An echo of the input data (2) Statistical parameters of the steady-state concentration at the receptor well (e.g., mean, median, standard deviation, selected percentiles, etc.). This file is the one of primary interest to the user.8-2 E:\Projects\EPA\R08-99.217.wpd The second type of output file is named { OUTPUT }.CDF where { OUTPUT } is the user-specified primary output file name. This file contains a list of concentration values, sorted in ascending order, and the corresponding cumulative probability values. For st eady-state, continuous source analyses this output file can be directly converted to the CDF of DAF values. For finite source simulations the primary output required for determining leachate and waste concentration limits is contained in the { OUTPUT } n .SAT files. These files contain the Monte Carlo output of receptor well concentrations of each of the n different ratios of C w/C Lthat were analyzed. In other words, EPACMTP will generate { OUTPUT }1.SAT, { OUTPUT }2.SAT, etc., corresponding to the 1st, 2nd, etc. different ratio of C w/C Lfor which EPACMTP has computedthe receptor well concentration values. For a degrading contaminant, these files c ontain the receptor well concentrations of the parent compound, plus all daughter products. The third type of Monte Ca rlo output file is named { OUTPUT }.BND. This optional output file contains the random variable iden tification code, the random variable value, and the upper and lower bounds specified for that random variable, if the randomvariable is generated out of bounds in either of the two subroutines RANGEN or ASSIGN. S ubroutine RANGEN generates random numbers from specified probability distributions, and subroutine A SSIGN computes random variables which are derived from other random variable s and constants. The { OUTPUT }.BND file is useful when a large number of generated and/or derived random variables occurred out of bounds, and the user wishes to see which variables these were. Note that bounds checking on th e generated random variable s will only be performed if the logical variables CHECK and LDRIV are set to TRUE in the Monte Carlo input file. Therefore, the { OUTPUT }.BND file will be empty if: (1) CHECK and LDRIV are set to FALSE, or (2) no random or derived variables are genera ted out of their specified bounds. The fourth Monte Carlo output file type consists of files which contai n the randomly generated values used for each Monte Carlo run. There are five of these optional files, and their names and contents are listed in Table 8.1, along with brief descri ptions of the other Monte Carlo output files. When the Monte Carlo option is used and the numbe r of runs is greater than one, most of the EPACMTP formatted output is suppressed to limit file size and disk storage requirements to reasonable levels. However, data read from the EPACMTP input file is always echoed to the formatted output file. 8.2.1 Model Output Processing POSTCMTP is an interactive, menu-driven pos tprocessor for EPACMTP. It reads in the EPACMTP Monte Carlo output files, presents the results in graphical and tabular form on the screen, and provides the option to generate hardcopy output on a wide variety of pr inters and plotters. Details on how to use EPACMTP postprocessor are pres ented in Section A.5.1, Appendix A.8-3 E:\Projects\EPA\R08-99.217.wpd Table 8.1 Monte Carlo output files.

Opened in Unit No. Name Description
MAIN 6 User-specified Echo of EPACMTP input, statistical parameters of receptor well concentration. MAIN 8 { OUTPUT }.CDF 1) Downgradient steady-state well concentrations sorted in ascending order and corresponding cumulative probability. (CDF of concentrations) MAIN 13-40 { OUTPUT } n .SAT 1) Downgradient receptor well concentrations corresponding to the n-th value of C w /C Lfor finite source scenario. MAIN 7 { OUTPUT }.BND 1) Randomly generate d or derived variables which were out of bounds. MAIN 12 { OUTPUT }.CVR 1) Values of chemical variables generated for Monte Carlo simulations. MAIN 11 { OUTPUT }.SVR 1) Values of source variables generated for Monte Carlo simulations. MAIN 13 { OUTPUT }.UVR 1) Values of unsaturated zone flow and transport variables generated for Monte Carlo simulations. MAIN 14 { OUTPUT }.AVR 1) Values of aquifer variables generated for Monte Carlo simulations.

1) { OUTPUT } represents the file name of the primary output file. The same file name is used, but with a different file extension to indi cate the type of ancillary output file.R-1 E:\Projects\EPA\R08-99.217.wpd REFERENCES

Allison, J.D., D.S. Brown, and K.J. Novo- Gradac, 1991. MINTEQA2/PRODEFA2 geochemical assessment model for environmental systems: Version 3.00 User Manua l U.S. Environmental Protection Agency, Athens, GA. Bear, J., 1979. Hydraulics of Groundwater. McGraw Hill, New York. Carsel, R.F., and R.S. Parrish, 1988. Developing joint probability dist ributions of soil water retention characteristics. Water Resour. Res. 29:755-770. CRC, 1981. Handbook of Chemistry a nd Physics. 62nd Ed. CRC Press. Davis, S.N., 1969. Porosity and permeability of natural materials. In: Flow Through Porous Media, R.J.M. de Wiest, Editor, Academic Press, NY. Enfield, C.G., et al., 1982. Approximating po llutant transport to groundwater. Ground Water, 10(6):711-722. EPA, 1989. Subtitle D Municipal Landfill Survey. Pr epared for Office of Solid Waste by DPRA, Inc., Washington, D.C., 20460. EPA, 1992. Background document for finite source methodology for wastes containing metals. U.S. EPA, Office of Solid Waste, Washington, D.C., 20460. EPA, 1995. Hazardous Waste Identification rule (HWIR), Background document fo Groundwater Pthway Results. U.S. EPA, Office of Solid Waste, Washington, DC, 20460. EPE, 1996a. Background Document for EPACMTP: User s Guide. U.s. EPA, Office of solid Waste, Washington, DC 20460. EPA, 1996b. Modeling approach for simulating three-di mensional migration of land disposal leachate with transformation products. Volume I: Background doc ument for the unsaturated zone and saturated zone modules. Office of SolidWaste, Washington, D.C., 20460. EPA, 1996c. Background document for EPACMTP: Fi nite source methodology for degrading chemicals with transformation products. U.S. EPA, O ffice of Solid Waste, Washington, D.C., 20460. EPA, 1996d. Background document for EPACMTP: Fate and Transport Modeling of Metals. U.S. EPA Office of Solid Waste, Washington, D.C., 20460 EPA, 1993. Parameter values for the EPA’s co mposite module for landfills (EPACML) used in developing nationwide regulati ons: Toxicity Characteristic Rule, Office of Solid Waste, Washington, D.C., 20460. EPRI, 1985. A review of field scale physical solute transport processes in saturated and unsaturated porous media. Electric Power Res. Inst., Palo Alto, CA.R-2 E:\Projects\EPA\R08-99.217.wpd Freeze, R.A., and J. Cherry, 1979. Groundwater. Prentice-Hall, Englewood Cliffs, NJ. Gelhar, L.W., C. Welty, K.R. Rehfeldt, 1992. A cr itical review of data on field-scale dispersion in aquifers. Water Res our. Res., 28(7), 1955-1974. HydroGeoLogic, Inc., 1992. A Revised Procedure for Determining Dispersivity Values for Use in EPA Fate and Transport Models. H ydroGeoLogic, Inc., Herndon, VA. Jordan, E.C., Co., 1985. Analysis of engineered controls for Subtitle C facilities for land disposal restrictions determinations. Revi sed distributions of leaching rates. Draft report ECJ Project No. 4756, prepared for U.S. EPA, Office of Solid Waste, Washington, D.C., 20460. Karickhoff, S.W., 1985. Sorption protocol for eval uation of OSW chemicals. U.S. EPA, Athens Environmental Research laboratory, Athens, GA. Kollig, H.P., J.J. Ellington, E.J. Weber, and N.L. Wo lfe. Pathway analysis of chemical hydrolysis for 14 RCRA chemicals. U.S. EPA Envir onmental Research Brief, EPA/600/M-89/009. Loux, N.T., C.R. Chafin, and S.M. Hassan, 1990. St atistics of aquifer materi al properties and empirical pH-dependent partitioning relationships. U.S. Environmental Protection Agency, Environmental Research Laboratory, Athens, GA. McGrath, E.J., and D.C. Irving, 1973. Techniques fo r efficient Monte Carlo simulation. Technical report prepared for Department of the Navy, Office of Naval Research, Arlington, VA. McWorter, D.B., and D.K. Suna da, 1977. Groundwater Hydrology and Hydraulics, Water Resources Publications, Fort Collins, CO. Mills, T., et al., 1981. Laboratory protocols for evalua ting the fate of organic chemicals in air and water. US EPA Technology Development and A pplications Branch, Athens, GA. Newell, C. J., L. P. Hopkins, and P. B. Bedien t. A hydrogeologic data base for groundwater modeling. Ground Water 28(5):703-714, 1990. Schroeder, P.R., et al., 1984. The hydrologic eval uation of landfill performance model (HELP): Volume 1 - Users guide for version I and Volume II - Documentation for version I. EPA/530-SW-84-009, U.S. EPA, Washington, D.C., 20460 Schanz, R., and A. Salhot ra, 1992. Subtitle D Landf ill Characteristics. Center for Modeling and Risk Assessment, Woodward-Clyde C onsultants, Oakland, CA. Shea, J.H., 1974. Deficiencies of elastic particles of certain sizes. Journal of Sedimentary Petrology, 44: 985-1003. van Genuchten, M.Th., 1980. A closed form e quation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J., 44:892-898.R-3 E:\Projects\EPA\R08-99.217.wpd Wolfe, N.L., 1985. Screening of hydrolytic re activity of OSW chemicals. US EPA Athens Environmental Research Laboratory, Athens, GA.A-1 E:\Projects\EPA\R08-99.217.wpd APPENDIX A
SIMULATION USING EPACMTP: DATA INPUT GUIDE

A.1 GENERAL Using EPACMTP to perform a fate and transpor t simulation involves the following three general steps: 1. Create input data file(s) 2. Execute EPACMTP 3. Perform post-processing output analysis All information needed by EPACMTP to perform a simulation is read from the data input file, which is an ASCII text file. The data input file canbe created using a standard ASCII text editor (Section A.2). Execution of EPACMTP is discussed in Secti on 7.3 and post-processing an alysis is discussed in Section A.5.
A.2 INPUT FILE PREPARATION Detailed format descriptions of EPACMTP data files are provided in Section A.2.1. These format instructions should be followed when using a text edito r to create or change an EPACMTP data input file.

A.2.1 Data Input Formats This section describes the format of data input files for EPACMTP. The data input file consists of a collection of records (a record corresponds to one line of information in the data file). There are two basic types of records. The first type of record is the data record. Thes e records must follow in a specified order and each record must conform to a pr escribed format. There are various different types of data records, each of which is distinguished by a two-character code described below. The second type of record is the comment record . Comment records are not used by EPACMTP; their purpose is to elucidate the meaning of the data r ecords to the user and improve readab ility of the data file to the user. Comment records may be interspe rsed freely with data records. Each record type is identified by a two-character code in the left-most 5 columns of the record. The following codes are used:A-2 E:\Projects\EPA\R08-99.217.wpd Data — Records * * * * * * * * * * * TI - Title records GP - General parameter records DC - Decay Chain data records OT - Observation Time data records SS - Source Specific data records FS - Finite Source Specific data records CS - Chemical Specific data records MT - Metals Specific data records US - Unsaturated Zone Specific data records AS - Aquifer Specific data records ** - Comment records Every record in the input file must be identifie d by one of the above code s. Data record groups must also be entered in the same sequential order in which they are listed above. The formats of the data record groups are describe d below. In order to become familiar with the input formats, it is recommended that the user also study the example data files listed at the end of this manual. A.2.1.1 Problem Title Records No. of Records: As many as desired Record Format: A5, A75 Col . Variable 1-5 GRPCOD Record identifier must be ‘TI’ always 6-80 TITLE Descriptive title for problem

A.2.1.2 General Parameter Records Record No. GP01: Control Parameters Format: A5, L5, 5I5, 2L5, I5 Col. Variable 1-5 GRPCOD Record identifier; must be ‘GP’ always 6-10 MC Monte Carlo control parameter = F(alse) for deterministic run = T(rue) for Monte Carlo run (Default).A-3 E:\Projects\EPA\R08-99.217.wpd 11-15 IVADOS Control parame ter for unsaturated zone simulation. = 0 if no unsaturated zone modeling is required, = 1 if unsaturated zone modeling is required (Default). 16-20 ISTMOD Control parame ter for saturated zone simulation. = 0 if no saturated zone modeling is required, = 1 if saturated zone modeling is required (Default). 21-25 NSPECI Number of contaminant component species in the system. Default = 1. 26-30 KFDM Simulation method = 0, finite element = 1, finite difference 31-35 KFS Control parameter for select ing continuous (infinite) source or finite source modeling option = 0 for continuous source option = 2 for landfill finite source option. 36-40 FULL3D Logical control parameter fo r selecting fully 3D or quasi-3D saturate zone modeling option = T(rue) for fully 3D simulation = F(alse) for quasi-3D simulation Note: FULL3D = F(alse) should be used for Monte Carlo simulations 41-45 METAL Logical control para meter for metals simulation = T(rue) for metals modeling = F(alse) for non-metals modeling 46-50 KDEVAL Integer control paramete r for selecting the scheme for determining the metals sorption isotherm (Leave blank if METAL = FALSE) = 1 Use the method of Loux for calculating k d from pH = 2 Use linearized MINTEQA2 isotherm = 3 Use nonlinear MINTEQA2 isothermA-4 E:\Projects\EPA\R08-99.217.wpd 51-55 LTCHK Logical variable to check minimum transmissivity = F (alse) if no = T (rue) if yes (default) 56-60 IFREGNL Logical variable for Regional Site-based Analysis = F (alse) for Nationwide Analysis = T (rue) if yes (default) 61-65 ISRC_TYP Control parameter for se lecting the type of waste source = 0 for landfill = 1 for surface impoundment = 2 for waste pile = 3 for land treatment
Notes: 1) MC is a logical variable: it should be set to e ither ‘F’ or ‘T’ (right-justified in input field). 2) The maximum number of component species thatthe code can handle is seven (NSPECI=7). 3) The default option FULL3D=FALSE reduces CPU time and memory requirements of the EPACMTP code. 4) Finite source option A (KFS=0) selects th e methodology developed for EPACML (see EPA, 1993). Finite source option B (KFS=1) selects th e methodology developed for EPACMTP (see EPA, 1992: Background document for EPACMTP: Finite Source Methodology for degrading chemicals with transformation products). 5) The different options for metals modeling are documented and explained in the metals background document for EPACMTP (EPA, 1996d). 6) If IFREGNL = True option is selected, regional s ite-based parameters must be supplied in a Input data file. The records in the data file are arranged under the following codes: DP contains information on number of s ites, climatic and groundwater regions. SP Contains site areas, depths, groundwater te mperature and their corresponding site-climatic regions, groundwater regions and weights IN Infiltration rates for different soil types for different climatic regions RE Recharge rates for different soil types for different climatic region HG Groundwater hydrological parameters, hydraulic gradient, unsaturated and saturated zone thickness and aquife r hydraulic conductivityA-5 E:\Projects\EPA\R08-99.217.wpd Sample data file listing is provided in Appendix A. Record No. GP02: Monte Carlo Control Parameters *** Omit this record for a deterministic *** model run (MC = F(alse)) Format: A5, I5, 7L5, F10.0 Col. Variable 1-5 GRPCOD Record identifier; must be ‘GP’ always 6-10 NRUN Number of iterations in Monte Carlo run. Recommended value is 1000 or greater. 11-15 ECHO Logical variable for creating output ‘VR’ files. = F(alse) if no; VR files should not be created (Default), = T(rue) if yes; VR files should be created. 16-20 CHECK Logical variable indicating if generated random variables are restricted to lie within specified upper and lower bounds, = F(alse) if no, = T(rue) if yes (Default). 21-25 CORR Logical variable indicating if statistical correlation is to be used in generating unsaturated zone parameters, = F(alse) if no (Default), = T(rue) if yes. 26-30 LDRIV Logical variable indicating if derived Monte Carlo variables are constrained to lie within specified upper and lower bounds, = F(alse) if no (Default), = T(rue) if yes. 31-35 LYCHK Logical variable indicating if observation well is constrained to lie within the approximate areal extent of the plume, = F(alse) if no, = T(rue) if yes (Default).A-6 E:\Projects\EPA\R08-99.217.wpd 36-40 LZCHK Logical variable indicating if observation well depth is constrained to lie within the approximate vertical plume depth. = F(alse) if no (Default), = T(rue) if yes. 41-45 LBNDOT Logical variable indicating if the ‘.BND’ output file is to be created, = F(alse) if no (Default), = T(rue) if yes. 46-50 ALLRUN Logical control parame ter to set aggregation option = T(rue) to automatically aggr egate Monte Carlo results over all soil and cover types = F(alse) to perform run only for the selected soil and cover type. 51-60 TCUTOFF Value of the upper time lim it, in years, for determining the receptor well maximum concentration in finite source modeling.
Notes: 1) If the ALLRUN = True option is selected, silt-loaminfiltration rate and r echarge rate values in the source-specific group mustbe used. The soil parameters in the unsaturated zone specific group must also be set to the values for the silt loam soil type. ALLRUN is notused if the regional sitebased analysis is selected (IFREGNL=True). 2) The option to incorporate statistical correlati on while generating unsatur ated zone parameters (CORR = (T)rue) has not been implemented in th is version of EPACMTP; hence, always set CORR = (F)alse.A-7 E:\Projects\EPA\R08-99.217.wpd Record No. GP03: Determ inistic Control Parameters *** Omit this record if a Monte Carlo simulation *** is performed with continuous source condition (MC=T(rue) and KFS=0) Format: A5, 7I5, G10.0, 2I5 Col. Variable 1-5 GRPCOD Record identifier; must be ‘GP’ always. 6-10 ISBC Contaminant source boundary condition = 0 if contaminant flux is given (Default), = 1 if contaminant concentration is given. 11-15 IBAT Control parameter fo r decaying source boundary condition, = 0 if no (continuous source or non-degrader infinite source) = 1 Biochemical decay (hydrolysis) = 2 Physical decay due to leaching (source depletion) = 3 Combine 1+2 (IBAT >=1 for degrader finite sources) Note: If IBAT = 1 or 3 is selected, it is assumed that the effective hydrolysis transformation coefficients in the waste source are the same as in the unsaturated and saturated zone. 16-20 IUSTED Control parameter indicating whether transport in the unsaturated zone is steady-state or transient, = 0 for transient (if KFS=1 or 2), = 1 for steady-state (if KFS=0). Leave blank if IVADOS (see record GP01)=0. 21-25 ISSTED Control parameter indicating whether transport in the saturated zone is steady-state or transient = 0 for transient (if KFS=1 or 2) = 1 for steady-state (if KFS=0). Leave blank if ISTMOD (see record GP01) = 0. 26-30 NUTOBS Number of time values at wh ich concentration at the water table is to be computed Leave blank if ISTMOD = 1, and/or IUSTED=1, and/or MC=T(rue). 31-35 NTS Number of time values at wh ich receptor well concentrations in the saturated zone are to be computed Leave blank if ISTMOD=0, or ISSTED=1.A-8 E:\Projects\EPA\R08-99.217.wpd Note: Set NTS = -1 or 0 to run finite source option in MonteCarlo mode. 36-40 NWELLS Number of receptor wells in the saturated zone. Leave blank if ISTMOD (see record GP01) = 0. 41-45 QRMAX Maximum groundwater vertical flux ratio (see Eq. 2.3.34) for selecting between analytical and numerical saturated zone contaminant transport solution. Recommended value is 0.02. Leave blank if ISTMOD (see record GP01) = 0. 51-55 NRATIO Number of ratios of C W /C Lto be used for finite source scenario (KFS=2). Default value is 8. Leave blank for a continuous source analysis (KFS=0) or a finite source analysis with a constant source leaching duration. 56-60 ICRW Control parameter indicati ng the time-dependent receptor well concentration to be computed for the finite source analysis = 0 compute peak receptor well concentration (Default) = 1 compute temporally averagedreceptor well concentration When ICRW=1 is used, the averaging period for each of the species must be specified in va riable CARC, in the chemicalspecific data records. The default period is 70 years.
Notes: 1) The maximum value for NUTOBS is 20. NUTOB S is automatically set to 0 if a steadystate simulation is performed (IUSTED=1). 2) The maximum value for NTS is 60. NTS is automatically set to 1 if a steady-state simulation is performed (ISSTED=1). 3) The maximum value for NWELLS is 40. 4) The maximum value for NRATIO is 9.

A.2.1.3 Finite Source Data Records When the finite source option is selected, with KF S=2 (see record FP01), the following additional data record group is required. The finite group is indicat ed by the record identifier “FS”. The record group contains the values of the ratios C W /C Lthat are to be used in the analysis. Format: A5,8G10.0 Col. Variable 1-5 GRPCOD Record identifier; must be “FS” always.A-9 E:\Projects\EPA\R08-99.217.wpd 6-15, CRATIO(1) Sequential values (i n increasing order) of the ratios C W /C Lto be 16-25 " used in the finite source Monte Carlo analysis.

" "

" " etc. CRATIO (NRATIO) Note: Up to 8 values can be entere d on one line; if the value of NRATIO is greater than 8, the data record should be repeated, but without the group code.

A.2.1.4 Chain Decay Data Records (DC)
****** This entire group is used only for chain decay modeling with multiple contaminant species (NSPECI > 1). Omit this group if NSPECI = 1. ****** The following input records, DC01 through DC03, are to be repeated a total of NSPECI-1 times, for each of the component species in the decay ch ain, except the first component species.
Record No. DC01: Number of Parent Species Format: A5, 2I5 Col. Variable 1-5 GRPCOD Record identifier; must be ‘DC’ always. 6-10 I Species number, from 2 to NSPECI. 11-15 NPA(I) Number of immediate parents for I-th species.

Record No. DC02: Parent Numbers *** Omit this record if the number of parents (NPA) = 0 for the current species ***
Format: 4I5 Col. Variable 1-5 GRPCOD Record identifier; must be ‘DC’ always.A-10 E:\Projects\EPA\R08-99.217.wpd 6-10 KPAREN(I,1) Sequential species numbers of the . . immediate parents of the current . . species, where NUMPAR = NPA(I).

. . 21-25 KPAREN(I,NUMPAR)
Record No. DC03: Decay Speciation Factors *** Omit this record if the number of parents (NPA) = 0 for the current species *** Format: A5, 4G10.0 Col. Variable 1-5 GRPCOD Record identifier; must be ‘DC’ always. 6-15 APAREN(I,1) Fraction of each parent that decays . . into species I, where NUMPAR = . . NPA(I). 36-45 APAREN(I,NUMPAR)

A.2.1.5 Observation Time Records (OT)
****** Omit this group when a Monte Carlo simulation is performed (MC = TRUE). ****** Record No. OT01: Unsaturated Z one Concentration Output Times *** Omit this record when unsaturated zone *** simulation is not performed (IVADOS = 0. see record GP01). Format A5, 8G10.0 Col. Variable 1-5 GRPCOD Record identifier; must be ‘OT’ always 6-15, UTOBS(1) Time values for which concentrations 16-25 . are to be computed.

. . etc. UTOBS(NUTOBS)A-11 E:\Projects\EPA\R08-99.217.wpd Note: The maximum allowed value for NUTOBS is 20. Up to eight time values can be entered on each line of the input file. This record should be re peated as many times as necessary (maximum of 3) to enter all time values.

Record No. OT02: Saturated Zone Observation Time Values *** Omit this record if only unsaturated zone *** simulation is performed (ISTMOD = 0, see record GP02) Format: A5, 8G10.0 Col. Variable 1-5 GRPCOD Record identifier; must be ‘OT’ always 6-15 T(1) Time values for which concentrations in 11-20, . saturated zone observation wells are to etc. . be computed. T(NTS) Note: The maximum allowed value for NTS is 60. Up to eight time values can be entered on each line of the input data file. This record should be repeated as many times as necessary (maximum of 8) to enter all time values.
A.2.1.6 Source-Specific Data Records (SS) The records in this group contain the parameters that describe the contaminant source. Records in this group are identified by the code ‘SS’. All records in this group have a common format which is described in Table A.6. Each record in the group corresponds to one source parameter. To indicate this correspondence, each source parameter is assigned a number. The appropriate number is entered as the second variable (IVAR) of each reco rd following the record identifier. A list of the records (parameters) in the source specific data group is given below in Table A.1.A-12 E:\Projects\EPA\R08-99.217.wpd Table A.1 Input parameters for source-specific gr oup. See Table 7.6 for data input formats.

IVAR EPACMTP Parameter Description
1 AREA Area of disposal unit (m 2 ) 2 XW Length (x-direction) of disposal unit (m) 3 YD Width (y-direction) of disposal unit (m) 4 CZERO(I) Leachate concentration emanating from the waste disposal facility (mg/L). For a degrad ing constituent, this record should be repeated for the pa rent and all daughter products, i.e., NSPECI times. For finite source degrader simulations CZERO represents the initial leaching concentration; the value should be set to zero for all daughters. The value for the parent should be set to a large value (10 6 is presently used) to avoid numerical difficulties associated with very small concentration values fo r degrading constituents. The value specified in the data input file is a dummy value only; the Monte Carlo results are adjusted for a range of actual C L values as a post-processing step. 5 RECHRG Areal recharge rate (m/y) 6 SINFIL Infiltration rate from disposal unit (m/y) 7 HZERO Ponding depth of surface impoundment unit (m) 8 DLINR Thickness of liner (m) 9 CLINR Hydraulic conductivity of liner (m/y) 10 TSOURC Duration of leaching period (y) for finite source option (KFS=1)TSOURC should be specified as a derivedvariable if KFS=2 (i.e. for landfills).A-13 E:\Projects\EPA\R08-99.217.wpd Table A.1 Input parameters for source-specific group. (continued)

IVAR EPACMTP Parameter Description

11 DEPTH Depth of the waste disposal facility (m) for landfill finite source option. 12 FRACT Fraction of hazardous waste in the waste disposal facility for landfill finite source option. 13 PWS Dummy parameter. 14 CTDENS Density of hazardous waste (g/cm 3 )

Notes: 1) For a deterministic simulation with decaying source conditions (IBAT=1, see group GP04), CZERO represents the initial concentration. 2) Parameters 7-9 (HZERO, DLINR, CLINR) ar e required only for the surface impoundment scenario when an unsaturated zone simulation is perf ormed (IVADOS=1, see group GP01) and the source ponding option is used. In this case, parameter 6 (SINFIL) should be specified as a derived parameter.A-14 E:\Projects\EPA\R08-99.217.wpd A.2.1.7 Chemical-Specific Data Records (CS) The records in this group contain the parameters that describe the degradation, adsorption and diffusion characteristics of each of th e chemical species being simulate d. Records in this group are identified by the code ‘CS’. All records in this gr oup have a common format whic h is described in Table A.6. Each record corresponds to one chemical speci fic parameter. To indicate this correspondence, each chemical specific parameter is assigned a number. The appropriate number is entered as the second variable (IVAR) of each record. A list of records (p arameters) in the chemical specific group is provided below in Table A.2. Table A.2 Input parameters for chemical-specific group. See Table A.6 for data input formats.

IVAR EPACMTP Parameter Description

1 RLAM2(I) Sorbed phase hydrolysis d ecay rate (1/y). For a multispecies simulation, this reco rd should be repeat ed for each of the components in the decay chain, i.e., NSPECI times. 2 RLAM1(I) Dissolved phase hydrolysis decay rate (1/y). For a multispecies simulation, this record should be repeated for each of the components in the decay chain, i.e., NSPECI times. 3 AHYDR(I) Second-order acid catalyzed hydrolysis rate constant at reference temperature (1/m ol-y). For a multispecies simulation, this record should be repeated for each of the components in the decay chain, i.e., NSPECI times. 4 NHYDR(I) Neutral hydrolysis rate c onstant at reference temperature (1/y). For a multispecies simulation, this record should be repeated for each of the compone nts in the decay chain, i.e., NSPECI times. 5 BHYDR(I) Second-order base cataly zed hydrolysis rate constant at reference temperature (1/mole-y). For a multispecies simulation, this record should be repeated for each of the components in the decay chain, i.e., NSPECI times. 6 RTEMP Reference temperature (°C). 7 KOC(I) Normalized distribution coefficient for organic carbon (ml/g). For a multispecies simulation, th is record should be repeated for each of the components in the decay chain, i.e., NSPECI times.A-15 E:\Projects\EPA\R08-99.217.wpd Table A.2 Input parameters for chem ical-specific group. (continued)

IVAR EPACMTP Parameter Description
8 DSTAR(I) Effective molecular diffusion coefficient (m 2 /y). For a multispecies simulation, this r ecord should be repeated for each of the components in the decay chain, i.e., NSPECI times. 9 DWS(I) Drinking water standard (mg/ L), i.e., HBL or MCL value for comparison against model predicted average receptor well concentrations. For a degrader, this record should be repeated for the parent and all daughterproducts, i.e., NSPECI times. 10 CARC(I) Base exposure period (y) for calculating average receptor wellconcentration. Set to 70 y for carcinogens, and 35 y for noncarcinogens. Always specify this parameter as a constant. For a degrader, this record should be repeated for the parent and all daughter products, i.e., NSPECI times.

Notes: 1) The parameters 1-7 in this group are used to compute degradation rates and partition coefficients for each of the contaminant component species. These parameters are required only when the overall degradation rate and sorp tion parameters for the unsatur ated and saturated zone, (see groups US and AS) are given as derived para meters, otherwise this group may be omitted. 2) If variables 1-2 (RLAM2 and RLAM1) are specifiedas derived parameters, they will be computed from the acid, neutral and base cataly zed hydrolysis rates (variables 3-5). 3) If variables 1-2 (RLAM2 and RLAM1) are specified as random with statistical distributions, variables 3-6 (hydrolysis rates) are not used, and may be omitted. 4) If the adsorption isotherm is linear, the par tition coefficient can be calculated from the K ocvalue (variable 7). If on the other hand, the isothermis nonlinear, the Freund lich isotherm parameters for the unsaturated and saturated zone must be given themselves in the record groups ‘US’ and ‘AS’, respectively, In this case the K ocis not used and may be omitted. 5) For a multispecies simulation, it is required that the degradation and sorption parameters for all species are specified in a consistent manner. Fo r instance, if the degradation rate for the first species is a derived parameter and computed from the acid, neutral and base catalyzed hydrolysis rates (variables 3-5), this same procedure mu st also be used for the remaining species.A-16 E:\Projects\EPA\R08-99.217.wpd 6) Chemical-specific variable CARC (IVAR=10) is not required when a steady-state (infinite source) analysis is performed, or when the finite source analysis uses the peak instead of time-averaged receptor well concentration (ICRW=0, see input group GP03). A.2.1.8 Metals-Specific Data Record (MT) The records in this group contain the parameters that describe the master variables that control the selection of the effective metals sorption isotherm . Records in this group are identified by the code 

### < MT’. All records in this group follow the format describe d in Table A.6. Each record corresponds to one parameter. To indicate this correspondence, each me tal specific parameter is assigned a number. The appropriate number is entered as the second variable (IVAR) of each record, following the record identifier. A list of records (parameters) in th e metals-specific group is given below in Table A.3. Table A.3 Input parameters for metals specific group. See Table A.6 for data input formats.

IVAR EPACMTP Parameter Description 1 METAL_ID Identification number for the metal. 2 USPH Soil and aquifer pH. 3 FEOX Weight percentage of iron- hydroxide in the soil and aquifer. 4 LOM Concentration (mg/L) of dissolved organic carbon in the waste leachate. 5 USNOM Unsaturated zone pe rcentage organic matter. 6 ASNOM Aquifer fraction organic carbon.

Notes: 1) This group is required only fo r metals modeling (METALS = True, see group GP01); the group may be omitted if METAL = F(alse). 2) Presently the following METAL_ID codes are recognized: 1 = Barium; 2 = Cadmium; 3 = Chromium (4+); 4 = Mercury; 5 = Nickel; 6 = Lead; 7 = Silver; 8 = Zinc (6+); 9 = Copper; 10 = Vanadium; 13=Arsenic; 14=Chromium (tb); 15=Selenium; 16=Thallium; 17=Antimony. 3) The unsaturated zone percentage organic matter varies with soil type and should be the same as that specified under the unsaturated zone specific group (see section 6.4.5). 4) The saturated zone fraction organic carbon should be the same as that specified under the aquifer specific group (see section 7.5.12).A-17 E:\Projects\EPA\R08-99.217.wpd A.2.1.9 Unsaturated Zone Specific Data Record (US) The records in this group contain the parameters that describe unsaturated zone physical and chemical characteristics. Records in this group are id entified by the code ‘US’. All records in this group follow the format described in Table A.6. Each r ecord corresponds to one uns aturated zone parameter. To indicate this correspondence, each unsaturated zone parameter is assigned a number. The appropriate number is entered as the second variable (IVAR) of each record, following th e record identifier. A list of records (parameters) in th e unsaturated zone specific group is given below in Table A.4.
Table A.4 Input parameters for unsaturated zone specific group. See Table A.6 for data input formats.

IVAR EPACMTP Parameter Description
1 SATK Saturated hydraulic conductivity, K s , (cm/hr). 2 ALPHA Moisture retention parameter, " , (cm -1 ). 3 BETA Moisture retention parameter, $

. 4 WCR Residual water content, 2 r . 5 WCS Saturated water content, 2 s . 6 DSOIL Thickness of unsaturated zone (m). 7 DISPR Dispersivity, " , (m) 8 POM Percent organic matter 9 RHOB Bulk density, D b , (g/cm 3 ) 10 UFCOF(I) Leading coefficient in Freundlich adsorption isotherm, k 1 . For a multispecies simulation, th is record should be repeated for each of the components in the decay chain, i.e., NSPECI times. 11 UFEXP(I) Freundlich exponent, 0 . For a multispecies simulation, this record should be repeated for each of the components in the decay chain, i.e., NSPECI times. 12 UCLAM(I) Chemical degradation rate coefficient (1/y). For a multispecies simulation, this record should be repeated for each of the components in the decay chain, i.e., NSPECI times.

### Table A.4 Input parameters for unsaturated zone specific group. (cont)

IVAR EPACMTP Parameter Description

A-18 E:\Projects\EPA\R08-99.217.wpd

13 BIOU(I) Biological degradation rate coefficient (1/y). For a multispecies simulation, this r ecord should be repeated for each of the components in the decay chain, i.e., NSPECI times.

Notes: 1) This group may be omitted if no unsaturated z one simulation is performed (IVADOS=0, see group GP02). 2) If the adsorption isotherm is linear, variable 11(UFEXP) should be omitte d or set to a constant value of 1.0. In this case , variable 10 (UFCOF) corresponds to the partition coefficient, k d , (cm 3 /g). 3) IF UFCOF is specified directly (i.e., not derived), variable 8 (POM) may be omitted. 4) If the chemical degradation rate (UCLAM) is speci fied as a derived parameter, it will be calculated from the chemical specific decay parameters (CS-group). 5) For a multispecies simulation, it is required that the degradation and adsorption parameters for all species are specified in a consistent manner. For instance, if the chemical degradation rate for the first species is specified directly instead of de rived, the same procedure must be used for the remaining species. A.2.1.10 Aquifer-Specific Data Records (AS) The records in this group contain the parameters that describe the saturated zone physical and chemical characteristics. Records in this group are id entified by the code ‘AS’. All records in this group follow the format described in Table A.6. Each reco rd corresponds to one satura ted zone parameter. To indicate this correspondence, each saturated zone para meter is assigned a number. The appropriate number is entered as the second variable (IVAR) of each record , following the record identifier. A list of records (parameters) in the saturated zone sp ecific group is given below in Table A.5.A-19 E:\Projects\EPA\R08-99.217.wpd Table A.5 Input parameters for aquifer specific group. See Table A.6 for data input formats.

IVAR EPACMTP Parameter Description
1 DIAM Average particle diameter (cm) 2 POR Aquifer porosity 3 BULKD Aquifer bulk density (g/cm 3 ) 4 ZB Aquifer Saturated thickness (m) 5 XKX Longitudinal hydraulic conductivity, K x , (m/y) 6 ANIST Anisotropy ratio, K x /Kz 7 GRADNT Hydraulic gradient (m/m)

8 VXCS Regional groundwater seepage velocity (m/yr)
9 RETARD(I) Retardation coefficient. For a multispecies simulation, this record should be repeated for each of the components in the decay chain, i.e., NSPECI times 10 AL Longitudinal dispersivity, " L(m) 11 AT Transverse dispersivity, " T(m) 12 AV Vertical dispersivity, " V(m) 13 TEMP Temperature of ambient aquifer water (°C) 14 PH Ambient groundwater pH 15 FOC Fraction organic carbon (g/g) 16 RADIS(I) Radial distance of observation well from center of downstream edge of waste disposal unit (m). Repeat this record for each of the NWELLS observation wells. 17 ANGLE(I) Angle off-center of observation well measured counterclockwise (degrees). Repeat this record for each of the NWELLS observation wells. 18 XWELL(I) Distance in downstream direction (m) between downstream edge of the source and observation well. Repeat this record for each of the NWELLS observation wells.Table A.5 Input parameters for a quifer specific group. (continued)

IVAR EPACMTP Parameter Description

A-20 E:\Projects\EPA\R08-99.217.wpd

19 YWELL(I) Horizontal transverse distance of well from the plume centerline (m). Repeat this record for each of the NWELLS observation wells. 20 ZWELL(I) Depth of well below water table (m). Repeat this record for each of the NWELLS observation wells. Note ZWELL should be given as a fraction of the saturated zone thickness, unless distribution type 12 (Secti on 6.5.13), i.e. constant well depth, is being used. 21 SFCOF(I) Leading coefficient of Freundlich adsorption isotherm (distribution coefficient). Fo r a multispecies simulation, this record should be repeated for each of the components in the decay chain, i.e., NSPECI times.

22 SFEXP(I) Freundlich isotherm exponent. For a multispecies simulation, this record should be repeated for each of the components in the decay chain, i.e., NSPECI times. 23 CSLAM(I) Chemical degradation rate coefficient (1/y). For a multispecies simulation, this r ecord should be repeated for each of the components in th e decay chain, i.e., NSPECI times. 24 BIOS(I) Biodegradation rate coe fficient (1/y). For a multispecies simulation, this record should be repeated for each of the components in the decay chain, i.e., NSPECI times.

Notes: 1) This entire group may be omitted when no saturated zone simulation is performed (ISTMOD=0, see group GP01). 2) For a Monte Carlo simulation, onl y one observation well is allowed. 3) The x-(longitudinal or downstream) and y-(horiz ontal transverse) location of the observation well(s) relative to the source can be determined in two ways. The first way is to give the x- and y-locations directly, through vari ables 18 and 19. The second way is to give these locations in terms of the radial distance between the well(s) and the center of the downstream side of theA-21 E:\Projects\EPA\R08-99.217.wpd source, and the angle between this radial directi on and the x-direction (see figure 6.1). If the latter option is used, the x and y locations (variables 18 and 19) should be give n as derived variables. 4) If the adsorption isotherm is linear, the Freund lich coefficient, SFEXP(I), should be set equal to 1.0 or omitted. In this case, SFCOF(I) corresponds to the partition coefficient k d . 5) If the retardation coefficient (RETARD) is specifi ed directly and not a derived parameter, then SFEXP, SFCOF and FOC can be omitted. This is valid only when the isotherm is linear. 6) When the degradation rate is not a derive d parameter, the aquifer pH is not needed. 7) For a multispecies simulation, it is required that the degradation and adsorption parameters for all species are specified in a consistent manner. For instance, if the chemical degradation rate for the first species is specified directly instead of de rived, the same procedure must be used for the remaining species. It is allowed that the sorpti on isotherm for some species is linear, while it is nonlinear for other species. In this case the isotherm parameters SFCOF and SFEXP must be specified for all species. A.3 EMPIRICAL DISTRIBUTIONS DATA RECORDS (EM) ****** Omit this group for a deterministic simulation (MC=False) or if no empirical distributions were specified in input groups SS-AS. ****** (i) Header record No. of records: 1 Format: 2A5, 2I5 Col. Variable 1-5 GRPCOD Data input group identifier. Should be ‘EM’ always. 6-10 EMPCOD Character variable indicating to which parameter group the empirical distribution belongs; enter SS, CS, US or AS. 11-15 IEMP Integer variable indicating parameter number (IVAR) to which the empirical distribution corresponds. 16-20 NPT Number of entry pairs of random values and cumulative probabilities for empirical distribution. (ii) Data records No. of records: NPT Format: A5, 2G10.0A-22 E:\Projects\EPA\R08-99.217.wpd Col. Variable 1-5 GRPCOD Data input group identifier. Should be ‘EM’ always. 6-15 VALUE (J,I) Value of J-th point in empirical distribution. 16-25 CDF (J,I) Cumulative probability of J-th point.
Notes: 1) Both the header and data records are to be rep eated for each of the specified empirical distributions in groups SS through AS. 2) The empirical distributions must be entered inthe same consecutive order in which they appear in groups SS through AS. Table A.6 Data format for input records in groups SS, CS, US and AS.

Format: A5, 2I5, 4G10.0, A25
Col. 1-5 GRPCOD: Character variable code to indicate group type of random variable; enter SS, CS, US, or AS. 6-10 IVAR: Integer variable indica ting which EPACMTP input parameter the record corresponds to. 11-15 IDIST: Integer variable indicating type of statistical distribution (see Table 5.1). 16-25 MEAN: Specified mean of statistical distribution. 26-35 SD: Standard deviation of statistical distribution. 36-45 LOWER: Lower bound of statistical distribution. 46-55 UPPER: Upper bound of statistical distribution. 56-80 VARNAM: Character variable givi ng name of the EP ACMTP parameter to which record corresponds.

Notes: 1) The first variable of each record is a two-charact er string that identifies the input group, e.g. ‘CS’.A-23 E:\Projects\EPA\R08-99.217.wpd 2) The second variable gives the parameter number. Each EPAC MTP parameter has a number that is unique within each group. This number is used by the code to identify the correct model parameter. 3) The third variable determines whether the parameter should be held constant (IDIST=0), or be considered a Monte Carlo variable (IDIST $ 1). A negative value indicates that the parameter is derived, i.e., computed from another EPACMTP i nput parameter. The codes used are described in Table 5.1. This variable is ignored during a deterministic simulation, unless the parameter is derived (indicated by a negative distribution code). 4) The fourth variable gives the mean of the pr obability distribution for a Monte Carlo simulation, and the actual parameter value to be used for a deterministic simulation, or when the parameter is held constant. This variable is not used when the parameter is derived. 5) The fifth variable gives the standard deviati on of the probability distribution for a Monte Carlo simulation. This variable is not used when the parameter is held constant or for a deterministic simulation. 6) The sixth and seventh variable correspond to the lower and upper bounds, respectively, of the probability distribution of the parameter. Setting both to zero means that the bounds will be ignored by EPACMTP. They are not used for a deterministic simulation or when the parameter is held constant. 7) The eighth and final variable for each record is and optional 25-character string that may be used to identify the model parameter corresponding to the input record. 8) The input records and parameters for each of the source specific (SS), Chemical Specific (CS), Unsaturated zone Specific (US), and Aquifer Speci fic (AS) input groups are listed in Tables A.1 through A.5. It is useful to note that often, especially while performing deterministic simulations, the model may not require input records for each and every para meter described above. For instance, the source area can be omitted if the x- and y-dimensions of the source are given individually. If an input parameter is not actually used by EPACMTP, and its corresponding record is still present in the data file, EPACMTP will simply skip this reco rd. To determine whether an input record is required or not, the user must be aware of the possible dependencies between input parameters. This is especially important for derived parameters. Failure to enter all necessary records/parameters may lead to unpredictable results! A.4 RUNNING EPACMTP EPACMTP can be used to simulate either a singl e problem at a time, or to process a number of problems in batch mode. When EPACMTP is started, the code will first ask the user whether a batch run is desired. If the user returns N(o), a single pr oblem (either Monte Carlo or deterministic) will be simulated. The user is then prompted by EPACMTP to provide the name of the input data file containing the problem definition, and the name to be given to the output file.A-24 E:\Projects\EPA\R08-99.217.wpd If a batch run is selected, the user must provide the name of the batch file. The batch file is simply a separate file containing a list of all the EPACMTP data input files thatare to be processed in the batch run. EPACMTP will then sequentially execute all the i nput data files in the batch file. The output files for each problem will be created automa tically; they will have the same f ile name as the data input file, but with different extensions. A.5 MODEL OUTPUT POST PROCESSING The output from EPACMTP consists of predicted c oncentration levels, either at the water table (output from the unsaturated zonemodule), and/or at observation we lls located in the saturated zone (output from the saturated zone module). When a deterministic simulation is performed, all output is written to the primary output file, and generally doe s not require additional post processing. When a Monte Carlo simulation is performed, the output of interest consists of the receptor well concentration values generated by each individual simulation. For continuous source runs, the entire cumulative distribution function (CDF) of the receptor well concentrations is written to the { OUTPUT }.CDF file. For finite source runs, the Monte Carlo output is written to a series of { OUTPUT }n.SAT files, with each file corresponding to a different value of the ra tio between total waste and leachate concentration. These files are subsequently used by POSTCMTP post- processor to provide the regulatory limits on waste and leachate concentration for any protection level. It may be noted that the finite source option of POSTCMTP does not allow aggregation of Monte Carlo re sults. Rather, it is assumed that the aggregation has already been performed inside EPACMTP, usi ng the ALLRUN option of the model. ALLRUN option is set to FALSE when regional site-based an alysis is performed, i.e. IFREGNL=TRUE. A.5.1 Using POSTCMTP POSTCMTP is an interactive, menu-driven pos tprocessor for EPACMTP. It reads in the EPACMTP Monte Carlo output files and presents the re sults in graphical and tabular form on the screen, and also provides the option to gene rate hard copy output on a wide va riety of printers and plotters. To use the postprocessor, simply type # POSTCMTP’. The first time you run the program, you will automatically be taken into the setup menu to se lect the type of printer you have and to specify how it is connected to your PC . After completing the initial setup, you can always change the configuration by selecting the # (I)nitialize Plotting Device’ option from the ma in menu. The main menu also allows you to select either # (C)ontinuous Source Analysis’ or # (F)inite Source Analysis’. You must select the analysis type that corresponds to the EPACMTP Monte Carlo si mulation that you want to analyze. For example, it is not valid to select (F)inite Source Analysis if a steady-state Monte Carlo run was performed.A-25 E:\Projects\EPA\R08-99.217.wpd Continuous Source Analysis After selecting the (C)ontinuous Source option, you will be prompted to provide the name of the EPACMTP output file. You must enter the file name only, without an extension. After entering the filename, select the (G)raph option to view the simu lated DAF curve. After the graph has been drawn on the screen, you can either save the graph on disk using the (S)ave option, or send the graph to your printer or plotter using the (H)ard Copy option. Please note that printing on a laser printer is quite slow; it may take more than 5 minutes to complete the plot. From the (C)ontinuous Source menu, you may also press the (S)pecify option. This allows you to compute the DAF at any arbitrary level of probab ility, or to compute the probability that the DAF will exceed a specified value. Press (E)xit to return to the POSTCMTP main menu. Finite Source Analysis After selecting the (F)inite Source Analysis opti on from the main menu, you will first be asked to supply the name of the EPACMTP output file you want to analyze. This file name must be entered without the file extension. After reading in the files, th e program will perform the finite source post-processing analysis. By default, it will compute th e values of total waste concentration (C w ) and leachate concentration (C L ) that correspond to an 85th percentile protection level. To change this, select the (S)pecify option, and enter the desired protection level. To view the post-processing results graphically, se lect the (G)raph option. After the graph has been drawn on the screen, you may (S)ave the graph on disk, or print it on you printer or plotter using the (H)ard Copy option. Please note that printing to a la ser printer is quite slow; it may take more than 5 minutes to complete the plot. Press (E)xit to return to the Main POSTCMTP menu.B-1 E:\Projects\EPA\R08-99.217.wpd APPENDIX B
INPUT AND OUTPUT FILES FOR A MONTE CARLO ANALYSIS SAMPLE PROBLEMC-1 E:\Projects\EPA\R08-99.217.wpd APPENDIX C FREQUENCY DISTRIBUTIONS OF MONTE CARLO INPUT PARAMETERS This Appendix contains frequenc y distribution plots of EPACMTP input parameters. The plots are represented in the form of histograms that disp lay different intervals of parameter values and the corresponding frequency of obs ervation. The observation frequency is given as a fraction of the total population. For example, a normalized frequency of 0.50 means that the corresponding interval contains half of the total population. The length of each inte rval is the distance between the mid points of two successive bars. A log scale axis has been used in some plots in order to achieve appropria te representation of the range of values for some of the input paramete rs such as area, hydraulic conductivity, and hydraulic gradient. The data used to plot the frequency distributions of the source related parameters (area, depth, infiltration, and recharge) is unique for each wast e management scenario (landfills, surface impoundments, and land application units). Data for the area and de pth were obtained from the OPPI Survey database. Infiltration and recharge rates were computed by the HELP model for individual waste management scenarios. The data for unsaturated and saturated z one parameters is the same for all waste management scenarios and represent nationwide distributions, obtai ned through aggregation of the distributions of the twelve individual hydrogeological environments. The values were genera ted by running EPACMTP in Monte Carlo mode.