Unsaturated Zone and Saturated Zone Transport Properties Rev 00, ICN 02

ANL-NBS-HS-000019

December 2001

1. PURPOSE
This Analysis/Model Report (AMR) summarizes transport properties for the lower unsaturated 
zone hydrogeologic units and the saturated zone at Yucca Mountain and provides a summary of 
data from the Busted Butte Unsaturated Zone Transport Test (UZTT). The purpose of this report 
is to summarize the sorption and transport knowledge relevant to flow and transport in the units 
below Yucca Mountain and to provide backup documentation for the sorption parameters 
decided upon for each rock type. Because of the complexity of processes such as sorption, and 
because of the lack of direct data for many conditions that may be relevant for Yucca Mountain, 
data from systems outside of Yucca Mountain are also included. The data reported in this AMR 
will be used in Total System Performance Assessment (TSPA) calculations and as general 
scientific support for various Process Model Reports (PMRs) requiring knowledge of the 
transport properties of different materials. 
This report provides, but is not limited to, sorption coefficients and other relevant 
thermodynamic and transport properties for the radioisotopes of concern, especially neptunium 
(Np), plutonium (Pu), uranium (U), technetium (Tc), iodine (I), and selenium (Se). The 
unsaturated-zone (UZ) transport properties in the vitric Calico Hills (CHv) are discussed, as are 
colloidal transport data based on the Busted Butte UZTT, the saturated tuff, and alluvium. These 
values were determined through expert elicitation, direct measurements, and data analysis. The 
transport parameters include information on interactions of the fractures and matrix. In addition, 
core matrix permeability data from the Busted Butte UZTT are summarized by both percent 
alteration and dispersion. Other data from C-wells testing for use in the saturated-zone (SZ) 
Process Model Report (PMR) (CRWMS M&O 1999a) are also included. 
The limitations of this AMR are that all conditions and properties on all rock relevant to Yucca 
Mountain have not, and cannot, be directly measured in the time frame of this project, and the 
key properties summarized in Section 6 are the best estimates based on data available at this 
time. These values are considered to be conservative and, thus, should provide conservative 
estimates for repository performance assessment calculations. 
This report is governed by the Office of Civilian Radioactive Waste Management (OCRWM) 
AMR Development Plan entitled U0100 UZ/SZ Transport Properties Data, Rev 00 (CRWMS 
M&O 1999b). As per this Development Plan, Tables 2a and b in Section 6.4 summarize the 
sorption data that will be used in flow and transport models and the TSPA. Solubility data was 
relegated to another AMR (CRWMS M&O 2000a). The Interim Change Notice (ICN), ICN 1, of 
this AMR was prepared as part of activities being conducted under Technical Work Plan (TWP), 
TWP-NBS-HS-000001, Technical Work Plan for Unsaturated Zone (UZ) Flow and Transport 
Process Model Report (CRWMS M&O 2000c). The planning document for the technical scope, 
content, and management of Interim Change Notice (ICN), ICN 2, to this AMR is Technical 
Work Plan (TWP), TWP-NBS-HS-000001 (Rev. 1), Technical Work Plan for Unsaturated Zone 
(UZ) Flow and Transport Process Model Report (BSC 2001a). The scope for the TBV 
resolution actions in ICN 2 is described in TWP-MGR-MD-000004, Rev. 02, Technical Work 
Plan For: Integrated Management of Technical Product Input Department, (Addendum B, 
Section 4.1) (BSC 2001b). 

Sections 6.8.6 and 6.8.7 document the use and validation of the UZTT model, which is based on 
a conceptual model that accounts for various radionuclide dilution and retardation mechanisms 
including sorption, matrix diffusion, dispersion, and colloid transport. The importance of the 
UZTT model to Performance Assessment (PA) is that it will be used to analyze data from the 
UZTT and to demonstrate and refine capability to model radionuclide transport at Yucca 
Mountain using the FEHM V2.00 (STN: 10031-2.00-00) code. 
Sections 6.8 and 6.9 assess the applicability of laboratory-derived parameters to the prediction of 
transport in the saturated and unsaturated zones at the field scale. Section 6.9 summarizes the 
field-scale estimates of transport model parameters in the Bullfrog and Prow Pass tuffs and 
validates the conceptual dual-porosity transport model in the saturated zone. 
The analyses and model presented in this AMR are appropriate for the intended use of this 
report. 

2. QUALITY ASSURANCE
The activities documented in this AMR were evaluated in accordance with QAP-2-0, Conduct of 
Activities, and were determined to be subject to the requirements of the U.S. DOE Office of 
Civilian Radioactive Waste Management (OCRWM) Quality Assurance Requirements and 
Description (QARD) (DOE 2000). This evaluation is documented in CRWMS M&O (1999c 
and d) and Wemheuer 1999 (activity evaluations for work packages WP 1401213UM1 and WP 
1401213SM1). The activities associated with the preparation of this ICN 1 were determined to 
be subject to the requirements of the QARD pursuant to the Activity Evaluation conducted in 
accordance with AP-2.21Q, Quality Determinations and Planning for Scientific, Engineering, 
and Regulatory Compliance Activities and documented in the TWP (CRWMS M&O 2000c). The 
Activity Evaluation for ICN 2 is contained in (BSC 2001a). The development plan (CRWMS 
M&O 1999b) was prepared in accordance with AP-2.13Q, Technical Product Development 
Planning, and the Technical Work Plans were developed in accordance with AP-2.21Q. The 
conclusions in this AMR do not affect the repository design or permanent items as discussed in 
QAP-2-3, Classification of Permanent Items. 
This document is a compilation and synthesis of data and information collected under other 
activities and reported elsewhere in published literature and in Yucca Mountain Site 
Characterization Project (YMP) reports and technical databases. All of the YMP site 
characterization work or activities summarized in this report were subject to QARD 
requirements. The quality assurance (QA) status of the YMP data used in this report is 
determined by the activities under which they were generated, with the specific controls noted in 
scientific notebooks associated with those activities. 
The work activities documented in this AMR depend on electronic media to store, maintain, 
retrieve, modify, update, and transmit quality-affecting information. The applicable process 
controls identified through AP-SV.1Q, Control of Electronic Management of Information, are 
implemented for the activities documented in this AMR through procedure LANL-YMP-QP-
S5.01, Electronic Data Management. 

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3. COMPUTER SOFTWARE AND MODEL USAGE
The computer software codes used in this AMR are listed below. The qualification status of 
each code is indicated in the electronic Document Input Reference System (DIRS) database. The 
software was obtained from Configuration Management (CM) unless otherwise stated. Input 
files used with the software codes are identified in the respective discussions in Section 6; the 
outputs are listed in Section 7.3. 
1. Software: FEHM Version (V) 2.00 [Software Tracking Number (STN): 10031-2.00-
00], Sun Ultra Sparc, Unix System 
Used for: Transport simulations 
FEHM is a finite-element heat and mass transfer numerical code (Zyvoloski et al. 1995). 
Version 2.00 of the FEHM application has been tested and verified for a variety of 
different types of transport problems, including matrix and fracture reactive transport. 
Detailed information about the verification can be found in the report by Dash et al. 
(1997). The software is appropriate for the application and was used only within a range 
for which it was validated. 
2. Software: TRACRN V1.0 (STN: 10106-1.0-00), Sun Ultra 2, Unix System 
Used for: Solving flow and transport equations 
The TRACRN V1.0 computer code solves the equations of transient two-phase flow and 
multicomponent transport in deformable, heterogeneous, sorptive, porous media. 
Solution is obtained by an implicit finite difference scheme for flow and a semi-implicit 
or implicit approach for transport. TRACRN can be used to study radioactive waste 
migration from repositories in unsaturated and saturated media, soil water movement, 
environmental restoration of chemically polluted soils and groundwaters, and the 
migration of volatile organic plumes. The software is appropriate for the application, and 
was used only within a range for which it was validated. 
3. Software: RTA V1.1 (STN: 10032-1.1-00), Sun, Unix System 
Used for: Obtaining field and laboratory transport predictions and preliminary 
interpretations of transport data acquired in tracer tests in saturated media 
RTA (Reactive Transport Application) is a software package that consists of two 
complementary computer models that can be used to predict and interpret tracer 
responses in laboratory or field tracer tests in dual-porosity media. The two models are 
the semianalytical code, RELAP, and the 2-D finite-difference code, RETRAN. The 
software is appropriate for the application and was used only within a range for which it 
was validated. 

4. Software: LAGRIT V1.0 (STN: 10212-1.0-00), SUN Solaris, Unix System, 
verification in process according to AP-SI.1Q, Software Management 
Used for: Developing the grid for the Busted Butte Phase-1A model 
The LAGRIT code has been adapted from its original application for use in generating 
hydrogeologic computational grids. Computational grids are generated using any of a 
number of mechanisms, from hand numbering, to simple automated rectilinear 
numbering, to LAGRIT. All grids have been tested for accuracy by running test 
simulations (including a linear heat gradient and steady-state flow calculations). A 
procedure for qualifying grids, independent of the method of generation, is currently 
being developed, and all grids will be fully tested to this procedure. The software is 
appropriate for the application and was used only within a range for which it was 
developed. 
5. Software: Zombie V3.0 (STN: 10431-3.0-00), Unix System, verification in process 
Used for: Collection and processing of electrical-resistance tomography (ERT) data 
Zombie V3.0 is data-acquisition-control software written in LabView V3.0. The 
computer codes and software routines that comprise Zombie are to be qualified in 
accordance with AP-SI.1Q, Software Management. The data-acquisition-control and 
data-processing software is used as part of the electrical resistance tomography (ERT) 
system. Electrical resistance tomography is a geophysical imaging technique that is used 
to map subsurface resistivity. The ERT measurements consist of a series of voltages and 
current measurements from buried electrodes using an automated data-collection system. 
The data are then processed to produce electrical resistive tomographs using state-of-the-
art data inversion algorithms. These measurements are used to calculate tomographs that 
show the spatial distribution of changes in subsurface resistivity. The software is 
appropriate for the application and was used only within a range for which it was 
developed. 
6. Software: STO-UNSAT V1.0 LV (STN: 10292-1.0LV-00), Unix System, 
verification in process according to AP-SI.1Q, Software Management. 
Used for: Stochastic method simulations for Busted Butte Phase-1A fluid flow 
STO-UNSAT is a numerical code for multiphase flow using a stochastic differential 
equation approach. It is currently being tested and verified for a range of multiphase flow 
problems. The software is appropriate for the application and was used only within a 
range for which it was developed. 
7. Software: CART V1.0 (STN: 10046-1.0-00), Sun, Unix System 
Used for: Collection and processing of ground-penetrating radar tomography 
(GPR-T) data 

The GPR-T method involves the emplacement of modified surface radar into a rock 
formation and transmission of high-frequency electromagnetic (radar) signals through the 
formation to a receiving antenna. The electrical properties of the subsurface material, 
which are determined in part by its moisture and chemical content, greatly influence the 
propagation of the transmitted signal and whether it travels at a high or low velocity. The 
transmitted signals may be represented as multiple-ray paths crossing through a zone of 
interest within the block. If sufficient ray paths are recorded, a tomographic image may 
be obtained through computer processing using CART V1.0. The information extracted 
from such data consists of the transit time, which depends on the wave velocity. This 
information, in the form of a processed radar velocity tomogram, offers a high-resolution 
approach to monitoring the changes in moisture and chemical content occurring in the 
rock over the duration of the tracer-injection experiment at the Busted Butte underground 
test facility. The software is appropriate for the application and was used only within a 
range for which it was validated. 
In addition, the following commercially available software products were used in this AMR. 
These software products are exempt software applications in accordance with AP-SI.1Q, 
Software Management. Only built-in standard functions were used. No software routines or 
macros were used with this software. The software is appropriate for the application and was 
used only within a range for which it was developed. 
1. Software: DeltaGraph, Version 4.0.1, Macintosh 
Used for: Plotting graphs 
The software was used for illustration purposes only. The results were not used in any 
subsequent analysis or modeling subject to QARD requirements. 
2. Software: Microsoft Excel, Version 5, Macintosh 
Used for: Spreadsheet analysis of geochemical data 
Only standard Excel functions were used. 
3. Software: Microsoft Excel 97 SR-1 
Used for: Calculating averages and standard deviations, plotting and graphing 
results, and performing linear regressions on specific data sets. 
Only built-in standard functions were used. 
The UZTT model presented in this AMR is a three-dimensional flow and transport model in the 
unsaturated zone. It encompasses field-scale experiments, laboratory experiments and analyses, 
geophysical methods, and numerical modeling. No previously documented models are used to 
support the analyses or modeling activities reported in this AMR. 

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4. INPUTS
4.1 DATA AND PARAMETERS 
Locations, brief descriptions, and data tracking numbers (DTN) that were used as input for this 
AMR are listed in Tables 1a through 1f. The qualification status of data inputs is indicated in the 
electronic Document Input Reference System (DIRS) database. All input data are appropriate 
for the intended use of this AMR. Data qualification efforts, as needed, will be conducted in 
accordance with AP-SIII.2Q, Qualification of Unqualified Data and the Documentation of 
Rationale Accepted Data, and documented separately from this AMR. 
Input data described in Tables 1a through 1d and used in Sections 6.4 through 6.7 of this report 
include laboratory results of radionuclide experiments using waters either collected from Yucca 
Mountain or synthesized to reflect Yucca Mountain waters and materials either collected from 
the field or synthesized in the laboratory. Parameters used are the radionuclide and colloid type 
and concentration, percent sorbed onto various substrates, and attachment/detachment rates for 
radionuclides onto and off of various substrates. 
Table 1a gives the input data for sorption and sorption modeling studies discussed in Section 6.4. 
Table 1a. Sorption and Sorption Modeling Studies 
Data Tracking Number Description Location in this AMR 
LA0002JC831341.001 Depth intervals and bulk densities of alluviums Table 9 
LA0002JC831341.002 Quantitative X-ray diffraction (QXRD) results of three 
alluviums 
Table 10 
LA0010JC831341.002, Batch sorption distribution coefficients for plutonium, Table 4, Figs. 18, 
LA0010JC831341.003, neptunium, cesium, strontium, selenium, and uranium onto Section 6.4.2, Table 11 
LA0010JC831341.004, various tuffs and minerals in different groundwaters 
LA0010JC831341.005, 
LA0010JC831341.006, 
LA0010JC831341.007 
LAIT831361AQ95.003 Transport data of H-3, Np, and Tc-95m collected to calculate Table 3 
retardation coefficients using J-13 and UE-25 p#1 waters 
LA0004AM831341.001 Uranium sorption coefficients for minerals and tuff under Tables 7 and 8 
oxidizing conditions in J-13 water 
LA0004AM831341.002 Np sorption onto clinoptilolite-rich tuff in J-13 water under Tables 5, 6, 8 
atmospheric conditions with Ka, Kd, and SA 
LAAM831311AQ98.005 Geochemical field measurements for UE-25 WT#17, 27-Jan-Sec. 6.4.3 
98 
LAAM831311AQ98.007 Flow-thru cell and static measurements at UE-25 WT#3, 22Sec. 
6.4.3 
Jun-98 
LAAM831311AQ98.008 Analysis of bailed sample for UE-25 WT#17, 04-Jun-98 Sec. 6.4.3 
LAAM831311AQ98.010 Static measurements for US-25 WT#17, 01-Jul-98 Sec. 6.4.3 
LA9907AM831234.003 Downhole Eh and pH measurements for NC-EWDP-01S, 11Sec. 
6.4.3 
Jan-99 
LA0004AM831234.001 Flow-through cell measurements for NC-EWDP-01S, 22-Feb-Sec. 6.4.3 

Data Tracking Number Description Location in this AMR 
99 and 23-Feb-99 
LA9907AM831234.009 Flow-through cell measurements for NC-EWDP-01S, NC-
EWDP-03S, NC-EWDP-09SX, 5/17/99, 5/18/99. 5/20/99 
Sec. 6.4.3 
LA9907AM831234.010 Flow-through cell measurements for SD6-ST1, 02-Jun-99 and 
08-Jun-99 
Sec. 6.4.3 
LA9907AM831234.011 Flow-through cell measurements for AD-2, 10-Jun-99 Sec. 6.4.3 
LA0004AM831234.002 Downhole probe measurements for NC-EWDP-03S, 23-Feb-
99 
Sec. 6.4.3 
MO0007MAJIONPH.011 Chemical composition data and laboratory analyses for 
groundwater from Yucca Mountain test wells 
Sec. 6.4.3 
MO0007MAJIONPH.003 Major ion and pH for borehole USW-G2 Sec. 6.4.3 
MO0007MAJIONPH.013 Chemical composition of groundwater and the locations of 
permeable zones in the Yucca Mountain area 
Sec. 6.4.3 
GS950808312322.001 Field, chemical, and isotopic data describing water samples 
collected in Death Valley National Monument and at various 
boreholes and around Yucca Mountain, Nevada, between 
1992 and 1995 
Sec. 6.4.3 
MO0007MAJIONPH.005 Field, chemical, and isotopic data from precipitation sample 
collected behind service station in Area 25 and groundwater 
samples collected at various boreholes, 10/06/97 to 07/01/98 
Sec. 6.4.3 
GS990808312322.001 Field and isotopic data from groundwater samples from wells 
in the Amargosa Valley and NTS 
Sec. 6.4.3 
LA0109MD831341.001 Alluvium sorption data for 237Np Figs. 9, 10 
LA0109MD831341.002 Alluvium sorption data for 99Tc Figs. 11, 12 
LA0109MD831341.003 Alluvium sorption data for 129I Fig. 13 
Table 1b gives the input data for the dynamic transport studies discussed in Section 6.5. 
Table 1b. Dynamic Transport Studies 
Data Tracking Number Description Location in this AMR 
LA000000000106.001 Np sorption column measurements Table 11 
LA0001JC831361.001 Radionuclide transport through saturated fractures Figs. 2326 
LA0001JC831361.002* Radionuclide transport through saturated fractures Figs. 25, 26 
LA0002JC831341.003 Selenium batch adsorption on nonwelded zeolitic tuff Table 12 
LA0002JC831361.001 Column studies using G4-268 devitrified tuff with J-13 well 
water and radionuclides (H-3 and Pu-239) 
Fig. 16 
LA0002JC831361.002 Column studies using G4-268 devitrified tuff with synthetic 
UE-25 p#1 water and radionuclides (H-3 and Pu-239) 
Fig. 17 
LA0002JC831361.003 Column studies using G4-268 devitrified tuff with J-13 well 
water and radionuclides (H-3 and Tc-95m) 
Fig. 18 
LA0002JC831361.004 Column studies using GU3-1414 vitric tuff with J-13 well 
water and radionuclides (H-3 and Tc-95m) 
Fig. 19 

Data Tracking Number Description Location in this AMR 
LA0002JC831361.005 Column studies using G4-1533 zeolitic tuff with J-13 well 
water and radionuclides (H-3 and Tc-95m) 
Fig. 20 
LA0004JC831361.001* Preliminary retardation data for selenium transport through 
unsaturated tuffs 
Fig. 21 
LA0004JC831224.001* Preliminary unsaturated hydraulic conductivities of tuffs from 
Yucca Mountain boreholes, Tunnel Bed 5 (G-Tunnel) and 
Bandelier tuff (Los Alamos) 
Fig. 22 
LA0010JC831341.007 Batch sorption distribution coefficients for neptunium onto 
various tuffs and minerals in different groundwaters 
Table 11 
LAIT831361AQ95.001 Radionuclide elution data through crushed tuff columns Figs. 14 and 15 
LAIT831361AQ95.003 Characteristics of column experiments and batch sorption 
values 
Tables 14, 15 
* used as corroborative information 
Table 1c gives the input data for diffusion transport studies in the laboratory discussed in Section 
6.6. 
Table 1c. Diffusion Transport Studies in the Laboratory 
Data Tracking Number Description Location in this AMR 
LA000000000034.001 Diffusion of sorbing and non-sorbing radionuclides Figs. 2730, Table17 
LA000000000034.002 Diffusion measurements data of rock beaker experiments 
(modeled using TRACRN), 11/25/1991 to 03/25/1992 
Table 16 
LAIT831362AQ95.001 Diffusion data for various radionuclides in various tuffs in 
different groundwaters 
Figs. 31-33 
Table 1d gives the input data for colloid-facilitated radionuclide transport discussed in Section 
6.7. 
Table 1d. Colloid-Facilitated Radionuclide Transport 
Data Tracking Number Description Location in this AMR 
LA0003NL831352.002 Kd values of 239Pu on colloids of hematite, montmorillonite, 
and silica in natural and synthetic groundwaters 
Sec. 6.7.3 
LA0005NL831352.001 Sorption distribution coefficients of 243Am on colloids of 
hematite, montmorillonite, and silica as a function of time, 
temperature, and concentration in natural and synthetic 
waters 
Sec. 6.7.3 
LA0002SK831352.001 Total colloidal particles concentration and size distribution in 
groundwaters from the Nye County early warning drilling 
program 
Sec. 6.7.2 
LA0002SK831352.002 Total colloidal particles concentration and size distribution in 
groundwaters around Yucca Mountain 
Sec. 6.7.2 
LA9910SK831341.005 Total colloidal particles concentration and size distribution in 
NTS-ER-20-5-1, NTS-ER-20-5-3, and J-13 groundwater 
Sec. 6.7.2 
LAIT831341AQ97.002 Reversibility of radionuclide sorption Sec. 6.7.3 

Input data described in Table 1e and discussed in Section 6.8 generally come from two sources: 
measured data from samples taken from the UZTT or data derived from model simulations of the 
UZTT. Measured data include: mineralogy, hydrologic parameters, sorption, solubility, tracer 
concentrations, and breakthroughs from collection pad analyses, etc. Simulation input data 
include: as-needed measured data (from the above list) and some data from other YMP sources 
as noted in the text. Simulation output data include: fluid distributions, tracer distribution in the 
rock, and tracer breakthrough times. 
Table 1e. Busted Butte Unsaturated Zone Transport Test 
Data Tracking Number Description Location in this AMR 
GS990308312242.007 Physical and hydraulic properties of core samples from Sec. 6.8.3.3 
Busted Butte boreholes 
GS990708312242.008 Physical and hydraulic properties of core samples from Sec. 6.8.3.3 
Busted Butte boreholes 
GS000408312231.003, Physical and hydraulic properties of cores from Yucca Sec. 6.8.6.1.2.1 
GS940508312231.006, Mountain boreholes 
GS950408312231.004, 
GS950608312231.008 
GS951108312231.009, 
GS960808312231.001, 
GS960808312231.003, 
GS960808312231.005, 
GS990408312231.001 
LB970601233129.001 The site-scale unsaturated zone model of Yucca Mountain, Sec. 6.8.6.1.2.1 
Nevada 
LA9909WS831372.001, Measurements of tracer breakthrough concentrations Figs. 58ae 
LA9909WS831372.002 (bromide, 2,6-DFBA, fluorescein, pyridone, and lithium) in 
UZTT Borehole 6 
LA0004WS831372.002 Radionuclide sorption of Np, Pu, and Am on rock samples Table 28 
from Busted Butte 
LA9909WS831372.005 Descriptions of outcrop samples collected from Busted Butte; Table 20 
quantitative x-ray diffraction results for samples from lower Table 21 
Tpt section 
LA9909WS831372.006* Mineral abundances in Calico Hills; surface samples from Table 22 
Busted Butte 
LA9909WS831372.007 Quantitative X-ray diffraction results for USW H-5 core and Table 24 
drill cuttings 
LA9909WS831372.010 Mineral abundances in Calico Hills Formation (Tac) samples Table 23 
from auger hole AUG-1 in the floor of the Busted Butte test 
alcove 
LA9909WS831372.011* Preliminary measured sorption coefficients for lithium, Table 26 
manganese, cobalt, and nickel 
LA9909WS831372.014 Measurements and specifications of fluorescent polystyrene Sec. 6.8.5.4 
microspheres 
LA9909WS831372.015 Chemical composition of Busted Butte pore water with UE-25 Table 29 
J-13 groundwater for comparison using ICPAES analysis 
LA9909WS831372.016 Chemical composition of Busted Butte pore water with UE-25 Table 29 
J-13 groundwater for comparison using ion chromatography 

Data Tracking Number Description Location in this AMR 
LA9909WS831372.017 Chemical composition of Busted Butte pore water with UE-25 
J-13 groundwater for comparison using pH measurements 
Table 29 
LA9909WS831372.018 Chemical composition of Busted Butte pore water with UE-25 
J-13 groundwater for comparison using gravimetric moisture 
content analysis 
Table 29 
LA9910WS831372.008 Busted Butte transport test: Gravimetric moisture content and 
bromide concentration in selected Phase 1A rock samples 
Sec. 6.8.5.3.1.2 
LA9910WS831372.009 QXRD data for UZTT Busted Butte samples Sec. 6.8.5.1.2.4 
LASC831321AQ98.003 Quantitative X-ray diffraction results for samples from drill 
hole USW SD-6 
Table 25 
LB00032412213U.001 Ground penetrating radar (GPR) tomography data Sec. 6.8.4.1.4, Figs. 
4550 
LL990612704244.098 ERT data for Busted Butte Sec. 6.8.4.2.7, Figs. 
55, 56 
MO0004GSC00167.000 Coordinate of boreholes in the test alcove and running drift, 
Busted Butte test facility 
Sec. 6.8.4.1.4, Figs. 
45-49; Sec. 6.8.7.2, 
Figs. 87-89 
* used as corroborative information 
Table 1f gives the input data for C-wells field and laboratory transport testing discussed in 
Section 6.9. 
Table 1f. C-Wells Field and Laboratory Transport Testing 
Data Tracking Number Description Location in this AMR 
GS010508312315.001 Concentrations of 2,6 DFBA and pyridone from tracer tests Table 54 
conducted at the C-wells complex, 1/8/977/11/97 
GS981008312314.002 Pump test data collected at the C-wells complex, 1/8/97 Sec. 6.9 
3/31/97 (used for packer locations) 
GS981008312314.003* Pumping test data collected at the C-wells complex, 5/7/96 Sec. 6.9 
12/31/96 (bottom hole coordinates) 
LA0002PR831231.001 Bullfrog reactive tracer test data Figs. 96, 98, Table 54 
LA9909PR831231.003 Transport parameters deduced from fits of the Bullfrog Tuff Tables 51, 53, 55, 
tracer responses Fig. 100 
LA9909PR831231.005 Transport parameters deduced from fits of the Prow Pass Tables 52, 53, 55 
(See Assumption 26) Tuff tracer responses Fig. 102 
LAPR831231AQ99.001 Normalized pentafluorobenzoic (PFBA) acid responses in two 
different tracer tests in the Bullfrog Tuff, Fig. 98; Log 
normalized tracer responses in the Prow Pass Tuff multiple 
tracer test, Fig. 99 
Figs. 97-99 
MO9906GPS98410.000 List of boreholes Fig. 94 
MO0012BRLI25C2.000 Br and Li data from laboratory tests Table 53 
MO0012PERMCHOL.000 Permeability data from C-holes core Table 55 
MO0012POROCHOL.000 Porosity data from C-holes core Table 55 
MO0012SORBCHOL.000 Sorbing element concentration data for C-holes Fig. 101 
MO0012CATECHOL.000 Cation exchange capacity data for C-well tuff Sec. 6.9.3.1 
MO0012MINLCHOL.000 Mineral abundance data from C-hole tuff Sec. 6.9.3.1 
* Information for which confirmation is not required. 

Table 1g is a listing of the scientific notebooks used in this AMR. 
Table 1g. Scientific Notebooks Used 
Description of 
Information 
Notebook 
Identifier 
PageNumber 
Reference YMP M&O SNR Location in 
this AMR 
Phase-2 testing LA-EES-5-NBK-
98-010 
79 Bussod 
(1998) 
SN-LANL-SCI-040-V1 Sec. 6.8.2.2.2 
Phase-1A results LA-EES-5-NBK-1-61 Soll (1997) SN-LANL-SCI-048-V1 Sec. 5, 
98-018 Assumption 
16, Sec. 6.8.8 
Phase-1A results LA-EES-5-NBK-
98-017 
1-78 Zhang 
(1998) 
SN-LANL-SCI-047-V1 Sec. 6.8.8 
4.2 CRITERIA 
This AMR complies with the DOE interim guidance (Dyer 1999). Subparts of the interim 
guidance that apply to this analysis are those pertaining to the characterization of the Yucca 
Mountain site (Subpart B, Section 15), the compilation of information regarding geochemistry 
and mineral stability of the site in support of the License Application (Subpart B, Section 
21(c)(1)(ii)), and the definition of geochemical parameters and conceptual models used in 
performance assessment (Subpart E, Section 114(a)). 
4.3 CODES AND STANDARDS 
No specific formally established codes or standards have been identified as applying to this 
analysis and modeling activity. This activity does not directly support License Application (LA) 
design. 

5. ASSUMPTIONS 
Assumptions used in the sorption work include the following. 
Assumption 1. It is assumed that radionuclide sorption parameters measured in laboratory 
experiments are not significantly affected by microbial activity. The rationale for accepting this 
assumption is that microbial growth in the test apparatus is not expected to be significant given 
the short times that are typical of sorption experiments (days to a few weeks), that the solutions 
used in the experiments are filtered through a 0.05 m filter before use (Daniels et al. 1982 p. 73; 
Triay et al. 1996 p. 3), and the presence of signficant microbial activity would be marked by 
turbid conditions in the solutions. This condition was not reported in the laboratory 
documentation (e.g., see scientific notebook in records package linked to DTN: 
LA0002JC831361.004) and references used in support of this AMR identified no areas of 
concern with respect to microbial activity. This assumption primarily applies to sorption data 
obtained for elements that have different redox states under the environmental conditions 
expected at Yucca Mountain and affects parts of Section 6 in which sorption data for these 
radionuclides are discussed (Sections 6.4, 6.5, 6.6, 6.7, 6.8.5, and 6.9.3). On the basis given 
above, and the consideration that this information is used simply for TSPA-SR purposes, no 
further confirmation is required. 
Assumption 2. It is assumed that sorption parameters determined in laboratory experiments using 
crushed tuff are applicable to transport through solid tuff matrix in the field. Experiments with 
solid rock columns are generally infeasible because of the long time required to elute sorbing 
radionuclides from such columns. To investigate the effects of crushing, results of sorption 
experiments on thin (2-mm) intact tuff wafers were compared to those for columns of crushed 
tuff for alkali and alkaline earth elements, which are simple cations (strontium, cesium, barium). 
The two data sets were found to be quite similar (Rundberg 1987, p. 18). Furthermore, 
experiments with sorption using different particle sizes of tuff material also yielded similar 
results for cesium, strontium, and neptunium, suggesting that sorption parameters are not a 
strong function of the degree of crushing (Rogers and Meijer 1993). In addition to the effect of 
crushing on affinity for sorption, there is also the potential for the measured sorption coefficient 
(Kd) to be affected by the use of a water/rock ratio in the laboratory that is much higher than that 
for in-situ rock. Crushed-rock column experiments involve a lower water/rock ratio than used in 
crushed-rock batch experiments and yield consistent results for alkali and alkaline earths and are 
reasonably presumed to apply to actinides. This assumption applies to all sorption results and 
affects parts of Section 6 in which sorption data are discussed (Sections 6.4, 6.5, 6.6, 6.7, 6.8.5, 
and 6.9.3). On the basis given above, and the consideration that this information is used simply 
for TSPA-SR purposes, no further confirmation is required. 
Assumption 3. It is assumed that waters from wells J-13 and p#1 bound the chemistry of 
groundwaters at Yucca Mountain with respect to major cations and anions. Sorption is a 
function of water chemistry and the type of tuff at Yucca Mountain. The concentrations of the 
major cations and anions in unsaturated-zone groundwaters at Yucca Mountain appear to be 
intermediate between the saturated-zone tuffaceous waters (e.g., from well J-13) and waters from 
the Paleozoic carbonate aquifer (from well p#1) (Meijer, 1992, pp. 18-19). Consequently, the 
presumption made for the PA recommendations was that waters from wells J-13 and p#1 bound 

the chemistry of groundwaters at Yucca Mountain, and these compositions were used in the 
sorption experiments. (The compositions of natural and synthetic J-13 or p#1 waters used in 
each experiment are found in the documentation associated with the DTNs for those 
experiments.) It was recognized that pH and Eh (i.e., redox state) of in-situ waters may lie 
outside of the range of the values measured for these two waters. For this reason, sorption 
experiments were done under a variety of pH conditions. However, Eh was not directly 
controlled in the sorption experiments; therefore, the potential range of in-situ Eh conditions in 
Yucca Mountain waters were not directly addressed by the experiments. Additionally, it is 
presumed that the lack of Eh control did not affect the validity of this assumption and of the 
experiment design, because the redox state of the natural groundwaters from J-13 and p#1 may 
vary considerably depending on sampling and analytical error, degree of water saturation, etc., 
and may vary vertically and horizontally in the rock units as well. In any case, the Eh of 
groundwaters in both the saturated and unsaturated zone are oxidizing. This assumption 
influences parts of Section 6 in which sorption data are discussed (Sections 6.4, 6.5, 6.6, and 
6.7). On the basis given above, and the consideration that this information is used simply for 
TSPA-SR purposes, no further confirmation is required. 
Assumption 4. It is assumed that the sorption coefficient distributions for a given rock type can 
be determined from a limited number of batch experiments, and that the available data are 
adequately representative of the hydrogeologic rock types used in the transport calculations. 
From the perspective of transport modeling, it is presumed that hydrogeologic strata at the site 
can be classified into five representative rock types: iron oxides, devitrified tuff, vitric tuff, 
zeolitic tuff (Wilson et al. 1994, section 9.3.1), and alluvium material. For the performance 
assessment calculations, these rock types are assigned sorption coefficient distributions for each 
radionuclide of interest (Tables 2a and 2b). The number of experimental determinations may be 
more limited for some hydrogeologic rock types (e.g., alluvium) than others, and should be 
considered in applying the distributions in Tables 2a and 2b. However, the recommended 
distributions are considered adequate for TSPA-SR purposes since they are based on the best 
available technical information at this time for performance assessment objectives. Therefore, 
no further confirmation is required to support TSPA-SR. This assumption applies to all sorption 
results and affects parts of Section 6 in which sorption data are discussed (Sections 6.4, 6.5, 6.6, 
and 6.7). 
Assumption 5. It is assumed that sorption parameters measured using a single radionuclide are 
applicable to the case where more than one radionuclide is present, i.e., it is assumed that 
competitive effects are negligible. For transport in the far field, the rationale for accepting this 
assumption is that solutes emanating from the repository would be transported at different rates 
(due to different sorption characteristics) such that the groundwater in the far field would not 
contain multiple radionuclides at significant concentrations. The assumption is also applied to 
near-field sorption behavior. This assumption applies to all sorption results and affects parts of 
Section 6 in which sorption data are discussed (Sections 6.4, 6.5, 6.6, and 6.7). 
Assumption 6. Nonlinear isotherms imply the sorption coefficient is not a constant value. It is 
assumed that the variability of the sorption parameter as a function of concentration can be 
adequately captured by lowering the minimum Kd value defined for the sorption distribution 
function so as to include the reduced Kd expected under high concentration conditions. This 

assumption does not require confirmation because radionuclide concentrations in the 
groundwater are not expected to reach concentrations where the non-linearity would be 
significant. This assumption applies to all sorption results and affects parts of Section 6 in which 
sorption data are discussed (Sections 6.4, 6.5, 6.6, and 6.7). 
Assumption 7. It is conceivable that flow rates in the natural system may be sufficiently fast that 
sorption equilibrium may not be achieved during solute transport through the matrix or fractures. 
If so, then a smaller sorption coefficient may apply than for the case where equilibrium is 
assumed. It is assumed that the possible presence of non-equilibrium conditions is adequately 
addressed by lowering the minimum Kd value assumed in the sorption coefficient distributions 
for those radionuclides with slow sorption reaction kinetics (primarily, Pu). This assumption 
applies to all sorption results and affects parts of Section 6 in which sorption data are discussed 
(Sections 6.4, 6.5, 6.6, and 6.7). 
Assumption 8. It is assumed that sorption experiments conducted under saturated conditions 
yield results that are also applicable to unsaturated conditions. The rationale for accepting this 
assumption is that it has been verified, to an extent, in experiments using Se as the sorbing ion 
(as suggested by the comparison of data contained in DTN: LA0010JC831341.004 with data 
contained in DTN: LA0002JC831341.003). Results for batch experiments under saturated 
conditions are similar to those obtained for unsaturated solid rock core (DTN: 
LA0010JC831341.004 and Section 6.5.2.2). This assumption applies to all sorption results and 
affects parts of Section 6 in which sorption data are discussed (Sections 6.4, 6.5, 6.6, and 6.7). 
On the basis given above, and the consideration that this information is used simply for TSPASR 
purposes, no further confirmation is required. 
Assumption 9. It is assumed that the characteristics of J-13 or p#1 groundwaters that influence 
sorption parameters can be adequately represented by solutions prepared in the laboratory to 
simulate these water compositions. Because variations in groundwater chemistry may have an 
impact on the sorption behavior of the radionuclides of interest, a strategy was developed to 
account for the potential impacts of these variations on sorption coefficients. Accordingly, it was 
assumed that the major ion compositions of wateres from Wells J-13 and p#1 are bounding for 
purposes of quantification of the sorption behavior of the radionuclides of interest (Assumption 
3). However, the pH and Eh variations of groundwaters in the Yucca Mountain flow system are 
not fully adressed by this choice in bounding water compositions. The pH of J-13 and p#1 
waters in contact with atmospheric carbon dioxide levels is generally in the range of 8.5 to 9 
(Triay, Cotter, Huddleston et al. 1996 p. 7). To address the lower pH values observed among 
saturated-zone waters in the Yucca Mountain flow system, the pH of aliquots of J-13 and p#1 
waters were adjusted to values near 7.0 by imposing an overpressure of carbon dioxide in a glove 
box. The Eh of the waters used in the experiments was presumed to be oxidizing because the 
experiments were carried out in contact with atmospheric oxygen levels. This assumption 
applies to all sorption results and affects parts of Section 6 in which sorption data are discussed 
(Sections 6.4, 6.5, 6.6, and 6.7). On the basis given above, and the consideration that this 
information is used simply for TSPA-SR purposes, no further confirmation is required. 

Assumption 10. It is assumed that decreases in radionuclide concentrations in solution during 
sorption experimentswhich is the basis for estimating the value of the sorption coefficient, or 
Kdare due to sorption and not precipitation of the radionuclide being studied. The validity of 
this assumption has been tested by comparing the Kd values obtained from batch-sorption tests 
for consistency with those obtained from crushed-rock and solid-rock column studies (Section 
6.5; Triay et al. 1997, Ch. V, Sections A and B). 
Assumptions used in the UZTT are listed below (Assumptions 11 through 21). The UZTT 
results presented in this report are preliminary. Work that is currently being conducted on this 
activity directly addresses many of these assumptions. 
Assumption 11. The rocks identified as Calico Hills vitric (CHv) and Topopah Spring welded 
(TSw) hydrogeologic units at Busted Butte are part of the same-named units that exist under the 
repository and are also representative of those same units under the repository. The basis for this 
assumption is the equivalent location of the units within the rock sequence at Busted Butte and 
Yucca Mountain (including the repository), as well as an understanding of the geologic processes 
that formed the region. Mineralogic analyses of samples from Busted Butte, compared to those 
collected from boreholes on Yucca Mountain, support this assumption (Section 6.8.3.2; Bussod 
et al. 1997, Section 2.2 and 2.3). This assumption is used in Section 6.8. 
Assumption 12. The presence of boreholes does not unduly influence the results of the transport 
test. This assumption has been tested through numerical assessment of borehole influence as 
shown in Section 6.8.2. Figure 42 of that section shows that solute travel time is disturbed by 
less than 20%, which is well within the uncertainties inherent in the calculation. This 
assumption is used in Section 6.8. This assumption does not require confirmation. 
Assumption 13. It is assumed that the principal barrier to radionuclide migration in the 
unsaturated zone at Yucca Mountain is the Calico Hills nonwelded hydrogeologic unit (Montazer 
and Wilson, 1984; Ortiz et al., 1985), and the Busted Butte test location sufficiently represents 
the vitric portions of this unit to produce data applicable to Yucca Mountain flow and transport 
models. The information in Section 6.8.3 and Bussod et al. (1997, Sections 2.2 and 2.3) 
documents the representativeness of the Busted Butte site with respect to lithology, mineralogy, 
and hydrologic properties.. This assumption is used in Section 6.8. 
Assumption 14. It is assumed that the vitric units of the Calico Hills Formation play a significant 
role as a barrier to transport (beyond the zeolitic CH). This is a primary assumption that the 
UZTT was designed to test. Laboratory studies supporting this assumption are discussed in 
Section 6.5. The assumption is used in all of Section 6.8 but primarily in Section 6.8.1. This 
assumption does not require confirmation. 
Assumption 15. The test block was minimally disturbed (saturation, in situ water distribution, 
fractures, faults) during construction of the test and is assumed to represent natural conditions. 
Precautions, such as dry drilling, were taken to avoid disturbance of the test block during 
construction, and no unexpected disturbances have been observed during visual inspection of the 
integrity of the test block. In addition, it has been assessed that the effects of an undetected 

disturbance on subsequent tests will be small compared to intentionally induced effects.. This 
assumption is used throughout Section 6.8 and particularly in Sections 6.8.2, 6.8.6, and 6.8.7. 
This assumption does not require confirmation. 
Assumption 16. The UZTT test blocks were at a steady-state background moisture distribution 
before injection. The UZTT is located in an otherwise undisturbed area of the Yucca Mountain 
site. It is assumed that the construction of the UZTT and the test design caused minimal 
disturbance of the system (see previous assumption), and that any change caused by construction 
would quickly return to an equilibrium state within the time between tunnel excavation and 
beginning injection. Models indicate that any perturbations would disappear in less than 14 days 
(Soll 1997, p. 21, Phase 1A results), which is before injection started. Therefore, this assumption 
does not require verification. This assumption is used throughout Section 6.8. 
Assumption 17. The different emitters in any given borehole are all injecting at the same rate. 
All emitters are identical. Total injection quantity is carefully monitored, and any variation can 
be identified and incorporated into analyses. Because each emitter is designed to be identical, 
this assumption does not require confirmation. This assumption is used in Section 6.8.5. 
Assumption 18. In selecting the tracers, fluorescein, bromide, and FBAs were assumed to be 
significantly less sorbing than the metals and were referred to as nonreactive. These tracers are 
accepted by the hydrologic community as conservative. This assumption has been confirmed in 
practice, and no further confirmation is required. This assumption is used in Section 6.8.5. 
Assumption 19. Hydrogeologic parameters for the same units available in the YM database are 
reasonable estimates for the parameters at Busted Butte. Prior to Busted Butte specific 
parameters being available, the best estimate is reported data from the same hydrologic unit at 
Yucca Mountain. This assumption does not require confirmation. This assumption is used in 
Sections 6.8.6 and 6.8.7. 
Assumption 20. This assumption not used. 
Assumption 21. The stochastic parameters contained in Stochastic Hydrogeologic Units and 
Hydrogeologic Properties Development for Total-System Performance Assessments, (Schenker 
et al. 1995), are representative of Busted Butte properties. The intended use of this information 
is for scoping and sensitivity only; therefore, these data are acceptable to use as a baseline and 
require no further confirmation. This assumption is used in Section 6.8.6. 
Assumptions used in the C-wells work include the following. 
Assumption 22. It is assumed that all tracers experience the same mean residence time and 
longitudinal dispersivity. The rationale for accepting this assumption as valid is that the tracers 
were injected simultaneously. This assumption influences the interpretation of the tracer tests 
and affects all of Section 6.9.2 where tracer tests are discussed. This assumption does not 
require confirmation. 

Assumption 23. Bromide and PFBA tracers were assumed to be conservative (nonsorbing). This 
assumption is supported by laboratory experiments in which the Kd values for these tracers were 
statistically indistinguishable from zero (i.e., no sorption) (Reimus et al. 1999). This assumption 
influences the interpretation of the tracer tests and affects all of Section 6.9.2 where tracer tests 
are discussed. This assumption does not require confirmation. 
Assumption 24. It is assumed that bromide and PFBA diffusion coefficients differ by a factor of 
3, with the bromide coefficient being the larger of the two, and that the lithium diffusion 
coefficient is twice that of PFBA. This assumption is supported by laboratory experiments for 
bromide and PFBA diffusion coefficients (DTN: LA9909PR831231.004), and by values 
published for lithium (Newman 1973, p. 230). This assumption influences the interpretation of 
the tracer tests and affects all of Section 6.9.2 where tracer tests are discussed. This assumption 
does not require confirmation. 
Assumption 25. The microspheres were assumed to not diffuse into the matrix (i.e., the diffusion 
coefficient is effectively zero). The rationale for accepting this assumption results is that the 
diffusion coefficient for microspheres is smaller than those for solutes by about three orders of 
magnitude (based on application of the Stokes-Einstein equation) (Bird et al. 1960, p. 514). 
Consequently, the low diffusivity for microspheres, in combination with matrix tortuosity, limits 
the rate and extent to which microspheres can diffuse into the matrix. This assumption 
influences the interpretation of the tracer tests and affects all of Section 6.9.2 where tracer tests 
are discussed. This assumption does not require confirmation. 
Assumption 26. It is assumed, for this version of the analysis, that the data presented in Tables 
52, 53, 54, and Figure 101 are reasonably representative of the results of the tests. Some of the 
interpretations of the C-Wells tracer data contained in these tables and figures are derived from 
unqualified DTN: LA9909PR831231.005. The interpretations were originally considered 
unqualified because the RTA software had not been qualified and C-Wells core and related 
information had not been qualified. Although the RTA software is now qualified and C-Wells 
Core information has been qualified (see DTN: MO0011DQRCWELC.001), the investigator for 
DTN: LA9909PR831231.005 has determined that minor differences between the qualified 
information and original input values used in the DTN do not allow DTN: 
LA9909PR831231.005 to be qualified. The information displayed on the tables and figures is 
considered sufficiently accurate for the purposes of this analysis and to support the conclusions 
in Section 6.9.4. This assumption is not used to produce the recommended values in DTNs: 
LA0003JC831362.001 and LA0003AM831341.001 and is therefore not critical to the principal 
output of the analysis. 

6. ANALYSIS/MODEL
6.1 INTRODUCTION 
This analysis directly supports four Principal Factors for the Post-Closure Safety Case as 
discussed in AP-3.15Q Managing Technical Product Inputs: (1) Solubility Limits of Dissolved 
Radionuclides, (2) Dilution of Radionuclide Concentrations in the Geologic Setting, (3) 
Retardation of Radionuclide Migration in the Unsaturated Zone, and (4) Retardation of 
Radionuclide Migration in the Saturated Zone. Therefore, this AMR is deemed to be of Level 1 
importance in addressing the factors associated with the post-closure safety case. 
This section summarizes field and laboratory data and interpretations that were collected or 
developed in laboratory activities and that are relevant to the development and testing of 
conceptual and numerical transport models of the saturated zone at Yucca Mountain. These data 
include sorption coefficients for the radionuclides of interest in various hydrologic units, 
transport data and modeling results from the C-Wells activity and the Busted Butte UZTT 
activity, measurements of hydrochemistry and Eh-pH conditions in groundwater, and parameters 
related to colloidal transport. 
6.2 APPROACH 
Radionuclide migration from a potential repository would be inhibited by several barriers, 
including the geochemical barriers of solubility and sorption. Sorption coefficients for 
radionuclides of interest were obtained using water and rock samples from the site (Assumptions 
110 in Section 5). Sorption coefficients were obtained in batch-sorption experiments and 
saturated-column experiments. Experiments were performed at several pH levels to evaluate the 
impact of pH variations on the sorption coefficient. In general, oxidation/reduction conditions 
were oxidizing in all the experiments. A limited number of experiments were performed to 
evaluate the sorption of radionuclides during fracture flow. Similarly, a limited number of 
column experiments were carried out to evaluate whether or not batch-sorption coefficients could 
be used to model transport of reactive species in a dynamic (that is, flowing) system 
(Assumption 7 in Section 5). The potential effects of organics on actinide sorption were 
evaluated in batch-sorption experiments with model organic compounds in waters and rock 
samples from the site. Models were developed to explain the sorption coefficient data and to 
allow prediction of coefficient values under anticipated conditions. Batch experiments were also 
done to evaluate the sorption of radionuclides onto colloidal-sized materials. In this set of 
experiments, the issue of reversibility of the radionuclide sorption reactions was also addressed. 
Effective diffusion coefficients for the radionuclides of interest were obtained in experiments 
with specially designed diffusion cells and beakers made of rock samples from the site. These 
experiments were performed with representative water and rock compositions from the site. 
The applicability of this approach to the derivation of transport parameters was evaluated with 
two major field tests in which sorbing and nonsorbing tracers were injected into and recovered 
from hydrogeologic units representative of units between the proposed repository and the 

accessible environment. The results of these tests were modeled with the codes to be used to 
model transport from the proposed repository at Yucca Mountain. 
6.3 SOLUBILITY STUDIES 
One of the objectives stated in CRWMS M&O (1999b) was an assessment of laboratory-derived 
radionuclide solubility limits to be used in performance assessment modeling of the site 
(CWRMS M&O 1999b). Solubility of radionuclide phases is the subject of a separate AMR 
(CRWMS M&O 2000a); none of these results are used in this AMR. 
6.4 SORPTION STUDIES 
6.4.1 Introduction 
This section provides the sorption-coefficient data to be used in performance assessment 
calculations. This section also provides analysis of the sorption data for the elements of interest 
obtained in laboratory experiments by the YMP and from the literature. The laboratory data 
obtained include batch-sorption coefficients, crushed rock column and solid column experiments, 
fractured rock column experiments, and diffusion experiments. 
6.4.2 Sorption Coefficients for Performance Assessment 
The sorption-coefficient data to be used in performance assessment calculations are provided in 
the form of sorption-coefficient distributions. These sorption-coefficient distributions are 
contained in the Technical Product Output DTN: LA0003AM831341.001. 
Sorption coefficients are required for the following chemical elements that represent the various 
radionuclides of interest: 
 
americium, thorium, zirconium, actinium, samarium, niobium, lead 
 
radium, strontium, cesium, lead, tin, plutonium 
 
neptunium, uranium, selenium, nickel, protactinium 
 
carbon, chlorine, technetium, iodine. 
As will be shown later, this listing is generally in order of decreasing sorption potential. 
The selection of the distributions provided in this section were based on a review of previous 
estimates (Wilson et al. 1994) and the qualified and corroborating data presented in this report. 
The results reflect the current best judgement of the author on the implications of these sources 
for estimating sorption-coefficient distributions to be used for the saturated zone. Many factors 
that determine sorption behavior were taken into account, such as: rock type, mineralogy, water 
chemistry, pH, reaction kinetics, competitive effects among sorbing constituents, and the 
presence or absence of microbial activity. The author also used his best judgement for 

estimating distribution parameter values for the Unsaturated Zone, for which conditions differ 
from those in the Saturated Zone insofar as conditions are always oxidizing and porewaters 
generally have higher ionic strengths; hence more competitive ion effects, especially in zeolitic 
units. The distributions elicited for the Unsaturated Zone sorption parameter values are biased 
toward the tests that used p#1-water compositions, and those for the Saturated Zone parameter 
values. As in previous estimates (Wilson et al. 1994), two key assumptions were used to 
formulate the sorption-coefficient distributions. These assumptions were as follows (their 
technical basis is described in Section 5): 
 
Waters from Wells J-13 and p#1 bound the major ion chemistry of the groundwaters at 
Yucca Mountain (Assumption 3 in Section 5). 
 
The variations in rock type in Yucca Mountain can be reduced to three main classes: 
devitrified tuff, vitric tuff, and zeolitic tuff. Iron oxide was also added as a class to 
represent sorption on waste-package material (alluvium was added subsequent to the 
expert elicitation to represent sorption in the far field). As discussed in Assumption 4, it 
is assumed that hydrogeologic strata at the site can be classified as one of these 
representative rock types, that sorption coefficient distributions for a given rock type can 
be determined from a limited number of batch experiments, and that the sorption data are 
adequately representative of the rock types used in the transport calculations (Assumption 
4 in Section 5). 
Additional assumptions also underlie the selection of reasonable and technically defensible 
distribution parameters. These are described in Section 5 and include the following: 
 
It is assumed that sorption parameters determined in laboratory experiments using 
crushed tuff are applicable to transport through solid tuff matrix in the field (Assumption 
2 in Section 5). 
 
It is assumed that sorption parameters measured for a single radionuclide are applicable 
to the case where more than one radionuclide is present, that is, it is assumed that 
competitive effects are negligible (Assumption 5 in Section 5). 
 
It is assumed that the variability of the sorption parameter as a function of concentration 
can be adequately captured by lowering the minimum Kd value defined for the sorption 
distribution function so as to include the reduced Kd value expected under high 
concentration conditions (Assumption 6 in Section 5). 
 
It is assumed that in-situ flow rates are sufficiently slow that sorption equilibrium is 
achieved during solute transport (Assumption 7 in Section 5). 
 
It is assumed that sorption experiments conducted under saturated conditions yield results 
that are also applicable to unsaturated conditions (Assumption 8 in Section 5). 
 
It is assumed that sorption parameters measured in laboratory experiments have not been 
significantly affected by microbial activity (Assumption 1 in Section 5). 

Table 2a shows the parameters for the sorption-coefficient distributions recommended for 
performance assessment for the unsaturated-zone units, and Table 2b shows the same parameters 
for saturated-zone units (DTN: LA0003AM831341.001). The parameters differ slightly for 
these two general types of environments because the higher ionic strength and higher redox 
potential of unsaturated-zone waters are expected to affect the sorption behavior of some 
elements relative to their behaviors in saturated-zone waters. The types of distributions 
considered include uniform, beta, and exponential beta distributions. The minimum and 
maximum values for each distribution are provided along with the expected value (E[x]) and the 
coefficient of variation (COV) for two of the types of distributions. The COV is defined as 
s[x]/E[x], where s[x] is the standard deviation of the distribution. 
The information given in Tables 2a and b reflects the best judgment of the author regarding the 
shape of each distribution. These judgments erred on the side of conservatism by choosing 
minimum and maximum values that were smaller than the bounds dictated by the available data. 
This action was done in acknowledgement of the fact that the available database was incomplete. 
The experimental data on which the distributions are based are discussed in the following section 
as well as in the documentation associated with DTN: LA0003AM831341.001. 
Americium, Thorium, Actinium, Samarium, Zirconium and Niobium 
The sorption-coefficient distributions for these elements in Yucca Mountain tuffs and iron oxides 
given in Tables 2a and b were inferred from data presented by Thomas (1987, pp. 3499, 
parameters srd1 and srd2), Triay et al. (1991), and Meijer (1992, pp. 2224) and from the review 
of the sorption characteristics of these elements in Triay et al. (1997, pp. 99107). 
Plutonium 
The sorption-coefficient distributions for plutonium given in Tables 2a and b were inferred from 
data presented by Thomas (1987, pp. 3499, parameters srd1 and srd2) and Meijer (1992, pp. 
2224) and from data discussed in Section 6.4.4 (DTN: LA0010JC831341.006). 
Uranium 
The sorption-coefficient distributions for uranium given in Tables 2a and b were inferred from 
data presented by Thomas (1987, pp. 3499, parameters srd1 and srd2) and Meijer (1992, pp. 24, 
2629) and from data discussed in Section 6.4.4 (DTN: LA0010JC831341.005). 
Neptunium 
The sorption-coefficient distributions for neptunium on tuffs and iron oxide given in Tables 2a 
and b were inferred from data presented by Thomas (1987, pp. 3499, parameters srd1 and 
srd2), Meijer (1992, pp.2429), and Triay, Robinson et al. (1993) and from data discussed in 
Section 6.4.4.1.4.2 (DTN: LA0010JC831341.007). Sorption-coefficient distributions for 
neptunium on alluvium are based on Section 6.4.5 (DTN: LA0109MD831341.001). 

Table 2a. Sorption-Coefficient Distributions for Unsaturated-Zone Units 
Element Rock type Min Kd (mL g1) Max Kd (mL g1) E[x] COV* Distribution type 
Americium (also Devitrified 100 2000   Uniform 
Actinium, Niobium, Vitric 100 1000 400 0.20 Beta 
Samarium, Thorium, Zeolitic 100 1000   Uniform 
Zirconium) Iron oxide 1000 5000   Uniform 
Plutonium Devitrified 5 70   Uniform 
Vitric 30 200 100 0.25 Beta 
Zeolitic 30 200 100 0.25 Beta 
Iron oxide 1000 5000   Uniform 
Uranium Devitrified 0 2.0 0.5 0.3 Beta 
Vitric 0 1.0 0.5 0.3 Beta 
Zeolitic 
Iron oxide 
0 
100 
10.0 
1000 
4.0 
 
1.0 
 
Beta (exp) 
Uniform 
Neptunium Devitrified 
Vitric 
Zeolitic 
0 
0 
0 
1.0 
1.0 
3.0 
0.3 
0.3 
0.5 
0.3 
1.0 
0.25 
Beta 
Beta (exp) 
Beta 
Iron oxide 500 1000   Uniform 
Radium Devitrified 70 300   Uniform 
Vitric 50 100   Uniform 
Zeolitic 
Iron oxide 
800 
0 
2000 
500 
 
30 
 
1.0 
Uniform 
Beta (exp) 
Cesium Devitrified 10 700   Uniform 
Vitric 10 100   Uniform 
Zeolitic 300 3000   Uniform 
Iron oxide 0 300 30 1.0 Beta (exp) 
Strontium Devitrified 5 30   Uniform 
Vitric 0 20   Uniform 
Zeolitic 200 2000   Uniform 
Iron oxide 0 20 10 0.25 Beta 
Nickel Devitrified 0 200 50 0.33 Beta 
Vitric 0 50 30 0.33 Beta 
Zeolitic 0 200 50 0.33 Beta 
Iron oxide 0 500   Uniform 
Lead Devitrified 100 500   Uniform 
Vitric 100 500   Uniform 
Zeolitic 
Iron oxide 
100 
100 
500 
1000 
 
 
 
 
Uniform 
Uniform 
Tin Devitrified 20 200   Uniform 
Vitric 20 200   Uniform 
Zeolitic 100 300   Uniform 
Iron oxide 0 5000   Uniform 
Protactinium Devitrified 
Vitric 
0 
0 
100 
100 
 
 
 
 
Uniform 
Uniform 
Zeolitic 0 100   Uniform 
Iron oxide 500 1000   Uniform 
Selenium Devitrified 
Vitric 
Zeolitic 
Iron oxide 
0 
0 
0 
0 
1 
1 
1 
200 
0.1 
0.1 
0.2 
30 
1.0 
1.0 
1.0 
1.0 
Beta (exp) 
Beta (exp) 
Beta (exp) 
Beta (exp) 
Carbon Iron oxide 10 100   Uniform 
Chlorine, 
Technetium, Iodine 
All rock 
types 
0 0    
DTN: LA0003AM831341.001 
NOTES: *Coefficient of variation: COV = s[x]/E[x]; in the table, where E[x] is the expected value of the distribution 
and s[x] is the standard of deviation of the distribution. 
 means this parameter is not applicable. 
ANL-NBS-HS-000019, Rev 00, ICN 02 45 November 2001 

Table 2b. Sorption-Coefficient Distributions for Saturated-Zone Units 
Element Rock type Min Kd (mL g1) Max Kd (mL g1) E[x] COV* Distribution type 
Americium (also Devitrified 100 2000   Uniform 
Actinium, Niobium, Vitric 100 1000 400 0.20 Beta 
Samarium, Thorium, Zeolitic 100 1000   Uniform 
Zirconium) Iron oxide 1000 5000   Uniform 
Plutonium Devitrified 5 100 50 0.15 Beta 
Vitric 50 300 100 0.15 Beta 
Zeolitic 50 400 100 0.15 Beta 
Iron oxide 1000 5000   Uniform 
Uranium Devitrified 
Vitric 
Zeolitic 
0 
0 
5 
5.0 
4.0 
20.0 
N/A 
N/A 
7.0 
N/A 
N/A 
0.3 
Uniform 
Uniform 
Beta 
Iron oxide 
Alluvium 
100 
0 
1000 
8.0 
N/A 
N/A 
N/A 
N/A 
Uniform 
Uniform 
Neptunium Devitrified 
Vitric 
Zeolitic 
0 
0 
0 
2.0 
2.0 
5.0 
0.5 
0.5 
1.0 
0.3 
1.0 
0.25 
Beta 
Beta (exp) 
Beta 
Iron oxide 500 1000   Uniform 
Alluvium 0 100 18 1.0 Beta 
Radium Devitrified 
Vitric 
Zeolitic 
Iron oxide 
100 
100 
1000 
0 
500 
500 
5000 
1500 
 
 
 
30 
 
 
 
1.0 
Uniform 
Uniform 
Uniform 
Beta (exp) 
Cesium Devitrified 20 1000   Uniform 
Vitric 10 100   Uniform 
Zeolitic 500 5000   Uniform 
Iron oxide 0 500 30 1.0 Beta (exp) 
Strontium Devitrified 10 200   Uniform 
Vitric 20 50   Uniform 
Zeolitic 
Iron oxide 
2000 
0 
5000 
30 
 
10 
 
0.25 
Log uniform 
Beta 
Nickel Devitrified 0 200   Uniform 
Vitric 0 50   Uniform 
Zeolitic 0 200   Uniform 
Iron oxide 0 1000   Uniform 
Lead Devitrified 
Vitric 
Zeolitic 
100 
100 
100 
500 
500 
500 
 
 
 
 
 
 
Uniform 
Uniform 
Uniform 
Iron oxide 100 1000   Uniform 
Tin Devitrified 20 200   Uniform 
Vitric 20 200   Uniform 
Zeolitic 100 300   Uniform 
Iron oxide 0 5000   Uniform 
Protactinium Devitrified 
Vitric 
Zeolitic 
0 
0 
0 
100 
100 
100 
 
 
 
 
 
 
Uniform 
Uniform 
Uniform 
Iron oxide 500 1000   Uniform 
Selenium Devitrified 
Vitric 
0 
0 
1.0 
1.0 
0.1 
0.1 
1.0 
1.0 
Beta (exp) 
Beta (exp) 
Zeolitic 
Iron oxide 
0 
0 
1.0 
500 
0.2 
30 
1.0 
1.0 
Beta (exp) 
Beta (exp) 
Carbon Iron oxide 10 100   Uniform 
Chlorine, Technetium, 
Iodine 
All tuffs 0 0    
Technetium Alluvium 0.27 0.62   Uniform 
Iodine Alluvium 0.32 0.63   Uniform 
DTN: LA0003AM831341.001 
NOTES: *Coefficient of variation: COV = s[x]/E[x]; in the table, where E[x] is the expected value of the distribution 
and s[x] is the standard of deviation of the distribution. 
 means this parameter is not applicable. 
ANL-NBS-HS-000019, Rev 00, ICN 02 46 November 2001 

Radium 
The sorption-coefficient distributions for radium given in Tables 2a and b were inferred from 
data presented by Thomas (1987, pp. 3499, parameters srd1 and srd2), Meijer (1992, pp. 24 
25), Triay et al. (1991), and Section 6.4.4.1.4.9. . 
Cesium 
Cesium sorption-coefficient distributions given in Tables 2a and b were inferred from data 
presented by Thomas (1987, pp. 3499, parameters srd1 and srd2), Meijer (1992, pp. 2325), 
and Triay et al. (1991) and DTN: LA0010JC831341.002. 
Strontium 
Strontium sorption-coefficient distributions given in Tables 2a and b were inferred from data 
presented by Thomas (1987, pp. 3499, parameters srd1 and srd2), Triay et al. (1991) and DTN: 
LA0010831341.003. 
Nickel 
Nickel sorption-coefficient distributions given in Tables 2a and b were inferred from data 
presented by Meijer (1992, p. 25). For iron oxides, the nickel sorption-coefficient distribution 
was inferred from the data presented by Siegel et al. (1992; 1993, p. 355). The distributions for 
vitric tuff and iron oxide were also inferred from data presented in Triay et al. (1997, pp. 122 
123). 
Lead 
The sorption-coefficient distributions for lead given in Tables 2a and b were inferred from data 
presented by Triay et al. (1997, pp. 122123. 
Tin 
The sorption-coefficient distributions given in Tables 2a and b were inferred from the work by 
Andersson (1988); the uniform distributions chosen were the result of uncertainty about the 
sorption of tin. 
Protactinium 
The element protactinium was given the same distribution parameters as the element neptunium. 
The protactinium sorption-coefficient distributions presented in Tables 2a and b were inferred 
from data for protactinium presented by Allard (1982, pp. 3233) and Rundberg et al. (1985, p. 
63). 

Selenium 
Selenium sorption-coefficient distributions given in Tables 2a and b were inferred from data 
presented by Thomas (1987, pp. 3499, parameters srd1 and srd2) and data discussed in Section 
6.4.5 (DTN: LA0010JC831341.004). 
Carbon 
Carbon is a special case because transport is expected to occur primarily in the gaseous phase as 
carbon dioxide. The major retardation mechanism is exchange of carbon-14 with the carbon in 
the carbon dioxide dissolved in the groundwater. Carbon sorption-coefficient distributions given 
in Tables 2a and b were inferred from data presented by Russell et al. (1975). 
Iodine, Technetium, and Chlorine 
Iodine, chlorine, and technetium do not appear to sorb onto tuffs under oxidizing conditions and, 
therefore, are assigned to have sorption coefficients of zero (see Section 6.4.4.1.4.5 for 
discussion of Tc sorption). Sorption-coefficient distributions for technetium and iodine in 
alluvium are based on data discussed in Section 6.4.5. The values given in Table 2b were based 
source DTNs that have been superceded by the information added in ICN 2. The corrected data 
(DTN: LA0109MD831341.002 and LA0109MD831341.003) result in somewhat lower sorption 
values than were used to generate the sorption coefficients in Table 2b. However, both data sets 
generally indicate low sorption coefficients in the +1 range. As noted by Triay, Cotter, 
Huddleston et al (1996, p. 7), values in this range are considered not to be significant. This is 
because the value is calculated by subtracting two large numbers (the initial isotope 
concentration in solution and the isotope concentration after sorption) to obtain a small number 
(the amount of isotope in the solid phase). Therefore, values in this range (particularly negative 
numbers) may be artifacts resulting from minor counting errors in measuring the activity of the 
samples. 
6.4.3 Hydrochemistry and Eh-pH Characteristics of the Saturated Zone 
The hydrochemistry of the saturated zone at Yucca Mountain controls the solubility and 
speciation of radionuclides in the groundwater and, hence, their transport characteristics. For the 
purposes of this report, the main concern is not the details of the hydrochemical variations but 
the total variation in water chemistry to be expected in the Yucca Mountain flow system. That 
is, what is required are bounding values for hydrochemical parameters in the saturated zone at 
Yucca Mountain. As discussed in Meijer (1992), the total variation in water chemistry in the 
Yucca Mountain flow system can be reasonably bounded by the compositions of waters from 
Wells J-13 and p#1 (Table 3) with some provisos (Assumption 3 in Section 5). The provisos 
involve the parameters pH and Eh. That is, the waters from Wells J-13 and p#1 have pH and Eh 
values that do not bound the range of these two parameters in waters in the saturated zone at 
Yucca Mountain. For pH, the range is approximately 6.5 to 9.5 (DTNs: GS950808312322.001, 
GS990808312322.001, MO0007MAJIONPH.011, MO0007MAJIONPH.013, 
MO0007MAJIONPH.005, MO0007MAJIONPH.003). The pH values for J-13 and p#1 waters 
(6.9 and 6.7) are at the lower end of this range. For Eh, the range is approximately 100 to +400 
mV (Standard Hydrogen Electrode, SHE) (DTN: LAAM831311AQ98.005, LAAM831311AQ98.007, 

LAAM831311AQ98.008, LAAM831311AQ98.010, LA9907AM831234.003, LA0004AM831234.001, 
LA9907AM831234.009, LA9907AM831234.010, LA9907AM831234.011, LA0004AM831234.002). The Eh 
values for J-13 and p#1 waters are both at the upper end of this range. 
Table 3. Groundwater Compositions of Wells J-13 and p#1 
Constituent 
Concentration (mg L1) 
J-13 water p#1 water 
Sodium 45 171 
Potassium 5.3 13.4 
Magnesium 1.8 31.9 
Calcium 11.5 87.8 
Silicon 30 30 
Fluoride 2.1 3.5 
Chloride 6.4 37 
Sulfate 18.1 129 
Bicarbonate 143 698 
pH 6.9 6.7 
DTN: LAIT831361AQ95.003 (SEP Table S98491.002) 
6.4.4 Sorption Experiments 
Sorption coefficients are generally obtained from batch-sorption experiments. Such experiments 
are simple in design, fast, and inexpensive compared to other sources of sorption-coefficient 
data. However, batch experiments have some drawbacks in that they are not sensitive to the 
possibility that, for a given radionuclide, some species may exist in the solution (e.g., in a 
different oxidation state) that sorb less than other species of the same nuclide in that solution and 
that are not in equilibrium with those species. If the poorly sorbing species constitutes only a 
small fraction of the total species in solution, a large sorption coefficient could be obtained in a 
batch experiment. However, the less sorptive species in solution would be transported through 
the rock much more readily than would be predicted by the value of the batch-sorption 
coefficient. To test for such a possibility, column experiments are carried out. In the column 
experiments, the existence of a poorly sorbing species in solution would be evident in the 
breakthrough curve. That is, this species would elute from the column before the major species 
were eluted from the column. The results of a limited number of both crushed-rock and solid-
rock column experiments are discussed in this section. The potential influence of organic 
constituents in groundwater on the sorption behavior of neptunium and plutonium is evaluated in 
batch experiments. 
Because variations in groundwater chemistry have an impact on the sorption behavior of the 
radionuclides of interest, a strategy was required to account for the potential impact of these 
variations on sorption coefficients. The strategy developed assumes that the major ion 
compositions of waters from Wells J-13 and p#1 are bounding for purposes of quantification of 
the sorption behavior of the radionuclides of interest (Assumption 3 in Section 5). However, the 
pH and Eh variations of groundwaters in the Yucca Mountain flow system are not fully 
addressed by this choice in bounding water compositions. The pH of J-13 and p#1 waters in 
contact with atmospheric carbon dioxide levels is generally in the range of 8.2 to 8.5. To address 
the lower pH values observed among saturated-zone waters in the Yucca Mountain flow system, 

the pH of aliquots of J-13 and p#1 waters were adjusted to values near 7.0 by imposing an 
overpressure of carbon dioxide in a glove box. The Eh of the waters used in the experiments was 
assumed to be oxidizing because the experiments were carried out in contact with atmospheric 
oxygen levels. 
6.4.4.1 Batch-Sorption Experiments 
Batch-sorption coefficients for radionuclides of interest were obtained using waters and rock 
samples from the site. Because of the large numbers of experiments required to address the 
sorption behavior of every radionuclide and every rock/water system of interest, some process 
for focusing the experimental program was required. The process developed has been called the 
minimum Kd approach (Meijer 1992, p. 9). The essence of this concept is that a minimum 
Kd exists for each radionuclide according to which the radionuclide will not reach the accessible 
environment through a groundwater pathway over the regulatory period of interest allowing for 
an adequate margin of error. Radionuclides that can be shown to possess this minimum Kd value 
in rock/water systems similar to those at Yucca Mountain, as based on literature data and any 
experimental data available for Yucca Mountain rock and water samples, would not require as 
much detailed investigation as radionuclides that do not. Those radionuclides with essentially no 
sorption potential were eliminated from further consideration. This approach allowed the 
experimental program to be focused on those radionuclides that would have the maximum 
potential for impacts on doses at the accessible environment over the regulatory time frame of 
interest. 
6.4.4.1.1 The Distribution Coefficient 
The batch-sorption distribution coefficient, Kd, was calculated using 
Kd = F = phase solid of g per deradionucli of moles 
. (Eq. 1)
Csolution of mL per deradionucli of moles 
Kd thus has units of mL g1. 
The Kd approach used here is by mass balance, that is, loss of solute from solution is assumed to 
have sorbed onto the solid. Some researchers measure solute concentrations in both the solution 
and on the solid. Also, because of mass measurements, results are sometimes given in units of 
g g1 instead of mL g1, which are the same in dilute aqueous solutions. Only mL g1 will be 
used here. 
Determination of very small or very large batch-sorption distribution coefficients results in large 
uncertainties in the Kd values calculated. When very little sorption occurs, calculations can yield 
negative Kd values; the error is the result of subtracting two large numbers (the initial 
radionuclide concentration in solution and the radionuclide concentration after sorption) to 
obtain a small number (the amount of radionuclide left in the solid phase). Therefore, very small 
Kd values are not very precise. On the other hand, when a great deal of sorption occurs, there can 
be large uncertainties associated with the measurement of the small amount of radioactivity left 

in solution after sorption. This fact also results in large uncertainties in the calculated Kd. 
Because of these uncertainties, most Kd values are only reported to one significant figure. 
6.4.4.1.2 Linear Versus Nonlinear Sorption 
The sorption distribution coefficient, Kd, for the species being sorbed, is the ratio of its concentration 
in the solid phase, F, to its concentration in the solution phase, C, which implies a linear 
relationship between the concentrations: 
F = KdC . (Eq. 2) 
Nonlinear adsorption isotherms have been reviewed by de Marsily (1986, p. 258). A useful 
nonlinear relationship, Freundlichs isotherm, is given by the equation 
F = KcC1/n, (Eq. 3) 
where Kc and n are positive constants (with n = 1). 
Another nonlinear relationship is Langmuirs isotherm, given by (de Marsily 1986, p. 258) 
K1C
F = 
1 + K2C, (Eq. 4) 
where K1 and K2 are positive constants. Part of the research discussed in this report was an 
attempt to assess the validity of using the linear distribution coefficients as opposed to other 
isotherm functional forms to describe retardation by sorption in transport calculations. However, 
in recommending sorption distribution coefficients for use in transport calculations, it was 
assumed in this AMR that the variability of the sorption parameter as a function of concentration 
can be adequately captured by lowering the minimum Kd value defined for the sorption 
distribution function so as to include the reduced Kd expected under high concentration 
conditions (Assumption 6 in Section 5). 
6.4.4.1.3 Experimental Procedures 
All batch-sorption experiments on Yucca Mountain samples reported here were performed at 
room temperature. The standard procedure first involved pretreating the solid phase with the 
groundwater being studied in the ratio of 1 g of solid to 20 mL of solution. The pretreated solid 
phase was then separated from the groundwater by centrifugation and exposed to 20 mL of a 
radionuclide solution (in the groundwater being studied) for approximately 3 weeks. After 
sorption, the phases were separated by centrifugation. The compositions of the groundwaters 
used were documented in the laboratory notebooks referenced by the DTNs for the experiments; 
these groundwaters were either natural or synthetic solutions of groundwaters from Wells J-13 or 
p#1 (see Assumptions 3 and 9 in Section 5). The nomenclature used for the tuff rock samples 
typically listed the borehole identifier followed by the sample depth in feet, for example, sample 
G1-2901 is tuff collected (as drillcore) from a depth of 2901 feet in borehole G-1. 

The amount of radionuclide in solution initially and then after sorption was either determined 
with a liquid-scintillation counter (for neptunium and plutonium) or with inductively coupled 
plasma mass spectrometry (for uranium). The amount of radionuclide in the solid phase was 
determined by difference. Container tubes without solid phases in them were used as controls to 
monitor radionuclide precipitation and sorption onto the container walls during the sorption 
experiment. The difference in the concentration of the radionuclide in the initial solution and 
that in the control-tube solution generally was only a few percent. In particular, results for the 
plutonium solution showed a small amount of sorption onto the container walls. Even in this 
case, the difference in concentration between the initial plutonium solution and the plutonium 
solution in the control tube never exceeded 7 percent for the experiments reported. Nevertheless, 
in the case of plutonium, the amount of radionuclide sorbed onto the solid phase was calculated 
by taking the difference of the final plutonium solution concentration both with the initial 
solution concentration and with the solution concentration in the control tube. The latter 
approach is conservative because plutonium may sorb to container walls only in the absence of 
the geologic material. 
Batch-sorption experiments were performed under atmospheric conditions or inside glove boxes 
with a carbon-dioxide overpressure. The pH of the J-13 and p#1 waters under atmospheric 
conditions was approximately 8.5 and 9, respectively, and inside the glove boxes was 7 (the CO2 
overpressure was adjusted to bring the pH of both waters down to 7). A limited number of batch 
experiments were carried out with different initial radionuclide concentrations in solution as 
described below. The results of these experiments were used to gauge whether the sorption 
isotherm for the rock/water system of interest was linear or not (Assumption 6 in Section 5). 
To investigate the kinetics of sorption reactions (i.e., the degree to which the reactions were 
instantaneous), batch experiments were carried out over different times (e.g., one day, one week, 
2 weeks, 3 weeks) (Assumption 7 in Section 5). Three weeks was generally enough time for the 
sorption reactions to reach a steady state. The issue of the reversibility of a given sorption 
process was investigated by performing desorption experiments on the solid samples remaining 
after a sorption experiment. In this case, the water initially added to the experiment was free of 
the radionuclide of interest. 
6.4.4.1.4 Data from Batch-Sorption Tests 
Data from batch-sorption tests were obtained from several sources. Most of the data reported 
here were obtained by the YMP. Corroborative data and data for some of the less important 
radionuclides were obtained from literature sources. 
6.4.4.1.4.1 Plutonium 
Data from Sorption Experiments Reported in the LiteratureThe data discussed in this section 
are provided to show trends for the sorption of plutonium. Allard (1982, pp. 6061) reported 
results on experiments involving plutonium sorption on quartz, apatite, attapulgite, 
montmorillonite, and various minerals rich in ferrous iron in a dilute groundwater containing 
plutonium at 1.8 x 1011 M. For all the minerals, the sorption coefficients were greater than 103 
mL g1 over a pH range from 4 to 9. Apatite, attapulgite, biotite, and montmorillonite showed 

sorption coefficients greater than 104 mL g1 over this pH range. Torstenfelt et al. (1988, pp. 
115116) presented data for plutonium sorption on feldspars, clays, and granite in contact with 
J-13 water. The sorption coefficients reported by them are generally between 100 to 200 mL g1 
in neutral to alkaline solutions. These authors emphasized the importance of proper 
experimental technique in the determination of sorption-coefficient values for plutonium and 
noted the potential for colloid formation in these types of experiments. Data indicating high 
affinity of plutonium for ferric oxyhydroxide, manganese oxide, and carbonate mineral surfaces 
were presented by Means et al. (1978), Keeney-Kennicutt and Morse (1985, Figs. 2, 46), and 
Sanchez et al. (1985). Means et al. noted that manganese oxides sorb plutonium more strongly 
than ferric oxyhydroxides in natural environments (presumably as a result of redox reactions on 
the manganese-oxide surface). 
Data from Laboratory Sorption Experiments with Yucca Mountain Rock and Water Samples 
Obtained prior to 1993Measurements of plutonium sorption coefficients involving Yucca 
Mountain rock samples and J-13 groundwater were summarized by Thomas (1987, p. 21 and 
Appendix). The values measured for the plutonium sorption coefficient range from 20 to greater 
than 4,500 mL g1 with most values lying between 100 to 2,000 mL g1 within a pH range of 8.2 
to 8.8. The coefficients determined during the desorption experiments were occasionally in the 
range of the sorption-coefficient values, but more typically, they were 10 to 20 times larger, 
reflecting the irreversibility of the sorption reactions. Zeolitic samples typically had lower 
sorption-coefficient values than vitric or devitrified samples. It appears that rocks that have 
essentially no reduction capacity remaining (that is, samples lacking ferrous iron or sulfide) show 
the lowest sorption coefficients for plutonium. Samples with calcite or clay showed the largest 
sorption coefficients (> 4,500 mL g1 for samples with 30 percent calcite). There are clays in the 
vitric tuff that increase Pu sorption. Pu is not strongly sorbed by zeolites in general. Therefore, 
the relative amounts of clays and zeolites should be known for reasonable prediction of sorption, 
not just the average fines content. 
Based on the eight experiments for which data are available (Meijer 1992), there was up to a 
factor of 12 variation in sorption coefficients as a function of groundwater composition. Water 
from Well p#1 was associated with the largest values (240 to 540 mL g1, sorption-desorption) 
with waters from Wells H-3 and J-13 showing the lowest values (20 to 230 mL g1). The higher 
values obtained with p#1 water may reflect calcite precipitation. There did not appear to be a 
dependence of the sorption coefficient on pH over the range from 7 to 9, although the available 
data are limited on this issue. Finally, there was less than a factor of four dependence of the 
sorption coefficient on radionuclide concentration over the range from 109 to 1012 M. 
Data from Laboratory Sorption Experiments with Yucca Mountain Rock and Water Samples 
Obtained after 1993Plutonium sorption coefficients have been measured on a variety of solid 
samples in contact with Yucca Mountain groundwaters J-13 and p#1 under atmospheric 
conditions (i.e., oxidizing conditions and pH = 8.2 to 8.6). The data obtained are summarized in 
Table 4. As shown in the table, plutonium sorption coefficients are greater than 100 mL g1 for 
vitric and zeolitic tuffs under these conditions. For devitrified tuffs, sorption coefficients are less 
than 100 mL g1 in both water compositions. 

Table 4. Plutonium Sorption Distribution Coefficients 
(under atmospheric conditions) 
Solid 
phase 
Kd range in 
J-13 water (mL g1) 
Kd range in synthetic 
p#1 water (mL g1) 
Vitric tuff 6002,000 100400 
Zeolitic tuff 300500 100400 
Devitrified tuff 40100 2070 
Synthetic hematite > 10,000 > 10,000 
Montmorillonite > 10,000 > 10,000 
Clinoptilolite 6003,000 2,0005,000 
Calcite 2001,000 100800 
Gibbsite 010 1090 
Albite 310 < 10 
Quartz < 10 < 10 
DTN: LA0010JC831341.006 
The sorption of plutonium onto the three main types of tuff in J-13 water at a pH of 7.0 was also 
studied using a carbon-dioxide overpressure to maintain a pH of 7. These experiments were also 
conducted under oxidizing conditions (i.e., atmospheric oxygen concentrations). The affinity of 
tuffs for plutonium at pH = 7 is, in decreasing order, zeolitic > vitric > devitrified (Triay et al. 
1997, Figure 37). Compared to the data summarized in Table 4, plutonium appears to sorb 
somewhat less at pH 7 than at pH values between 8.2 and 8.6 (i.e., atmospheric conditions), 
particularly on devitrified tuff (Kd < 10 mL g1 at pH 7). 
To evaluate which minerals in the tuffs were responsible for most of the plutonium sorption, 
sorption experiments with pure mineral separates were carried out. The minerals investigated 
included hematite, clinoptilolite, albite, and quartz. The results of the batch-sorption 
experiments for plutonium on these minerals are shown in Table 4. The relative affinities of 
these minerals for plutonium are, in decreasing order, hematite > montmorillonite > clinoptilolite 
> calcite >> gibbsite > albite = quartz. These data suggest that montmorillonite and zeolite 
minerals are likely responsible for most of the plutonium sorption onto the bulk tuffs. The trace 
amounts of hematite found in the tuffs do not appear to have a significant impact based on 
sorption data for neptunium and uranium (Triay et al. 1997, pp. 126, 133, and 145). However, 
the presence of calcite in the tuffs can have a significant impact depending on the amounts 
present and on the surface area of the calcite present. 
As stated above, sorption coefficients are not necessarily constant with increasing concentration 
of the sorbing element. That is, sorption isotherms can be linear or nonlinear. To evaluate the 
shape of the plutonium sorption isotherm with increasing plutonium concentration, experiments 
were conducted over a range of solution concentrations with various rock/water combinations. 
The data obtained indicate that the plutonium sorption isotherm is generally nonlinear on tuffs 
from Yucca Mountain (Triay et al. 1997, Figures 3842 and 44). The cause of the nonlinearity is 
not known. The solution concentrations in these experiments range from 3 x 1010 to 2 x 107 M. 
Because the upper limit of this range is close to the solubility of plutonium in Yucca Mountain 

waters, the concentration of plutonium transported in the flow system will likely not exceed this 
value. Experiments conducted with concentrations at the low end of the range produce sorption 
coefficients that are higher than experiments conducted with solution concentrations at the high 
end of the range. Therefore, the use of sorption coefficients in performance assessment 
calculations obtained with the more concentrated solutions will result in conservative predictions 
of plutonium transport rates (Assumption 6 in Section 5). 
The sorption of plutonium onto tuffs and minerals in J-13 and synthetic p#1 water under 
atmospheric conditions was studied as a function of time and initial plutonium solution 
concentration. The resulting data (Triay et al. 1997, Figure 38) indicate that it takes more than a 
couple weeks for the plutonium sorption reactions to reach steady state. Even after 32 days, a 
steady-state concentration in solution had not been achieved in these experiments. This slowness 
in reaching a steady state may be due to redox reactions at solid surfaces in the samples. 
Nitsche et al. (1993, pp. 52, 5862) report that, even when a plutonium solution in J-13 or p#1 
water is prepared starting in the +4 (IV) oxidation state, the predominant final oxidation state is 
+5, or Pu(V). The solution used for plutonium sorption experiments was prepared from a well-
characterized Pu(V) acidic stock in J-13 well water. Consequently, it has been assumed that, 
during the few weeks over which the sorption experiments have been conducted (e.g., 30 days), 
the plutonium remained predominantly in the +5 oxidation state although, given more time, it 
may not have remained in that state. 
Comparison of the data for plutonium sorption coefficients with similar data for neptunium and 
uranium indicates that significant plutonium sorption occurred in tuffs and minerals that exhibit 
very small sorption coefficients for Np(V) and U(VI). This result is puzzling; if plutonium in 
J-13 well water is predominantly Pu(V) and Pu(VI) (Nitsche et al. 1993, pp. 5862), it is 
expected that its sorption behavior would have been similar to that observed for Np(V) and 
U(VI). Several possible explanations for this apparent discrepancy are: 
 
The data of Nitsche et al. (1993, pp. 5862) for the oxidation states are incorrect, and the 
predominant plutonium oxidation state in J-13 well water at a pH of 7 is Pu(IV), not 
Pu(V) and Pu(VI) 
 
The Pu(IV) species is what sorbs from J-13 water but a re-equilibration in the solution 
phase produces more Pu(IV) to maintain equilibrium (which implies that the kinetics in 
plutonium speciation are fast in solution, but slow on the solid) 
 
Pu(V) and Pu(VI) reduce to Pu(IV) at solid surfaces (as a result of changes in the solution 
redox potential in the presence of the solid phases). 
In general, slow sorption kinetics should generally result in conservative predictions of transport 
rates of plutonium in Yucca Mountain from the batch-test sorption coefficients (Assumption 7 in 
Section 5). However, the great complexity of unsaturated flow, in which the residence time of 
solutions in the matrix versus fractures at any particular time can change dramatically, means 
that one has to be cautious in interpreting batch tests for unsaturated flow systems. 

Conclusions Regarding Sorption Behavior of Plutonium in the Yucca Mountain Flow System 
On the basis of the discussion in the previous subsections, it appears the most important factors 
controlling the sorption of plutonium from oxidizing groundwater onto Yucca Mountain tuffs are 
the abundances of montmorillonitic clays and zeolite minerals in the tuffs. Calcite, if present, 
may also result in high plutonium sorption coefficients. The affinity of Yucca Mountain tuffs for 
plutonium is highest in zeolitic tuffs, slightly lower in vitric tuffs, and lowest in devitrified tuffs. 
Groundwater compositional parameters that appear to have the most impact on plutonium 
sorption behavior are redox potential (i.e., Eh) and pH. Under less oxidizing redox potentials 
than those maintained in the batch experiments, plutonium sorption coefficients would be larger. 
Therefore, the sorption coefficients reported here will result in conservative predictions of 
plutonium transport rates. The change in sorption coefficients that may result from variations in 
groundwater pH are accounted for in the distributions reported in Tables 2a and b. Similarly, the 
impact of potential variations in plutonium concentration are incorporated in the distributions by 
assuming the high end of the range of potential plutonium concentrations in groundwater pertain 
to the Yucca Mountain flow system (Assumption 6 in Section 5). Although the kinetics of the 
plutonium sorption reactions appear to be relatively slow compared to elements with simpler 
solution chemistry (e.g., cesium), the sorption coefficients reported here should result in 
conservative predictions of plutonium transport rates. 
6.4.4.1.4.2 Neptunium 
Neptunium, protactinium, selenium, and uranium share a common characteristic in that they all 
tend to show small values for sorption coefficients in the rock-water systems expected at Yucca 
Mountain under oxidizing conditions. Under more reducing conditions, they would all have 
much lower solubilities and higher sorption affinities in Yucca Mountain groundwaters. In 
solutions representative of oxidized water compositions expected within the Yucca Mountain 
flow system, neptunium will be predominantly in a +5 oxidation state. In this oxidation state, 
neptunium is quite soluble when compared to lower oxidation states. If reducing conditions are 
encountered along the flow path between the proposed repository and the accessible 
environment, neptunium could be reduced to the +4 oxidation state. 
Data from Sorption Experiments Reported in the LiteratureThe results of neptunium sorption 
experiments with pure mineral separates have been reported by Allard (1982, pp. 1517, 5159) 
and Meijer et al. (1989). On the basis of these results, it is evident that in oxidizing solutions, 
neptunium has a high affinity for ferric oxides and oxyhydroxides, apatite, and attapulgite 
(magnesium-rich clay). It has a somewhat lower affinity for carbonates (such as calcite), sulfates 
(anhydrite), and manganese minerals (cryptomelane). It has a low affinity for most silicate 
minerals. Neptunium also shows high affinities for minerals that contain ferrous iron (such as 
pyrite, olivine, augite, magnetite, hornblende, epidote, biotite, and chlorite). This affinity is 
likely due to the reduction of Np(V) to Np(IV) by Fe(II) on the surfaces of these minerals. 
Although ferrous iron-bearing minerals are, at best, minor species in Yucca Mountain tuffs (Bish 
and Chipera 1989, Appendices A and B), they could be of considerable significance to 
neptunium sorption where present in the flow system. 
In addition to the nature of the available mineral surfaces, it is evident that pH is also a critical 
parameter in neptunium sorption. In general, neptunium sorption increases with increasing pH. 

This effect is particularly evident in the experiments with iron oxyhydroxides (Hobart 1990, 
p. 403). However, similar behavior is evident in the sorption experiments with silicate minerals 
(Allard 1982, pp. 1516). In the latter case, the sorption edge (as a function of pH) is located at a 
higher pH (89) than the edge associated with the ferric oxyhydroxides (a pH of 67). 
Neptunium does not appear to have a high affinity for ion-exchange reactions on clays and 
zeolites (Allard 1982; Triay, Robinson et al. 1993, Table 3a). This phenomenon may be due to 
the small charge-to-radius ratio and the large size of the neptunyl ion. 
Data from Laboratory Sorption Experiments with Yucca Mountain Rock and Water Samples 
Obtained Prior to 1993The results of neptunium sorption experiments involving Yucca 
Mountain rock and water samples have been reported by Daniels et al. (1982, pp. 78, 79, 90, 98, 
108), Thomas (1987, Appendix; 1988, pp. 3537), and Triay, Robinson et al. (1993, Table 3a). 
These experiments indicate that neptunium has a low affinity (for example, Kd values of 0 to 5 
mL g1) for the surfaces in Yucca Mountain tuffs over most of the pH range and water 
compositions expected in the Yucca Mountain flow system. The sorption mechanisms are 
apparently not entirely reversible as coefficients obtained from desorption experiments are 
commonly larger than those obtained from sorption experiments even though the isotherms are 
linear in the concentration range covered by these experiments. There is some indication of 
increased sorption coefficients (5 to 40 mL g1) at the highest pH values (8.5 to 9.0). Torstenfelt 
et al. (1988, p. 115) suggest that this result reflects increased hydrolysis of the neptunyl ion, 
resulting in an increase in surface-adsorption reactions. However, in Yucca Mountain rock-
water systems, it could also reflect increased potential for calcite precipitation at high pH. 
In the pH range from 6.5 to 8.5, the small but consistent affinity of neptunium for the tuffs most 
likely reflects the existence of a limited number of favorable adsorption sites for neptunium. 
This number apparently does not involve ion-exchange sites because zeolitic rock samples also 
show low sorption coefficients. For example, Thomas (1988, Table V) describes a case in which 
a zeolitic tuff sample (G4-1608) with a cation-exchange capacity of approximately 1.5 meq g1 
(based on the average value reported for other zeolitic tuff samples listed in the table cited) 
appears to have essentially the same affinity for neptunium as a devitrified tuff sample (GU3433) 
with an exchange capacity of approximately 0.02 meq g1. These sites are apparently not 
present in the same abundance on all tuff samples. That is, some zeolitic, vitric, and devitrified 
tuff samples have almost no affinity for neptunium over the pH range from 6.5 to 8.5, whereas 
other samples with similar proportions of major minerals show sorption coefficients in the range 
of 5 to 10 mL g1 (Meijer 1992). This result suggests, but does not prove, that the favorable sites 
are associated with some minor primary or secondary phase that has variable abundance. 
Hematite and calcite are candidates for this phase based on pure mineral studies. Because ferric 
oxides are present at trace levels in most of the rock units within Yucca Mountain, they could be 
the source of the low but consistent values (0.5 to 2 mL g1) observed in experiments on 
devitrified and zeolitic tuffs. Alternatively, neptunium may be sorbed (through reduction to 
Np(IV)) by the small amounts of ferrous-iron-bearing minerals present in the rock samples used 
in the sorption experiments. 
The increased sorption of neptunium on tuffaceous samples known to contain calcite suggests 
this mineral is of considerable potential significance to neptunium sorption on Yucca Mountain 
tuffs. If so, prediction of the adsorption behavior of neptunium will depend on knowledge of the 

surface areas of calcite in the various hydrologic units or on the saturation state of calcite in 
groundwaters present in these units. Because even small amounts of calcite appear to 
significantly increase neptunium sorption coefficients, current mineral identification techniques 
may not be adequate for prediction of neptunium sorption behavior involving calcite. For vitric 
units lacking iron oxides and calcite, neptunium may not be sorbed at all. 
Data from Laboratory Sorption Experiments with Yucca Mountain Rock and Water Samples 
Obtained after 1993 (data discussed in this section are reported in DTN: 
LA0010JC831341.007)Sorption coefficients for Np(V) on individual samples of the three 
main types of tuff under atmospheric conditions (pH = 8.28.6; oxidizing) are shown in Figure 1. 
Note that the sorption coefficients for all samples are less than 5.0 mL g1. The values less than 
1.0 are generally for vitric and devitrified samples. Those greater than 1.0 are for zeolitic 
samples. 
Kd (mL/g) 
G1-732G1-1271 
G1-1405G1-1436 
G1-1936G1-1941 
G2-767G2-770 
G2-1813G2-1951G2-2000G2-2222G4-268G4-270 
G4-272 
G4-1067 
G4-1395G4-1505G4-1506 
G4-1510G4-1529 
G4-1566 
G4-1625G4-1772 
G4-2077G4-2570GU3-747GU3-1394GU3-1405GU3-1407 
GU3-1555GU3-2325 
GU3-1992 
5 
4 
3 
2 
1 
0 
Sorption distribution coefficient, 
Tuff samples 
DTN: LA0010JC831341.007 
Note: These values of the batch-sorption distribution coefficient, Kd, obtained in separate experiments, illustrate the 
limited sorption of neptunium onto a large range of Yucca Mountain tuffs in J-13 well water under atmospheric 
conditions. The initial neptunium concentration ranged from 6 to 8 x 107 M. The tuffs were wet-sieved to 
particle sizes that ranged from 75 to 500 m. The pretreatment period was 2 to 14 days; the sorption period 
was 3 to 23 days. Samples are shown in order of borehole and depth. Figure from Triay et al. (1997, Fig. 66). 
Figure 1. Neptunium Sorption in J-13 Well Water 
Zeolitic tuffs show substantial variation in the neptunium sorption coefficient in different 
samples and under different pH conditions. Some zeolitic samples show very little affinity for 
neptunium, although more at a pH value of 8.5 than at 7.0 (Figure 2). Other zeolitic samples 
(e.g., G4-1510 and GU3-1992) show a higher affinity (that is, higher Kd), particularly at a pH 
value of 7.0. Why some zeolitic samples show substantially higher neptunium sorption 
coefficients is not entirely clear. The explanation likely revolves around the type of zeolite 
structure and the chemistry of the zeolite. 
The impact of pH variations on neptunium sorption behavior was also investigated with 
experiments on devitrified and vitric tuff and albite and quartz in J-13 water (under oxidizing 

conditions) at two pH values (7 and 8.5). It was found that in J-13 water neptunium sorbs only 
sparingly onto devitrified and vitric tuffs under both pH conditions. 
Experiments with pure clinoptilolite indicate that sorption increases with decreasing pH for 
Np(V). Because the major constituent of tuff sample G4-1510 is clinoptilolite, predictions of the 
Ka (Kd divided by the solid-phase surface area per unit mass) were made for neptunium sorption 
onto this tuff by assuming that clinoptilolite is the only sorbing phase. Table 5 shows measured 
and predicted values of Ka for the clinoptilolite-rich tuff sample G4-1510 at two different pH 
values. Because sorption is correlated with surface area, similar calculations (Table 6) were 
made for a series of tuff samples containing various amounts of clinoptilolite for which the 
surface area had been measured. The values in these two tables indicate that reasonable 
predictions can be made based on Np sorption data for pure clinoptilolite (assuming clinoptilolite 
is the only sorptive mineral). 
K d (mL/g) 
G2-767, vitricG2-1813, zeoliticG2-1951, zeoliticG2-2000, zeoliticG2-2222, zeoliticG4-270, devitrifiedG4-1395, 22% clinoptiloliteG4-1510, zeolitic, 59% clinoptiloliteGU3-1407, vitricGU3-1992, zeolitic, 76% clinoptilolite 
6 
5 
4 
3 
2 
1 
0 
Carbon-dioxide atmosphere, pH ~ 7 
Air atmosphere, pH ~ 8.5 
Sorption distribution coefficient, 
Tuff samples 
DTN: LA0010JC831341.007 
Note: Experimental values of Kd for the sorption of neptunium onto tuffs in J-13 water at initial concentrations of 6 to 
7 x 107 M are compared for atmospheric conditions (pH ~8.5) and a carbon-dioxide overpressure (pH ~7). 
Tuffs were wet-sieved (75 to 500 m); the pretreatment period was 2 to 3 days; the sorption period was 3 to 5 
days. Samples are shown in order of borehole and depth. Figure from Triay et al. (1997, Figure 62). 
Figure 2. pH Dependence of Neptunium Sorption onto Tuffs at 107 M 
Table 5. Prediction of Neptunium Sorption on Clinoptilolite-Rich G4-1510 Tuff in J-13 Watera 
Ka (m) Ka (m) 
7 7 775 
8.5 8 7 
Initial concentration (M) pH Measured Predicted 
1 x 101 x 101 x 10 to 3 x 106 x 101 x 10
DTN: LA0004AM831341.002.
Note: aAssuming clinoptilolite is the only sorbing mineral in the tuff, present at 59 weight %.
ANL-NBS-HS-000019, Rev 00, ICN 02 59 November 2001 

Table 6. Neptunium Sorption onto Clinoptilolite-Rich Tuffs in J-13 Watera 
Tuff sample Measured Ka (m) Predicted Ka (m) Clinoptilolite % 
G4-1505 8 x 108 1 x 107 74  7 
G4-1506 2 x 107 1 x 107 62  7 
G4-1510 6 x 108 1 x 107 59  7 
G4-1529 6 x 108 1 x 107 59  8 
G4-1625 7 x 108 1 x 107 61  7 
G4-1772 9 x 108 1 x 107 63  5 
G4-2077 1 x 108 1 x 107 51  8 
DTN: LA0004AM831341.002 
NOTE: aAtmospheric conditions; initial neptunium concentrations ranged from 6 to 8 x 107 M; tuffs 
were wet-sieved to particle sizes ranging from 75 to 500 m; the pretreatment period was 2 
to 14 days; the sorption period was 3 to 23 days; and the pH was 8.5  0.3. 
The dependence of neptunium sorption on neptunium concentrations for zeolitic tuffs and pure 
zeolites was tested in two samples. The sorption of neptunium onto zeolitic tuffs and 
clinoptilolite appears to be linear in the concentration range from 1 x 107 to 3 x 105 M and can 
be fitted using a constant Kd. In a zeolite-rich tuff at pH = 7.0, the Kd = 3 mL g1; whereas, at pH 
= 8.5, the Kd = 1.5 mL g1 (Figure 3). Similar results were obtained with a pure zeolite sample 
(Figure 4). The higher sorption of neptunium onto zeolites at a pH of 7 might be explained by 

the larger amount of NpO2 
+ relative to NpO2CO3 in J-13 well water at a pH value of 7 compared 
to that at a pH of 8.5 (Nitsche et al. 1993, Table VII; CRWMS M&O 2000a, Table 3). 
The relatively small amount of sorption observed in the zeolitic tuffs, given the large cation-
exchange capacity of zeolites, suggests that the mechanism for neptunium sorption onto 
clinoptilolite is a surface reaction involving only the cation sites accessible on the zeolite surface. 
One possible explanation for this behavior is that the shape and large size of the neptunyl cation 
prevents it from entering the pores in the zeolite structure, thereby gaining access to most of the 
exchange sites. This ion likely has a trans-dioxol configuration normal to a puckered equatorial 
ring containing six bound water molecules. 
Because neptunium was thought to sorb with a surface mechanism even in zeolitic tuffs and 
because the batch experiments are conducted with crushed tuff samples (i.e., increased surface 
area), the sorption coefficient for neptunium was investigated as a function of sieving procedure 
for devitrified (G4-270) and zeolitic (G4-1506) tuffs and calcite in J-13 and p#1 well waters. 
The data obtained in these experiments indicate that dry-sieving probably produces artificially 
high Kd values because of the increased surface area contributed by the small particles. As 
previously determined by Rogers and Meijer (1993), the optimal batch-sorption procedure 
involves wet-sieving the tuff samples to a size of 75 to 500 m. 
The sorption of neptunium onto pure iron oxides (hematite) in J-13 water was also measured. 
The measured values of Kd for hematite range from 100 to 2000 mL g1 (Triay, Cotter, Kraus et 
al. 1996, p. 15). Although the sorption onto the pure iron oxide hematite is very large, 
neptunium sorption onto devitrified tuffs, which appear to have traces of hematite (1 percent 

pH 7 
pH 8.5 
Concentration of neptunium in solution, C (micromoles/L) 
Concentration of neptunium in solid phase, F 
(nanomoles/g) 
Kd = 3 mL/g 
Kd = 1.5 mL/g 
0 
80 
60 
40 
20 
0 
10 20 30 
DTN: LA0010JC831341.007 
NOTE: 
A plot is shown of the concentration, F, of neptunium in the solid phase of the clinoptilolite-rich tuff G4-1510 
versus the concentration, C, of neptunium in the solution phase of J-13 well water and linear (Kd) fits to the 
data for two values of pH. From Triay et al. (1997, Figure 55). 
Figure 3. Neptunium 
Sorption onto Clinoptilolite-Rich Tuff 
pH 7 
pH 8.5 
Concentration of neptunium in solution, C (micromoles/L) 
Concentration of neptunium in solid phase, F 
(nanomoles/g)
Kd 
= 2.6 mL/g 
Kd 
= 1.4 mL/g 
0 
100 
80 
60 
40 
20 
0 
10 20 30 
DTN: LA0010JC831341.007 
NOTE: 
A plot is shown of the concentration, F, of neptunium in the solid phase of clinoptilolite versus the 
concentration, C, of neptunium in the solution phase of J-13 well water and linear (Kd) fits to the data for two 
values of pH. From Triay et al. (1997, Figure 56). 
Figure 4. Neptunium Sorption onto Clinoptilolite 

 1), is close to zero (Triay, Cotter, Kraus et al. 1996, p. 10). This result could be due to 
differences in the surface chemistry of pure hematite compared to hematite in tuff. For example, 
it could be due to passivation of the hematite surfaces in the tuff by elements (such as the rare 
earths) that have a higher affinity for hematite than neptunium and thus occupy the sorption sites. 
Alternatively, there may be too little hematite present in the tuffs to provide an adequate number 
of sorption sites. 
The kinetics of neptunium sorption onto tuffs and pure minerals were investigated, and it was 
found that the sorption of neptunium onto tuffs and clinoptilolite appears to be fast, with steady-
state conditions reached in 5 to 7 days, with no significant changes thereafter, in experiments 
conducted for up to 30 days (Triay et al. 1997, Figure 59). Although the data are scant, they can 
be used as guidelines. This is not the case for pure minerals that tend to sorb by means of a co-
precipitation mechanism (such as calcite) or by surface complexation (such as hematite) (Triay et 
al. 1997, Figure 60). The dissolution/precipitation reactions that may accompany the co-
precipitation of neptunium with calcite appear to be slow compared with other sorption 
mechanisms. 
Experiments with p#1 water indicate that neptunium sorption onto tuffs and zeolites is very 
limited (Kd < 1 mL g1) in this water regardless of conditions (pH and neptunium concentration) 
(Triay, Cotter, Huddleston, et al. 1996, pp. 2749, 56). If clinoptilolite is the only mineral 
affecting neptunium sorption on tuffs and if ion exchange at the surface is the dominant sorption 
mechanism, then the lack of neptunium sorption onto clinoptilolite could be the formation of the 
neptunium carbonado complex (NpO2CO3) in p#1 water to the exclusion of the neptunyl cation. 
Another possibility is that in p#1 water there is strong competition for sorption sites due to the 
higher ionic strength of this water compared with J-13 water. 
Figures 5 and 6 summarize the sorption of neptunium under atmospheric conditions for tuffs and 
minerals as a function of water type. Sorption onto zeolitic tuffs decreases considerably with 
increasing carbonate content and ionic strength of the water (compare sorption measured using 
carbonate-rich p#1 waters to those obtained using J-13 waters in Figure 5). Figure 6 shows that 
calcite and hematite have high affinities for neptunium, particularly in p#1 water. The calcite-
rich tuff G2-723 (34 percent calcite), exhibits considerable sorptive capacity for neptunium. 
Assuming that the calcite in the tuff sample has the same surface area as the natural calcite used 
for the experiments (and that calcite is the only sorptive mineral in the tuff), one would predict 
from neptunium sorption on pure calcite a log Kd for tuff G2-723 of 1.5. This prediction agrees 
well with the measured Kd (Figure 6). 
Conclusions Regarding Sorption Behavior of Neptunium with Respect to Variations in 
Groundwater CompositionThe mechanisms by which neptunium appears to sorb onto mineral 
surfaces in the Yucca Mountain flow system appear to be ion exchange or surface complexation 
on zeolitic phases and co-precipitation and surface adsorption involving carbonate minerals. The 
ion-exchange/surface-complexation mechanism appears to be responsible for the 0.5 to 5.0 
mL g1 range in sorption-coefficient values consistently measured in zeolitic rock samples. The 
high end of this range may reflect other mechanisms, such as the presence of trace minerals with 
high affinities for neptunium. 

K d (mL/g)
G4-270, 
devitrified 
G4-1506, 
zeolitic 
G4-1510, 
zeolitic 
GU3-1394, 
vitric 
GU3-1407, 
vitric 
2.0 
J-13 water 
UE-25 p#1 water3.0 
3.5 
1.0 
0 
1.5 
2.5 
0.5Sorption distribution coefficient, 
Tuff samples 
DTN: LA0010JC831341.007 
NOTE: Values of Kd for sorption of neptunium onto several tuffs that allow comparison of sorption (under 
atmospheric conditions) for the two types of groundwaters. The initial neptunium concentration ranged from 
6 x 107 to 8 x 107 M. The tuffs were wet-sieved to particle sizes ranging from 75 to 500 m. The 
pretreatment period was 2 to 14 days, and the sorption period was 3 to 23 days. From Triay et al. (1997, 
Figure 68). 
Figure 5. Dependence on Water for Sorption onto Tuffs 
Log K d (with K d in mL/g) 
G2-723, 
calcite rich 
Clinoptilolite 
Montmorillonite 
Calcite 
Hematite 
2.0 
3.0 
1.0 
0 
3.5 
2.5 
0.5 
1.5 
J-13 water 
UE-25 p#1 water 
DTN: LA0010JC831341.007 
NOTE: Values of Kd for neptunium onto several minerals and a calcite-rich tuff that allow comparison of sorption 
(under atmospheric conditions) for the two groundwaters. The initial neptunium concentration ranged from 
6 x 107 to 8 x 107 M. The tuff and the calcite were wet-sieved to particle sizes ranging from 75 to 500 m; 
the montmorillonite was dry-sieved; the clinoptilolite and hematite were not sieved; the sorption period was 
17 to 22 days. From Triay et al. (1997, Figure 69). 
Figure 6. Dependence on Water for Sorption onto Minerals 

6.4.4.1.4.3 Americium, Actinium, and Samarium 
The radionuclides of concern represented by these elements have the following characteristics in 
common: (1) In groundwater-rock systems of concern in this report, these elements are all 
present in the +3 oxidation state. (2) In aqueous solutions with compositions typical of 
groundwaters, the solubility of these elements tends to be controlled by sparingly soluble 
carbonates, phosphates, fluoride-carbonate complexes, and to a lesser extent, hydroxycarbonate 
compounds (Mariano 1989). The elements may also form solid solutions with carbonates, 
phosphates, fluorides, and oxides of the major cations in groundwaters. (3) The dominant 
solution species associated with these elements are generally complexes with carbonate, 
phosphate, and hydroxide ligands (Sillen and Martell 1964; Cotton and Wilkinson 1988, pp. 985987; 
Runde et al. 1992, p. 93). (4) The solution species tend to have high affinities for 
adsorption onto oxide surfaces as discussed below. The radionuclides represented by these 
elements are all in the strongly sorbing group discussed by Meijer (1992). 
Because the chemistry of all three of these elements is similar in aqueous solution and sorption 
reactions, they will be discussed as a group. 
Behavior in Solutions Representative of Yucca Mountain GroundwatersIn solution, 
americium, actinium, and samarium occur as simple (trivalent) cations, carbonate complexes, 
phosphate complexes, and hydrolysis products (Wood 1990). Complexes with other inorganic 
ligands (for example, Cl, F, and SO42) will not be of importance in the water compositions 
expected in the Yucca Mountain flow system. Therefore, speciation models for the rare-earth 
elements and trivalent actinides should consider pH, carbonate-ion concentration, and possibly 
phosphate-ion concentration as key variables. According to Byrne and Kim (1993), phosphate 
complexes will not be significant unless the ratio of the total phosphate concentration to the total 
carbonate concentration is greater than 1.3 x 103. This condition makes it unlikely that 
phosphate complexes will be important in Yucca Mountain groundwaters. Therefore, carbonate 
complexes are expected to dominate the solution species for these elements. The solubility-
controlling solids in Yucca Mountain groundwaters will likely be carbonates, hydroxycarbonates 
(Kerrisk 1984), and possibly phosphates (see the following section). 
According to Nitsche et al. (1993; 1995), the solubilities of americium compounds in solutions 
representative of water compositions expected within Yucca Mountain are approximately 1 to 
2 x 109 M in J-13 water and 3 to 30 x 107 M in p#l water as a function of pH at 25C. At 60C, 
the solubilities of americium compounds were 1 x 108 to 2.5 x 106 M in J-13 water and 7 x 10 
10 to 3 x 109 M in p#l water as a function of pH. The solubility-controlling solids were found to 
be hexagonal and orthorhombic forms of AmOHCO3. The speciation of americium in these 
solutions could not be determined due to the low solubilities of americium in these water 
compositions relative to the detection limits of the available spectroscopic techniques. 
Preliminary modeling calculations with the speciation code EQ3 suggest that carbonate 
complexes dominate in both J-13 and p#l waters at 25 and 60C (Ogard and Kerrisk 1984). 
Qualitative Evidence for Behavior in the Surficial EnvironmentAlthough the geological 
community generally regards the rare-earth elements as immobile during most water-rock 

alteration processes (Taylor and McLennan 1988), detailed studies of weathering profiles suggest 
that these elements may be redistributed within these profiles during weathering. Duddy (1980) 
studied a weathering profile formed on a homogeneous sedimentary rock unit in southeastern 
Australia. This profile was formed in a cool temperate climate with 200 cm yr-1 precipitation. 
The profile contained bleached zones and ferruginous zones in which iron was reduced or 
oxidized, respectively. The rare-earth elements were up to 7 times enriched in the bleached 
portions of the profile. Based on the sorption data discussed in the following section, this result 
is somewhat puzzling as one might expect these elements to be coprecipitated or adsorbed to the 
secondary ferric oxides formed in the profile. In fact, the rare-earth elements appeared to be 
enriched in vermiculite, an expanding magnesium-ferrous iron trioctahedral clay that formed in 
the weathering profile as a result of the alteration of biotite. Up to 10 weight percent (wt %) of 
rare-earth elements was reported in vermiculites on the basis of electron-probe analyses. The 
elements originated from the dissolution of apatite (Ca5(PO4)3(F,Cl,OH)) and other minerals 
present higher in the profile. 
Banfield and Eggleton (1989) studied the rare-earth elements in an Australian weathering profile 
formed on granite. These authors also noted that these elements were mobile in the profile. 
However, they found that (primary) biotite crystals in the granite contained apatite inclusions 
rich in rare-earth elements or cavities resulting from the dissolution of apatite. The apatite 
crystals were apparently dissolved during weathering leaving behind fine-grained (< 10 m) 
rare-earth-element phosphate phases including florencite, rhabdophane (CePO4H2O), and an 
unidentified phosphate-free aluminum-rare-earth-element mineral, possibly a carbonate, 
hydroxycarbonate, or fluorocarbonate. Vermiculites were also present in this profile, but they 
were not analyzed for rare-earth-element contents. 
These two studies clearly indicate that the rare-earth elements can be mobilized in the surficial 
environment. However, they also suggest that this mobilization is generally of a local nature 
resulting in the precipitation of new rare-earth-element phases or the incorporation of these 
elements in other secondary phases, such as clays. These studies did not address the question of 
whether adsorption of the rare-earth elements onto the surfaces of other mineral phases is a 
significant process in controlling the mobility of these elements in surficial environments. 
Loubet and Allegre (1977) noted that the light rare-earth elements were not mobilized in the 
reactor zones at Oklo, Gabon. 
Data on the behavior of americium in the surficial environment is limited to anthropogenic 
examples. Americium was found to be very immobile in most of the studies located in the 
literature (for example, Means et al. 1978; Carpenter et al. 1987). The main uncertainty 
regarding the surficial behavior of americium appears to be the degree to which it is mobilized 
through colloidal transport (Penrose et al. 1990). 
Data from Laboratory Sorption ExperimentsIon-exchange studies involving the sorption of 
lanthanide ions on montmorillonitic clays have been reported by Frysinger and Thomas (1960), 
Aagaard (1974), Bruque et al. (1980), and Bonnot-Courtois and Jaffiezic-Renault (1982). These 
studies conclude that essentially all of the exchange capacity of the clays is available to 
lanthanide ions and that the exchange reactions are rapid (that is, minutes). Frysinger and 
Thomas noted that the Cs+-Y3+ binary exchange was not dependent on pH over the range from 3 

to 7. At low cesium concentrations, such as are likely to occur in the potential repository 
horizon, the clay showed a slight preference for the lanthanide ions relative to cesium, and this 
preference increased with temperature (3075C). 
Koeppenkastrop and De Carlo (1992; 1993) have evaluated the sorption of the rare-earth 
elements by iron oxides, manganese oxides, and apatite from high-ionic-strength aqueous 
solutions (ultraviolet-irradiated natural seawater). One nanomole of each rare-earth-element 
radiotracer was equilibrated with approximately 10 mg of the solid phase in 1 kg of seawater. 
The pH of the system was maintained at 7.8 in all the experiments. The percentage of rare-earth 
element adsorbed on FeOOH and MnO2 was measured in the presence and absence of carbonate. 
Carbonate appeared to affect the kinetics of the adsorption reactions but not the extent of 
adsorption at equilibrium. The sorption reactions equilibrated within tens of minutes. Under the 
conditions of the experiments, the rare-earth elements are shown to have very high affinities for 
the oxide and phosphate phases (Kd >> 1,000 mL g1). Koeppenkastrop and De Carlo (1993) 
further state that modeling of sorption data derived from experiments with natural particles 
indicates that desorption rate constants are much smaller than adsorption rate constants. 
The high affinity of the rare-earth elements for iron- and manganese-oxide phases suggests that 
these phases would act as getters for these elements in surficial environments. Yet, the data 
reported by Duddy (1980) suggest that the rare-earth elements in the weathering profile he 
studied were preferentially incorporated in vermiculite in the bleached zones and not adsorbed 
onto ferric oxides in the ferruginous zones. This effect suggests that there were other 
constituents in the solution phase of the profile investigated by Duddy (1980) that had higher 
affinities for the oxide surfaces than the rare-earth elements and that they were present in 
sufficient quantity to saturate the available surface sites. A possible candidate would be the Al3+ 
ion (see Brown et al. 1955). 
Stammose and Dolo (1990) reported on batch-sorption experiments with americium (108 M) on 
clay as a function of pH and ionic strength. The clay used in the experiments was a mixed-layer 
clay consisting of kaolinite and smectite. At ionic strengths of 0.01 and 0.1 M (NaClO4), the 
americium sorption coefficient was greater than 103 mL g1 over the entire pH range (310) 
addressed by the experiments. In the higher ionic-strength solutions (1 and 3 M), the sorption 
coefficients were low (10 mL g1) at a pH of 2 but increased to values in the range of 104 to 105 
mL g1 for pH values greater than 6. 
Overall, the data presented by these authors suggest: (1) the ion-exchange sites on the clay have 
a very high selectivity for americium at trace concentrations; (2) sodium ions at sufficiently high 
concentrations can displace the americium from these sites; (3) americium is also adsorbed in 
surface-complexation reactions; (4) the surface-complexation reactions define a sorption edge 
that has minimum values at low pH and reaches a maximum at a pH of approximately 7; (5) 
americium is adsorbed as an inner-sphere complex, and its adsorption affinity in surface-
complexation reactions is therefore not a function of ionic strength; and (6) at trace americium 
concentrations, carbonate complexation of americium may compete with surface-complexation 
reactions in the pH range from 8 to 10, leading to a slight decrease in adsorption in this range. 

Allard and Beall (1979) have presented americium sorption-coefficient data for a range of 
mineral types including clays, feldspars, carbonates, phosphates, oxides, oxyhydroxides, and 
other less common minerals. The sorption coefficients were measured over a range of pH from 4 
to 9 in a low ionic-strength (synthetic) groundwater similar in composition to an average Yucca 
Mountain groundwater. Initial americium solution concentrations were in the range from 1.8 to 
5.0 x 109 M. Data presented for clay minerals indicate that ion exchange occurred on these 
minerals in the lower pH range (< 6). Surface recrystallization reactions are evident in the low 
pH data for apatite (also, see Jonasson et al. 1985) and fluorite. On the remaining silicates and 
nonsilicates, americium appears to sorb dominantly by surface-complexation reactions. In all 
cases, the sorption-coefficient values are in excess of 103 mL g1 over the pH range likely to be 
encountered in the Yucca Mountain groundwaters (CRWMS M&O 2000a, Table 3). 
In summary, trivalent actinium, americium, and samarium likely sorb by at least two distinct 
mechanisms. At pH values less than approximately 6, ion-exchange reactions on clays and other 
ion-exchanging minerals may dominate the adsorption behavior of these elements in low ionic-
strength solutions. These reactions will show dependencies on ionic strength and ion selectivity. 
At pH values greater than 6, sorption appears to involve primarily inner-sphere surface-
complexation reactions. Although these reactions are independent of ionic strength, they will 
likely be subject to competition with other sorbing species at sufficiently high sorption densities. 
In the pH range from 8 to 10, carbonate-complexation reactions in solution may compete with 
the surface-complexation reactions involving these elements. However, the surface-
complexation reactions are expected to dominate over carbonate-complexation reactions in 
Yucca Mountain groundwaters. 
Sorption Data Obtained on Yucca Mountain SamplesSorption coefficients for cerium, 
europium, and americium have been determined for a variety of rock samples from Yucca 
Mountain and in several groundwater compositions from the site (Thomas 1987; Knight and 
Thomas 1987). The data are generally consistent with the conclusions stated in the previous 
section. However, several additional points should be emphasized. First, experiments with rock 
samples that contained calcite (for example, G1-2901 and G2-723) or groundwater that was 
saturated with calcite (such as p#l) showed very large sorption coefficients for these elements. 
This result suggests the radionuclides were either coprecipitated with carbonates (for example, 
calcite) or formed solid solutions on the surfaces of existing carbonates. Because groundwaters 
in the unsaturated zone at Yucca Mountain are likely near saturation with calcite, this 
observation suggests the trivalent lanthanides and actinides will not be mobile in the proposed 
repository horizon. Second, experiments on samples with more than a few percent clay (for 
example, G1-3658) also showed high sorption coefficients. For these rock types, the ionic 
strength of the groundwaters may play a role in determining the magnitude of the sorption 
coefficients for these elements. Third, experiments with groundwaters containing high carbonate 
concentrations (such as p#l) show large sorption coefficients for these elements, suggesting that 
carbonate complexation in solution does not lead to significant decreases in the sorption 
coefficients for these elements in Yucca Mountain groundwaters. 
Conclusions Regarding Sorption Behavior with Respect to Expected Variations in Ground-
watersThe impact of variations in groundwater compositional parameters within the ranges 
expected in Yucca Mountain on the sorption behavior of actinium, americium, and samarium 

should be relatively minor. Over the expected pH range (69), the trivalent actinides and 
lanthanides appear to sorb primarily by inner-sphere surface-complexation mechanisms. These 
mechanisms are not sensitive to variations in ionic strength. Further, these elements appear to 
have high affinities for the mineral surfaces typically available in the Yucca Mountain rock units 
over the entire pH range expected. This result suggests that the trivalent actinide and lanthanide 
radionuclides will be strongly sorbed (Kd > 100 mL g1) over the entire range of expected 
groundwater compositions. 
6.4.4.1.4.4 Uranium 
Behavior in Solutions Representative of Yucca Mountain GroundwatersUnder the redox 
potentials expected in Yucca Mountain groundwaters, particularly in the unsaturated zone, 
uranium should be in the +6 oxidation state. In this oxidation state, uranium will be present in 
4
solution in a variety of complexes including (UO2)2CO3(OH)3 , UO2(CO3)22, UO2(CO3)3 , 
UO2(OH)2(aq), UO2(CO3)(aq), and other minor species. Phosphate, fluoride, or sulfate species 
will not be significant within the concentration ranges for these anions and the pH range 
expected in Yucca Mountain groundwaters (CRWMS M&O 2000a, Table 3). 
Qualitative Evidence for Behavior in the Surficial EnvironmentData on the behavior of 
uranium in the surficial environment are available from various sources. Several types of 
uranium ore deposits have been studied as natural analogs to repository settings. Other sources 
of data include studies of uranium mill-tailings piles, waste-stream outfalls, and other uranium 
ore deposits. Only the natural analog studies will be discussed in this subsection. 
The deposits that have been studied as natural analogs include the deposits at Oklo, Gabon, the 
Alligator Rivers region in Australia, Cigar Lake in Canada, Poos de Caldas in Brazil, and Pea 
Blanca in Mexico. Each of these deposits has been studied in considerable detail to define the 
geochemical behavior of uranium and its daughter products in the environments in which the ore 
deposits are found. Although none of the environments are completely analogous to the Yucca 
Mountain site, the Pea Blanca deposit is at least situated in Tertiary volcanic tuffs similar to 
those present at Yucca Mountain. 
A critical aspect of any analog for potential uranium migration at the Yucca Mountain site is that 
the uranium source must be subject to redox potentials similar to those expected at Yucca 
Mountain, particularly in the unsaturated zone. This fact eliminates from detailed consideration 
data from the Cigar Lake and probably the Oklo deposits (Goodwin et al. 1989; Cramer and 
Sargent 1994; Brookins 1983). 
The Alligator Rivers deposits are exposed to oxidizing conditions in a surficial environment 
(Giblin and Snelling 1983). Uranium isotope-disequilibrium studies at this site indicate that 
uranium migration has occurred relatively recently (Snelling and Dickson 1979). However, 
evidence for recent transport does not by itself provide an estimate of the rate of transport and, 
more importantly, of the chemical controls on this rate. The latter type of information could be 
very useful to the YMP. 

At the Koongarra deposit, uranium migration is significantly retarded by the precipitation of 
uranyl phosphate minerals (Snelling 1980). Although phosphate concentrations in local 
groundwaters are not high (0.01 to 0.1 mg L1), significant phosphate concentrations are found in 
the country rocks in minerals such as apatite. The phosphate in the rocks is apparently 
redistributed locally by groundwater, resulting in the precipitation of uranyl phosphate minerals 
within the zone of weathering (Snelling 1980). This retardation mechanism is not expected to be 
important at Yucca Mountain, given the low phosphate concentrations found in Yucca Mountain 
rock units (Broxton et al. 1986). 
Uranium in the zone of weathering at Alligator Rivers also appears to be associated with and is 
probably retarded by ferric-iron compounds (Payne et al. 1990). Sorption experiments have been 
carried out involving uranium sorption on whole-rock samples and on pure mineral samples 
(Payne et al. 1990). The results of these experiments suggest that ferric hydroxides are strong 
sorbers of uranium in this system over a pH range of 5 to 9. This result is not particularly new as 
similar results on ferric oxyhydroxides have been reported by others (for example, Hsi and 
Langmuir 1985). A potentially important result from these studies would be the derivation of 
some defensible estimate of the rate of transport of uranium in this system using the 
experimentally derived chemical constraints on uranium adsorption behavior and a valid 
groundwater flow model. However, the complicated nature of the flow system of the site may 
preclude the development of defensible flow models. 
The Pea Blanca uranium deposits in Mexico provide a potentially more appropriate analog site 
in relation to Yucca Mountain. The primary uranium deposits at this site are hydrothermal in 
origin and were emplaced in structural features associated with Tertiary silicic volcanic tuffs that 
overlie Mesozoic calcareous basement (George-Aniel et al. 1991). In addition to the 
hydrothermal deposits, which contain sulfide minerals as well as uranium oxides, supergene 
deposits have formed locally through the leaching of uranium from the volcanic rocks and 
subsequent precipitation as uranyl silicate minerals, including uranophane (Murphy 1992). The 
supergene deposits are hosted by kaolinitized and silicified rhyolite and do not appear to contain 
sulfide minerals. The absence of sulfide minerals is important because sulfides, such as pyrite, 
oxidize readily in the surficial environment to produce acidic conditions unlike those expected 
within Yucca Mountain. The supergene deposits are thought to have formed in the surficial 
environment (George-Aniel et al. 1991), and their study may offer useful insight into the 
potential for migration of uranium from the proposed repository within Yucca Mountain. No 
data on the present-day sorption behavior or rate of migration of uranium in these deposits has 
been reported to date. However, several geochemical studies are currently underway to provide 
such data (Murphy 1992). 
A qualitative study by Rosholt et al. (1971) established that uranium was leached from 
devitrified tuff samples but not from hydrated glassy samples obtained from a given geologic 
unit. This and other data presented suggest devitrification makes the uranium in tuffs more 
mobile in the surficial environment. Zielinski et al. (1986) and Flexser and Wollenberg (1992) 
observed that uranium in Yucca Mountain devitrified tuffs was commonly associated with 
manganese oxides. This fact suggests that, although uranium may be mobile in the unsaturated 
devitrified tuffs in Yucca Mountain, it could be retarded to the extent that there are manganese 
oxides present along the flow path with sufficient capacity to sorb the potential flux of uranium 

from the proposed repository horizon. Given the amount of uranium to be emplaced in the 
potential repository, it would seem the sorption capacity of the manganese oxides present in the 
mountain (Bish and Chipera 1989) would be rapidly saturated. Nonetheless, manganese oxides 
may significantly retard the movement of uranium in some of the fracture-flow scenarios. 
Data from the LiteratureData have been presented on the adsorption of uranium as U(VI) onto 
a variety of pure mineral phases in simple electrolytes. Among the solid phases investigated are 
goethite (for example, Hsi and Langmuir 1985), hematite (Ho and Miller 1986), silica gel 
(Zielinski 1980), clays (Tsunashima et al. 1981), and zeolites (Ames et al. 1983). The results 
reported are sometimes difficult to reconcile. For example, Hsi and Langmuir (1985) report that 
hematite sorbs very little of the uranium in solutions with 5 x 105 M uranium and 103 M total 
carbonate, whereas Ho and Miller (1986) report that hematite sorbs up to 100 percent of the 
uranium in their experiments with similar uranium and bicarbonate solution concentrations. 
Both sets of experiments had similar hematite surface areas. The main difference was that the 
solution phase in the Hsi and Langmuir (1985) experiments also contained 0.1 M NaNO3. 
However, NaNO3 is generally considered to be a nonreactive electrolyte, and nitrate does not 
form complexes with uranium in the pH range addressed in these experiments. Why there is a 
difference in these results is unclear. One possibility is that the surface characteristics of the 
solid phases used were not the same in the two sets of experiments. 
Silica gel appears to have a clear affinity for uranium as established by the results of laboratory 
experiments and by observations on the association of uranium with opals in nature (Zielinski 
1980). According to Maya (1982), the uranium is adsorbed to silica gel as the uranyl ion, free of 
carbonate ligands. Zielinski has shown that sorption of uranium onto silica gel is sensitive to the 
total carbonate concentration of the solution phase when this concentration is above 0.01 M. 
Experiments carried out at elevated temperatures (65 to 80C) resulted in somewhat higher 
sorption coefficients. Data regarding competitive effects on silica gel between uranium and 
other constituents in groundwaters at near-neutral pH have not been found in the literature. 
Sorption of uranium by clays has been investigated in some detail. Borovec (1981) has 
presented data that indicate montmorillonite has a high selectivity for uranyl ions relative to 
divalent ions of zinc, manganese, calcium, magnesium, cobalt, cadmium, and nickel at a pH 
value of 6 in chloride solutions. However, Tsunashima et al. (1981) found montmorillonite has a 
greater selectivity for calcium, magnesium, and barium ions than for uranyl ions in nitrate 
solutions over the pH range from 4.0 to 4.5. Montmorillonite was found to have a greater 
selectivity for the uranyl ion than for sodium and potassium ions in the same solutions. Ames et 
al. (1983) found that uranium was strongly sorbed to montmorillonite from 0.01 M NaCl 
solutions but weakly sorbed from 0.01 M NaHCO3 solutions in the pH range from 8 to 9. 
Because groundwaters in Yucca Mountain contain significant concentrations of bicarbonate, 
calcium, and magnesium ions, these data suggest overall that uranyl ions may not compete 
favorably for exchange sites on clay minerals in Yucca Mountain, although quantitative 
prediction of the extent of exchange would require more detailed analysis. 
Data available on uranium sorption on zeolitic minerals are very limited. Ames et al. (1983) 
report that clinoptilolite has a low affinity for trace levels of uranium in the pH range from 8 to 9 
in 0.01 M NaHCO3. Doi et al. (1975) found that uranium at concentrations of 106 g per g of 

solution was strongly sorbed onto clinoptilolite from perchlorate solutions in the pH range from 
4 to 8.5. 
Data on uranium sorption on alluvium from the general vicinity of Yucca Mountain were 
obtained in two studies. Wolfsberg (1978, pp. 3, 7, 14) measured sorption of U(VI) on three 
alluvium samples obtained from NTS drillholes in Frenchman Flat (hole U5e, also called RNM1) 
and Yucca Flat (hole U3bv). Measured values of Kd using groundwater from the alluvial 
aquifer in Frenchman Flat (hole RNM-2S) ranged from 6 to 9 mL g1. Wolfsberg et al. (1983, 
pp. 47) measured sorption of U(VI) on alluvial material collected from a trench at the Beatty, 
Nevada, Disposal Facility and from borehole U3hr in Yucca Flat. Water used for these sorption 
experiments was collected from supply wells located near the locations from which the alluvial 
materials were obtained. Average Kd values for the integral samples ranged from 1 to 3 mL g1; 
slightly higher Kd values of 6 to 9 mL g1 were obtained for the silt and clay fractions. 
Data from Laboratory Sorption Experiments with Yucca Mountain Rock and Water Samples 
Obtained Prior to 1993Data on uranium sorption coefficients for Yucca Mountain rock/water 
systems were reported by Thomas (1987) and discussed by Meijer (1990; 1992). The affinity of 
the devitrified and vitric tuffs for trace levels of uranium is generally small (Kd < 5 mL g1) over 
the pH range from 6 to 9 in J-13 water. For zeolitic tuffs, the Kd is near zero at a pH value of 9 
but increases with decreasing pH to values of approximately 25 mL g1 at a pH of 6 in J-13 
water. This behavior suggests the uranyl cations can exchange with the major cations in zeolites. 
Uranium batch-sorption experiments in p#1 water were only carried out in the pH range from 8.3 
to 9.3 with the result that measured sorption coefficients were small (0 to 2.7 mL g1; Thomas 
1988). A devitrified sample showed the largest sorption coefficient. In the pH range from 6 to 
8, it is expected that the sorption coefficients for uranium in p#1 water will increase with 
decreasing pH (because of predominance by UO2CO30 at higher pH values), but they will likely 
be smaller than the coefficients obtained for the same rock samples in J-13 water over this pH 
range. In H-3 groundwater, sorption coefficients were also low for zeolitic and devitrified rock 
types over the pH range from 9.2 to 9.3, presumably reflecting the elevated carbonate content of 
this water. However, data for a vitric sample showed a value of 6.2 mL g1 for the uranium 
sorption coefficient at a pH value of 9. This relatively high value has not been explained. 
Data from Laboratory Sorption Experiments with Yucca Mountain Rock and Water Samples 
Obtained after 1993The sorption of U(VI) onto samples of the three types of tuff in J-13 water 
(under oxidizing conditions) at the two pH values (7 and 8.5) was studied. However, to identify 
the sorbing minerals in the tuffs, sorption onto the pure minerals hematite, clinoptilolite, albite, 
and quartz was also studied. It was found that uranium in J-13 water does not sorb onto 
devitrified and vitric tuffs, albite, and quartz (Table 7). 
Wet-sieved tuffs, albite, and quartz samples with particle sizes in the range from 75 to 500 m 
were used. Initial uranium concentrations ranged from 8 x 108 to 1 x 104 M. The pretreatment 
period was 2 to 4 days, and the sorption period, 3 to 4 days. The negative values reported in 
Table 7 are the result of analytical error for the case of very little sorption (that is, a small 
number obtained as the difference of two large numbers). For the experimental conditions cited, 

uranium sorption onto zeolitic tuffs and clinoptilolite is nonlinear and can be fitted with 
Freundlich and Langmuir isotherms (Figures 7 and 8). 
Table 7. Uranium Sorption in J-13 Water under Oxidizing Conditions 
Solid phase pH Kd (mL g1) 
G4-268, devitrified tuff 7 1 x 101 
8.5 7 x 101 
GU3-1405, vitric tuff 7 4 x 101 
8.5 5 x 101 
Quartz 7 3 x 101 
8.5 4 x 102 
Albite 7 5 x 102 
8.5 1 x 101 
DTN: LA0004AM831341.001 
For the clinoptilolite-rich zeolitic tuff sample G4-1510, the scatter in the data makes it 
impossible to conclude whether there is a significant difference between the experiments 
performed under a carbon-dioxide overpressure and a pH of 7 or at atmospheric conditions and a 
pH of 8.5 (Figure 7). However, the experiments with pure clinoptilolite indicate that sorption 
increases with decreasing pH for U(VI) (Figure 8), as is the case for Np(V). Because the major 
constituent of tuff sample G4-1510 is clinoptilolite, predictions of the Ka (Kd divided by the 
solid-phase surface area; Triay, Cotter, Kraus et al. 1996) were made for uranium sorption onto 
this tuff by assuming that clinoptilolite is the only sorbing phase. Inspection of Table 8 indicates 
that predictions obtained with this assumption are within a factor of 3 of the measured values for 
both pH conditions. 
pH 7 
pH 8.5 
1.0 0.5 1.50.5 1.0 
0.5 
Log of the concentration (ppm) of uranium in solution, log C 
0.5 
1.5 
2.0 
2.5 
pH 7 
pH 8.5 
Langmuir isotherm 
K1 = 18, K2 = 0.25 
Freundlich isotherm 
K = 8, n = 1.33Log of the concentration (g/g)of uranium in solid phase, log F 
2.0 
0 
DTN: LA0010JC831341.005 
NOTE: 
The graph is a log-log plot of the concentration of uranium in the solid phase, F, of the clinoptilolite-rich tuff 
G4-1510 versus the concentration of uranium in the solution phase, C, of J-13 well water. The tuff was wet-
sieved to give particles that ranged in size from 75 to 500 .m. The period of pretreatment was 2 to 4 days; 
the period of sorption was 3 to 4 days. The data for a pH of 7 have been fitted with a Langmuir isotherm; the 
data for a pH of 8.5 have been fitted with a Freundlich isotherm. 
Figure 7. Uranium Sorption onto Clinoptilolite-Rich Tuff 

Log of the concentration (g/g)
of uranium in solid phase, log F 
pH 7 
pH 8.5 
2.0 1.5 0.5 1.50.5 1.0 
0.5 
0.5 
1.5 
2.0 
1.0 
Log of the concentration (ppm) of uranium in solution, log C 
Langmuir isotherm 
K1 = 23, K2 = 0.7 
Langmuir isotherm 
K1 = 9.5, K2 = 0.5 
0 
DTN: LA0010JC831341.005 
NOTE: 
This is a log-log plot of the concentration of uranium in the solid phase, F, of clinoptilolite versus the 
concentration of uranium in the solution phase, 
C, of J-13 water. The mineral was unsieved. The period of 
pretreatment was 2 to 4 days; the period of sorption was 3 to 4 days. 
The 
data for each pH (7 and 8.5) have 
been fitted with a Langmuir isotherm. 
Figure 8. Uranium Sorption onto Clinoptilolite 
Table 8. Prediction of 
Uranium Sorption on 
Clinoptilolite-Rich G4-1510 Tuff in J-13 Water 
Initial concentration (M) 
pH 
Measured Ka (m) 
Predicted Ka (m)a 
2 x 107 to 4 x 107 
7 4 x 107 
8 x 107 
8.5 
5 x 107 
2 x 107 
DTN: LA0004AM831341.001 (Kd) and LA0004AM831341.002 (surface area). 
NOTE: aAssuming clinoptilolite is the only sorbing mineral in the tuff, present at 59 wt. %. 
The sorption of uranium onto pure iron oxides (such as hematite) is very large (and large 
uncertainties in the Kd values result from measuring the small amounts of radionuclide left in 
solution after sorption). 
Although the measured sorption of uranium onto pure hematite is very 
large, sorption onto devitrified tuffs, which appear to have traces of hematite (1 percent  1), is 
essentially zero. As with neptunium, this result could be due to differences in the surface of pure 
hematite compared to hematite in tuff. Alternatively, it could be due to passivation of the 
hematite surfaces in the tuff by elements (such 
as the rare earths) that have a 
higher affinity for 
hematite than uranium and, thus, occupy the sorption sites. 
Conclusions Regarding Sorption Behavior of Uranium with Respect to Expected Variations in GroundwatersThe dominant groundwater compositional controls on the sorption behavior of 
uranium on Yucca Mountain rock samples will likely be pH, carbonate content, and the 
concentrations of calcium and magnesium ions in solution. The pH and carbonate contents 

influence the sorption largely as a result of the decrease in carbonate complexation of uranium 
with decreasing pH. These two parameters are therefore not entirely independent. However, 
different water compositions can have different carbonate contents at a given pH. The 
expectation is that waters with higher carbonate contents will be associated with lower sorption 
coefficients. This trend would apply to both ion-exchange and surface-complexation sorption 
mechanisms. However, decreasing pH will have different effects on uranium sorption behavior 
in zeolitic and clay-rich samples versus devitrified and vitric samples. In the former samples, the 
uranium sorption coefficient will likely increase with decreasing pH due to the increase in uranyl 
ion concentrations with decreasing pH. For a given rock-water system, the magnitude of this 
increase will depend on the concentrations of competing ions such as calcium and magnesium in 
the water. For high calcium and magnesium waters, the competition effects will be substantial. 
Because unsaturated-zone waters are relatively enriched in calcium and magnesium, uranium 
sorption coefficients in the unsaturated zone may be on the low end of the range reported to date 
(Thomas 1987; 1988) unless the low total carbonate concentrations in these waters balance the 
effect of the elevated calcium and magnesium concentrations. 
6.4.4.1.4.5 Technetium 
Technetium appears to show nonzero, although minimal, retardation in Yucca Mountain rock-
water systems (Ogard and Vaniman 1985; Rundberg et al. 1985; Thomas 1988). However, the 
cause of this retardation has not been identified, and it may simply be an experimental artifact. If 
sufficiently reducing conditions could be shown to exist in portions of the flow system down-
gradient of the proposed repository, retardation of technetium by the precipitation and sorption of 
Tc4+ species would provide a barrier for this element. 
6.4.4.1.4.6 Protactinium 
Behavior in Solutions Representative of Yucca Mountain GroundwatersIn aqueous systems, 
protactinium appears to exist dominantly in the +5 oxidation state, although the +4 state may 
occur in reducing environments (Brookins 1988). In both oxidation states, protactinium is 
strongly hydrolyzed and forms highly insoluble compounds (Cotton and Wilkinson 1988). This 
result implies that the +5 solution chemistry of protactinium is more akin to that of Nb(V) than to 
+
other actinides in +5 oxidation states, such as PuO2+ or NpO2 . If this interpretation is correct, 
the solution parameter of greatest importance to protactinium sorption behavior would be pH. 
Sorption Data from the LiteratureBatch-sorption experiments with protactinium have yielded 
some interesting results. In dilute to intermediate ionic-strength solutions, Allard (1982) report 
large values (104 mL g1) for the protactinium sorption coefficient on alumina and silica at pH 
values greater than about 7 but much lower values (90 to 500 mL g1) at pH values less than 7. 
Data from Laboratory Sorption Experiments with Yucca Mountain Rock And Water Samples 
Obtained Prior to 1993Rundberg et al. (1985) report protactinium sorption coefficients in the 
range from 3.7 to 8.2 mL g1 for a zeolitic tuff in contact with J-13 water spiked with 1011 to 
1014 M protactinium at pH values of 6.3 to 6.7. Combined with the data reported by Allard 
(1982), these data suggest that protactinium sorbs by a surface-complexation mechanism and that 

there is a rather steep sorption edge for protactinium as a function of pH at a pH value of 
approximately 7. 
Conclusions Regarding Sorption Behavior of Protactinium with Respect to Expected Variations 
in GroundwatersBatch-sorption data for protactinium suggest that sorption coefficients for this 
element will be small (< 10 mL g1) at lower pH values. Because protactinium sorption 
experiments on rock samples from Yucca Mountain have only been carried out in the low pH 
range, no firm conclusions can be stated concerning sorption coefficients on Yucca Mountain 
tuffs at pH values from 7 to 9. 
6.4.4.1.4.7 Selenium 
Behavior in Solutions Representative of Yucca Mountain GroundwatersSelenium will occur as 
anionic species in all water compositions expected at Yucca Mountain. Although the two 
oxidation states of +4 and +6 (Howard 1977) are found for selenium in surficial waters in contact 
with atmospheric oxygen, the +4 state predominates under the conditions expected for 
groundwaters at Yucca Mountain (Howard 1977; White et al. 1991). In that state, selenium is 

found as the SeO32 and HSeO3 selenite ions. In the +6 oxidation state, selenium occurs as the 

SeO42 and HSeO4 selenate ions. 
Evidence for Behavior in the Surficial EnvironmentSelenium behavior in the surficial 
environment is very closely tied to the redox potential of different parts of the near-surface 
environment. Under reducing conditions, selenium is immobilized as FeSe2 at low pH (< 5) and 
as native selenium at higher pH (Howard 1977). The stability range for native selenium extends 
nearly to surface redox conditions. When in contact with atmospheric oxygen levels, selenium is 
apparently stabilized as the selenite ion (SeO32). At higher redox potentials, selenium is 
oxidized to the selenate ion (SeO42), which appears to be more mobile in the surficial 
environment than the selenite ion (Howard 1977). 
Sorption Data from the LiteratureBecause selenium occurs as anionic species in the surficial 
environment, its adsorption behavior is controlled primarily by surface-complexation reactions 
on oxide minerals including iron oxides and oxyhydroxides (Balistrieri and Chao 1987), 
manganese oxides and oxyhydroxides, clays (Bar-Yosef and Meek 1987), and other minerals 
with affinities for anionic species. These surface-complexation reactions are quite sensitive to 
pH. For example, adsorption on iron oxyhydroxides decreases for both selenite and selenate ions 
with increasing pH (Balistrieri and Chao 1987). Selenate ions appear to sorb dominantly in the 
outer layer of the electrical double layer present on oxide surfaces, whereas selenite tends to sorb 
in the inner layer (Hayes et al. 1987). Selenate ions are subject to ionic-strength effects as well 
as competitive effects with sulfate and other anions in solution, presumably because they sorb in 
the outer layer. Selenite ions are not subject to ionic-strength effects but may be subject to 
competition from other anions sorbing on inner-layer sites (Hingston et al. 1971). 
Studies of selenite adsorption on soils in the pH range expected for Yucca Mountain 
groundwaters indicate relatively limited adsorption (< 30 percent) from 0.05 N chloride solutions 
containing 0.16 to 0.63 mg L1 selenium (Neal et al. 1987). This limited sorption potential will 
likely be further decreased in natural waters containing high concentrations of competing anions. 

Data from Laboratory Sorption Experiments with Yucca Mountain Rock And Water Samples 
Obtained Prior to 1993Data for selenium sorption coefficients on Yucca Mountain rock 
samples in contact with J-13 water have been summarized by Thomas (1987). Most measured 
values are less than 5 mL g1, and they do not appear to correlate with rock type. A puzzling 
feature of the data is that, for a given rock sample, sorption coefficients are larger in the higher 
pH experiments (pH of 8.8) compared to the lower pH experiments (pH of 6.0). This result is 
contrary to the pH dependence predicted on the basis of double-layer theories. Neal et al. (1987) 
noted a similar effect for selenium sorption on soils for a solution phase enriched in calcium. 
They suggested the effect may be due to the formation of a calcium-rich surface precipitate or, 
alternatively, a change in surface charge due to the adsorption of divalent calcium cations. 
Benjamin (1983) made similar observations involving other divalent cations. These data suggest 
that in groundwaters relatively enriched in calcium, and perhaps other divalent cations, selenium 
adsorption may be somewhat enhanced in the alkaline pH range. 
Conclusions Regarding Sorption Behavior of Selenium with Respect to Expected Variations in 
GroundwatersSorption coefficients for selenium on Yucca Mountain rock samples have only 
been measured in J-13 water. These experiments do not show the expected decrease in sorption 
coefficient with pH. Therefore, variations in pH over the range expected in Yucca Mountain 
groundwaters do not appear to be the most important groundwater compositional parameter in 
the sorption behavior of this element. Based on the data obtained in other studies, divalent 
cations may have a significant impact on the sorption behavior of this element in Yucca 
Mountain rock/water systems. Additional experiments with waters enriched in divalent cations 
(such as p#1 water) may be productive and may enlarge the range of selenium sorption-
coefficient values appropriate for use in performance-assessment calculations. 
6.4.4.1.4.8 Carbon, Chlorine, and Iodine 
Because carbon, chlorine, and iodine are unlikely to have significant sorption affinity in the 
rock/water systems expected at Yucca Mountain, their sorption behavior will not be discussed in 
detail. For carbon, the most robust retardation mechanism will be isotopic exchange with stable 
carbon isotopes in groundwater and on carbonate mineral surfaces (Meijer 1993). 
Chloride and iodide ions will have no significant retardation in Yucca Mountain rock/water 
systems and may even have slightly enhanced migration rates due to anion-exclusion effects 
(Ogard and Vaniman 1985). If conditions were to become sufficiently oxidizing to convert 
iodide to iodate, some retardation of iodine might occur in the flow system. Such conditions 
might occur locally, for example, due to radiolysis in the near field. 
6.4.4.1.4.9 Cesium, Radium, and Strontium 
Behavior in Solutions Representative of Yucca Mountain GroundwatersThese elements show 
relatively simple solution behavior in typical groundwaters. They are not subject to changes in 
oxidation state in the groundwater compositions expected in Yucca Mountain. Radium and 
cesium are invariably present as the simple Ra2+ and Cs+ cations in the expected groundwater 
compositions (Ogard and Kerrisk 1984). Strontium exists primarily as the Sr2+ ion in these 
waters but may also be present as the neutral aqueous species SrSO4 at concentrations of a few 

percent of the total strontium solution concentration (Ogard and Kerrisk 1984). The data of 
Langmuir and Riese (1985) indicate that RaSO4/Ra2+ will be greater or equal to 0.6 when the 
sulfate ion concentration is greater than 103 M. These numbers suggest that RaSO4 will be a 
significant species (RaCO3 and SrCO3 may also be significant). 
Sorption Data from the LiteratureThe literature on the behavior of cesium, radium, and 
strontium in the surficial environment is voluminous and will not be reviewed here. Their 
sorption behavior is fairly well understood and is largely controlled by ion-exchange reactions 
(Bolt and Bruggenwert 1976), although surface-complexation reactions involving these elements 
have also been discussed (for example, Balistrieri and Murray 1982). The dominant controls on 
the ion-exchange reactions are the cation-exchange capacities of the minerals in the system, the 
abundances of these ion-exchanging minerals, their selectivity coefficients for the various cations 
in the solution phase, and the concentrations of the competing cations in the solution phase. The 
selectivity of most clays and zeolites for cesium, radium, and strontium is greater than the 
selectivities for the major cations in solution. Further, pH does not have a significant effect on 
the sorption behavior of these elements over the pH range of interest. Because their sorption 
behavior is fairly well understood and because this behavior depends strongly on local 
conditions, data from sites other than Yucca Mountain will not be reviewed here. 
Data from Laboratory Sorption Experiments with Yucca Mountain Rock And Water Samples 
Obtained Prior to 1993Sorption coefficients for cesium, radium, and strontium were reviewed 
by Daniels et al. (1983), Thomas (1987), and Meijer (1990). For cesium at low concentrations 
(108 M), sorption coefficients are greater than 100 mL g1 for all water-rock combinations tested 
except p#1 water in contact with vitric tuff (Knight and Thomas 1987). Cesium sorption 
coefficients for the devitrified-tuff/J-13-water system show a clear concentration dependence that 
has been modeled with a Fruendlich isotherm (Polzer and Fuentes 1988). The coefficients for 
this particular rock/water system are greater than 100 mL g1 for cesium solution concentrations 
below 5 x 105 M. For p#1 water in contact with this rock type, the coefficient would be 100 mL 
g1 at somewhat lower solution concentrations. In any case, in the higher ionic-strength waters 
(0.02 eq L1), including unsaturated-zone waters, the sorption coefficients for cesium on 
devitrified and vitric samples may be less than 100 mL g1 if solution concentrations of cesium 
exceed 106 M. For zeolitic tuffs, cesium sorption coefficients are greater than 100 mL g1 for all 
water compositions and cesium concentrations anticipated in the potential repository 
environment. 
Radium appears to have a somewhat higher affinity for sorption onto Yucca Mountain tuffs than 
cesium. In addition, the solubility of RaSO4 limits the concentrations in solution to trace levels 
(107108 M; Ogard and Kerrisk 1984). At concentrations below the solubility limit for RaSO4, 
sorption coefficients for radium are greater than 100 mL g1 in essentially all rock-water 
combinations tested, using barium as an analog for radium (Knight and Thomas 1987). This fact 
suggests that a minimum sorption coefficient of 100 mL g1 can be used for radium in all 
rock/water systems. For zeolitic samples, minimum values of 1,000 mL g1 can be used. 
Strontium sorption behavior is more sensitive to mineral and water compositions than the other 
two elements discussed in this subsection. For devitrified and vitric tuffs, sorption coefficients 
for the higher ionic-strength waters (such as p#1) are in the range of 10 to 30 mL g1 (Knight and 

Thomas 1987). These sorption coefficients will decrease as the solution concentration of 
strontium is increased above approximately 105 M (Thomas 1987). However, this concentration 
is close to the solubility limit for SrCO3 in these waters so that the 10 to 30 mL g1 range is likely 
appropriate for use in performance-assessment calculations in the devitrified or vitric tuffs. For 
zeolitic tuffs, a minimum value of 1,000 mL g1 would be appropriate (Knight and Thomas 
1987). 
Conclusions Regarding Sorption Behavior of Cesium, Radium, and Strontium with Respect to 
Expected Variations in GroundwatersThe existing sorption-coefficient database for cesium, 
radium, and strontium should be adequate for performance-assessment calculations. The main 
concern would be the concentration of cesium in the solution phase in contact with devitrified 
and vitric tuffs. If this concentration is over 105 M, the appropriate value for the sorption 
coefficient may be less than the minimum recommended value of 100 mL g1. The sorption 
coefficients for strontium in devitrified and vitric tuffs will be as low as 10 to 30 mL g1 in 
higher ionic-strength waters. If additional experiments were to be carried out for this group of 
elements, they should focus on strontium in contact with devitrified and vitric tuffs in the higher 
ionic-strength waters. 
6.4.4.1.4.10 Nickel and Lead 
Behavior in Solutions Representative of Yucca Mountain GroundwatersThe aqueous solution 
behavior of nickel and lead is relatively simple. Within the range of groundwater compositions 
expected in the Yucca Mountain flow system, these elements are present in solution primarily as 
simple divalent cations. Several percent of the total nickel concentration will be present as the 
NiSO4 (aq) complex. NiCO3 may also be a significant aqueous species. Similarly, several 
percent of the total lead concentration will be present as the PbCl+ complex. 
Sorption Data from the LiteratureThe behavior of nickel and lead in the surficial environment 
has been studied in some detail (for example, Snodgrass 1980). These elements are generally 
quite particle-reactive. The dominant mechanisms that control their sorption behavior are ion 
exchange on clay minerals (Bowman and OConnor 1982) and adsorption onto various oxides 
(Theis and Richter 1980). The selectivities of clay minerals for nickel and lead are large relative 
to the major cations (such as Mg2+) in typical groundwaters (Decarreau 1985). Solution 
compositional parameters that can influence this adsorption behavior include pH, ionic strength, 
concentrations of competing ions, and concentrations of complexing agents (see review by Rai 
and Zachara 1984). 
Data on sorption of transition metals on synthetic zeolites suggest that Pb2+ has a high affinity for 
ion exchange compared with Sr2+, whereas Ni2+ has a lower affinity relative to Sr2+ (Barrer and 
Townsend 1976; Obeng et al. 1981; Blanchard et al. 1984). This result suggests the zeolitic 
zones within Yucca Mountain could be significant barriers to lead migration. 
Data from Laboratory Sorption Experiments with Yucca Mountain Rock And Water Samples 
Obtained Prior to 1993Data on the sorption behavior of nickel in Yucca Mountain rock-water 
systems were reported by Knight and Lawrence (1988). Sorption and desorption ratios were 
determined in several water compositions in the pH range from 8.3 to 9.0 with nickel 

concentrations in solution of approximately 108 M. For devitrified and zeolitic samples, 
sorption coefficients were in the range of 200 to 400 mL g1. Sorption coefficients obtained in 
the desorption step were generally a factor of two larger than the sorption coefficients. In the 
only vitric sample analyzed, sorption coefficients ranged from approximately 30 to 70 mL g1. 
For the desorption step, the coefficients were in the range of 33 to 72 mL g1 for this rock type. 
References to the adsorption behavior of lead on tuffaceous or even granitic rock samples were 
not found. 
Conclusions Regarding Sorption Behavior of Nickel and Lead with Respect to Expected 
Variations in GroundwatersBased on information in the literature, the sorption behavior of 
these elements will be determined largely by the free-ion activities in solution and the cation-
exchange capacity of the host rock (for example, Bowman and OConnor 1982; Rai and Zachara 
1984). Solution pH and oxide-mineral abundances may be a factor in rocks in which nickel and 
lead sorb primarily by surface-complexation mechanisms. In any case, lead appears to sorb more 
strongly than nickel in most surficial environments, and both elements appear to sorb more 
strongly than strontium (Bowman and OConnor 1982). The nickel sorption coefficients 
discussed in the previous subsection could reasonably be used as default values for lead in 
performance-assessment calculations. For nickel, a minimum sorption coefficient of 100 mL g1 
could be used in the devitrified and zeolitic zones. For the vitric zones, the performance-
assessment calculations could be done using random sampling and a normal distribution ranging 
from 0 to 50 mL g1. 
6.4.4.1.4.11 Thorium, Niobium, Tin, and Zirconium 
The radionuclides of concern represented by these elements have several characteristics in 
common. First, in groundwater/rock systems of concern in this report, these elements have 
stable oxidation states. Niobium is present in a +5 oxidation state, whereas the others are 
typically in +4 oxidation states (Brookins 1988). Second, in aqueous solutions with 
compositions typical of Yucca Mountain groundwaters, these elements tend to occur as sparingly 
soluble oxides or silicates (Brookins 1988). They may also form solid solutions with other, more 
common, sparingly soluble oxides, such as titania (TiO2). Third, the dominant solution species 
associated with these oxides are hydrolysis products (Baes and Mesmer 1986). Fourth, the 
hydrolyzed solution species tend to have high affinities for adsorption onto oxide surfaces as 
discussed further below. The radionuclides represented by these elements are in the strongly 
sorbing group discussed by Meijer (1992). 
Niobium 
Behavior in Solutions Representative of Yucca Mountain GroundwatersAccording to Baes and 
Mesmer (1986), at a dissolved niobium concentration of 106 M, the dominant solution species in 
pure water are the neutral species Nb(OH)5 and the anionic species Nb(OH)6. The anionic 
species predominates at values of pH above 7, and the neutral species is stable below a pH of 7. 
At surficial temperatures and pressures, evidence for significant complexation of niobium by 
nonhydroxide ligands in natural aqueous solutions is lacking. As discussed below, carbonate 
complexation may occur at higher temperatures and pressures. 

The concentrations of niobium in surficial aqueous solutions are extremely low, presumably due 
to the low solubility of the pentavalent oxide (Baes and Mesmer 1986) and to sorption onto 
mineral surfaces. In geologic systems, niobium may substitute as a trace element in the more 
abundant oxide phases such as micas, titanium oxides (for example, rutile), and clays 
(Goldschmidt 1954). This effect also leads to low solution concentrations. 
Qualitative Evidence for Behavior in the Surficial EnvironmentThe geologic literature contains 
numerous papers that qualitatively discuss the mobility, or more accurately, the immobility of 
niobium in rocks during alteration processes (for example, Cann 1970). In various studies of 
soils or altered, weathered, or metamorphosed rocks, geological, geochemical, and statistical 
evidence has been presented that supports the conclusion that niobium is essentially immobile in 
the surficial environment. Although some of these studies deal with rocks that have been altered 
under conditions of low fluid-to-rock ratios, the general lack of evidence for niobium mobility 
suggests that this element would also be immobile in systems with higher water/rock ratios, such 
as the Yucca Mountain flow system. For example, Brookins (1983) notes that 100 percent of the 
niobium produced by fission at the natural reactor at Oklo, Gabon, has been retained by the host 
pitchblende even though the reactor was active in water-bearing sandstones that were subjected 
to elevated temperatures during and after the critical (that is, nuclear) stage of the reactor. 
Grimaldi and Berger (1961) studied the concentrations of niobium in twenty lateritic soils from 
West Africa and concluded that silica is depleted more rapidly from these soils than is niobium 
and niobium more rapidly than aluminum. Further, these workers note that there is a strong 
association of niobium with the clay-sized fraction and also with titanium. They propose that the 
association of niobium with the clay fraction may be due to the presence of niobium-rich 
authigenic rutile in the clays. The observation that niobium was mobilized more readily than 
aluminum in this environment does not necessarily imply niobium was transported out of the 
system as a dissolved solution species. The tendency of elements such as niobium, titanium, tin, 
and so forth to form very fine-grained precipitates is well known. Such colloidal-sized particles 
can be transported by soil solutions and surface waters. 
Evidence for niobium mobility during greenschist metamorphism of mafic rocks has been 
presented by Murphy and Hynes (1986). These workers suggest that carbonate-rich 
metamorphic solutions can mobilize and transport niobium (as well as titanium, zirconium, 
phosphorus, and yttrium). Presumably, carbonate can form mobile complexes with niobium 
under conditions of elevated temperature and pressure. No references were found that address 
the ability of carbonate to complex niobium under low temperatures and near atmospheric 
pressures. 
Conclusions Regarding Sorption Behavior of Niobium with Respect to Expected Variations in 
GroundwatersOn the basis of the geological evidence and because niobium forms primarily 
hydrolyzed species in groundwaters of the type associated with Yucca Mountain, niobium should 
be very insoluble in Yucca Mountain groundwaters and strongly sorbed onto mineral phases 
present in Yucca Mountain tuffs from the whole range of groundwater compositions expected at 
the site. 

Thorium 
Behavior in Solutions Representative of Yucca Mountain GroundwatersLangmuir and Herman 
(1980) have compiled and critically reviewed thermodynamic data for thirty-two dissolved 
thorium species and nine thorium-bearing solid phases. In the groundwater compositions 
expected within Yucca Mountain, thorium will be fully hydrolyzed (Th(OH)4), and thorium 
complexing with other inorganic ligands will be insignificant based on the data presented in 
Langmuir and Herman (1980). Thorium compounds are among the most insoluble in the group 
of elements considered in this report. Solubilities of the order of 1050 M are common for 
thorium compounds (for example, thorianite (ThO2) and thorite (ThSiO4)). Nevertheless, 
concentrations well above this value have been found in various natural waters and appear to 
reflect complexation with organic ligands in organic-rich waters. Such waters are not expected 
at Yucca Mountain. 
Qualitative Evidence for Behavior in the Surficial EnvironmentThorium is one of the elements 
considered to be immobile in most surficial environments (Rose et al. 1979). Studies of the 
isotopic disequilibrium in the uranium and thorium decay series found in natural aquifers suggest 
that thorium isotopes are strongly retarded in these flow systems relative to other members of the 
decay series (Krishnaswami et al. 1982). Studies of the migration of thorium away from thorium 
ore bodies also indicate that it is extraordinarily immobile in these environments (Eisenbud et 
al. 1984). Brookins (1983) found that thorium was immobile in the Oklo reactor environment. 
Studies of thorium concentration gradients with depth in seawater also point to high sorption 
affinities for this element on oceanic particulate matter (Moore and Hunter 1985). 
Data from Laboratory Sorption ExperimentsHunter et al. (1988) carried out thorium sorption 
experiments on MnO2 and FeOOH in artificial sea-water and in a simple NaCl solution. The 
primary objective was to determine the effects of major ions (for example, Mg2+ and SO42) on 
the adsorption of thorium by goethite (FeOOH) and MnO2 relative to sorption in a pure NaCl 
electrolyte system. The effects of magnesium and calcium ions on thorium adsorption were very 
small (probably within the margin of experimental error), but the presence of sulfate at seawater 
concentrations (0.028 M) increased the adsorption edge on FeOOH by one-half of a pH unit. 
Because the adsorption edge is in the range of pH values from 3 to 5 in all the experiments, this 
effect is not considered important for thorium sorption behavior at the Yucca Mountain site. 
LaFlamme and Murray (1987) evaluated the effects of carbonate on the adsorption 
characteristics of thorium on goethite. They found that carbonate alkalinity could decrease 
thorium sorption onto goethite at alkalinity values greater than 100 meq L1. Because the 
alkalinity values expected in the Yucca Mountain flow system are orders of magnitude lower 
than this value, carbonate alkalinity is not expected to affect thorium adsorption behavior in this 
system. 
According to Langmuir and Herman (1980), the adsorption of thorium from water onto clays, 
oxides, and organic material increases with pH and approaches 100 percent by a pH of about 6.5. 
As the thorium ion is largely hydrolyzed above a pH of about 3.2, it follows that hydroxy 
complexes of thorium are primarily involved in adsorption processes (in carbonate-poor 
systems). Using a mixed quartz-illite soil as a sorbent, Rancon (1973) measured a Kd value of 5 
mL g1 at a pH of 2, which increased to 5 x 105 mL g1 at a pH of 6. With a quartz-illite-calcite-

organic-matter soil, Rancon found that the Kd decreased from 106 mL g1 at a pH of 8 to 100 
mL g1 at a pH of 10. This change was attributed to the dissolution of soil humic acids and the 
formation of thorium-organic complexes at this high pH. 
Lieser and Hill (1992) reported thorium sorption coefficients for rock/water systems associated 
with the Gorleben site in Germany. They found that thorium was strongly sorbed in such 
systems (Kd = 103 to 105 mL g1). However, they also found that colloidal transport may be of 
potential significance to the migration of thorium in the surficial environment. 
Thorium sorption experiments on Yucca Mountain rock samples in J-13 groundwater were 
reported by Rundberg et al. (1985) and Thomas (1988). The sorption coefficients obtained in 
these experiments ranged from 140 to 23,800 mL g1. No correlations were noted between the 
values obtained for the sorption coefficient and rock type or pH (5.37.5). The large range in 
sorption coefficients obtained in these experiments may be explained by the presence of fine 
colloidal particles in the solution phase used to obtain the sorption coefficients (for example, 
Lieser and Hill 1992). 
Conclusions Regarding Sorption Behavior of Thorium with Respect to Expected Variations in 
GroundwatersThe sorption coefficients for thorium are expected to be large (> 100 mL g1) in 
all hydrochemical environments associated with Yucca Mountain in the present day or in the 
future. This conclusion is based on the dominance of hydrolysis reactions in solution, the low 
solubility of thorium oxides and silicates, and the large values measured for thorium sorption 
coefficients in different water compositions, including seawater, combined with the general lack 
of evidence for mobility of thorium in the surficial environment. 
Tin 
Behavior in Solutions Representative of Yucca Mountain GroundwatersThe dominant tin 
solution species in surficial waters appears to be Sn(OH)4. The concentrations of tin in natural 
groundwaters are extremely low due to the ion solubility of the tetravalent oxides (about 109 M 
in pure water; Baes and Mesmer 1986). Cassiterite (SnO2) should be the solubility-limiting 
oxide in most groundwaters. Tin could also coprecipitate with other insoluble oxides or silicates 
such as niobium pentoxide, zirconium and thorium dioxide, and thorium silicate. In natural 
waters with high sulfide concentrations, tin sulfide minerals could control tin solubility. 
However, such water compositions are not expected in association with the proposed repository 
site at Yucca Mountain. 
Qualitative Evidence for Behavior in the Surficial EnvironmentTin is one of the elements 
considered to be immobile in most near-surface geologic environments (Rose et al. 1979). This 
assignment is based on various types of data, including observations on the mobility of tin in and 
around tin ore deposits. However, De Laeter et al. (1980) note that some tin has migrated out of 
the pitchblende at the natural reactor at Oklo, Gabon. The cause for this migration has not been 
established but may reflect the existence of reducing conditions during some phase of the history 
of the reactor. 
Data from Laboratory Sorption Experiments Carried out Prior to 1993Sorption experiments 
with tin have been carried out on several whole-rock samples from Yucca Mountain in contact 

with J-13 water and p#l water and several other water compositions separately spiked with 
sodium sulfate, sodium bicarbonate, and calcium chloride (Knight and Thomas 1987). The 
measured sorption coefficients ranged from 77 to 35,800 mL g1 at pH values in the range of 8.4 
to 9.2. Coefficients obtained from desorption experiments were generally larger (30052,500 
mL g1) than those obtained from sorption experiments. The devitrified tuff samples produced 
the highest sorption and desorption-coefficient values (> 2900 mL g1), whereas the vitric and 
zeolitic tuff samples produced lower values. Sorption coefficients were generally highest in the 
p#l water and the calcium-chloride-spiked J-13 water. Apparently, high calcium concentrations 
in the solution phase result in high sorption-coefficient values for tin. Alternatively, high 
calcium concentrations cause the precipitation of some type of tin-bearing compound. As with 
thorium, the large range in sorption coefficients observed in the experiments may reflect the 
presence of colloidal-size particles in the solution phase used to obtain the coefficients. 
Conclusions Regarding Sorption Behavior of Tin with Respect to Expected Variations in 
GroundwatersThe sorption coefficients for tin are expected to be large (> 100 mL g1) in all 
hydrochemical environments associated with Yucca Mountain in the present day or in the future. 
This conclusion is based on the dominance of hydrolysis reactions in solution, the low solubility 
of tin oxides, and the large values measured for tin sorption coefficients in different water 
compositions, combined with the general lack of evidence for mobility of tin in the surficial 
environment. 
Zirconium 
Behavior in Solutions Representative of Yucca Mountain GroundwatersIn near-neutral 
solutions, the dominant zirconium solution species appear to be hydrolysis products, such as 
Zr(OH)4. The degree to which zirconium forms complexes with other inorganic ligands present 
in Yucca Mountain groundwaters is insignificant. The solubility of zirconium in dilute solutions 
is extremely small (Baes and Mesmer 1986, pp. 152156; Cotton and Wilkinson 1988, pp. 780 
782), although the identity of the solubility-controlling solid is uncertain. The solubility-
controlling compounds for zirconium in most natural groundwaters are likely zircon (ZrSiO4) or 
baddeleyite (ZrO2). Zirconium solubilities in surficial environments may also reflect 
coprecipitation in other sparingly soluble oxides or silicates. The concentrations of zirconium in 
natural waters may be predominantly controlled by sorption reactions. 
Qualitative Evidence for Behavior in the Surficial EnvironmentZirconium is one of the 
elements considered to be immobile in most near-surface geologic environments (Rose et al. 
1979). Studies of zirconium concentrations in altered and unaltered or less-altered rocks from 
the same original geologic unit (Cann 1970) form part of the basis for this conclusion. Other 
evidence includes the persistence of zircon (ZrSiO4) in the weathering zone and the low 
concentrations of zirconium in waters associated with zirconium-rich rocks. Brookins (1983) 
noted that zirconium was retained within the reactor zones at Oklo, Gabon, although it may have 
been subject to very local-scale redistribution. 
Sorption Data from the LiteratureData on the sorption behavior of zirconium in 
soil/rock/water systems have been reported by Rhodes (1957), Spitsyn et al. (1958), Prout 
(1959), and Serne and Relyea (1982). Rhodes (1957) has presented data on zirconium sorption 
coefficients for a soil-water system that show large values (> 1980 mL g1) up to a pH of 8.0 

followed by a decrease to 90 mL g1 at a pH of 9.6 and a return to high values at a pH of 12. He 
attributed the decreased sorption for values of pH from 8 to 12 to the stabilization of colloidal 
components in solution in this pH range. Spitsyn et al. (1958) observed little movement of 
zirconium through a sandy soil in a field test under both acidic and alkaline conditions. Serne 
and Relyea (1982) report large values for zirconium sorption coefficients in all media tested. 
Conclusions Regarding Sorption Behavior of Zirconium with Respect to Expected Variations in 
GroundwatersThe dominance of zirconium hydrolysis reactions in solution suggests that pH 
will be the dominant groundwater compositional parameter controlling zirconium solubility and 
sorption behavior. The lack of evidence for zirconium transport in field tests under both acidic 
and alkaline conditions and the general lack of evidence for mobility of zirconium in the surficial 
environment combined with the large values of the sorption coefficient reported in the literature 
for zirconium suggest that in all hydrochemical environments associated with Yucca Mountain in 
the present-day or in the future this elements sorption coefficients will be large (> 100 mL g1). 
6.4.4.2 Effects of Organics on Actinide Sorption 
Naturally occurring organic compounds generated during the transformation of plant and animal 
debris over time and as a result of the synthetic activities of microorganisms are ubiquitous in 
surface and subsurface environments. For example, pore water from a well-developed soil 
environment usually contains dissolved organic carbon in quantities greater than 20 mg L1 in 
top soils and in quantities of about 5 mg L1 in subsoils. Dissolved organic carbon 
concentrations in groundwaters typically depend on the environment and are usually below 2 
mg L1 (Drever 1988). The decrease in concentrations of organic materials with increasing depth 
is attributed to chemical and biological degradation as well as to sorption on mineral surfaces. 
Sorption of organic materials onto mineral surfaces is considered the dominant contributing 
factor to the removal of organics from solution during percolation through the subsurface. 
The interaction between organic materials and mineral surfaces in the natural environment is 
important to mineral surface geochemistry. Sorption of organic material onto mineral surfaces 
affects not only the solubility and charge of the organic materials in solution but also the 
properties of the mineral surfaces, such as their charge and hydrophobicity, thereby altering the 
reactivity of the mineral toward metal ions. A clear understanding of the effects of the organic 
materials that frequently coat mineral surfaces in natural environments will lead to improvements 
in the sorption models used to predict the mobility of radionuclides in natural aquatic 
environments (Choppin 1992). 
Triay et al. (1997) presented laboratory results for the effect of organic materials on the sorption 
of plutonium and neptunium on selected mineral oxides and Yucca Mountain tuff. Triay et al. 
(1997) investigated Pu and Np sorption onto various Yucca Mountain tuffs, devitrified tuff (G4270 
and G4-275), vitric tuff (Gu3-1496) and zeolitic tuff (G4-1529), in natural J-13 and synthetic 
p#1 waters, in the presence of catechol, alanine, DOPA (dihydroxyphenylalanine), and NAFA 
(Nordic aquatic fulvic acid). Alanine is an amino acid that will complex with the hard acid form 
of metal ions in solution. Catechol is a phenolic compound that can chelate metal ions and 
undergo redox reactions with the metal. DOPA, a naturally occurring amino acid commonly 
found in plant seedlings, pods, and broad beans, was chosen because it contains well-defined 

organic functional groups such as carboxylic acid, amine, and phenols. Triay et al. (1997) 
concluded the following: 
 
The sorption of organic material DOPA on oxide surfaces follows the order aluminum 
oxide > iron oxide. For a given sorbent, the higher the pH, the more DOPA is sorbed. 
Surface complexation is the most likely sorption mechanism. 
 
The sorption of plutonium generally follows the order hematite > ferrihydrite > goethite. 
The sorption of neptunium on iron oxide is higher than that on aluminum oxide. The 
sorption of neptunium on crushed tuff material was much lower than that on oxide 
surfaces. 
 
The sorption of plutonium and neptunium on iron oxides increases as the solution pH is 
raised, although the range in pH investigated was narrow (see Assumption 3 in Section 
5). The sorption of plutonium is much higher than that of neptunium on hematite, 
goethite, and ferrihydrite. The applicability of these sorption data for modeling sorption 
onto waste packages is not known because the range of pH values for waters that might 
be in contact with a waste package is currently unbounded. 
 
The amount of neptunium sorption was not affected by any of the organic materials that 
were studied. The presence of the organic materials alanine, catechol, DOPA, and NAFA 
did not influence the sorption of neptunium on tuff or on iron and aluminum oxides. This 
lack of an observable effect is presumably a result of the weak complexation between 
neptunium and the model organics. Therefore, under the conditions that the experiments 
were conducted, the types of organics studied should have little effect on Np sorption. 
 
The sorption of plutonium was influenced by the presence of DOPA on goethite and 
ferrihydrite. Increasing the amount of DOPA resulted in higher sorption of plutonium on 
goethite and ferrihydrite. Alanine decreased the sorption of plutonium. However, in the 
system containing catechol, plutonium sorption was increased. The enhancement of 
plutonium sorption in the presence of catechol is probably due to the reduction of Pu(V) 
to Pu(IV) by the organic. The inhibition of plutonium sorption in the presence of alanine 
is probably caused by the lowering of the free plutonium-ion activity in solution by 
formation of an alanine-plutonium complex. No observable effect of organics on 
plutonium sorption was found in the hematite system under the conditions that the 
experiments were conducted, which is probably due to a relative high sorptivity of 
plutonium on the hematite surface. 

6.4.5 Adsorption of Radionuclides by Alluvium 
Alluvium is the generic name for clay silt, sand, gravel, or similar detrital material deposited by 
running water. Alluvium provides another natural barrier to migration of radionuclides along the 
flow path from Yucca Mountain. Because the alluvium through which a radionuclide may travel 
is relatively far from the repository, its retardation properties are important to PA with respect to 
the most mobile radionuclides, particularly 237Np, 99Tc, and 129I. Consequently, the apparent 
distribution coefficient, Kd (mL g1), of these three radionuclides in alluvium has been 
determined for use in PA. 
The water used in the experiments is groundwater from the alluvial aquifer, filtered through a 
0.05-m filter. Tracer solutions were prepared as a dilution using the filtered water from a stock 
solution, then passed through a 0.02-m filter before use. The alluvial samples used in the 
experiments come from the three boreholes shown in Table 9. Also shown are the density values 
for the samples used in the sorption experiments that reflect the samples as they were prepared 
for the experiments. Standard batch adsorption experiments were performed on the 75- to 500-
m fraction. 
Table 9. Depth Intervals (below the surface) and Bulk Densities of Alluvial Samples 
Borehole NC-EWDP-02D (02D) Borehole NC-EWDP-09Sx (09Sx) Borehole NC-EWDP-03S (03S) 
Depth (ft) Density (g cm3) Depth (ft) Density (g cm3) Depth (ft) Density (g cm3) 
395400 1.3 145150 1.3 6065 1.3 
400405 1.2 150155 1.3 6570 1.2 
405410 1.3 155160 1.3 7075 1.3 
410415 1.3 160165 1.2 7580 1.2 
DTN: LA0002JC831341.001 
NOTE: Densities were measured in the laboratory and do not represent in-situ conditions. 
6.4.5.1 Results and Discussion 
Table 10 lists the QXRD results for the three samples used for the first adsorption kinetic 
experiments, which are the deepest samples tested from each borehole suite. The QXRD results 
show that the major mineral phase in these alluvial samples is feldspar, and that the amount of 
feldspar in the three samples is about the same. The amount of poorly sorbing minerals 
tridymite, cristobalite, and quartzis also about the same in these three samples. The important 
differences among these samples are the presence of smectite, clinoptilolite, calcite, and 
hematite. 
6.4.5.1.1 Adsorption of 237Np 
Figure 9 presents the results of adsorption of 237Np on the three alluvial samples. In general, the 
samples from Borehole 02D and Borehole 03S have relatively high retardation capacity. The Kd 
value for 237Np on is 77 mL g1 for the alluvium from Borehole 02D, 400405 ft, and almost 45 
mL g1 for the samples from Borehole 03S, 6065 ft. The highest 237Np Kd value is for the 

sample with the highest amount of the sorptive phases: calcite, smectite, clinoptilolite and 
hematite (Table 10). Calcite has a high affinity for 237Np at this pH. 
Table 10. Quantitative X-ray Diffraction (QXRD) Results of Three Alluvial Samples 
Minerals 
Mineral Percentage in Samples 
NC-EWDP-02D 
410415 ft, 75500 m 
NC-EWDP-03S 
7580 ft, 75500 m 
NC-EWDP-09Sx 
160165 ft, 75500 m 
Smectite 2  1 1  1 6  2 
Kaolinite 1  1 1  1  
Clinoptilolite 4  1 13  1 3  1 
Tridymite 3  1  1  1 
Cristobalite 16  1 10  1 18  1 
Quartz 18  1 17  1 14  1 
Feldspar 54  8 53  8 58  8 
Calcite  4  1  
Mica Trace 1  1 Trace 
Hematite 1  1  Trace 
Hornblende Trace Trace  
Unidentified Phases Trace   
Total 99  8 100  8 100  8 
DTN: LA0002JC831341.002 
NOTE:  means mineral not detected 
The deepest sample from each borehole was chosen to carry out the adsorption kinetic 
experiments. The results, depicted in Figure 10, suggest that adsorption of 237Np on alluvium is 
fast. 
6.4.5.1.2 Adsorption of 99Tc 
The results of adsorption of 99Tc are presented for the three alluvial samples in Figure 11. 
Although the degree of retardation of 99Tc on alluvium is low, it is non-zero for some samples 
and even this small degree of retardation could be significant for long-term performance. 
However, all Kd values are less than 1 and several are negative values. Values in this range are 
generally considered not to be significant (Triay, Cotter, Huddleston et al. 1996 p. 7). This is 
because the value is calculated by subtracting two large numbers (the initial isotope 
concentration in solution and the isotope concentration after sorption) to obtain a small number 
(the amount of isotope in the solid phase). Therefore, values in this range (particularly negative 
numbers) may be artifacts resulting from minor counting errors in measuring the activity of the 
samples. 
Figure 12 indicates that adsorption of 99Tc increases in the early part of the test before decreasing 
at longer times. Assuming the trends are real, other mechanisms besides simple adsorption may 
be operating, such as redox reactions. Although no sulfides or other reduced minerals were 
indicated by the QXRD analyses, only trace amounts need be present to greatly affect the 

reactivity of the surfaces. The accuracy of QXRD is poor below a few percent and, also, if the 
phases are poorly crystalline. 
6.4.5.1.3 Adsorption of 129I 
Experiments to determine the overall Kd values for 129I are not yet complete, but have yielded 
some preliminary Kd values. Similar to 99Tc, retardation of 129I on alluvium is small but positive 
at short intervals but then decreases to negative values at longer times, as indicated in Figure 13. 
As with 99Tc, these low values may not be significant. 

Adsorption of Np as Function of Stratigraphic Position (Borehole-02D) 
0 
20 
40 
60 
80 
( 
)
Kd or % Adsorbed 
K dm L/g) 
P e r c en t adsor bed ( % 
395 400 405 410 415 
Depth (ft) 
Adsorption of Np as a Function of Stratigraphic Position (Borehole-09Sx) 
0 
10 
20 
30 
( 
)
Kd or % Adsorbed 
K dm L/g) 
P e r c en t adsor bed ( % 
145 150 155 160 165 
Depth (ft) 
Adsorption of Np as a Function of Stratigraphic Position (Borehole-03S) 
0 
20 
40 
60 
80 
( 
)
Kd or % Adsorbed 
K dm L/g) 
P e r c en t adsor bed ( % 
606570 7580 
Depth (ft) 
DTN: LA0109MD831341.001 
NOTE: Borehole-02D signifies Borehole NC-EWDP-02D, Borehole-09Sx signifies Borehole NC-EWDP-09Sx, 
and Borehole-03S signifies Borehole NC-EWDP-03S. Value plotted at center of 5-foot sample interval. 
Figure 9. Adsorption of 237Np on Three Alluvial Samples 

Adsorption of Np as a Function of Tim e 
20 
15 
Kd(mL/g)
ll-
B o re h o e -0 2 D 
B o re h o e -0 9 S x 
B o r e hold e 03 S 
10 
5 
0 
0 2 00 40 0 6 0 0 
Tim e (hr) 
Adsorption of Np as a Function of Tim e 
50 
40 
Percent Adsorbed (%) 
30 
20 
10 
0 
-
B o r e hole-0 2 D 
B o r e hole-0 9 S x 
B o r e holde 0 3 S 
0 2 00 40 0 6 0 0 
Tim e (hr) 
DTN: LA0109MD831341.001 
NOTE: The top panel shows the change in sorption coefficient (Kd) with time; the bottom panel, the percent 
adsorbed. Borehole-02D signifies Borehole NC-EWDP-02D, Borehole-09Sx signifies Borehole NC-EWDP-
09Sx, and Borehole-03S signifies Borehole NC-EWDP-03S. 
Figure 10. Kinetics of 237Np Adsorption on Three Alluvial Samples 

Adsorption of Tc as a Function of Stratigraphic Position (Borehole-02D) 
0 
( 
) 
-0 . 8 
-0 . 4 
0.4 
0.8 
1.2 
1.6 
Kd or % Adsorbed 
K dm L/g) 
P e rc e n t a d s o rb e d (% 
39 5 4 00 40 5 4 10 41 5 
Depth (ft) 
Adsorption of Tc as a Function of Stratigraphic Position (Borehole-09Sx) 
0 
( 
) 
-0 . 4 
0.4 
0.8 
1.2 
1.6 
Kd or % Adsorbed 
K dm L/g) 
P e rc e n t a d s o rb e d (% 
14 5 1 50 15 5 1 60 16 5 
Depth (ft) 
Adsorption of Tc as a Function of Stratigraphic Position (Borehole-03S) 
1. 6 
1. 2 
0. 8 
0. 4 
0 
Kd ( /g ) 
)
Kd or % Adsorbed 
m L 
P e rc e n t a d s o rb e d (% 
60 65 7 0 75 80 
Depth (ft) 
DTN: LA0109MD831341.002 
NOTE: Borehole-02D signifies Borehole NC-EWDP-02D, Borehole-09Sx signifies Borehole NC-EWDP-09Sx, 
and Borehole-03S signifies Borehole NC-EWDP-03S. Value plotted at center of 5-foot sample interval. 
Figure 11. Adsorption of 99Tc on Three Alluvial Samples 
ANL-NBS-HS-000019, Rev 00, ICN 02 91 November 2001 

Adsorption of Tc as a Function of Tim e 
0. 2 
0 
Kd (mL/g)
-0 . 2 
-0 . 4 
0 2 00 40 0 600 
l 
l 
l 
B o r e ho e -0 2D 
B o r e ho e -0 9S x 
B o r e ho e -0 3S 
Tim e (hr) 
Adsorption of Tc as a Function of Tim e 
1 
0 
-1 
-2 
0 2 0 0 40 0 600 
Percent Adsorbed (%) 
l 
l 
l 
B o re h o e -0 2 D 
B o re h o e -0 9 S x 
B o re h o e -0 3 S 
Tim e (hr) 
DTN: LA0109MD831341.002 
NOTE: The top panel shows the change in sorption coefficient (Kd) with time; the bottom panel, the percent 
adsorbed. Borehole-02D signifies Borehole NC-EWDP-02D, Borehole-09Sx signifies Borehole NC-EWDP-
09Sx, and Borehole-03S signifies Borehole NC-EWDP-03S. 
Figure 12. Kinetics of 99Tc Adsorption on Three Alluvial Samples 

Adsorption of I as a Function of Tim e 
-1 
0 
1 
(
lll-0 . 5 
0.5 
KdmL/g)
B o r e h o e-02 D 
B o r e h o e-09 S x 
B o r e h o e-03 S 
0 200 4 0 0 6 00 
Tim e (hr) 
Adsorption of I as a Function of Tim e 
-4 
-2 
0 
2 
4 
) 
lllPercent Adsorbed (%
B o r e h o e-02 D 
B o r e h o e-09 S x 
B o r e h o e-03 S 
0 2 00 4 0 0 6 00 
Tim e (hr) 
DTN: LA0109MD831341.003 
NOTE: The top panel shows the change in sorption coefficient (Kd) with time; the bottom panel, the percent 
adsorbed. Borehole-02D signifies Borehole NC-EWDP-02D, Borehole-09Sx signifies Borehole NC-EWDP-
09Sx, and Borehole-03S signifies Borehole NC-EWDP-03S. 
Figure 13. Kinetics of 129I Adsorption on Three Alluvial Samples 
6.4.5.2 Conclusions about Sorption onto Alluvium 
Although the available data cannot be used to make any strong conclusions, the alluvium does 
appear to be more sorptive than previously expected. Values of Kd for 237Np ranged from about 
4 to 77 mL g1; values of Kd for 99Tc ranged from about 0.15 to 0.3 mL g1; and Kd values for 
129I ranged from about 0.71 to 0.49 mL g1. Sorption was much faster for 237Np than for 99Tc or 
129I. The differences in sorptive properties among samples probably results from differences in 
the amounts of the sorptive phasessmectite, clinoptilolite, calcite, and hematiteand perhaps 
from the presence of organic carbon and trace amounts of sulfides, which may explain the slow 
sorption response for 99Tc and 129I. Biological activity, or simple sorption onto organic material, 
could also be important and account for the slow sorption response for 99Tc and 129I. 

6.4.6 Effects of Temperature Perturbations on Adsorption of Radionuclides 
Little work has been done on the effects of repository perturbations on the transport of 
radionuclides. Some of the obvious effects involve increased temperatures as the repository 
heats up. These effects will be important for the drift and near-field environments. Increased 
temperature will affect the solubilities of existing phases, the precipitation of new phases, the 
generation and stability of colloids, and the overall aqueous geochemistry of the drift and near-
field environments. This section discusses the effect of temperature on radionuclide adsorption 
(Kd values). 
Temperature will affect adsorption by shifting equilibria among solution species, by changing 
the zero point of charge of the substrate surfaces, and by changing the ratio of adsorbed to 
solution-phase species. The magnitude of these effects can be modeled with standard 
thermodynamic relationships if solution and adsorption enthalpy data are available (Machesky 
1990, pp. 283288). Relationships such as the vant Hoff equation and Boltzmann functions 
have temperature in them and can be used to predict the effects of temperature (Machesky 1990, 
p. 283). This calculation should be done for all radionuclides of concern for Yucca Mountain. 
There is general agreement that increasing temperature increases the sorption of cations and 
decreases the sorption of anions (Machesky 1990, p. 287; Beckman et al. 1988, p. 13). The few 
data that exist support this assertion. Machesky (1990, p. 290) used the vant Hoff equation to 
predict a doubling of Kd values with every increase of 20C. 
Beckman et al. (1988, Figure 2) presented data that show barium adsorption onto tuff was 
increased by an order of magnitude going from 25C to 70C and described similar effects for 
cerium, europium, cesium and strontium. They also concluded that temperature effects are 
overwhelmingly more important than effects of concentration or particle size. 
The effect of temperature on sorption coefficients was also reviewed by Meijer (1990, p. 17). 
Again, measured sorption coefficients onto tuffs were higher at elevated temperatures for all 
elements studied: americium, barium, cerium, cesium, europium, plutonium, strontium, and 
uranium. Consequently, the conclusion can be drawn that sorption coefficients measured at 
ambient temperatures should be applicable and generally conservative when applied to 
describing aqueous transport from a hot repository. This conclusion must be tempered by the 
possibility that high temperatures, sustained for long time periods due to potential high thermal 
loads, could result in changes in the near-field mineralogy and water chemistry at Yucca 
Mountain that are not predictable by short-term laboratory and field experiments. 
As a preliminary evaluation, the effect of temperature in a perturbed repository will increase 
adsorption of cationic species and decrease adsorption of anionic species. Because anions do not 
adsorb very well at ambient temperatures, a conservative estimate is their Kd values at higher 
temperatures will be zero. However, the Kd values of cationic species at higher temperatures will 
increase significantly over those listed in Table 2a by as much as 10 times at repository 
temperatures above 70C; more precise numbers should be estimated by modeling efforts. 

6.5 DYNAMIC TRANSPORT STUDIES 
Batch-sorption experiments are most commonly used to obtain sorption coefficients because 
such experiments are fast, easy, and inexpensive compared to other ways of obtaining sorption 
coefficients. However, batch-sorption experiments are appropriate for use in transport 
calculations only if the sorption reaction for a given radionuclide meets certain conditions. 
These conditions are the following (de Marsily 1986, Chapter 10). 
 
Microscopic equilibrium is attained between solution species and the adsorbate (sorption 
reaction is reversible) (Assumption 7 in Section 5). 
 
Only one soluble chemical species is present (or if more than one is present, they 
interchange rapidly relative to the time scale of the experiment) (Assumption 5 in Section 
5). 
 
The radionuclides are sorbed and not precipitated (Assumption 10 in Section 5). 
 
The dependence of sorption on concentration is described by a linear isotherm 
(Assumption 6 in Section 5). 
Although batch-sorption experiments can be used to test for the first and last conditions, they do 
not provide information on the second and third conditions. To test whether or not the latter 
conditions are met for a given radionuclide in the Yucca Mountain flow system, additional 
experiments must be carried out. The easiest way to test for these conditions is to perform 
column tests in which a solution bearing the radionuclide of interest is added to the top of a 
column of crushed rock and eluted from the bottom of the column. The rate at which the 
radionuclide is eluted from the column (the elution curve) provides information on the degree to 
which the conditions are met. Column studies are also the easiest way to investigate the sorption 
behavior of radionuclides during flow in unsaturated media. In this case, solid-rock columns are 
used. Finally, column studies allow the investigation of radionuclide transport along fractures in 
dense rock. 
This section discusses the results of crushed-rock, solid-rock, and fractured-rock column 
experiments. 
6.5.1 Crushed-Rock Columns 
6.5.1.1 Approach 
Column elution curves can be characterized by two parameters: the time of arrival of the 
radionuclide eluted through the column and the broadness (dispersion) of the curve. The arrival 
time depends, among other factors, on the retardation factor, Rf, which, for soluble radionuclides, 
depends, in turn, on the sorption distribution coefficient, Kd, together with the water content and 
bulk density of the solid phase. Significant deviations (those larger than expected based on 
sampling variability) in arrival times from those predicted on the basis of the batch-sorption 
distribution coefficients indicate one of the following problems: 

 
The presence of more than one chemical species that are not readily exchanged and that 
have different selectivities for tuff minerals 
 
The presence of the radionuclide as a colloid 
 
Extremely slow sorption kinetics 
 
Hydrologic parameters (preferential flow paths) 
 
Experimental artifacts. 
The broadness, or apparent dispersion, of the curve depends on: 
 
The kinetics and reversibility of sorption 
 
The linearity of the isotherm that describes the dependence of sorption on radionuclide 
concentration. 
The most comprehensive explanation of the fate of reactive and nonreactive solutes and 
suspended particles in porous and fractured media has been presented by de Marsily (1986, 
Chapter 10). The transport of radionuclides in porous media is governed by advection, diffusion, 
and kinematic dispersion. Advection is the mechanism in which dissolved species are carried 
along by the movement of fluid. Diffusion causes species to be transferred from zones of high 
concentration to zones of low concentration. Kinematic dispersion is a mixing phenomenon 
linked to the heterogeneity of the microscopic velocities inside the porous medium. The 
migration of a solute in a saturated porous medium is described by the following transport 
equation 
.(D.C -CU)=e.Q 
+Q , (Eq. 5)
.t 
where D is the dispersion tensor, C is the concentration of solute in the solution phase, U is the 
filtration velocity (Darcys velocity), e is the porosity, t is time, and Q is a net source or sink 
term that accounts for such things as reactivity or adsorption. 
For the case of a sorbing, nonreactive solute, the equation becomes 
.F
.(D.C -CU)=e.Q 
+..t 
, (Eq. 6)
.t b 
where .b is the dry bulk density of the medium and F is the mass of solute sorbed per unit mass 
of solid. 
Dispersion has three components: the longitudinal dispersion coefficient in the direction of the 
flow, DL, and the transverse dispersion coefficient, DT, in the two directions at right angles to the 
velocity of the flow. These components are given by 

DL = ed + aL|U| and (Eq. 7) 
DT = ed + aT|U| , 
where d is the effective diffusion coefficient in the medium and a is dispersivity. 
The characteristics of the sorption determine the actual relationship between F and C. For the 
case in which sorption is linear, reversible, and instantaneous, the ratio between F and C is 
simply equal to the sorption distribution coefficient: 
F 
= Kd (Eq. 8)
C
Substitution of Equation 8 into Equation 6 yields 
.(D.C -CU)=e.1+
.b Kd 
. .C 
(Eq. 9)
. e. .t 
The expression in brackets in Equation 9 corresponds to the retardation factor, Rf, given by 
Rf = 1 + 
.b Kd (Eq. 10)
e
where .b is the dry bulk density (including pores) and e is the porosity (Hiester and Vermeulen 
1952, Eq. 74). Thus, there is a way to compare sorption coefficients obtained under advective, 
diffusive, and dispersive conditions with sorption coefficients obtained from batch-sorption 
experiments. However, this approach is valid only if sorption is linear, reversible, and 
instantaneous. 
6.5.1.2 Results and Discussion 
Elution of neptunium, plutonium, and technetium were measured as a function of water velocity 
through zeolitic, devitrified, and vitric crushed tuff columns with J-13 well water and with 
synthetic p#1 water. Each experiment used the most thermodynamically stable species of the 
radionuclide of interest in oxidizing waters: Np(V), Pu(V), and pertechnetate (TcO4-). Porosities 
for these experiments were calculated as the free column volumes divided by the total column 
volumes. Empirical values of Rf were then calculated for the column experiments by dividing 
the free column volume into the volume of solution that had to be eluted to recover 50 percent of 
the injected radionuclide. This method does not assume linear equilibrium sorption and is just an 
empirical method for assigning a Rf value to column data. From these values of Rf, Equation 10 
was used to calculate column sorption-distribution coefficients. 
6.5.1.2.1 Neptunium Results 
Elution curves for the Np(V) column have been previously published (Triay, Furlano et al. 1996, 
Appendix A). The sorption-distribution coefficients obtained for these column experiments are 
listed in Table 11. Inspection of Table 11 indicates good agreement between the values of Kd 
obtained by the two approaches (batch and column experiments), which means that the arrival 
time of 237Np, as defined by C/C0 = 0.5, can be predicted from a value for Kd. On the other hand, 

the broad, dispersive shapes of the elution curves (Triay, Furlano et al. 1996, Appendix A) 
indicate that sorption of neptunium onto zeolitic and vitric tuffs is either nonlinear, 
nonreversible, or noninstantaneous. Previous work has found that sorption of neptunium onto 
clinoptilolite-rich tuffs is rapid (Triay, Cotter, Huddleston et al. 1996, Figure 7) and can be fit 
with a linear isotherm (Triay, Cotter, Kraus et al. 1996, Figure 4). Consequently, the degree of 
reversibility of neptunium sorption onto zeolitic and vitric tuffs may be the most likely reason for 
the broadening observed in the tuff-column elution curves. 
Table 11. Comparison of Neptunium Kd Values from Batch and Column Measurements 
Column number Tuff type Water type Batch Kd (mL g1)a Column Kd (mL g1)a 
1 zeolitic J-13 1.7  0.4 (G4-1510) 1.7 (G4-1508) 
2 zeolitic J-13 1.7  0.4 (G4-1510) 1.2 (G4-1508) 
3 zeolitic J-13 2.1  0.4 (G4-1505) 2.8 (G4-1505) 
4 zeolitic Syn. p#1 0.2  0.3 (G4-1506) 0.4 (G4-1505) 
5 zeolitic Syn. p#1 0.2  0.3 (G4-1506) 0.2 (G4-1505) 
6 zeolitic Syn. p#1 0.2  0.3 (G4-1506) 0.2 (G4-1505) 
7 devitrified J-13 0.04  0.2 (G4-268) 0.07 (G4-268) 
8 devitrified J-13 0.04  0.2 (G4-268) 0.01 (G4-268) 
9 devitrified J-13 0.04  0.2 (G4-268) 0.02 (G4-268) 
10 devitrified J-13 0.04  0.2 (G4-268) 0.01 (G4-268) 
11 devitrified Syn. p#1 0.2  0.3 (G4-270) 0.06 (G4-272) 
12 devitrified Syn. p#1 0.2  0.3 (G4-270) 0.03 (G4-268) 
13 devitrified Syn. p#1 0.2  0.3 (G4-270) 0.03 (G4-268) 
14 vitric J-13 0.1  0.5 (GU3-1407) 0.2 (GU3-1407) 
15 vitric J-13 0.1  0.5 (GU3-1407) 0.1 (GU3-1407) 
16 vitric J-13 0.03  0.2 (GU3-1405) 0.1 (GU3-1405) 
17 vitric Syn. p#1 0.2  0.4 (GU3-1407) 0.1 (GU3-1405) 
18 vitric Syn. p#1 0.2  0.4 (GU3-1407) 0.1 (GU3-1405) 
19 vitric Syn. p#1 0.2  0.4 (GU3-1407) 0.1 (GU3-1405) 
DTN: LA000000000106.001 (column Kd, SEP Table S99009.001), LA0010JC831341.007. Water compositions are described in the 
laboratory notebooks referenced by documentation associated with these DTNs. 
NOTE: aSample identifiers given in parentheses represent borehole code and drillcore depth in feet. 
The elution curves also reveal that, regardless of the water being studied, the elution of 237Np 
does not precede the elution of tritium for any of the tuffs. This observation is extremely 
important because if charge-exclusion effects were to cause the neptunyl-carbonato complex (an 
anion) to elute faster than neutral tritiated water molecules, significant neptunium releases could 
occur at Yucca Mountain. Another important observation that can be drawn from these 
experiments is that a batch Kd value can be used to obtain conservative estimates for neptunium 
transport through Yucca Mountain tuffs, assuming matrix flow. 

6.5.1.2.2 Plutonium and Technetium Results 
This section discusses the results from experiments in which Pu(V) was eluted through crushed-
rock columns using J-13 well water and synthetic p#1 water. The elution curves for experiments 
in which vitric and zeolitic rock samples were used with J-13 water are shown in Figures 14 and 
15. As shown in these figures, a small fraction of the Pu(V) breaks through early with the 
nonreactive tritium tracer. In the experiment with zeolitic tuff (Figure 15), an additional fraction 
breaks through between 10 and 20 column volumes followed by a slowly increasing amount of 
breakthrough. The early breakthrough observed in these experiments indicates there is a form of 
plutonium that is essentially unretarded under the experimental conditions. However, the data 
also indicate that the dominant fraction of plutonium in the experiments is retarded even after 50 
column volumes have passed through the columns. The early breakthrough of Pu(V) is 
inconsistent with the batch retardation coefficients measured for similar rock samples in similar 
water compositions as discussed in Section 6.4.4.1.4.1 (Table 4). This inconsistency likely 
reflects slow kinetics for the plutonium sorption reaction in these rock/water systems. One 
possible explanation for such slow reaction kinetics is that the sorption reaction is coupled to a 
reduction reaction in which Pu(V) and Pu(VI) are reduced to Pu(IV) when in contact with the 
crushed-rock samples. 
DTN: LAIT831361AQ95.001 (SEP Tables S98490.001 and .002) 
NOTE: This plot shows the elution curves for tritium and plutonium-239 through vitric tuff sample GU3-1405 with J-13 
well water. 
Figure 14. Plutonium through Vitric Tuff 

DTN: LAIT831361AQ95.001 (SEP Tables S98940.001 and .002) 
NOTE: This plot shows the elution curves for tritium and plutonium-239 through zeolitic tuff sample G4-1533 with 
J-13 well water. 
Figure 15. Plutonium through Zeolitic Tuff 
The results of column experiments with devitrified tuff are presented in Figures 16 and 17. With 
this rock composition, the early breakthrough fraction, under flow conditions similar to those 
pertaining to the vitric and zeolitic column experiments discussed above, is approximately 60% 
in J-13 water and 20% in p#1 water. However, this fraction decreased substantially as the flow 
rate through the column was decreased. For the experiment with p#1 water, the early 
breakthrough fraction is absent when the flow rate is decreased to 0.4 mL g1. In J-13 water, a 
small (<10%) early breakthrough fraction is present even at a flow rate of 0.4 mL g1. These 
results reinforce the concept that plutonium sorption reactions on these types of tuffs are slow. 
An important question is, at what threshold velocity is the early breakthrough fraction eliminated 
for the various rock/water combinations encountered in the Yucca Mountain flow system? This 
question cannot be answered with the available data. Therefore, no definitive statements can be 
made regarding the applicability of batch-sorption coefficient data for plutonium to modeling of 
plutonium transport in the Yucca Mountain flow system. 
The elution of pertechnetate (TcO4) was also studied in columns of crushed devitrified, vitric, 
and zeolitic tuffs in J-13 and synthetic p#1 waters as a function of flow velocity. Inspection of 
the elution curves (Figures 18 to 20) indicate that anion-exclusion effects for pertechnetate in 
crushed tuff are essentially negligible except in the case of technetium transport through zeolitic 
tuff in J-13 well water (Figure 20). In this case, the anion-exclusion effect is small but 
measurable. 

DTN: LA0002JC831361.001 
NOTE: This plot shows elution curves for tritium and plutonium-239 at different flow rates with J-13 water through 
devitrified tuff sample G4-268. 
Figure 16. Plutonium in Devitrified Tuff at Various Flow Rates (J-13 Water) 
DTN: LA0002JC831361.002 
NOTE: This plot shows elution curves for tritium and plutonium-239 at different flow rates in synthetic p#1 water and 
tuff sample G4-268. 
Figure 17. Plutonium in Devitrified Tuff at Various Flow Rates (p#1) 

DTN: LA0002JC831361.003 
NOTE: This plot shows the elution curves for tritium and technetium-95m at different flow rates with J-13 well water 
through devitrified tuff sample G4-268. 
Figure 18. Technetium in Devitrified Tuff 
DTN: LA0002JC831361.004 
NOTE: This plot shows the elution curves for tritium and technetium-95m at different flow rates with J-13 well water 
through vitric tuff sample GU3-1414. 
Figure 19. Technetium in Vitric Tuff 

DTN: LA0002JC831361.005 
NOTE: This plot shows the elution curves for tritium and technetium-95m at different flow rates with J-13 well water 
through zeolitic tuff sample G4-1533. 
Figure 20. Technetium in Zeolitic Tuff 
6.5.2 Solid-Rock Columns 
Direct measurements of transport parameters in actual subsurface materials under subsurface 
conditions can provide defensible modeling of contaminant transport in host rocks and 
engineered barriers surrounding nuclear and hazardous waste repositories. The hydraulic 
conductivity, K, and the retardation factor, Rf, along with the associated distribution coefficient, 
Kd, are poorly known transport parameters for real systems but are key input parameters to 
existing and developing contaminant release models. Unsaturated Rf and K were experimentally 
determined for core samples of Yucca Mountain vitric-member tuff and zeolitic nonwelded tuff 
(from G Tunnel at Rainier Mesa about 45 km northeast of Yucca Mountain) with respect to J-13 
well water with a selenium concentration (as selenite) of 1.31 mg L1 (ppm) at 23C. The intent 
was to demonstrate that a method in which flow is induced with an ultracentrifuge could rapidly 
and directly measure Rf and K in whole-rock tuff cores and then to compare these directly 
measured unsaturated Rf values with those calculated from Kd values obtained through traditional 
batch tests on the same materials. 
6.5.2.1 Methodology 
6.5.2.1.1 Retardation 
Retardation factors can be determined in flow experiments where Rf for a particular species is the 
ratio of the solution velocity to the species velocity. The retardation factor, a dimensionless 
parameter, for that species is given by (Bouwer 1991, p. 41): 

V
gw Kd
R
f = 
V=1 +.d e 
, (Eq. 11) 
sp 
where Vgw is the velocity of carrier fluid (cm1), Vsp is the velocity of the species (cm1), .d is the 
dry bulk density (g cm3), e is the porosity (dimensionless), and Kd is defined as the moles of the 
species per g of solid divided by the moles of the species per mL of solution (mL g1). If none of 
a particular species is lost to the solid phase, then Kd = 0 and Rf = 1 for that species. In column 
experiments, a breakthrough curve is obtained for the particular species and Rf is determined as 
the pore volume at which the concentration of the species in the solution that has passed through 
the column is 50 percent of the initial concentration (C/C0 = 0.5). It is now generally assumed 
that, for unsaturated systems, e= ., where . is the volumetric water content (Bouwer 1991, 
p. 41). The study described in this section experimentally addresses this concern under 
unsaturated conditions in whole rock and evaluates the use of data from batch experiments in 
determining Rf in whole rock. 
Solutions were prepared using J-13 well water with a selenite concentration of 1.31 mg L1 
(ppm). Selenium concentrations were measured with an inductively coupled, argon-plasma, 
atomic-emission spectrometer, with a selenium detection limit of about 0.1 mg L1. The 
speciation of selenium in solution was determined by ion chromatography. All selenium in the 
starting and effluent solutions was found to exist as selenite. 
6.5.2.1.2 Hydraulic Conductivity 
One way to drive fluid through rock is to use centripetal acceleration as the driving force. A new 
technology, the Unsaturated Flow Apparatus (UFA), was used to produce hydraulic steady-state; 
to control temperature, degree of saturation, and flow rates in all retardation experiments; and to 
measure the hydraulic conductivity. A specific advantage of this approach is that centripetal 
acceleration is a whole-body force similar to gravity. This force acts simultaneously over the 
entire system and independently of other driving forces, such as gravity or matrix suction. It has 
been shown that capillary bundle theory holds in the UFA method (Conca and Wright 1992, pp. 
5, 19). 
The UFA instrument consists of an ultracentrifuge with a constant, ultralow flow-rate pump that 
provides fluid to the sample surface through a rotating seal assembly and microdispersal system. 
Accelerations up to 20,000 g are attainable at temperatures from 220 to 150C and flow rates as 
low as 0.001 mL hr1. The effluent is collected in a transparent, volumetrically calibrated 
container at the bottom of the sample assembly. The effluent collection chamber can be 
observed during centrifugation using a strobe light. 
The current instrument has two different rotor sizes that hold up to 50 and 100 cm3 of sample, 
respectively. Three different rotating-seal assemblies facilitate various applications and 
contaminant compatibilities: a face seal, a mechanical seal, and a paramagnetic seal. The large 
sample option with the paramagnetic seal is a configuration that is optimal for adsorption and 
retardation studies. 

Numerous studies have compared use of the UFA approach with traditional methods of 
measuring hydraulic conductivities in unsaturated soils and clays, and the agreement is excellent 
(Conca and Wright 1992, p. 20; Nimmo et al. 1987, pp. 128134). Good agreement is expected 
because the choice of driving force does not matter provided the system is Darcian (see next 
paragraph) and the sample is not adversely affected by a moderately high driving force (= 1000 g 
for all samples run in these experiments); both of these provisions hold for most geologic 
systems. Additionally, all techniques for estimating hydraulic conductivity, K(.), are extremely 
sensitive to the choice of the rock or soil residual water content, .r, and to the saturated hydraulic 
conductivity, Ks; minor variations in .r 
or Ks produce order-of-magnitude changes in K(.) 
(Stephens and Rehfeldt 1985, p. 12). 
The UFA technology is effective because it allows the operator to set the variables in Darcys 
Law, which can then be used to determine hydraulic conductivity. Under a centripetal 
acceleration in which water is driven by both the potential gradient, d./dr, and the centrifugal 
force per unit volume, ..2r, Darcys Law is 
q = K(.) .d.-.. 2 r. , (Eq. 12)
. dr .
where q is the flux density into the sample (cm s1); K, the hydraulic conductivity (cm s1), is a 
function of the matric suction, ., and, therefore, of water content, .; r is the radius from the axis 
of rotation; . is the fluid density (g cm3); and . is the rotation speed (radians per second). The 
gradient term, d./dr  ..2r, is dimensionless. When multicomponent and multiphase systems 
are present in the UFA instrument, each component reaches its own steady state with respect to 
each phase, as occurs in the field. Appropriate values of rotation speed and flow rate into the 
sample are chosen to obtain desired values of flux density, water content, and hydraulic 
conductivity in the sample. Above speeds of about 300 rpm, depending upon the material and 
providing that sufficient flux density exists, d./dr << ..2r. Under these conditions, Darcys 
Law is given by q = K(.) [..2r]. Rearranging the equation and expressing hydraulic 
conductivity as a function of water content, Darcys Law becomes 
K(.) = 
q 
. (Eq. 13)
.. 2r
As an example, a whole-rock core of Topopah Spring Member Tuff accelerated to 7,500 rpm 
with a flow rate into the core of 2 mL hr1 achieved hydraulic steady-state in 30 hr with a 
hydraulic conductivity of 8.3 x 109 cm s1 at a volumetric water content of 7.0 percent. Previous 
studies have verified the linear dependence of K on flux and the second-order dependence on 
rotation speed (Nimmo et al. 1987, pp. 124126), and several comparisons between the UFA 
method and other techniques have shown excellent agreement (Conca and Wright 1992, p. 20). 
Because the UFA method can directly and rapidly control the hydraulic conductivity, fluid 
content, temperature, and flow rates, other transport properties can then be measured as a 
function of fluid content by associated methods either inside or outside the UFA instrument 
during the overall run. 

Fundamental physics issues involving flow in an acceleration field have been raised and 
successfully addressed by previous research and in numerous forums (Conca and Wright 1992, 
pp. 16, 18; Nimmo et al. 1987, pp. 124128; Nimmo and Akstin 1988, p. 303; Nimmo and Mello 
1991, p. 1268). These studies have shown, first, that compaction from acceleration is negligible 
for subsurface soils at or near their field densities. Bulk density in all samples remains constant 
because a whole-body acceleration does not produce high point pressures. A notable exception 
is surface soils, which can have unusually low bulk densities; special arrangements must be 
made to preserve their densities. Whole rock cores are completely unaffected. 
The studies have also shown that three-dimensional deviations of the driving force with position 
in the sample are less than a factor of 2, but moisture distribution is uniform to within 1 percent 
in homogeneous systems because water content depends only upon ., and unit gradient 
conditions are achieved in the UFA instrument in which d./dr = 0. Hydraulic steady-state is not 
as sensitive to changes in rotation speed as to flux density. In heterogeneous samples or 
multicomponent systems such as rock, each component reaches its own hydraulic steady state 
and water content, as occurs for such materials under natural conditions in the field. This last 
effect cannot be reproduced with pressure-driven techniques but only under a whole-body force 
field, such as with gravity columns or centrifugal methods. The ratio of flux to rotation speed is 
always kept high enough to maintain the condition of d./dr = 0. 
6.5.2.2 Results and Discussion for Vitric and Zeolitic Tuff 
6.5.2.2.1 Column Breakthrough Test Results 
For these experiments, the rotation speed was set at 2,000 rpm with a flow rate into each sample 
of 0.2 mL hr1. The experiment was run for 9 days with an initial selenium concentration of 1.31 
mg L1. Figure 21 shows the breakthrough curves for selenite (C/Co is given for selenium as 
selenite) in the Yucca Mountain vitric member at 62.6% saturation and in the zeolitic nonwelded 
tuff at 52.8% saturation. Pore volume is given as water-filled, or effective pore volume, the 
same as the volumetric water content, ., and is dimensionless. The experiment was stopped 
before full breakthrough in the zeolitic nonwelded tuff, but the C/Co = 0.5 point was reached. 
The retardation factor for each tuff sample is 2.5. The Kd for each tuff sample can be calculated 
by rearranging Equation 11 into Kd = (Rf  1)(water content)/(bulk density). The water content is 
the total porosity multiplied by the degree of saturation. For the Yucca Mountain vitric-member 
tuff Kd = (2.5  1)(0.626)(0.23)/1.54 = 0.14 mL g1, and for the zeolitic nonwelded tuff, Kd = 
(2.5  1)(0.528)(0.4)/1.21 = 0.26 mL g1. 
During these experiments, the unsaturated hydraulic conductivity, K, for each sample at these 
water contents was 2.5 x 108 cm s1 for the Yucca Mountain vitric-member tuff and 1.2 x 108 
cm s1 for the zeolitic nonwelded tuff. Figure 22 gives the characteristic curves, K(.), for these 
tuffs determined in separate experiments, as well as measurements for other tuffs and materials 
for comparison. 

0 1 2 3 4 5 6 7 8 9 
1.6 
1.4 
1.2 
1.0 
0.8 
0.6 
0.4 
0.2 
0.0 
3; 52.8% saturation; K = 1.2 x 108 cm/s) 
3; 62.6% saturation; K = 2.5 x 108 cm/s) 
Pore volume, . 
C/C o for selenium 
10 11 12 13 14 15 
Zeolitized nonwelded G-tunnel tuff (1.21 g/cmYucca Mountain vitric-member tuff (1.54 g/cm
DTN: LA0004JC831361.001 
NOTE: The UFA column data plotted here for a Yucca Mountain tuff retardation experiment show the breakthrough 
curves for selenium. The initial concentration, C0, of selenium (as selenite) was 1.31 mg L1 in J-13 well 
water. 
Figure 21. Selenium Breakthrough Curves 
6.5.2.2.2 Batch-Sorption Test Results 
Batch-sorption tests were conducted using the same J-13 well water with the slightly lower 
selenium concentration, as selenite, of 1.1 mg L1 and the same zeolitic nonwelded tuff as in the 
UFA column breakthrough test. The batch-adsorption tests consist of crushing and wet-sieving 
the tuff, pretreating the tuff with J-13 water, placing the selenium solution in contact with the 
tuff, separating the phases by centrifugation, and determining the amount of selenium in each 
phase by difference using inductively coupled plasma mass spectrometry. Control samples were 
used to determine the sorption of selenium onto the walls of the sorption containers. The control 
procedure consisted of following the described batch-sorption procedure with a sample 
containing the selenium solution, except with no tuff added. The results of the control 
experiments indicate no loss of selenium due to precipitation or sorption onto the walls of the 
container during the batch-sorption experiment. The sorption distribution coefficients obtained 
are given in Table 12. The Eh of all solutions, measured after the sorption experiments, varied 
from 140 to 150 mV. 

DTN: LA0004JC831224.001 
NOTE: These UFA column data for various Yucca Mountain and Bandelier tuffs and other soil samples show the 
unsaturated hydraulic conductivity, K, as a function of volumetric water content, .. The name and the 
density of each tuff is given in the legend. 
Figure 22. Unsaturated Hydraulic Conductivity 
Table 12. Selenium Batch Adsorption on Nonwelded Zeolitic Tuff a 
Pretreatment period (days) Sorption period (days) Kd (mL g1) 
6.9 0.04 0.2 
6.9 0.04 0.3 
6.8 13.9 0.0 
6.8 13.9 0.2 
DTN: LA0002JC831341.003 
NOTE: a Experimental conditions: J-13 water; 20C; 75500 m tuff particle sizes; 1.1 mg L1 initial selenium 
concentration; solution pH after sorption of 8.4; and samples from the same location as the tuff used in the 
column experiments. 
The data presented in Table 12 and Figure 21 indicate agreement between the column and the 
batch-sorption experiments. At a selenium concentration of ~1 mg L1, no sorption of the 
selenium by the tuff is observed for the zeolitic tuff used in batch experiments (average Kd = 0.08 
 0.22 mL g1 from Table 12), and minimal sorption is observed for the zeolitic tuff used in the 
unsaturated column experiments (Kd of 0.26 mL g1 from Section 6.5.2.2.1). The method used 
for the batch-sorption experiments to determine Kd values (by difference) involves subtracting 
the selenium concentration in solution after equilibration with the solid phase from the initial 
selenium concentration in solution. This method yields large scatter in the data when the batch-
sorption distribution coefficient is small because two large numbers are subtracted to get a small 

number. Inspection of Table 12 also suggests that the kinetics of selenium sorption onto tuff are 
fast. 
6.5.2.2.3 Conclusions 
This study demonstrated the feasibility of using the UFA technology to measure retardation 
factors and hydraulic conductivities rapidly and directly in whole-rock cores of tuff under the 
unsaturated conditions that exist in the field. In UFA column breakthrough tests, the retardation 
factor for the selenite species was only 2.5 in both Yucca Mountain vitric member tuff at 
62.6 percent saturation and zeolitic nonwelded tuff at 52.8 percent saturation for a selenium 
concentration in J-13 water of 1.31 mg L1, corresponding to Kd values of 0.14 mL g1 and 0.26 
mL g1, respectively. In batch tests on the same material with an initial selenium concentration 
of 1.1 mg L1, the average Kd was 0.08  0.2 mL g1, which gives retardation factors that are 
slightly lower than those from the UFA column breakthrough experiments. This finding 
suggests that using batch-sorption coefficients to predict radionuclide transport through 
unsaturated tuff will yield conservative results. The unsaturated hydraulic conductivities during 
the experiments were 2.5 x 108 cm s1 for the Yucca Mountain vitric-member tuff and 1.2 x 108 
cm s1 for the zeolitic nonwelded tuff. 
6.5.3 Radionuclide Transport Through Fractures 
6.5.3.1 Overview 
Among other reasons, Yucca Mountain was chosen as a potential site for a high-level nuclear-
waste repository because its geochemistry is believed to form both a physical and a chemical 
barrier to radionuclide migration. As a result of regional tectonics and volcanism, many faults 
and fractures were produced within the tuffaceous units of Yucca Mountain as well as the 
surrounding region. In addition, volcanic tuffs are commonly fractured as a result of cooling. 
The numerous fractures present at Yucca Mountain represent a potential breach in the natural 
barrier, providing a fast pathway for radionuclide migration. 
Radionuclide transport estimates commonly assume that radionuclides can travel through 
fractures unimpeded. This assumption is too simplistic and leads to overconservative predictions 
of radionuclide releases to the accessible environment. The assumption ignores two main 
mechanisms by which retardation of radionuclides migrating through fractures can occur: (1) 
diffusion of the radionuclides from the fractures into the rock matrix, and (2) sorption of 
radionuclides onto the minerals coating the fractures. 
Minerals coating the fracture walls are generally different from the host-rock mineralogy due to a 
variety of factors ranging from precipitation of hydrothermal waters or meteoric waters to 
alteration of the pre-existing minerals. A review of the literature (Carlos 1985, Table I; 1987, 
Table I; 1989, Table II; 1994, Table 1; Carlos et al. 1990, Table II; 1993, Table 1) has provided a 
list of the minerals lining the fractures found at Yucca Mountain (Table 13). 

Table 13. Minerals Coating Fracture Walls in Yucca Mountain Tuffs 
Zeolites 
Heulandite . Clinoptilolite Ca4Al8Si28O7224H2O . (Na, K)6Al6Si30O7224H2O 
(range of compositions with arbitrary division of Si/Al < 4.4 for heulandite and Si/Al > 4.4 for clinoptilolite) 
Mordenite (Ca,Na2,K2)4Al8Si40O9628H2O 
Analcime NaAlSi2O6H2O 
Chabazite CaAl2Si4O126H2O 
Phillipsite (K2,Na2,Ca)Al2Si4O1245H2O 
Erionite (Ca,Na2,K2)4Al8Si28O7227H2O 
Stellerite CaAl2Si7O187H2O 
Silica 
Quartz SiO2low-temperature polymorph of silica 
Tridymite SiO2high-temperature polymorph of silica 
Cristobalite SiO2highest-temperature polymorph of silica 
Opal SiO2nH2O 
Feldspars 
Plagioclase (albite) Solid solutions of albite (NaAlSi3O8) and anorthite (CaAl2Si2O8) 
K-feldspar (sanidine) Solid solutions of orthoclase (KAlSi3O8) and albite (NaAlSi3O8) 
Clays 
Smectite family: 
Dioctahedral (montmorillonite) (Na,K,Mg0.5,Ca0.5,possibly others)0.33Al1.67Mg0.33Si4O10(OH)2nH2O 
Trioctahedral (saponite) (Ca0.5,Na)0.33(Mg,Fe)3(Si3.67Al0.33)O10(OH)24H2O 
Sepiolite Mg4(Si2O5)3(OH)26H2O 
Palygorskite (Mg,Al)2Si4O10(OH)4H2O 
Illite (H3O,K)y(Al4Fe4Mg4Mg6)(Si8yAly)O20(OH)4 
Manganese oxides/hydroxides 
Pyrolusite MnO2 (1x1 tunnel structure) 
Cryptomelane family: A02(Mn4+,Mn3+)8(O,OH)16 (2x2 tunnel structure) 
Cryptomelane A = K 
Hollandite A = Ba 
Coronadite A = Pb 
Romanechite (Ba,H2O)2Mn5O10 (2x3 tunnel structure) 
Todorokite (Na,Ca,Ba,Sr)0.30.7(Mn,Mg,Al)6O123.24.5H2O (3x3 tunnel structure) 
Aurorite (Mn2+,Ag,Ca)Mn3O73H2O 
Lithiophorite m{Al0.5Li0.5MnO2(OH)2}n{Al0.667(Mn4+,Co,Ni,Mn2+)O2(OH)2}pH2O 
Rancieite (Ca,Mn2+)(Mn4+)4O93H2O 
Iron oxides/hydroxides 
Hematite Fe2O3 
Magnetite (Fe,Mg)Fe2O4 
Carbonates 
Calcite CaCO3 
Halides 
Fluorite CaF2 
Source: Carlos (1985, Table I; 1987, Table I; 1989, Table II; 1994, Table 1); Carlos et al. (1990, Table II; 1993, Table 1) 

The transport of radionuclides through fractures from Yucca Mountain was examined to assess 
the retardation that can be provided by radionuclide diffusion into the matrix and sorption onto 
the minerals coating the Yucca Mountain fractures. 
6.5.3.2 Experimental Procedures 
GroundwatersThe groundwaters used for the experiments presented in this section were 
waters from Well J-13 (filtered through a 0.05-m filter) and two sodium bicarbonate buffers 
that simulated the water chemistry of the groundwaters from Wells J-13 and p#1. The synthetic 
J-13 water was prepared by dissolving 0.03 g of Na2CO3 and 1.92 g of NaHCO3 in 
10 L of deionized water; the synthetic p#1 water by dissolving 0.39 g of Na2CO3 and 8.90 g of 
NaHCO3 in 10 L of deionized water. The reasons for having to use synthetic waters for the 
fracture-column experiments was the unavailability of water from Well p#1 and the prevention 
of microbial activity in the columns. 
Fractured-Tuff SamplesTuff samples with natural fractures from drill holes at Yucca 
Mountain were selected from the YMP Sample Management Facility in Mercury, Nevada. The 
tuff matrix of all samples consisted of devitrified tuff, and the minerals lining the fractures were 
stellerite, magnetite, hollandite, and romanechite. The sampling criteria were confined to cores 
with natural fractures, determined by the presence of secondary mineral coatings, and fractures 
with removable fracture walls that could be repositioned to their original orientation. Based on 
this criteria, it was concluded that of the fractured-tuff cores selected (USW G1-1941, 
UZ-16 919, USW G4-2981, and USW G4-2954) all consisted of natural fractures except sample 
G1-1941, the only core sample that did not have secondary minerals coating its fracture. The 
fracture in sample G1-1941 is apparently induced. 
Radionuclide SolutionsThe radionuclide solutions (tritium, pertechnetate, and neptunium) 
were prepared in the same manner as for the crushed-tuff column experiments (Section 6.5.1.2). 
Fractured-Column ProcedureThe experimental setup was the same as that for the crushed-tuff 
column experiments except the column was replaced with a fractured-tuff column. The column 
was submerged in a beaker containing either synthetic p#1 or synthetic J-13 water. The beakers 
were subjected to a vacuum for a minimum of 2 weeks until all evacuating gas bubbles ceased. 
After saturation, the columns were connected, via one of the outflow ports, to a syringe pump, 
and the second outflow port was connected to a pressure transducer. The tracer was injected 
through the bottom. A constant flow rate was established, and a radionuclide tracer was 
introduced into the system through an injection valve. The column elutions were collected as a 
function of time and analyzed, using standard radiometric techniques, for the percentage of 
radionuclide tracer recovered. The aperture of the fractures has not yet been determined, but 
Table 14 gives the other characteristics of the four columns. 
Batch-Sorption ExperimentsFor comparison with the fractured-column experiments, batch-
sorption tests of neptunium onto the fracture minerals stellerite, hollandite, romanechite, and 
magnetite were conducted. These tests were performed under atmospheric conditions using J-13 
well water with a Np(V) concentration of 6.7 x 107 M. The batch-sorption tests consisted of:: 

 
Crushing and wet-sieving the minerals to a size of 75 to 500 m 
 
Pretreating the minerals with J-13 water 
 
Placing the neptunium solution in contact with the minerals for a period of three days 
(using a solid to solution ratio of 1 g to 20 mL) 
 
Separating the phases by centrifugation 
 
Determining the amount of neptunium in each phase by difference using liquid 
scintillation counting. 
Table 14. Characteristics of Fractured Devitrified-Tuff Columns 
Characteristic Column #1 Column #2 Column #3 Column #4 
Sample identifier G1-1941 UZ-16 919 G4-2981 G4-2954 
SMF barcode 
number 
N/A 0029365 0029366 0029368 
Major minerals in tuff 
matrix 
Alkali feldspar and 
quartz 
Alkali feldspar and 
Quartz 
Alkali feldspar and 
opal CT 
Alkali feldspar and 
opal CT 
Minerals coating the None (apparent Stellerite Hollandite Hollandite 
fracture induced fracture) Magnetite Romanechite Romanechite 
Water type Synthetic J-13 Synthetic p#1 Synthetic J-13 Synthetic J-13 
pH 8.6 8.8 8.6 8.6 
Concentration of 
237Np (M) 
1.4 x 105 4.8 x 106 1.4 x 105 1.4 x 105 
Length (cm) 12.6 6.1 6.0 not determined 
Diameter (cm) 6.1 5.2 5.2 not determined 
Volumetric flow rate 
(mL hr1) 
0.5 0.5 0.5 0.5 
DTN: LAIT831361AQ95.003 (SEP Table S98491.001) 
NOTE: Sample identifier is a combination of the borehole identifier and depth in feet. 
Control samples were used to determine the sorption of neptunium onto the walls of the sorption 
containers. The control samples consisted of following the described batch-sorption procedure 
with a sample containing the neptunium solution only with no solid added. The results of the 
control experiments indicate no loss of neptunium from precipitation or sorption onto the walls 
of the container during the batch-sorption experiment. The pH of the water in these experiments 
was approximately 8.5. 
6.5.3.3 Results and Discussion 
As discussed earlier, neptunium does not sorb onto devitrified tuff (Triay, Cotter, Kraus et al. 
1996, p. 18), which constitutes the matrix of all the fractures studied. Retardation during fracture 
flow occurs by diffusion of the radionuclides into the tuff matrix or by sorption of the 
radionuclides onto the minerals coating the fractures. Table 15 lists the results of batch-sorption 

experiments describing the sorption of neptunium onto natural minerals that exist along flow 
paths in the tuff. 
Table 15. Batch-Sorption Results for 237Np in J-13 Well Water 
Major mineral 
in solid phase 
Kd 
(mL g1) 
Solid-phase 
composition a 
Stellerite ~ 0 not analyzed 
Hollandite 700 100% Hollandite 
Romanechite 600 not analyzed 
Magnetite 7 85% Magnetite 
12% Hematite 
3% Goethite 
DTN: LAIT831361AQ95.003 (SEP Table S98491.003) 
NOTE: a Determined by x-ray-diffraction analysis. 
Although the extrapolation from these experiments to Yucca Mountain tuffs containing the same 
minerals is not immediate, the data of Table 15 show some important trends. Neptunium has a 
high affinity for hollandite and romanechite, whereas sorption onto the stellerite is not 
significant. If ion exchange is the main mechanism for neptunium sorption onto stellerite, 
changing the water from J-13 to p#1 will only result in less sorption (due to the formation of a 
larger amount of the neptunyl carbonado complex and competitive effects as a result of the 
higher ionic strength in the p#1 water). The sorption of neptunium onto magnetite does not 
appear to be significant either. As shown in Table 15, the magnetite sample studied contains 
hematite, which could account for the entire observed sorption (Triay, Cotter, Kraus et al. 1996, 
Figure 17). 
Because no secondary minerals coating the fractures were observed for the G1-1941 fractured 
sample (Table 14, column #1, and Figure 23), it can be concluded that the retardation of 
neptunium observed for that column is due to diffusion into the matrix. 
The total neptunium recovery of 70 percent in the UZ-16 919 fractured sample (Table 14, 
column #2, and Figure 24) could be due to minimal sorption onto the stellerite and magnetite 
coating that fracture or due to diffusion into the matrix. It is important to note that in changing 
the water for this column from synthetic J-13 to synthetic p#1, the speciation of neptunium 
changes from a mixture of neptunyl and carbonado complex to almost 100 percent carbonado 
complex (which can be excluded from tuff pores due to size and charge). 
Neptunium seems to be significantly retarded even during fracture-flow in the sample G4-2981 
fractured sample (Figure 25) that is coated with hollandite and romanechite. The recovery of 
neptunium in this fracture is less than 10 percent, and its first appearance is delayed with respect 
to tritium and technetium. 

DTN: LA0001JC831361.001 
NOTE: This plot shows the elution curves for tritium and neptunium-237 in synthetic J-13 water through a fractured 
column of devitrified tuff sample G1-1941. 
Figure 23. Neptunium in Fractured Tuff G1-1941 
DTN: LA0001JC831361.001 
NOTE: This plot shows the elution curves for tritium and neptunium-237 in synthetic p#1 water through a fractured 
column of devitrified tuff UZ-16 919. 
Figure 24. Neptunium in Fractured Tuff UZ-16 919 

DTN: LA0001JC831361.001, LA0001JC831361.002 
NOTE: Elution curves for 3H, 237Np, and 95mTc in synthetic J-13 water through a fractured column of tuff G4-2981. 
Figure 25. Neptunium and Technetium in Fractured Tuff 
Results illustrated in Figures 25 and 26 (columns #3 and #4 of Table 14) indicate that diffusion 
from the fracture into the matrix has taken place because recovery of tritium was only 80 percent 
compared to 90 percent for technetium. This trend agrees with diffusion data that were 
previously obtained for 3H and 95mTc in devitrified tuff and water from well J-13. These data 
were fitted to the diffusion equation (Triay, Birdsell et al. 1993, Eq. 1) using the transport code 
TRACRN V1.0 (STN: 1010601.0-00), which yielded diffusion coefficients for saturated 
22
devitrified tuffs that were of the order of 106 cm s1 for tritiated water and 107 cm s1 for 
technetium. Anion exclusion, in which the large pertechnetate anion is excluded from tuff pores 
due to its size and charge, may be operative in this case. The alternative explanation, that 3H is 
retarded relative to pertechnetate due to sorption, is ruled out; the Kd for 3H is so infinitesimally 
small because the mass of 3H in the water far exceeds that associated with clays or other minerals 
in the rock. 
Continuing with the explanation by de Marsily (1986, Chapter 10) of the fate of reactive and 
nonreactive solutes in porous and fractured media, that was started in the earlier section on 
crushed-rock columns, the equation for a sorbing, nonreactive solute (Equation 6) can be 
expanded to account for a solute that also undergoes radioactive decay: 
.(D .C - CU)=e 
.Q 
+.C 
+.b 
.F 
+.F 
(Eq. 14)
.t 
.t 
where . is related to the half-life, t1/2, of the decaying radionuclide by the relationship . = 
0.693/t1/2. 
ANL-NBS-HS-000019, Rev 00, ICN 02 115 November 2001 

DTN: LA0001JC831361.001, LA0001JC831361.002 
NOTE: This plot shows elution curves for 95mTc and 3H in synthetic J-13 water through a fractured column of 
devitrified tuff sample G4-2954. 
Figure 26. Technetium in Fractured Tuff 
As was pointed out earlier, the mechanism of sorption determines the relationship between F and 
C. If the linear, reversible, and instantaneous relationship for sorption is substituted, that is 
F = KdC, Equation 14 becomes 
.(D .C - CU)=e1 + 
.
b 
e
K
d 
.C
+.C 
(Eq. 15)
.t 
The expression inside the first set of parentheses in Equation 15 is the retardation factor, Rf, 
which, of course, is only valid if sorption is linear, reversible, and instantaneous. 
For radionuclide elution through fractures, the porous medium and the fractured medium are 
treated separately, each with its own Darcys velocity and porosity (de Marsily 1986, Chapter 
10), then coupled by a convection and a dispersion-exchange term in the transport code. The 
radionuclide elution data through fractured media were reduced and analyzed using the transport 
code FEHM V2.00 (STN: 10031-2.00-00) and reported in Robinson et al. (1995, pp. 6370). 
The report on 237Np elution through fractured rock made it clear that the data are consistent with 
very large values of Kd, at least compared to the typical value of 2.5 for 237Np on zeolitic tuff. 
The report also indicated that it is possible that minerals present in trace quantities in the bulk 
rock that appear to contribute insignificantly to sorption may be quite effective at retarding 237Np 
transport when concentrated on fracture surfaces. 
The most significant conclusion of the work presented here is that, contrary to previous 
assumptions about the role of fractures in radionuclide retardation, preliminary results from these 
experiments indicate that fracture flow does not necessarily result in a fast pathway for actinide 

migration through fractures. As can be seen in the experiments described above, the migration 
of actinides through fractures could be significantly retarded by sorption onto minerals coating 
the fractures and by diffusion into the tuff matrix. This is corroborated by the Busted Butte and 
C-wells results in Sections 6.8 and 6.9. 
6.6 DIFFUSION TRANSPORT STUDIES IN THE LABORATORY 
Solute transport in fractured rock in a potential radionuclide waste repository has been discussed 
by Neretnieks (1990, p. 22) who concluded that most rocks (even dense rocks such as granites) 
have small fissures between the crystals that interconnect the pore system containing water. 
Small molecules of radioactive materials can diffuse in and out of this pore system. The inner 
surfaces in the rock matrix are much larger than the surfaces in the fractures on which the water 
flows. The volume of water in the microfissures is much larger than the volume in fractures. 
Therefore, over a long time scale, diffusion can play an important role in radionuclide 
retardation. 
The objective of diffusion experiments was to provide diffusion information for nonsorbing 
neutral molecules and anions and sorbing radionuclides. Because the uptake of radionuclides by 
tuff is measured as a function of time, the experiments also yield information on kinetics of 
sorption. 
6.6.1 Rock-Beaker Experiments 
Rock-beaker experiments measure the diffusive loss of radionuclides into a rock from a solution 
placed in a cavity drilled into the rock. The radionuclides used in these experiments were 3H,
95mTc, 237Np, 241Am, 85Sr, 137Cs, and 133Ba. Batch-sorption results are used to correct for 
decreases in radionuclide concentrations in the solution due to sorption. 
6.6.1.1 Experimental Procedure 
The experimental technique involved fabricating rock beakers of tuff. The beaker sits inside a 
Plexiglas container surrounded by groundwater. A stopper is used to prevent evaporation. 
The cavity in the rock beaker has a radius of approximately 1.4 cm and a length of 2.5 cm. The 
beaker itself has a length of approximately 5 cm and a radius of 3.1 cm. A solution (prepared 
with groundwater from Well J-13) containing the radionuclide of interest was placed in the rock 
cavity and then aliquots of the solution from the beaker for the remaining radionuclide 
concentration were analyzed as a function of time. Also performed were batch-sorption 
experiments with J-13 water and the tuffs under study. 
6.6.1.2 Data Analysis 
The results of the rock-beaker experiments were corroboratively modeled using TRACRN V1.0 
(STN: 10106-1.0-00), which is a 3-D geochemical/geophysical-model transport code. TRACRN 
is documented in Travis and Birdsell (1989). Using the criterion for validation of visual 
judgment of goodness of fit to the analytical solution, TRACRN was validated against an 
analytic solution by Kelkar and Travis (1999). The numerical and analytical solutions agree 
within 0.5%. In rock-beaker experiments the geometry is known; therefore, the mesh is 

validated by inspection, and transport is by diffusion only in saturated rock. Consequently, the 
results are independent of the hydrogeologic properties of the rock. Because the geometry of the 
rock beaker is complex, an analytical solution is not available for this system. The concentration 
profiles of the diffusing tracer are fitted to the transport equation (de Marsily 1986, Chapter 10): 
.(ed.C)=e.C 
+Q , (Eq. 16)
.t 
where e is the total porosity of the tuff, d is the diffusion coefficient through the tuff, C is the 
concentration of the diffusing tracer in solution, and the source term, Q, is zero for a nonreactive 
tracer but for a sorbing solute 
.F
Q =..t 
, (Eq. 17)
b 
where F is the amount of tracer sorbed per unit mass of solid and .b is the bulk tuff density 
(.b = (1  e).s, where .s is the density of the solid particles). 
As discussed in previous sections, the mechanism of sorption determines the relationship 
between F and C. When sorption is linear, reversible, and instantaneous, the relationship 
between F and C is given by the sorption distribution coefficient 
F
Kd = . (Eq. 18)
C
Substitution of this equation and Equation 17 into Equation 16 yields 
.(ed.C)=eRf 
.C 
, (Eq. 19)
.t
where, once again, the retardation factor, Rf, is given by 
Rf = 1 + 
.e 
b Kd , (Eq. 20) 
Equation 20 provides a means of comparing results for sorption coefficients obtained under 
diffusive conditions with sorption coefficients obtained from batch-sorption experiments and is 
valid only if sorption is linear, reversible, and instantaneous (the Langmuir and the Freundlich 
isotherms are examples of nonlinear relationships between F and C). 
Consequently, the diffusion coefficient can be determined by fitting concentration profiles for 
the nonsorbing tracers, and sorption parameters, such as Kd, can be determined by fitting 
concentration profiles for the sorbing tracers. 
6.6.1.3 Results and Discussion 
Figure 27 shows an example of a set of diffusion data for a rock beaker experiment in which the 
feldspar-rich tuff sample G4-737 and solutions of tracers in J-13 water were used. The 
concentration of tracer, C, remaining in the solution inside the cavity of the rock-beaker divided 
by the initial concentration, Co, is plotted as a function of elapsed time. 

Neptunium-237 
Americium-241 
Cesium-137 
Strontium-85 
Barium-133 
1.0 
0.8 
0.6 
0.4 
0.2 
0 
Concentration of tracer relativeto initial concentration, C/C 0 
0 
6 
8 x 106 
12 x 106 
Tritium 
Technetium-95m 
4 x 10Time (s) 
DTN: LA000000000034.001 (Fig. 2) 
NOTE: 
These data for diffusion of tracers in J-13 water and in rock beakers made of tuff sample G4-737 show the 
concentration, C, of tracer (relative to the initial concentration, C0) remaining in the beaker as a function of 
elapsed time. 
Figure 27. Diffusion Data 
The solid lines in Figure 28 are a fit of these same data to the diffusion equation (Equation 16) 
using the TRACRN V1.0 transport code for the two nonsorbing radionuclides, tritium and 
technetium-95m. 
The diffusion coefficients obtained in this manner for these radionuclides for 
all the tuff samples studied (Table 16) agree well with previous results (Rundberg et al. 1987, 
Table VI). 
These two tracers diffuse essentially as tritiated water and the pertechnetate 
anion, 
TcO4  . 
Large anions are excluded from tuff pores because of their size and charge, which can 
account for the lower diffusivity of TcO4  . 
If sorption is linear, reversible, and instantaneous, then F/C is equal to a sorption coefficient, Kd. 
To test this assumption, values of Kd in batch-sorption experiments using the tuffs under study 
(Table 17) were determined. An expected diffusion curve was calculated using, for each tuff, the 
diffusion coefficient measured for tritiated water and the batch-sorption coefficient measured for 
each sorbing radionuclide. Figure 29 shows these calculated diffusion curves for devitrified tuff 
sample G4-737. 
Comparison of the calculated curves with the actual measured data (see the 
example in Figure 30) shows that the concentration of the sorbing radionuclides remaining in the 
rock beaker drops 
faster than predicted on the basis of a linear Kd. 
This result indicates that the 
diffusion of the sorbing radionuclides could not be fitted by assuming reversible, instantaneous, 
and linear sorption. These results also indicate that transport calculations using a batch-sorption 
Kd value and the diffusion coefficient measured for tritiated water will result in conservative 
predictions for the transport of sorbing radionuclides. Note that Cs appears to diffuse much 
faster than the tritium in tritiated water (
Figure 30) because of the 
combined effects of diffusion 
and sorption of Cs, giving a conservative prediction (less apparent diffusion than observed) when 
using HTO diffusion and batch Kds for Cs. 

d = 2 x 106 
4 
) 
d = 4 x 107 
1.0 
0.8 
0.6 
0.4 
0.2 
Concentration of tracer relativeto initial concentration, C/C 0 
Neptunium-237 
0 6 8 x 106 12 x 106 
Tritium (HTO) 
Technetium-95m (TcO4 x 10Time (s) 
DTN: LA000000000034.001 (Fig. 3) 
NOTE: The solid curves are fits to the diffusion data by the TRACRN V1.0 code for the nonsorbing tracers tritium 
and technetium in the rock-beaker experiments with tuff sample G4-737. 
Figure 28. Diffusion Data Curve Fits 
Table 16. Rock-Beaker Diffusion Results for Nonsorbing Radioisotopes and Devitrified Tuffs 
Tuff sample Major minerals Porosity Diffusion coefficient, d (cm2 s1) 
HTO TcO4 
 
G4-737 Alkali feldspar 68% 
Cristobalite 28% 
0.07 2.2 x 106 3.9 x 107 
GU3-304 #1 Alkali feldspar 75% 0.06 1.5 x 106 3.0 x 107 
GU3-304 #2 Cristobalite 25% 1.6 x 106 3.0 x 107 
GU3-433 Alkali feldspar 76% 
Cristobalite 15% 
0.10 3.5 x 106 Not determined 
GU3-1119 Alkali feldspar 70% 
Quartz 19% 
0.10 2.0 x 106 4.9 x 107 
Topopah 
outcrop 
Alkali feldspar 59% 
Cristobalite 23% 
Quartz 12% 
0.07 1.0 x 106 1.0 x 107 
DTN: LA000000000034.002 

DTN: LA000000000034.001 (Table 2) 
Table 17. Batch-Sorption Coefficients for Devitrified Tuffs 
Tuff 
sample 
Majorminerals 
Sorption coefficient, Kd (mL g1) 
Np Am Cs Sr Ba 
G4-737 Alkali feldspar 68% 
Cristobalite 28% 
8 134 532 52 28 
GU3-304 Alkali feldspar 75% 
Cristobalite 25% 
8 no data 342 18 19 
GU3-433 Alkali feldspar 76% 
Cristobalite 15% 
9 154 1264 20 61 
GU3-1119 Alkali feldspar 70% 
Quartz 19% 
8 136 494 42 27 
Topopah 
outcrop 
Alkali feldspar 59% 
Cristobalite 23% 
Quartz 12% 
9 no data 465 20 25 
1.0 
0.8 
0.6 
0.4 
0.2 
0 
Concentration of tracer relativeto initial concentration, C/C 0 
104 105 106 107 108 
d = 2 x 106 
Sorbing tracers: 
d = 2 x 106 
Neptunium: Kd = 8 
x Barium: Kd = 28 
. Strontium: Kd = 52 
Kd = 134 
Cesium: Kd = 532 
Nonsorbing tracers: 
Kd = 0 
d = 4 x 107 
Time (s) 
Tritium 
+ Americium: 
Technetium 
DTN: LA000000000034.001 (Fig. 4) 
NOTE: These curves were calculated for tuff sample G4-737 using the diffusion coefficient, d, measured for tritiated 
water and the batch-sorption coefficients, Kd, measured for the sorbing radionuclides (Table 17). Diffusion 
curves for tritium and technetium are also shown. 
Figure 29. Calculated Diffusion Curves 

1.0 
0.8 
0.6 
0.4 
0.2 
0 
Concentration of tracer relativeto initial concentration, C/C 0 
Calculated 
Kd = 532 
d = 2 x 106 
102 103 104 105 106 107 108 
Actual cesium 
cesium diffusion 
Time (s) 
diffusion 
DTN: LA000000000034.001 (Fig. 5) 
NOTE: 
The solid curve is the diffusion curve calculated for cesium using a Kd value and the diffusion coefficient for 
tritium (Figure 29); the squares are the actual diffusion data for cesium with tuff sample G4-737 (Figure 27). 
Figure 30. Comparison of Calculated and Actual Diffusion Data 
The results obtained from rock-beaker experiments agree with previous results (Rundberg 1987, 
Tables VI, VII). 
Experiments were performed on the uptake of sorbing radionuclides by tuff and 
it was found that rate constants for uptake of the sorbing cations from solution onto tuff were 
consistent with a diffusion-limited model in which diffusion occurs in two stages. In 
the first 
stage, the cations diffuse into rock through water-filled pores; in the second stage, they diffuse 
into narrower intracrystalline channels. This diffusion model yielded sorption coefficients for 
cesium, strontium, and barium, and these values agree well with the sorption coefficients 
determined by batch techniques (Rundberg 1987, Table VII)
. 
6.6.2 
Diffusion-Cell Experiments 
Another experimental technique for deriving the diffusion coefficient is through the use of a 
diffusion cell, in which two chambers containing groundwater are separated by a slab of tuff. 
Radioactive tracers are added to one chamber, and the other (untraced) chamber is periodically 
sampled for the presence of radioactivity. The only driving force in this 
experimental setup is 
the chemical concentration gradient; thus, the solute flux is purely diffusive. The apparent time 
of arrival depends on the porosity, the heterogeneity of the pore structure, the retardation factor 
for a given radionuclide, and the sensitivity of radionuclide measurements. 
The rate of 
concentration increase in the untraced chamber depends on the ionic diffusivity, 
the tuff porosity, 
and the tuff tortuosity/constrictivity factor. Thus, by measuring the movement of sorbing and 
nonsorbing tracers through tuff slabs as a function of time, 
the rock-dependent diffusion 
parameters can be measured. 

This technique was applied to the determination of diffusion coefficients for 3H, 95mTc, natural 
U(VI), 237Np(V), and 239Pu(V) in devitrified and zeolitic tuff. 
6.6.2.1 Experimental Procedures and Data Analysis 
The dimensions of the diffusion cells used are given in Table 18. 
Table 18. Dimensions of Diffusion Cells 
Diameter of tuff slab 6 cm 
Length of tuff slab 1 cm 
Volume of traced chamber 750 cm3 
Volume of untraced chamber 80 cm3 
Source: Weaver et al. (1996), Attachment I, p. 1Reference only 
The two major rock types used for the diffusion-cell experiments were zeolitic tuff (sample 
1362) and devitrified tuff (sample G4-287). The zeolitic tuff has a porosity of 0.4 and a bulk 
density of 1.5 g mL1. The devitrified tuff has a porosity of 0.2 and a bulk density of 2.3 g mL1. 
The major component of the zeolitic tuff is clinoptilolite; the major component of the devitrified 
tuff is alkali feldspar. 
The solutions used for these experiments were prepared by taking an aliquot of a 3H, 95mTc, 
natural U(VI), 237Np(V), or 239Pu(V) acidic stock and diluting it in the water being studied. The 
actinide concentration of the solutions used for the diffusion experiments was very close to the 
solubility limit of the actinides in the groundwaters. At 25C and for nominal pH values between 
6 and 8.5, the experimentally determined solubilities of plutonium range from 2 x 107 M (J-13 
water at a pH of 7) to 1 x 106 M (p#1 water at a pH of 8.5) and of neptunium range from 7 x 10 
6 M (p#1 water at a pH of 8.5) to 5 x 103 M (J-13 water at a pH of 6) (Nitsche et al. 1993, 
Figures 1 and 15; Nitsche et al. 1995, Figures 1 and 9). 
The experimental setup for the diffusion cells can be described by a 1-D diffusion model. Thus, 
Equation 19 (on rock-beaker experiments) can be rewritten as (Bradbury et al. 1986): 
.
2 C .C
D.x2 =a.t 
, (Eq. 21)
e 
where x is the axis along the direction of tracer diffusion, De is the effective diffusivity (= ed), 
and a is the rock-capacity factor (= eRf). This equation yields an analytic solution to diffusion 
through a slab. 
Bradbury et al. (1986) solved Equation 21 for a porous rock. For the experimental setup, the 
boundary conditions can be taken to be: 
 
At x = 0, a constant source concentration, Co, is maintained 

 
At x = L, where L is the tuff-slab thickness, the concentration measured at the initially 
untraced cell, Ct, is much smaller than the source concentration (Ct << Co). 
For these conditions, the total quantity, Qt, diffused through a tuff slab of area A after a time t is 
given by the equation 
Den2p2t
n
8()
a2a-1
-
p
Dt
e
Q
t = 
ALC
o 
L
2 
-
-
L2a 
. 
(Eq. 22)e 
2 
2
6 
n=1 n
As t .8, the asymptotic solution becomes 
ACDACLa
oe o
Q
t = t - . (Eq. 23)
L 6
Consequently, a plot of Qt versus t yields the effective diffusivity, De, from the slope and the 
rock-capacity factor, a, from the intercept on the time axis of the extrapolated linear region. For 
a nonsorbing species, Kd = 0, Rf = 1, and a= e; for a sorbing species, Kd may be calculated from 
the value of a. 
The diffusion coefficient, d, can be calculated from the effective diffusivity (De = ed). The 
difference between the diffusion coefficient, ds, for a tracer diffusing in the solution phase and 
the diffusion coefficient, d, for a tracer passing through tuff pores is given by (Neretnieks 1990, 
p. 23)
d
d = 
t2 d s , (Eq. 24) 
where d is the constrictivity and tis the tortuosity of the tuff pore structure. 
6.6.2.2 Results and Discussion 
The diffusion of 3H, 95mTc, natural U(VI), 237Np(V), and 239Pu(V) through devitrified and zeolitic 
tuffs was studied using water from Well J-13 and synthetic p#1 water. The radionuclides 3H, 
natural U(VI), and 239Pu(V) were studied together in four diffusion cells (devitrified and vitric 
tuff cells, each with both types of water). Likewise, the radionuclides 95mTc and 237Np(V) were 
studied together in another four diffusion cells. Typical results for these experiments are shown 
in Figures 31 to 33. 

0.25 
0.20 
0.15 
0.10 
0.05 
0 
Relative concentration, C/C 0 ,
of radioisotope in untraced cellUranium 
Plutonium 
0 
500 
1000 
1500 
2000 
2500 
Elapsed time (hr) 
Tritium 
DTN: LAIT831362AQ95.001 (SEP Table S99010.001) 
NOTE: 
The data show the concentration in synthetic p#1 water of 3H, 239Pu(V), and natural U(VI) (relative to the 
concentration in the traced cell, C/C0) diffusing through devitrified tuff sample G4-287 into the untraced cell as 
a function of time. 
Figure 31. Tritium, Plutonium, and Uranium Diffusion through Devitrified Tuff 
The results indicate that the diffusion of nonsorbing radionuclides into saturated tuff (illustrated 
by the diffusion of tritiated water in Figures 31 to 33) is slower in devitrified tuffs 
than in zeolitic 
tuffs, probably because of the greater porosity of the zeolitic tuffs. 
Large anions such as 
pertechnetate (which are excluded from the tuff pores by size and charge) diffuse slower through 
the pores than tritium regardless of the groundwater or tuff type (as also observed in the rock-
beaker experiments, Figure 29). The migration of plutonium through tuff under diffusive 
conditions is dominated by sorption (as shown by Figures 31 to 33). 
The migration of Np(V) 
and U(VI) through tuff depends on tuff type and water chemistry. 
In cases for 
which the 
reported sorption of neptunium is essentially zero, such as for devitrified tuff samples (Triay, 
Cotter, Kraus et al. 1996, pp. 14, 18; Triay, Cotter, Huddleston et al. 1996, pp. 3236), the 
diffusion of neptunium through the tuff is slower than the diffusion of tritium but comparable to 
the diffusion of a nonsorbing, large anion, such as pertechnetate (Figure 32). 

ANL-NBS-HS-000019, Rev 00, ICN 02126November 20010.140.120.100.080.060.040.020Relative concentration, C/C0,
of radioisotope in untraced cellTechnetiumNeptunium0500100015002000250030003500Elapsed time (hr)
DTN: LAIT831362AQ95.001 (SEP Table S99010.002)
NOTE:
The data show the concentration in synthetic p#1 water of 95mTc and 237Np (relative to the concentration inthe traced cell, C/C0) diffusing through devitrified tuff sample G4-287 into the untraced cell as a function oftime.
Figure 
32. Technetium and Neptunium Diffusion through Devitrified Tuff0.60.50.40.30.20.10Relative concentration, C/C0,
of radioisotope in untraced cellUraniumPlutonium05001000150020002500Elapsed time (hr)
TritiumDTN: LAIT831362AQ95.001 (SEP Table S99010.001)
NOTE:
The data show the concentration in synthetic p#1 water of 3H, 239Pu(V), and natural U(VI) (relative to theconcentration in the traced cell, C/C0) diffusing through zeolitic tuff sample #1362 into the untraced cell as afunction of time.
Figure 
33. Tritium, Plutonium, and Uranium 
Diffusion through Zeolitic Tuff

6.6.3 Distribution Parameters for Matrix Diffusion Coefficients 
The following distribution parameters for matrix diffusion coefficients (DTN: LA0003JC831362.001) 
were developed based on a qualitative analysis of the data from reviews of the literature and 
results described above in section 6.6.1.3, Table 16 and Figures 29 and 30. For anions, the 
2 2
average matrix diffusion coefficient is 3.2 x 1011 m s1 (3.2 x 107 cm s1) with a standard 
2 2
deviation of 1 x 1011 m s1, a minimum value of zero and a maximum value of 109 m s1 (105 
2 1)
cm s with a Beta distribution. For cations, the average matrix diffusion coefficient is 
221 2
1.6 x 1010 m s1 (1.6 x 106 cm s) with a standard deviation of 0.5 x 1010 m s1, a minimum 
221
value of zero and a maximum value of 109 m s1 (105 cm s) with a Beta distribution. 
6.7 COLLOID-FACILITATED RADIONUCLIDE TRANSPORT 
The potential role of colloids in the transport of radionuclides through the subsurface at Yucca 
Mountain was reviewed by Triay et al. (1997, Chapter V, Section D). These authors pointed out 
that radioactive-waste-derived colloids include the following three types: 
 
Degradation colloids generated directly from the waste form by disaggregation or 
spalling of actinide solid phases 
 
Precipitation colloids generated from solutions supersaturated with respect to actinide 
solid phases, including real actinide colloids produced by the agglomeration of 
hydrolyzed actinide ions, traditionally referred to as radiocolloids 
 
Pseudocolloids generated by the attachment of radionuclides (in soluble or colloidal 
form) to other colloids, such as naturally occurring groundwater colloids consisting of 
inorganic or organic constituents or microorganisms. 
Triay et al. (1997, Chapter V, Section D) concluded that existing data in the literature suggest 
that colloidal species can enhance radionuclide transport in the unsaturated and saturated zones 
but that existing information was inadequate to assess the significance of this transport 
mechanism for Yucca Mountain. The present section summarizes the available data that are 
relevant to Yucca Mountain, including colloid types and concentrations, percent sorbed onto 
various substrates, and attachment/detachment rates for radionuclides interacting with various 
substrates. 
6.7.1 Review of Geochemical Controls on Colloid Stability 
Colloid concentrations in groundwater are a function of the colloid phase stability in the 
hydrochemical system. Key factors that affect colloid stability are pH, redox potential, salt (Na, 
Ca) concentrations, the presence of dissolved organics, and the extent to which the system exists 
at steady state with respect to chemistry and flow (Degueldre, Grauer et al. 1996; Degueldre, 
Pfeiffer et al. 1996; OMelia and Tiller 1993). For an aquifer in a steady-state situation, 
decreases of the concentration of alkali elements (Na, K) below 102 M and of alkali-earth 
elements (Ca, Mg) below 104 M contribute to an increase in the colloid stability and 
concentration (Degueldre, Grauer et al. 1996; Degueldre, Pfeiffer et al. 1996). Mixing of waters 

of different compositions and large concentrations of organic carbon also contribute to an 
increase in colloid stability and concentration. The presence of transient situations, such as 
changes of temperature, flow rate, or chemistry (pH, salt, or redox potential) in the aquifer 
induces larger colloid concentrations. Conversely, high ionic strength waters, low organic 
carbon concentrations, and stable conditions reduce the potential for colloid stability. 
6.7.2 Colloid Concentrations at Yucca Mountain 
Colloid concentration measurements for groundwaters collected in the vicinity of the Yucca 
Mountain site showed that the concentration of colloids in the 50 nm to 200 nm size range 
ranged between 1 x 106 (J-13) and 2 x 109 particles mL1 (UE-25 WT#17) (DTN: 
LA0002SK831352.001, LA0002SK831352.002, LA9910SK831341.005), which is high enough 
to cause concern about colloid-facilitated radionuclide migration in any groundwater at Yucca 
Mountain. 
6.7.3 Review of Sorption Behavior of Radionuclides on Colloids 
The degree of reversibility of radionuclide sorption onto colloids has dramatic implications for 
colloid-facilitated radionuclide migration. Previous results have shown that the transport rate of 
a given radionuclide is not significantly affected if its sorption onto colloids is fully reversible 
(Noell et al. 1998). If the sorption reaction is irreversible, then the retardation properties of the 
radionuclide are determined in part by the stability of the colloid. 
Studies of sorption rates of Pu and Am onto colloids of iron oxide, clays, and silica in 
groundwater show that colloidal Pu(IV), as well as soluble Pu(V), is rapidly sorbed by colloids 
of hematite, goethite, montmorillonite, and silica in both natural and synthetic J-13 and p#1 
groundwaters (DTN: LAIT831341AQ97.002, SEP Table S97458.002). For example, after a 10minute 
contact period, hematite sorbed about 57 percent to 66 percent of Pu(IV) colloids and 44 
percent to 82 percent of soluble Pu(V), whereas goethite sorbed 29 percent to 34 percent of 
Pu(IV) colloid and 19 percent to 63 percent of Pu(V) (DTN: LAIT831341AQ97.002, SEP Table 
S97458.003). In contrast, desorption rates for Pu(IV) and Pu(V) are slow and insignificant on a 
laboratory timescale. After 30 days of desorption, Pu(V) was not desorbed from hematite, and 
less than 0.01 percent of Pu(V) desorbed from goethite (DTN: LA0003NL831352.002). Less 
than 0.01 percent of Pu(IV) colloids was desorbed from hematite, and less than 0.1 percent of 
Pu(IV) was desorbed from goethite. 
Adsorption of 243Am by hematite colloids was faster and higher than by montmorillonite and 
silica colloids (DTN: LA0005NL831352.001). Maximum sorption of 243Am occurred at 1 hour 
for hematite, 48 hours for silica, and 96 hours for montmorillonite. After these time periods, 
partial desorption of 243Am from colloids occurred. With the maximum sorption, Kd values for 
243Am were on the order of 104 mL g1 for silica and 105 mL g1 for hematite and 
montmorillonite. 
These findings suggest that these types of inorganic colloids may facilitate transport of 239Pu and, 
possibly, 243Am along potential flowpaths. Uncertainties in the data summarized in this section 
do not significantly affect these generalizations because the degree to which colloid-facilitated 

radionuclide transport is affected by hydrochemical conditions, colloid stability, and reversibility 
of sorption, has not been quantified and the available data can only be used to indicate expected 
trends. Measured desorption rates were so low as to not be quantifiable over the experimental 
period. However, even a low but finite desorption rate over thousands of years could decrease 
colloid-facilitated radionuclide transport to insignificant levels even though the colloids 
themselves may be transported. The development of a colloid transport model to test this 
conclusion is documented in CRWMS M&O 2000b. 

6.8 BUSTED BUTTE UNSATURATED ZONE TRANSPORT TEST 
FEHM V2.00 (STN: 10031-2.00-00) and STO-UNSAT V1.0 (STN: 10292-1.0LV-00) are used 
for the numerical analyses in Section 6.8 of this AMR. The model describes a meso-scale 
(approximately 12m x 12m x 12m) experiment in the Calico Hills and Topopah Spring units at 
Busted Butte. The FEHM models are deterministic two- or three-dimensional models of two of 
the phases of the Unsaturated Zone Transport Test (UZTT) (Phase 1A and Phase 2). Phase 1B 
has not been modeled for this report. The STO-UNSAT model is a two-dimensional stochastic 
flow representation of the UZTT Phase 1A. The simulations here represent the best knowledge 
at the time for the stratigraphy and hydrogeologic parameters at the site. The model description 
is detailed in Sections 6.8.6 and 6.8.7. 
Visual inspection of model outputs presented in Sections 6.8.6 and 6.8.7 (and comparison with 
the transport behavior expected for sorbing and nonsorbing tracers) confirms that the models 
used in Section 6 of this AMR are appropriate for their intended use. This inspection also 
confirms that the input data, including material properties and Kd values, are appropriate for their 
intended use. 
6.8.1 Overview 
6.8.1.1 Unsaturated Zone Transport Test Location 
The Busted Butte test facility is located in Area 25 of the Nevada Test Site (NTS) approximately 
160 km northwest of Las Vegas, Nevada, and 8 km southeast of the potential Yucca Mountain 
repository area. The site was chosen based on the presence of a readily accessible exposure of 
the Topopah Spring Tuff and the Calico Hills Formation and the similarity of these units to those 
beneath the potential repository horizon. The test facility consists of an underground excavation 
along a geologic contact between the Topopah Spring Tuff (Tpt) and the Calico Hills Formation 
(Tac). This facility also provides access to the contact between the Topopah Spring welded 
(TSw) hydrogeologic unit and the Calico Hills nonwelded (CHn) hydrogeologic unit (which is 
comprised of the nonwelded portion of the basal vitrophyre (Tptpv1) of the Topopah Spring Tuff 
and the Calico Hills Formation). Details of the test configuration are given in Section 6.8.2. 
6.8.1.2 Unsaturated Zone Transport Test Concept 
The test block was located at Busted Butte where the exposure of Calico Hills rocks represents a 
distal extension of the formation located immediately beneath the potential repository horizon. 
Because of its location, the UZTT experimental blocks are in the vitric Calico Hills. This 
location means that the site is not an analog site but, to the best of our knowledge, represents 
both the vitric Calico Hills Formation and the Topopah Spring Tuff units as they exist beneath 
the potential repository horizon west of the Ghost Dance fault. 
The UZTT is comprised of three integrated efforts: the field test, a parallel laboratory-scale 
testing program, and validation and assessment of models used for PA. The field test involves 
design of the test, analysis of the geology, identification of tracer breakthrough using 
geochemical analyses, in-situ imaging of liquid and tracer migration through geophysical 
techniques, and ultimately, destructive testing to identify tracer migration. 

The UZTT was designed for two test phases. The first phase, including test Phases 1A and 1B, 
was designed as a scoping study to assist in design and analysis of Phase 2. The second phase is 
the mesoscale study, which incorporates a larger region than Phase 1 with a broader, more 
complex scope of tracer injection, monitoring, and collection. 
In addition to field testing, parallel laboratory analytical and testing programs in geochemistry, 
tracer evaluation, hydrology, and mineralogy are designed to help interpret the field results. The 
geochemistry program includes measurement of in-situ pore-water chemistry and development 
of a synthetic injection matrix. The tracer evaluation program includes batch-sorption studies on 
Busted Butte samples using Phase-1 and Phase-2 conservative and reactive analog and 
radioactive tracers. The lab program also includes modeling of the geochemical behavior of 
those tracers in the ambient water chemistry. The hydrology program involves the measurement 
of the matric potentials and conductivities as a function of saturation for core samples from 
Busted Butte. The porosity of each sample is also characterized. The mineralogy/petrology 
(Min/Pet) activities involve the mineralogic characterization of the Busted Butte samples from 
cores taken from Phases 1 and 2. When possible, splits from the core samples are used in all 
three characterization programs. 
Because the principal objective of the test is to evaluate the validity of the flow and transport 
site-scale process models used in PA abstractions, a flow and transport modeling program has 
also been implemented. This effort will allow us to update the site-scale flow and transport 
model by simulating and predicting experimental field results and by addressing the effects of 
scaling from laboratory to field scales. Initial predictions of the field tests are included in 
Sections 6.8.6 and 6.8.7. 
6.8.1.3 Unsaturated Zone Transport Test Project Objectives 
The principal objectives of the test are to address uncertainties associated with flow and transport 
in the UZ site-process models for Yucca Mountain. These include but are not restricted to the 
following. 
 
The effect of heterogeneities on flow and transport in unsaturated and partially saturated 
conditions in the Calico Hills Formation. In particular, the test aims to address issues 
relevant to fracture/matrix interactions and permeability contrast boundaries. 
 
The migration behavior of colloids in fractured and unfractured Calico Hills rocks. 
 
The validation through field testing of laboratory sorption experiments in unsaturated 
Calico Hills rocks. 
 
The evaluation of the 3-D site-scale flow and transport process model (i.e., equivalent-
continuum/dual-permeability/discrete-fracture-fault representations of flow and transport) 
used in the PA abstractions for LA. 
 
The effect of scaling from lab scale to field scale and site scale. 

The discussion in Section 6.8 presents relevant data and background on all aspects of the UZTT, 
which is a complex medium-scale coupled field/laboratory/analyses test. Section 6.8.2 presents 
an overview of the design of the test. Section 6.8.3 covers geology and geologic/ 
hydrogeological properties of the units existing in the UZTT test blocks, and Section 6.8.4 
presents the geophysical effort. Geochemistry is discussed in Section 6.8.5. Section 6.8.6 gives 
details of the Phase-1 computational modeling, and 6.8.7 covers computational modeling of 
Phase 2. Model validation is discussed in Section 6.8.8. In Section 6.8.9 the UZ transport 
testing results at Busted Butte are discussed in view of their importance to PA needs to build 
confidence in and reduce the uncertainty of site-scale flow and transport models and their 
abstractions for performance. 
6.8.2. Test Design 
The UZTT is comprised of the main drift tunnel, which is 75 m in length, and a test alcove, 
which is 19 m in length. The configuration of the UZTT site is shown in Figure 34. 
6.8.2.1 Site Description 
Design, construction, and scientific teams were all involved in insuring that the test block itself 
remained undisturbed by construction activities. Minimal disturbance of the in-situ test block in 
the initial stages of unsaturated tracer transport testing was the foremost objective. Shotcrete and 
sodium silicate glass applications to the tunnel walls were coordinated so as to optimize safety 
concerns and testing requirements. Details of the design and construction criteria can be found 
elsewhere (Sub Terra, Inc. 1998, pp. 9oe21, 33oe44). 
The site characterization of the potential test block involved the mapping of the main drift wall, 
core sampling for min/pet, and recovery of samples from outcrops. These samples were used for 
the initial laboratory characterization studies of hydrologic properties and mineralogy. The 
geological context and lithological descriptions of core samples from the test site were used to 
provide further information on the geometry of the beds at the site to guide the construction of 
the tunnel. 
Samples were collected from the dry drilling of the boreholes from the main drift and the test 
adit to provide core samples for geologic, hydrologic, and geochemical laboratory investigations 
and scoping calculations. The boreholes were then surveyed and instrumented for the injection 
tests. Laboratory measurements of hydrologic, mineralogic, and tracer sorption and matrix 
diffusion properties of the core samples collected once the tunnel was excavated are now 
providing important information for predictive modeling studies. 
6.8.2.2 Experimental Design: Test Phases 
6.8.2.2.1 Test Phase 1 
Phase 1 represents a simple test program that serves both as a precursor or scoping phase to 
Phase 2 and as a short-term experiment aimed at providing initial transport data for early fiscal 
year 1999 model updates. Phase 1 involves six single-point injection boreholes and two 
inverted-membrane collection boreholes. All Phase-1 boreholes are 2 m in length and 10 cm in 
diameter. A mixture of conservative tracers (bromide, fluorescein, pyridone, and fluorinated 

benzoic acids (FBAs)), a reactive tracer (lithium), and fluorescent polystyrene microspheres are 
being used to track flow, reactive transport, and colloid migration, respectively. 
Phase 1A, located in the nonwelded Calico Hills (CHn) hydrogeologic unit spanning both the 
geologic Calico Hills Formation (Tac) and the nonwelded subzone of the lowermost Topopah 
Spring Tuff (Tptpv1), is a noninstrumented or blind test consisting of four single-point 
injection boreholes. Continuous injection started on April 2, 1998. Injection rates varied from 1 
mL hroe1 (boreholes 2 and 4) to 10 mL hroe1 (boreholes 1 and 3). The field test was completed 
through excavation by mini-mineback and auger sampling in March/April, 1999. Test 
predictions are included in this report. Initial model predictions associated with Phase 1A 
(presented in Section 6.8.6) were done blind and are meant to test our ability to predict the 
flow and transport results given present YMP databases and modeling capabilities. 
Phase 1B involved both injection and collection membranes. Injection started on May 12, 1998, 
in the lower section of the Topopah Spring Tuff (Tptpv2), and ended November 18, 1998. Phase 
1B involved two injection rates, 1 mL hroe1 in borehole 7 and 10 mL hroe1 in borehole 5. Because 
of the paucity of data on fracture/matrix interactions in these lithologies, this test serves as a 
calibration test for fracture/matrix interactions to be used in Phase-2 conceptual models. 
Geochemical analysis results of Phase 1B are presented and discussed in Section 6.8.5. 
6.8.2.2.2 Test Phase 2 
Phase-2 testing involves a large 7-m high, 10-m wide, and 10-m deep block comprising all the 
lithologies of Phase 1 (Figure 34). Unlike the single-point injection geometries in Phase 1, the 
injection systems in Phase 2 are designed to activate large surfaces of the block. Due to the short 
time frame available for testing, both upper and lower injection planes are used for testing in 
Phase 2. The injection points for this phase are distributed in two horizontal, parallel planes 
arranged to test the properties of the lower Topopah Spring Tuff (Tptpv2) and the hydrologic 
Calico Hills (Tptpv1 and Tac). There are 4 upper injection holes and 4 lower injection holes. 
Note that six upper injection holes were originally drilled, but two were accidentally grouted in 
and so were not used in the test. Phase-2 mixed-tracer solutions include those used in Phase 1 
plus three additional fluorinated benzoic acids (FBAs), a mixture of new reactive tracers (Ni2+, 
Co2+, Mn2+, Sm3+, Ce3+, and Rhodamine WT), and starting in August 1999, an additional 
conservative tracer (Ioe). 
Phase 2 is subdivided into three subphases (2A, 2B, and 2C) according to location and the 
injection rates used. Phase 2A consists of a single borehole in the upper injection plane 
instrumented with 10 injection points and 10 moisture sensors, one at each injection point. The 
injection rate is 1 mL hroe1 per injection point, which corresponds to an overall infiltration rate of 
30 mm yroe1 (Bussod 1998). This borehole is restricted to the Tptpv2 lithology, which consists of 
fractured, moderately welded tuff from the basal vitrophyre. Phase-2A injection began on July 
23, 1998, and is ongoing. A completion date is not fixed but is anticipated around October, 
2000. Results from the ongoing test will be reported as available in further report revisions. 
Phase 2B consists of four injection boreholes in the lower injection plane, each instrumented 
with 10 injection points and 10 moisture sensors, one at each injection point. The injection rate 
is 10 mL hroe1 per injection point, which corresponds to an overall infiltration rate of 380 mm yroe1 

(Bussod 1998). This injection plane is restricted to the Calico Hills Formation (Tac) and is 
meant to activate the lower section of the test block simultaneously with the upper section 
(Phases 2A and 2C). Phase-2B injection began on July 30, 1998, and is ongoing. 
N/A  For illustration purposes only 
NOTE: This schematic of the Busted Butte UZTT shows the relative locations of the different experiment phases and 
borehole locations. 
Figure 34. Busted Butte Unsaturated Zone Transport Test 

Phase 2C consists of three upper injection boreholes, each instrumented with 9 injection points 
and 12 moisture sensors, one at each injection point and two additional sensors located toward 
the borehole collar to detect tracer movement towards the front of the borehole. The injection 
rate is 50 mL hroe1 per injection point, which corresponds to an overall infiltration rate of 1550 
mm yroe1 (Bussod 1998). As in Phase 2A, this injection system is restricted to a horizontal plane 
in the Tptpv2 lithology. Phase-2C injection was initiated on August 5, 1998, and is ongoing. 
A geochemistry-based discussion of the current status of Phase 2 is included in Section 6.8.5. 
Natural infiltration rates at Yucca Mountain vary between 0.01 and 250 mm yroe1 with an average 
of 5 mm yr-1 (Flint et al. 1996). Phase 2A falls within the range of natural present-day 
infiltration rates at Yucca Mountain, whereas Phase 2B lies at the high end of predicted values 
for a pluvial climate scenario. Phase-2C infiltration rates are artificially higher than expected 
natural infiltration rates for the region but provide for the best testing conditions given the short 
duration of the experiment. Further, these high injection rates may provide insight into system 
behavior during unnaturally high flow potentially caused by repository heating. Model 
simulations indicate that even at these high injection rates, the system is expected to remain 
unsaturated. 
The upper injection plane consists of fractured Topopah Spring Tuff Tptpv2. As in Phase 1B, 
this unit represents the base of the TSw basal vitrophyre and is characterized by subvertical 
fractured surfaces representing columnar joints. Thirty-seven injection points distributed along 4 
injection holes (Phase 2A and 2C) approximately 8 m deep each are used for tracer injection 
along a horizontal surface. The natural fracture pattern present in this unit serves as the conduit 
for tracer migration into the non-welded Calico Hills. The lower horizontal injection plane is 
located in the Calico Hills Formation (Tac). There are 40 injection points distributed in 4 
horizontal and parallel boreholes. This test (Phase 2B) is meant to activate the lower part of the 
block in the event that the top injection system does not activate the entire block in the short 
duration of the testing program (2 years maximum). 
Whereas all injection boreholes are located in the Test Alcove, the 12 collection boreholes 
associated with Phase 2 are located in the Main Adit. These boreholes are 8.5 to10.0 m in 
length, and each contains 15 to 20 collection pads evenly distributed on inverted membranes. 
Because of the complexity of the flow fields expected in this block, two techniques [i.e., 
electrical resistance tomography (ERT) and ground-penetrating radar tomography (GPR-T)] are 
used to image the 2- and 3-D saturation state of the block in monthly to bimonthly intervals. 
6.8.2.3 Borehole Injection And Sampling Systems 
Injection and sampling of the liquid tracers was accomplished by two pneumatically inflated 
borehole sealing and measurement systems (Figure 35). To allow visual inspection of the 
injection points under both standard and ultraviolet (UV) illumination, a transparent packer 
system was developed for the tracer-injection systems (Figure 36). Moisture sensing and 
sampling were accomplished using pneumatically emplaced inverting membranes. To 
accomplish moisture sampling in the collection boreholes, inverting membranes were fabricated 

ANL-NBS-HS-000019, Rev 00, ICN 02136November 2001N/A  For illustration purposes onlyNOTE: This figure shows a vertical cross-section of the test configuration.
Figure 35. Injection and Collection System ConfigurationN/A  For illustration purposes onlyFigure 36. Injection-System Transparent Packerwith mesh pockets to retain absorbent sample pads. The inverting membranes are removed fromthe boreholes regularly (as frequently as weekly) for sample-pad removal and replacement,
whereas the injection packers remain in the holes for the duration of the test program. Eachsystem is maintained at slight overpressure (1.7 to 3.5 kPa) to maintain contact between thesampling/injection pads and the tuff and, also, to prevent circulation of air within the borehole.
~10 mInjection ports (~1m spacing)
Absorbent samplers on invertingmembrane system (~1mspacing)
Inverting membraneemplacementcanisterTransparentpneumatic packerassemblyFracture flowMatrix flowFront SealPressurization Line2" Polycarbonate PipePolyethylene Sleeve (6 ml)
Moisture Sensor (typ.)
To Pressurization ManifoldMoisture Sensor Terminal BlockRear Seal 

6.8.2.3.1 Moisture Sensors 
Simple resistive moisture sensors were installed to diagnose the relative moisture state of the 
injection pads and the arrival of liquid tracer at the sampling-pad membranes. These sensors 
consisted of two wires separated a fixed distance apart and embedded in an absorbent-pad 
assembly. Their signal level was sensed by the Campbell Scientific dataloggers, using an 
alternating polarity resistance measurement technique to avoid charge polarization. The sensors 
operate by measuring resistance across the exposed leads of the wires. Moisture absorbed by the 
fabric reduces the resistance between the two exposed wires. The wetter the fabric, the lower the 
resistance. Although the sensor output is not quantitative, the values successfully indicated the 
general state of the sensing location: dry pads before installation tended to be in the 300- to 500kohm 
range, pads equilibrated with the tuff moisture showed 80- to 100-kohm resistance, and 
pads sensing the arrival of the more conductive tracer mixture were distinctly lower in resistance 
at 10 to 30 kohms. These moisture indications were meant to guide the inverting-membrane 
sampling operations (indicating tracer arrival) and diagnose the injection-pad moisture state, 
indicating loss of injection or over injection. 
6.8.2.3.2 Phase-1 and Phase-2 Data Collection 
Campbell Scientific dataloggers are being used to collect measurement data from sensors and 
instrumentation. These data can be used to either help understand or validate the collected 
experimental chemical data or aid in ongoing decisions in conducting the Busted Butte saturated-
flow tracer experiments. Environmental and experimental control data are measured and 
collected with two dataloggers. The data are stored in the dataloggers at user-defined intervals. 
A computer outside the tunnel portal connects to the dataloggers periodically via a short-haul 
modem and downloads the data. The data can then be transferred to a remote computer using a 
phone link and modem. 
Phase 1 
For the Phase-1A Busted Butte test, the dataloggers measured the pressure in the 
injection/sampling manifold, 12 to 14 moisture sensors, the datalogger panel temperature and 
battery voltage, the number of times the syringe pumps cycled in a given period of time, and the 
relative humidity, air temperature, and atmospheric pressure in the experimental area. For the 
Phase-1B test, the same data were collected only for a total of 32 moisture sensors and with the 
addition of an anemometer in the tunnel. 
Phase 2 
For the Phase-2 experiment, over 200 different sensors were measured. The data that are (or can 
be) collected include: 
 
Environmental information, such as ambient pressure, temperature, and relative humidity 
and wind speed in the vent system. 
 
Experimental control information, such as injection pressure, the number of times pumps 
are activated, and relative saturation at injection points, at the face of boreholes or along 
sampling membranes. 

6.8.2.4 Conservative and Reactive Tracers and Microspheres 
To predict the performance of the Calico Hills barrier to radioactive waste migration at Yucca 
Mountain under different percolation flux scenarios, a series of process models in flow and 
transport have been developed by the project based on theory and on field and laboratory studies. 
For viability assessment, site suitability, and licensing, the effectiveness and reliability of the 
geologic barriers will be determined using modeling predictions of radionuclide migration to the 
accessible environment. Measurements on a small scale can be conducted in the laboratory, but 
validating the extrapolation of these data in the presence of larger-scale heterogeneities requires 
field-tracer tests. However, the behavior of actual radionuclides of concern has been extensively 
studied in the laboratory; regulatory and environmental concerns prevent the use of these 
materials in the field. For the Busted Butte field tests, analog conservative and reactive tracers 
are used as surrogates for radionuclides. To validate the use of these tracers and the site-scale 
use of the Kd approach to modeling sorption and the processes of matrix diffusion and colloid 
migration, laboratory batch studies of radionuclide and tracer sorption onto Busted Butte core 
samples have been completed. The tracers were chosen so that conservative, reactive, and 
colloid-like behaviors could be monitored in a single continuous injection scenario. The tracers 
were mixed together to normalize the hydrologic conditions of the injection. The tracer matrix 
was synthetic pore water, which is based on the measured composition of Busted Butte pore 
waters (Section 6.8.5). The recipe for the synthetic water is provided in Section 6.8.2.4.3. 
6.8.2.4.1 Phase-1 Tracers 
Phase-1 tracers were chosen based on the list of tracers permitted for use in the C-wells tests. 
Analog conservative and reactive tracers and colloids are mixed together so as to normalize the 
hydrologic conditions they experience and provide for higher accuracy of the results. The tracers 
used in the Busted Butte experiments of Phase 1 include the following: 
 
Lithium bromide 
 
Fluorescent polystyrene latex microspheres 
 
Sodium fluorescein
 Pyridone (3-carbomoyl-2(1H)-pyridone)
 
2,6-difluorobenzoic acid (2,6-DFBA) 
 
Pentafluorobenzoic acid (PFBA). 
The reactive tracer used is lithium (Kd = 1.0), and the colloid analogs are fluorescent polystyrene 
latex microspheres of two sizes: 0.3 and 1 m diameter. The 2,6-DFBA and PFBA are 
conservative tracers used to tag the various injection boreholes according to injection rates (i.e., 
1 and 10 mL hroe1 rates). Sodium fluorescein and pyridone are UV fluorescent and are used as 
conservative tracer markers that can be detected in the field at a concentration level of 
approximately 10 ppm using UV illumination. Borehole numbers are shown in Figure 37 for 
Phase 1A and Figure 38 for Phase 1B and Phase 2. 

Borehole 1 
Tptpv1 
Calico Hills 
Borehole 2 
Borehole 3 
Borehole 4 
N/A  For illustration purposes only 
Figure 37. Phase-1A Borehole Numbers and Relative Locations 
Phase 1B 
Phase 2 
N/A  For illustration purposes only 
Figure 38. Phase-1B and Phase-2 Borehole Numbers and Relative Locations 

Phase 1A10 mL hr1 Injection Rate; Boreholes 1 and 3: 
 
500 mg kgoe1 lithium bromide 
 
500 mg kgoe1 sodium fluorescein 
 
100 mg kgoe1 2,6-DFBA 
 
1 mL kgoe1 fluorescent polystyrene microspheres. 
Phase 1A1 mL hr1 Injection Rate; Boreholes 2 and 4: 
 
500 mg kgoe1 lithium bromide 
 
500 mg kgoe1 sodium fluorescein 
 
100 mg kgoe1 PFBA 
 
1 mL kgoe1 fluorescent polystyrene microspheres. 
Phase 1B10 mL hr1 Injection Rate; Borehole 5: 
 
500 mg kgoe1 lithium bromide 
 
500 mg kgoe1 sodium fluorescein 
 
100 mg kgoe1 2,6-DFBA 
 
100 mg kgoe1 pyridone 
 
1 mL kgoe1 fluorescent polystyrene microspheres. 
Phase 1B1 mL hr1 Injection Rate; Borehole 7: 
 
500 mg kgoe1 lithium bromide 
 
500 mg kgoe1 sodium fluorescein 
 
100 mg kgoe1 PFBA 
 
100 mg kgoe1 pyridone 
 
1 mL kgoe1 fluorescent polystyrene microspheres. 
6.8.2.4.2 Phase-2 Tracers 
Phase-2 tracers include those used in Phase 1 but with three additional FBAs (2,4-DFBA, 2,4,5-
triFBA, 2,3,4,5-tetraFBA), iodide, a fluorescent reactive tracer (Rhodamine WT), and additional 
reactive ions that serve as analogs for neptunium, plutonium, and americium. (See Figure 38 for 
Phase-2 borehole locations.) 
 
Neptunium Analogs (NpO2+, Np(V)): 
- 
Nickel (Ni2+) 
- 
Cobalt (Co2+) 
- 
Manganese (Mn2+) 
 
Plutonium Analog (Pu3+): 
- Samarium (Sm3+) 
 
Plutonium Analogs (colloidal form): 
- Polystyrene microspheres 
 
Americium Analog (Am3+): 

- Cerium (Ce3+). 
Phase-2 tracer recipes are as follows. 
Phase 2A1 mL hr1 Injection Rate; Borehole 23: 
 
1000 mg kgoe1 lithium bromide 
 
10 mg kgoe1 sodium fluorescein 
 
100 mg kgoe1 2,4,5-TriFBA 
 
10 mg kgoe1 pyridone 
 1 mL kgoe1 microspheres, 
and starting October 7, 1998: 
 
10 mg Loe1 rhodamine WT 
 
10 mg kgoe1 NiCl2fl6H2O (2.47 mg/kg of Ni2+) 
 
10 mg kgoe1 MnCl2fl4H2O (2.78 mg/kg of Mn2+) 
 
10 mg kgoe1 CoCl2fl6H2O (2.48 mg/kg of Co2+) 
 
5 mg kgoe1 SmCl3fl6H2O (2.06 mg/kg of Sm3+) 
 
5 mg kgoe1 CeCl3fl7H2O (1.88 mg/kg of Ce3+). 
On September 30, 1999, the Phase-2A recipe was changed with the elimination of the 
microspheres and the addition of 500 mg kgoe1 potassium iodide. 
Phase 2B10 mL hr1 Injection Rate; Boreholes 24, 25, 26, 27: 
 
1000 mg kgoe1 lithium bromide 
 
10 mg kgoe1 sodium fluorescein 
 
100 mg kgoe1 2,6-DFBA (Borehole #26,Borehole #27) 
 
100 mg kgoe1 2,3,4,5-TetraFBA (Borehole #24, Borehole #25) 
 
10 mg kgoe1 pyridone 
 
10 mg kgoe1 rhodamine WT 
 1 mL kgoe1 microspheres 
and starting September 2, 1998: 
 
10 mg kgoe1 NiCl2fl6H2O (2.47 mg/kg of Ni2+) 
 
10 mg kgoe1 MnCl2fl4H2O (2.78 mg/kg of Mn2+) 
 
10 mg kgoe1 CoCl2fl6H2O (2.48 mg/kg of Co2+) 
 
5 mg kgoe1 SmCl3fl6H2O (2.06 mg/kg of Sm3+) 
 
5 mg kgoe1 CeCl3fl7H2O (1.88 mg/kg of Ce3+). 
On August 18, 1999, the Phase-2B recipe was changed with the elimination of the microspheres 
and the addition of 500 mg kgoe1 potassium iodide. 
Phase 2C50 mL hr1 Injection Rate; Boreholes 18, 20, 21: 
 
1000 mg kgoe1 lithium bromide 
 
10 mg kgoe1 sodium fluorescein 
 
100 mg kgoe1 2,6-DFBA (Borehole #18) 
 
100 mg kgoe1 PFBA (Borehole #20) 
 
100 mg kgoe1 2,4-DFBA (Borehole #21) 
 
10 mg kgoe1 pyridone 

 
10 mg kgoe1 rhodamine WT 
 1 mL kgoe1 microspheres 
and starting September 2, 1998: 
 
10 mg kgoe1 NiCl2fl6H2O (2.47 mg/kg of Ni2+ ) 
 
10 mg kgoe1 MnCl2fl4H2O (2.78 mg/kg of Mn2+ ) 
 
10 mg kgoe1 CoCl2fl6H2O (2.48 mg/kg of Co2+ ) 
 
5 mg kgoe1 SmCl3fl6H2O (2.06 mg/kg of Sm3+ ) 
 
5 mg kgoe1 CeCl3fl7H2O (1.88 mg/kg of Ce3+ ). 
On August 18, 1999, the Phase-2C recipe was changed with the elimination of the microspheres 
and the addition of 500 mg kgoe1 potassium iodide. 
6.8.2.4.3 Synthetic Pore-Water Recipe 
To minimize the reactivity of the tracer solution with the country rock, a synthetic pore water, 
based on measured in-situ composition (Section 6.8.5.2), is used as a matrix for the tracers in 
solution. 
Phase-1 Synthetic Pore Water: 
 
76.8 mg kgoe1 SiO2flnH2O (amorphous silica), 
 
36.8 mg kgoe1 CaCl2fl2H2O (calcium chloride dihydrate) 
 
44.8 mg kgoe1 Ca(NO3)2fl4H2O (calcium nitrate tetrahydrate) 
 
3.8 mg kgoe1 NaF (sodium fluoride) 
 
10.7 mg kgoe1 Na2SO4 (sodium sulfate) 
 
51.2 mg kgoe1 NaHCO3 (sodium bicarbonate) 
 
9.0 mg kgoe1 KHCO3 (potassium bicarbonate) 
 
36.9 mg kgoe1 MgSO4fl7H2O (magnesium sulfate heptahydrate) 
 
7.8 mg kgoe1 Ca(OH)2 (calcium hydroxide). 
Phase-2 Synthetic Pore Water: 
Phase 2 is identical to Phase 1 with the exception that, due to the large quantities required, 
the source of water is J-13 water that has been deionized (DI) using resin cartridges. This 
results in a DI water with approximately 30 ppm Si, so that no additional silica is added. 
6.8.2.5 Use of Numerical Simulations for Test Design 
6.8.2.5.1 Sample Collection Analyses Simulating Performance of a Sampling Pad 
Increasing attention has been paid in recent years to the collection of comprehensive large-scale 
field data. Considerable effort has been expended during the last few decades for various 
modifications in solution sampler design and improved collection techniques, leading to a better 
performance and ease of operation in various sampling conditions. To date, a variety of 
modified sampler types are available depending on their shapes, materials, functions, and 
operations. Unfortunately, little attention has been paid to how such instruments and the related 
operations might influence their surroundings and alter the background flow field and the 
resulting solute transport. 

The objective of this study was to test the performance of the pad sampling system used in 
Busted Butte field test site using numerical experiments. The simulation results were 
subsequently analyzed to evaluate any significant effect the sampler pads may have on the 
interpretation of transport phenomena and the underlying process hypothesis. 
Numerical Experiments 
Numerical simulations were designed to closely approximate the experimental setup of the 
sampler system at the Busted Butte field test site. For the purpose of testing the general 
performance of this new methodology, soil hydraulic properties representing Calico Hills tuff 
(Tac) (DTN: GS990308312242.007, GS990708312242.008) and a loamy sand (Carsel and 
Parrish 1988) were chosen for this study. The simulations were conducted using the FEHM 
V2.00 (STN: 10031-2.00-00) code assuming two-dimensional air-water flow under isothermal 
conditions. 
Model Description 
The performance of the pad sampling system was tested by applying a narrow pulse of solute 
after a sufficient period of constant, uniform irrigation so as to establish a reasonably steady 
water flow field within the simulation domain. The subsequent leaching of solute is observed 
frequently at selected nodes representing a sampling pad during the numerical experiments. In 
general, a pad is replaced periodically after the water potential within the pad reaches 
equilibrium with its ambient flow condition. The time required to reach this equilibrium depends 
highly upon the hydraulic properties of the pad and the soil. 
The simulations were conducted in a homogeneous two-dimensional vertical cross-section of 
2 m by 2 m with a 0.1-m-diameter access borehole located at the center of the domain. A 3.175-
mm thick pad of 0.05-m diameter was located on the inner upper center of the borehole. The 
hydraulic properties of the pad and the two selected soils are given in Table 19. The lateral 
boundaries were assumed to be a no-flux condition for both water flow and solute transport. A 
prescribed water potential was assumed on the surface boundary. The corresponding water input 
rate was calculated internally by FEHM (V2.00, STN: 10031-2.00-00) and used subsequently for 
solute flux calculations. The pulse of solute input lasted for one day. The lower boundary was 
assumed to be a gravity-drained or natural drainage flux condition for water flow. At the bottom 
boundary, solute leaves the system freely with water at a water flux rate equal to the unsaturated 
hydraulic conductivity of a given water potential. 
Table 19. Hydraulic Parameter Sets used to Define the Material Properties for the Simulations 
Material 
[Source] 
Permeability(m2) 
Residual 
saturation 
a 
(m1) n Porosity 
Calico Hills (Tac) 
[DTN: GS990308312242.007, 
GS990708312242.008] 
5 x 1012 0.0001 3.5 1.19 0.50 
Loamy Sand 
[Carsel and Parrish (1988)] 
4.65 x 1012 0.139 12.4 2.28 0.41 
Pad 
[SEA (1992)] 
2.19 x 1011 0.05 17.0 1.12 0.85 
DTN: LA9909WS831372.019 (except loamy sand) 

The pad sampling system contains three major components that can adversely affect the flow 
field and the resulting solute transport. These three components are: the borehole, the pad, and 
the effect due to periodic replacement of the pad. A borehole within the unsaturated flow 
domain acts like an obstacle to water flow (Philip et al. 1989, pp. 16oe28). The collection pad 
disturbs the flow field due to the difference of its hydraulic properties from the neighboring 
porous material even under equilibrium condition with the ambient flow field. Finally, the 
periodic replacement of a new pad causes a highly transient flow condition at least in the vicinity 
of the sampling location. To best evaluate and analyze the influence of the experimental setup 
on the overall behavior of the physical system, each component is tested separately during steady 
water flow conditions. 
Modeling Results 
Water Flow 
Simulations were run for both the Tac and the loamy sand. The background water saturation for 
steady-state flow for Tac was assumed to be 0.35, whereas for the loamy sand, it was 0.5. When 
a borehole is constructed within an unsaturated domain, water tends to build up on the upstream 
side of the borehole and creates different shapes of so-called roof-drip lobes, depending largely 
upon the soil hydraulic properties (Philip et al. 1989, pp. 16oe28). The addition of a pad slightly 
changes the pattern of the water potential (or saturation) distributions. Figure 39 shows the 
saturation distributions for the Tac when a borehole and a pad are added to the system. Notice 
that the contour levels used for plotting the results of a borehole and a borehole plus a pad are the 
same for easy identification of the influences between different tests. Differences in flow 
patterns are not distinguishable for the loamy sand soil for the cases with and without a pad. 
The dynamic responses of both systems to a pad plotted at three selected observation nodes are 
shown in Figure 40. The results show the water extraction rate and the equilibrium time needed 
when a new pad is added to a steady-state system. The background saturation during steady-
state flow was 0.35 for the Tac and 0.5 for the loamy sand. Two observation nodes were located 
within the pad. The rock node in Figure 40 indicates an observation immediately upstream of 
the pad within the porous media domain. The results revealed that for Tac, it took approximately 
10 days to reach equilibrium, whereas for loamy sand, the equilibrium time was less than half a 
day. 
Solute Transport 
Figure 41 shows the solute resident concentrations plotted as a function of time for various 
simulation scenarios for the Tac. Solute concentration is in moles of solute per kg of liquid 
water. The solute transport was delayed when a borehole and a pad were added to the physical 
system. On the other hand, the periodic replacement of a pad causes an early arrival for the 
travel times as illustrated for a step input of solute. The pad was assumed to be replaced every 
week during the simulations. As can be observed on the upper panel of Figure 42, the 
concentrations dropped periodically to a value of zero corresponding to the replacement 
operations. The periodic change of concentrations on a nearby porous rock node, as depicted on 
the lower panel of Figure 42, also shows a reduction of concentration when the soil water 
solution was absorbed into the pad. 

N/iillyDTN:Asmulaton resuts for illustrative purposes on
NOTE: The left frame shows the influence of a borehole and the right frame shows the same condition with the addition of a pad.
Figure 39. Distributions of Saturation under Steady-State Water Flow Conditions Within the Tac
ANL-NBS-HS-000019, Rev 00, ICN 02 145 November 2001 

N/iillyDTN:Asmulaton resuts for illustrative purposes on
Figure 40. Water Saturation as a Function of Time for a New Pad Attached to the System 
ANL-NBS-HS-000019, Rev 00, ICN 02 146 November 2001 

DTN: N/Asimulation results for illustrative purposes only 
NOTE: The solute resident concentrations above were observed at a point corresponding to a position at the 
center of the pad during steady-state water flow conditions in Tac. Concentration is in moles of solute per 
kg of liquid water. The parameter S is water saturation. 
Figure 41. Solute Resident Concentrations in Tac 

DTN: N/Asimulation results for illustrative purposes only 
NOTE: A point injection of 10 mL hr1 with a step input of solute located 30 cm above a collection pad was 
assumed. Concentration is in moles of solute per kg of liquid water. 
Figure 42. Performance of a Collection Pad in Tac 
The dimensionless mean and variance of travel times were plotted as a function of steady-state 
fluid saturation in Figure 43 for Tac. The normalized dimensionless quantities give a relative 
quantification of each separate effect that influences system behavior. For instance, the 
influence of a pad can be evaluated on a relative basis when one normalizes the effect caused by 
a borehole plus pad with the corresponding quantities for a borehole without pad. Notice again 
that the resident concentrations observed as a function of time at a given location do not have an 
obvious physical implication in terms of parameter estimation and moment analysis as discussed 
above. The results revealed that the disturbance of both a borehole and a pad to the solute 
transport is highly dependent upon the fluid saturation of the system. In a natural situation when 
the flow field is inherently transient, the saturation-dependent condition must be integrated into 
the system for the evaluation of the possible influence on solute transport. 

DTN: N/Asimulation results for illustrative purposes only 
NOTE: The plots show influence of a borehole and a pad on (left) the dimensionless mean E*(t)/E(t) and (right) the 
dimensionless variance Var*(t)/Var(t) of solute travel times plotted as a function of fluid saturation for the 
Tac. 
Figure 43. Influence of Borehole and Pad on Solute Travel Times for Tac 
Future Efforts 
The last step of converting this effort to an application tool is to derive relationships between 
measured data and undisturbed true system behavior using available information. After that, this 
methodology can be applied to the Phase-1A and Phase-1B tests using hydraulic properties data 
(DTN: GS990308312242.007; GS990708312242.008) as part of model-calibration and 
validation practices. The calibrated model can then be applied to the Phase-2 test evaluation and 
will help PA represent anthropogenically altered sites more accurately. 
6.8.2.5.2 Performance Measures 
Determining the validity of current concepts for unsaturated-zone flow and transport, and 
simultaneously demonstrating and documenting the improvement of the model as new data are 
incorporated, requires making predictions throughout the different stages of the testing program. 
The cyclic process of prediction, measurement, and model refinement adopted in this work will 
result in increased confidence in the site-scale unsaturated-zone flow and transport model. The 
predictive simulations will serve to document our ability to forecast the experimental results 
using current YMP databases and models. 
Numerical simulations are, therefore, intimately involved in the prediction and analysis of the 
test phases as well as the scaling of parameters for the site-scale models. In support of the 
numerical models, hydrologic, geologic, mineralogic, and geochemical parameters from the test 
block are being measured. Furthermore, the physical state of the block (e.g., saturation and pore-

water chemistry) and the boundary conditions are being measured for incorporation in the 
numerical models. Due to the time constraints of the field test, these measurements are being 
collected in conjunction with the test instrumentation rather than prior to it. However, the 
impacts of this procedure on formal predictions are minimal due to the phased development of 
the testing program. That is, parameters and boundary conditions necessary for the large block 
simulations are being measured during the single-borehole testing phase. 
Scoping calculations, test design, and Phase-1 predictions were done using a high-resolution 
unstructured grid. These predictions are described in Section 6.8.6. For Phase 2, the mapped 
tunnel walls are being incorporated into a 3-D computational grid with 500,000+ nodes for the 
test block using LAGRIT software (V1.0, STN: 10212-1.0-00). A series of numerical 
simulations have been carried out in support of the design of the unsaturated-zone transport tests 
at Busted Butte. These calculations are presented in Section 6.8.7. 
Because of the paucity of data for the properties of the Calico Hills rocks, the predictions are 
likely to produce results having a high range of uncertainty. However, this exercise serves to 
document the full impact of the test at Busted Butte on both YMP databases and models. As 
more data become available, these predictions will be updated and modified. The UZTT 
provides field and laboratory experimental data and modeling analyses that increase the amount 
of data and understanding of the Calico Hills unit. 

6.8.3 Geology, Mineralogy, and Hydrologic Properties 
The unsaturated-zone flow and transport test at Busted Butte was sited in the same stratigraphic 
units as those underlying the potential repository at Yucca Mountain. As mentioned in Section 
6.8.1.2, this was done to achieve optimal applicability of the test results at least to the portion of 
the repository west of the Ghost Dance fault. This section describes the current early state of the 
characterization effort documenting the degree of lithologic, mineralogic, and hydrologic 
correspondence between Busted Butte and western Yucca Mountain. 
One objective of the Busted Butte test, as stated in Section 6.8.1.3, is to investigate the effects of 
heterogeneities on flow and transport in unsaturated rocks. Permeability changes are associated 
with boundaries between rocks of different textures and constituents and with different 
mineralogic alteration (Loeven 1993, pp. 1520). Initial observations of the Phase-1A tracer test 
suggest that this is true for the rock units of the Busted Butte test facility (Section 6.8.5.3.1.1). 
The mineralogic composition of rocks in the test block is a potentially relevant factor in the 
analysis of reactive-tracer movement and the prediction of radionuclide transport. The effects of 
faults and fractures on flow and transport are also a topic of study; no results are available for this 
analysis. The rock-characterization results presented here represent a preliminary stage of the 
complete lithologic analysis. 
6.8.3.1 Geology of the Busted Butte Test Facility 
Busted Butte is a small (2.5 km by 1 km) north-trending mountain block primarily made up of 
thick ignimbrite deposits of the Paintbrush Group. This fault-block uplift is bound by northeast-
and north-trending normal faults, and it is split by a north-trending down-to-the-west normal 
fault that gives Busted Butte its distinctive appearance. Tuff units generally have dips less than 
10 except where affected by drag near large faults. Small windows of older volcanic units 
below the Paintbrush Tuff, including the Calico Hills Formation, Wahmonie Formation, and 
Prow Pass Tuff, are exposed through colluvial deposits on the north and southeast sides of 
Busted Butte. 
The test facility is located within a small horst on the southeast side of Busted Butte. The horst is 
300 to 350 m wide. Geologic units exposed in the vicinity of the test facility include, in 
ascending stratigraphic order, the Wahmonie Formation, the Calico Hills Formation, and the 
Topopah Spring Tuff (Figure 44). The test facility is constructed in the Topopah Spring Tuff and 
the Calico Hills Formation. The Wahmonie Formation, which is not present below the potential 
repository, is similarly absent from the UZTT test block itself (see Section 6.8.3.2). 
A brief description of geologic units in the underground test facility is given below (Bussod et al. 
1997, pp. 3032). Nomenclature and symbols for subunits of the Topopah Spring Tuff follow 
the usage of Buesch et al. (1996, pp. 58) 

N/A - For illustration purposes only 
NOTE: The plot is a geologic map of the area around the underground test facility in the southeastern part of 
Busted Butte. The contour interval is 10 feet. The tunnel entrance is at the southern end of the facility. 
Figure 44. Busted Butte Geologic Map 
6.8.3.1.1 Calico Hills Formation 
Up to several meters of nonwelded Calico Hills Formation are exposed in the test area of the 
facility in the lower walls of both the Main Adit and the Test Alcove. The exposed Calico Hills 
Tuff consists of alternating beds of poorly cemented salmon-pink massive tuff and variably 
cemented, white ash beds. 
The salmon-pink tuffs contain round to slightly elongated white vitric pumices that are generally 
less than a centimeter in diameter. The matrix is a mixture of fine ash, phenocrysts, and locally 
abundant fragments of black glass. The salmon-pink tuffs gradually become more deeply colored 

upsection, suggesting that the upper parts of these units are more oxidized and may represent 
weakly developed paleosols. The clay content of these tuffs is low (see Table 22). 
There are two variably cemented ash beds intercalated with the salmon-pink tuffs in the Main 
Adit and Test Alcove. These ash beds are about 130 cm apart on the right rib of the Main Adit. 
The ash beds are 15 to 20 cm thick and typically form resistant ledges in outcrops outside of the 
test facility and resistant layers inside the facility. 
6.8.3.1.2 Topopah Spring Tuff 
Tptpv1The lowermost 1 to 1.5 m of the Topopah Spring Tuff is nonwelded ash-flow unit 
Tptpv1. The base of Tptpv1 is locally marked by a 3- to 4-cm coarsely bedded ashfall deposit. 
This deposit consists of 0.5- to 3-cm pumice fragments and 0.25-cm black perlitic lava clasts. 
This thin deposit pinches out laterally and is similar to thin discontinuous beds of ashfall deposits 
at the base of the Topopah Spring Tuff in outcrops outside of the test facility. A 4.5-cm-thick, 
crudely laminated shardy tuff overlies the ashfall deposit. The shardy tuff is also discontinous 
laterally. 
Above the thin bedded deposits, Tptpv1 consists of light-gray nonwelded ignimbrites. The 
ignimbrite flow units contain medium-gray pumice clasts in a pink-gray matrix. Near the top of 
Tptpv1, the pumice clasts are tan. Glassy lava fragments and red-brown lithics are common. 
Pumice clasts increase in size and abundance upsection in individual ignimbrite flow units. The 
two lowermost ignimbrite flow units are separated by one or more bedded tuffs 0.2 to 8 cm thick. 
The lithology of the bedded tuffs is variable, consisting of laminated shardy tuff in some places 
and clast-supported pumiceous deposits in others. Because of relief differences on the surface on 
which these bedded tuffs were deposited, they fall within Tptpv1 in the Test Alcove and within 
Tptpv2 toward the back of the Main Adit. 
In the Test Alcove, the upper part of Tptpv1 contains a distinctive zone of clay alteration 
typically about 70 cm thick. The clay occurs both as rinds around pumice clasts and as complete 
replacement of the pumice clasts. The clays are typically reddish brown but also include small 
round bodies of white clay within the reddish-brown clays (giving it a mottled appearance). In 
some replaced pumice clasts, white clay overlies layers of reddish-brown clay. Clay alteration 
also occurs in the tuff matrix and along subhorizontal fractures. One such fracture contains four 
different layers of clay up to 1.5 cm thick. The lower boundary of clay alteration is undulatory 
and has up to 0.5 m of relief. 
Tptpv2Tptpv2 is the highest stratigraphic unit exposed in the back of the Main Adit and in the 
Test Alcove area. It is characterized by tan, partly welded ignimbrite that has well-developed 
columnar joints. The matrix of the ignimbrite has a distinctive salt and pepper appearance due to 
the presence of black glass shards in a tan ashy matrix. Pumice clasts are typically 1 to 6 cm in 
their long dimension and exhibit flattening ratios from 6:1 to 8:1. Welding increases upsection 
through Tptpv2. 

6.8.3.2 Mineralogy of the Busted Butte Locality 
Samples from outcrops were collected at the Busted Butte site for mineralogic and petrologic 
analysis. Some of these samples were also used for determinations for hydrologic properties. 
Other samples were collected from the test block walls throughout the year for the study, and 
descriptions of the lithologies present in the test area were gathered. 
The tables below summarize the mineralogic data for outcrop samples at the Busted Butted test 
locality. Tables 20 and 21 are for the Tpt samples. Table 20 provides stratigraphic descriptions 
for the samples in Table 21. The calcite and gypsum reported in the Tpt samples represent 
pedogenic calcrete contamination in the surface samples. 
Table 20. Descriptions of Outcrop Samples Collected from Busted Butte 
Sample Lithology 
SPC #517962 densely welded perlitic vitrophyre (upper Tptpv3) 
SPC #517963 densely to moderately welded vitric tuff (mid Tptpv2) 
SPC #517964 moderately welded to nonwelded vitric tuff (mid Tptpv1) 
LANL #2814 densely welded perlitic vitrophyre (lower Tptpv3) 
LANL #2815 densely to moderately welded vitric tuff (upper Tptpv2) 
LANL #2816 densely to moderately welded vitric tuff (mid Tptpv2) 
LANL #2817 densely to moderately welded vitric tuff (lower Tptpv2) 
LANL #2818 moderately welded to nonwelded vitric tuff (upper Tptpv1) 
LANL #2819 moderately welded to nonwelded vitric tuff (lower Tptpv1) 
LANL #2820 vitric pumice swarm at base of the Topopah Spring Tuff (basal Tptpv1) 
DTN: LA9909WS831372.005 
The samples from the Tptpv2 and Tptpv1 intervals show that the poorly welded to nonwelded 
vitric portions of the lower Topopah Spring Tuff at this site are largely unaltered, without 
zeolites but with modest smectite occurrences. The Tptpv3 interval, although not part of the 
transport test section, is also largely unaltered. 
The mineralogy of the Calico Hills Formation and the Wahmonie Formation is available from 
two locations at Busted Butte (Tables 22 and 23). The three lowest samples from auger hole 
AUG-1 in the floor of the Busted Butte Alcove were analyzed (Table 23) for comparison with the 
mineralogy of the vitric Calico Hills Formation near the crest of Yucca Mountain (see Section 
6.8.3.2.1). Alteration in the alcove samples shown on Table 23 is generally similar, with 
smectite > clinoptilolite in all three samples. The low biotite and feldspar contents of the AUG-1 
samples are characteristic of the Calico Hills Tuff, indicating that Wahmonie deposits are at least 
396 cm below the present alcove floor and are not expected to have measurable influence on the 
test results. Table 22 presents corroborating mineralogic data for the Calico Hills Formation and 
the Wahmonie Formation from surface samples at the Busted Butte site. These samples are 
arranged in Table 22 by relative depth and generally corroborate the results for the vitric interval 
sampled in AUG-1. The samples also indicate that the lowermost part of the Calico Hills 
Formation (Tac) contains appreciable amounts of 

Table 21. Quantitative X-ray Diffraction Results for Samples from Lower Tpt Section (weight %) 
Sample 
Smectite 
Opal-CT/ 
Cristob. Quartz 
Feldspar 
Glass/ 
Amorph 
Hematite 
Mica/ 
Illite 
Calcite 
Gypsum 
Kaolinite 
Total 
Tptpv3 
SPC #517962 1  1 2  1 tr 4  1 93  2  tr tr   100  2 
LANL #2814p1 1  1 4  1 tr 3  1 92  2  tr    100  2 
Tptpv2 
LANL #2815p1  4  1 1  1 5  1 90  2   tr   100  2 
SPC #517963 1  1  tr 3  1 95  2 tr tr 1  1 tr  100  2 
LANL #2816p1 2  1 4  1 tr 4  1 90  2  tr    100  2 
LANL #2817p1 3  1 3  1 tr 3  1 91  2  tr    100  2 
Tptpv1 
SPC #517964 tr  tr 2  1 95  1  tr 3  1   100  1 
LANL #2818p1 tr 2  1 tr 2  1 96  1  tr tr   100  1 
LANL #2819p1 3  1 1  1 tr 3  1 91  2  tr tr  2  1 100  2 
LANL #2820p1 2  1 1  1 4  1 8  1 84  2 1  1 tr tr   100  2 
DTN: LA9909WS831372.005 
NOTE:  = not detected. tr = Trace abundance of < 0.5 wt %. Errors are conservative 2-sigma values. 
The p1 appended to LANL samples is a designator for the X-ray diffraction split of the sample. 
Table 22. Mineral Abundances (weight %) in Calico Hills 
Formation (Tac) and Wahmonie Formation Surface Samples from Busted Butte 
Sample 
Smectite 
Clinoptilolite 
Crist./ 
OpalCT Quartz 
Feldspar 
Glass 
Hematite 
Biotite 
Total 
vitric Tac 
DEB 3/90-10 1(1)   2(1) 11(1) 86(2)  tr 100(2) 
DEB 3/90-9 6(2)   4(1) 15(2) 74(3)  tr 100(3) 
DTV-97-2 2(1)  1(1) 1(1) 2(1) 94(2)  tr 100(2) 
DEB 3/90-8a   1(1) 1(1) 1(1) 97(2)  tr 100(2) 
DEB 3/90-8b 1(1)  1(1) 4(1) 7(1) 86(2)  tr 100(2) 
DEB 3/90-7 3(1) 1(1) 1(1) 7(1) 12(1) 76(2) tr tr 100(2) 
zeolitic Tac 
DTV-97-3  9(1) 1(1) 7(1) 16(2) 64(3) 1(1) 2(1) 100(3) 
DEB 3/90-6 1(1) 12(1) 1(1) 7(1) 16(2) 62(3)  1(1) 100(3) 
Wahmonie 
DEB 3/90-5 5(2)   1(1) 26(3) 54(5) 1(1) 9(3) 100(5) 
DEB 3/90-4 4(1)   2(1) 24(3) 46(6) 2(1) 17(5) 100(6) 
DTN: LA9909WS831372.006 
NOTE:  = not detected. tr = trace abundance. Numbers in parentheses are 2-sigma standard errors. 
ANL-NBS-HS-000019, Rev 00, ICN 02 155 November 2001 

Table 23. Mineral Abundances (weight %) in Calico Hills Formation (Tac) 
Samples from Auger Hole AUG-1 in the Floor of the Busted Butte Test Alcove 
Sample 
Depth 
(cm) 
Smectite 
Clinoptilolite 
Crist./ 
OpalCT Quartz 
Feldspar 
Glass 
Hematite 
Biotite 
Total 
vitric Tac 
AUG-1-P 375 
383 
3(1) 1(1) 1(1) 8(1) 12(2) 74(3) tr 1(1) 100(3) 
AUG-1-Q 383 
389 
6(2) 1(1) 1(1) 8(1) 18(3) 65(4) tr 1(1) 100(4) 
AUG-1-R 389 
396 
3(1) 1(1) 1(1) 7(1) 11(2) 76(3) tr 1(1) 100(3) 
DTN: LA9909WS831372.010 
NOTE: tr = trace abundance. Numbers in parentheses are 2-sigma standard errors. 
clinoptilolite. The upper part of the Calico Hills Formation at this site, however, is 
characterized more by smectite than by zeolite alteration. Access to both types of alteration is, 
therefore, possible at this site. 
6.8.3.2.1 Mineralogic Comparison with Yucca Mountain: Boreholes H-5 and SD-6 
The excavated section at Busted Butte is in the lower Topopah Spring Tuff and the upper Calico 
Hills Formation (Tac). The vitric nature of this section and the relatively low abundances of 
smectite and clinoptilolite alteration are similar to that in drill holes near the crest of Yucca 
Mountain, such as USW H-5 (Table 24) and USW SD-6 (Table 25). The increase in 
zeolitization at the base of the Calico Hills Formation, particularly in the bedded tuff (Tacbt) 
unit, is comparable to the localized zeolitization in the lower part of the Calico Hills Formation 
at Busted Butte (Tables 22 and 23). The data indicate a distribution of alteration similar to that at 
Busted Butte. In considering the SD-6 and H-5 data, however, it is important to bear in mind that 
both drill holes had only partial core or cuttings recovery, potentially skewing the mineralogic 
information. A much more accurate picture of these poorly indurated vitric units is obtained by 
excavation, as was accomplished at Busted Butte. 
6.8.3.3 Hydrologic Properties 
Samples of the Calico Hills Formation and Topopah Spring Tuff exposed in Busted Butte 
outcrops were used to determine the hydrologic properties of the formations in the test block. 
These results are reported in DTNs: GS990308312242.007 and GS990708312242.008. 

Table 24. Quantitative X-ray Diffraction Results (weight %) for USW H-5 Core and Drill Cuttings 
Sample Depth (m) 
Smectite 
Clinoptilolite 
Mordenite 
Tridymite 
Cristobalite 
Opal-
CT Quartz 
Feldspar 
Glass Mica 
Hematite 
Calcite 
Hornblende 
Total 
Tptpv2 505.7509.6 
Tptpv1 509.6517.9 
Tpbt1 517.9519.7 
Tac 519.7573.0 
1710/1720 522.7 3  2    5  3  5  3 20  10 70  10     103  15 
1750 (DC) 533.4  tr?   5  5  2  3 5  5 85  5     97  9 
1762 (SW) 537.1     1  1  1  1 5  5 95  5     102  7 
1760/1770 538.0  6  1    4  1 3  1 6  1 81  2     100  2 
1800 (SW) 548.6     2  3  2  3 10  5 85  5 1  1    100  8 
1820/1830 556.3 tr 4  1    2  1 4  1 10  1 80  2     100  2 
1852 (SW) 564.5       2  3 7  3 90  5 1  1    100  7 
1875 (SW) 571.5  tr?    tr? 2  3 5  5 92  3     99  7 
Tacbt 573.0592.0 
1890/1900 577.6 1  1 11  1    3  1 4  1 6  1 75  2     100  2 
1900/1910 580.6 tr 10  1    1  1 3  1 8  1 78  2 tr    100  2 
1910/1920 583.7 tr 18  1    5  1 5  1 7  1 65  2 tr    100  2 
1917 (SW) 584.3  25  5    10  5 30  5 35  10  tr    100  13 
1920/1930 586.7 3  1 52  3    7  2 14  1 16  3 6  5 2  1    100  5 
1930 (DC) 588.3 2  3 50  10     15  5 30  5  1  1    98  13 
DTN: LA9909WS831372.007 
NOTE:  = not detected. tr = trace abundance. DC = drill-bit cutting sample. SW = sidewall core sample. 
ANL-NBS-HS-000019, Rev 00, ICN 02 157 November 2001 

Table 25. Quantitative X-ray Diffraction Results (weight %) for Samples from Drill Hole USW SD-6 
Sample 
Depth 
(m) 
LANL 
number a 
Smectite 
Clinoptilolite 
Tridymite 
Cristobalite 
Opal-
CT Quartz 
Feldspar 
Glass 
Hematite 
Mica 
Hornblende 
Calcite 
Total 
Tptpv2 455.7461.5 
1496.5/1496.7 456.2 2984p1     17  4 1  1 11  2 71  4  tr   100  5 
1500.1/1500.2 457.3 2985p1 2  1    17  4 1  1 12  2 68  4  tr   100  5 
1503.3/1503.4 458.2 2986p1 2  1    20  6 2  1 16  2 60  6 tr tr   100  6 
1506.2/1506.3 459.1 2987p1 3  1    18  5 1  1 13  2 65  5 tr tr   100  6 
1509.2/1509.3 460.0 2988p1 3  1    15  4 1  1 11  2 70  4 tr    100  5 
1512.1/1512.2 460.9 2989p1 4  1    14  4 1  1 12  2 69  4  tr   100  5 
Tptpv1 461.5471.7 
1516.9/1517.0 462.4 2990p1 4  1    9  2 1  1 10  1 76  2 tr tr   100  3 
1521.5/1521.6 463.8 2991p1 5  2    4  1 1  1 5  1 85  2  tr   100  3 
1524.9/1525.0 464.8 2992p1 5  2  3  1 2  1  2  1 11  2 77  3 tr tr   100  3 
1527.8/1527.9 465.7 2993p1 3  1  2  1 4  1  5  1 21  3 65  3 tr tr   100  4 
1546.5/1546.6 471.4 2994p1 1  1   1  1  5  1 10  1 83  2 tr tr   100  2 
Tpbt1 471.7473.8 
Tac 473.8526.5 
1560.8/1560.9 475.8 2995p1 b tr 4  1 6  1 10  1  27  2 53  8  tr tr   100  8 
1563.3/1563.4 476.5 2996p2 b  6  1 5  1 13  1  25  2 53  8  tr tr   102  8 
1563.3/1563.4 476.5 2996p1 4  1 4  1  2  1  2  1 7  1 81  2  tr   100  2 
1566.7/1566.8 477.6 2997p1  2  1 1  1 2  1  4  1 10  1 81  2 tr tr   100  2 
1570.3/1570.5 478.7 2998p1  1  1 1  1 3  1  8  1 16  2 71  2 tr tr   100  3 
DTN: LASC831321AQ98.003 
NOTES:  = not detected tr = Trace abundance 
abThe p1 or p2 appended to LANL numbers of samples denote specific X-ray diffraction splits.
The analyses 2995p1 and 2996p2 are from large (> 5 cm) lithic clasts that occur at these depths. 
ANL-NBS-HS-000019, Rev 00, ICN 02 158 November 2001 

6.8.4 Tomographic Studies: Geophysical Techniques at the Busted Butte Unsaturated 
Zone Test FacilityOverview 
Real-time geophysical monitoring techniques may be used to provide real-time data on the 
advance of fluid fronts and tracer fronts through the block and enable us to optimize our 
collection-pad sampling schedule to collect data. Combining two techniques enables the 
collection of detailed, high-resolution, 3-D, calibrated, real-time monitoring of moisture and 
tracer movement through the unsaturated fractured medium. Specifically, electrical resistance 
tomography (ERT) provides 3-D global coverage and ground-penetrating radar tomography 
(GPR-T) provides high spatial resolution. The techniques are listed below, along with their 
characteristics and the advantages they bring to the test program at Busted Butte. 
Electrical Resistance Tomography (ERT): 
 
3-D snapshot of moisture content and tracer front 
 
Covers entire test block 
 
Half-meter spatial resolution 
 
Measurements on demand (probably weekly or monthly). 
Ground Penetrating Radar Tomography (GPR-T): 
 
2-D slices of moisture content 
 
Selected locations through available boreholes 
 
1- to 10-cm spatial resolution 
 
Measurements at opportunistic intervals (tied to collection-borehole sampling frequency) 
 
Can determine matrix permeabilities. 
Ongoing geophysical monitoring is presently helping us guide the timing of collection-pad 
retrieval for detailed tracer analysis. Independent physical measurement techniques (collection 
pads and block mineback) will ultimately provide field-scale evaluation of geophysical 
techniques, increasing their applicability and acceptability for other YMP activities. None of 
these techniques interfere physically with the test or with the testing schedule. 
6.8.4.1 Ground-Penetrating Radar Tomography 
6.8.4.1.1 Experimental Objective and Status 
The objective of the ground penetrating radar (GPR) data acquisition is to monitor the tracer 
injection of the Busted Butte UZTT both spatially and temporally and to investigate the nature of 
fluid migration through the Calico Hills member of the Yucca Mountain lithologic sequence. 
The data collected, analyzed, and submitted to the Technical Data Management System (TDMS) 
thus far include the pre-injection baseline radar velocity measurements as well as the subsequent 
velocity measurements made after the start of tracer injection (seven data collection visits 
through September 1999). Additional measurements shall continue to be made on a regular 
schedule approximately every three to four months. All analyzed data are periodically compared 
to the other available geophysical data as well as to the tracer breakthrough data in order to better 
constrain the interpretation of fluid/tracer migration within the block. 

6.8.4.1.2 Background and Experimental Approach 
The borehole radar method is one in which modified surface radar antennae are emplaced into a 
rock formation and high-frequency electromagnetic signals are transmitted through the formation 
to a receiving antenna. The electrical properties of the subsurface material greatly influence the 
transmitted electromagnetic signal. In particular, the dielectric permittivity of the rock has a 
strong influence on the propagation of the signal and whether it travels at a high or low velocity. 
Moisture content has such an effect. The high dielectric permittivity (.) of water (. ~ 80) or wet 
rock (. ~ 2030) in contrast to drier rock (. ~ 36) typically results in greatly reduced signal 
velocities. Changing chemical compositions (i.e., tracers) may also alter the bulk dielectric 
permittivity of the rock and, hence, the velocity of propagation of the radar wave. Because such 
changes in signal character are what are to be measured over the course of the Busted Butte 
UZTT, any increase (or decrease) in background moisture content or chemical composition 
resulting from the tracer injection (or rock dry-out) should result in changes in the received radar 
wave velocity. 
The transmitted signals may be represented as multiple ray paths crossing through a zone of 
interest within the block. If sufficient ray paths are recorded, a tomographic image may be 
obtained through computer processing. The information extracted from such data consists of the 
transit time, which depends on the wave velocity. This information, in the form of a processed 
radar velocity tomogram, offers a high-resolution approach to monitoring the changes occurring 
in the rock over the duration of the tracer-injection experiment. 
6.8.4.1.3 Equipment Description, Component Specifications, Operating Principles, and 
Survey Methodology 
A description of the equipment used, the component specifications, the operating principles, and 
the GPR-T survey methodology can be found in the Technical Implementing Procedure 
governing all GPR-T data acquisition done in support of the Yucca Mountain site 
characterization effort (YMP-LBNL-TIP/GP 5.0, Rev. 0, Mod. 0). 
6.8.4.1.4 Results of Busted Butte Unsaturated Zone Transport Test Radar Data 
Acquisition 
The radar data were acquired in eight of the Phase-2 collection boreholes orthogonal to the 
direction of the Phase-2 injection boreholes. Additionally, two of the Phase-2 injection 
boreholes were used to acquire data only one time, after the boreholes were apparently affected 
by grout infiltration resulting from nearby electrical resistance tomography (ERT) borehole 
grouting. The 10 boreholes include the following: 9, 11, 13, 15, 16, 46, 47, and 48 (Phase-2 
collection); 19 and 22 (Phase-2 injection). The configuration and layout of the boreholes used 
are illustrated in Figure 38. 
The radar data are acquired in the two-dimensional planes defined by two boreholes, more 
commonly referred to as well pairs. The well pairs acquired in support of the Busted Butte 
UZTT are the following: 15-13, 48-46, 47-11, 46-9, 46-16, and 22-19 (one time only for the last 
pair listed). The decision to acquire data in these particular well pairs was made based on their 

relative proximity to the injection boreholes. Data from both the upper horizontal well pair 1513 
and the vertical well pair 46-16 are acquired to monitor tracer injection associated with the 
upper injection boreholes 18, 20, 21, and 23. Data from the lower horizontal well pairs 46-9, 4711, 
and 48-46 are acquired to monitor tracer injection associated with the lower injection 
boreholes 24, 25, 26, and 27. The vertical well pair 46-16 may also be used to image any tracer 
injection associated with the lower injection boreholes and the progress of the tracer beneath the 
horizontal well pair 15-13. 
Thus far the data collected have been processed for travel times with the result being radar 
velocity tomograms (DTN: LB00032412213U.001). Differencing or subtraction of the velocity 
tomograms over time has also been completed for each of the well pairs. Such differencing or 
subtraction allows for the highlighting of the tracer or moisture front as it changes spatially and 
temporally. In essence, the background formation remains static in those areas not affected by 
the changing tracer or moisture front. By subtracting one velocity tomogram from another, we 
are able to discount those areas remaining static while emphasizing those areas where change is 
occurring. 
Two of the well pairs differ slightly in the acquisition method used between the baseline and the 
post-injection surveys. These well pairs are 46-16 and 46-9. Data for well pair 46-16 was 
collected at a higher frequency (200 MHz) during the post-injection survey to better match the 
data collected in all of the other well pairs. Higher frequencies generally result in data of higher 
resolution (approximately 10.0 cm for 200 MHz), so the highest-frequency antennae should be 
used if at all possible. Data were not originally acquired in well pair 46-9 because it was 
believed that well pair 48-46 provided sufficient coverage in the area of the lower injection 
boreholes. A decision was subsequently made after tracer injection began to gather more spatial 
information below the lower injection boreholes and, hence, well pair 46-9 was added to the 
GPR acquisition list. Also, it should be noted that the pre-injection baseline data for several of 
the well pairs differs significantly from data acquired just one month after tracer injection began. 
The differences are likely the result of changes in the overall block assemblies (e.g. grouting of 
the ERT boreholes, addition of the injection apparatus, etc.) rather than the immediate 
consequence of the tracer injection. In order to enhance the subsequent differencing 
tomography, the baseline set of velocity tomograms chosen are those collected in August-
September 1998 approximately one month after tracer injection began. Comparison with tracer 
breakthrough data on the collection pads indicates that tracer had not yet significantly entered 
those regions imaged by the GPR tomograms. Therefore, it was determined that the August 
September 1998 data would provide an adequate starting point from which to evaluate the 
changes in the block over time. 
Each of the well pairs have witnessed some degree of velocity change over the course of the 
experiment. For the purposes of this AMR, however, only three of the well pairs will be 
discussed in detail: 46-16, 46-9 and 15-13. This is done in an effort to be concise as the results 
for each well pair are essentially similar. Again, all of the data from each of the well pairs have 
been submitted to the TDMS and are available for review. 

Well Pair 46-16 
This well pair represents the only vertical slice through the block (approximately 9.5 m in length 
and 3.5 m in height). It images tracer and moisture contributions from both the upper and the 
lower injection boreholes. When evaluating changes in velocity over time, one would expect 
such changes to occur in the regions directly surrounding the injection boreholes with decreased 
velocities representing areas of increased moisture content. This is exactly what is seen in the 
differenced tomograms. Figures 45 to 47 represent several time steps throughout the course of 
the experiment (dates of data acquisition are noted above each tomogram). As can be seen, 
decreases in the velocity relative to the baseline (AugustSeptember 1998) data are immediately 
obvious surrounding the high and low injection boreholes (these locations are marked on the 
tomogram as small white dots). Furthermore, the zones of decreased velocity can be seen to 
expand away from the injection boreholes over time both in a vertical as well as a horizontal 
direction. Such vertical and horizontal spreading is to be expected as a result of matrix or 
capillary-driven flow and was, in fact, confirmed in the Phase-1A excavation. Precluding a 
similar excavation of the rock included by this well pair, the GPR data would seem to indicate a 
similar mechanism of flow for the Phase-2 block. 
Also of note is the seemingly large extent of decreased velocity. It should be restated that low 
velocities are indicative of zones of higher dielectric permittivity; zones of higher dielectric 
permittivity are indicative of zones of elevated moisture content. That being the case, those 
zones of decreased velocity may represent regions of elevated moisture content and not simply 
the presence of tracer. This subtlety is born out when comparing the tracer breakthrough data 
with the tomography results. The zones of increased moisture content (i.e., decreased velocity) 
do not directly overlay the tracer breakthrough within boreholes 46 or 16. In fact, the locations 
of tracer breakthrough are contained within the zones of decreased velocity. This result implies 
that as the fluid front containing the tracer spreads away from the injection boreholes, some of 
the tracer may be retarded in relation to the spread of the moisture front. In effect, the tracer may 
be moving more slowly through the block than its associated fluid or water component. 
Conversely, the fluid front leaving the injection boreholes may be simply displacing existing 
pore fluids and mobilizing them within the block. The radar velocities are insensitive to this 
effect and are thus incapable of distinguishing between existing pore waters, introduced pore 
waters or tracers. Again, comparing the tomography results with those recorded in the tracer 
breakthrough logs, it appears that some form of fluid breakthrough is occurring in the collection 
boreholes which is not comprised of tracer. This is evidently what is being imaged by the 
differenced radar tomograms and it is not an inconsequential finding. 

DTN: LB00032412213U.001; MO0004GSC00167.000 (for location) 
Figure 45. Tomography (GPR-T) Results for Well Pair 46-16, December 1998 
DTN: LB00032412213U.001; MO0004GSC00167.000 (for location) 
Figure 46. Tomography (GPR-T) Results for Well Pair 46-16, March 1999 

DTN: LB00032412213U.001; MO0004GSC00167.000 (for location) 
Figure 47. Tomography (GPR-T) Results for Well Pair 46-16, April 1999. 
Well Pair 46-9 
This well pair represents a horizontal slice (approximately 8.0 m in length and 2.6 m in width) 
through the block and images the tracer/moisture front associated with the lower injection 
boreholes. Figures 48 and 49 represent two time steps throughout the course of the experiment 
(dates of data acquisition are noted above each tomogram). As can be seen, decreases in the 
velocity relative to the baseline (August-September 1998) data are immediately obvious 
surrounding the low injection boreholes (these locations are marked on the tomogram as 
orthogonal tubes). Furthermore, the zones of decreased velocity can be seen to expand away 
from the injection boreholes over time in a horizontal direction. Because a horizontal well pair 
cannot capture the vertical flow of moisture away from the boreholes, only the extent of the 
horizontal flow can be imaged. What is observed is that the decrease in velocity (i.e., the 
increase in moisture content), moves rapidly away from the injection boreholes early on in the 
experiment and then remains relatively constant aside from some localized changes. This would 
seem to imply that much of the moisture front moves away from the injection apparatus to its 
greatest possible horizontal extent at which time it can no longer spread in such a direction. 
Presumably, the majority of fluid flow from this time on continues in a direction other than 
horizontal. 

ion)DTN: LB00032412213U.001; MO0004GSC00167.000 (for locat
Figure 48. Tomography (GPR-T) Results for Well Pair 46-9, December 1998. 
ion)DTN: LB00032412213U.001; MO0004GSC00167.000 (for locat
Figure 49. Tomography (GPR-T) Results for Well Pair 46-9, April 1999. 

The results implied by the radar tomograms are in concurrence with the tracer breakthrough logs 
for boreholes 46 and 9. Again, those zones of decreased velocity overlay those locations in the 
boreholes where tracer has been seen to break through. The additional contribution of the 
moisture front relative to the tracer (as described above for well pair 46-16) does not appear to be 
as significant for this horizontal well pair. It is not yet clear whether this is because the region 
imaged is smaller or spatially closer to the injection boreholes. 
Well Pair 15-13 
This well pair represents a horizontal slice (approximately 9.5 m in length and 2.0 m in width) 
through the block and images the tracer/moisture front associated with the upper injection 
boreholes. Figure 50 represents one time step throughout the course of the experiment (dates of 
data acquisition are noted above each tomogram). As can be seen, decreases in the velocity 
relative to the baseline (August-September 1998) data are immediately obvious surrounding the 
upper injection boreholes (these locations are marked on the tomogram as orthogonal tubes). 
Furthermore, the zones of decreased velocity can be seen to expand away from the injection 
boreholes over time in a horizontal direction. What is observed is that the decrease in velocity 
(i.e., the increase in moisture content), moves steadily away from the injection boreholes 
throughout the course of the experiment. This varies a bit from the analogous well pair 46-9. 
Rather than reaching a maximum extent, the moisture front appears to be continually expanding 
away from the boreholes. This is probably the result of the well pairs increased distance 
beneath the injection boreholes and the much larger volume of fluid being introduced by the 
upper injection boreholes. 
The results implied by the radar tomograms are in concurrence with the tracer breakthrough logs 
for boreholes 15 and 13. Again, those zones of decreased velocity overlay those locations in the 
boreholes where tracer has been seen to break through. The additional input of the moisture 
front relative to the tracer (as described above for well pair 46-16) does not appear to be as 
significant for this well pair. As for well pair 46-9, it is not yet clear whether this is because the 
region imaged is smaller or spatially closer to the injection boreholes. Also, the much larger 
volume of tracer injected into the region of this well pair may account for the lack of a 
discrepancy (i.e. there is simply more tracer in the area of the collection boreholes). 
Additionally, the neutron probe data collected in these two boreholes implies a very similar 
pattern of elevated moisture content. Those zones that appear to be wetting as well as those that 
remain dry agree nicely with the same regions on the tomograms. The ERT data in this region 
appear to indicate an area of changing resistivity with time. It remains to be seen if this 
anomalous zone of resistivity correlates with the zone of decreased velocity imaged by the radar 
tomograms. Further analysis is planned to resolve this issue. 

DTN: LB00032412213U.001 
Figure 50. Tomography (GPR-T) Results for Well Pair 15-13, April 1999 
6.8.4.1.5 Conclusions 
The radar data collected thus far in support of the Busted Butte UZTT suggest that the method is 
an appropriate one for investigating subsurface velocity anomalies that may be related to tracer 
injection and moisture migration. Such anomalies are the result of changes in the dielectric 
permittivity of the rock mass. As noted above, such changes are most likely the result of some 
combination of the injected tracer and its associated fluid component. The regions of low 
velocity (i.e. elevated moisture content) appear to be in very close agreement with the other 
complementary evidence, including the tracer breakthrough logs and the neutron logging results. 
At this time, it appears very likely that the differenced radar tomograms are defining the total 
extent of elevated moisture content within those zones defined by the radar well pairs. By 
defining the extent of this front, the radar tomography should provide an excellent control 
mechanism for any planned excavation of the Phase-2 block or any hydrologic flow modeling 
done to date. 
6.8.4.2 Electrical-Resistance Tomography 
6.8.4.2.1 Experimental Objective 
The objective of this work is to provide 3-D electrical-resistance tomography (ERT) images of 
the movement of a tracer through the test block at the UZTT at Busted Butte. ERT was chosen 
as an appropriate technology based on its success at many other locations, including the Drift 
Scale Test at Yucca Mountain. This report describes the results obtained during four separate 
data collections starting in July and ending in early September 1998. 

6.8.4.2.2 Description of the Electrical-Resistance Tomography Method 
Electrical-resistance tomography (ERT) is a new geophysical imaging technique that can be used 
to map subsurface liquids as flow occurs during natural or man-induced processes and to map 
geologic structure. Man-induced processes, such as tank leaks and clean-up processes such as 
steam injection, can create changes in a rocks electrical properties that are readily measured. 
ERT is a technique for reconstruction of subsurface electrical resistivity. The result of such a 
reconstruction is a 2- or 3-D map of the electrical resistivity distribution underground made from 
a series of voltage and current measurements from buried electrodes. The ERT approach we 
follow here relies on detection and mapping of the changes in electrical resistivity associated 
with the movement of a tracer through the test block at the UZTT site. 
ERT surveys are performed using a number of electrodes in boreholes and/or at the rock surface 
to image the resistivity distribution between two boreholes. Using an automatic data-collection 
and switching system, we collect a few hundred electrical-resistance measurements. The data 
are then processed to produce electrical-resistivity tomographs using state-of-the-art data-
inversion algorithms. We use these measurements to calculate tomographs that show the spatial 
distribution of the subsurface resistivities. 
6.8.4.2.3 Description of 2-D Algorithms 
Finite-element Iterative Algorithm 
This algorithm involves solving both the forward and inverse problems. The forward and 
inverse algorithms used in this work are described in detail by LaBrecque et al. (1996, pp. 538 
548) and summarized below. The solution to the forward problem uses the finite-element 
method (FEM) to compute the potential electrical response of a 2-D earth to a 3-D source. To 
avoid the difficulty of numerically solving a 3-D problem, Poissons equation is formulated in 
the wave-number domain using the Fourier transformation in the strike (y) direction. The 
governing equation is: 
.
.x 
s
.x 
.V
+
.
.z 
s
.z 
.V
 .2sV =Idx(),
()dz
(Eq. 25) 
where V is the potential in the Fourier transform domain, sis the conductivity, .is the Fourier-
transform variable, I is the source current, and d(x) is the delta function. 
Using the FEM, we can calculate the potentials for a discrete number of transform variables at 
the nodes of a mesh of quadrilateral elements. We can then transform the potentials back into 
the Cartesian domain. The boundary conditions are assumed to be Neumann (zero potential 
gradient, no vertical current flow) at the ground-air interface and Dirichlet (potential set to zero) 
along the other three boundaries. 
The inverse algorithm iteratively finds the maximum value of the stabilization parameter ato 
minimize the objective function for the stabilization parameter: 
Y P2 P() (Eq. 26)
()=.()+aW P

.
where P is the vector of unknown parameters, W(P) is the roughness operator, and .2 is the chi-
squared statistic. Minimizing this function yields a value of .2(P) equal to an a-priori value, 
2a-priori, which in our work, is assumed to equal the number of data points. The inverted 
parameters are the natural logarithms of the conductivities of pixels, where each pixel contains 
the elements of a rectangular region of a FEM mesh. The chi-squared statistic is given by: 
2 TW1[D
()] FP
.=[D FP()] (Eq. 27) 
where D is the vector of known data values, F(P) is the forward solution, T signifies transpose, 
and W is the data covariance matrix. 
The roughness operator stabilizes and removes ambiguity in the resistivity inversion by 
minimizing the model roughness, which is referred to as smoothest inversion. The roughness 
operator W(P) is given by: 
W P(), (Eq. 28)
()=PTRP
where, here, R is the roughness matrix. 
At the ith iteration, our algorithm begins by approximating the forward solution by a first-order 
Taylors series of the form: 
()=FPi(
FP()+AP-Pi ), (Eq. 29) 
where F(P)is the forward solution, A is the sensitivity matrix, and Pi is the vector of estimated 
parameters at the ith iteration. 
Using a root-finding algorithm, we estimate a for this linearized system. We then use a 
modified Marquardt method iteration to find the parameters that minimize the objective function 
(Equation 26) for the estimated value of a. Iteration is repeated until the changes in aand .2 
from one iteration to the next are suitably small. 
Single-pass Image Reconstruction 
The computational demands and potential convergence failure of a formal inverse solution such 
as that above has prompted the development of a number of image-reconstruction algorithms that 
are purely qualitative. We use an algorithm that computes a gray scale associated with each 
element j =1, 2, ... m according to a simple matrix-vector product: 
'
Pj = in 
=1 Si , j ln(Ti /Ti) j = 1, 2, .... m (Eq. 30) 
' where n is the number of independent measurements, Ti and T i are the ith-measured boundary 
transfer resistances before and after a change in resistivity within the region, and Si,j is a 
sensitivity coefficient for element j and independent measurement i derived in the same manner 
as the smoothness algorithm in the previous section. 

The sensitivity matrix is computed on a finite-element mesh with uniform resistivity. Because 
no inverse matrices are required, the algorithm is computationally efficient and very fast as only 
one matrix vector product is required for each image. 
6.8.4.2.4 Description of the 3-D Imaging Algorithm 
Our 3-D inversion algorithm requires a forward solution, which can numerically solve the 
potential equation: 
.
.x 
s
.x 
. 
+ 
.
.y 
s
.y 
. 
.
.
s
.z 
V
= 
I(x, y,z) 
, (Eq. 31)
V 
V 
+
.z 
where V is the scalar electrical potential and I(x,y,z) is the distribution of electrical current 
sources and sinks. We convert the differential equation (Equation 25) into a system of linear 
equations. This system of equations is then solved iteratively using the diagonally weighted 
preconditioned-conjugate-gradient method. The boundary conditions are assumed to be 
Neumann (zero potential gradient, no vertical current flow) at the ground-air interface and 
Dirichlet (potential set to zero) along the other five boundaries. 
Three-dimensional inversion is by nature strongly underdetermined, and so, inverse solutions 
that consider only the fitting of the forward model to field data are nonunique. Therefore, we 
implemented a regularized solution that jointly minimizes the misfit of the forward model to the 
field data and stabilizes the inverted value of the parameters. To find the optimal value of the 
parameter vector P, our algorithm finds the maximum value of a, the stabilization parameter, 
for which minimizing: 
Y(P) = .2(P) + a PTRP (Eq. 32) 
gives 
2
.2(P) = .a-priori (Eq. 33) 
In Equation 32, we have chosen to use, R, the solution roughness, as the stabilizing functional. 
This parameter is approximated by: 
R = xTx + yTy + zTz , (Eq. 34) 
where x, y, and z are the first-order difference operators in the x, y, and z directions. Also in 
2
Equation 33, .a-priori is equal to the number of data points, and .2 is given by: 
.2 = (D  F(P))TW(D  F(P)) , (Eq. 35) 
where D is the vector of known data values, F(P) is the forward solution and W is a data weight 
matrix. The diagonal elements of W are the reciprocals of the data variances and the 
nondiagonal elements are zero. This assumes noncorrelated data errors. 

The parameters, P, are the natural logarithms of the conductivity of the FEM elements. In the 
foreground (the part of the FEM mesh between the boreholes and near the boreholes), each 
parameter corresponds to a single finite element. In the background (the region away from the 
boreholes), we lump several finite elements together into a single parameter. 
The nonlinear inversion is carried out iteratively as: 
P
k +1 = Pk +.Pk , (Eq. 36) 
where Pk is the vector of parameters from the previous iteration and .Pk is the parameter change 
vector. The .Pk vector is found by solving the linear problem: 
-1 
T
.Pk =(A k WA k +aR)(W .Dk -aRPk ), (Eq. 37) 
where Ak is the sensitivity matrix and .Dk = F(P) -D . The elements of the sensitivity matrix, 
ai,j, are: 
Pk(Eq. 38)
a
i, j =
.Fi () ,
.pj
where pj is the jth element of Pk and Fi(Pk) is the forward solution for the ith data point. Solving 
Equation 37 exactly is not practical because the system is very large (50,000 by 50,000), full, and 
ill-conditioned. Instead, we use the conjugate-gradient method described by Mackie and 
Madden (1993, pp. 215219) to give a stable, approximate solution to this linear system. The 
details will not be repeated here, but note that the solution does not require the calculation of the 
full sensitivity matrix, only the calculation of the sensitivity matrix and its transpose multiplied 
by a vector. 
Our routine differs from that of Mackie and Madden in three ways. First, we use a method 
similar to that described by Rodi (1976, pp. 483506) to calculate ATu and Av where u and v are 
vectors. Since we calculate a forward model for every electrode, this method does not require 
any additional forward solutions during the conjugate gradient iterations. Second, we use more 
conjugate gradient iterations than Mackie and Madden (1993). For the magnetotelluric inverse 
problem, Mackie and Madden found that the nonlinear inversion routine converged well with 
three conjugate gradient iterations. We usually require between 10 and 40 conjugate gradient 
iterations to achieve adequate convergence. Third, we use smoothness instead of comparison 
with an a-priori model to stabilize the inverse solution. 
We found that choosing the correct value of a is critical for both achieving rapid convergence of 
the nonlinear inversion and for finding a good final parameter estimate. With our method, a new 
value of a is estimated at the end of each nonlinear iteration. The estimate uses the assumption 
that the relation between .2 and a can be approximated by the rational function: 

.
2 . 
b a 
. (Eq. 39)
a+ a
The constant a is estimated from the values of a and .2 of the previous iteration. If the misfit is 
2
.
2 
k and the desired misfit is .target, then the new estimate of a, ak+1, is: 
b 
-1
2 
=
.k ak . (Eq. 40)
a
k +1 b -1 
.
2 
target 
22
The value of .2target is chosen as the larger of .a-priori and 0.5.k. 
Although the approach is simplistic, it usually converges to the correct value of .2 in 10 to 20 
iterations. 
6.8.4.2.5 Electrical-Resistance Tomography Data-Collection and Processing Codes 
Computer codes are used for both data collection and processing/presentation. Figure 51 
contains a block diagram flow chart summarizing these codes and a description of how they are 
used. 
6.8.4.2.6 Electrical-Resistance Tomography Data-Collection System 
As shown in the block diagram of Figure 52, the data-collection system is composed of three 
main parts: a transmitter, a receiver, and a laptop computer to control the system and archive the 
data. The action of the system can be described briefly, as follows. The computer sets the 
switches in the MX-30 multiplexer according to a predefined schedule so as to connect the 
transmitter to a particular electrode pair and the receiver channels in the GDP-32 receiver to 
other sets of electrode pairs. The GDP-32 tells the ZT-30 to apply current to the transmitter pair 
and measures the resulting potentials at the other pairs. The data are then sent back to the 
computer and stored. A new transmitter pair is selected according to the schedule, and the 
process begins again until all the combinations in the schedule have been used. 

N/A  For illustration purposes only 
Figure 51. Flow Chart of Computer Codes Used for Electrical-Resistance 
Tomography Data Collection and Processing at Busted Butte 
ANL-NBS-HS-000019, Rev 00, ICN 02 173 November 2001 

N/A  For illustration purposes only 
NOTE: This block diagram illustrates the ERT data-collection system used at Busted Butte. 
Figure 52. Electrical-Resistance Tomography Data-Collection System 

6.8.4.2.7 Results of Data CollectionsJuly to Early September 
ERT data were collected four times: July 2, July 14, August 19, and September 9, 1998. The 
intent was to make comparisons between the baseline condition on July 2 and data collected at 
later times. Comparisons between July 2 and August 19 and between July 2 and September 9 are 
presented because the data from July 14 were of questionable quality. These data have been 
submitted to the YMP Technical Data Management System (DTN: LL990612704244.098). 
Sixty ERT electrodes were installed in the test block as shown in Figure 53. The electrodes were 
placed in six drilled holes, ERT-1 though ERT-6, and two surface arrays (upper and lower). 
Holes ERT-3, 4, 5, and 6 and the surface arrays were drilled perpendicular to and from the 
instrumentation alcove. Holes ERT-1 and 2 were drilled from the main drift. The electrodes 
were grouped into boreholes 1 through 4 as shown in Figure 54. As is evident, each borehole is 
L-shaped and contains 15 electrodes. For example, borehole 1 is composed of the 8 electrodes in 
hole ERT-3 along with the 7 electrodes in the upper-surface array. 
N/A - For illustration purposes only 
NOTE: This diagram gives the layout of drilled holes, ERT electrode locations, and spacing in the UZTT test block 
at Busted Butte. 
Figure 53. Electrical-Resistance Tomography Layout 

N/A  For illustration purposes only 
NOTE: This diagram gives the layout of the ERT boreholes and electrode assignments in the UZTT test block at 
Busted Butte. 
Figure 54. Electrical-Resistance Tomography Electrode Assignments 
The ERT data are collected between borehole pairs. Thus, the data are collected between 
boreholes 1 and 2 (upper horizontal plane), 3 and 4 (lower horizontal plane), 1 and 3 (left vertical 
plane), 2 and 4 (right vertical plane), 1 and 4 (diagonal), and finally 2 and 3 (diagonal) for a total 
of six data sets. The total number of data values collected is 2430. These 2430 values provide 
the 3-D sampling of the test block resistivity, and the 3-D inversion algorithm operates on these 
data to produce a reconstruction of the 3-D resistivity distribution, a 3-D ERT image, of the 
block. 
One could look at absolute ERT images or comparison images. It is most useful to look at 
comparison images when changes are taking place over time. The results presented here 
consider difference images that compare the resistivity of the block on August 19 and September 
9 to July 2. Because the water injected during Phase 2 of the UZTT experiment was 
approximately eight times more conductive than the pore water, resistivity decreases in the 
images are of interest. 

6.8.4.2.8 Absolute Electrical-Resistance Tomography Images of the Block 
Figure 55 shows an absolute image of the baseline condition of July 2 (top) and the difference 
between August 19 and July 2 (bottom). The baseline image shows a layered structure consistent 
with the lithology in the rear half of the block. That is, a high-resistivity layer over most of the 
middle of the block, Tptpv1, with a lower-resistivity region, Tptpv2, at the top, and a low-
resistivity region, Tac, at the bottom. The image also shows an anomalously low resistivity 
region in the front half of the block, particularly near the bottom. 
6.8.4.2.9 Difference Electrical-Resistance Tomography Images of the Block 
The difference image of Figure 55 shows regions of resistivity decrease near injection holes 18, 
20, and 21, as one would expect from the injection of conductive water. It is apparent that a 
pronounced resistivity decrease exists in the slice 2.66 m from the front of the block, which 
could be associated with water moving downward in the block. The region of the block between 
1.33 and 4.0 m, which contains this slice, also appears to be a low-resistivity region in the 
absolute image. 
The September 9 to July 2 difference (Figure 56) also shows regions of resistivity decrease near 
injection holes 18, 20, and 21. The effect is even stronger in the 5.33-m slice. Moreover, the 
effect of water moving down into the block seems to be more pronounced in the 
4.0-m slice compared to August 19. 
6.8.4.2.10 Conclusions 
The ERT baseline images show a resistivity structure that is consistent with the known lithology 
in the rear part of the block. There appears to be a low-resistivity region in the front half of the 
block, particularly near the bottom. This is not well understood and should be confirmed, if 
possible, by other means. 
The difference images from August 19 and September 9 show clear and consistent resistivity 
decreases in the region near holes 18, 20, and 21 that can be associated with the injection of 
conductive water. This effect appears to be stronger on September 9 in the 5.33-m slice. The 
images show very little effect in the region around the other injection holes, 23 and 24 through 
27, where far less water was injected. 
In addition, the difference images from August 19 and September 9 show resistivity decreases 
that could be interpreted as water moving down into the block between the 1.33-m and 4.0-m 
slices. This is the same region that has an anomalously low resistivity in the baseline image. 
These results should be considered preliminary and subject to change based on new information, 
such as borehole radar data and, perhaps, neutron data. 

(g) 
(g) 
Borehole numbers 24 
to 27 see Fiure 50Borehole numbers 18 
to 23 see Fiure 50
DTN: LL990612704244.098 
NOTE: The diagram shows vertical slices through block at 0, 1.33 2.66, 4.0, 5.33,6.66, and 8.0 m. The top series is an absolute image (baseline, July 2), and the 
bottom series is the August 19July 2 difference images. 
Figure 55. Electrical-Resistance Tomography Images of Test Block Viewed from Test Alcove: Baseline and August Differences 
ANL-NBS-HS-000019, Rev 00, ICN 02 178 November 2001 

(see Fig) 
(see Fig) 
DTN: LL990612704244.098 
Borehole numbers 18 
to 23 ure 50Borehole numbers 24 
to 27 ure 50
NOTE: The diagram shows vertical slices through the block at 0, 1.33, 2.66, 4.0, 5.33, 6.66, and 8.0 m that represent September 9July 2 difference images. 
Figure 56. Electrical-Resistance Tomography Images of Test Block Viewed from Test Alcove: September Differences 
ANL-NBS-HS-000019, Rev 00, ICN 02 179 November 2001 

6.8.5 Geochemistry and Tracer Migration - Laboratory and Field Tests 
This section discusses all aspects of chemical and geochemical measurements that have been 
conducted in association with the Busted Butte project. These include laboratory measurements 
of sorption of both radionuclides and tracers onto Busted Butte rock samples, measurements of 
in-situ pore-water chemistry used in formulating the field tracer mixture, and measurements of 
field-scale tracer transport. 
6.8.5.1 Laboratory Sorption Studies 
As discussed in Section 6.8.2.4, analog conservative and reactive tracers are used as surrogates 
for radionuclides. To validate the use of these tracers and the site-scale use of the minimum-Kd 
approach for sorption and the processes of matrix diffusion and colloid migration, a series of 
laboratory batch-sorption studies have been conducted. Preliminary tracer sorption studies used 
in tracer selection are complete, detailed radionuclide sorption studies are complete, and detailed 
tracer sorption studies are in progress. Each type of study will be discussed in turn. 
6.8.5.1.1 Preliminary Studies 
A large number of possible tracers were proposed in the Busted Butte work plan. Final 
determination of tracer selection and concentration was dependent on both rock and pore-water 
characteristics. Rock and pore-water samples became available in early 1998, and a set of fast-
turnaround batch studies and geochemical modeling efforts were initiated. 
Preliminary batch-sorption studies were conducted using proposed reactive tracers and two rock 
samples from the Main Adit at Busted Butte. Tracers tested included lithium, manganese, cobalt, 
nickel, molybdate, and perrhenate (this list differs from the final list of tracers used in the field 
and described in section 6.8.2.4.1 and 6.8.2.4.2); rocks were samples of the Calico Hills (Tac) 
and Topopah Spring (Tptpv2) from Phase-1 boreholes 4 and 7. The results of the preliminary 
sorption studies for lithium, manganese, cobalt, and nickel are presented in Table 26. The results 
indicate that the Tac sample sorbed the metals more strongly than the Tptpv2 sample and that, on 
both samples, the metals showed a consistent sequence of sorption: Li << Mn << Ni < Co. 
Based on these results, all four of these metals show significant sorption and may be useful 
reactive tracers in the field. Neither of the proposed pertechnetate analogues (molybdate and 
perrhenate) displayed any significant sorption and were, therefore, eliminated from further 
consideration in our testing. 
Table 26. Preliminary Measured Sorption Coefficients 
Rock 
sample 
Measured Kd (mL g1) 
Li Mn Co Ni 
Tac (Phase 1, borehole 4) = 1 16 38 34 
Tptpv2 (Phase 1, borehole 7) = 1 6 14 13 
DTN: LA9909WS831372.011 

6.8.5.1.2 Detailed Studies 
6.8.5.1.2.1 Sample Description 
Three core samples were selected for detailed sorption and mineralogic characterization. These 
samples are described in Table 27. These rock samples were uniformly ground, sieved, and 
homogenized, and subsamples were used for radionuclide sorption, tracer sorption, and 
quantitative x-ray diffraction studies (QXRD). 
Table 27. Sorption Mineralogy Samples 
Sample Source Sample Name Short Name Geologic Unit 
Phase 1A, borehole 3 UZTT-BB-PH1-3 PH1-3 Tptpv1 
Phase 1A, borehole 4 UZTT-BB-PH1-4 PH1-4 Tac 
Phase 1B, borehole 7 UZTT-BB-PH1-7 PH1-7 Tptpv2 
NOTE: Borehole locations and lithology are from Figures 37 and 38. 
6.8.5.1.2.2 Radionuclide Sorption 
The sorption of Np, Pu, and Am to the three Busted Butte rock samples was measured at five 
different concentration levels; each measurement was conducted in duplicate. The radionuclide 
sorption studies were completed in fiscal year 1999, and the results are summarized in Table 28. 
Table 28. Summary of Radionuclide Sorption Results 
Sample 
Approximate Average Kd (mL g1) 
Np Am Pu 
PH1-3 0.3 380 19 
PH1-4 1.4 470 2500 
PH1-7 1.1 460 1100 
DTN: LA0004WS831372.002 
6.8.5.1.2.3 Tracer Sorption 
Laboratory batch measurements of the sorption of the field tracers onto the same three rock 
samples were begun in fiscal year 1999. The laboratory procedures and the chemical analysis of 
the sorption supernatant are underway. 
6.8.5.1.2.4 Quantitative X-ray Diffraction 
QXRD analysis have been obtained for subsamples of the same three rock samples (DTN: 
LA9910WS831372.009). 

6.8.5.2 In-Situ Pore-Water Chemistry 
Field-scale transport behavior is primarily a function of the ambient flow field and the 
interactions between the geologic host and the material being transported. Secondary influences 
include details of the pore-water chemistry, including pH, Eh, ionic strength, and chemical 
composition. Changes in any of these variables may affect solute sorption behavior and colloid 
stability and may lead to dissolution or precipitation of minerals resulting in permeability 
changes. These considerations lead to a fundamental conflict in field-tracer studies. Alteration of 
the in-situ water chemistry should be limited to minimize the artificial perturbations introduced 
by chemistry variations. Introduction of any artificial tracer will inherently alter water 
chemistry. (One exception might be the use of miniscule amounts of isotopic tracersnot a 
practical alternative at this phase of the Busted Butte studies.) 
The plan at Busted Butte was to introduce artificial tracers in a matrix designed to mimic natural 
pore-water chemistry as closely as practical, acknowledging that some alterations were 
inevitable. Accordingly, pore-water samples from rock cores collected in the Adit were 
analyzed, a recipe for synthetic Busted Butte water that closely resembled the in-situ chemistry 
was developed, and this synthetic water was used as our injection matrix. Results of the 
chemical analyses are presented here, and details of the synthetic water recipe are presented in 
Section 6.8.2.4.3. 
A set of rock samples were collected in the Test Alcove on January 30, 1998. These samples 
were collected from the Tac horizon by hand augering and immediately sealed. A series of 
samples were collected in sequence, starting with sample 3A at the Adit wall, extending to 
Sample 3U, 1.93 m away from the tunnel into the wall. 
Pore water was extracted from a subset of these samples by ultracentrifugation. Gravimetric 
moisture contents of the rock samples were determined by weight difference upon drying, and 
the chemical composition of the extracted pore water was determined using standard ion 
chromatography (IC) and inductively coupled-plasma/atomic-emission spectrometry (ICP/AES). 
Full results are presented in Table 29. Note that the IC analyses involve a bicarbonate buffer 
and, thus, no direct measurement of bicarbonate is possible. The bicarbonate concentrations 
listed in Table 29 are estimated by charge balance. 
The results in Table 29 show that the pore water is a mixed-ion water (Ca-Na-HCO3-SO4) with 
an average total dissolved solids (TDS) of approximately 200 mg L1. Compared to more typical 
groundwater compositions, the pore water shows high nitrate (probably due to soil biological 
activity) and high silica (due to relatively rapid equilibration with amorphous silica in the tuff). 
Sample 3B, from near the Adit wall, differs somewhat from the other samples, perhaps due to the 
influence of construction water and atmospheric CO2 levels. The compositions of the other three 
pore-water samples were averaged (as shown in the table), and these average values were used to 
develop the synthetic pore-water recipe presented in Section 6.8.2.4.3. 
Also listed in Table 29 are pH values measured on extracted pore water. Despite obvious 
opportunities for pH alteration due to CO2 exchange during sample collection, extraction, and 
analysis, these pH values were the best available at the time of Phase-1 planning. Thus, Phase-1 

tracer mixtures were pH-adjusted to a value of 8.4  0.1. During Phase-2 installation, attempts 
were made to measure pH in situ by inserting pH paper into boreholes for a few days. Results 
were mixed but seemed to indicate lower in-situ pH values than those measured in the laboratory 
(consistent with degassing of excess soil CO2 before lab analysis). Accordingly, Phase-2 tracer 
mixtures were pH adjusted to a value of 7.0  0.1. 
Table 29. Chemical Composition of Busted Butte 
Pore Water with J-13 Groundwater for Comparison 
Constituent 
Concentrations (mg L1) 
Sample 
3B 
Sample 
3N 
Sample 
3Q 
Sample 
3U 
Average 
3N  3U 
J-13 
water 
Br 0.06 0.07 0.06 0.06 0.06  
Ca 17.73 24.35 21.16 19.81 21.77 12.5 
Ce < 0.5 < 0.5 < 0.5 < 0.5 < 0.6  
Cl 16.13 19.06 17.71 16.74 17.84 6.5 
Co < 1.0 < 1.0 < 1.0 < 1.0 < 1.1  
F 2.36 1.82 1.85 1.41 1.69 0.53 
Fe < 0.1 < 0.1 < 0.1 < 0.1 < 0.2 < 0.05 
HCO3 (est.) 33.0 52.7 45.6 40.6 46.3 137.2 
K 4.14 3.35 3.37 3.44 3.39 4.5 
Li 0.11 0.11 0.10 < 0.1 0.10 < 0.1 
Mg 3.20 4.13 3.64 3.19 3.66 2.1 
Mn < 0.5 < 0.5 < 0.5 < 0.5 < 0.6 < 0.01 
Mo < 1.0 < 1.0 < 1.0 < 1.0 < 1.1  
Na 17.67 21.36 19.63 17.89 19.63 44.6 
Ni < 1.0 < 1.0 < 1.0 1.34 1.34  
NO3 22.76 26.48 22.62 20.99 23.36 1.3 
PO4 < 0.1 < 0.1 < 0.1 < 0.1 < 0.2  
Re < 1.0 < 1.0 < 1.0 < 1.0 < 1.1  
Si 29.69 31.85 34.10 31.00 32.32 29.6 
Sm < 0.5 < 0.5 < 0.5 < 0.5 < 0.6  
SO4 31.29 33.63 31.36 30.08 31.69 18.6 
Sr 0.37 0.49 0.42 0.38 0.43  
TDS 178.5 219.4 201.6 186.9 203.6 257.4 
pH 8.20 8.48 8.45 8.28 8.40 7.3oe8.4 
Gravimetric 
moisture content: 0.123 0.134 0.158 0.109 0.133 n/a 
DTN: LA9909WS831372.015, LA9909WS831372.016, LA9909WS831372.017, LA9909WS831372.018 

6.8.5.3 Field-Scale Tracer Transport 
6.8.5.3.1 Phase 1A 
6.8.5.3.1.1 Description 
Phase 1A consisted of four 2-m injection boreholes (boreholes 14). The Phase-1A tracer 
mixture was described in Section 6.8.2.4.1. The tracer mixture was injected at 10 mL hr1 in 
boreholes 1 and 3, and at 1 mL hr1 in boreholes 2 and 4. Phase-1A injection ran continuously 
from April 2, 1998, until January 12, 1999. Mineback of the Phase-1A test block began on 
January 15, 1999, and ended on March 3, 1999. During mineback, as successive layers of the 
Adit wall were removed, digital photographs under visible and UV illumination were taken, rock 
samples were collected by augering, and the exposed face was accurately surveyed. 
6.8.5.3.1.2 Results 
The visualization of the tracer plume using UV illumination of the fluorescein tracer was very 
successful, and the digital imagery resulting from this effort serves as the primary result of Phase 
1A. Detailed comparison of the digital plume imagery and numerical modeling results is 
underway and will be completed this year. 
A small number of augered rock samples have been analyzed for bromide and moisture content 
(DTN: LA9910WS831372.008). Values from these analyses will be used for re-calibrating the 
Phase 1A model (Section 6.8.6 discusses the blind modeling results). 
6.8.5.3.2 Phase 1B 
6.8.5.3.2.1 Description 
Phase 1B consisted of two 2-m injection boreholes (5 and 7) and two 2-m collection boreholes (6 
and 8). The Phase-1B tracer mixture was described in Section 6.8.2.4.1. The tracer mixture was 
injected at 10 mL hr1 in borehole 5 and at 1 mL hr1 in borehole 7. Phase-1B injection began on 
May 12, 1998. Borehole 7 injection was terminated on November 9, 1998, and borehole 5 
injection was terminated on November 18, 1998. Throughout the experiment, rock pore-water 
samples were collected at regular intervals using collection pads installed in boreholes 6 and 8. 
A total of 558 pad samples were collected and shipped to the laboratory for analysis. 
At the conclusion of the experiment, overcoring of the Phase-1B boreholes was conducted as 
follows: moisture pad collection was conducted on collection hole 8 directly below injection hole 
7 until injection shut down of hole 7 on November 9, 1998. Tracer injection and moisture pad 
collection was continued in holes 5 and 6 while two 10-inch-diameter overcores were drilled 
approximately tangential to one another with their centerlines in a vertical plane and contained in 
the area between the top of injection borehole 7 and the bottom of collection borehole 8. When 
injection hole 5 was shut down, three 10-inch-diameter overcores were drilled approximately 
tangential to one another with their centerlines in a vertical plane and contained in the area 
between the top of injection borehole 5 and 10 inches below the bottom of collection borehole 6. 

As soon as each of the injection holes was turned off, the injection and collection holes were 
surveyed as well as video and neutron logged. 
6.8.5.3.2.2 Results 
There were 176 selected pads extracted for tracers, and the extracts were analyzed by IC, 
ICP/MS, HPLC, spectrofluorimetry, and epifluorescent microscopy. The extraction/analysis 
procedure is shown schematically in Figure 57. Also, 883 individual analyses were conducted, 
and full results were submitted (DTNs: LA9909WS831372.001 and LA9909WS831372.002). 
Breakthrough of all 5 solute tracers was detected in borehole 6, directly below the 10 mL hr1 
injection site in borehole 5. No breakthrough was detected in borehole 8 below the 1 mL hr1 
injection site in borehole 7. No clear evidence of microsphere breakthrough was detected in 
either borehole, but this may be due to analytical difficulties, discussed below in Section 
6.8.5.3.5. The borehole-6 breakthrough results are summarized in Figure 58 (a through e), which 
shows tracer concentration in pad moisture (C) normalized by the theoretical input tracer 
concentration (C0) listed in Table 30. 
Table 30. Tracer C0 Values for Phase 1B Injection 
Tracer C0 (mg/kg) 
Lithium 40 
Bromide 460 
2,6-DFBA (Borehole #5 only) 100 
Pyridone 100 
Sodium fluorescein 500 
Note: Initial chemical concentration calculated from information presented in Section 6.8.2.4.1. 
All five tracers shown in Figure 58 give clear evidence of breakthrough by the end of the 
experiment. All of the figures show peak concentrations at a (horizontal) depth of approximately 
130 cm, directly below the injection port in borehole 5; but maximum recovery varies greatly. 
Bromide and 2,6-DFBA, both anionic supposedly nonreactive tracers, show similar and 
reasonable breakthrough patterns, with initial breakthrough detected in mid-late June 1998, after 
approximately 1 month of injection. Both bromide and 2,6-DFBA reached 50% injection 
concentrations in mid-July, after 2 months of injection. The fluorescein breakthrough pattern is 
more erratic. In particular, the peak concentration measured is over twice the injected 
concentration, which is clearly not reasonable. These anomalies probably reflect analytical 
difficulties associated with the extremely high concentration of fluorescein injected. The high 
concentration succeeded in improving field visualization of the plumes during mineback and 
overcore, even though it hurt the laboratory quantification. This analytical problem will be less 
severe for Phase 2, in which injected fluorescein concentrations are just 1/50 of that used in 
Phase 1. The later breakthrough and lower detected concentrations of pyridone may also reflect 
analytical difficulties; if real, they may indicate either sorption or degradation of this supposedly 
conservative tracer. Ongoing laboratory sorption and degradation studies will provide more 
information. Finally, although detected lithium concentrations are quite low, their contrast with 
background levels and their consistent location both in time and space indicate that true lithium 

breakthrough was observed in the field. The low and late breakthrough indicate that lithium was 
sorbed quite significantly. Ongoing numerical analyses will provide quantitative field 
retardation estimates, which can be compared with lab sorption estimates. 
Pad 
0.2 m 
filtration 
Filtration 
Br 
HPLC: 
FBAs 
Li 
ly) 
Collection 
Bicarbonate Buffer 
Extraction 
Extract 
Filtrate 
Membrane 
Epifluorescent Microscope: 
Microspheres 
Spectrofluorometer: 
Fl, Pyr., Rhod. 
Ion Chromatograph 
ICP/MS: 
(Selected 
Pads On
N/A - For illustration purposes only 
Figure 57. Phase-1B Pad Extraction/Analysis Scheme 

9/
10/
10/
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Relative 
Pore-Water 
Concentration 
(C/Co) 
Depth alongBorehole (cm) 
Collection date 
6/9/ 
6/16/
6/23/
7/1/
7/8/ 
7/15/
7/22/
7/30/
8/12/
8/26/
9/2/
9/9/ 
9/15/
10/6/ 
10/20/
11/18/ 
DTN: LA9909WS831372.001; LA9909WS831372.002 
Figure 58a. Bromide Concentrations in Borehole 6 for Phase 1B 
9/
10/
10/
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Relative 
Pore-Water 
Concentration 
(C/Co) 
Depth alongBorehole (cm) 
Collection date 
6/9/ 
6/16/
6/23/
7/1/
7/8/ 
7/15/
7/22/
7/30/
8/12/
8/26/
9/2/
9/9/ 
9/15/
10/6/ 
10/20/
11/18/ 
DTN: LA9909WS831372.001; LA9909WS831372.002 
Figure 58b. 2,6-DFBA Concentrations in Borehole 6 for Phase 1B 
ANL-NBS-HS-000019, Rev 00, ICN 02 187 November 2001 

9/
10/
10/
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Relative 
Pore-Water 
Concentration 
(C/Co) 
Depth alongBorehole (cm) 
Collection date 
6/9/ 
6/16/
6/23/
7/1/
7/8/ 
7/15/
7/22/
7/30/
8/12/
8/26/
9/2/
9/9/ 
9/15/
10/6/ 
10/20/
11/18/ 
DTN: LA9909WS831372.001; LA9909WS831372.002 
Figure 58c. Fluorescein Concentrations in Borehole 6 for Phase 1B 
6/9/986/16/986/23/987/1/987/8/987/15/987/22/987/30/988/12/988/26/989/2/989/9/989/15/989/29/9810/6/9810/13/9810/20/9810/27/9811/18/98 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Relative 
Pore-Water 
Concentration 
(C/Co) 
Depth alongBorehole (cm) 
Collection date 
DTN: LA9909WS831372.001; LA9909WS831372.002 
Figure 58d. Pyridone Concentrations in Borehole 6 for Phase 1B 
ANL-NBS-HS-000019, Rev 00, ICN 02 188 November 2001 

9/
10/
10/
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Relative 
Pore-Water 
Concentration 
(C/Co) 
Depth alongBorehole (cm) 
Collection date 
6/9/ 
6/16/
6/23/
7/1/
7/8/ 
7/15/
7/22/
7/30/
8/12/
8/26/
9/2/
9/9/ 
9/15/
10/6/ 
10/20/
11/18/ 
DTN: LA9909WS831372.001; LA9909WS831372.002 
Figure 58e. Lithium Concentrations in Borehole 6 for Phase 1B 
6.8.5.3.3 Phase 2 
6.8.5.3.3.1 Description 
Phase 2 involves eight injection boreholes and 12 collection boreholes drilled into the Phase-2 
test block from the Test Alcove. The injection boreholes are subdivided into three subphases. 
Phase 2A consists of a single horizontal borehole (23) in the Tptpv2 horizon. The borehole has 
10 injection points, each injecting tracer at 1 mL hr1. Phase-2A injection began on July 23, 
1998, and is ongoing. Phase 2B consists of four parallel horizontal injection boreholes (24, 25, 
26, and 27) in the Tac horizon. Each borehole is fitted with 10 injection points flowing at 10 
mL hr1. Phase-2B injection began on July 30, 1998, and is ongoing. Phase 2C consists of three 
parallel horizontal injection boreholes (18, 20, and 21) coplanar with the Phase-2A borehole in 
the Tptpv2 horizon. Each borehole is equipped with 9 injection points flowing at 50 mL hr1. 
Phase-2C injection was initiated on August 5, 1998, and is ongoing. Details on the tracer 
mixtures for each borehole are presented in Section 6.8.2.4.2. 
The 12 collection boreholes were drilled into the Phase-2 test block from the Main Adit and are, 
thus, perpendicular to the injection boreholes. Ten of the collection boreholes are horizontal, 
whereas the two deepest boreholes (11 and 47) are dipping downward beneath the block. The 
collection boreholes are arranged to allow interception of the tracer plumes after varying travel 
distances. Figure 59 is a schematic layout of the collection boreholes. 

jj12 13 
16 
15 14 
17 
10 
9 
11 
46 
47 
48 
Trace of Inection Plane/ Phase 2B 
Trace of Inection Plane/Phases 2A and 2C 
Figure 59. Schematic Layout of Phase-2 Collection Boreholes (not to scale) 
N/A oe For illustration purposes only 
6.8.5.3.3.2 Status 
Between the initiation of Phase-2 activities and September 21, 1999, a total of 9,188 sampling 
pads have been collected. Of these, over 2,200 have been extracted for the Phase-2 extraction/ 
analysis scheme, shown in Figure 60. Over 10,000 individual analyses have been conducted by 
the various instrument laboratories shown in Figure 59, and more are being received on a day-to-
day basis. The results will help guide decisions on the future course of the field test and will be 
reported in future revisions of YMP documents. 

Selected
Collection 
Extract 0.2 m
Pad Pads 2% HNO3 
filtration 
Filtrate 
Bicarbonate Buffer
Extraction
ICP/MS: 
Co, Li, Mn, Ni, 
Extract 
Ce, and Sm 
0.2 m
filtration
Epifluorescent Microscope: 
Filtration Microspheres 
Membrane 
Filtrate 
Spectrofluorometer: 
Fl, Pyr., Rhod. 
Ion Chromatograph: 
Br 
HPLC: 
FBAs 
N/A oe For illustration purposes only 
Figure 60. Phase-2 Pad Extraction/Analysis Scheme 
6.8.5.4 Special TopicMicrosphere Measurements 
Fluorescent polystyrene microspheres were injected in both Phase-1 and Phase-2 tracer solutions. 
Two sizes of microspheres have been used at Busted Butte: 0.3-m-diameter nile red spheres, 
and 1.0-m-diameter blue spheres (these are nominal diameters; actual measurements are 
detailed in DTN: LA9909WS831372.014). Stock microsphere suspensions were prepared by 
taking aliquots of commercially produced microsphere latex (colloidal suspension) and diluting 
them with deionized water to give approximately 109 to 1010 nile red particles per mL and 108 to 
109 blue particles per mL (the higher concentration of particles was used for Phase 1; the lower 
concentration for Phase 2). 

Stock microsphere suspensions are then added in sufficient amounts to tracer solutions to further 
reduce the number of microspheres to ~106-nile-red particles mL1 for Phase 2 and 107-nile-red 
particles per mL for Phase 1. Concentrations of blue particles for Phase 1 and Phase 2 were ~106 
and ~105 particles per mL, respectively. 
Initial microscopic examination of a few collection pads from both Phase 1 and Phase 2 
produced no evidence for microsphere transport; however, subsequent verification studies cast 
serious doubt on the ability of our simple extraction method to extract microspheres from the 
pads efficiently and reproducibly. Progress on resolution of this issue will be reported in future 
YMP documents. 
6.8.5.5 Special TopicTracer Degradation 
One concern that has been raised is the possible biodegradation of some of the tracers on the 
collection pads during transportation and storage. This concern only applies to the organic 
tracers (dyes and FBAs); the metals, inorganic anions (bromide and iodide), and polystyrene 
microspheres are not subject to degradation. Among the organic tracers, FBAs are unlikely to 
degrade rapidly due to their strong fluorine-carbon bonds; the dyes primary purpose is field-
screening, so degradation, if it occurs, is not particularly damaging to the overall goals of the 
test. Nevertheless, to address this concern, a long-term tracer biodegradation study was initiated 
in 1999 and will be concluded in 2000, at which time data and discussion of the potential impacts 
of tracer degradation will be reported. 
6.8.5.6 Forward Efforts 
Current plans (as of February 2000) call for continued operation of the Phase-2 field study 
through at least September 2000. Collection pads from the collection boreholes shown in Figure 
59 will continue on a regular basis and the pads will be analyzed for both nonreactive and 
reactive tracers. Three new collection boreholes will be drilled in the February/March 2000 
timeframe; selected rock samples collected during drilling will be analyzed for tracers, collection 
pad membranes will be installed in the holes, and these new collection boreholes will be added to 
the ongoing pad collection schedule. 

6.8.6 Phase-1A Predictions 
6.8.6.1 Deterministic Model 
This section reports predictions of the behavior of Phase 1A of the UZTT. The numerical 
experiments presented here were set up to determine the ability of numerical models to predict 
actual field response of flow and transport in porous media. These predictions were done 
previous to any actual experiment data being available. The simulations are intended to provide 
insight into the quality and extent of the information needed to accurately represent a physical 
system and to identify physical processes that are not currently adequately represented in 
numerical models. 
Here, parameters from the available Yucca Mountain hydrologic database, as well as initial 
laboratory values on samples taken from the Busted Butte site are used. At the time of this 
document, additional data from Phase 1A were just becoming available but not early enough to 
be incorporated into the modeling work for Rev 00 of this AMR. 
The computer code FEHM V2.00 (STN: 10031-2.00-00) is used in the development of the 
predictions presented here. FEHM is a multidimensional, multiphase, unsaturated and saturated, 
transient, finite-element code and is used by YMP for radionuclide migration predictions. 
6.8.6.1.1 Model Configuration and Parameter Set 
In these simulations of Phase 1A, the focus is on the injection of a conservative tracer into the 
vitric Calico Hills Formation via a single injection point in a borehole. The simulations of the 
Phase-1A field experiment were run in a model system that approximates the field configuration 
as closely as possible. The model system was a single borehole with a diameter of 0.10 m 
embedded in a matrix of tuff in the Tac unit. The domain size for the simulations is 6 m by 6 m 
by 1.5 m. The borehole extends the full 1.5-m length of the z direction, and gravity acts in the x 
direction. The system configuration is shown in Figure 61. Both 2-D and 3-D simulations of the 
system were run. The 2-D system was a vertical plane, an x-y slice through the injection point at 
0.75 m.
The model accurately captured the configuration of the injection pad, as well. The tracer 
solution is injected through a polypropylene pad located 0.75 m down the length of the borehole. 
The injection pad resided inside the borehole, centered at x = 0.0, y = 0.05, z = 0.75 (Figure 61). 
The pad was 0.05-m by 0.05-m polypropylene material, with material parameters shown at the 
bottom of Table 30. Injection occurred at a single point in the center of the pad, consistent with 
the actual physical injection system. For Phase 1A, the pad and injection point are located on the 
side of the borehole, 90 off vertical, as shown in Figure 61. 
Simulations were done before having detailed geologic and hydrologic property distributions in 
three dimensions for the UZTT. The simulation domain used a homogeneous, isotropic, 
unfractured description of the porous media. However, 3-D effects are likely to become 
important in representing the test. Therefore, the model is fully three dimensional from the 
outset to make it possible to capture any of these effects. 

DTN: LA9909WS831372.019 
NOTE: LAGRIT-generated computational grid for single-borehole simulations. The grid has 82,000 nodes. The 
domain is 6 m x 6 m x 1.5 m and includes the porous rock matrix (blue) and the injection pad (red). 
Figure 61. Computational Grid for UZTT Single-Borehole Simulations 

An 82,000-node, 3-D, unstructured grid represents the single borehole configuration. This grid 
was generated using the LAGRIT V1.0 (STN: 10212-1.0-00) computer code. It contains the full 
representation of the injection borehole, the 25-cm2 injection pad, and 54 m3 of the surrounding 
rock mass (Figure 61). 
The boundary conditions for the simulation were no flow for the lateral sides (y =  3 m) and the 
front face of the borehole (z = 0 m). The exposed face of the rock in the field (z = 0 m) has been 
sealed to minimize evaporative losses resulting from the experimental tunnel. The top and 
bottom faces (x =  3 m) of the model, as well as the back side (z = 1.5 m), were held at a fixed 
capillary pressure. Capillary pressures were chosen to match measured in-situ saturation and 
capillary-pressure conditions. The use of capillary-pressure boundary conditions provide the 
most accurate means of capturing the real saturation distribution of the system. Although for a 
homogeneous rock matrix, capillary pressure can readily be converted to a constant saturation 
boundary, in a heterogeneous system, the saturation may vary drastically around the boundary, 
though the capillary pressure is relatively constant. In the vadose zone, the capillary pressure 
provides a much better representation of the steady-state condition of the system, and measured 
in-situ saturations can be much better captured by a model. 
In the simulations, the influence of a number of model parameters were assessed that can, at best, 
be only approximately known. These parameters include rock permeability, relative 
permeability, porosity, and in-situ conditions (saturation). For these simulations, the constitutive 
relationships (relative permeability versus saturation and capillary pressure versus saturation) are 
characterized using the van Genuchten curve fit (van Genuchten 1980, pp. 892898). The van 
Genuchten method fits the data points of permeability versus saturation measured in the 
laboratory to a two-parameter function. The two parameters are typically denoted as aand n. 
The a parameter represents the air entry pressure and is given here in units of m1. The n 
parameter controls the slope of the capillary-pressure saturation curve, and is nondimensional. 
Tables 31 and 32, respectively, list the different parameter combinations that were run in 2-D 
and 3-D representations of Phase 1A. The base case represents our current best knowledge of 
the properties and conditions of the system. Using available data from Flint (1998, Figure 3, p. 
22-23; Figure 9, p. 30; Figure 10, p. 31; Table 7, p. 44; and Table 8, p. 45) and DTN: 
LB970601233129.001, a range for each parameter was simulated. Calculations were initiated in 
January 1998. Since these calculations are only scoping calculations and since a range of values 
was being used, the exactness of the data is not critical to the results presented here. Note that 
this situation also applies to data used for Phase-1A Monte Carlo simulations, Section 6.8.6.3, 
and Phase-2 modeling, Section 6.8.7. 
The response of the system to various rates of injection of the tracer fluid was also assessed. 
Injection rates simulated were 1, 10, and 50 mL hr1. These injection rates were chosen to span 
the range of rates being applied in the various UZTT phases. 

Table 31. Hydrologic Parameters Used for the 2-D Simulations 
Parameter List for 2-D Simulations 
Simulation 
variable Porosity 
Permeability(m2) 
a 
(m1) n 
Saturation, S 
(in situ) 
Inj. rate(mL hr1) 
1. Base Case 0.30 1.3 x 1012 0.82 1.31 0.30 10 
2. Matrix perm.=0.1x base 
case perm. 
0.30 1.3 x 1012 0.82 1.31 0.30 10 
3.Matrix perm.=10x base 
case perm. 
0.30 1.3 x 1012 0.82 1.31 0.30 10 
4. Porosity = 0.20 0.20 1.3 x 1012 0.82 1.31 0.30 10 
5. Porosity = 0.40 0.40 1.3 x 1012 0.82 1.31 0.30 10 
6. alow, n low 0.30 1.3 x 1012 0.60 1.20 0.30 10 
7. ahigh, n low 0.30 1.3 x 1012 1.20 1.20 0.30 10 
8. alow, n high 0.30 1.3 x 1012 0.60 1.80 0.30 10 
9. ahigh, n high 0.30 1.3 x 1012 1.20 1.80 0.30 10 
10. In situ S = 0.2 0.30 1.3 x 1012 0.82 1.31 0.20 10 
11. In situ S = 0.4 0.30 1.3 x 1012 0.82 1.31 0.40 10 
12. In situ S = 0.5 0.30 1.3 x 1012 0.82 1.31 0.50 10 
13. In situ S = 0.6 0.30 1.3 x 1012 0.82 1.31 0.60 10 
14. In situ S = 0.7 0.30 1.3 x 1012 0.82 1.31 0.70 10 
15. In situ S = 0.8 0.30 1.3 x 1012 0.82 1.31 0.80 10 
16. In situ S = 0.9 0.30 1.3 x 1012 0.82 1.31 0.90 10 
17. In situ S = 0.2, Inj = 1 0.30 1.3 x 1012 0.82 1.31 0.20 1 
18. In situ S = 0.3, Inj = 1 0.30 1.3 x 1012 0.82 1.31 0.30 1 
19. In situ S = 0.4, Inj = 1 0.30 1.3 x 1012 0.82 1.31 0.40 1 
20. In situ S = 0.5, Inj = 1 0.30 1.3 x 1012 0.82 1.31 0.50 1 
21. In situ S = 0.6, Inj = 1 0.30 1.3 x 1012 0.82 1.31 0.60 1 
22. In situ S = 0.7, Inj = 1 0.30 1.3 x 1012 0.82 1.31 0.70 1 
23. In situ S = 0.8, Inj = 1 0.30 1.3 x 1012 0.82 1.31 0.80 1 
24. In situ S = 0.9, Inj = 1 0.30 1.3 x 1012 0.82 1.31 0.90 1 
25. In situ S = 0.3, Inj = 50 0.30 1.3 x 1012 0.82 1.31 0.30 50 
26. In situ S = 0.9, Inj = 50 0.30 1.3 x 1012 0.82 1.31 0.90 50 
Polypropylene pad 0.85 2.2 x 1011 17.0 1.12 N/A N/A 
DTN: LA9909WS831372.019 
NOTE: aThe simulation-variable column shows the parameter being varied (tested) in the simulation. Permeability 
is the intrinsic value (value under saturated conditions). The variables aand n are the van Genuchten 
function parameters taken from laboratory measurements; Inj. stands for injection rate in mL hr1. 

Table 32. Hydrologic Parameters Used for the 3-D Simulations 
Parameter List for 3-D Simulations 
Simulation 
variable Porosity 
Permeability(m2) 
a 
(m 1) n 
Saturation, S 
(in situ) 
Inj. rate(mL hr1) 
1. Base Case 0.35 1.3 x 1012 0.82 1.31 0.35 10 
2. Matrix perm.=0.1x base 
case perm 
0.35 1.3 x 1012 0.82 1.31 0.35 10 
3. Porosity = 0.20 0.20 1.3 x 1012 0.82 1.31 0.35 10 
4. ahigh, n low 0.35 1.3 x 1012 1.20 1.20 0.35 10 
5. ahigh, n high 0.35 1.3 x 1012 1.20 1.80 0.35 10 
6. In situ S = 0.2 0.35 1.3 x 1012 0.82 1.31 0.20 10 
7. In situ S = 0.6 0.35 1.3 x 1012 0.82 1.31 0.60 10 
8. In situ S = 0.9 0.35 1.3 x 1012 0.82 1.31 0.90 10 
9. In situ S = 0.2, Inj = 1 0.35 1.3 x 1012 0.82 1.31 0.20 1 
10. In situ S = 0.35, Inj = 1 0.35 1.3 x 1012 0.82 1.31 0.35 1 
11. In situ S = 0.6, Inj = 1 0.35 1.3 x 1012 0.82 1.31 0.60 1 
12. In situ S = 0.9, Inj = 1 0.35 1.3 x 1012 0.82 1.31 0.90 1 
13. In situ S = 0.2, Inj = 50 0.35 1.3 x 1012 0.82 1.31 0.20 50 
14. In situ S = 0.35, Inj = 50 0.35 1.3 x 1012 0.82 1.31 0.35 50 
DTN: LA9909WS831372.019 
NOTE: The simulation-variable column shows the parameter being varied (tested) in the simulation. Permeability is 
the intrinsic value (value under saturated conditions). The variables aand n are the van Genuchten function 
parameters taken from laboratory measurements; Inj. stands for injection rate in mL hr1. 
6.8.6.1.2 Modeling Results 
All simulated concentrations presented in Section 6.8.6 are normalized (C/C0) concentrations 
and, as such, are dimensionless. 
6.8.6.1.2.1 Overview of Simulations 
The large 3-D system size required relatively long simulation times. In an effort to minimize 
computer time and use the time most effectively, 2-D simulations were run first. These 
simulations were used as scoping calculations to identify important simulations to run in three 
dimensions. As shown in the numerical results, the 2-D simulations showed shorter travel 
distances for the tracer than did the same simulation in 3-D. The differences in tracer movement 
between 2-D and 3-D simulations at the same effective injection rate are primarily due to the 
effective volume of injection. The 2-D system is implicitly 1 m in depth, resulting in a lower 
effective point-injection rate. Therefore, all quantitative predictions are made using values from 
the 3-D simulations; however, the 2-D simulations can be used to identify the relative response 
of one set of conditions versus another. The results indicate that trends in the 2-D simulations 
mirror those in the 3-D simulations. 

A series of 2-D simulations (Table 31) were run to identify the relative importance of different 
parameters and injection scenarios. Figure 62, a graph of concentration versus distance from the 
borehole center, indicates that this system is relatively insensitive to many of the parameters but 
is quite sensitive to some. Three-dimensional simulations were then chosen from the parameter 
sets to which the system was most sensitive; the parameter combinations are listed in Table 32. 
The most influential parameter, based on Figure 62, is the value of the van Genuchten power n. 
Figure 62 shows the two simulations with a high n value dramatically increase concentration at a 
given distance over the other simulations. The values of both van Genuchten parameters, aand 
n, for the base case were taken from laboratory measurements made on cores from the Busted 
Butte site. Available data for the Tac unit were also collected from other sources 
(GS000408312231.003, GS951108312231.009, GS960808312231.003, GS960808312231.005, 
GS950408312231.004, GS990408312231.001, GS940508312231.006, GS960808312231.001, 
GS950608312231.008, LB970601233129.001) to try to capture the uncertainty in these 
parameters. From these data, high and low reported values of a and n were selected, testing the 
range of responses for the combinations of those values. The value of n strongly controls the 
relative influence of capillary forces and gravity forces. Increasing n decreases the capillary 
forces, resulting in more gravity-driven flow. 
Another parameter that clearly influenced the tracer transport was the porosity. Although flow is 
only slightly affected by even relatively large changes in porosity, transport is more strongly 
affected. Porosity affects transport because the bulk velocity of the fluid is divided by the 
porosity to get the pore velocity. Two-dimensional and 3-D simulations were run with porosity 
increased and decreased by 10% to 15%. The higher porosity did not substantially change the 
transport, but the lower porosity made an observable difference. 
A third factor affecting tracer transport was the injection rate. Prior to starting Phase 1A, 
simulations were run using different injection rates to help select a rate for Phase 1A that would 
allow the tracer to move sufficiently far to produce readily measurable distributions but not so 
far that the tracer could not be fully recovered. Testing multiple injection rates was also 
intended to help select injection rates for Phase 2. An injection rate of 10 mL hr1 was chosen 
for Phase 1A. Therefore, the discussion below focuses on simulation results using 10 mL hr1. 
Results with other injection rates are presented later, for completeness. These other injection 
scenarios are also useful for making predictions for Phase 2. 
Simulation results are presented as both spatial and time-history concentration profiles. For 
Phase 1A, however, the concentration distribution between 180 days and 365 days after 
injection is of most interest. Temporal snapshots are presented for 180 days, as this is the time 
corresponding to the original mineback and auger schedule for Phase 1A. The mineback was 
actually delayed until 284 days. 

1.00 
Concentration 
0.80 
0.60 
0.40 
0.20 
0.00 
Base-case 
k_base(S=1)*0.1 
k_base(S=1)*0.01 
Porosity=0.20 
vG(a_high,n_high) 
vG(a_high,n_low) 
k_base(S=1)*10 
Porosity=0.40 
In-situ.S=0.20 
In-situ.S=0.40 
vG(a_low,n_high) 
vG(a_low,n_low) 
-1.000 -0.800 -0.600 -0.400 -0.200 0.000
X [m] 
DTN: LA9909WS831372.019 
NOTE: Normalized concentration as a function of vertical distance from the injection point for different 2-D 
sensitivity runs. Parameter values for each case shown in the legend are listed in Table 31. 
Figure 62. 2-D Sensitivity Runs 
6.8.6.1.2.2 Discussion of Simulations: Base Case 
Tables 33 and 34 present the results from the 3-D base-case simulation, against which all other 
runs were compared. Using the experimental injection rate of 10 mL hr1, after 180 days, the 
model predicts tracer transport distances as shown in the Table 32. (In all the simulations 
discussed in this section, the tracer was injected at a concentration of 1, and cited 
concentration values are relative to this initial value.) 
Measurements from core samples suggested in-situ saturations in the range of 20 to 40%. In the 
simulations, an initial capillary pressure of 5.6 MPa corresponds to a saturation of 20%, 0.55 
MPa corresponds to 35%, and 0.07 MPa corresponds to 60%. Comparing locally measured 
values and values reported for the Tac unit, the in-situ saturation of approximately 35% most 
closely represented reality. 
Table 33. Predicted Transport Distances for a Given Concentration 
Normalized concentration (C/C0) Distance traveled 
0.01 0.85 m 
0.1 0.67 m 
0.25 0.56 m 
0.50 0.45 m 
DTN: LA9909WS831372.019 

The predicted distribution of the tracer is generally uniform in all directions, that is, a spherical 
distribution (see Figure 62). Changes in saturation from the background (in-situ) level are 
generally small. Table 34 shows the saturation at different distances from the injection point at 
180 days for the base-case simulation. Note that the initial background saturation for this 
simulation is 35%, based on measured moisture contents of 14.0  2.5% and a porosity of 
approximately 0.5. 
Table 34. Simulation Results at 180 Days 
Distance from injection point (m) Saturation at 180 days 
0.125 0.402 
0.25 0.381 
0.50 0.366 
1.00 0.357 
DTN: LA9909WS831372.019 
The saturation of the system changed only slightly over the 180-day period for the base case. 
After 180 days, at a distance of 0.125 m below the injection point, the saturation had increased 
by only 5%, whereas at a distance of 1 m it had increased by only a fraction of a percent (0.7%). 
The tracer distributes relatively evenly in all directions, centered at the injection point. Some 
asymmetry is introduced, however, by the presence of the borehole and by injecting 90 degrees 
off vertical. Water and tracer must move around the borehole to flow in the negative horizontal 
direction where there is no impedance in the positive horizontal direction. Thus, flow and 
transport are somewhat asymmetric. 
The relatively even distribution of tracer and the lack of increase in saturation near the borehole 
indicate that this system is dominated by capillary forces over gravitational forces. 
Gravitationally dominated flows have a much more asymmetric character. These simulations 
show that a 10-mL hr1 injection rate should not introduce enough water to change the overall 
flow and transport processes that occur in the undisturbed system. 
At 180 days, for the 2-D run, the approximate radius of the tracer at a normalized concentration 
of 0.01 is 0.75 m and at a concentration of 0.5 is 0.30 m. Normalized concentrations of 0.01 and 
0.5 for the 3-D simulation occur, respectively, at 0.85 m and 0.45 m. Figure 63 plots the 
concentration as a function of distance from the borehole center at 180 days for the 2-D and 3-D 
systems. In the 3-D simulation, at a distance of 0.125 m, the normalized concentration is almost 
1 (0.96), whereas at 1 m the concentration has fallen to 2 x 104. 

1.00 
0.80 
0.60
Concentration 
0.40 
0.20 
0.00 
Base-case (2d) 
Base-case (3d) 
-1.00 -0.80 -0.60 -0.40 -0.20 0.00 
X [m] 
DTN: LA9909WS831372.019 
NOTE: The graph shows tracer concentration as a function of vertical distance from the injection point. Although 
the shapes of the curves are similar, the 2-D system lags the 3-D system by a concentration of 
approximately 0.20. Parameter values used in these simulations are listed in row 1 of Tables 31 and 32. 
Figure 63. Tracer Concentration versus Distance for 2-D and 3-D Simulations 
Figure 64 plots the time history of concentration at a vertical distance of 0.125 m, 0.25 m, 0.5 m, 
and 1m below the center of the borehole for the 3-D simulation. Figure 64 shows that, at 180 
days and a distance of 0.125 m, the tracer concentration has almost reached a value of 1.0, 
whereas at 0.5 m, the concentration is still increasing rapidly. 
6.8.6.1.2.3 Discussion of Simulations: Sensitivity Analyses 
Knowledge of the actual hydrologic and material properties will always contain some 
uncertainty. Furthermore, these properties vary somewhat within the hydrogeologic units, as 
reflected in variations in measured values from different cores. Using stochastic methods, these 
uncertainties can be incorporated directly into the calculations. Such predictions will be 
presented in later sections. Within this deterministic modeling approach, an attempt has been 
made to account for and understand the influence of such uncertainty by assessing the sensitivity 
of the simulation to various system parameters (Table 32). Figures 65 to 68 compare the results 
of these simulations. Figure 65 shows the effect of water injection on matrix saturation with 
time for the different sensitivity runs. Figure 66 plots concentration against distance from the 
borehole for the 3-D simulations 1 through 6 in Table 32. Figure 67 shows the same information 
plotted as concentration versus time with each graph plotting a different distance from the 
borehole. Figure 68 compares the 3-D concentration-versus-time values against those for 2-D. 

DTN: LA9909WS831372.019 
NOTE: The graph shows plots of normalized tracer concentration as a function of time for different vertical distances 
(x in meters) from the injection point. Curves for both 2-D and 3-D simulations (2d and 3d in legend, 
respectively) are shown. 
Figure 64. Time Profiles of Tracer Concentrations 
DTN: LA9909WS831372.019 
NOTE: The graph shows changes in saturation through time at a distance of 0.25 m from the injection point. Note 
that in most cases there is little change in saturation over a year. Parameter values for the cases shown in 
the legend are listed in Table 32 (rows 1, 2, 3, 5, 4, 7, 10, and 14, respectively). 
Figure 65. Effect of Water Injection on Matrix Saturation 
Figure 65 shows that saturation for the Phase-1A model system is not greatly affected by any of 
the simulation scenarios. Further, the saturation is not particularly sensitive to many of the 
parameters. The biggest changes are observed for conditions that increase the relative influence 
of gravity forces over the otherwise prevailing capillary forces. Both a higher a and the much 

higher injection rate produce relatively rapid and readily apparent increases in saturation. 
Otherwise little change in saturation is observed. 
At 180 days, Figure 66 demonstrates that, based on simulated measurements at a distance of 0.25 
m from the borehole, it is not likely that actual field measurements at such a distance would be 
useful for evaluating how well the system characteristics have been captured by the model for a 
10 mL hr1 injection rate. At 0.25 m, there is negligible difference in concentrations among the 
different simulations. At a distance of 0.5 m, however, variations in system characteristics result 
in a 60% range of concentrations. Figure 66 shows that a system with an order-of-magnitude 
lower intrinsic permeability k is indistinguishable from one with a value of van Genuchten power 
n that is at the low end of reported values. Both of these cases are very similar to the base case 
as well. This fact indicates that transport in this system is not particularly sensitive to the values 
of k or a; errors in these values are not expected to demonstrably influence the accuracy of 
predictions in this system. 
DTN: LA9909WS831372.019 
NOTE: The graph shows differences in normalized concentration for different sensitivity runs at three distances (x in 
meters) from the injection point. The sensitivity to the system parameters is not captured at the smallest 
distances but is apparent at a distance of 0.5 m. Parameter values for the cases shown along the horizontal 
axis are listed in Table 32 (rows 1, 2, 3, 5, 4, 7, and 10, respectively). 
Figure 66. Tracer Concentration for Different Sensitivity Runs 
If the reported porosity is in error by as much as 15% (0.20 instead of 0.35), tracer concentration 
at 0.5 m and 180 days is expected to be 0.58, which is a difference of 25% from the base value. 
By 365 days, as seen in Figure 67, the difference in concentration has decreased somewhat but is 
still a substantial 10%. At shorter distances from the injection point, the low-porosity system is 
very close to the fastest transport systemone with high van Genuchten nand leads the base 
prediction by 10% concentration. 

DTN: LA9909WS831372.019 
NOTE: These plots show concentration changes as a function of time for a distances from the injection point of (a) 
0.25 m, (b) 0.5 m, and (c) 1.0 m. Note that at a 1-m distance, there is, in most cases, little change in 
concentration after injecting tracer for 1 year. Parameter values for the cases shown in the legend are listed 
in Table 32 (rows 1, 2, 3, 5, 4, 7, and 10, respectively). 
Figure 67. Concentration versus Time at Various Distances from Injection 

Flow in the simulation that starts with an in-situ saturation of 60%, instead of the estimated 35%, 
is dominated by gravity rather than capillarity. For this simulation, tracer is carried much farther 
down than in any of the lateral directions. At 180 days and a vertical distance of 0.5 m below the 
borehole, the concentration is 0.57 versus 0.11 laterally. The transport rate is substantially faster 
than the base case, as demonstrated by much higher concentrations at greater distances (Figure 
66). At a distance of 0.5 m below the borehole, decreased porosity and increased saturation runs 
are virtually indistinguishable but differ significantly in 3-D distribution. The lower-porosity 
transport is capillary-driven, producing a relatively uniform tracer cloud, whereas the high-
saturation system produces a highly elongated tracer profile. Note that saturation as high as 60% 
is not indicated by the reported capillary pressures measured in this system. The measured 
pressures indicate an in-situ saturation in the vicinity of 25 to 35%. 
The simulation results were most sensitive to the value of the van Genuchten parameter n. 
Reported values for this parameter ranged from 1.2 to 1.8, with the value from Busted Butte 
samples being 1.31. At a value of n = 1.8, transport was strongly gravity-dominated. The 
resulting concentration profiles were long vertically and thin laterally. This effect was much 
stronger than that observed for the high-saturation simulation. Concentration at a vertical 
distance of 0.5 m was 0.88 versus 0.04 at the same lateral distance. At a distance of 0.5 m, tracer 
concentration was 60% higher than the base case and 30% higher than the high-saturation case. 
Furthermore, by 180 days, this system had just about reached its steady-state distribution, 
whereas even at 365 days (Figure 67), the other systems were continuing to change. If the actual 
value of n at Busted Butte is significantly different from the value used here, it should be 
recognizable by the distinct, long and thin, tracer distribution and by the high vertical 
concentrations. 
The 2-D simulations (Figure 62) indicated that the value of a appears to have little influence on 
the system as compared to n. As a result, 3-D simulations were only done for the two different 
variations in van Genuchten parameters presented. 
6.8.6.1.2.4 Discussion of Simulations: 2-D versus 3-D 
The 2-D and 3-D simulations followed very much the same trends in tracer distribution and 
concentrations in space and time. Figure 68 shows concentration as a function of time, measured 
at a distance of 0.5 m vertically below the borehole center for both 2-D and 3-D simulations. 
The numerical difference in concentration between equivalent 2-D and 3-D systems remained 
relatively constant, approximately 10 to 15% after 90 simulation days. Concentration values 
between the 2-D runs and the 3-D runs were much closer at early times and began to converge at 
later times, as all concentrations approached unity. 
6.8.6.1.3 Implications for Unsaturated Zone Transport Test Phase-2 Design and Analysis 
Using different tracers at various injection rates and injection separation distances can provide an 
opportunity to differentiate controlling processes and material features. These simulations 
provide us with a tool to select injection rates for the Phase-2 experiment, as well as to help 
understand how the injection rates used influence what is observed at different monitoring 
locations. For example, after 180 days at an injection rate of 10 mL hr1, normalized 

concentrations had increased to greater than 0.20 within a radius of 0.4 m from the injection 
point but fell off rapidly beyond that and were below 1% beyond 0.9 m. Thus, a 2-m spacing 
between boreholes and injection points within boreholes would probably produce a system in 
which each injection location was distinct from every other. 
0.00 
0.10 
0.20 
0.30 
0.40 
0.50 
0.60 
0.70 
0.80 
0.90 
1.00 
Base-case (3d) 
k_base(S=1)*0.1 (3d) 
Porosity=0.20 (3d) 
vG(a_high,n_high) (3d) 
vG(a_high,n_low) (3d) 
Base-case (2d) 
k_base(S=1)*0.1 (2d) 
Porosity=0.20 (2d) 
vG(a_high,n_high) (2d) 
vG(a_high,n_low) (2d) 
Concentration 
DTN: LA9909WS831372.019 
NOTE: These plots of normalized concentration versus time show that differences between 2-D and 3-D values are 
fairly consistent between different runs. The concentration is measured at a distance of 0.5 m vertically 
below the borehole center. Parameter values for the cases shown in the legend are listed in Table 32 for 3D 
cases and in Table 31 for 2-D cases. 
Figure 68. Differences in 2-D and 3-D Predicted Concentrations 
Faster injection rates, such as 50 mL hr1, can be expected to strongly modify natural flow 
patterns, producing gravity-dominated flow. At this injection rate, concentrations 0.5 m from the 
injection point are predicted to rise to 0.80 in only 3 months. Thus, boreholes or injection points 
spaced 1 m apart are expected to start influencing each other very early in the experiment. 
On the other hand, an injection rate of 1 mL hr1 is seen to hardly influence the system at all. 
Even after 1 yr, normalized concentrations do not rise even to 0.05 at the 0.5-m distance. At 
such a low injection rate, it would take an extremely long time to characterize the behavior of the 
system or identify important physical and chemical processes that are occurring. Further, at such 
slow rates of movement, it is very difficult to distinguish differences in tracer movement that 
might arise due to geochemical effects. 
6.8.6.2 Stochastic Model 
6.8.6.2.1 Introduction 
The purpose of this section is to document the use of stochastic predictions made for Phase 1A of 
the Busted Butte testing program. At this time, there is a paucity of physical-properties 
information on the lithologies of that site, and the spatial variability of rock properties cannot be 
accurately depicted. Although this lack of information results in uncertainties for any flow and 

transport prediction, this section attempts to use stochastic predictions for the site using current 
YMP databases. The input uncertainties from these databases are directly incorporated into flow 
predictions through a recently developed stochastic model. The predictions represent the first 
two moments (expected value and standard deviation) of flow quantities, and these two moments 
are used to construct confidence intervals for the flow quantities. 
It is expected that the field-test results will fall within the predicted confidence intervals with a 
68% probability. However, because the statistical and other input parameters are taken or 
estimated from the YMP databases, these parameters may or may not represent the rock 
properties at the test site, thereby introducing another level of uncertainty in the analyses. Some 
sensitivity analyses were performed on these parameters and found that the flow predictions are 
sensitive to the background saturation, the mean and variance of pore-size-distribution parameter 
a, the mean and variance of the logarithm of the saturated hydraulic conductivity, and the 
injection rate. This indicates that the refinement of these parameters is important. 
6.8.6.2.2 Stochastic Modeling 
Although geologic media exhibit a high degree of spatial variability, rock properties, including 
fundamental parameters such as permeability and porosity, are usually observed only at a few 
locations due to the high cost associated with subsurface measurements. This combination of 
significant spatial heterogeneity with a relatively small number of observations leads to 
uncertainty about the values of material properties and, thus, to uncertainties in predicting flow 
and solute transport in such media. It has been recognized that the theory of stochastic processes 
provides a natural method for evaluating flow and transport uncertainties. In the last two 
decades, many stochastic theories have been developed to study the effects of spatial variability 
on flow and transport in both saturated (e.g., Gelhar and Axness 1983, pp. 161180; Dagan 
1984, pp. 151177; Neuman et al. 1987, pp. 453466; Graham and McLaughlin 1989, pp. 2331 
2355; Zhang and Neuman 1995, pp. 3951) and unsaturated zones (e.g., Yeh et al. 1985, pp. 
457464; Mantoglou and Gelhar 1987, pp. 3746; Russo 1995, pp. 16471658; Harter and Yeh 
1996, pp. 15851595; Zhang and Winter 1998, pp. 10911100). In the unsaturated zone, the 
problem is complicated by the fact that the flow equations are nonlinear because unsaturated 
hydraulic conductivity depends on pressure head. 
In many of these previous theories, there are a number of simplifying assumptions such as 
gravity-dominated flow (for steady-state cases) and slow-varying gradient (for transient flow). 
These assumptions restrict the applicability of existing theories to modeling the UZTT results. 
For example, the assumption of gravity-dominated flow excludes the presence of domain 
boundaries and the existence of a water table. In addition, a slow-varying gradient does not 
permit local injection or fast-varying recharge. Recently, a stochastic model for transient 
unsaturated flow in bounded domains free of the above mentioned assumptions (STO-UNSAT, 
V1.0, STN: 10292-1.0LV-00) was developed. The model results are the first two moments of 
the flow quantities, which may be used to construct confidence intervals for these quantities. 
The confidence intervals are a measure of the uncertainty caused by incomplete knowledge of 
material heterogeneities. 

6.8.6.2.3 Phase-1A Modeling 
For Phase-1A simulations, the input uncertainties from YMP databases are directly incorporated 
into flow predictions using STO-UNSAT, V1.0 (STN: 10292-1.0LV-00). This model requires 
that the first two statistical moments for rock properties, such as saturated hydraulic conductivity 
and pore-size-distribution parameter a, be specified. Because the variabilities of porosity and 
residual water content are likely to be small compared to that of hydraulic conductivity, both of 
them are assumed to be known with certainty. To model unsaturated flow, the constitutive 
relationships between capillary pressure and unsaturated hydraulic conductivity and between 
capillary pressure and saturation must also be specified. Although the parameters characterizing 
these relationships are reported in the YMP databases (Schenker et al. 1995; Flint 1998), these 
are based on the van Genuchten model (van Genuchten 1980, pp. 892808). Conversely, STOUNSAT, 
V1.0 assumes the constitutive relationships to obey the Gardner-Russo model (Gardner 
1958, pp. 228232; Russo 1988, pp. 453459). In this study, the Gardner-Russo parameter ais 
estimated from the reported van Genuchten parameters by matching the main features of the 
retention curves for these two models. The first row of Table 35 summarizes the relevant 
parameters that are taken or estimated from the YMP databases (Schenker et al. 1995). No 
information is found with respect to the correlation lengths of the logarithm of the hydraulic 
conductivity and pore size distribution. The value of 20 cm is assumed for both of these 
correlation lengths. 
Baseline Case 
In the baseline case, the size domain is 200 cm by 200 cm with material properties specified in 
Table 35. The steady-state simulations are run with the following boundary conditions: specified 
flux at the top, a constant head of 488 cm at the bottom, and no-flow boundaries at the sides. 
The specified flux is consistent with the constant head at the bottom such that at steady-state (t = 
0), the flow is gravity-dominated with a constant mean pressure head (h = 488 cm) and a 
constant saturation (S = 30%) through the whole domain. Specifically, the initial mean 
saturation is assumed to be 30% and then the initial mean head is computed to be 488 cm using 
the gravity-dominated condition and the specified characteristic curves. However, the head 
standard deviation is not uniform in such a bounded domain. The head standard deviation is zero 
at the bottom boundary, increases with distance from there, and reaches its maximum at the top. 
Figure 69 shows the confidence intervals for the pressure head h and the saturation S along 
horizontal (y) and vertical (z) lines passing through the injection point. The profiles are obtained 
by adding one standard deviation to the result and subtracting it from the mean quantity. This 
result corresponds to the 68% confidence intervals for the flow quantities. By comparing the 
vertical profiles for pressure head and saturation (Figure 69), it is seen that unlike the head 
standard deviation, the saturation standard deviation is not zero at the bottom boundary of 
constant head. This result happens because the uncertainty in saturation comes from the 
uncertainty in the soil parameter a, even though the head is specified with certainty there. 
An injection of rate Q = 1 mL hr1 starts at time t = 0, and lasts for 150 days. The actual 
injection at the field test site is a point in 3-D, whereas the model is in 2-D. In the model, the 
injection is approximated by a line source of length L3 perpendicular to the 2-D domain. 
Therefore, the injection rate is Q/L3 in 2-D. In this baseline case, L3 = 50 cm. It should be 
realized that the 2-D representation is an approximation and the accuracy of this approximation 

depends strongly on the choice of L3, which is, in turn, a strong function of injection rate. The 2D 
predictions should have the same general trend as the 3-D predictions. 
Table 35. Case Description 
Case Description 
1 
.f. = 6.258, s2 
f = 2.459, .f = 20 cm, .a. = 0.01 cm1 , s2 
a = 9 x 106 (cm1)2 , .a = 20 cm, 
f= 0.5, L1 = L2 = 200 cm, L3 = 50 cm, Q = 1 mL hr1 , So = 30% 
2 Same as in Case 1, except .f = .a = 30 cm 
3 Same as in Case 1, except L1 = L2 = 400 cm 
4 Same as in Case 1, except So = 20% 
5 Same as in Case 1, except So = 40% 
6 Same as in Case 1, except So = 60% 
7 Same as in Case 1, except f= 0.3 
8 Same as in Case 1, except .f. = 4.258 and L1 = L2 = 400 cm 
9 Same as in Case 1, except .a. = 0.02 cm1 
10 Same as in Case 1, except s2 
a = 0 
11 Same as in Case 1, except Q = 10 mL hr1 
12 Same as in Case 8, except Q = 50 mL hr1 , L3 = 100 cm 
13 Same as in Case 1, except .f. = 4.258 and L1 = L2 = 400 cm, and Q = 10 mL hr1 
14 Same as in Case 13, except Q = 50 mL hr1 and L3 = 100 cm 
Data Source: Schenker et al. 1995; DTN: LA9909WS831372.020 
NOTE: f = ln Ks is the log-transformed saturated hydraulic conductivity (for Case 1, the mean .f. and variance s2 
f 
are obtained with .Ks. = 6.552 x 103 cm hr1 and sKs = 2.143 x 102 cm hr1), ais a parameter related to 
pore-size distribution, .f and .a are the respective correlation lengths of f and a, fis porosity, L1 and L2 are 
the vertical and horizontal dimensions of the domain, L3 is the length of the third dimension used to calculate 
the injection rate for 2-D simulation, So is the background saturation, and Q is the actual injection rate. 
Figure 70 shows the vertical and horizontal profiles of pressure head and saturation at 150 days. 
It is seen that the impact of injection is the increase of pressure head and saturation in the vicinity 
of injection. The effects seem to be even in all directions near the injection. This result is 
caused by the fact that the injection rate is overwhelmingly large compared to the background 
unsaturated hydraulic conductivity. The approximate radius of noticeable pressure head and 
saturation changes is 40 cm. 
Other flow quantities (e.g., flux and velocity) and their associated uncertainties can be given 
similarly. In principle, the concentration field and its associated uncertainty may be predicted 
based on this information. However, at this stage, there is no existing model for solute transport 
in a nonstationary, unsaturated flow field. Our ongoing related research may provide us with a 
model during Phase-2 prediction. 

DTN: LA9909WS831372.020 
NOTE: Horizontal and vertical profiles of pressure head h and saturation S under steady-state conditions. 
Figure 69. Steady-State Profiles 
DTN: LA9909WS831372.020 
Figure 70. Case 1. Horizontal and Vertical Profiles of Pressure 
Head h and Saturation S at 150 Days after Injection Started 

Sensitivity Cases 
Because the number of actual measurements at the site is too few to perform any statistical 
analysis, the statistical parameters are either taken or estimated from the relevant YMP 
databases. There is another level of uncertainty associated with inaccurate statistical and other 
parameters. Sensitivity studies, presented below, are performed using the stochastic model. 
As mentioned before, there is no information regarding the correlation lengths of the log of the 
saturated hydraulic conductivity and the rock pore-size distribution parameter a. Case 2 
investigates the effect of the correlation lengths by changing it from 20 cm to 30 cm. Figure 71 
shows the corresponding profiles of pressure head and saturation. Comparison of Figures 70 and 
71 reveals that the prediction is insensitive to the correlation lengths. 
In the base case, the domain size is taken to be L1 = 200 cm by L2 = 200 cm. However, the real 
domain is much larger than this. The size of the domain is expected to affect the standard 
deviations of flow quantities to some extent. In Case 3, L1 = 400 cm and L2 = 400 cm. It is seen 
from Figure 72 that, in areas away from the boundaries, the confidence intervals are quite 
insensitive to the domain size. 
DTN: LA9909WS831372.020 
Figure 71. Case 2. The Same as Case 1 in Figure 70 but with .f = .a = 30 cm 
Cases 4 through 6 from Table 35 investigate the effect of background saturation by changing the 
specified flux at the top and the constant head at the bottom. Figures 73 to 74 show the cases for 

So = 20%, 40%, and 60%, respectively. It is seen that the peak pressure head and saturation at 
the injection location decrease with the increase of the background saturation, but the impact 
radius increases with it. The width of confidence intervals for pressure head decreases with the 
increase of background saturation, whereas that for saturation profiles is quite insensitive to the 
background saturation. 
In Case 7, the porosity is varied from f= 0.5 to 0.3 (Figure 76). Comparison of Figure 76 and 
Figure 77 reveals that a lower porosity results in a slight increase in both the peak saturation and 
the radius of influence. However, the effect of porosity on solute transport is expected to be 
greater. 
DTN: LA9909WS831372.020 
Figure 72. Case 3. The Same as Case 1 in Figure 70 but with L1 = L2 = 400 cm 
In Case 8, the mean of the log of the saturated hydraulic conductivity K is increased from .f. =  
6.258 to 4.258 (where f = ln K). As one should expect, a larger hydraulic conductivity renders a 
lower peak saturation and a larger radius of influence (Figure 77). In this case, the size of the 
domain was changed to 400 cm by 400 cm to accommodate the increase of saturation at large 
distance. In Case 9, the mean of a is varied from 0.01 to 0.02 cm-1 while the variance of ais 
kept the same. It is seen that the mean head has increased significantly with a larger a for a 
given saturation (Figure 78). The confidence intervals are qualitatively similar to those in Figure 
70, but the intervals are tighter in Figure 79. This difference occurs because the variability in a 
is actually reduced by keeping the same variance but with an increased mean value. As 
expected, the predictionin particular, the width of confidence intervalsis sensitive to the 

variabilities in saturated hydraulic conductivity and pore size distribution a. Case 10 shows the 
case in which the variability of a is zero. It is seen from Figure 79 that the width of the 
confidence intervals is significantly reduced in the absence of variability in a. In this case, the 
uncertainties in the prediction are entirely caused by the variability in saturated hydraulic 
conductivity Ks. In the baseline case, the coefficient of variation CVa = sa/.a . = 0.3, while 
CVKs = 3.27. That is to say, the variability in saturated hydraulic conductivity Ks is much larger 
than that in pore size distribution a. Therefore, it may be concluded that the results are much 
more sensitive to the variability in a than to that in Ks. 
DTN: LA9909WS831372.020 
Figure 73. Case 4. The Same as Case 1 in Figure 70 but with So = 20% 
As one should expect, the behaviors of pressure head and saturation profiles are very sensitive to 
the injection rate. In Case 11, the injection rate is Q = 10 mL hr1 (Figure 80). The peak 
saturation is much higher than that in Case 1 (Figure 70) and the radius of influence is also 
larger. This difference is even clearer from Case 12 (Figure 81), for which Q = 50 mL hr1. 
In the last two cases, the mean saturated hydraulic conductivity .Ks . is taken to be 4.68 cm hr1. 
This value is based on some site-specific measurements and is three orders of magnitude larger 
than the value found in the YMP databases mentioned earlier. As for sensitivity runs, this value 
is taken as the mean and CVKs = sKs/.Ks. = 3.27, as in the baseline case. Equivalently, .f. = 
0.314 and s2f = 2.459. In Case 13, Q = 10 mL/hr and L3 = 50 cm (Figure 82); in Case 14, Q = 50 
mL hr1 and L3 = 100 cm (Figure 83). As found in Case 8 (Figure 77), a larger hydraulic 
conductivity renders a lower peak saturation and a larger radius of influence. It is expected that 
the tracer travels significantly faster in these cases. 

DTN: LA9909WS831372.020 
Figure 74. Case 5. The Same as Case 1 in Figure 70 but with So = 40% 
DTN: LA9909WS831372.020 
Figure 75. Case 6. The Same as Case 1 in Figure 70 but with So = 60% 

Figure 76. Case 7. The Same as Case 1 in Figure 70 but with f= 0.3 
DTN: LA9909WS831372.020 
DTN: LA9909WS831372.020 
Figure 77. Case 8. The Same as Case 1 in Figure 70 but with .f. = 4.258 and L1 = L2 = 400 cm 

Figure 78. Case 9. The Same as Case 1 in Figure 70 but with .a. = 0.02 cm1 
DTN: LA9909WS831372.020 
DTN: LA9909WS831372.020 
Figure 79. Case 10. The Same as Case 1 in Figure 70 but with sa = 0 

Figure 80. Case 11. The Same as Case 1 in Figure 70 but with Q = 10 mL hr1 
DTN: LA9909WS831372.020 
DTN: LA9909WS831372.020 
Figure 81. Case 12. The Same as Case 8 in Figure 77 but with Q = 50 mL hr1 and L3 = 100 cm 

Figure 82. Case 13. The Same as Case 1 in Figure 70 but with 
.f. = 0.314, L1 = L2 = 400 cm, and Q = 10 mL hr1 
DTN: LA9909WS831372.020 
DTN: LA9909WS831372.020 
Figure 83. Case 14. The Same as Case 13 in Figure 82 but with Q = 50 mL hr1 and L3 = 100 cm 

6.8.6.2.4 Nonstationarity 
The stochastic model of transient fluid flow in unsaturated, stationary (statistically 
homogeneous) media was modified to account for nonstationary features such as distinct layers 
in the rock properties. This extension was then implemented into the stochastic model and tested 
with some two-dimensional examples. In these examples, some special cases of medium 
nonstationarity are considered: trending in the statistical moments of the log of saturated 
hydraulic conductivity and pore size distribution parameter; zones of different rock properties 
existing in the domain; and different layers present in the domain. The effect of an embedded 
thin layer on fluid flow for the Phase-1A test was investigated with this modified stochastic 
model. It was found that this thin layer acts like a barrier to fluid flow and induces lateral fluid 
spreading. Only after accounting for this thin layer did the stochastic model produce fluid 
redistribution behaviors that are qualitatively similar to those observed by the mineback. A 
quantitative comparison will be made after the site-specific statistical parameters are obtained 
from the on-going geostatistical analysis of the rock property measurements at the site. In 
addition, it was found that the flow nonstationarity under unsaturated conditions significantly 
affects the behaviors of solute migration in such flow fields. The effect of the thin layer in the 
Phase-1A area on the migration of injected tracers can be assessed with site-specific statistical 
parameters. 
6.8.6.3 Monte Carlo Flow and Transport Simulations 
6.8.6.3.1 Introduction 
To augment the results of the sensitivity analyses for the homogeneous-model calculations and 
the stochastic-model results, a series of Monte Carlo analyses were carried out in two 
dimensions. The goal of these simulations is to bracket the range of possible transport behaviors 
that could arise due to variability in the hydrologic parameters. To accomplish this goal, a 
refined 2-D grid was generated (Figure 84) for performing flow and transport calculations. As in 
the homogeneous simulations using FEHM (V2.00, STN: 10031-2.00-00), the top and bottom 
boundaries of the model are held at constant capillary pressure. A single realization of the model 
consists of two simulations: a background simulation (without fluid injection at the borehole) to 
establish a steady-state flow condition followed by a simulation in which fluid of unit 
concentration (arbitrary concentration units) is injected for 180 days, the planned duration of the 
Phase-1A experiments. To simulate a heterogeneous system, the model is populated with a 
distribution of permeability values with a given mean value and an assumed correlation length. 
Figure 85 shows a permeability distribution chosen at random from the Case-1 simulations (see 
Table 36 for a summary of the different cases treated in the Monte Carlo simulations; detailed 
discussion of the individual cases considered is described below). Contrasting permeability 
values within the region of rock in which fluid is injected is expected to affect the flow and 
transport behavior by providing preferential pathways for fluid migration through the rock. 

DTN: LA9909WS831372.021 
NOTE: Top: full view (6 m x 6 m); Middle: intermediate close-up view (0.4 m x 0.5 m); Bottom: close-up of borehole. 
Figure 84. Finite-Element Grid Used in the Monte Carlo Simulation 
ANL-NBS-HS-000019, Rev 00, ICN 02 220 November 2001 

Permeability (m2) 
DTN: LA9909WS831372.021 
NOTE: This plot shows the permeability distribution for the Monte Carlo realization selected for discussion. 
Figure 85. Permeability Distribution 
Table 36. Summary of Monte Carlo Cases* 
Case number Correlation of aand permeability Correlation length of heterogeneities 
1 None 0.2 m 
2 Altman et al. (1996, Eq. on p. 34) 0.2 m 
3 Altman et al. (1996, Eq. on p. 34) 0.1 m 
4 Altman et al. (1996, Eq. on p. 34) 0.5 m 
DTN: LA9909WS831372.021 
NOTE: *All cases assume ln(ks) = 1.54, where ks is in cm hr1. 
6.8.6.3.2 Methodology 
The Monte Carlo approach considers the results of all individual realizations to be equally likely 
outcomes of the behavior of the system. Therefore, once a metric is chosen for quantifying the 
behavior of the system, statistical properties of the behavior of the system can be established. In 
the present study, the movement of a conservative solute injected with the fluid is used. The 
maximum penetration distances in all four directions away from the injection point are recorded 
for several different concentrations to establish the direction of movement of tracer. Then, for all 
50 realizations in a given case, the mean values for these distances are recorded along with the 
standard deviation. The mean values establish the general location of the concentration front, 

whereas the standard deviation is a measure of the uncertainty in the predictions of 
concentration-front movement due to the heterogeneities of the rock mass. 
6.8.6.3.3 Statistical Results 
One advantage of the Monte Carlo approach is that an individual realization can be examined in 
detail to understand the behavior of the system, after which, the multiple realizations can be used 
to quantify the uncertainty. The behavior of the flow and transport system is now examined 
(Figure 86) for the permeability distribution shown in Figure 85. The background saturation 
distribution shows little or no variability. This result is in contrast to the variability in predicted 
fluid saturation for the cases in which permeability and van Genuchten aare correlated. 
For that type of heterogeneous field, the fluid saturation is a strong function of a. The left-hand 
panels of Figure 86 show the movement of the saturation front into the rock mass for various 
times during the injection phase, and the right-hand panels are the concentrations of the 
conservative tracer for those same times. For this rock at this injection rate, there appears to be a 
relatively uniform migration of fluid and tracer away from the injection point in all directions, 
even upward. Under these conditions, the capillary-pressure driving forces are strong enough to 
pull water against the force of gravity. The presence of the borehole produces a shadow effect 
in which fluid must migrate around the borehole to reach the rock on the opposite side of the 
injection pad. Regarding the influence of heterogeneities, there is some tendency for fluid to be 
drawn preferentially into portions of the rock with higher capillary suction. The resulting 
saturation and concentration fronts exhibit an irregular pattern that tracks the heterogeneities. 
Nevertheless, the general patterns of movement of fluid and solute match fairly closely those of 
the homogeneous simulations. 
Now the results of the statistical analyses of the Monte Carlo simulations are examined. Table 
37 shows the mean and standard-deviation values for the four cases summarized in Table 36. 
The y coordinate in the table represents the vertical direction, with negative values below the 
borehole injection point. The x coordinate is laterally away from the borehole, with positive 
values located on the side at which the injection pad is located. First consider the results of Case 
1, in which the permeability field is assumed to vary but the van Genuchten a value is constant. 
The mean values for the minimum and maximum y values illustrate the degree to which the 
transport occurs uniformly in upward and downward directions. The Case-1 results show that 
capillary forces tend to pull water (and tracer) uniformly upward and downward with little or no 
tendency for downward migration due to gravity. The C = 0.01 isoconcentration value is meant 
to represent the migration of the front edge of the concentration plume; it travels approximately 
40 to 45 cm in the upward, downward, and outward (positive x) directions, on average. The 
injection point is located at approximately x = 5 cm and y = 0. The travel distance for the C = 
0.5 isoconcentration value is more indicative of bulk plume movement, rather than the leading 
edge. This front travels approximately 30 cm in the three directions. The xmin values suggest a 
slight asymmetry in plume migration. This asymmetry is caused by the shadow effect due to 
the presence of the borehole, as described above. 

DTN: LA9909WS831372.021 
15 days 
45 days 
90 days 
135 days 
180 days 
2 m 
NOTE: These plots show fluid saturations (left panels) and tracer concentrations (right panels) at various times 
during a Monte Carlo simulation. Note the restricted range of saturation values used. 
Figure 86. Fluid Saturations and Tracer Concentrations 

Table 37. Statistical Results of Monte Carlo Simulations 
C = 0.01 C = 0.1 C = 0.5 
xmin xmax ymin ymax xmin xmax ymin ymax xmin xmax ymin ymax 
Case 1 
Mean 0.35 0.53 0.45 0.42 0.28 0.46 0.37 0.35 0.20 0.37 0.29 0.27 
Std. dev. 0.11 0.12 0.082 0.093 0.098 0.11 0.077 0.085 0.08 0.098 0.070 0.068 
Case 2 
Mean 0.30 0.47 0.42 0.41 0.23 0.41 0.35 0.35 0.16 0.33 0.27 0.27 
Std. dev. 0.019 0.014 0.015 0.012 0.016 0.013 0.013 0.013 0.010 0.012 0.011 0.013 
Case 3 
Mean 0.29 0.48 0.41 0.41 0.22 0.41 0.35 0.35 0.15 0.33 0.27 0.27 
Std. dev. 0.014 0.012 0.011 0.011 0.011 0.012 0.011 0.011 0.007 0.013 0.011 0.010 
Case 4 
Mean 0.28 0.49 0.41 0.42 0.22 0.42 0.35 0.35 0.15 0.34 0.27 0.27 
Std. dev. 0.027 0.024 0.022 0.024 0.022 0.020 0.019 0.018 0.015 0.017 0.018 0.017 
DTN: LA9909WS831372.021 
NOTE: Results based on 50 realizations for each case listed in Table 36. 
The borehole causes the plume to have more difficulty migrating in the negative x direction. The 
standard deviation values reflect the uncertainty in the predicted migration of the plume caused 
by the heterogeneous permeability distribution. For Case 1, the uncertainty in the C = 0.01 
isoconcentration value is about 8 to 12 cm (depending on direction), whereas for the C = 0.5 
value, the uncertainty ranges from about 7 to 10 cm. Therefore, for Case 1, the heterogeneous 
permeability field (with no variability in a) adds considerable uncertainty to the predictions. 
A comparison of Cases 1 and 2 illustrates the influence of imposing a correlation of permeability 
and aon the uncertainty of the predictions. The mean values for the spreading of the plume in 
all directions are very similar for the two cases, but the uncertainty due to heterogeneity is much 
smaller when a is assumed to be correlated with permeability (Case 2). The correlation imposes 
a larger a for lower permeability, resulting in a larger capillary suction for regions of the rock 
with lower permeability. This result counteracts the tendency for fluid to travel preferentially 
through higher permeability rock, as in Case 1. Therefore, the spreading of tracer in Case 2 is 
more uniform, and the standard deviation values are consequently smaller. 
Finally, the influence of correlation length on plume spreading can be examined by comparing 
Cases 2, 3, and 4, which assumed correlation lengths of 0.2 m, 0.1 m, and 0.5 m, respectively. 
The mean behavior of the plumes is very insensitive to the correlation length. Regarding the 
uncertainty in plume prediction (as measured by the standard deviation), there is a trend toward 
larger uncertainty as the correlation length increases, as expected. Nevertheless, the uncertainty 
for these cases is much smaller than the correlation length itself. This result is caused by the 
assumed correlation of permeability and afor each of these cases, which, as just discussed, 
largely negates the distribution of permeability values. Therefore, the largest uncertainty in these 
simulations appears to be the nature of the correlation (or lack thereof) of different hydrologic 

properties. Permeability and the van Genuchten a parameter were correlated in these 
simulations. Altman et al. (1996, p. 34) also propose correlations between permeability and 
porosity for Yucca Mountain tuffs. Therefore, the most important data that could be collected to 
further constrain these predictions are hydrologic property measurements on a much larger set of 
samples from the test block. A full suite of property measurements (porosity, permeability, and 
unsaturated hydrologic parameters) on samples collected from known locations in the block 
would be useful to set correlations between parameters and assign correlation lengths for future 
simulations. 
6.8.6.3.4 Interpretations of the Monte Carlo Phase-1A Study 
The modeling analyses for Phase 1A indicate that strong capillary forces in the rock matrix of the 
Tac unit are likely to modulate fracture flow from overlying units, thereby dampening pulses of 
infiltrating water and providing a large degree of contact between radionuclides and the rock 
matrix. Several modeling approaches, from deterministic to Monte Carlo to stochastic models, 
were used to simulate the Phase-1A experiments. All yielded similar qualitative results. From 
these results, the tentative conclusion is that the deterministic modeling approach taken at the site 
scale may be adequate. As the data from the UZTT become available, parameterizations used in 
these calculations will be updated. 
A particularly interesting observation from the Phase-1B experiment is that, even when injection 
occurs immediately adjacent to a fracture, water appears to be imbibed quickly into the 
surrounding matrix. The transport times observed immediately below the injection point were on 
the order of 30 days, whereas pure fracture flow would have resulted in travel times of minutes 
to hours at this flow rate. Site-scale models must be evaluated in light of this observation. 
Models that predict significant fracture flow at percolation rates low enough for the matrix to 
transmit the flow may be inconsistent with the Phase-1B experiment. 
6.8.6.3.5 Forward Efforts 
In Phase 1A, the fluorescein image information is being incorporated into the modeling effort. 
Also, a small number of moisture and bromide samples from the Phase-1A rock are being 
analyzed. The strengths and weaknesses of the three conceptual approaches presented in this 
section are being assessed based on their relative accuracy in predicting flow and concentration. 
Predictions will be compared with flow and transport experimental data. 
Measurements of the tracer concentrations from collected samples are to be conducted after that 
date. After auger samples are collected and analyzed and mineback completed, the numerical 
predictions will be compared against the measured values, and the accuracy of the model 
configuration will be addressed. 

6.8.7 Initial Phase-2 Model Predictions 
6.8.7.1 Introduction 
In this section, conservative and reactive tracer breakthrough times are predicted for each of the 
sampling boreholes for up to one year from the start of Phase-2 injection. This work constitutes 
the first blind prediction on the behavior of the Phase-2 block of the Busted Butte transport test 
prior to injection. These predictions were made and presented prior to starting Phase-2 injection. 
These predictions are intended to test the current modeling concepts and tools available to the 
integrated site-scale model and the validity of the abstractions of that model for performance 
assessment. The predictions use parameters from currently available Yucca Mountain 
hydrologic and geochemical databases. At this stage, no model calibration to the UZTT has been 
performed. As data become available from the various phases of the UZTT, they will be 
incorporated into refined versions of the model. The new information will be used to make 
improved predictions. 
The computer code FEHM (V2.00, STN: 10031-2.00-00), which was used in the site-scale UZ 
flow and transport model and its abstractions for performance assessment, is also used in the 
development of the 3-D model presented in this report. Specifically, this code is used for 
radionuclide migration predictions using calibrated site-scale flow solutions. Although 
detailed geologic and hydrologic property distributions in three dimensions are not available at 
present for the UZTT, it is anticipated that during the course of the testing, these data will 
become available. Three-dimensional effects will probably become important as data specific to 
the test block become available for the Phase-2 block. The model is, therefore, being developed 
in three dimensions at the outset to capture these effects and to anticipate the 3-D property 
database that will be collected for the test block. 
6.8.7.2 Model Description 
The Phase-2 test block at Busted Butte encompasses, from top to bottom, the lower section of the 
Topopah Spring Tuff vitrophyre (Tptpv2) and the hydrologic Calico Hills unit (CHn, comprised 
of Tptpv1 and Tac). The first step in constructing a 3-D finite-element model of the Phase-2 test 
is to build a finite-element mesh using the coordinates of the injection and collection boreholes. 
The file used in this work contains the surveyed local coordinates of the boreholes and the 
layered stratigraphy at the site. 
Figure 87 gives a representation of the Phase-2 block with the boreholes represented as colored 
lines. The simulation block is approximately 7-m high by 12-m deep by 12-m wide and contains 
28 boreholes ranging from 7.5 m to 10.0 m in length. The 8 injection boreholes (shown in red) 
all originate in the left rib of the Test Alcove (located in front of the figure). These boreholes are 
subparallel, distributed along two horizontal planes, and are perpendicular to the 12 collection 
boreholes (dark blue) coming from the right rib of the Main Adit (to the left of the vertical yz 
plane). The other boreholes (light blue) are dedicated to ERT and GPR-T measurements. 

DTN: MO0004GSC00167.000 (for location); LA9909WS831372.022 
NOTE: The red lines represent injection boreholes; the dark blue lines collection boreholes; and the light blue lines 
are devoted to tomography. In this view, the Test Alcove is located in front of the figure, and the Main Adit is 
to the left of the figure (beyond the x = 2 plane). Dimensions are in meters. 
Figure 87. Three-Dimensional View of the Injection and Collection Boreholes 
The model domain extends from +2 to +14 m in the x-direction, +60 to +72 m in the y-direction, 
and 8.2845 to +2.5015 m in the z-direction. These coordinates are consistent with the surveyed 
local coordinates and the stratigraphy of the block. Figure 88 shows a top view of the finite-
element grid with the borehole locations. 
In general, the mesh was refined at locations between the injection and collection boreholes to 
accurately capture the migration of the tracers and heterogeneities at scales smaller than the layer 
thickness. In the x-direction, a grid spacing of 0.25 m at locations close to the boreholes was 
chosen. In both the x-and y-directions, a coarse mesh spacing at the block boundaries was 
chosen because no transport is expected at these locations. In the y-direction, a mesh spacing of 
0.125 m at locations close to boreholes was chosen. A slightly finer grid spacing was used in the 
y-direction than the x-direction to capture accurately the location of the injection points, which 
are spaced 0.61 m apart in the y-direction (10 injection points per injection borehole). In the z-
direction, the stratigraphy is represented with 6 distinct layers: 5 layers to represent the Calico 
Hills hydrogeologic unit (Tac: 3 layers; Tptpv1: 2 layers), and 1 layer to represent Tptpv2. The 
discretization in the z-direction is dependent on the particular layer because some layers are 
thicker than others. The discretization ranged from 0.15 to 0.25 m. The entire model is 

DTN: MO0004GSC00167.000 (for location); LA9909WS831372.022 
NOTE: As in Figure 87, injection boreholes are depicted as red lines, collection boreholes as blue, and ERT 
boreholes as green. The horizontal axis represents the x-direction, increasing to the right. The vertical axis 
represents the y-direction, increasing bottom to top. 
Figure 88. Top View of Finite-Element Grid and the Injection and Collection Boreholes 
comprised of 128,570 nodes. Figures 89 and 90 show views of the grid from the Test Alcove 
and the Main Adit, respectively. Once the mesh was constructed, the next step was to assign 
properties to the model. Table 38 contains the property sets used in the different layers. These 
properties are based upon measurements collected from the same units in the Yucca Mountain 
area but not actually from Busted Butte. 

DTN: MO0004GSC00167.000 (for location); LA9909WS831372.022 
NOTE: The blue colored lines represent collection boreholes. Because the injection boreholes originate in the Test 
Alcove, they are perpendicular to the plane of the figure and are depicted as circles. The horizontal axis 
represents the x-direction. The vertical axis represents the y-direction. 
Figure 89. Finite-Element Grid as seen from the Test Alcove 
Table 38. Property Sets for the Phase-2 Test 
Busted 
Butte 
Layer 
Matrix Material 
(Flint 1998) 
Fracture Material 
Layer 
am 
(m1) nm 
Km 
(m2) Layer 
af 
(m1) nf 
Kf 
(m2) ff . 
Tac1 CHv 3.5 1.19 5 x 1012 CH1v 11.52 3.0 2.43 x 109 7.14 x 105 0.5 
Tac2 CHv 3.5 1.19 5 x 1012 CH1v 11.52 3.0 2.43 x 109 7.14 x 105 0.5 
Tac3 CHv 3.5 1.19 5 x 1012 CH1v 11.52 3.0 2.43 x 109 7.14 x 105 0.5 
Tptpv1 BT1 0.56 1.31 1 x 1013 CH1v 11.52 3.0 2.43 x 109 7.14 x 105 0.5 
Tptpv1 BT1 0.56 1.31 1 x 1013 CH1v 11.52 3.0 2.43 x 109 7.14 x 105 0.5 
Tptpv2 PV2 2.2 1.25 1 x 1016 TSW2 0.91 2.92 6.6 x 109 1.29 x 104 0.25 
DTN: LA9909WS831372.022, LB970601233129.001 
NOTE: Here, a and n are the van Genuchten parameters, K is permeability, fis volume fraction, and .is porosity. 
The subscript m signifies matrix material and the subscript f signifies fracture material. 
ANL-NBS-HS-000019, Rev 00, ICN 02 229 November 2001 

DTN: LA9909WS831372.022 
NOTE: The horizontal red lines represent the location of injection boreholes, which are perpendicular to the 
collection boreholes (blue). The collection boreholes originate in the Main Adit, but several are plunging 
down and, thus, appear as blue lines rather than circles. The green line represents one of the tomography 
boreholes. The horizontal axis represents the y-direction, and the vertical axis represents the z-direction. 
Figure 90. Finite-Element Grid as seen from the Main Adit 
For this preliminary investigation, layers 1 through 3 were combined into a single Calico Hills 
unit (Tac), layers 4 to 5 were assigned Tptpv1 properties, and layer 6 was assigned Tptpv2 
properties. As additional data become available, layers 1 through 3 and 4 through 5 will all be 
treated as distinct layers. Porosity values were obtained from a few samples from the Busted 
Butte site. Permeabilities and van Genuchten relative-permeability parameters for the matrix 
were obtained from Flint (1998). This study (Flint 1998) was chosen because it represents the 
existing YMP database for the unsaturated zone and contains sufficient samples to generate 
statistics on the variability of key parameters, such as matrix permeability and matrix van 
Genuchten parameters. Fracture van Genuchten parameters were taken from the calibrated 
flow model in DTN: LB970601233129.001. These data were obtained by fitting field data at the 
site scale. Although there is a great amount of uncertainty in fracture properties, this data set is 
considered to be a reasonable representation of YMP material properties applicable to Busted 
Butte. 
For this blind investigation, and in view of the absence of data on fracture-matrix interactions 
in the Calico Hills, the equivalent-continuum model (ECM) was used to model Phase 2. The 

ECM was also used in scoping calculations done during design of the test and is currently being 
used to understand both Phases 1A and 1B. 
Tracer breakthrough has occurred in the 10 mL hr-1 injection system of Phase 1B (borehole 6). 
Specifically, tracer was detected at the pad 35 days after injection. This result means the time of 
breakthrough occurred between 28 and 35 days in relation to the collection-pad schedule. To 
compare model predictions with test breakthrough times as defined by the appearance of 
fluorescein tracer on a collection pad, the concentration of the tracer must be known. For 
example, if the time of breakthrough is defined to be when the normalized concentration reaches 
0.5, then for Tptpv2, the ECM predicts a breakthrough at 47 days for a distance traveled by the 
tracer of 28 cm (Figure 91). At a normalized concentration of 0.3, the model predicts 
breakthrough at 31 days for the same distance, which is close to the observed breakthrough time 
for borehole 6. 
For the first phase of predictive modeling and in the absence of appropriate data, the property 
sets listed in Table 38 were used with no attempt at calibration. The background flow conditions 
were obtained by setting a capillary pressure at the top and bottom boundaries and allowing the 
block to equilibrate to a steady-state saturation profile. A capillary pressure of 200 m of water 
was chosen, which is within the range of capillary-pressure measurements at Yucca Mountain 
(Altman et al. 1996, p. 34). The capillary pressure was set so that a saturation of about 0.35 to 
0.45 was obtained in the block. Moisture measurements and preliminary porosity data from test-
block lithologies indicate that these saturation values are reasonable. Once the background 
conditions are set, the next step is to begin pumping and injecting tracer. 
Three different pumping rates were used for injection boreholes in the Phase 2 experiment: (a) 1 
mL hr1 (1 upper borehole), (b) 10 mL hr1 (4 lower boreholes), and (c) 50 mL hr1 (3 upper 
boreholes). The 1 mL hr1 rate is equivalent to an infiltration rate of approximately 30 mm yr1, 
which is well within the range of infiltration rates at Yucca Mountain. The predictions made 
(given below) show that during a 1-yr test, the 1 mL-hr1 pumping rate is not expected to 
transport any tracer to the sampling boreholes. Injection borehole 23 will be the only one that 
pumps at 1 mL hr1. The 10-mL-hr-1 injection rate is equivalent to an infiltration rate of 
approximately 380 mm yr1, which is slightly higher than the highest anticipated infiltration that 
could occur at Yucca Mountain. The lower injection boreholes 24, 25, 26, and 27 will operate at 
10 mL hr1. Finally, 50 mL hr1 is equivalent to an approximate infiltration rate of 1550 mm yr 
1 
. This infiltration rate is far higher than what is expected at Yucca Mountain even under wetter, 
future climate scenarios. The purpose of the 50 mL hr1 rate is to obtain enough separation in 
travel times between the conservative and reactive tracers so as to be visible and distinct in the 
field test. Injection boreholes 18, 20, and 21 will pump continuously at 50 mL hr1. 
6.8.7.3 Predictions 
The predictions below are borehole specific and can, therefore, be used to compare directly 
to test-block results. Phase-2 borehole numbers and relative locations were presented in 
Figure 38 (Section 6.8.2.4.1). Table 39 shows the distance between the closest sampling 
point and the injection planes. Tables 40 through 48 predict the tracer breakthroughs at 
each of these locations. 

DTN: LA9909WS831372.022, file: inject.trc 
NOTES: The plots show the conservative breakthrough of tracer 28 cm from the injection pad: (a) saturation 
breakthrough, (b) concentration breakthrough 
Figure 91. Phase-1B Breakthrough Predictions Using 
Equivalent-Continuum Model and Tptpv2 Properties 
Table 39. Closest Sampling Point to the Injection Planes 
Within Each Collection Borehole 
Collection 
borehole 
Distance from top 
injection plane (m) 
Collection 
borehole 
Distance from bottom 
injection plane (m) 
16 0.61 46 0.175 
17 0.80 48 0.175 
14 1.17 9 0.175 
15 1.17 10 0.59 (above plane) 
13 1.17 
12 1.17 
DTN: LA9909WS831372.022 
For all predictions, three criteria were used for tracer-breakthrough times: (a) a 5% concentration 
limit, (b) a 50% concentration limit, and (c) the concentration after 1 year from the time of 
injection (the time of submittal of results for TSPA-LA). Note that it was assumed that the 
concentration of tracer in the injection fluid is unity. Also, note that tracers are continuously 
injected for the duration of the test. 

2
A diffusion coefficient of 1 x 1011 m s1 was used for all tracers. Longitudinal and transverse 
dispersivities were zeroed out for this preliminary set of calculations. However, as with any 
finite-element model, some numerical dispersion is present. Due to the fine mesh spacing and 
small time steps taken in these simulations, numerical dispersion is not expected to play a 
significant role in these simulations. A bulk-rock density of 2,580 kg m3 was used for all layers. 
This parameter only affects the reactive-tracer breakthrough times. As more data become 
available, all of these parameters will be adjusted. However, the values chosen are reasonable 
representations of Yucca Mountain properties, given the existing database. 
Conservative Tracers 
Travel times were first predicted for fluorescein, a conservative tracer. Uniform properties were 
assumed for porosity, permeability, and van Genuchten model parameters within each of the six 
layers of the test block. The effect of heterogeneous property distributions is discussed at the 
end of this subsection. Figure 92 depicts a concentration plume for fluorescein after 1 year. 
Table 40. Fluorescein from Upper Injection Boreholes 
Borehole 
number 
5% Breakthrough 
concentration 
50% Breakthrough 
concentration 
Normalized 
concentration at 1 yr 
16 27 days 68 days 1.0 
17 48 days 118 days 1.0 
14 118 days 238 days 0.87 
15 103 days 218 days 0.90 
13 103 days 218 days 0.90 
12 212 days > 1 yr 0.46 
Remaining collection 
boreholes 
> 1 yr > 1 yr 0.0 
DTN: LA9909WS831372.022, file: cons.trc 
Table 41. Fluorescein from Lower Injection Boreholes 
Borehole 
number 
5% Breakthrough 
concentration 
50% Breakthrough 
concentration 
Normalized 
concentration at 1 yr 
46 4 days 30 days 1.0 
48 20 days 91 days 0.98 
9 53 days 166 days 0.91 
10 171 days > 1 yr 0.37 
DTN: LA9909WS831372.022, file: cons.trc 

x 
z 
y 
DTN: LA9909WS831372.022, file: inject.10004_con_node 
NOTES: The figure depicts the concentration plume after 1 year of conservative-tracer injection. The green 
isosurface represents a normalized concentration of 0.5; the red dots represent the sampling points along 
the collection boreholes. 
Figure 92. A Conservative Tracer Concentration Plume 

Tables 40 and 41 show the predicted breakthrough times of tracer for the upper and lower 
collection sampling points, respectively. As expected, sampling locations closer to the injection 
planes exhibit tracer breakthrough times that are earlier than those from more distant locations. 
As discussed in the next section, for simulations involving heterogeneous property distributions, 
this result may be modified due to preferential flow paths. The results indicate that tracer 
breakthrough is expected at several sampling locations within the first year, and some are 
expected within the first month. Conservative tracer breakthroughs could occur at earlier times 
than predicted by the ECM model if the model assumptions are erroneous. Fracture flow 
through Tptpv2, for example, could result in faster travel times. Even so, an additional year of 
operation may be required to achieve transport distances on the order of the entire length of the 
block. 
Nonconservative Tracers 
Table 42 shows the distribution coefficients, Kd, for the reactive, nonconservative tracers 
determined by parallel laboratory studies and used in Phase 2 for the various units. The 
measurements are preliminary but provide a starting point for the modeling effort. Travel times 
for reactive tracers are extremely sensitive to these distribution coefficients, and errors in these 
parameters strongly bias the results. One major deficiency in the preliminary measurements is 
that these results do not include reversible sorption, and equilibrium may not have been achieved 
when obtaining the distribution coefficients. 
Table 42. Retardation of Reactive Tracers 
Tracer Tptpv1, Tac Kd (mL g1) Tptpv2 Kd (mL g1) 
Lithium 1.0 0.0 
Manganese 15.6 6.5 
Nickel/cobalt 34.0 13.0 
DTN: LA9909WS831372.022 
The next set of tables shows the predicted breakthroughs for the three reactive tracers: lithium 
Tables 43 and 44), manganese (Tables 45 and 46), and nickel or cobalt (Tables 47 and 48). 
The data indicate that lithium does not sorb in Tptpv2 but mildly sorbs in Tac and Tptpv1. 
Although lithium sorption in Tac is mild when compared to manganese and nickel or cobalt, the 
sorption has a large effect on travel times over the time scale of interest. The lithium only breaks 
through at locations that are extremely close to the injection boreholes (i.e., boreholes 16, 17, 46, 
48 and 9). 
Manganese is predicted to sorb much more strongly than lithium. For this reason, manganese is 
only expected to break through at boreholes 46 and 48 within a one-year time span. Cobalt or 
nickel sorbs even more strongly than manganese and is not expected to break through at any of 
the boreholes during the time of the test. 

Table 43. Lithium from Upper Injection Boreholes 
Borehole 
number 
5% Breakthrough 
concentration 
50% Breakthrough 
concentration 
Normalized 
concentration at 1 yr 
16 52 days 193 days 0.79 
17 257 days > 1 yr 0.12 
14 > 1 yr > 1 yr 0.0 
15 > 1 yr > 1 yr 0.02 
13 > 1 yr > 1 yr 0.0 
12 > 1 yr > 1 yr 0.0 
Remaining collection 
boreholes 
> 1 yr > 1 yr 0.0 
DTN: LA9909WS831372.022, file: chem.trc 
Table 44. Lithium from Lower Injection Boreholes 
Borehole 
number 
5% Breakthrough 
concentration 
50% Breakthrough 
concentration 
Normalized 
concentration at 1 yr 
46 28 days 267 days 0.62 
48 63 days 327 days 0.55 
9 242 days > 1 yr 0.12 
10 > 1 yr > 1 yr 0.0 
DTN: LA9909WS831372.022, file: chem.trc 
There are many caveats that could strongly affect the predicted travel times of the reactive 
tracers. First, the model is extremely sensitive to the values of Kd, and the current Kd 
measurements are uncertain at this time. A simple Kd may not be sufficient to model sorption of 
these tracers due to chemical heterogeneities and nonlinear reactions. Finally, these immobile 
reactive tracers may sorb onto colloids, thereby enhancing their mobility. 
Heterogeneous System 
A major assumption of the above modeling results is that properties are homogeneous within a 
layer. In this section, the effects of the heterogeneity of properties within the layers are explored. 
In these simulations, permeability values are distributed within each layer. The means of the 
permeability values for each layer are assumed to be the same as the permeability values used in 
the homogeneous simulations. In each layer, a log-normal distribution of permeability with a ln 
(k) variance of 2.0 and a correlation length of 1 m in the x, y and z directions is assumed. In the 
Tac and Tptpv1 units, an equation has been proposed to represent the correlation between the 
van Genuchten parameter am and matrix permeability (Altman et al. 1996, unnumbered equation 
on p. 34). To explore the sensitivity of the model results to this type of correlation, this relation 
is used to distribute am throughout these units. 

Table 45. Manganese from Upper Injection Boreholes 
Borehole 
number 
5% Breakthrough 
concentration 
50% Breakthrough 
concentration 
Normalized 
concentration at 1 yr 
16 > 1 yr > 1 yr 0.0 
17 > 1 yr > 1 yr 0.0 
14 > 1 yr > 1 yr 0.0 
15 > 1 yr > 1 yr 0.0 
13 > 1 yr > 1 yr 0.0 
12 > 1 yr > 1 yr 0.0 
Remaining collection 
boreholes 
> 1 yr > 1 yr 0.0 
DTN: LA9909WS831372.022, file: chem.trc 
Table 46. Manganese from Lower Injection Boreholes 
Borehole 
number 
5% Breakthrough 
concentration 
50% Breakthrough 
concentration 
Normalized 
concentration at 1 yr 
46 277 days > 1 yr 0.06 
48 328 days > 1 yr 0.06 
9 > 1 yr > 1 yr 0.0 
10 > 1 yr > 1 yr 0.0 
DTN: LA9909WS831372.022, file: chem.trc 
Figure 93 shows the background saturation profile and the saturation profile after one year of 
continuous injection. The saturation profile shows that the 50 mL hr1 boreholes have a strong 
effect on the saturation profile. This effect is for two reasons, including the obvious reason that 
50 mL hr1 is the high injection rate. The second reason is that the 50 mL hr1 boreholes inject 
into the Tptpv2 layer, which has a much lower matrix permeability than the Calico Hills 
hydrogeologic unit (Tac and Tptpv1). The 10-mL hr1 injections in the Tac unit do not have a 
large effect on the saturation profile. The simulations indicate that capillary action is an 
important process around the 10 mL hr1 injections, which is mostly due to the high matrix 
permeabilities in this unit. 

Table 47. Nickel or Cobalt from Upper Injection Boreholes 
Borehole 
number 
5% Breakthrough 
concentration 
50% Breakthrough 
concentration 
Normalized 
concentration at 1 yr 
16 > 1 yr > 1 yr 0.0 
17 > 1 yr > 1 yr 0.0 
14 > 1 yr > 1 yr 0.0 
15 > 1 yr > 1 yr 0.0 
13 > 1 yr > 1 yr 0.0 
12 > 1 yr > 1 yr 0.0 
Remaining collection 
boreholes 
> 1 yr > 1 yr 0.0 
DTN: LA9909WS831372.022, file: chem.trc 
Table 48. Nickel or Cobalt from Lower Injection Boreholes 
Borehole 
number 
5% Breakthrough 
concentration 
50% Breakthrough 
concentration 
Normalized 
concentration at 1 yr 
46 > 1 yr > 1 yr 0.0 
48 > 1 yr > 1 yr 0.0 
9 > 1 yr > 1 yr 0.0 
10 > 1 yr > 1 yr 0.0 
DTN: LA9909WS831372.022, file: chem.trc 
Tables 49 and 50 show the breakthrough times for two realizations. As expected, heterogeneities 
do add some fluctuations in the trends observed previously; however, many of the trends still 
hold. 
6.8.7.4 Summary and Interpretation 
This section constitutes a preliminary blind prediction of the behavior of the Phase-2 block of 
the UZTT at Busted Butte. The prediction is intended to test the current modeling concepts and 
tools available to the integrated site-scale model and their abstractions for performance 
assessment. This prediction uses parameters from the available Yucca Mountain hydrologic and 
geochemical databases and is considered to be preliminary because calibrations have not been 
performed using information from Busted Butte. 
Modeling results for fluorescein, a conservative tracer, indicate that tracer breakthrough is 
expected at several sampling locations within the first year of testing. For some sampling 
locations, tracer breakthrough is predicted for travel times of less than a month. Tracer 
breakthroughs could be even quicker than predicted if the ECM assumption does not hold. 

Table 49. Fluorescein from Upper Injection Boreholes with Physical Heterogeneities 
Borehole 
number 
5% Breakthrough 
concentration 
50% Breakthrough 
concentration 
Normalized 
concentration at 1 yr 
Real.* 1 Real. 2 Real. 1 Real. 2 Real. 1 Real. 2 
16 28 days 36 days 70 days 106 days 1.0 0.96 
17 70 days 60 days 238 days 161 days 0.7 0.91 
14 173 days 170 days > 1 yr > 1 yr 0.35 0.38 
15 126 days 126 days 278 days 267 days 0.72 0.76 
13 118 days 142 days 247 days 347 days 0.80 0.54 
12 222 days 247 days > 1 yr > 1 yr 0.42 0.29 
Remaining 
collection 
boreholes 
> 1 yr > 1 yr > 1 yr 
0.0 0.0 
DTN: LA9909WS831372.022, files: real1.trc and real2.trc 
NOTE: *Real. = realization 
Table 50. Fluorescein from Lower Injection Boreholes with Physical Heterogeneities 
Borehole 
number 
5% Breakthrough 
concentration 
50% Breakthrough 
concentration 
Normalized 
concentration at 1 yr 
Real.* 1 Real. 2 Real. 1 Real. 2 Real. 1 Real. 2 
46 4 days 4 days 37 days 32 days 1.0 1.0 
48 24 days 22 days 106 days 96 days 0.97 0.98 
9 52 days 47 days 156 days > 1 yr 0.94 0.94 
10 197 days 202 days > 1 yr > 1 yr 0.28 0.26 
DTN: LA9909WS831372.022, files: real1.trc and real2.trc 
NOTE: *Real. = realization 
Fracture flow through the Topopah Spring (Tptpv2) could result in faster travel times. The 
fracture parameters for the van Genuchten model are not known to a high degree of accuracy. 
Another caveat in these modeling results is the effect of physical heterogeneities within each 
layer. Small-scale heterogeneities could result in preferential flow paths, which results in faster 
flow paths in some parts of the block and slower flow paths in other parts of the block. Monte 
Carlo simulations and more elegant stochastic techniques could be employed to capture the 
uncertainty in the travel times. 
More uncertainty exists in the predicted travel times of the reactive tracers when compared with 
the conservative-tracer predictions. The strongly sorbing tracers manganese and cobalt (or 
nickel) are not expected to break through within the first year of testing. Even weakly sorbing 

DTN:ili and injix 
y 
z 
x 
y 
z 
LA9909WS831372.022, fes: bg.10002_sca_node.npect.10002_sca_node.np 
NOTES: These saturation profiles are from 10 to 60% of the heterogeneous Realization 1: (a) background 
saturation, (b) saturation profile after 1 yr of injection. In the figure, injection points are red dots and 
collection points are blue dots. 
Figure 93. Predicted Saturation Profiles for Phase 2 

lithium only reaches a few collection boreholes. Therefore, an additional year of operation may 
be required to achieve transport distances that reach more sampling points. At this stage, it is 
important to note that the model is extremely sensitive to the values of Kd used, which are 
preliminary. In addition, a linear Kd model may not be sufficient to model sorption of these 
tracers due to chemical heterogeneities and nonlinear reactions. More rigorous reactive transport 
models could be used to check the linear Kd assumption. Finally, these immobile reactive tracers 
may sorb onto colloids, thereby enhancing their mobility. 
6.8.8 Model Validation 
Model validation is a process to demonstrate and document that a model is appropriate and 
adequate for its intended use. Models used in this AMR express a conceptual model of 
unsaturated flow as mathematical equations, which are solved by computer codes that execute 
numerical methods. Input to the problem includes rock properties and, in the case of FEHM 
V2.00 (STN: 10031-2.00-00), a mesh or grid that expresses the geometry. Validation includes 
developing confidence in the grid, the input values, and the code. 
The conceptual model underlying the models used in this AMR is the standard model of 
unsaturated Darcy flow with constitutive relationships defined by either van Genuchten or 
Gardner-Russo equations (van Genuchten 1980; Gardner 1958; Russo 1988), which are generally 
accepted by the scientific community. For this work, all rock property values were measured on 
samples either from the Busted Butte site or from the same geologic units at Yucca Mountain 
(Table 1e). Mesh validation was done by plotting the location of features such as boreholes and 
geologic contacts and comparing them visually with the known geometry, such as shown in 
Figures 34, 45, and 87. The codes were validated by comparing outputs for simple problems 
against analytical solutions where such exist. Close correspondence between the analytical and 
numerical methods was judged visually by inspection of plotted output. Output was also 
inspected visually to ensure that the behavior of the system conformed to what is expected for 
this conceptual model. 
The UZTT is an integration of field experiment, laboratory analysis, and conceptual and 
numerical simulation. The UZTT is designed to verify and validate the project's ability to 
capture transport, dispersion, fracture flow, and other features significant to the Yucca Mountain 
site in a computational model. 
The UZTT applies a variety of computer codes for the computational analyses. The primary 
flow and transport modeling code being used is FEHM. Computational grids for FEHM were 
generated using LAGRIT V1.0 (STN: 10212-1.0-00). The software code STO-UNSAT V1.0 
(STN: 10292-1.0LV-00) is used for stochastic flow modeling. STO-UNSAT numerically solves 
the moment differential equations that describe transient unsaturated flow in randomly 
heterogeneous porous media (Zhang 1999). Verification of this code included running solutions 
to steady state and then comparing with published one- and two-dimensional steady-state 
solutions (Zhang et al. 1998; Zhang and Winter 1998). All other computational tools used are 
standard commercial software as listed in Section 3.0 of this AMR. 
Computational grids were generated using site data from the TDMS (Table 1e). Domain size, 
stratigraphy, and borehole configuration were taken from site survey data and measurements. 

These configurations generated by LAGRIT were visually compared against the input data and 
judged to be accurate representations of these data. The correctness of the computational grids 
themselves was tested by running simple steady-state flow- and heat-conduction simulations 
with homogeneous material properties. Phase-1A deterministic and Monte Carlo simulations 
and Phase-2 simulations were run using FEHM V2.00, which is the primary code used to 
simulate the flow and transport of the field experiment. FEHM was chosen because it is the code 
that will be used to simulate flow and transport at the proposed repository for PA. FEHM has 
been used extensively within the YMP, and no modifications of FEHM were made for the UZTT 
modeling. In addition to extensive verification and validation of FEHM reported in Dash et al. 
(1997), these FEHM simulations were initially validated for simple homogeneous flow under 
uniform saturation. Plots of model results at a range of saturations showed the expected 
transition from capillary-dominated to gravity-dominated between 60% and 90% initial 
saturation. This agreement with expected behavior and the previously documented agreement 
with analytical solutions (Dash et al. 1997) indicates that the computational code was running 
correctly for this configuration (Soll 1997, pp. 18 to 26). 
Material properties used in the model were either site-specific when available or otherwise were 
derived from field and laboratory measurements made at or near Yucca Mountain. The ultimate 
validation of the UZTT models will be with data collected from the UZTT itself. The model 
results presented in this AMR are predictions that will be compared against actual field results. 
Full validation of these models at this time is, thus, premature. The only data currently available 
to validate the Phase-2 FEHM model qualitatively are those depicting the first detection of 
fluorescein (Figure 91). The Phase-2 model reported here adequately met the criterion that the 
tracer concentration predicted in the time interval when breakthrough actually occurred must be 
between 25% and 75%. 
STO-UNSAT, a stochastic differential equation code for modeling flow and transport, was used 
to develop a 2-D model for Phase-1A predictions. The STO-UNSAT model was validated by 
first running a simple homogeneous case (Zhang and Winter 1998, Figures 1 and 2) and 
comparing against the analytical results derived in Zhang et al. 1998 (Equations 63 and 46, 
respectively). Good agreement was also found between Phase-1A stochastic simulations using 
STO-UNSAT and Phase-1A deterministic predictions using the FEHM model. Further examples 
are contained in Zhang 1998 (pp. 12 to 21, 76). 
Note that the UZTT is itself a model validation process. As additional data become available, 
they will be incorporated into the UZTT computational models to improve their 
representativeness. Validation of the UZTT models to date has shown that they adequately 
reproduce input data and provide similar results in comparison tests. Because of the limited 
comparisons of UZTT model predictions and test data that are available to date, a final 
conclusion regarding the validity of these models for simulating the test conditions and 
environments of the UZTT cannot be made at this stage of the testing and modeling program. 
However, the results obtained thus far, as described in this AMR, support the conclusion that 
these models are adequate for their intended use. 

6.8.9 Summary: Implications for Performance Assessment 
The UZ transport test is designed to provide information suitable for assessing the validity of 
flow and transport models used in the site characterization and performance assessment 
programs for Yucca Mountain. Observations of the available data collected to date for Phase 1 
and Phase 2, and the modeling of these data, lead to several key conclusions of relevance to 
performance assessment. These conclusions are summarized below, categorizing them with 
respect to the particular field or modeling activity in this report. 
1. 
Laboratory measurements: The collection of unsaturated hydrologic property data using 
the UFA provides data of particular relevance to flow and transport models because they 
are direct measurements under unsaturated conditions rather than indirect, model-derived 
parameters. The Monte Carlo analyses (Section 6.8.6) indicate that the nature of the 
correlations between parameters such as permeability and the van Genuchten a parameter 
have a strong impact on the predictability of the flow and transport system. Therefore, 
additional measurements of hydrologic and transport parameters under unsaturated 
conditions could be used to constrain models and develop correlations. 
2. 
Phase-1A and -1B model results: In addition to the point just made, the modeling 
analyses for Phase 1A indicate that strong capillary forces in the rock matrix of the Tac 
unit are likely to modulate fracture flow from overlying units, thereby damping pulses of 
infiltrating water and providing a large degree of contact between radionuclides and the 
rock matrix. Several modeling approaches, from deterministic to Monte Carlo and 
stochastic models, were used to simulate the Phase-1A experiments (Sections 6.8.6 and 
6.8.7). All yielded similar qualitative results. From this we conclude tentatively that the 
deterministic modeling approach taken at the site scale may be adequate. The 
parameterizations used in performing these calculations must be evaluated after data from 
the UZTT are available. 
A particularly interesting observation from the Phase-1B experiment is that, even when 
injection occurs immediately adjacent to a fracture, water appears to be imbibed quickly 
into the surrounding matrix. The transport times observed immediately below the 
injection point were on the order of 30 days, whereas pure fracture flow would have 
resulted in travel times of minutes to hours at this flow rate. Site-scale models must be 
evaluated in light of this observation. Models that predict significant fracture flow at 
percolation rates low enough for the matrix to transmit the flow may be inconsistent with 
the Phase-1B experiment. 
3. 
Phase-2 modeling: Significant uncertainties uncovered by the modeling include the 
adequacy of continuum models in nonwelded units of high matrix permeability and the 
nature of the transition from fracture flow to matrix flow at contacts between 
hydrogeologic units. These are exactly the issues being studied within the UZTT. 
Primary focus over the past year has been on flow and transport field data collection. Data 
collection/analysis for Phase 2 will be continuing through September 2000, with continuing 
compilation, analysis, and interpretation of the field data. 

6.9 C-WELLS FIELD AND LABORATORY TRANSPORT TESTING 
6.9.1 Introduction 
To test conceptual SZ transport models for YMP, two major cross-hole, forced-gradient tracer 
tests were conducted at the C-wells (UE-25c#1, c#2, and c#3), which are located approximately 
2 km southeast of the potential repository footprint (Figure 94), and completed in fractured 
volcanic tuffs. Groundwater flow at this location is thought to be toward the southeast, which 
puts the C-wells directly downgradient of the southern end of the potential repository. The tracer 
tests were conducted in two different saturated intervals that differed in horizontal hydraulic 
conductivity by about 2 orders of magnitude: the lower Bullfrog Tuff, with a conductivity of 27 
to 52 m d1 (Geldon et al. 1997, p. 34 in Hydraulic Tests section), and the lower Prow Pass Tuff, 
with a conductivity of 0.8 to 1.0 m d1 (Reimus et al. 1999, p. A.7). Figure 95 depicts the 
hydrogeology of the C-wells and shows the packer locations in the tracer tests. Note from Figure 
95 that the vast majority of the water produced at the C-wells comes from a small number of 
relatively discrete zones, most of which are located in the lower half of the Bullfrog Tuff. Both 
tracer tests were conducted between wells c#2 and c#3 (a linear distance of approximately 30 m), 
with c#3 being the production well in the Bullfrog Tuff test and c#2 the production well in the 
Prow Pass Tuff test. 
The two tracer tests featured the simultaneous injection of several different tracers having 
different physical and chemical characteristics: (1) nonsorbing solutes with different diffusion 
coefficients (Br and pentafluorobenzoate), (2) a weakly-sorbing solute (Li+), and (3) 
carboxylate-modified latex (CML) polystyrene microspheres, which served as colloidal tracers 
(Reimus et al. 1999, pp. 5.1-5.2 and C.1-C.2). Two additional tracer tests in the Bullfrog Tuff 
were conducted, each of which involved the injection of only a single nonsorbing solute (Reimus 
et al. 1999, Appendix C, Section C.2). These additional tests were conducted primarily to 
determine the optimal injection well for the test involving multiple tracers, and they will not be 
discussed further here except in the context of how they supported the interpretation of the 
multiple tracer test in the Bullfrog Tuff. The simultaneous injection of multiple tracers offers 
significant advantages over single tracer injections because it allows transport processes to be 
better distinguished and quantified by comparing the responses of the different tracers. 
A series of laboratory studies were conducted in parallel with the field testing efforts to help 
support and constrain the interpretations of the field tests. These studies included (1) batch-
sorption tests to characterize Li+ sorption to C-wells tuffs, (2) diffusion-cell tests to determine 
matrix diffusion coefficients of tracers used in the field, and (3) dynamic-transport tests to study 
tracer transport in fractured and crushed tuffs under more controlled conditions than in the field. 
The batch-sorption tests and dynamic-transport tests have provided estimates of lithium sorption 
parameters for comparison with sorption parameters derived from the field tests. Such 
comparisons are important because they offer an indication of whether laboratory-derived 
radionuclide sorption parameters can be used defensibly in field-scale predictive calculations. 

)DTN: MO9906GPS98410.000 and GS981008312314.003 (borehole coordinates
Figure 94. Location and Layout of the C-Wells Complex 
ANL-NBS-HS-000019, Rev 00, ICN 02 245 November 2001 

DTN: GS970708312314.007 (lower Bullfrog packer locations); GS990408312315.002 (Prow Pass); GS981008312314.002; all are 
reference only 
NOTE: Packer locations indicate intervals in which hydraulic and tracer tests were conducted (the two tracer tests 
involving multiple tracers were conducted between c#2 and c#3). Packer locations for lower Bullfrog tests 
were derived from Geldon et al. (1997, Table 2). Porosity data are from Geldon (1993, Table 13), and 
fracture density data are from Geldon (1993, Tables 6 and 7); these data sets are shown only for reference 
purposes and have not been used in any of the analyses discussed in this report. 
Figure 95. Stratigraphy, Lithology, Matrix Porosity, Fracture Density, 
and Inflow from Open-Hole Flow Surveys at the C-Wells 

6.9.2 Summary of Field Test Results and Interpretations 
Figure 96 shows the normalized tracer responses (concentrations divided by injection masses) in 
the multiple tracer test in the Bullfrog Tuff. Figure 97 shows the normalized tracer responses in 
the Prow Pass multiple tracer test. Note that the concentrations and times for the Bullfrog Tuff 
test in Figure 96 are shown on log-log axes. The test conditions and tracer injection masses in 
the two tests are described in detail in Reimus et al. (1999), Chapter 5 (Prow Pass Tuff) and 
Appendix C (Bullfrog Tuff). Both tests featured partial recirculation of the water produced from 
the pumped well: ~3.5% recirculation in the Bullfrog Tuff and ~30% recirculation in the Prow 
Pass Tuff. 
The most striking feature of the tracer breakthrough curves in the Bullfrog Tuff test (Figure 96) 
is their bimodal (double-peaked) behavior. This behavior is attributed to a relatively small 
fraction (~13%) of the tracer solution exiting the injection borehole in short-residence-time 
pathways in the upper half of the injection interval, whereas the remaining tracer mass exited the 
borehole primarily in pathways of longer travel time deeper in the interval. The greater density 
of the tracer solution (injected just below the top packer) relative to the groundwater would have 
caused it to preferentially sink to the bottom of the relatively long (and unmixed) injection 
interval. Figure 98 shows that there was only one pentafluorobenzoate (PFBA) peak in a tracer 
test conducted earlier in the Bullfrog Tuff (same interval, same flow rates). The only difference 
between the two tests was that ~1,000 L of tracer solution was injected in the first test, and 
~12,000 L was injected in the second test. The packed-off injection interval volume was ~4300 
L, so in the first test, only about one-fourth of an interval volume was injected. Therefore, it is 
likely that only flow pathways in the lower part of the injection interval conducted tracers out of 
the borehole because of the tendency of the tracers to sink. In contrast, in the second test, 
approximately three interval volumes of tracer solution were injected, so the volume between the 
packers should have eventually filled with tracer solution, and tracers would have, thus, accessed 
flow pathways throughout the entire length of the interval. The flow survey information 
depicted in Figure 95 suggests that the zone of highest flow in the injection well (c#2) occurred 
in the upper half of the injection interval. 
The PFBA and bromide responses in both the Bullfrog Tuff tracer test and the Prow Pass Tuff 
test show clear qualitative evidence of matrix diffusion. The peak-normalized PFBA 
concentrations are higher than the peak-normalized bromide concentrations in both tests, and the 
second bromide peak in the Bullfrog Tuff test is somewhat delayed relative to the PFBA with a 
tail that appears to cross over the PFBA at long times. These features are all consistent with 
greater matrix diffusion of the more diffusive tracer (bromide) relative to the less diffusive tracer 
(PFBA). Another qualitative indication of matrix diffusion in the Prow Pass Tuff test is the 
jump(s) in solute tracer concentrations after each of the three major flow interruptions during 
the tailing portion of the test, which indicate diffusion of tracers out of the matrix and into 
fractures during the interruptions. Thus, the two tests support the concept of dual-porosity 
behavior (where flow occurs primarily in fractures, but there is a large amount of stagnant water 
available for tracer/contaminant storage in the near-stagnant water of the rock matrix) in the 
saturated, fractured system at the C-wells. 

DTN: LA0002PR831231.001 (SEP Tables S00086.001 and S00086.004) 
NOTE: Normalized tracer responses in the Bullfrog Tuff multiple tracer test (note both axes are log scale). Tracer recoveries were ~69% for PFBA, ~69% for 
bromide, ~39% for lithium, and ~15% for microspheres. Concentrations are normalized to mass injected. 
Figure 96. Normalized Tracer Responses in the Bullfrog Tuff Multiple Tracer Test 
ANL-NBS-HS-000019, Rev 00, ICN 02 248 November 2001 

ANL-NBS-HS-000019, Rev 00, ICN 02November 2001249DTN: LAPR831231AQ99.001 (SEP Tables S99126.001, S99126.002, S99126.003 and S99126.004)
NOTE:Normalized tracer responses in the Prow Pass Tuff multiple tracer test. Microsphere responses are shown more clearly in Figure 99. Tracerrecoveries were 52% for PFBA, 49% for Cl, 43% for Br, 19% for Li, 0.3% for 640-nm spheres, and 0.1% for 280-nm spheres.
Figure 97. Normalized Tracer Responses in the Prow Pass Tuff Multiple Tracer Test05010015020025030035040045050003006009001200150018002100240027003000Time, hrsNormalized Concentration, 10^9/LPFBABrClLi640-nm Spheres280-nm SpheresUnplanned flow 14-
hr flow interruption 
on 11/14/98 
Planned 7-day flow 
interruption (11/23-30/98) Planned 14-day flow 
interruption (12/21/98 
to 1/4/99)

DTN: LA0002PR831231.001 (SEP Tables S00086.001 and S00086.003) 
NOTE: The axes are both linear. These tests differed only in the amount of tracer solution injected.
Figure 98. Normalized PFBA Responses in Two Different Tracer Tests in the Bullfrog Tuff
ANL-NBS-HS-000019, Rev 00, ICN 02 250 November 2001 

The lithium responses in the two tests show obvious attenuation relative to the nonsorbing 
tracers, which is indicative of lithium sorption. The attenuation in the Prow Pass test and in the 
first peak of the Bullfrog Tuff test is almost exclusively a lowering of the peak concentration 
with little or no delay in arrival time. This behavior is consistent with lithium sorption in the 
matrix (after diffusion into the matrix). The attenuation in the second peak of the Bullfrog Tuff 
test involves a clear time delay along with a dramatic lowering of concentration. This behavior 
is consistent with sorption in both the fracture flow pathways and the matrix. 
The CML microsphere responses in the two tests indicate that the microspheres were 
significantly attenuated relative to the solute tracers in both tests with the attenuation relative to 
solutes being greater in the Prow Pass Tuff test. The microsphere responses in the Prow Pass test 
are shown more clearly on a plot of log-normalized concentration versus time in Figure 99. The 
responses in both tests (including both sizes of spheres in the Prow Pass test) are characterized 
by truncated tails relative to the solutes but with measurable concentrations that persist 
throughout the tests. This behavior is consistent with filtration followed by some sort of 
nonlinear or stochastic resuspension of the microspheres. 
The tracer responses in both tests were interpreted by simultaneously fitting the breakthrough 
curves using a semianalytical, dual-porosity transport model, RELAP, which is described in 
detail in Reimus et al. (1999, Appendix D). RELAP is part of the Reactive Transport 
Application (RTA) V1.1 (STN: 10032-1.1-00) software package. It solves Laplace domain 
transfer functions that describe tracer injection, well-bore mixing, recirculation, and tracer 
transport in a dual-porosity system. The equations used for transport in the flow system can take 
various forms depending on whether: (1) flow is assumed to be linear or radial, (2) the matrix is 
assumed to be finite or semi-infinite, or (3) sorption is assumed to be equilibrium or rate limited 
(Reimus et al. 1999, Appendix D). 
The interpretation strategy in the Bullfrog Tuff test was to (1) fit the first tracer peaks, (2) 
subtract the fitted responses from the complete breakthrough curve(s), and (3) fit the resulting 
residual second peak(s) with a second set of transport parameters. In the Prow Pass test, a single 
set of transport parameters was sufficient to fit the single modal tracer responses. The sequence 
of fitting the different tracer responses in each test was as follows. 
1. 
The Br and PFBA responses were fit simultaneously using RELAP. It was assumed that 
both tracers experienced the same mean residence time and the same longitudinal 
dispersivity (because they were injected simultaneously), but they had diffusion coefficients 
that differed by a factor of 3. Both tracers were assumed to be conservative (nonsorbing). 
(Assumptions 22, 23, and 24 in Section 5). 
2. 
The lithium response was fit by assuming that the lithium experienced the same mean 
residence time and dispersivity as the bromide and PFBA, but it had a diffusion coefficient 
two-thirds that of bromide and twice that of PFBA. (Assumptions 22 and 24 in Section 5). 
The only parameters adjusted to obtain a fit to the lithium data were sorption parameters. 

ANL-NBS-HS-000019, Rev 00, ICN 02November 2001252DTN: LAPR831231AQ99.001NOTE: The time scale (x axis) is linear. The microspheres appeared to respond after pressure transients associated with mixing the injection well bore (c#3)
and an unplanned flow interruption.
Figure 99. Log Normalized Tracer Responses in the Prow Pass Tuff Multiple Tracer Test0.1110100100003006009001200150018002100240027003000Time, hrsNormalized Concentration, 10^9/LPFBABrClLi640-nm Spheres280-nm SpheresMixing of C#3Flow Interruption 
on 11/14/98

3. 
The microsphere responses were fitted assuming the same mean residence times and 
dispersion coefficients as the solute tracers but without any diffusion into the matrix 
(diffusion coefficient of zero) (Assumptions 22 and 25 in Section 5). The rate-limited 
sorption features of RELAP were used to adjust filtration and resuspension rate constants 
until a fit to the data was obtained. 
4. 
To simulate the flow interruptions in the Prow Pass Tuff tracer test, a numerical code called 
RETRAN, also part of the RTA software package, was used. Unlike RELAP, RETRAN is 
capable of simulating nonlinear behavior and flow transients (Laplace transforms are 
limited to linear functions and steady-state flow behavior). RELAP fits were extended 
beyond the flow interruptions by using the transport parameters obtained from RELAP (up 
until the time of the flow interruptions) as inputs to RETRAN. RETRAN was also used to 
simulate nonlinear sorption behavior of lithium. 
The model RETRAN solves the concentration of tracer only at the breakthrough point, using a 
semi-analytical method. No mesh is required. The breakthrough curve is obtained by numerical 
inversion of the solution in Laplace space. Complete model documentation is reported in 
Reimus and Dash (1999), including discussion on stability and accuracy of solutions. RETRAN 
was validated for this application by fitting the observed breakthrough curves and visually 
judging the goodness of fit as shown in Appendix C of Reimus et al. (1999). RETRAN was 
chosen for this application because no data other than breakthrough data are available to fit. 
Therefore, there is no advantage to using a model that predicts concentrations everywhere. The 
model fits to the tracer breakthrough curves are not shown here graphically; the reader is referred 
to Reimus et al. (1999, Chapter 5 and Appendix C) for graphical representations of the fits. All 
transport parameters obtained from the fits, with the exception of lithium sorption parameters, 
are listed in Tables 51 and 52 for the Bullfrog and Prow Pass Tuff tests, respectively. Note that 
there are separate estimates of mean residence times and dispersivities depending on whether 
radial or linear flow is assumed (the two possible extremes for flow to a pumped well in a 
confined aquifer). These values reflect the parameter uncertainty associated with not knowing 
the true nature of the flow field. Figure 100 shows the range of longitudinal dispersivities 
deduced from the C-wells tracer tests on a plot of dispersivity versus length scale taken from 
Neuman (1990, Figure 3). It is apparent that the C-wells longitudinal dispersivities (the 
darkened box) are in relatively good agreement with Neumans published relationship of 
dispersivity versus length scale. Note that the lower end of the range of length scales associated 
with the darkened box corresponds to the interwell separation in the tracer tests and the upper 
end corresponds to the test interval thickness (used as an upper bound for the transport distance). 
The effective flow porosities listed in Tables 51 and 52 were calculated from the deduced mean 
tracer residence times using the following expression (Reimus et al. 1999, Section 5.6), which 
assumes radial convergent flow in a homogeneous, isotropic medium: 
. = 
pQR2 
t 
L 
(Eq. 41) 
where 
. = flow porosity R = distance between boreholes (m) 
Q = production rate (m3/hr) L = formation thickness (m). 
t = mean tracer residence time (hr) 

Table 51. Transport Parameters Deduced from Fits of the Bullfrog Tuff Tracer Responses d 
Parameter Pathway 1 e Pathway 2 
mass fraction in pathway 0.12 0.59 
residence time, linear flow (hr) 37 995 
longitudinal dispersivity, linear flow (m) 5.3 18.8 
residence time, radial flow (hr) 31 640 
longitudinal dispersivity, radial flow (m) 3.6 10.7 
effective flow porosity, linear (radial) a 0.0029 (0.0025) 0.026 (0.017) 
f 
b 
Dm for bromide (sec1/2) b 0.00158 0.000458 
microsphere filtration rate constant (hr1) c 0.2 0.04 
DTN: LA9909PR831231.003 
NOTES: a Based on flow log information (see Figure 95), it was assumed that 75% of the production flow 
contributed to the pathway 1 responses and 25% of the flow contributed to the pathway 2 responses. 
Equation 41 was used to estimate the flow porosity. 
b This parameter is an effective matrix diffusion mass transfer coefficient (Reimus et al. 1999, Section 5.6). 
The value of the parameter for PFBA was assumed to be 0.577 times that for bromide. 
c Multiple resuspension/detachment rate constants were assumed in each pathway to obtain a fit to the 
microsphere responses (see Reimus et al. 1999, Section C.5). Filtration coefficients (m1) can be 
calculated from the filtration rate constants (kfilt) using kfilt/V, where V = average linear velocity determined 
from mean fluid residence times. 
d See Table 53 for lithium sorption parameters. 
e Note that Pathway 1 refers to pathways that resulted in the first tracer peak, and Pathway 2 refers to 
pathways that resulted in the second peak. 
Table 52. Transport Parameters Deduced from Fits of the Prow Pass Tuff Tracer Responses 
Parameter Value 
mass fraction participating in test 0.75 
residence time, linear flow (hr) 1230 
longitudinal dispersivity, linear flow (m) a 23.1 
residence time, radial flow (hr) 620 
longitudinal dispersivity, radial flow (m) a 6.3 
effective flow porosity, linear (radial) 0.0068 (0.0034) 
f 
b 
Dm for bromide (sec1/2) b 0.000968 
640-nm sphere filtration rate constant (hr1) c 0.043 
280-nm sphere filtration rate constant (hr1) c 0.07 
DTN: LA9909PR831231.005 (see Assumption 26) 
NOTES: a Longitudinal dispersivities calculated after subtracting out apparent dispersion due to the 
recirculating flow field (see Reimus et al. 1999, p. 5.14). 
b See Table 51 for footnotes. 
c See Table 53 for lithium sorption parameters. 

DTN: LA9909PR831231.003 
NOTES: This figure shows the range of C-wells values derived from interpretations of the Prow Pass and Bullfrog 
reactive tracer tests (darkened box). Plot taken from Neuman (1990, Figure 3). Note that the right edge of 
the box corresponds to the interwell separation distance and the left edge of the box corresponds to the test 
interval thickness (taken to be the upper limit of transport distance). 
Figure 100. Longitudinal Dispersivity versus Length Scale of C-Wells Values 
from Interpretations of the Prow Pass and Bullfrog Reactive Tracer Tests 

The mass tracer solution (which may have resulted in tracers sinking out of the zone of 
influence of the pump). Finite but slow flow through the matrix may also have resulted in some 
tracer being pushed out of the injection well into the matrix (rather than fractures) where it would 
have been effectively lost from the fracture flow system. 
The parameter describing matrix diffusion in Tables 51 and 52 is actually a lumped parameter 
(Reimus et al. 1999 pp. C.7 and 5.15) which could be called the mass transfer coefficient, 
f 
D
m
b 
consisting of the matrix porosity, f, the fracture half-aperture, b, and the matrix diffusion 
coefficient, Dm. These parameters cannot be easily separated because it is not possible to obtain 
independent estimates of their in-situ values. It should be noted that the simultaneous fits to the 
nonsorbing tracer responses were not significantly improved by assuming a finite matrix (versus 
an infinite matrix) or by assuming multiple pathways with different matrix diffusion mass 
transfer coefficients (versus a single mass transfer coefficient). 
Lithium sorption parameters deduced from the field tracer tests are listed in Table 53. 
Laboratory-derived lithium sorption parameters (see below) are also listed in Table 53 to allow a 
comparison between field-and laboratory-derived sorption data. Table 53 indicates that lithium 
sorption in the field was always approximately equal to or greater than the sorption measured in 
the laboratory. It should be noted that although Table 53 lists only linear equilibrium sorption 
parameters (Kd values), the first lithium peak in the Bullfrog Tuff tracer test was actually a better 
fit assuming either rate-limited sorption or nonlinear sorption. A discussion of these and other 
alternative approaches to fitting the early lithium peak in the Bullfrog Tuff test is provided by 
Reimus et al. (1999, Section C.4.2). For the second lithium peak in the Bullfrog Tuff test and the 
only peak in the Prow Pass test, the assumption of linear equilibrium sorption provided very 
good fits to the data. 
The microsphere fitting procedure in the Bullfrog Tuff test is described in detail in Reimus et al. 
(1999, Section C.5). Two sets of pathways were assumed, each having a unique linear filtration 
rate constant. However, to fit the long tailing behavior, it was necessary to assume that there 
were multiple resuspension rate constants for different mass fractions of the spheres within each 
pathway. To match the low recovery of the spheres, a relatively large fraction of mass in each 
pathway was assumed to be irreversibly filtered (a resuspension rate constant of zero). The 
filtration rate constants resulting in a good fit to the complete microsphere response are given in 
Table 51. In the Prow Pass Tuff test, the microsphere responses were fit assuming only linear 
forward filtration with no resuspension (Reimus et al. 1999, p. 5.10). The resulting filtration rate 
constants are provided in Table 52. The fits provided a good match to the data up until the tails 
of the breakthrough curves began to flatten out. The long flat tails could probably be explained 
by multiple resuspension rate constants, but this was not attempted. 

Table 53. Comparison of Field- and Laboratory-Derived Sorption Parameters for Lithium Ion a 
Parameter Field Kd Laboratory Kd 
b 
Prow Pass matrix Kd assuming 
Central Prow Pass Tuff 
0.66 0.13 (0.26 at infinite dilution) 
Prow Pass matrix Kd assuming 
Lower Prow Pass Tuff 
1.68 0.084 (0.44 at infinite dilution) 
Bullfrog matrix Kd in Pathway 1 
assuming Central Bullfrog Tuff c 0.24 0.19 (0.44 at infinite dilution) 
Bullfrog matrix Kd in Pathway 1 
assuming Lower Bullfrog Tuff c 0.97 0.32 (1.64 at infinite dilution) 
Bullfrog matrix Kd in Pathway 2 
assuming Central Bullfrog Tuff c 0.67 0.19 (0.44 at infinite dilution) 
Bullfrog matrix Kd in Pathway 2 
assuming Lower Bullfrog Tuff c 2.75 0.32 (1.64 at infinite dilution) 
DTN: LA9909PR831231.003 (field Kd for Bullfrog); MO0012BRLI25C2.000 (laboratory Kd); LA9909PR831231.005 (see
Assumption 26) (field Kd for Prow Pass)
NOTES: a This comparison assumes linear sorption isotherms. 
b Values at infinite dilution obtained from slopes of Langmuir isotherm fits to the data (asymptotic slope at 
very low concentrations). Other values obtained from a simple linear fit to the entire range of data. 
c Pathway 1 refers to pathways that resulted in the first tracer peak in the Bullfrog reactive tracer test, and 
Pathway 2 refers to pathways that resulted in the second peak in this test. Kd values were calculated from 
the smallest matrix retardation factors obtained from alternative interpretations of the test (see Reimus et al. 
1999, Section C.4.2, Table C-8). 
Only one other C-wells tracer test involved the simultaneous injection of more than one tracer 
(the combined iodide and 2,4,5-trifluorobenzoate (TFBA) injection in the Prow Pass Tuff in June 
1998). This test exhibited the same qualitative behavior as the multiple tracer tests described 
above, that is, the iodide was more attenuated than the TFBA due to greater matrix diffusion. 
The comparisons of tracer responses resulting from injections into well c#1 and into either well 
c#2 while pumping well c#3 provided some insights into flow heterogeneity/ 
anisotropy in the lower Bullfrog Tuff at the C-wells. Table 54 lists the ratios of peak response 
times or first arrival times for conservative tracers between c#1 and c#3 and between c#2 and 
c#3 for the two tests in which a comparison was possible. For a homogeneous, isotropic 
medium, the response times under radial flow conditions are expected to vary as R2, which is the 
distance between injection and production well squared. The ratios of R2 values corresponding 
to both cases are also listed in Table 54. Note that the ratios of tracer response times and R2 
values are in reasonably good agreement in both cases, suggesting that anisotropy in the lower 
Bullfrog Tuff at the C-holes may be relatively small despite the apparent orientation of the 
fracture network in the general direction of c#1 to c#2 (Geldon 1993, pp. 44-46). 

Table 54. Ratios of Observed Tracer Arrival Times and Distances Squared for C-Wells Tracer Tests 
Tests Time c#1/Time c#2c#3 R2 c#1/R2 c#2c#3 
Bullfrog, PFBA (c#2) and Iodide (c#1) a 6 7.4 
Bullfrog, 2,6-DFBA (c#2) and Pyridone (c#1) b 10 7.4 
DTN:GS010508312315.001, LA0002PR831231.001. 
a
NOTES: Both tests conducted with 2.5 to 3.5% recirculation into injection well. Peak tracer arrivals compared. 
b Both tests conducted with no recirculation. First tracer arrivals compared. 
6.9.3 Summary of Laboratory Test Results and Interpretations 
6.9.3.1 Batch-Sorption Testing of Lithium Ion 
Batch-sorption tests were conducted to determine equilibrium sorption isotherms of lithium ion 
to seven different C-wells tuff lithologies over a three order-of-magnitude range of lithium 
solution concentrations that effectively spanned the range of concentrations in field tests (~1 to 
1000 mg L1). The resulting best-fitting Langmuir isotherms for each lithology are shown in 
Figure 102 (without the fitted data, which would excessively clutter the plot) (Reimus et al. 
1999, Section 6.2, Figures 6-3 to 6-9). In most cases, a Langmuir isotherm offered a better fit to 
the data than either a linear or Freundlich isotherm because of the tendency of the isotherm to 
plateau at higher concentrations, suggesting a saturation of the surface with lithium. These 
tests and their results are described in more detail in Reimus et al. (1999, Section 6.2). 
In conjunction with the batch-sorption tests, all tuffs tested were analyzed quantitatively for 
major mineral phases by x-ray diffraction (XRD) (DTN: MO0012MINLCHOL.000). Cation 
exchange capacity experiments were also conducted on each rock (DTN: 
MO0012CATECHOL.000). Not surprisingly, the tuffs that had the highest lithium sorption 
capacity (Bedded Prow Pass and Lower Bullfrog) also had the highest percentages of smectite 
clays and/or zeolites and the highest cation exchange capacities (Reimus et al. 1999, Table 6-4, 
Figure 6-2). The primary cations exchanging with the lithium in the cation exchange capacity 
tests were sodium and calcium. 
6.9.3.2. Diffusion Cell Testing 
Laboratory diffusion cell tests involved measuring the diffusion coefficients of the nonsorbing 
tracers used in field tests in intact blocks of saturated matrix material. The experimental 
apparatus and details of the test interpretations are presented in Reimus et al. (1999, Chapter 8). 
The diffusion coefficients of PFBA and bromide were measured in five different tuff lithologies, 
as listed in Table 55. Each of these lithologies could have been involved in the field tests. Table 
55 also gives the measured matrix porosities and permeabilities of the tuffs. It is evident that the 
diffusion coefficients are positively correlated with both of these rock properties, although the 
correlation is better with permeability. The two diffusion cell tests conducted in the lower Prow 
Pass Tuff indicate very good experimental reproducibility using two different pieces of core 
from this lithological unit. 

DTN: MO0012SORBCHOL.000, LA9909PR831231.005 (see Assumption 26) 
NOTES: See Figure 95. 
The units are ordered in the legend from shallowest to deepest. Lithium concentrations in the field tests 
ranged from less than 0.1 mg L1 to 2600 mg L1. 
These isotherms are based on log amount sorbed versus log solution concentration. 
Figure 101. Fitted Langmuir Sorption Isotherms for Lithium Sorption 
onto C-Wells Tuffs from Different Units/Lithologies 

The ratio of bromide to PFBA diffusion coefficients in the diffusion cell tests was consistently 
about 3:1, regardless of the absolute values of the diffusion coefficients. This ratio was used as a 
constraint when simultaneously fitting the responses of these tracers in the field tests. 
Table 55. Laboratory-Measured Matrix Diffusion Coefficients of 
Bromide and PFBA in Various C-Wells Tuffs a 
Tuff Porosity 
Permeability 
(mDarcy) 
L 
(cm) b 
Matrix diff. coeff. (cm2 s1 x 106) 
Br PFBA Br/PFBA 
Central Bullfrog 0.094 0.00107 1.16 0.45 0.13 3.46 
Lower Bullfrog 0.298 0.0949 0.84 1.0 0.35 2.86 
Upper Prow Pass 0.272 4.72 0.91 6.0 1.9 3.16 
Central Prow Pass 0.138 0.000786 1.23 0.4 0.13 3.08 
Lower Prow Pass-1 c 0.288 0.455 2.27 3.0 1.1 2.73 
Lower Prow Pass-2 c 0.288 0.455 1.82 3.0 1.0 3.0 
DTN: LA9909PR831231.003, MO0012PERMCHOL.000, MO0012POROCHOL.000, LA9909PR831231.005 (see Assumption 26) 
NOTES: a The porosities and permeabilities of the tuffs are also listed in this table. 
b L = thickness of tuff wafer used in diffusion cell experiment. 
c Duplicate experiments were conducted in the lower Prow Pass Tuff. 
6.9.3.3 Flowing Transport Experiments in Crushed and Fractured Tuffs 
Two types of flowing transport experiments were conducted to support the field testing efforts: 
(1) crushed-tuff column experiments, and (2) fractured-core column experiments. In the former, 
lithium bromide solutions were eluted through columns of the same crushed Central Bullfrog 
Tuff at different concentrations and different flow rates. These tests showed that lithium 
sorption under flowing conditions was in very good agreement with batch-sorption 
measurements and demonstrated that lithium sorption kinetics were rapid enough that lithium 
sorption in the field should be well approximated by assuming local equilibrium between the 
solution and solid phases. More details of the crushed-tuff column experiments and their results 
are given in Reimus et al. (1999, Section 7.1). The tests were interpreted using the RELAP and 
RETRAN components of the RTA (STN: 10032-1.1-00) software package. 
The fractured-core experiments offered more realistic laboratory simulations of the field tracer 
tests. These tests were conducted in induced (unnatural) fractures in both the Upper and Central 
Prow Pass Tuff lithologies (1617 cm long). Experimental methods are described in Reimus et 
al. (1999, Section 7.2). Two different sets of tests were conducted in each fracture: (1) multiple 
tests at different flow rates using only iodide as a tracer and (2) tests involving the simultaneous 
injection of lithium, bromide, and PFBA (analogous to the field experiments). The first set of 
tests consistently exhibited higher peak concentrations of iodide at higher flow rates, consistent 
with matrix diffusion (more iodide would be expected to diffuse into the matrix as residence 
times increase). Simultaneous RELAP fits of these data sets allowed estimates of matrix 
diffusion parameters in the columns. The second set of tests exhibited behavior that was very 
consistent with the field observations, that is, peak normalized PFBA concentrations that were 
greater than peak Br concentrations and lithium concentrations that were lower yet but not 
significantly attenuated in time (Reimus et al. 1999, pp. 7.8 to 7.9, Figures 7-8 to 7-9, 7-11). All 

of these features are consistent with dual-porosity transport behavior in the columns. These tests 
were interpreted using the same techniques (RTA) as the field tests involving multiple tracers. 
For brevity, the test results and interpretations are not presented here (see Reimus et al. 1999, 
Section 7.2). In general, apparent matrix-diffusion-mass-transfer coefficients were greater in the 
laboratory than in the field, and lithium sorption parameters in the laboratory were smaller than 
in the field. 
6.9.4 Conclusions from C-Wells Field and Laboratory Testing 
The C-wells field and laboratory tests have resulted in the following conclusions relevant to 
performance assessments of the potential Yucca Mountain repository. 
 
The responses of nonsorbing tracers in all field and laboratory tracer tests in fractured 
rocks have been consistent with matrix diffusion behavior. This result supports the use of 
a dual-porosity conceptual model to describe radionuclide transport through the saturated, 
fractured volcanic rocks near Yucca Mountain. 
 
Sorption of lithium ion in the field was greater than or equal to its measured sorption in 
the laboratory (see Table 53 for a comparison of lab- and field-derived sorption 
parameters). Although lithium does not behave identically to any radionuclide, this result 
suggests that the use of laboratory-derived radionuclide sorption parameters in field-scale 
transport predictions is defensible and may even be conservative. 
 
The effective flow porosities deduced from the field tests were less than 1% for all tracer 
responses except for the second tracer peak(s) in the Bullfrog Tuff test, for which the 
deduced flow porosity was several percent (even after apportioning 75% of the 
production flow to the first tracer peak). However, if the second peak was really the 
result of only 10% of the production flow, then the effective flow porosity would be only 
~1%, and if it were only 5% of the production flow, then the flow porosity would be less 
than 1%. Such small percentages of production flow resulting in the second tracer 
response(s) are entirely possible in a heterogeneous fracture flow system such as that at 
the C-wells. 
 
The longitudinal dispersivities deduced from the field-scale experiments are consistent 
with published relationships of dispersivity versus length scale (see, for example, Figure 
100). 
 
Matrix-diffusion-mass-transfer coefficients in the field experiments were less than in the 
laboratory experiments, and they generally decreased as tracer residence times increased. 
There are several possible explanations for this behavior including (1) larger average 
fracture apertures as length (and time) scales increase, (2) an increasingly greater 
influence of true matrix diffusion, as opposed to diffusion into stagnant free water as test 
durations increase, and/or (3) a greater tendency to encounter diffusion barriers in longer 
duration tests for which characteristic diffusion distances are greater. It should be noted 
that all tracer responses could be adequately fitted assuming a single matrix-diffusion-
mass-transfer rate for each tracer peak. 

INTENTIONALLY LEFT BLANK

7. CONCLUSIONS
Sorption is a function of water chemistry and the type of tuff at Yucca Mountain. It is assumed 
for the performance-assessment recommendations that the waters from wells J-13 and p#1 bound 
the major-ion chemistry of the groundwaters at Yucca Mountain, and that the number of 
sorption-coefficient distributions elicited to four per radionuclide: iron oxides, devitrified tuff, 
vitric tuff, and zeolitic tuff. The basis for this grouping is the fact that sorption of radionuclides 
is the result of a chemical reaction between the radionuclide in the groundwater and the minerals 
in the tuff. The mineralogy of the different strata of the same rock group is very similar, and the 
sorption coefficients can be grouped in terms of these rock types. Iron oxides were added to the 
list of rock types to reflect the containers to be used in the repository and the possibility that 
the corrosion by-products of a massive multipurpose container could become a substrate for 
sorption. Measured sorption coefficients onto tuffs were higher at elevated temperatures for all 
elements studied: americium, barium, cerium, cesium, europium, plutonium, strontium, and 
uranium. Therefore, sorption coefficients measured at ambient temperatures should be 
applicable and generally conservative when applied to describing aqueous transport from a hot 
repository. Table 2a gives recommended parameters for the sorption-coefficient probability 
models for performance assessment for unsaturated-zone units, and Table 2b shows the same 
parameters for the saturated zone. 
The alluvium exhibited significant sorptive properties with respect to Np, Tc, and I. Distribution 
coefficients (Kds) varied as a function of depth and borehole. Kd values for Np ranged from 
about 5 to 77 mL g-1; Kd values for Tc ranged from about 0.35 to 0.8 mL g-1; and preliminary Kd 
values for 129I ranged from about 0.41 to 0.75 mL g-1. Sorption was much faster for Np than for 
Tc or 129I. The differences in sorptive properties among samples probably result from 
differences in the amount of the sorptive phasesmectite, clinoptilolite, calcite, and hematite 
and perhaps from the presence of organic carbon and trace amounts of sulfides, which may 
explain the slow sorption response for Tc and 129I. Biological activity, or simply sorption onto 
organics, could also be important and account for the slow sorption responses for Tc and 129I. 
During these tests, significant amounts of colloids were also found, but their transport properties 
could not be investigated. These values are incorporated into Table 2a and b. 
Conclusions drawn from available data regarding radionuclide transport through fractured-rock 
columns show that, contrary to previous views about the role of fractures in radionuclide 
retardation, fracture flow does not necessarily result in a fast pathway for actinide migration 
through fractures. The migration of actinides through fractures can be significantly retarded by 
sorption onto minerals coating the fractures and by diffusion into the tuff matrix, a result 
consistent with the results of the Busted Butte tests. 
The results obtained from rock-beaker experiments agree with previous results and found that 
rate constants for uptake onto tuff of the sorbing cations from solution were consistent with a 
diffusion-limited model in which diffusion occurs in two stages. In the first stage, the cations 
diffuse into rock through water-filled pores; in the second stage, they diffuse into narrower 
intracrystalline channels. This diffusion model yielded sorption coefficients for cesium, 
strontium, and barium that agree well with the sorption coefficients determined by batch 
techniques. 

The UZTT at Busted Butte was designed to provide information suitable for assessing the 
validity of flow and transport models used in the site characterization and performance 
assessment programs for Yucca Mountain. Critical evaluation and iterative improvement of the 
flow and transport conceptual and numerical models await the collection of further data, which is 
currently in progress. The first step in this process was reported in this AMR, namely the 
predictions of flow and transport behavior for both the Phase-1 and Phase-2 experiments and the 
preliminary results of those experiments. As discussed in Sections 6.8.6 and 6.8.7, the models 
are doing a reasonable job of capturing the flow phenomenon at this site. The major prediction is 
that fracture flow is not dominant at this site. 
Although flow and transport field data collected to date are limited, observations of the available 
data collected so far, and the modeling of these data, lead to several key conclusions of relevance 
to performance assessment. These conclusions are summarized below, categorized with respect 
to the particular field or modeling activity in this report. 
 
Laboratory measurements: The collection of unsaturated hydrologic property data using 
the Unsaturated Flow Apparatus (UFA) provides data of particular relevance to flow and 
transport models because they are direct measurements under unsaturated conditions 
rather than indirect, model-derived parameters. The Monte Carlo analyses (Section 
6.8.6) indicate that the nature of the correlations between parameters such as permeability 
and the van Genuchten a parameter have a strong impact on the predictability of the flow 
and transport system. This impact could be better defined by conducting a full suite of 
measurements of hydrologic and transport parameters on rock samples to constrain 
models and develop correlations. 
 
Phase-1A and -1B model results for UZTT: The modeling analyses for Phase 1A indicate 
that strong capillary forces in the rock matrix of the Tac unit are likely to modulate 
fracture flow from overlying units, thereby damping pulses of infiltrating water and 
providing a large degree of contact between radionuclides and the rock matrix. Several 
modeling approaches, from deterministic to Monte Carlo and stochastic models, were 
used to simulate the Phase-1A experiments (Sections 6.8.6 and 6.8.7). All yielded similar 
qualitative results. From this we conclude tentatively that the deterministic modeling 
approach taken at the site scale may be adequate. The parameterizations used in 
performing these calculations will be confirmed as data from the UZTT are available. 
A particularly interesting observation from the Phase-1B experiment is that, even when 
injection occurs immediately adjacent to a fracture, water appears to be imbibed quickly 
into the surrounding matrix. The transport times observed immediately below the 
injection point were on the order of 30 days, whereas pure fracture flow would have 
resulted in travel times of minutes to hours at this flow rate. Site-scale models must be 
evaluated in light of this observation. Models that predict significant fracture flow at 
percolation rates low enough for the matrix to transmit the flow are inconsistent with the 
Phase-1B experimental data results. This behavior was demonstrated in the Phase 1B 
experiments that occurred in the lower section of the TS (tptpv2), but the same general 
behavior should occur in the Calico Hills unit because the matrix capillarity is even 
stronger than the TS unit, and the in situ saturation is low. 

 
Phase-2 modeling: Because there were not field-test results at the time of preparation of 
this AMR, it is difficult to draw conclusions relevant to the evaluation of models. 
Significant uncertainties uncovered by the modeling include the adequacy of continuum 
models in nonwelded units of high matrix permeability, and the nature of the transition 
from fracture flow to matrix flow at contacts between hydrogeologic units. These are the 
issues being studied within the UZTT. Therefore, preliminary answers to these important 
questions are anticipated after test data have been collected and analyzed. 
The UZTT has demonstrated that in the vitric Calico Hills unit, fluid movement is dominated by 
capillary forces and, thus, is predominantly matrix flow with little influence of fractures. Even 
with an overlying fractured unit (Topopah Spring welded tuff), the Calico Hills Formation below 
it strongly damps any fracture-dominated influence. These results suggest that commonly held 
views regarding the Calico Hills as a fast path for contaminants may be overstated. However, 
the UZTT is still in progress, and additional data regarding the influence of sorption and 
movement of radionuclides, as well as non-reactive contaminants, are still being collected for 
evaluation. 
Some conclusions from the C-wells field and laboratory tests are relevant to performance 
assessment of the potential repository. The responses of nonsorbing tracers in all field and 
laboratory tracer tests in fractured rocks have been consistent with matrix diffusion behavior. 
This result supports the use of a dual-porosity conceptual model to describe radionuclide 
transport through saturated, fractured volcanic rock near Yucca Mountain. Results also suggest 
that the use of laboratory-derived radionuclide sorption parameters in field-scale transport 
predictions is defensible and conservative. The effective flow porosities deduced from the field 
tests were less than 1% for all tracer responses except for the second tracer peak in the Bullfrog 
Tuff test, for which the deduced flow porosity was several percent. Such small percentages of 
production are entirely possible in a heterogeneous fracture flow system such as that at the C-
wells. The longitudinal dispersivities deduced from the field-scale experiments are consistent 
with published relationships of dispersivity versus length scale. Matrix diffusion mass transfer 
coefficients in the field experiments were less than in the laboratory experiments, and they 
generally decreased as tracer residence times increased. There are several possible explanations 
for this behavior, including (1) larger average fracture apertures as length (and time) scales 
increase, (2) an increasingly greater influence of true matrix diffusion, as opposed to diffusion 
into stagnant free water, as test durations increase, and/or (3) a greater tendency to encounter 
diffusion barriers in longer duration tests in which characteristic diffusion distances are greater. 
It should be noted that all tracer responses could be adequately fitted assuming a single matrix 
diffusion mass transfer rate for each tracer peak. 

7.1 RECOMMENDATIONS 
Sorption 
The sorption data suggest that there are definite upper and lower bounds for the Kd values 
recommended to PA. Unfortunately, the ranges can be large, and large uncertainties in Kd values 
between 0 and 5 can result in very large uncertainties in predicted travel times. Further 
experiments under a wide variety of conditions relevant to the repository situation could reduce 
uncertainty in Kd values. 
Because sorption is sensitive to the mineralogy, it is critical to either have an adequate 
understanding of the mineralogy and distributions along the flow pathway to the accessible 
environment or to accommodate this uncertainty in the PA evaluations. As an example, sorption 
of Np by hematite is thousands of times greater than sorption by the vitric tuff, and fracture 
coatings of hematite will influence transport well beyond what is indicated by its mass fraction in 
the system. 
Sorption of Tc and I by the alluvium may be an important mechanism to be considered in PA 
evaluations. The preliminary data show non-zero Kd values of these radioisotopes in the 75-to-
500-m fraction of the alluvium (the standard size range for YMP sorption experiments). 
However, although this size range is appropriate for crushed whole-rock tuff samples, it omits 
the most sorptive components of the alluvium. Therefore, the alluvium may have higher Kd 
values than those measured to date. 
Colloid-facilitated transport 
The degree to which colloid-facilitated radionuclide migration will be a problem at Yucca 
Mountain depends very strongly on water chemistry, the specific radionuclide, type and size of 
the colloid, and ambient conditions, including degree of saturation. Unfortunately, the few 
colloid transport studies that have been documented have been conducted on systems and under 
conditions not relevant to the YMP. The data collected for this AMR are also of limited 
usefulness because they lack accompanying physical colloid transport data, such as colloid 
transport through fractured rock columns or invert/backfill materials in the laboratory or through 
tuff units in the field. In addition, a few well-designed studies on YMP-specific problems could 
resolve most of the colloid issues with respect to the repository environment. 
1. 
The chemistry and type of colloids present in groundwaters should be determined. 
2. 
Column experiments should be performed to determine the transport properties of 
colloids in Yucca Mountain materials. 
3. 
The stability of colloids under various repository conditions should be determined. 
4. 
The radionuclide and colloid attachment/detachment properties should be determined for 
all colloids anticipated in the repository environment. 

5. 
The flux and water chemistry through the drift should be determined or more accurately 
bounded. 
Busted Butte UZTT and C-Wells 
The Busted Butte UZTT shows that there is no significant fracture flow through unsaturated 
nonwelded units and that the travel speed through these units are presently overestimated by 
orders of magnitude. This observation, together with the likely retardation properties of the 
alluvium units for Tc, I, and Np, and the corroboration of a dual-porosity conceptual model to 
describe radionuclide transport through the saturated, fractured volcanic rocks near Yucca 
Mountain determined from the C-wells field and laboratory tests, provides the greatest technical 
support for a successful LA, and it is recommended that their completion be a major focus of 
efforts leading to the LA. 
7.2 IMPACTS 
Kd values and diffusion coefficients enter into the Principal Factor of Dilution of Radionuclide 
Concentrations in the Geologic Setting and perhaps some of the Other Factors For the Post-
Closure Safety Case. Consequently, PA calculations using these data will better illustrate the 
sensitivity of radionuclide release rates to the data, and an exact impact will be determinable; 
similarly, for Busted Butte and C-wells results. 
Users of the output from this AMR should consider the limitations on the results imposed by the 
assumptions listed in Section 5 and the limitations resulting from the available experimental 
determinations discussed in Sections 7.1 and 6. The uncertainties resulting from these 
limitations may affect how the output should be used in other analyses or models. 
The software codes used in this AMR (LANL 2000a and 2000b) have not been verified. 
However, their use is considered suitable for the purposes of this AMR with respect to site 
recommendation performance assessment, and it is expected that verification of the codes will 
not affect the results presented. 
7.3 OUTPUT DATA 
Data developed by this analysis are included in the following. 
 
Sorption parameter values for both the unsaturated and the saturated zones can be found 
in DTN: LA0003AM831341.001. 
 
The matrix diffusion coefficients recommended for use in PA can be found in DTN: 
LA0003JC831362.001. 
 
Output data for the Busted Butte simulations are found in DTN: LA9909WS831372.019, 
LA9909WS831372.020, LA9909WS831372.021, and LA9909WS831372.022. 

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8. INPUTS AND REFERENCES
8.1 DOCUMENTS CITED 
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Francais des Argiles, 26, (2), 193199. Paris, France: Centre National de la Recherche 
Scientifique. Copyright Requested Library Tracking Number: 236921. 
Allard, B. 1982. Sorption of Actinides in Granitic Rock. KBSTR 82-21. Goteborg, Sweden: 
Chalmers University of Technology. TIC: 205892. 
Allard, B. and Beall, G.W. 1979. Sorption of Americium on Geologic Media. Journal of 
Environmental Health, A14, (6), 507518. New York, New York: Marcel Dekker. TIC: 
224102. 
Altman, S.J.; Arnold, B.W.; Barnard, R.W.; Barr, G.E.; Ho, C.K.; McKenna, S.A.; and Eaton, 
R.R. 1996. Flow Calculations for Yucca Mountain Groundwater Travel Time (GWTT-95). 
SAND96-0819. Albuquerque, New Mexico: Sandia National Laboratories. ACC: 
MOL.19961209.0152. 
Ames, L.L., Jr.; McGarrah, J.E.; and Walker, B.A. 1983. Sorption of Trace Constituents from 
Aqueous Solutions onto Secondary Minerals: 1. Uranium. Clays and Clay Minerals, 31, (5), 
321334. Long Island City, New York: Pergamon Press. TIC: 238914. 
Andersson, K. 1988. SKI Project-90: Chemical Data. SKI Technical Report 91:21. 
Stockholm, Sweden: Swedish Nuclear Power Inspectorate. TIC: 242643. 
Baes, C.F., Jr. and Mesmer, R.E. 1986. The Hydrolysis of Cations. Malabar, Florida: Krieger 
Publishing Company. TIC: 223481. 
Balistrieri, L.S. and Chao, T.T. 1987. Selenium Adsorption by Goethite. Soil Science Society 
of America Journal, 51, (5), 11451151. Madison, Wisconsin: Soil Science Society of America. 
TIC: 223172. 
Balistrieri, L.S. and Murray, J.W. 1982. The Adsorption of Cu, Pb, Zn, and Cd on Goethite 
from Major Ion Seawater. Geochimica et Cosmochimica Acta, 46, 12531265. New York, 
New York: Pergamon Press. TIC: 238913. 
Banfield, J.F. and Eggleton, R.A. 1989. Apatite Replacement and Rare Earth Mobilization, 
Fractionation, and Fixation during Weathering. Clays Minerals Society, 37, (2), 113127. Long 
Island, New York: Pergamon Press. TIC: 236948. 
Bar-Yosef, B. and Meek, D. 1987. Selenium Sorption by Kaolinite and Montmorillonite. Soil 
Science, 144, (1), 1119. Philadelphia, Pennsylvania: Lippincott, Williams & Wilkins. TIC: 
237321. 

Barrer, R.M. and Townsend, R.P. 1976. Transition Metal Ion Exchange in Zeolites: Part 1. 
Thermodynamics of Exchange of Hydrated Mn2+, Co2+, Ni2+, Cu2+, and Zn2+ Ions in Ammonium 
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Los Alamos National Laboratory. TIC: 222337. 
8.2 CODES, STANDARDS, REGULATIONS, AND PROCEDURES 
AP-2.13Q, Rev. 0, ICN 4. Technical Product Development Planning. Washington, D.C.: U.S. 
Department of Energy, Office of Civilian Radioactive Waste Management. ACC: 
MOL.20000620.0067. 
AP-2.21Q, Rev. 0, ICN 0. Quality Determinations and Planning for Scientific, Engineering, and 
Regulatory Compliance Activities. Washington, D.C.: U.S. Department of Energy, Office of 
Civilian Radioactive Waste Management. ACC: MOL.20000802.0003. 
AP-3.15Q, Rev. 3. Managing Technical Product Inputs. Washington, D.C.: U.S. Department 
of Energy, Office of Civilian Radioactive Waste Management. ACC: MOL.20010801.0318. 
AP-SIII.2Q, Rev. 0, ICN 3. Qualification of Unqualified Data and the Documentation of 
Rationale Accepted Data. Washington, D.C.: U.S. Department of Energy, Office of Civilian 
Radioactive Waste Management. ACC: MOL.20001002.0152. 
AP-SI.1Q, Rev. 2, ICN 4, ECN 1. Software Management. Washington, D.C.: U.S. Department 
of Energy, Office of Civilian Radioactive Waste Management. ACC: MOL.20001019.0023. 
AP-SV.1Q, Rev. 0, ICN 2. Control of Electronic Management of Information. Washington, 
D.C.: U.S. Department of Energy, Office of Civilian Radioactive Waste Management. ACC: 
MOL.20000831.0065. 
LANL-YMP-QP-S5.01, Rev 0. 2000. Electronic Data Management. Los Alamos, New 
Mexico: Los Alamos National Laboratory. ACC: MOL.20000621.0094. 
QAP-2-0, Rev. 5. Conduct of Activities. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.19980826.0209. 

QAP-2-3, Rev. 10. Classification of Permanent Items. Las Vegas, Nevada: CRWMS M&O. 
ACC: MOL.19990316.0006. 
8.3 SOFTWARE 
DeltaGraph, V4.0.1, Macintosh.
LBNL 1999. Software Code: CART V1.0. 10046-1.0-00. SUN, UNIX.
LLNL 2000. Software Code: ZOMBIE V3.0. 10431-3.0-00.
Los Alamos National Laboratory 1999. Software Code: FEHM V2.00. V2.00. SUN Ultra
Sparc. 10031-2.00-00.
LANL (Los Alamos National Laboratory) 1999. Software Code: RTA V1.1. V1.1. SUN.
10032-1.1-00.
LANL (Los Alamos National Laboratory) 1999. Software Code: TRACRN V1.0. V1.0. SUN
Ultra 2. 10106-1.0-00.
LANL 2000. Software Code: LAGRIT V1.0. 10212-1.0-00.
LANL 2000. Software Code: STO-UNSAT V1.0 LV. 10292-1.0LV-00.
Microsoft Excel, V5, Macintosh.
Microsoft Excel 97 SR-1.
8.4 SOURCE DATA, LISTED BY DATA TRACKING NUMBER 
GS000408312231.003. Relative Humidity Calculated Porosity Measurements on Samples from 
Borehole USW SD-9 Used for Saturated Hydraulic Conductivity. Submittal date: 04/10/2000. 
GS940508312231.006. Core Analysis of Bulk Density, Porosity, Particle Density, and In Situ 
Saturation for Borehole UE-25 UZ#16. Submittal date: 05/04/1994. 
GS950408312231.004. Physical Properties and Water Potentials of Core from Borehole USW 
SD-9. Submittal date: 03/01/1995. 
GS950608312231.008. Moisture Retention Data from Boreholes USW UZ-N27 and UE-25 
UZ#16. Submittal date: 06/06/1995. 
GS950808312322.001. Field, Chemical, and Isotopic Data Describing Water Samples Collected 
in Death Valley National Monument and at Various Boreholes and around Yucca Mountain, 
Nevada, between 1992 and 1995. Submittal date: 08/16/1995. 

GS951108312231.009. Physical Properties, Water Content, and Water Potential for Borehole 
USW SD-7. Submittal date: 09/26/1995. 
GS960808312231.001. Water Permeability and Relative Humidity Calculated Porosity for 
Boreholes UE-25 UZ#16 and USW UZ-N27. Submittal date: 08/28/1996. 
GS960808312231.003. Moisture Retention Data for Samples from Boreholes USW SD-7, USW 
SD-9, USW SD-12, and UE-25 UZ#16. Submittal date: 08/30/1996. 
GS960808312231.005. Water Permeability and Relative Humidity Calculated Porosity for 
Samples from Boreholes USW SD-7, USW SD-9, USW SD-12, and USW UZ-14. Submittal 
date: 08/30/1996. 
GS970708312314.007. Results of Hydraulic Conservative Tracer Tests in Miocene Tuffaceous 
Rocks at the C-Hole Complex, 1995-1997, Yucca Mountain, Nevada. (Submitted Input Request 
Tracking Number 00067.R). Submittal date: 07/31/1997. 
GS010508312315.001. Concentration of Difluorobenzoic Acid and Pyridone from Tracer Tests 
Conducted at the C-Well Complex, 1/8/97  7/11/97. Submittal date: 06/01/01 
GS981008312314.002. Pump Test Data Collected at the C-wells Complex, 1/8/973/31/97. 
(Submitted Input Request Tracking Number 00067.R) Submittal date: 10/28/1998. 
GS981008312314.003. Pumping Test Data Collected at the C-wells Complex, 5/7/9612/31/96. 
Submittal date: 10/28/1998. 
GS990308312242.007. Laboratory and Centrifuge Measurements of Physical and Hydraulic 
Properties of Core Samples from Busted Butte Boreholes UZTT-BB-INJ-1, UZTT-BB-INJ-3, 
UZTT-BB-INJ-4, UZTT-BB-INJ-6, UZTT-BB-COL-5, AND UZTT-BB-COL-8. Submittal 
date: 03/22/1999. 
GS990408312231.001. Saturated Hydraulic Conductivity of Core from SD-9, 2/273/27/95. 
Submittal date: 04/27/1999. 
GS990408312315.002. Transducer, Barometric Pressure, and Discharge Data Collected from 
4/18/98 through 11/24/98 in Support of the Ongoing Hydraulic and Tracer Tests Being 
Conducted at the UE-25 C-Well Complex, Nevada. Submittal date: 04/06/1999. 
GS990708312242.008. Physical and Hydraulic Properties of Core Samples from Busted Butte 
Boreholes. Submittal date: 07/01/1999. 
GS990808312322.001. Field and Isotopic Data from Ground Water Samples from Wells in the 
Amargosa Valley and NTS. Submittal date: 08/23/1999. 
LA000000000034.001. Diffusion of Sorbing and Non-Sorbing Radionuclides. Submittal date: 
01/22/1993. 

LA000000000034.002. Diffusion of Sorbing and Non-Sorbing Radionuclides. Submittal date: 
06/22/1993. 
LA000000000106.001. Report on the Study of Kinetic Effects Using Crushed Tuff Columns: 
Neptunium in Sodium Bicarbonate Waters. Submittal date: 11/01/1994. 
LA0001JC831361.001. Radionuclide Transport through Saturated Fractures. Submittal date: 
01/14/2000. 
LA0001JC831361.002. Radionuclide Transport through Saturated Fractures. Submittal date: 
01/14/2000. 
LA0002JC831341.001. Depth Intervals and Bulk Densities of Alluviums. Submittal date: 
03/08/2000. 
LA0002JC831341.002. Quantitative X-ray Diffraction (QXRD) Results of Three Alluviums. 
Submittal date: 03/08/2000. 
LA0002JC831341.003. Selenium Batch Adsorption on Nonwelded Zeolitic Tuff. Submittal 
date: 03/08/2000. 
LA0002JC831361.001. Column Studies Using G4-268 Devitrified Tuff with J-13 Well Water 
and Radionuclides (H-3 and Pu-239). Submittal date: 03/16/2000. 
LA0002JC831361.002. Column Studies Using G4-268 Devitrified Tuff with Synthetic UE-25 
p#1 Water and Radionuclides (H-3 and Pu-239). Submittal date: 03/16/2000. 
LA0002JC831361.003. Column Studies Using G4-268 Devitrified Tuff with J-13 Well Water 
and Radionuclides (H-3 and Tc-95m). Submittal date: 03/16/2000. 
LA0002JC831361.004. Column Studies Using GU3-1414 Vitric Tuff with J-13 Well Water and 
Radionuclides (H-3 and Tc-95m). Submittal date: 03/16/2000. 
LA0002JC831361.005. Column Studies Using G4-1533 Zeolitic Tuff with J-13 Well Water and 
Radionuclides (H-3 and Tc-95m). Submittal date: 03/16/2000. 
LA0002PR831231.001. Bullfrog Reactive Tracer Test Data. Submittal date: 02/04/2000. 
LA0002SK831352.001. Total Colloidal Particles Concentration and Size Distribution in 
Groundwaters from the Nye County Early Warning Drilling Program. Submittal date: 
02/24/2000. 
LA0002SK831352.002. Total Colloidal Particles Concentration and Size Distribution in 
Groundwaters around Yucca Mountain. Submittal date: 02/25/2000. 

LA0003NL831352.002. The Kd Values of 239Pu on Colloids of Hematite, Ca-Montmorillonite, 
and Silica in Natural and Synthetic Groundwater. Submittal date: 03/29/2000. 
LA0004AM831234.001. Flow-through Cell Measurements for NC-EWDP-01S, 22-Feb-99 and 
23-Feb-99. Submittal date: 04/17/2000 
LA0004AM831234.002. Downhole Probe Measurements for NC-EWDP-03S, 23-Feb-99. 
Submittal date: 04/17/2000. 
LA0004AM831341.001. Uranium Sorption Coefficients for Minerals and Tuff under Oxidizing 
Conditions in J-13 Water. Submittal date: 05/01/2000. 
LA0004AM831341.002. Np Sorption onto Clinoptilolite-rich Tuff in J-13 Water under 
Atmospheric Conditions with Ka, Kd, and SA. Submittal date: 05/03/2000. 
LA0004JC831224.001. Preliminary Unsaturated Hydraulic Conductivities of Tuffs from Yucca 
Mountain Boreholes, Tunnel Bed 5 (G-Tunnel) and Bandelier Tuff (Los Alamos). Submittal 
date: 05/01/2000. 
LA0004JC831361.001. Preliminary Retardation Data for Selenium Transport through 
Unsaturated Tuffs. Submittal date: 04/19/2000. 
LA0004WS831372.002. Sorption of Np, Pu, and Am on Rock Samples From Busted Butte, NV. 
Submittal date: 04/19/2000. 
LA0005NL831352.001. The Kd Values of 243Am on Colloids of Hematite, Montmorillonite, and 
Silica in Natural and Synthetic Groundwater. Submittal date: 05/03/2000. 
LA0010JC831341.002. Radionuclide Retardation Measurements of Sorption Distribution 
Coefficients for Cesium. Submittal date: 10/19/2000. 
LA0010JC831341.003. Radionuclide Retardation Measurements of Sorption Distribution 
Coefficients for Strontium. Submittal date: 10/19/2000. 
LA0010JC831341.004. Radionuclide Retardation Measurements of Sorption Distribution 
Coefficients for Selenium. Submittal date: 10/19/2000. 
LA0010JC831341.005. Radionuclide Retardation Measurements of Sorption Distribution 
Coefficients for Uranium. Submittal date: 10/19/2000. 
LA0010JC831341.006. Radionuclide Retardation Measurements of Sorption Distribution 
Coefficients for Plutonium. Submittal date: 10/19/2000. 
LA0010JC831341.007. Radionuclide Retardation Measurements of Sorption Distribution 
Coefficients for Neptunium. Submittal date: 10/19/2000. 

LA0109MD831341.001. Adsorption of NP-237 in Three Types of Alluvium as a Function of 
Time and Stratigraphic Position. Submittal date: 09/14/2001. 
LA0109MD831341.002. Adsorption of TC-99 in Three Types of Alluvium as a Function of 
Time and Stratigraphic Position. Submittal date: 09/14/2001. 
LA0109MD831341.003. Adsorption of I-129 in Three Types of Alluvium as a Function of Time 
and Stratigraphic Position. Submittal date: 09/13/2001. 
LA9907AM831234.003. Downhole Eh and pH Measurements for NC-EWDP-01S, 11-Jan-99. 
Submittal date: 01/27/2000. 
LA9907AM831234.009. Flow-through Cell Measurements for NC-EWDP-01S, NC-EWDP-
03S, NC-EWDP-09SX, 5/17/99, 5/18/99, 5/20/99. Submittal date: 01/27/2000. 
LA9907AM831234.010. Flow-through Cell Measurements for SD6-ST1, 02-Jun-99 and 08-Jun-
99. Submittal date: 01/27/2000. 
LA9907AM831234.011. Flow-through Cell Measurements for AD-2, 10-Jun-99. Submittal 
date: 01/27/2000. 
LA9909PR831231.003. Interpretations of Bullfrog Reactive Tracer Test DataModeling Data. 
Submittal date: 09/02/1999. 
LA9909PR831231.005. Interpretations of Tracer DataModeling Data. Submittal date: 
09/02/1999. 
LA9909WS831372.001. Busted Butte UZ Transport Test: Phase I Collection Pad Extract 
Concentrations. Submittal date: 09/29/1999. 
LA9909WS831372.002. Busted Butte UZ Transport Test: Phase I Collection Pad Tracer 
Loading and Tracer Concentrations. Submittal date: 09/30/1999. 
LA9909WS831372.005. QXRD Results for Samples from Lower Tpt Section. Submittal date: 
10/04/1999. 
LA9909WS831372.006. Mineral Abundances (weight %) in Calico Hills Formation. Submittal 
date: 10/01/1999. 
LA9909WS831372.007. Quantitative XRD Results for USW H-5 Core and Drill Cuttings. 
Submittal date: 10/04/1999. 
LA9909WS831372.010. Mineral Abundances (weight %) in Calico Hills Formation. Submittal 
date: 10/01/1999. 

LA9909WS831372.011. Preliminary Measured Sorption Coefficients. Submittal date: 
10/13/1999. 
LA9909WS831372.014. Microsphere Suspension Preparation for the Busted Butte UZ 
Transport Test. Submittal date: 09/30/1999. 
LA9909WS831372.015. ICPAES Porewater Analysis for Rock Samples from Busted Butte, 
NV. Submittal date: 10/01/1999. 
LA9909WS831372.016. Ion Chromatography Porewater Analysis for Rock Samples from 
Busted Butte, NV. Submittal date: 09/30/1999. 
LA9909WS831372.017. pH of Porewater of Rock Samples from Busted Butte, NV. Submittal 
date: 09/30/1999. 
LA9909WS831372.018. Gravimetric Moisture Content of Rock Samples from Busted Butte, 
NV. Submittal date: 09/30/1999. 
LA9910SK831341.005. Total Colloidal Particles Concentration and Size Distribution in NTS-
ER-20-5-1, NTS-ER-20-5-3, and J-13 Groundwater. Submittal date: 12/07/1999. 
LA9910WS831372.008. Busted Butte UZ Transport Test: Gravimetric Moisture Content and 
Bromide Concentration in Selected Phase 1A Rock Samples. Submittal date: 11/03/1999. 
LA9910WS831372.009. QXRD Data for UZTT Busted Butte Samples. Submittal date: 
11/03/1999. 
LAAM831311AQ98.005. Geochemical Field Measurements for UE-25 WT#17, 27-Jan-98. 
Submittal date: 09/14/1998. 
LAAM831311AQ98.007. Flow-through Cell and Static Measurements at UE-25 WT#3, 22-Jun-
98. Submittal date: 09/14/1998. 
LAAM831311AQ98.008. Analysis of Bailed Sample for UE-25 WT#17, 04-Jun-98. Submittal 
date: 09/14/1998. 
LAAM831311AQ98.010. Static Measurements for US-25 WT#17, 01-Jul-98. Submittal date: 
09/14/1998. 
LAIT831341AQ97.002. Reversibility of Radionuclide Sorption. Submittal date: 06/09/1997. 
LAIT831361AQ95.001. Crushed Rock Column Data to Obtain Sorption Coefficients for 
Pu(IV), Np, Tc-95m, Using J-13 and UE-25 p#1 Waters, Yucca Mountain, Nevada. Submittal 
date: 09/29/1995. 

LAIT831361AQ95.003. Transport Data of H-3, Np, and Tc-95m Collected to Calculate 
Retardation Coefficients Using J-13 and UE-25 p#1 Waters. Submittal date: 08/20/1997. 
LAIT831362AQ95.001. Solid Rock Diffusion Data Collected to Determine Rate of Movement 
for Np, U, and HTO through an Unsaturated Tuff. Submittal date: 09/29/1995. 
LAPR831231AQ99.001. Prow Pass Reactive Tracer Test Field Data. Submittal date: 
02/10/1999. 
LASC831321AQ98.003. Results of Real Time Analysis for Erionite in Drill Hole USW SD-6, 
Yucca Mountain, Nevada. Submittal date: 06/11/1998. 
LB00032412213U.001. Busted Butte Ground Penetrating Radar Data Collected June 1998 
through February 2000 at the Unsaturated Zone Transport Test (UZTT): GPR Velocity Data. 
Submittal date: 03/24/2000. 
LB970601233129.001. The Site-Scale Unsaturated Zone Model of Yucca Mountain, Nevada for 
the Viability Assessment. Submittal date: 06/09/1997. 
LL990612704244.098. ERT Data for Busted Butte. Submittal date: 06/21/1999. Submit to 
RPC URN-0386. 
MO0004GSC00167.000. As-built Coordinate of Boreholes in the Test Alcove and Running 
Drift, Busted Butte Test Facility. Submittal date: 04/20/2000. 
MO0007MAJIONPH.003. Major Ion Content of Groundwater from Yucca Mountain Project 
Borehole USW G-2, Extracted from ANL-NBS-HS-000021, Geochemical and Isotopic 
Constraints on Groundwater Flow Directions, Mixing, and Recharge at Yucca Mountain, 
Nevada. Submittal date: 07/27/2000. 
MO0007MAJIONPH.005. Major Ion Content of Groundwater from Boreholes UZ-14, WT-17 
and WT #3, Extracted from ANL-NBS-HS-000021, Geochemical and Isotopic Constraints on 
Groundwater Flow Directions, Mixing and Recharge at Yucca Mountain, Nevada. Submittal 
date: 07/27/2000. 
MO0007MAJIONPH.011. Major Ion Content of Groundwater from Selected Yucca Mountain 
Project Boreholes Extracted from ANL-NBS-HS-000021, Geochemical and Isotopic Constraints 
on Groundwater Flow Directions, Mixing and Recharge at Yucca Mountain, Nevada. Submittal 
date: 07/27/2000. 
MO0007MAJIONPH.013. Major Ion Content of Groundwater from Selected Yucca Mountain 
Project and Other Boreholes Extracted from ANL-NBS-HS-000021, Geochemical and Isotopic 
Constraints on Groundwater Flow Directions, Mixing, and Recharge at Yucca Mountain, 
Nevada. Submittal date: 07/27/2000. 

MO0012BRLI25C2.000. Bromide Abundance and Lithium Abundance Data Using Crushed 
Tuff Column Studies with Filtered J-13 Water from UE-25 C #2 at 2406 FT. Submittal date: 
12/04/2000. 
MO0012CATECHOL.000. Cation Exchange Capacity Data of C-Well Tuff from UE-25 C #1 
and UE-25 C #2. Submittal date: 12/05/2000 
MO0012MINLCHOL.000. Mineral Abundance Data using X-ray Diffraction Analyses of C-
Well Tuffs from UE-25 C #1, and UE-25 C #2. Submittal date: 12/05/2000. 
MO0012PERMCHOL.000. Permeability Data (using Filtered J-13 Water) from UE-25 C #1, 
UE-25 C #2, and UE-25 C #3. Submittal date: 12/05/2000. 
MO0012POROCHOL.000. Porosity Data (using Deionized Water) from UE-25 C #1, UE-25 C 
#2, and UE-25 C #3. Submittal date: 12/05/2000. 
MO0012SORBCHOL.000. Sorbing Element Concentration Data of J-13 and C-3 Well Water 
from UE-25 C #1 and UE-25 C #2. Submittal date: 12/05/2000. 
MO9906GPS98410.000. Yucca Mountain Project (YMP) Borehole Locations. Submittal date: 
06/23/1999. 
8.5 OUTPUT DATA, LISTED BY DATA TRACKING NUMBER 
LA0003AM831341.001. Preliminary Revision of Probability Distributions for Sorption 
Coefficients (Kds). Submittal date: 03/29/2000. 
LA0003JC831362.001. Preliminary Matrix Diffusion Coefficients for Yucca Mountain Tuffs. 
Submittal date: 04/10/2000. 
LA9909WS831372.019. Busted Butte Simulation Results 3-D Model. Submittal date: 
04/11/2000. 
LA9909WS831372.020. Summary of Monte Carlo Cases Including Correlation Length of 
Heterogeneities. Submittal date: 04/12/2000. 
LA9909WS831372.021. Busted Butte Phase 1-A Modeling Results for Stochastic Model. 
Submittal date: 04/11/2000. 
LA9909WS831372.022. Fracture and Matrix Property Sets Used in Simulations for the Phase-2 
Test. Submittal date: 04/11/2000.