Nominal Performance Biosphere Dose Conversion Factor Analysis Rev 01, ICN 00

ANL-MGR-MD-000009

December 2000

1. PURPOSE
Biosphere is one of the component process models of the Total System Performance Assessment 
(TSPA) model. The biosphere model considers the movement of radionuclides in the reference 
biosphere and human exposure to these radionuclides. The outcome of the biosphere model 
consists of radionuclide-specific biosphere dose conversion factors (BDCFs). BDCFs are 
included, together with other parameters, in the input for the TSPA code, which allows dose 
assessment from the postulated radionuclide releases from the potential repository. 
The objective of biosphere modeling is to evaluate human exposure from radionuclides present 
in environmental media, such as water, soil, air, and food. Such exposures may be internal or 
external in origin. Internal exposure pathways under consideration in the biosphere model 
include ingestion and inhalation of radionuclides. The external exposure pathway considered 
external irradiation from contaminated soil. BDCFs for nominal performance quantify internal 
and external exposure to the receptor of interest resulting from the unit activity concentration of 
a radionuclide in groundwater introduced to the environment. The integration of the biosphere 
modeling results for nominal performance with the other models of the system is shown in 
Figure 1. The figure shows the diagram of the component models contributing to the TSPA. 
The BDCFs for nominal performance provide the means of conversion of radionuclide 
concentration in groundwater to annual doses to the human receptor of interest. 
The assessments of nominal repository performance and of certain disruptive processes and 
events that may result in radionuclide releases into groundwater (e.g., intrusive volcanic event 
and human intrusion) share the same human exposure scenario. Therefore some additional 
radionuclides important from the perspective of disrupted performance were included in this 
analysis. They include relatively short-lived radionuclides, such as 90Sr, 137Cs, and 63Ni, which 
are of interest for consideration of human intrusion. 
The previously developed set of BDCFs for nominal performance (CRWMS M&O 2000a) was 
used as input to the TSPA-Site Recommendation (SR) Rev 0 (CRWMS M&O 2000b). This 
revision will be used in TSPA-SR Rev 1, which will support the Site Recommendation Report. 
The current analysis supplements the preceding one with: (1) investigation of the effects of 
climate change on the Biosphere Dose Conversion Factors for nominal performance; (2) addition 
of pathway analysis; (3) expansion of the radionuclide list to include radionuclides important for 
up to 1 million years postclosure; (4) addition of radionuclides important for the human intrusion 
scenario. In addition, the bounding case considered previously has been removed. The analysis 
and model report (AMR) title has been modified to better reflect the contents of the document 
and to be consistent with Project-wide terminology. 
The scope of the analysis encompasses development of radionuclide-specific nominal 
performance BDCFs for the twenty-four radionuclides of interest for the current conditions. The 
effects of the possible future climate on BDCFs are then evaluated. The analysis for the current 
climate includes consideration of radionuclide buildup in soil as the result of previous irrigation 
with contaminated groundwater. The analysis is augmented with the pathway and uncertainty 
studies. The model used to develop nominal performance BDCFs was appropriate for its 
intended use. It is analogous to the model used previously (CRWMS M&O 2000a). 

This analysis was conducted in accordance with the Office of Civilian Radioactive Waste 
Management (OCRWM) AP-3.10Q procedure, Analyses and Models and the Technical Work 
Plan for Biosphere Modeling and Expert Support (CRWMS M&O 2000c). 

(abq0063G298.ai) 
Figure 1. Process Models Included in Total System Performance Assessment - Site Recommendation 

2. QUALITY ASSURANCE
The activities documented in this AMR were evaluated in accordance with AP-2.21Q, Quality 
Determination and Planning for Scientific, Engineering, and Regulatory Compliance Activities, 
and were determined to be quality affecting and subject to the requirements of the 
U.S. Department of Energy (DOE) OCRWM Quality Assurance Requirements and Description 
(QARD) (DOE 2000). This evaluation is documented in the Technical Work Plan (CRWMS 
M&O 2000c). Consequently, the modeling or analysis activities documented in this AMR have 
been conducted in accordance with the Civilian Radioactive Waste Management System 
Management and Operating Contractor (CRWMS M&O) quality assurance program, using 
approved procedures. 
The primary implementing procedure for this work is OCRWM procedure AP-3.10Q, Analyses 
and Models. To perform this work, several other procedures are invoked by AP-3.10Q. These 
include the following: 
 
AP-2.14Q, Review of Technical Products 
 
AP-2.21Q, Quality Determinations and Planning for Scientific, Engineering, and 
Regulatory Compliance Activities 
 
AP-3.4Q, Level 3 Change Control 
 
AP-3.14Q, Transmittal of Input 
 
AP-3.15Q, Managing Document Inputs 
 
AP-3.17Q, Impact Reviews 
 
AP-6.1Q, Controlled Documents 
 
AP-17.1Q, Record Source Responsibilities for Inclusionary Records 
 
AP-SI.1Q, Software Management 
 
AP-SIII.2Q, Qualification of Unqualified Data and the Documentation of Rationale for 
Accepted Data 
 
AP-SIII.3Q, Submittal and Incorporation of Data to the Technical Data Management 
System 
 
AP-SIII.4Q, Development, Review, Online Placement, and Maintenance of Individual 
Reference Information Base Data Items 
 
AP-SV.1Q, Control of the Electronic Management of Data. 
Personnel performing work on this analysis were trained and qualified according to OCRWM 
procedures AP-2.1Q, Indoctrination and Training of Personnel, and AP-2.2Q, Establishment 

and Verification of Required Education and Experience of Personnel. Preparation of this 
analysis did not require the classification of items in accordance with CRWMS M&O procedure 
QAP-2-3, Classification of Permanent Items. This analysis is not a field activity. Therefore, a 
Determination of Importance Evaluation in accordance with CRWMS M&O procedure NLP-2-0 
was not required. 
Evaluation of electronic data management process control was performed and documented 
(CRWMS M&O 2000c) in accordance with AP-SV.1Q, Control of the Electronic Management 
of Data and was accomplished in accordance with the controls specified in the Technical Work 
Plan (CRWMS M&O 2000c). The control process ensuring accuracy and completeness as well 
as security of data included the following. Access to the data contained on the personal 
computer used to perform this work was controlled (password protected). The data were stored 
on the server drive, which was periodically backed up to the network and updated daily, as 
appropriate. 
The development of BDCF did not depend on electronic data transfer for the model input. Upon 
completion of model runs, the files generated by the computer code were transferred to the 
Modeling Warehouse Database of the Technical Data Management System. To accomplish this 
transfer, the files were compressed, using the WinZip utility to maintain data security and 
integrity. The list of the transferred files, including filename, file size, and date when the file 
was generated is included in Attachment IV. 

3. COMPUTER SOFTWARE AND MODEL USAGE
The computer code GENII-S V1.4.8.5 (Sandia National Laboratories 1998) was used to calculate 
nominal performance BDCFs. GENII-S is a computer program used to calculate statistical and 
deterministic values of radiation doses to humans from exposure to radionuclides in the 
environment. GENII-S is acquired software, which was qualified for use on the Yucca Mountain 
Project (CRWMS M&O 1998a). Justification for the selection of this software to perform 
calculation of radionuclide transport in the biosphere and uptake by a human receptor is 
documented in Harris (1997). 
The GENII-S computer code consists of an executable program and auxiliary files, all of which 
are maintained under Configuration Management (CM) (CSCI: 30034 V1.4.8.5). The software 
was obtained from CM, it is appropriate for this application, and was used within the range of 
applicability in accordance with AP-SI.1Q, Software Management, as described in the software 
qualification report (CRWMS M&O 1998a). The analysis was performed using Gateway 2000 
Personal Computers, CPU# 111161 and 111163 located in Building 3, Summerlin, in cubicles 
number 327D, and 327C, respectively. 
The biosphere model used by GENII-S was validated in accordance with AP-3.10Q (see 
Attachment II for details) and was used within the range of validation. The documentation, 
inputs, and outputs for the biosphere model for the current and the evolved climates is located in 
the Technical Data Management System (TDMS) Model Warehouse, 
DTN MO0010MWDPBD09.006, Biosphere Dose Conversion Factors for Nominal 
Performance. 
Prior irrigation periods (see Section 6.3.2, Table 3) were developed using Microsoft Excel 
version 97 SR-2. To calculate prior irrigation periods, standard Excel functions were used, as 
documented in Attachment V. 
BDCF pathway contributions (see table and figures in Section 6.7) were also developed using 
Microsoft Excel version 97 SR-2. To accomplish this, portions of GENII-S output files were 
copied into the spreadsheet. Determination of pathway contributions to BDCFs was done using 
the Pathway Contribution software routine described in Attachment VI of this report. 

4. INPUTS
4.1 DATA AND PARAMETERS 
Input parameters used in this AMR report were developed in a series of AMRs listed in Table 1. 
The data sets used to develop nominal performance BDCFs are listed in Table 1. Selection of 
the values, ranges, and distributions of input parameters as well as justification of the 
applicability of the selected data to the specific exposure scenarios considered for the potential 
repository is described in the corresponding AMRs, which are also listed in Table 1 together with 
the corresponding data tracking numbers (DTNs). Some input parameters were developed in this 
analysis and are documented in this report. Input parameters developed in this report include 
selected ingestion pathway parameters (see Attachment III for details) and selected dose 
coefficients (see Section 6.5.2). 
Input parameters listed as items 1 through 7 in Table 1 were developed specifically for the 
purpose of GENII-S analyses and are, therefore, appropriate for the use in this model. The 
values of dose conversion factors (DCFs) and dose coefficients (i.e., factors used to convert 
exposure to radionuclides to dose) apply to chronic low-level intakes and exposure conditions 
and are inappropriate for acute, high-level intakes and exposures. Therefore the BDCFs 
developed using such DCFs can not be applied to acute, high-level exposures to radionuclides. 
4.2 CRITERIA 
Total System Performance Assessment and Integration (TSPAI) Nuclear Regulatory Commission 
(NRC) Key Technical Issue (KTI) (NRC 2000) includes subissues and the related technical 
acceptance criteria that are applicable to this work scope because the biosphere model is one of 
the TSPA component models. The TSPAI subissues include: 
 
Subissue 1, System Description and Demonstration of Multiple Barriers 
 
Subissue 2, Scenario Analysis 
 
Subissue 3, Model Abstraction 
 
Subissue 4, Demonstration of the Overall Performance Objective 
The acceptance criteria associated with these subissues that are applicable to the development of 
BDCFs are relevant to several concepts which are addressed throughout this document. They 
include the following: 
 
The pedigree of data is clearly identified 
 
Input parameter development and basis for their selection is described 
 
Documents and reports are clear and consistent 
 
Data and model uncertainty is discussed 
 
Alternative modeling approaches are presented 

Table 1. Input Data 
No. 
Data Tracking Number/ Document Identifier 
DTN/Document Title Parameters/Comments 
1 MO0010SPAPET07.004, Environmental Transport Parameters 
Source of the DTN: 
ANL-MGR-MD-000007 REV 00/ICN 1, Environmental Transport Parameters Analysis 
(CRWMS M&O 2000e) 
Deposition velocity 
Crop resuspension factor 
Crop biomass 
Depth of surface soil 
Surface soil density 
Deep soil density 
Fraction of roots in surface soil 
Fraction of roots in deep soil 
Soil ingestion rate 
Weathering halflife 
Translocation factor 
Animal feed and water consumption rate 
Dry-to-wet ratio 
2 MO0010SPAPTC08.005, Transfer Coefficients 
Source of the DTN: 
ANL-MGR-MD-000008 REV 00/ICN 2, Transfer Coefficient Analysis (CRWMS M&O 2000f) 
Soil-to-plant transfer coefficients 
Soil-to-plant transfer scaling factor 
Animal feed-to-animal transfer coefficients 
Animal feed-to-animal transfer scaling factor 
Bioaccumulation factors 
3 MO0010SPAAAM01.014, Input Parameter Values for External and Inhalation Radiation 
Exposure Analysis 
Source of the DTN: 
ANL-MGR-MD-000001 REV 01, Input Parameter Values for External and Inhalation 
Radiation Exposure Analysis (CRWMS M&O 2000g) 
Mass loading 
Inhalation exposure time 
Chronic breathing rate 
Soil exposure time 
Home irrigation rate 
Duration of home irrigation 
ANL-MGR-MD-000009 REV 01 19 December 2000 

ANL-MGR-MD-000009 REV 01 20 December 2000 
Table 1. Input Data (Continued) 
No. 
Data Tracking Number/Document Identifier 
DTN/Document Title Parameters/Comments 
4 MO0002RIB00068.000, Ingestion Exposure Parameter Values 
Input and the source of the DTN: 
ANL-MGR-MD-000006 REV 00/ICN 0, Identification of Ingestion Exposure 
Parameters (CRWMS M&O 2000h) 
Irrigation water source 
Drinking water treatment 
Crop interception fraction 
Water contaminated fraction 
Irrigation water contamination fraction 
Aquatic food consideration 
Holdup time 
Storage time 
Dietary fraction 
Selected parameters from this data set (irrigation time, irrigation 
rate, food yield, grow time) were re-developed; in addition the 
additional set of ingestion exposure parameters for the evolved 
climate was developed as documented in Attachment III of this 
report. 
5 MO0007SPADMM05.002, Distributions, Mean, Minimum and Maximum 
Consumption Levels of Locally Produced Food by Type and Tap Water for the 
Amargosa Valley Receptor of Interest 
Source of the DTN: 
CAL-MGR-MD-000005 REV 00/ICN 0, Calculation: Values and Consumption 
Rates of Locally Produced Food and Tap Water for the Receptor of Interest 
(CRWMS M&O 2000i) 
Consumption rates for locally produced food and tap water 
6 MO9912RIB00066.000, Parameter Values for Internal and External Dose 
Conversion Factors 
Source of the DTN: 
ANL-MGR-MD-000002 REV 00/ICN 0, Dose Conversion Factor Analysis: 
Evaluation of GENII-S Dose Assessment Methods (CRWMS M&O 1999) 
Dose coefficients for exposure to contaminated soil (external dose 
conversion factors) 
External dose conversion factors for additional radionuclides have 
been developed in addition to coefficients included in this data set 
as described in Section 6.5.2. 

ANL-MGR-MD-000009 REV 01 21 December 2000 
Table 1. Input Data (Continued) 
Data Tracking Number/Document Identifier 
No. DTN/Document Title Parameters/Comments 
7 MO0004RIB00085.000, Soil and Radionuclide Removal by Erosion and 
Leaching 
Removal rates by leaching (leaching coefficients) 
Source of the DTN: 
ANL-NBS-MD-000009 REV 00/ICN 0, Evaluate Soil/Radionuclide Removal by 
Erosion and Leaching (CRWMS M&O 2000j) 
8 MO9912SPASUB02.001, Dose Coefficients for Air Submersion and Exposure to The source of the data is Federal Guidance Report No. 12 
Contaminated Soil (Eckerman and Ryman 1993). 

4.3 CODES AND STANDARDS 
At present, the regulatory framework for a potential repository at Yucca Mountain is evolving 
and the final applicable rules have not been released yet. Until the final rules become available, 
U.S. Department of Energy has issued interim guidance (Dyer 1999) pending the issuance of the 
NRC regulations. The interim guidance, which is based on the proposed rule 10 CFR 63 
(64 FR 8640), Disposal of High-level Radioactive Wastes in a Geologic Repository at Yucca 
Mountain, Nevada, has been taken into consideration for the development of the nominal 
performance BDCFs. Of particular relevance to this analysis are: (1) Section 114 of the 
guidance which details requirements for performance assessment, including treatment of 
features, events and processes (FEPs) with regard to their inclusion or exclusion from the model, 
and (2) Section 115 of this rule which specifies required characteristics of the reference 
biosphere and critical group. 
In parallel to the NRC rulemaking efforts, EPA is presently developing Environmental Radiation 
Protection Standards for Yucca Mountain, Nevada (64 FR 46976). Another proposed rule 
applicable to the potential repository at Yucca Mountain, is 10 CFR 963 (64 FR 67054), which 
outlines the Yucca Mountain site suitability guidelines. 

5. ASSUMPTIONS
It was assumed that a dietary fraction of fresh forage for the evolved climate was equal to one. 
This means that livestock are sustained year-round on fresh forage even though the cooler 
weather conditions of the glacial transition climate would not permit such situation. This 
assumption is conservative because it overestimates the amount of radionuclides available for 
uptake by animals. In addition this assumption simplifies calculations of BDCFs. This 
assumption is used in Section 6.5. 
The following assumptions were made to develop the parameter values for one climate evolution 
scenario, using Spokane, Washington as the analog site (see Attachment III): 
1. 
No irrigation occurs during the period of November through February, because the 
precipitation rate exceeds the evapotranspiration rate during this period in Spokane, 
WA (see Tables III-4 and III-5). 
2. 
The meteorological data for Spokane, WA were taken from the Western Regional 
Climate Center (1997) (see Table III-4). 
3. 
Crop planting, growing and irrigation schedules for Spokane, WA were taken from 
Washington Agricultural Statistics Service (1992) or Hogan (1988) (see Table III-3). 
4. 
Crop yields for Spokane, WA are the same as the ones for Amargosa Valley, except 
for alfalfa, because there are only three harvests and a shorter growing season in 
Spokane (Washington Agricultural Statistics Service 1999). A comparison made for 
some crop yields between the two locations indicates that those crop yields were very 
close (see Section III.2.1). 
5. 
To maximize farmland use, two harvests were assumed if the planting period was 
longer than the growing time. Crops in this category include spinach, lettuce, and 
carrots, similar to the ones selected for Amargosa Valley (CRWMS M&O 2000h). 
The use of two harvests in one year for the same crop simplifies calculation of 
growing period and other irrigation parameters and is conservative. 
The above assumptions concerning development of selected ingestion exposure parameters for 
the evolved climate were used in Sections 6.5.4.1 and 6.5.5.1. 
It was assumed that the activity concentration of water used for the fish farm is the same as that 
of groundwater. The basis for this assumption is that it assures that well water is used for all 
local food production purposes. This assumption means that, for the GENII-S input, activity 
concentration of surface water is assumed to be equal to 1 pCi/L. This assumption is used in 
Tables 7 and 10. 
A conservative assumption was made that the GENII-S beef consumption category included both 
beef and pork and that animal food-to-animal product transfer coefficients for beef can be 
applied to pork. This assumption allows including all types of locally produced meat using a 
single GENII-S food type category related to meat consumption. This assumption is used in 
Section 6.5.3. 

Parameters represented by their probability distribution functions included in this analysis are 
assumed to vary independently (except for plant-to-soil and animal food-to-animal product 
transfer factors). Therefore, for this analysis the covariance among parameters was not included 
in the biosphere model. In general, the effect of neglecting covariances is to estimate slightly 
wider confidence intervals on BDCFs. In other words, ignoring positive correlations amongst 
input parameters, the uncertainty in the BDCFs will be underestimated. This assumption is used 
for entering GENII-S input (Sections 6.5.4.1 and 6.5.5.1). 

6. ANALYSIS/MODEL
This chapter describes development of radionuclide- and exposure scenario-specific BDCF. 
First the context of analysis is presented in terms of selection of radionuclides of interest 
(Section 6.1), definition of the human receptor (Section 6.2) and the conditions of the receptors 
exposure to radiation (Section 6.3). Application of the biosphere model, including the modeling 
inputs and outcomes, to calculate BDCFs for the current and the evolved climates are discussed 
in Section 6.4. Uncertainly and pathway analyses are discussed in Section 6.6 and 6.7, 
respectively. The last section of the chapter (Section 6.8) addresses alternative models. 
Radionuclide-specific BDCFs developed in this analysis are expressed in terms of total effective 
dose equivalent (TEDE) (10 CFR 20) for the average member of the critical group resulting from 
an annual internal and external exposure to radionuclides in the environmental media, from unit 
of activity concentration of a radionuclide in groundwater released to the environment. Exposure 
pathways included in the BDCFs are specific to the exposure scenario under consideration. In 
general, they include ingestion, inhalation, and external exposure (see Section 6.3 for the 
description of exposure scenario and pathways). The BDCFs were calculated in a probabilistic 
analysis through multiple realizations of the model outcome from multiple sampling the model 
input parameters. The results of model realizations are subsequently abstracted into BDCF 
probability distribution functions, which is outside the scope of this model. The abstraction is 
carried out in Abstraction of BDCF Distributions for Irrigation Periods, ANL-NBS-MD-000007 
REV 00/ICN 1 and in Distribution Fitting to the Stochastic BDCF Data, ANL-NBS-MD-000008 
REV 00/ICN 1. 
The biosphere model used in this analysis has been validated for the intended use in development 
of nominal performance BDCFs for the current and evolved climates. Model validation is 
discussed in Attachment II. The present analysis is analogous to the one conducted previously to 
develop non-disruptive event BDCFs (CRWMS M&O 2000a) and is appropriate for generation 
of input for the TSPA model. 
6.1 RADIONUCLIDES OF INTEREST 
BDCFs were developed for a combined list consisting of twenty-four radionuclides. 
Sixteen radionuclides were identified as important for nominal performance during the 
compliance period of up to 10,000 years (CRWMS M&O 2000k). These are: 14C, 99Tc, 129I,
127Ac, 229Th, 232U, 233U, 234U, 236U, 238U, 237Np, 238Pu, 239Pu, 240Pu, 241Am, and 243Am. 
It was recommended that for the assessment of the consequences of human intrusion the 
following nineteen radionuclides are considered (CRWMS M&O 2000k): 14C, 63Ni, 90Sr, 99Tc,
129I, 137Cs, 227Ac, 229Th, 232U, 233U, 234U, 236U, 238U, 237Np, 238Pu, 239Pu, 240Pu, 241Am, and 243Am. 
For estimating potential dose beyond the compliance period of 10,000 years to the expected time 
of peak dose or 1 million years, the following five radionuclides were included in addition to 
those listed above (CRWMS M&O 2000k): 210Pb, 231Pa, 226Ra, 230Th, and 242Pu. This selection 
applies to both nominal performance and human intrusion scenarios. 

6.2 RECEPTOR OF INTEREST 
Receptor of interest is defined as a hypothetical individual for whom the dose consequences of 
the postulated radionuclide release from the potential repository are assessed. For this analysis 
an average member of the critical group is a receptor of interest. The critical group is defined as 
the group of individuals reasonably expected to receive the greatest exposure to radioactive 
releases from the potential repository over time, considering the circumstances under which the 
analysis is carried out. 
The critical group used in this analysis has been developed in (CRWMS M&O (2000l). The 
critical group characterized in that report is a relatively homogeneously exposed group residing 
within a farming community. It is expected to receive the highest exposure for the exposure 
scenario under consideration (see Section 6.3 for the description of exposure scenario). It 
consists of full-time residents that are involved in agricultural activities for a significant part of 
the day; spending part of the day recreating outdoors; and consuming locally grown food, some 
of which is grown in their own gardens. The average member of the critical group defined in 
such a manner is an individual who represents the exposure scenario based on site-specific, but 
prudently conservative, exposure assumptions and parameter values. The average member of the 
critical group is represented by mean values of behaviors and characteristics, which were derived 
from each individual exposure parameters frequency distribution for the critical group. In terms 
of the biosphere model outcome, BDCFs for the average member of the critical group are 
represented by the mean value of the BDCF probability distribution for the critical group. 
The proposed NRC rule requires that, The behaviors and characteristics of the average member 
of the critical group shall be based on the mean value of the critical groups variability range 
(Dyer 1999, Section 115 (b)(4) of the attachment). In the previous revision of this analysis, for 
all exposure characteristics of the average member of the critical group, the mean fixed values 
were used, while the parameters associated with the environmental transport of radionuclides 
were allowed to be represented by probability distribution functions (see Sections 6.5.2 and 6.5.3 
for the discussion of the model parameters). In the current analysis, exposure parameters (e.g., 
locally produced food consumption rates) for the critical group are represented by their 
probability distributions to include the uncertainty in these important parameters in the overall 
uncertainty of the modeling outcome. However, the mean values of the parameters distributions 
were calculated such that they coincide with either the food consumption survey mean values, in 
the case of food consumption rates, or with the best estimates of the parameter value in the case 
of other parameters. By doing so, the mean values of the BDCF distributions are the same as the 
mean values of the BDCF distributions calculated using fixed mean values of exposure 
parameters. However, the latter BDCFs distributions are broader because they include 
variability in behavioral characteristics. 
6.3 EXPOSURE CONDITIONS 
The main objective of biosphere modeling is to provide the numerical values of BDCFs, which 
represent annual doses to a human receptor of interst from a radionuclides under consideration 
per unit of activity concentration of this radionuclide postulated to be released to the 
environment in groundwater. The circumstances of human exposure to radionuclides present in 

the biosphere were defined in terms of features, events, and processes (FEPs) that were identified 
as applicable to the Yucca Mountain region. 
One of the requirements that the reference biosphere must meet is that FEPs that describe the 
reference biosphere shall be consistent with present knowledge of the conditions in the region 
surrounding the Yucca Mountain site (Dyer 1999, Section 63.115 (a)(1) of the attachment; 
64 FR 8640, Section 63.115 (a)(1)). The selection of applicable FEPs forms a basis of the 
definition of human exposure conditions. The selection should comprehensively address site-
specific aspects of human exposure to radionuclides released to the environment from the 
potential repository. The list of FEPs potentially applicable to the Yucca Mountain Site 
Characterization Project has been constructed and described in detail (CRWMS M&O 2000m). 
The evaluation of the applicability of biosphere-related FEPs is being performed and the 
screening arguments for inclusion or exclusion of specific FEPs are being developed and 
documented (Evaluation of the Applicability of Biosphere-Related Features, Events, and 
Processes (FEP), ANL-MGR-MD-000011, REV 01). Table 2 lists FEPs that are addressed in 
this AMR and describes how and where a specific FEP is addressed within this document. 
BDCFs, developed for the average member of the critical group, account for transfer of 
radionuclides through the human food chain and for human dosimetry. The mechanisms of 
radionuclide transfer through the biosphere, called exposure pathways, were first identified and 
then modeled. These exposure pathways are described in the following section. 

ANL-MGR-MD-000009 REV 01 28 December 2000 
Table 2. Consideration of Features, Events, and Processes Considered Applicable to YMP within the Biosphere Model for Calculation of 
Nominal Performance Biosphere Dose Conversion Factor 
FEP Name 
YMP FEP 
Database Number Reference/Comment 
Climate change, 
global 
1.3.01.00.00 Climate change may affect the long-term performance of the repository. Consideration of climate change 
based on the geologic record from the Yucca Mountain region, has been included in the analysis. BDCFs 
for the evolved climate were developed (see Section 6.5.5). 
Wells 1.4.07.02.00 This FEP is considered as the source of radionuclides entering the environment, here water for human use 
(e.g., drinking water) and agriculture ( e.g., irrigation, animal watering). This FEP is a part of the exposure 
pathway selection described in Section 6.3.1. 
Soil type 2.3.02.01.00 Soil type FEP is considered for the selection of the values for soil-to-plant transfer coefficient (values for 
sandy soils were selected, if available) controlling radionuclide transfer from soils to plants, as well as for 
the calculation of leaching factors, which quantify fraction of activity removed from the top soil layer by 
leaching, and development of soil-related parameters, such as soil density. (See Sections 6.5.3, 6.5.4.1, 
and 6.5.5.1 for the specific use of these parameters within the model.) These parameters were developed 
and documented in the AMRs and are used as input to this analysis. 
Radionuclide 
accumulation in soils 
2.3.02.02.00 Radionuclide accumulation in soil as the result continuing irrigation practices has been included in the 
model. See Section 6.3.2 for the description of the method and Sections 6.5.4.2 and 6.5.5.2 for calculation 
of buildup factors and incorporation of radionuclide buildup in soil into BDCFs for nominal performance. 
Soil and sediment 
transport 
2.3.02.03.00 Removal of radionuclides from top layer of soil by the process of wind erosion (aeolian processes) is 
addressed within the biosphere model when considering long-term effects of agricultural land use. This 
process is not directly included in the calculation of the nominal performance BDCFs, but rather it is applied 
within the TSPA model to modify/adjust the contamination source term. 
Precipitation 2.3.11.01.00 Precipitation (precipitation rate) is a parameter which is not used directly in the model, but rather it is used 
to derive the values of other input parameters that depend on the overall water balance, such as leaching 
rate, irrigation rates for various crops, as well as home irrigation rate. The usage of precipitation is 
addressed in the AMRs, which document development of input parameters for the biosphere model. (See 
Sections 6.5.2, 6.5.3, 6.5.4.1, and 6.5.5.1 for the description of input and its sources). 
Surface runoff and 
flooding 
2.3.11.02.00 Evapotranspiration and infiltration are the factors in the water balance equation used to derive certain input 
parameters described above (see comment for FEP 2.3.11.01.00). 
Biosphere 
characteristics 
2.3.13.01.00 Biosphere characteristics such as climate, vegetation, fauna and flora are included in the model as the 
components of the reference biosphere. Specific relationships between these components form the 
foundation of the biosphere model used in this analysis and are shown in Figure 2. 
Biosphere transport 2.3.13.02.00 See comment above, i.e., for FEP 2.3.13.01.00. 
Human 
characteristics 
(physiology, 
metabolism) 
2.4.01.00.00 Human characteristics, such as physiology and metabolism, are considered as elements of human 
dosimetry and they are inherent to dose conversion factors (conversion factors from radionuclide intake to 
dose). Selection of the specific set of dose conversion factors is discussed in Section 6.8.2. Also see 
Section 6.5.2 for the additional description of dosimetric input. 

ANL-MGR-MD-000009 REV 01 29 December 2000 
Table 2. Consideration of Features, Events, and Processes Considered Applicable to YMP within the Biosphere Model for Calculation of 
Nominal Performance Biosphere Dose Conversion Factor (Continued) 
FEP Name 
YMP FEP 
Database Number Reference/Comment 
Diet and fluid intake 2.4.03.00.00 Consumption rates of locally produced food and tap water for the critical group are included among the 
model input parameters which were developed in the input AMRs. See Sections 6.5.4.1 and 6.5.5.1 for the 
description of input and its sources for the current climate and for the glacial transition climate, respectively.) 
Human lifestyle 2.4.04.01.00 Conditions of human exposure are based on the assumed lifestyle of the receptor of interest (farming 
community). This is apparent in the selection of specific values of exposure parameters (which, in addition 
to food and water consumption rates, include amount of time spent outdoor for work and recreation, 
inhalation exposure time) specific to the critical groups lifestyle. The human exposure parameters are 
described in the input AMR. (See Section 6.2 for the discussion of human receptor and Sections 6.5.4.1 
and 6.5.5.1 for the description of input and its sources for the current climate and for the glacial transition 
climate, respectively.) 
Dwellings 2.4.07.00.00 Type of dwellings and the habits of human receptor are addressed in the AMR, which develops the critical 
group. (See Section 6.2 for the discussion of human receptor and Sections 6.5.4.1 and 6.5.5.1 for the 
description of input and its sources for the current climate and for the glacial transition climate, respectively.) 
Agricultural land use 
and irrigation 
2.4.09.01.00 Agricultural land use by the human receptor is addressed in the AMRs that develops the critical group (see 
Section 6.2). Irrigation characteristics are included in biosphere model input. (See sections 6.5.4.1 and 
6.5.5.1 for the description of input parameters and their sources for the current climate and for the glacial 
transition climate, respectively.) 
Animal farms and 
fisheries 
2.4.09.02.00 Contamination of animal products is considered in the model. (See mathematical representation of the 
biosphere model in Attachment I of this AMR for the description of submodels addressing radionuclide 
transfer to animal products and fish.) 
Drinking water, 
foodstuffs and drugs, 
contaminant 
concentrations in 
3.3.01.00.00 Consumption rates of locally produced food and tap water for the critical group are included among the 
model input parameters which were developed in the input AMRs. See Sections 6.5.4.1 and 6.5.5.1 for the 
description of input and its sources for the current climate and for the glacial transition climate, respectively.) 
Plant uptake 3.3.02.01.00 Contamination of crops is considered in the model by using steady-state transfer factors. (See 
mathematical representation of the biosphere model in Appendix I for the description of submodels 
addressing radionuclide transfer to crops.) 
Animal uptake 3.3.02.02.00 Contamination of animal products is considered in the model by using steady-state transfer factors. (See 
mathematical representation of the biosphere model in Appendix I for the description of submodels 
addressing radionuclide transfer to animal products.) 
Bioaccumulation 3.3.02.03.00 The process of accumulation of radioactive contaminants in food products is considered in the model. See 
mathematical representation of the biosphere model in Appendix I for the description of submodels 
addressing radionuclide transfer to crops, animal products and fish. 

ANL-MGR-MD-000009 REV 01 30 December 2000 
Table 2. Consideration of Features, Events, and Processes Considered Applicable to YMP within the Biosphere Model for Calculation of 
Nominal Performance Biosphere Dose Conversion Factor (Continued) 
FEP Name 
YMP FEP 
Database Number Reference/Comment 
Ingestion 3.3.04.01.00 Ingestion of locally produced food and tap water is included in the model as one of the exposure pathways. 
Consumption rates of locally produced food and tap water for the critical group are included among the 
model input parameters which were developed in the input AMRs. See Sections 6.5.4.1 and 6.5.5.1 for the 
description of input and its sources for the current climate and for the glacial transition climate, respectively. 
Inhalation 3.3.04.02.00 Inhalation of resuspended particular matter is included in the model as one of the pathways. See 
mathematical representation of the biosphere model in Attachment I. Parameters for the inhalation pathway 
are developed in the input AMR  see Sections 6.5.4.1 and 6.5.5.1 for the description of input parameters 
and their sources. 
External exposure 3.3.04.03.00 External exposure pathway is includedin the model. (See mathematical representation of the biosphere 
model in Appendix I. Parameters for the external exposure pathway are developed in the input AMR  see 
Sections 6.5.4.1 and 6.5.5.1 for the description of input parameters and their sources. 
Radiation doses 3.3.05.01.00 Calculation of radiation doses is carried out by the human dosimetry component of the biosphere model. 
(See mathematical representation of the biosphere model in Attachment I for the description of dosimetric 
component of the model.) 

6.3.1 Exposure Pathway Identification and Modeling 
Modeling the movement of radionuclides through the food chain is accomplished by identifying 
specific routes, called pathways, taken by radionuclides through the biosphere from the source of 
contamination to a human receptor. Current regional land use and other local current conditions 
influence pathways that are considered significant. The analysis considered pathways that are 
typical for the current conditions for the critical group residing within a hypothetical farming 
community in the vicinity of Yucca Mountain (64 FR 8640, Section 63.115 (a)(2) and 
Section 63.115 (b)). The farming critical group was selected because farming activities typically 
involve more exposure pathways than other human activities identified in the Yucca Mountain 
region (64 FR 8640, Section VI of the Supplementary Information). The exposure pathways 
included ingestion of contaminated water, crops, and animal products; inhalation, and external 
exposure from contaminated soil. Specifically, the following exposure pathways were 
considered for the development of the nominal performance BDCFs: 
 
Consumption of tap water 
 
Consumption of locally produced leafy vegetables 
 
Consumption of other locally produced vegetables 
 
Consumption of locally produced fruit 
 
Consumption of locally produced grain 
 
Consumption of locally produced meat 
 
Consumption of locally produced poultry 
 
Consumption of locally produced milk 
 
Consumption of locally produced eggs 
 
Consumption of locally produced fish 
 
Inadvertent soil ingestion 
 
Inhalation of resuspended particulate matter 
 
External exposure to contaminated soil 
Pathway modeling is accomplished using simple mathematical formulations reflecting transfer 
compartments in the environment. This approach to biosphere modeling was used to calculate 
BDCFs and it is shown schematically in Figure 2. Modeling of radionuclide transport through 
the compartments of the biosphere model applied steady-state transfer factors, e.g., soil-to-plant 
and plant-to-animal product transfer coefficients, and bioaccumulation factors, e.g., 
bioaccumulation factor for fish. (See Appendix I for the numerical representation of the model, 
including the description of submodels and associated transfer parameters.) 
Pathway analysis results in determination of the total exposure of the individual to radionuclides. 
The human dosimetry element of the biosphere model was then involved to convert both internal 
exposure, through ingestion and inhalation, and external exposure from unit activity 
concentration in groundwater to TEDE for the human receptor resulting from an annual 
exposure. 

(00023DC_Biosphere_PMR Fig. 3-7a.ai) 
Figure 2. Block Diagram of Biosphere Conceptual Model for Nominal Performance of the Potential 
Repository 

6.3.2 Radionuclide Buildup in Soil 
The magnitude of radiation exposure from certain pathways depends on the radionuclide 
concentration in soil. The dynamics of the radionuclide concentration in the top layer of soil are 
governed by a conservation equation where the rate of change in radionuclide concentration in a 
volume of soil is equal to the quantity flowing in (from irrigation with contaminated 
groundwater) minus the amount being removed. Mechanisms of radionuclide removal from the 
soil considered in this analysis include radioactive decay, plant uptake, and leaching into the 
deeper soil layer. 
To address this buildup effect, it was necessary to determine whether buildup caused any 
significant change in the BDCF. For those radionuclides where buildup did not occur to a 
significant degree, the periods of irrigation that were used for the calculation were of no 
consequence. However, for those radionuclides where buildup was shown to be significant, an 
approach was required that would allow the BDCFs to be determined for an arbitrary previous 
irrigation period. The technique adopted to determine the functional relationship between the 
value of the BDCF and the duration of previous irrigation was that of curve fitting 
(CRWMS M&O 2000n) with subsequent use of the fitted curve for interpolation or 
extrapolation. To accomplish this, the BDCFs were calculated for several periods of prior 
irrigation times. (The period of prior irrigation represents the number of years that the land has 
been irrigated with contaminated water before the intake occurs.) 
The technique of curve fitting does not require that BDCFs be calculated for any specific time. 
The only requirement is that the dependent variable (BDCFs) is calculated over the range of the 
variable parameter of interest (irrigation periods). Considering this, the values of irrigation 
periods were calculated such that the series of BDCFs would be approximately equally spaced 
between the value for the first period and the asymptotic value after an infinitely long period of 
previous irrigation. Assuming the constant addition rate of radionuclide to soil via irrigation, 
BDCFs are proportional to the function f: 
f = 1 - e-.t (Eq. 1) 
where . is the effective removal constant for each radionuclide in soil, and t is the duration of 
previous irrigation. The effective removal constant was approximated by the sum of the 
radioactive decay constant and the leaching factor. The range between the BDCF for no prior 
irrigation (f = 0) and the equilibrium value of the BDCF for an individual radionuclide (f = 1) 
was arbitrarily divided into the six equal intervals to obtain a sufficient number of data points for 
the future curve fitting. Then the irrigation time periods were calculated based on the effective 
removal rate of radionuclides from the top 15 cm of soil using the following formula: 
. 1 . 
.. 
t = 
ln ... 1 - f . (Eq. 2)
. 
where f is the fraction of the BDCF at equilibrium. For the six irrigation time periods, f is equal 
to 1/6, 2/6, 3/6, 4/6, and 5/6 for the second through the sixth irrigation period, respectively. The 
first time period corresponds to no prior irrigation with contaminated water. Irrigation time 

periods calculated using this method are provided in Table 3. The calculations were performed 
using an Excel spreadsheet as shown in Attachment V. 

ANL-MGR-MD-000009 REV 01 35 December 2000 
Table 3. Prior Irrigation Time Periods 
Radionuclide 
Removal constant, 1/y Prior Irrigation Periods, y a 
Radioactive 
Decay b Leaching c Effective d 1 2 3 4 5 6 
14C 1.21E-04 1.32E-01 1.32E-01 0 1 3 5 8 14 
63Ni 6.92E-03 1.69E-03 8.61E-03 0 21 47 80 128 208 
90Sr 2.42E-02 4.47E-02 6.89E-02 0 3 6 10 16 26 
99Tc e 3.25E-06 2.77E+00 2.77E+00 0 1 2 3 4 5 
129I e 4.42E-08 5.92E-01 5.92E-01 0 1 2 3 4 5 
137Cs 2.30E-02 2.42E-03 2.54E-02 0 7 16 27 43 71 
210Pb 3.11E-02 2.51E-03 3.36E-02 0 5 12 21 33 53 
226Ra 4.33E-04 1.35E-03 1.78E-03 0 102 227 389 616 1005 
227Ac 3.18E-02 1.50E-03 3.33E-02 0 5 12 21 33 54 
229Th 9.44E-05 2.12E-04 3.06E-04 0 595 1323 2262 3586 5848 
230Th 9.00E-06 2.12E-04 2.21E-04 0 825 1835 3136 4971 8108 
231Pa 2.11E-05 1.23E-03 1.25E-03 0 146 324 554 878 1432 
232U 9.63E-03 1.93E-02 2.89E-02 0 6 14 24 38 62 
233U 4.35E-06 1.93E-02 1.93E-02 0 9 21 36 57 93 
234U 2.84E-06 1.93E-02 1.93E-02 0 9 21 36 57 93 
236U 2.96E-08 1.93E-02 1.93E-02 0 9 21 36 57 93 
238U 1.55E-10 1.93E-02 1.93E-02 0 9 21 36 57 93 
237Np 3.24E-07 1.32E-01 1.32E-01 0 1 3 5 8 14 
238Pu 7.90E-03 1.23E-03 9.13E-03 0 20 44 76 120 196 
239Pu 2.87E-05 1.23E-03 1.26E-03 0 145 322 551 873 1423 
240Pu 1.06E-04 1.23E-03 1.34E-03 0 136 304 519 822 1341 
242Pu 1.84E-06 1.23E-03 1.23E-03 0 148 329 563 892 1455 
241Am 1.60E-03 3.56E-04 1.96E-03 0 93 207 354 561 915 
243Am 9.39E-05 3.56E-04 4.50E-04 0 405 901 1541 2442 3983 
a
NOTES: Prior irrigation periods were calculated using Equation 2 in Excel spreadsheet (see Attachment V) 
b 
Rittmann 1993, p. 2-5, Removal constant for radioactive decay = (ln 2)/(half-life) 
c 
DTN: MO0004RIB00085.000 
d 
Effective removal constant = Sum of radioactive decay and leaching removal constants 
e 
Sixth irrigation period was less than five years, thus one year per period was conservatively assumed. 

6.4 CONSIDERATION OF CLIMATE CHANGE 
Geologic media provide the historical record of the types and periodicity of climate change in the 
Yucca Mountin region. The past-to-future climate analog forecasts that the modern-day climate 
at Yucca Mountain should persist from 400 to 600 yr., followed by a warmer and much wetter 
monsoon climate for 900 to 1,400 yr., followed by a cooler and wetter glacial transition climate 
for 8,000 to 8,700 yr. (CRWMS M&O 2000o, p. 6.5-3). The analysis performed to estimate 
climatic variables for the next 10,000 years (USGS 2000) identified representative 
meteorological stations selected to represent future climates. The stations selected provided an 
upper and lower climate bound for each future climate. The forecasted future climate states, 
analog sites (USGS 2000, Table 2) and the corresponding average long-term meteorological 
parameters (DTN GS000100001221.001) are listed in Table 4. 
Table 4. Annual Average Meteorological Parameters for Potential Future Climate States at Yucca 
Mountain and the Analog Sites for the Climate Change Analysis 
Climate State 
Representative 
Meteorological 
Stations 
Max 
Temperature 
(F) 
Min 
Temperature 
(F) 
Snowfall 
(inches) 
Precipitation 
(inches) 
Monsoon Climate Nogales 6N, Arizona 79.4 42.7 0.45 17.55 
Average Upper Bound Hobbs, New Mexico 76.5 47.8 5.18 16.44 
Monsoon Climate Yucca Mountain site 81.9 48.3 0.10 4.48 
Average Lower Bound and regional 
meteorological 
stations (e.g. 
Amargosa Farms) 
Glacial Transition Climate 
Average Upper Bound 
Spokane, 
Washington 
Rosalia, Washington 
58.1 
58.1 
60.9 
38.1 
36.1 
35.8 
42.14 
24.34 
25.78 
16.15 
18.10 
17.06 
St. John, 
Washington 
Glacial Transition Climate Beowawe, Nevada 65.0 30.8 14.39 8.64 
Average Lower Bound Delta, Utah 65.4 34.8 25.15 7.79 
Modern Interglacial 
Climate 
Yucca Mountain site 
and regional 
meteorological 
stations 
81.9 48.3 0.10 4.48 
(e.g. Amargosa 
Farms) 
DTN: GS000100001221.001 
Conditions representative of a glacial transition climate were selected to represent evolved 
climatic conditions at Yucca Mountain and evaluate the impact of climate change on BDCFs. A 
glacial transition climate was selected because it is predicted to occur for most of the next 10,000 
years (CRWMS M&O 2000o, p. 6.5-3). Also, the conditions at the Spokane, Washington station 
differ the most from the current conditions in the Yucca Mountain region. In Spokane, the 
average maximum temperature is the lowest, and the amount of overall precipitation (including 
snow) is the greatest from among the selected analog sites. Therefore this site, being the coolest 
and wettest of the analog sites, was chosen to represent bounding future-climate conditions for 
the biosphere analysis. 

To consider the effects of climate change on the biosphere modeling, BDCFs were developed for 
the two climate condition: (1) the current climate state, and (2) the evolved climate represented 
by the upper bound of the glacial transition climate, similar to that of Spokane, Washington. 
6.5 DEVELOPMENT OF BIOSPHERE DOSE CONVERSION FACTORS 
GENII-S, A Code for Statistical and Deterministic Simulations of Radiation Doses to Humans 
from Radionuclides in the Environment (Leigh et al. 1993), was chosen to support biosphere 
modeling for nominal performance. Using a comprehensive set of environmental pathways 
models, the code calculated the environmental transport of radionuclides following initial 
contamination of groundwater. Radionuclide concentrations in water, air, soil, and various 
foodstuffs, combined with human intake and external exposure conditions are then converted to 
internal and external radiation doses. A description of the environmental transport and uptake 
model implemented by the GENII-S code, which are important for biosphere modeling, is 
included in Attachment I. 
Conversion of radionuclide intake by ingestion or inhalation is accomplished in GENII-S by 
application of dose conversion factors, which represent dose per unit activity intake by ingestion 
or by inhalation. Conversion factors depend on the chemical and physical form of chemical 
compound of the radionuclide. For conservatism, BDCFs for nominal performance were 
calculated using the set of dose conversion factors for inhalation and ingestion for those 
compounds that result in the highest doses. 
The primary results from this analysis are radionuclide-specific BDCFs for the average member 
of the critical group for both current climate and the upper bound of the glacial transition 
climate. Each of these BDCF sets includes considerations of radionuclide buildup in soil due to 
previous irrigation. Probabilistic analysis was used to develop BDCF values, as described in the 
following section (Section 6.5.1). The general approach to the development of input parameters 
for the biosphere model is discussed in Section 6.5.2. The two following sections, Section 6.5.3 
and Section 6.5.4, contain specific description of modeling input and output for the current 
climate and the glacial transition climate, respectively. 
6.5.1 Probabilistic Analysis 
To develop BDCFs, the probabilistic approach was taken which allows statistical sampling of 
parameter values described by their probability distribution functions (PDF). This method, 
called Monte Carlo analysis, provides a quantitative evaluation of uncertainty and its impacts on 
the modeling outcome represented by distributions of potential modeling results  BDCFs. 
When performing BDCF calculations, a large quantity of parameters is encountered. GENII-S 
has the capability of representing some of the model parameters by their PDFs. Parameter values 
were sampled using the Latin Hypercube sampling (LHS) scheme. With LHS, the probability 
distribution is divided into intervals of equal probability. The GENII-S code then samples a 
value from each interval, which results in more even and consistent sampling compared with the 
conventional Monte Carlo random sampling scheme. 
There are more than 100 input parameters used in the GENII-S model (See Table 5). Some of 
these parameters can be represented by a distribution while the others are fixed. 

Table 5. GENII-S Parameters 
Parameter 
Distribution 
GENII-S Biosphere Selection 
RADIONUCLIDE CONCENTRATION IN SOIL 
Depth of Surface Soil Variable Fixed 
Surface Soil Density Variable Fixed 
Deep Soil Density Variable Fixed 
Prior Irrigation Duration Fixed Fixed 
Home Irrigation Rate Variable Variable 
Home Irrigation Duration Variable Fixed 
Leaching Factors Fixed Fixed 
Crop Yields Variable Variable 
RADIONUCLIDE CONCENTRATION IN AIR 
Deposition Velocity Variable Fixed 
Mass Loading Variable Variable 
RADIONUCLIDE CONCENTRATION IN PLANTS FOR HUMAN AND ANIMAL 
CONSUMPTION 
Resuspension Factor Variable Variable 
Growing Time Variable Variable 
Fraction of Roots in Upper Soil Variable Fixed 
Fraction of Roots in Deep Soil Variable Fixed 
Irrigation Rates Variable Variable 
Irrigation Times Variable Variable 
Interception Fraction (irrigation) Variable Variable 
Weathering Half-life Fixed Fixed 
Translocation Factors Fixed Fixed 
Soil-to-Plant Transfer Factors Fixed Fixed 
Soil-to-Plant Transfer Scaling Factor Variable Variable 
Crop Biomass Fixed Fixed 
Dry-to-Wet Ratio Fixed Fixed 
RADIONUCLIDE CONCENTRATION IN ANIMAL PRODUCTS 
Feed Storage Time Variable Fixed 
Dietary Fraction Variable Fixed 
Contaminated Water Fraction Variable Fixed 
Animal Feed and Water Consumption Rates Fixed Fixed 
Transfer Coefficients for Animal Products Fixed Fixed 
Animal Product Transfer Scaling Factor Variable Variable 
Bioaccumulation Factors for Fish Fixed Fixed 
HUMAN EXPOSURE 
Drinking Water Holdup Variable Fixed 
Water Consumption Rates Variable Variable 
Crop/Animal Product Holdup Variable Fixed 
Food Consumption Rates Variable Variable 
Soil Ingestion Rate Variable Fixed 
Inhalation Exposure Time Variable Variable 
Chronic Breathing Rate Fixed Fixed 
Soil Exposure Time Variable Variable 
DOSIMETRIC PARAMETERS 
Human Dose Scaling Factor Variable Fixed 
Dose Commitment Period Fixed Fixed 
DCFs/DFs Fixed Fixed 
Organ/Tissue Weighting Factors Fixed Fixed 

In general, parameters that could potentially be represented by a probability distribution function, 
but which were determined to have less influence on the final BDCF, were selected as fixed 
values. Such parameters include storage and holdup times (which are inconsequential for long-
lived radionuclides), parameters related to properties of the soils (which were determined based 
on site-specific information), and parameters whose values were maximized for conservatism 
(e.g. home irrigation rate, fraction of contaminated water used for animal watering). 
Representing less important parameters as fixed values helped to alleviate the computational 
burden on the software and allowed increasing the maximum possible number of model 
realization, thus improving statistical representation of the outcome. (Model realization is one of 
the possible model outcomes obtained as a result of a single round of sampling of the model 
input parameters.) To obtain statistically valid results using LHS, the minimum number of 
realizations, has to be 1.33 times greater than the number of sampled parameters (LaPlante and 
Poor 1997, page 3-2). There were 37 sampled parameters in this analysis, which gives the 
minimum number of realization of about 50. 
The code has a limitation on the combination of number of parameters represented by probability 
distributions and the number of realizations. That is, if more parameters are sampled from their 
probability distributions, the number of possible realizations decreases. The actual number of 
realizations that the code can process and produce output in a practical text format depends on 
the number of variables that are sampled and calculated. The code calculates 27 dependent 
variables, which when combined with 37 independent variables gives 64 variables whose values 
are included in the output file (MO0010MWDPBD09.006). It was determined that the 
maximum number of realizations can be calculated by dividing 10,000 by the number of 
variables. In this case the number of variables was 64, so the maximum number of realization 
would be 156. The number of realizations was therefore set to 150. 
The statistical sampling technique described above produces a set of 150 single model results 
(BDCFs) from a set of sampled parameter values (inputs). The 150 model realizations are then 
statistically summarized and characterized to produce probability distributions of BDCFs that 
can be used in the TSPA model. 
6.5.2 Development of Input Parameters 
As noted before, GENII-S uses large number of input parameters. These parameters can be 
classified into two main groups: (1) the parameters that influence, or are related to, radionuclide 
transport and accumulation in the biosphere, and (2) the parameters related to characteristics of 
the human receptor. Input parameters, whether characterized as fixed constants or uncertain 
parameters with associated probability distributions, have been developed for each of the model 
runs. When available, site-specific data were used to determine input parameter values. 
However, for many parameters, the only available data were not completely representative of the 
reference biosphere and the population being assessed. In this case, developed parameter values 
reflect expert judgement regarding the degree to which each parameter is unknown. In this 
aspect, the resulting frequency distribution may be to some degree subjective and should not be 
considered to represent only natural variability. However, the selection of the parameter values 
was guided by the general assessment philosophy, which was to use generally conservative 
assumptions to ensure that the results are unlikely to underestimate the corresponding values of 
BDCFs for the considered radionuclide transport and uptake conditions and mechanisms. 

A majority of the input parameters were developed in a series of analyses. The supporting 
documentation, including justification for the selection of parameter values, ranges, and 
distributions can be found in associated AMRs, as listed in Table 1. The remaining parameters 
are addressed below. 
Ingestion Exposure Parameters 
The previously developed set of ingestion exposure parameters (MO0002RIB00068.000) 
includes recommended values of thirteen parameters: water source, drinking water treatment, 
crop interception fraction, water contaminated fraction, irrigation water contamination fraction, 
irrigation time, irrigation rate, aquatic food consideration, food yield, grow time, holdup time, 
storage time, and dietary fraction. These parameters were developed for the current climate 
condition. If the weather becomes cooler and wetter, such as it is postulated for the evolved 
climate, the crop growing characteristics, such as the growing season or the irrigation rate will be 
affected. 
Selected ingestion exposure parameters were developed to address a cooler and wetter weather 
conditions using Spokane, WA as an analog site. In addition, to correct inconsistencies found in 
the method used to develop Ingestion Exposure Parameter Values (MO0002RIB00068.000), 
additional calculations of the parameters for the current climate were carried out (see 
Attachment III for details). The summary is presented in Table 6. 

ANL-MGR-MD-000009 REV 01 41 December 2000 
Table 6. Summary of Ingestion Parameters for the Current and Evolved Climates 
Parameter 
Current Climate a Evolved Climate b 
Distribution 
Reasonable 
Estimate 
Minimum 
Value 
Maximum 
Value Distribution 
Reasonable 
Estimate 
Minimum 
Value 
Maximum 
Value 
Growing Time (day) 
Leafy vegetables Uniform 57 45 68 Uniform 73 50 95 
Root vegetables Uniform 84 70 98 Uniform 122 75 168 
Fruit Uniform 136 88 184 Uniform 140 95 184 
Grain Fixed 244 244 244 Fixed 191 191 191 
Fresh feed for beef Triangular 47 46 135 Fixed 71 71 71 
Stored feed for poultry Fixed 140 140 140 Fixed 179 179 179 
Fresh feed for milk Triangular 47 46 135 Fixed 71 71 71 
Stored feed for eggs Fixed 140 140 140 Fixed 179 179 179 
Irrigation Time (mo/yr) 
Leafy vegetables Uniform 3.8 3.0 4.5 Uniform 4.8 3.3 6.2 
Root vegetables Uniform 3.9 3.2 4.6 Uniform 5.2 4.9 5.5 
Fruit Uniform 4.5 2.9 6.0 Uniform 4.6 3.1 6.0 
Grain Fixed 8.0 8.0 8.0 Fixed 6.2 6.2 6.2 
Fresh feed for beef Fixed 12 12 12 Fixed 7.0 7.0 7.0 
Stored feed for poultry Fixed 4.6 4.6 4.6 Fixed 5.7 5.7 5.7 
Fresh feed for milk Fixed 12 12 12 Fixed 7.0 7.0 7.0 
Stored feed for eggs Fixed 4.6 4.6 4.6 Fixed 5.7 5.7 5.7 
Irrigation Rate (in/yr) 
Leafy vegetables Uniform 36 28 43 Uniform 34 25 43 
Root vegetables Uniform 50 47 52 Uniform 40 39 41 
Fruit Uniform 38 30 45 Uniform 28 26 30 
Grain Fixed 56 56 56 Fixed 34 34 34 
Fresh feed for beef Fixed 95 95 95 Fixed 42 42 42 
Stored feed for poultry Fixed 75 75 75 Fixed 39 39 39 
Fresh feed for milk Fixed 95 95 95 Fixed 42 42 42 
Stored feed for eggs Fixed 75 75 75 Fixed 39 39 39 

ANL-MGR-MD-000009 REV 01 42 December 2000 
Table 6. Summary of Ingestion Parameters for the Current and Evolved Climates (Continued) 
Parameter 
Current Climate a Evolved Climate b 
Distribution 
Reasonable 
Estimate 
Minimum 
Value 
Maximum 
Value Distribution 
Reasonable 
Estimate 
Minimum 
Value 
Maximum 
Value 
Crop Yield (kg/m2) 
Leafy vegetables Uniform 4.6 4.4 4.8 Uniform 4.6 4.4 4.8 
Root vegetables Uniform 7.0 4.1 9.8 Uniform 7.0 4.1 9.8 
Fruit Uniform 2.0 1.6 2.3 Uniform 2.0 1.6 2.3 
Grain Uniform 0.5 0.3 0.7 Uniform 0.5 0.3 0.7 
Fresh feed for beef Uniform 1.1 1.0 1.2 Fixed 0.5 0.5 0.5 
Stored feed for poultry Uniform 0.7 0.6 0.8 Uniform 0.7 0.6 0.8 
Fresh feed for milk Uniform 1.1 1.0 1.2 Fixed 0.5 0.5 0.5 
Stored feed for eggs Uniform 0.7 0.6 0.8 Uniform 0.7 0.6 0.8 
a
NOTES: From Table III-2 (Attachment III) 
b From Table III-7 (Attachment III) 

Dose Coefficients 
A previously developed set of dose coefficients (DCs) for 15-cm layer of contaminated soil 
(MO9912RIB00066.000) was amended because the set did not include radionuclides considered 
important for up to 1 million years. For most radionuclides, their decay products must be taken 
into account in BDCF calculations. Decay products should match those considered in GENII-S 
for a given radionuclide. Following computational methods of GENII-S, DCs for some 
radionuclides include contributions from their own chains of short-lived radionuclides. 
DCs were developed using the same method as that described in CRWMS M&O (1999), using 
accepted data from Federal Guidance Report No. 12 (FGR-12) (Eckerman and Ryman 1993) 
(MO9912SPASUB02.001) as the source of the data in Table 7. DCs in the FGR-12 are listed in 
units of Sv/s per Bq/m3. GENII-S input should be in Sv/y per Bq/m3. DC were therefore 
converted to the units used in GENII-S input file by multiplying FGR-12 DCs by the conversion 
factor of 3.15107 s/y. In addition, for three radionuclides (222Rn, 225Ac, and 223Ra), 
contributions from their short-lived decay products were added to the DC of a parent, in a 
process described in (CRWMS M&O 1999). DCs for radionuclides of interest and their decay 
products are summarized in Table 7. 
Table 7. Dose Coefficients for 15-cm Layer of Contaminated Soil for Radionuclides of Interest (shown 
in bold type) and Their Decay Products 
Radionuclide 
Dose Coefficient for contaminated soil 
Sv/s per Bq/m3 a Sv/y per Bq/m3 b 
14C 7.20E-23 2.27E-15 
63Ni 0.00E+00 0.00E+00 
90Sr 3.72E-21 1.17E-13 
90Y 1.20E-19 3.78E-12 
99Tc 6.70E-22 2.11E-14 
129I 6.93E-20 2.19E-12 
137Cs + 137mBa 1.71E-17 5.39E-10 
210Pb 1.31E-20 4.13E-13 
210Bi 1.86E-20 5.86E-13 
210Po 2.45E-22 7.72E-15 
226Ra 1.65E-19 5.20E-12 
222Rn + decay products c 
218Po 
214Pb 
214Bi 
214Po 
(1.14E-20) 
(2.63E-22) 
(6.70E-18) 
(4.36E-17) 
(2.40E-21) 
1.58E-09 
210Pb d 
227Ac 2.62E-21 8.25E-14 

Table 7. Dose Coefficients for 15-cm Layer of Contaminated Soil for Radionuclides of Interest (shown 
in bold type) and Their Decay Products (Continued) 
Radionuclide 
Dose Coefficient for contaminated soil 
Sv/s per Bq/m3 a Sv/y per Bq/m3 b 
229Th 1.70E-18 5.36E-11 
225Ra 5.90E-20 1.86E-12 
225Ac + decay products c 
221Fr 
217At 
213Bi 
213Po at 97.84% 
209Tl at 2.16% 
209Pb 
(3.34E-19) 
(7.90E-19) 
(8.61E-21) 
(3.75E-18) 
(0) 
(1.25E-18) 
(4.08E-21) 
1.93E-10 
230Th 6.39E-21 2.01E-13 
226Ra d 
231Pa 9.62E-19 3.03E-11 
227Th 2.65E-18 8.35E-11 
223Fr 1.01E-18 3.18E-11 
223Ra + decay products c 
219Rn 
215Po 
211Pb 
211Bi 
207Tl 
(3.10E-18) 
(1.54E-18) 
(4.98E-21) 
(1.46E-18) 
(1.28E-18) 
(9.48E-20) 
2.36E-10 
232U 4.77E-21 1.50E-13 
233U 7.24E-21 2.28E-13 
229Th d 
234U 2.14E-21 6.74E-14 
236U 1.14E-21 3.59E-14 
238U 5.52E-22 1.74E-14 
234Th 1.29E-19 4.06E-12 
234Pa 5.38E-17 1.69E-09 
237Np 4.16E-19 1.31E-11 
233Pa 5.16E-18 1.63E-10 
238Pu 8.07E-22 2.54E-14 
234U d 
239Pu 1.52E-21 4.79E-14 
240Pu 7.84E-22 2.47E-14 
236U d 
242Pu 6.85E-22 2.16E-14 
241Am 2.34E-19 7.37E-12 
237Np d 

Table 7. Dose Coefficients for 15-cm Layer of Contaminated Soil for Radionuclides of Interest (shown 
in bold type) and Their Decay Products (Continued) 
Radionuclide 
Dose Coefficient for contaminated soil 
Sv/s per Bq/m3 a Sv/y per Bq/m3 b 
243Am 7.60E-19 2.39E-11 
239Np 3.90E-18 1.23E-10 
239Pu d 
a
NOTES: MO9912SPASUB02.001
b (Sv/s per Bq/m3)  (3.15  107 s/y) = Sv/y per Bq/m3
c 
Contribution from decay products listed below is added to the DC for this radionuclide 
d The DCs for this radionuclide and its progeny, if applicable, have been listed earlier in this Table. 
6.5.3 GENII-S Input 
GENII input consists of parameters included in input files and parameters entered using menu-
driven interface. Input files did not change between the current and evolved climates. The 
content of input files is shown, as images of the files, in Figures 3, 4, 5, and 6. Figure 3 shows 
the image of DEFSR.IN file containing input parameters for GENII-S simulations. (For some 
parameters, if the user does not enter a parameter value using a menu-driven interface, a value 
from the default file is assigned to this parameter. The file also contains certain conversion 
factors.) Parameters used in the calculation, included in this file, were developed in the AMR 
(CRWMS M&O 2000e) (MO0010SPAPET07.004) except for the chronic breathing rate 
(MO0010SPAAAM01.014). Parameters not used in the present analysis remained unchanged 
from the GENII-S original default file. 
The image of the FTRANSR.DAT file containing food transfer factors developed in 
CRWMS M&O (2000f) (MO0010SPAPTC08.005) is shown in Figure 4. Food transfer factors 
for beef are assumped to apply to both beef and pork (see Section 5). This file also contains 
leaching factors developed in a separate report (CRWMS M&O 2000j) 
(MO0004RIB00085.000). Figure 5 shows the image of the BIOACSR.DAT file containing 
bioaccumulation factors (used for assessment of radionuclide transfer from water to fish for the 
aquatic food ingestion pathway) (MO0010SPAPTC08.005) while Figure 6 shows the image of 
the GRDFSR.DAT file containing dose coefficients for external exposure. The majority of dose 
coefficients were developed previously (CRWMS M&O 1999) (MO9912RIB00066.000). The 
remaining ones were developed in this analysis based on accepted data 
(MO9912SPASUB02.001) and are documented in this report (Section 6.5.2). 
Among input parameters entered using menu-driven interface, many are specific to the exposure 
scenario. These parameters are listed in the subsequent sections dealing with the modeling input 
and output for the specific exposure scenarios. 

Figure 3. The Image of the DEFSR.IN File (Default Parameter Values and Conversion Factors) 

Figure 4. The Image of the FTRANSR.DAT File (Food Transfer Coefficients and Leaching Factors) 
Figure 5. Image of the BIOACSR.DAT File (Bioaccumulation Factors) 

Figure 6. Image of the GRDFSR.DAT File (Dose Coefficients for Soil Contaminated to 15 cm) 

6.5.4 Biosphere Dose Conversion Factors for the Current Climate at Yucca Mountain 
Present Yucca Mountain climate is arid to semi-arid and seasonally warm. Total rainfall is 
typically less than 10 in. The air temperatures range from over 40oC during summer days to 
below 0oC during some winter nights (CRWMS M&O 2000o, p. 6.2-4). BDCFs were developed 
for the climatic conditions corresponding to such precipitation and temperature characteristics. 
The receptor of interest was defined as an average member of the critical group, as described in 
Section 6.2. BDCFs were calculated in a series of GENII-S simulations for each of the 24 
radionuclides (see Section 6.1). Each simulation resulted in 150 model realizations. 
This section contains a listing of the modeling input data and the summary of the model output 
followed by the discussion of the radionuclide buildup in soil caused by irrigation with 
contaminated groundwater. 
6.5.4.1 Input Data 
As noted previously, GENII-S input consists of input files and parameters entered during the 
model runs using menu-driven interface. The content of the input files was the same for both 
climate conditions considered in this analysis and it is given in Section 6.4.3. The set of input 
values for the current climate, entered using GENII-S menu-driven interface is shown in Table 8. 
The values and selections in the table are listed in the format that they are entered in GENII-S. 

ANL-MGR-MD-000009 REV 01 50 December 2000 
Table 8. GENII-S Menu-Accessible Input Parameters for the Current Climate 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum 
Best 
Estimate Maximum 
PRE-GENII 
Scenario Options 
- Near-Field Scenario Y NA b NA NA NA 
Near-field 
scenario 
- Population Dose N NA NA NA NA 
- Acute Release N NA NA NA NA 
Transport Options No radionuclide 
- Air Transport N NA NA NA NA transport 
- Surface Water Transport N NA NA NA NA 
- Biotic Transport N NA NA NA NA 
- Waste From Degradation N NA NA NA NA 
Exposure Pathway Options Pathway 
- External Finite Plume N NA NA NA NA selection 
- External Infinite Plume N NA NA NA NA 
- External Ground Exposure Y NA NA NA NA 
- External Recreational Exposure N NA NA NA NA 
- Inhalation Uptake Y NA NA NA NA 
- Drinking Water Ingestion Y NA NA NA NA 
- Aquatic Food Ingestion Y NA NA NA NA 
- Terrestrial Food Ingestion Y NA NA NA NA 
- Animal Product Ingestion Y NA NA NA NAEdit Flags and Options 
- Inadvertent Soil Ingestion Y NA NA NA NA 
Deterministic Output Options Output selection 
- Both Committed and Cumulative N NA NA NA NA 
- EDE by Nuclide N NA NA NA NA 
- EDE by Pathway N NA NA NA NA 
Run Options 
- Inventory Unit Index (1-5) 
- Soil Inventory Unit Index (1-3) 
1, pCi 
1, per m2 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
Unit selection, 
Run selection, 
- Inventory Input Option (1-3) 2 NA NA NA NA Intake duration 
- Det Run/Stat Run/Both (1/2/3) 2 NA NA NA NA 
- Nuclide Intake Duration, yr 1 NA NA NA NA 
Select Radionuclide selection Y/N ----Section 6.1 
Nuclides 

ANL-MGR-MD-000009 REV 01 51 December 2000 
Table 8. GENII-S Menu-Accessible Input Parameters for the Current Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum 
Best 
Estimate Maximum 
PRE-GENII (Continued) 
Select Statistical 
Output 
- Statistical Committed Dose Summary 
- Statistical Committed Nuclide Dose 
- Statistical Committed Pathway Dose 
- Statistical Committed Organ Dose 
- Statistical Cumulative Pathway Dose 
- Statistical Cumulative Organ Dose 
- Statistical External Dose Summary 
Y 
N 
N 
N 
N 
N 
N 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
Output Selection 
MAIN EDITING MENU 
Titles And RunControls 
- Model Name 
- Title (2 lines) 
- Latin Hypercube (LHS) or Monte Carlo 
(MC) Sampling 
- The Number of Trials (<=500) 
- A Random Seed (0.0<=Seed<=1.0) 
LHS 
150 
0.333 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
Fixed Data Input 
Variable Distribution 
Population/Soil/Scenario Data 
- Total Population 
- Population Scale Factor 
- Soil/Plant Transfer Scale Factor, (-) 
- Animal Uptake Scale Factor, (-) 
- Human Dose Factor Scale Factor, (-) 
- Dose Commitment Period, yr 
- Surface Soil Depth, cm 
- Surface Soil Density, kg/m2 
- Deep Soil Density, kg/m3 
- Roots in Upper Soil, fraction 
- Roots in Deep Soil, fraction 
- Air Release Time Before Intake, yr 
- H2O Release Time Before Intake, yr 
1 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
0.0275 
0.117 
NA 
NA 
NA 
NA 
1 
1 
50 
15 
225 
1500 
1 
0 
0 
0 
NA 
36.4 
8.51 
NA 
NA 
NA 
-- 
Fixed 
Lognormal 
Lognormal 
Fixed 
NA 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
NA 
NA 
Not used 
Not used 
Input #2 
Input #2 
Input #6 
Input #1 
Input #1 
Input #1 
Input #1 
Input #1 
Not used 
Table 3 
Biotic Trans./Near Field Data 
Not used 
-- -- -- -- -- -- 

ANL-MGR-MD-000009 REV 01 52 December 2000 
Table 8. GENII-S Menu-Accessible Input Parameters for the Current Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum 
Best 
Estimate Maximum 
MAIN EDITING MENU (Continued) 
Fixed Data Input 
Variable Distribution 
External/Inhalation Exposure (cont.) 
- Chronic Plume Exposure Time, hr 
- Acute Plume Exposure Time, hr/phr 
- Inhalation Exposure Time, hr/yr 
- Resuspension Model Flag (0-2) 
- Mass Loading, g/m3 
- Transit Time to Rec. Site, hr 
- Swimming Exposure Time, hr 
- Boating Exposure Time, hr 
- Shoreline Exposure Time, hr 
- Type of Shoreline Index (1-4) 
- H2O/Sediment Transfer1/m2/yr 
- Soil Exposure Time, hr 
- Home Irrigation Flag (0/1 = N/Y) 
- Irrigation Water Index (1-2) 
- Home Irrigation Rate, in/yr 
- Home Irrigation Duration, mo/yr 
NA 
NA 
NA 
1 
NA 
NA 
NA 
NA 
NA 
0 
NA 
NA 
1 
1 
NA 
NA 
5793.5 
NA 
3.810-5 
NA 
2827 
NA 
NA 
51 
0 
0 
6073.5 
NA 
1.0510-4 
0 
0 
0 
0 
NA 
0 
3387 
NA 
NA 
74 
12 
6353.5 
NA 
1.7310-4 
NA 
3947 
NA 
NA 
96 
Fixed 
Fixed 
Uniform 
NA 
Normal 
Fixed 
Fixed 
Fixed 
Fixed 
NA 
Fixed 
Uniform 
NA 
NA 
Uniform 
Fixed 
Not used 
Not used 
Input #3 
Mass loading 
Input #3 
| 
| 
| Parameters 
| not used 
| 
| 
Input #3 
Contaminated 
water 
Input #3 
Input #3 
Ingestion Exposure 
- Food Production Option 
- Food-Weighted Chi/Q, kg-s/m3 
- Crop Resuspension Factor, 1/m 
- Crop Deposition Velocity, m/s 
- Crop Interception Fraction 
- Exported Food Dose (0/1 = N/Y) 
- Soil Ingestion Rate, mg/day 
- Swim H2O Ingestion Rate, l/h 
- Population Ingesting Aquatic Food 
- Bioaccumulation Flag (0/1 = N/Y) 
- Population Drinking Contaminated 
Water 
- Drink Water Source Index (0-3) 
- Drink Water Treated (0/1 = N/Y) 
- Drink Water Holdup Time, days 
- Drink Water Consumption, l/y 
0 
0 
NA 
NA 
NA 
0 
NA 
NA 
0 
0 
0 
0 
0 
NA 
NA 
NA 
1.710-10 
0.044 
NA 
NA 
NA 
NA 
NA 
NA 
0 
NA 
0 
4.710-10 
0.001 
0.259 
NA 
50 
0 
NA 
NA 
NA 
NA 
NA 
0 
752.85 
NA 
7.710-10 
0.474 
NA 
NA 
NA 
NA 
NA 
NA 
1487.45 
NA 
Fixed 
Normal 
Fixed 
Normal 
NA 
Fixed 
Fixed 
NA 
NA 
NA 
NA 
NA 
Fixed 
Uniform 
Not used 
Not used 
Input #1 
Input #1 
Input #4 
Not used 
Input #1 
Not used 
Not used 
Not used 
Whole 
population 
Groundwater 
Input #4 
Input #4 
Input #5 

ANL-MGR-MD-000009 REV 01 53 December 2000 
Table 8. GENII-S Menu-Accessible Input Parameters for the Current Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum 
Best 
Estimate Maximum 
MAIN EDITING MENU (Continued) 
Array Data Input (cont.) 
Variable Distribution 
Aquatic Food Ingestion 
- Use (0/1 = F/T) 
Fish 
Mollusc 
Crustacea 
Plants 
- Transit Time (hr) 
Fish 
Mollusc 
Crustacea 
Plants 
- Production (kg/y) 
Fish 
Mollusc 
Crustacea 
Plants 
- Holdup (days) 
Fish 
Mollusc 
Crustacea 
Plants 
- Consumption (kg/yr) 
Fish 
Mollusc 
Crustacea 
Plants 
1 
0 
0 
0 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
6.17E-8 
NA 
NA 
NA 
NA 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0.47 
0 
0 
0 
NA 
NA 
NA 
NA 
8.79 
NA 
NA 
NA 
NA 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Loguniform 
Input #5 
Input #5 
Input #5 
Input #5 
| 
| 
| 
| 
| 
| 
| 
| 
| Parameters 
| not used 
| 
| 
| 
| 
Input #5 

ANL-MGR-MD-000009 REV 01 54 December 2000 
Table 8. GENII-S Menu-Accessible Input Parameters for the Current Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum 
Best 
Estimate Maximum 
MAIN EDITING MENU (Continued) 
Array Data Input (cont.) 
Variable Distribution 
Terrestrial Food Ingestion 
- Use (0/1 = F/T) 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Growing Time, days 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Water Source Flag (0-2) 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Irrigation Rate, in/yr 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Irrigation Time, mo/yr 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Crop Yield, kg/m2 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
1 
1 
1 
1 
NA 
NA 
NA 
NA 
1 
1 
1 
1 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
45 
70 
88 
NA 
NA 
NA 
NA 
28 
47 
30 
3.0 
3.2 
2.9 
4.4 
4.1 
1.6 
0.3 
NA 
NA 
NA 
NA 
57 
84 
136 
244 
NA 
NA 
NA 
NA 
36 
50 
38 
56 
3.8 
3.9 
4.5 
8.0 
4.6 
7.0 
2.0 
0.5 
NA 
NA 
NA 
NA 
68 
98 
184 
NA 
NA 
NA 
NA 
43 
52 
45 
4.9 
4.6 
6.0 
4.8 
9.8 
2.3 
0.7 
NA 
NA 
NA 
NA 
Uniform 
Uniform 
Uniform 
Fixed 
NA 
NA 
NA 
NA 
Uniform 
Uniform 
Uniform 
Fixed 
Uniform 
Uniform 
Uniform 
Fixed 
Uniform 
Uniform 
Uniform 
Uniform 
Input #5 
Input #5 
Input #5 
Input #5 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
| 
| Contaminated 
| water 
| 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 

ANL-MGR-MD-000009 REV 01 55 December 2000 
Table 8. GENII-S Menu-Accessible Input Parameters for the Current Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum 
Best 
Estimate Maximum 
MAIN EDITING MENU (Continued) 
Array Data Input (cont.) 
Variable Distribution 
Terrestrial Food Ingestion (Continued) 
- Production, kg/yr 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Holdup, days 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Consumption Rate, kg/yr 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
1.16 
0.65 
0.18 
8.7910-11 
0 
0 
0 
0 
1 
14 
14 
14 
15.14 
7.81 
15.57 
0.48 
59.68 
29.86 
97.69 
12.33 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Loguniform 
Loguniform 
Loguniform 
Loguniform 
| 
| Parameter 
| not used 
| 
Input #4 
Input #4 
Input #4 
Input #4 
Input #5 
Input #5 
Input #5 
Input #5 
Animal Product Consumption 
- Use (0/1 = F/T) 
Beef 
Poultry 
Milk 
Eggs 
- Consumption Rate, kg/yr 
Beef 
Poultry 
Milk 
Eggs 
-Holdup, days 
Beef 
Poultry 
Milk 
Eggs 
1 
1 
1 
1 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
7.3410-7 
2.2210-5 
2.9110-9 
0.23 
NA 
NA 
NA 
NA 
2.93 
0.80 
4.14 
6.68 
20 
1 
1 
1 
NA 
NA 
NA 
NA 
53.11 
10.50 
100.36 
33.34 
NA 
NA 
NA 
NA 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Input #5 
Input #5 
Input #5 
Input #5 
Input #5 
Input #5 
Input #5 
Input #5 
Input #4 
Input #4 
Input #4 
Input #4 

ANL-MGR-MD-000009 REV 01 56 December 2000 
Table 8. GENII-S Menu-Accessible Input Parameters for the Current Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum 
Best 
Estimate Maximum 
MAIN EDITING MENU (Continued) 
Array Data Input (cont.) 
Variable Distribution 
Animal Product Consumption 
- Production, kg/yr 
Beef 
Poultry 
Milk 
Eggs 
- Contaminated Water Fraction 
Beef 
Poultry 
Milk 
Eggs 
Animal Products (Stored Feed Data) 
- Dietary Fraction 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
- Growing Time, days 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
- Water Source Flag 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
- Irrigation Rate, in/yr 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
- Irrigation Time, mo/yr 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
0 
1 
0 
1 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
0 
0 
0 
0 
1 
1 
1 
1 
0 
1 
0 
1 
0 
140 
0 
140 
NA 
NA 
NA 
NA 
0 
75 
0 
75 
0 
4.6 
0 
4.6 
NA 
NA 
NA 
NA 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
NA 
NA 
NA 
NA 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
| 
| Parameter 
| not used 
| 
Input #4 
Input #4 
Input #4 
Input #4 
Input #4 
Input #4 
Input #4 
Input #4 
Input #4 
Attachment III 
Input #4 
Attachment III 
Input #4 
Input #4 
Input #4 
Input #4 
Input #4 
Attachment III 
Input #4 
Attachment III 
Input #4 
Attachment III 
Input #4 
Attachment III 

ANL-MGR-MD-000009 REV 01 57 December 2000 
Table 8. GENII-S Menu-Accessible Input Parameters for the Current Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum 
Best 
Estimate Maximum 
Animal Products (Stored Feed Data) 
cont. 
- Feed Yield, kg/m2 
Beef 
Poultry (corn)
NA 
NA 
-
0.6 
0 
0.7 0.8 
Fixed 
Uniform 
Input #4 
Attachment III 
Milk 
Eggs (corn) 
NA 
NA 0.6 
0 
0.7 0.8 
Fixed 
Uniform 
Input #4 
Attachment III 
- Storage, days 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
NA 
NA 
NA 
NA 
0 
14 
0 
14 
Fixed 
Fixed 
Fixed 
Fixed 
Input #4 
Input #4 
Input #4 
Input #4 
Animal Products (Fresh Forage Data) 
- Dietary Fraction 
Beef (alfalfa)NA 1 Fixed Input #4 
Milk (alfalfa) NA 1 Fixed Input #4 
- Grow Time, daysVariable Distribution 
Beef (alfalfa)NA 46 47 135 Triangular Attachment III 
Milk (alfalfa) NA 46 47 135 Triangular Attachment III 
- H2O Source Flag 
Beef (alfalfa)0 NA NA NA NA Groundwater 
Array Data Input (cont.) 
Milk (alfalfa) 0 NA NA NA NA 
- Irrigation Rate, in/yr 
Beef (alfalfa)NA 95 Fixed Attachment III 
Milk (alfalfa) NA 95 Fixed Attachment III 
- Irrigation Time, mo/yr 
Beef (alfalfa)NA 12 Fixed Attachment III 
Milk (alfalfa) 
- Feed Yield, kg/m2NA 12 Fixed Attachment III 
Beef (alfalfa)NA 1.0 1.1 1.2 Uniform Attachment III 
Milk (alfalfa) NA 1.0 1.1 1.2 Uniform Attachment III 
- Storage, days 
Beef (alfalfa)NA 0 Fixed Input #4 
Milk (alfalfa) NA 0 Fixed Input #4 

ANL-MGR-MD-000009 REV 01 58 December 2000 
Table 8. GENII-S Menu-Accessible Input Parameters for the Current Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum 
Best 
Estimate Maximum 
MAIN EDITING MENU (Continued) 
Array Data Input(cont.)
VariableDistributionInventory  Basic Concentrations 
- Air, pCi/m3 
- Surface Soil, pCi/m2 
- Deep Soil, pCi/kg 
- Ground Water, pCi/l 
- Surface Water, pCi/l 
NA 
NA 
NA 
NA 
NA 
0 
1 
0 
0 
1 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed Section 5 
NOTES: aInput numbers identified in Reference/Comment column refer to input numbers in column 1 of Table 1. 
bNA as an entry means that a given selection/option/value does not appear in GENII-S. 

6.5.4.2 Modeling Output 
The outcome of the BDCF statistical calculation consists of 150 results of individual model 
realizations for each radionuclide for every irrigation period. The corresponding means and 
standard deviations are listed in Table 9 for the first and the sixth irrigation period. The ratio of 
BDCFs for these two periods allows determination of the degree of radionuclide buildup in soil 
represented by the buildup factor. The buildup factor is a ratio of the mean value of period 6 
(longest) to period 1 (no prior irrigation). Buildup factors are also listed in Table 9. For those 
radionuclides whose buildup factor was greater than 1.15, BDCFs were also calculated for the 
remaining irrigation periods, i.e., periods 2 through 5. Their means and standard deviations are 
listed in Table 10. 
Table 9. Summary Results of Biosphere Dose Conversion Factor Modeling for the Current Climate for 
First and Sixth Irrigation Periods, and the Corresponding Buildup Factors 
Radionuclide 
BDCF, 1st Irrigation Period 
rem/y per pCi/L 
BDCF, 6th Irrigation Period 
rem/y per pCi/L 
BuildupFactor cMean a 
Standard 
Deviation a Time, y b Mean a 
Standard 
Deviation a 
14C 5.19E-5 1.40E-4 14 5.19E-5 1.40E-4 1.00 
63Ni 7.22E-7 4.20E-7 208 1.16E-6 1.04E-6 1.61 
90Sr 1.55E-4 8.21E-5 26 2.57E-4 2.57E-4 1.66 
99Tc 3.34E-6 2.26E-6 5 3.39E-6 2.34E-6 1.01 
129I 3.10E-4 1.62E-4 5 3.11E-4 1.62E-4 1.00 
137Cs 1.26E-4 1.47E-4 71 4.54E-4 1.72E-4 3.60 
210Pb 6.67E-3 4.02E-3 53 6.86E-3 4.08E-3 1.03 
226Ra 1.10E-3 5.61E-4 1005 1.81E-2 8.36E-3 16.5 
227Ac 1.54E-2 8.07E-3 54 1.64E-2 8.09E-3 1.06 
229Th 4.01E-3 2.14E-3 5848 4.91E-2 1.13E-2 12.2 
230Th 6.05E-4 3.23E-4 8108 3.97E-2 1.64E-2 65.6 
231Pa 1.15E-2 6.05E-3 1432 4.55E-2 1.10E-2 3.96 
232U 1.45E-3 7.49E-4 62 2.34E-3 7.87E-4 1.61 
233U 3.19E-4 1.66E-4 93 3.62E-4 1.68E-4 1.13 
234U 3.13E-4 1.63E-4 93 3.52E-4 1.65E-4 1.12 
236U 2.97E-4 1.54E-4 93 3.33E-4 1.56E-4 1.12 
238U 2.86E-4 1.49E-4 93 3.36E-4 1.51E-4 1.17 
237Np 5.76E-3 3.02E-3 14 5.84E-3 3.04E-3 1.01 
238Pu 3.50E-3 1.84E-3 196 3.73E-3 1.84E-3 1.07 
239Pu 3.88E-3 2.04E-3 1423 5.78E-3 2.14E-3 1.49 
240Pu 3.88E-3 2.04E-3 1341 5.66E-3 2.12E-3 1.46 
242Pu 3.61E-3 1.90E-3 1455 5.41E-3 1.99E-3 1.50 
241Am 3.96E-3 2.08E-3 915 5.28E-3 2.16E-3 1.33 
243Am 3.95E-3 2.08E-3 3983 1.44E-2 3.19E-3 3.65 
NOTES: aThe values were taken from GENII-S runs (see Attachment IV). 
bTime for 6th irrigation period from Table 3. 
c Ratio of mean BDCF for 6th irrigation period to mean BDCF for 1st (no prior irrigation) period. 

ANL-MGR-MD-000009 REV 01 60 December 2000 
Table 10. Summary Results of Biosphere Dose Conversion Factor Modeling for the Current Climate for Radionuclides with Buildup Factor 
Greater than 1.15 
Radionuclide Biosphere Dose Conversion Factor for Prior Irrigation Periods 
rem/y per pCi/L 
1 2 3 4 5 6 
Mean STD Mean STD Mean STD Mean STD Mean STD Mean STD 
63Ni 7.22E-7 4.20E-7 8.10E-7 4.98E-7 8.98E-7 6.14E-7 9.84E-7 7.47E-7 1.07E-6 8.88E-7 1.16E-6 1.04E-6 
90Sr 15.5E-5 8.21E-5 1.78E-4 1.08E-4 1.97E-4 1.39E-4 2.16E-4 1.75E-4 2.38E-4 2.17E-4 2.57E-4 2.57E-4 
137Cs 1.26E-4 1.47E-4 1.90E-4 1.49E-4 2.57E-4 1.53E-4 3.21E-4 1.58E-4 3.86E-4 1.64E-4 4.54E-4 1.72E-4 
226Ra 1.10E-3 5.61E-4 4.38E-3 1.53E-3 7.86E-3 3.17E-3 1.13E-2 4.90E-3 1.48E-2 6.58E-3 1.81E-2 8.36E-3 
229Th 4.01E-3 2.14E-3 1.30E-2 3.19E-3 2.20E-2 5.02E-3 3.10E-2 7.03E-3 4.01E-2 9.20E-3 4.91E-2 1.13E-2 
230Th 6.05E-4 3.23E-4 5.19E-3 1.76E-3 1.30E-2 5.05E-3 2.18E-2 8.71E-3 3.07E-2 1.25E-2 3.97E-2 1.64E-2 
231Pa 1.15E-2 6.05E-3 1.73E-2 6.38E-3 2.43E-2 7.17E-3 3.13E-2 8.23E-3 3.83E-2 9.54E-3 4.55E-2 1.10E-2 
232U 1.45E-3 7.49E-4 1.58E-3 7.52E-4 1.77E-3 7.59E-4 1.96E-3 7.65E-4 2.15E-3 7.75E-4 2.34E-3 7.87E-4 
238U 2.86E-4 1.48E-4 2.96E-4 1.49E-4 3.06E-4 1.50E-4 3.16E-4 1.50E-4 3.26E-4 1.51E-4 3.36E-4 1.51E-4 
239Pu 3.88E-3 2.04E-3 4.26E-3 2.06E-3 4.63E-3 2.07E-3 5.01E-3 2.09E-3 5.39E-3 2.11E-3 5.78E-3 2.14E-3 
240Pu 3.88E-3 2.04E-3 4.23E-3 2.05E-3 4.59E-3 2.06E-3 4.94E-3 2.08E-3 5.29E-3 2.10E-3 5.66E-3 2.12E-3 
242Pu 3.61E-3 1.90E-3 3.97E-3 1.91E-3 4.33E-3 1.93E-3 4.69E-3 1.94E-3 5.05E-3 1.97E-3 5.41E-3 1.99E-3 
241Am 3.96E-3 2.08E-3 4.22E-3 2.10E-3 4.49E-3 2.11E-3 4.76E-3 2.12E-3 5.02E-3 2.14E-3 5.28E-3 2.16E-3 
243Am 3.95E-3 2.08E-3 6.03E-3 2.18E-3 8.10E-3 2.33E-3 1.02E-2 2.56E-3 1.23E-2 2.87E-3 1.44E-2 3.19E-3 
Source: The values were taken from GENII-S runs (see Attachment IV). 

The analysis of the model results indicates that for 10 radionuclides BDCFs increase only very 
insignificantly with the prior irrigation time (buildup factor is less than 1.15). Among these 
radionuclides were Technetium-99, Iodine-129, and Neptunium-237, which are considered the 
key radionuclides in the TSPA-SR analysis (CRWMS M&O 2000b, Figure 4.1-6). Buildup 
factor for the remaining 14 radionuclides ranged from 1.17 (Uranium-238) to 65.6 (Thorium230). 
However, for radionuclides that show significant buildup, BDCFs for the sixth irrigation 
period were calculated assuming thousands of years of continuous prior irrigation on the same 
plot of land, although this is not a typical agricultural practice. In addition, radionuclide removal 
by soil erosion has not been factored in because this mechanism is addressed later in the 
abstraction phase of the biosphere modeling. 
6.5.5 Biosphere Dose Conversion Factors for the Evolved Climate 
To model the effect of the future climate variations on biosphere, BDCFs were developed for the 
climatic conditions corresponding to the upper bound of the glacial transition climate, as 
described in Section 6.3. An average member of the critical group represented the receptor of 
interest, as for the case of the modern climate. Also, following the same calculation technique as 
the one used for the current climate, BDCFs were calculated in a series of GENII-S simulations 
for each of the 24 radionuclides. Each simulation resulted in 150 model realizations. 
This section contains a listing of the modeling input data and the summary of the model output 
followed by the discussion of the radionuclide buildup in soil caused by irrigation with 
contaminated water. 
6.5.5.1 Input Data 
The content of the input files, the same for both the current and the evolved climates, is 
presented in Section 6.4.3. The set of input values for the evolved climate, entered using 
GENII-S menu-driven interface, is shown in Table 11. The values and selections in the table are 
listed in the format that they are entered in GENII-S. 

ANL-MGR-MD-000009 REV 01 62 December 2000 
Table 11. GENII-S Menu-Accessible Input Parameters for the Evolved Climate 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum Best Estimate Maximum 
PRE-GENII 
Scenario Options 
- Near-Field Scenario Y NA b NA NA NA Near-field 
- Population Dose N NA NA NA NA scenario 
- Acute Release N NA NA NA NA 
Transport Options 
- Air Transport 
- Surface Water Transport 
- Biotic Transport 
- Waste From Degradation 
N 
N 
N 
N 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
No radionuclide 
transport 
Exposure Pathway Options 
- External Finite Plume N NA NA NA NA 
- External Infinite Plume N NA NA NA NA 
- External Ground Exposure Y NA NA NA NA 
- External Recreational Exposure 
- Inhalation Uptake 
- Drinking Water Ingestion 
N 
Y 
Y 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
Pathway 
selection 
- Aquatic Food Ingestion Y NA NA NA NA 
- Terrestrial Food Ingestion Y NA NA NA NA 
- Animal Product Ingestion Y NA NA NA NAEdit Flags and Options 
- Inadvertent Soil Ingestion Y NA NA NA NA 
Deterministic Output Options 
- Both Committed and Cumulative 
- EDE by Nuclide 
N 
N 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA Output selection 
- EDE by Pathway N NA NA NA NA 
Run Options 
- Inventory Unit Index (1-5) 
- Soil Inventory Unit Index (1-3) 
1, pCi 
1, per m2 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA Unit selection, 
- Inventory Input Option (1-3) 2 NA NA NA NA Run selection, 
- Det Run/Stat Run/Both (1/2/3) 2 NA NA NA NA Intake duration 
- Nuclide Intake Duration, yr 1 NA NA NA NA 
Select 
Nuclides Radionuclide selection Y/N ----Section 6.1 

ANL-MGR-MD-000009 REV 01 63 December 2000 
Table 11. GENII-S Menu-Accessible Input Parameters for the Evolved Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum Best Estimate Maximum 
PRE-GENII (Continued) 
Select Statistical 
Output 
-Statistical Committed Dose Summary 
- Statistical Committed Nuclide Dose 
- Statistical Committed Pathway Dose 
- Statistical Committed Organ Dose 
- Statistical Cumulative Pathway Dose 
- Statistical Cumulative Organ Dose 
- Statistical External Dose Summary 
Y 
N 
N 
N 
N 
N 
N 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
Output Selection 
MAIN EDITING MENU 
Titles And RunControls 
- Model Name 
- Title (2 lines) 
- Latin Hypercube (LHS) or Monte Carlo 
(MC) Sampling 
- The Number of Trials (<=500) 
- A Random Seed (0.0<=Seed<=1.0) 
LHS 
150 
0.333 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
Fixed Data Input 
Variable Distribution 
Population/Soil/Scenario Data 
- Total Population 
- Population Scale Factor 
- Soil/Plant Transfer Scale Factor, (-) 
- Animal Uptake Scale Factor, (-) 
- Human Dose Factor Scale Factor, (-) 
- Dose Commitment Period, yr 
- Surface Soil Depth, cm 
- Surface Soil Density, kg/m2 
- Deep Soil Density, kg/m3 
- Roots in Upper Soil, fraction 
- Roots in Deep Soil, fraction 
- Air Release Time Before Intake, yr 
- H2O Release Time Before Intake, yr 
1 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
0.0275 
0.117 
NA 
NA 
NA 
NA 
1 
1 
50 
15 
225 
1500 
1 
0 
0 
0 
NA 
36.4 
8.51 
NA 
NA 
NA 
-- 
Fixed 
Lognormal 
Lognormal 
Fixed 
NA 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
NA 
NA 
Not used 
Not used 
Input #2 
Input #2 
Input #6 
Input #1 
Input #1 
Input #1 
Input #1 
Input #1 
Not used 
Table 3 
Biotic Trans./Near Field Data 
Not used -- -- -- -- -- -- 

ANL-MGR-MD-000009 REV 01 64 December 2000 
Table 11. GENII-S Menu-Accessible Input Parameters for the Evolved Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum Best Estimate Maximum 
MAIN EDITING MENU (Continued) 
Fixed Data Input 
Variable Distribution 
External/Inhalation Exposure (cont.) 
- Chronic Plume Exposure Time, hr 
- Acute Plume Exposure Time, hr/phr 
- Inhalation Exposure Time, hr/yr 
- Resuspension Model Flag (0-2) 
- Mass Loading, g/m3 
- Transit Time to Rec. Site, hr 
- Swimming Exposure Time, hr 
- Boating Exposure Time, hr 
- Shoreline Exposure Time, hr 
- Type of Shoreline Index (1-4) 
- H2O/Sediment Transfer1/m2/yr 
- Soil Exposure Time, hr 
- Home Irrigation Flag (0/1 = N/Y) 
- Irrigation Water Index (1-2) 
- Home Irrigation Rate, in/yr 
- Home Irrigation Duration, mo/yr 
NA 
NA 
NA 
1 
NA 
NA 
NA 
NA 
NA 
0 
NA 
NA 
1 
1 
NA 
NA 
5793.5 
NA 
3.810-5 
NA 
2827 
NA 
NA 
26 
0 
0 
6073.5 
NA 
1.0510-4 
0 
0 
0 
0 
NA 
0 
3387 
NA 
NA 
37 
6 
6353.5 
NA 
1.7310-4 
NA 
3947 
NA 
NA 
48 
Fixed 
Fixed 
Uniform 
NA 
Normal 
Fixed 
Fixed 
Fixed 
Fixed 
NA 
Fixed 
Uniform 
NA 
NA 
Uniform 
Fixed 
Not used 
Not used 
Input #3 
Mass loading 
Input #3 
| 
| 
| Parameters 
| not used 
| 
| 
Input #3 
Contaminated 
water 
Input #3 
Input #3 
Ingestion Exposure 
- Food Production Option 
- Food-Weighted Chi/Q, kg-s/m3 
- Crop Resuspension Factor, 1/m 
- Crop Deposition Velocity, m/s 
- Crop Interception Fraction 
- Exported Food Dose (0/1 = N/Y) 
- Soil Ingestion Rate, mg/day 
- Swim H2O Ingestion Rate, l/h 
- Population Ingesting Aquatic Food 
- Bioaccumulation Flag (0/1 = N/Y) 
- Population Drinking Contaminated 
Water 
- Drink Water Source Index (0-3) 
- Drink Water Treated (0/1 = N/Y) 
- Drink Water Holdup Time, days 
- Drink Water Consumption, l/y 
0 
0 
NA 
NA 
NA 
0 
NA 
NA 
0 
0 
0 
0 
0 
NA 
NA 
NA 
1.710-10 
0.044 
NA 
NA 
NA 
NA 
NA 
NA 
0 
NA 
0 
4.710-10 
0.001 
0.259 
NA 
50 
0 
NA 
NA 
NA 
NA 
NA 
0 
752.85 
NA 
7.710-10 
0.474 
NA 
NA 
NA 
NA 
NA 
NA 
1487.45 
NA 
Fixed 
Loguniform 
Fixed 
Normal 
NA 
Fixed 
Fixed 
NA 
NA 
NA 
NA 
NA 
Fixed 
Uniform 
Not used 
Not used 
Input #1 
Input #1 
Input #4 
Not used 
Input #1 
Not used 
Not used 
Not used 
Whole population 
Groundwater 
Input #4 
Input #4 
Input #4 

ANL-MGR-MD-000009 REV 01 65 December 2000 
Table 11. GENII-S Menu-Accessible Input Parameters for the Evolved Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum Best Estimate Maximum 
MAIN EDITING MENU (Continued) 
Array Data Input (cont.) 
Variable Distribution 
Aquatic Food Ingestion 
- Use (0/1 = F/T) 
Fish 
Mollusc 
Crustacea 
Plants 
- Transit Time (hr) 
Fish 
Mollusc 
Crustacea 
Plants 
- Production (kg/y) 
Fish 
Mollusc 
Crustacea 
Plants 
- Holdup (days) 
Fish 
Mollusc 
Crustacea 
Plants 
- Consumption (kg/yr) 
Fish 
Mollusc 
Crustacea 
Plants 
1 
0 
0 
0 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
6.17E-8 
NA 
NA 
NA 
NA 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0.47 
0 
0 
0 
NA 
NA 
NA 
NA 
8.79 
NA 
NA 
NA 
NA 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Loguniform 
Input #5 
Input #5 
Input #5 
Input #5 
| 
| 
| 
| 
| 
| 
| 
| 
| Parameters 
| not used 
| 
| 
| 
| 
Input #5 

ANL-MGR-MD-000009 REV 01 66 December 2000 
Table 11. GENII-S Menu-Accessible Input Parameters for the Evolved Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum Best Estimate Maximum 
MAIN EDITING MENU (Continued) 
Array Data Input (cont.) 
Variable Distribution 
Terrestrial Food Ingestion 
- Use (0/1 = F/T) 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Growing Time, days 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Water Source Flag (0-2) 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Irrigation Rate, in/yr 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Irrigation Time, mo/yr 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Crop Yield, kg/m2 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
1 
1 
1 
1 
NA 
NA 
NA 
NA 
1 
1 
1 
1 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
50 
75 
95 
NA 
NA 
NA 
NA 
25 
39 
26 
3.3 
4.9 
3.1 
4.4 
4.1 
1.6 
0.3 
NA 
NA 
NA 
NA 
73 
122 
140 
191 
NA 
NA 
NA 
NA 
34 
40 
28 
34 
4.8 
5.2 
4.6 
6.2 
4.6 
7.0 
2.0 
0.5 
NA 
NA 
NA 
NA 
95 
168 
184 
NA 
NA 
NA 
NA 
43 
41 
30 
6.2 
5.5 
6.0 
4.8 
9.8 
2.3 
0.7 
NA 
NA 
NA 
NA 
Uniform 
Uniform 
Uniform 
Fixed 
NA 
NA 
NA 
NA 
Uniform 
Uniform 
Uniform 
Fixed 
Uniform 
Uniform 
Uniform 
Fixed 
Uniform 
Uniform 
Uniform 
Uniform 
Input #5 
Input #5 
Input #5 
Input #5 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
| 
| Contaminated 
| water 
| 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 

ANL-MGR-MD-000009 REV 01 67 December 2000 
Table 11. GENII-S Menu-Accessible Input Parameters for the Evolved Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum Best Estimate Maximum 
MAIN EDITING MENU (Continued) 
Array Data Input (cont.) 
Variable Distribution 
Terrestrial Food Ingestion (cont.) 
- Production, kg/yr 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Holdup, days 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
- Consumption Rate, kg/yr 
Leafy Vegetables 
Root Vegetables 
Fruit 
Grain 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
1.16 
0.65 
0.18 
8.7910-11 
0 
0 
0 
0 
1 
14 
14 
14 
15.14 
7.81 
15.57 
0.48 
59.68 
29.86 
97.69 
12.33 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Loguniform 
Loguniform 
Loguniform 
Loguniform 
| 
| Parameter 
| not used 
| 
Input #4 
Input #4 
Input #4 
Input #4 
Input #5 
Input #5 
Input #5 
Input #5 
Animal Product Consumption 
- Use (0/1 = F/T) 
Beef 
Poultry 
Milk 
Eggs 
- Consumption Rate, kg/yr 
Beef 
Poultry 
Milk 
Eggs 
-Holdup, days 
Beef 
Poultry 
Milk 
Eggs 
1 
1 
1 
1 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
7.3410-7 
2.2210-5 
2.9110-9 
0.23 
NA 
NA 
NA 
NA 
2.93 
0.80 
4.14 
6.68 
20 
1 
1 
1 
NA 
NA 
NA 
NA 
53.11 
10.50 
100.36 
33.34 
NA 
NA 
NA 
NA 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Input #5 
Input #5 
Input #5 
Input #5 
Input #5 
Input #5 
Input #5 
Input #5 
Input #4 
Input #4 
Input #4 
Input #4 

ANL-MGR-MD-000009 REV 01 68 December 2000 
Table 11. GENII-S Menu-Accessible Input Parameters for the Evolved Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum Best Estimate Maximum 
MAIN EDITING MENU (Continued) 
Array Data Input (cont.) 
Variable Distribution 
Animal Product Consumption 
- Production, kg/yr 
Beef 
Poultry 
Milk 
Eggs 
- Contaminated Water Fraction 
Beef 
Poultry 
Milk 
Eggs 
Animal Products (Stored Feed Data) 
- Dietary Fraction 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
- Growing Time, days 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
- Water Source Flag 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
- Irrigation Rate, in/yr 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
- Irrigation Time, mo/yr 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
0 
1 
0 
1 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
0 
0 
0 
0 
1 
1 
1 
1 
0 
1 
0 
1 
0 
179 
0 
179 
NA 
NA 
NA 
NA 
0 
39 
0 
39 
0 
5.7 
0 
5.7 
NA 
NA 
NA 
NA 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
NA 
NA 
NA 
NA 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
| 
| Parameter 
| not used 
| 
Input #4 
Input #4 
Input #4 
Input #4 
Input #4 
Input #4 
Input #4 
Input #4 
Input #4 
Attachment III 
Input #4 
Attachment III 
Input #4 
Input #4 
Input #4 
Input #4 
Input #4 
Attachment III 
Input #4 
Attachment III 
Input #4 
Attachment III 
Input #4 
Attachment III 

ANL-MGR-MD-000009 REV 01 69 December 2000 
Table 11. GENII-S Menu-Accessible Input Parameters for the Evolved Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum Best Estimate Maximum 
MAIN EDITING MENU (Continued) 
Array Data Input (cont.) 
Variable DistributionAnimal Products (Stored Feed Data) 
cont. 
- Feed Yield, kg/m2 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
- Storage, days 
Beef 
Poultry (corn) 
Milk 
Eggs (corn) 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
-
0.6 
0.6 
0 
0.7 
0 
0.7 
0 
14 
0 
14 
0.8 
0.8 
Fixed 
Uniform 
Fixed 
Uniform 
Fixed 
Fixed 
Fixed 
Fixed 
Input #4 
Attachment III 
Input #4 
Attachment III 
Input #4 
Input #4 
Input #4 
Input #4 
Animal Products (Fresh Forage Data) 
- Dietary Fraction 
Beef (alfalfa) 
Milk (alfalfa) 
- Grow Time, days 
Beef (alfalfa) 
Milk (alfalfa) 
- H2O Source Flag 
Beef (alfalfa) 
Milk (alfalfa) 
- Irrigation Rate, in/yr 
Beef (alfalfa) 
Milk (alfalfa) 
- Irrigation Time, mo/yr 
Beef (alfalfa) 
Milk (alfalfa) 
- Feed Yield, kg/m2 
Beef (alfalfa) 
Milk (alfalfa) 
- Storage, days 
Beef (alfalfa) 
Milk (alfalfa) 
NA 
NA 
NA 
NA 
0 
0 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
1 c 
1 c 
71 
71 
NA 
NA 
42 
42 
7.0 
7.0 
0.5 
0.5 
0 
0 
NA 
NA 
Fixed 
Fixed 
Fixed 
Fixed 
NA 
NA 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed 
Input #4 
Input #4 
Attachment III 
Attachment III 
Groundwater 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Attachment III 
Input #4 
Input #4 

ANL-MGR-MD-000009 REV 01 70 December 2000 
Table 11. GENII-S Menu-Accessible Input Parameters for the Evolved Climate (Continued) 
Menu(s) Option/-Parameter, Unit Selection 
Values 
Distribution 
Referencea/
Comments Minimum Best Estimate Maximum 
MAIN EDITING MENU (Continued) 
Array Data 
Input (cont.) 
Variable 
DistributionInventory  Basic Concentrations 
- Air, pCi/m3 
- Surface Soil, pCi/m2 
- Deep Soil, pCi/kg 
- Ground Water, pCi/l 
- Surface Water, pCi/l 
NA 
NA 
NA 
NA 
NA 
0 
1 
0 
0 
1 
Fixed 
Fixed 
Fixed 
Fixed 
Fixed Section 5 
NOTES: aInput numbers identified in Reference/Comment column refer to input numbers in column 1 of Table 1. 
bNA as an entry means that a given selection/option/value does not appear in GENII-S. 
c Dietary fraction for fresh forage equal to 1 means that cattle is sustained year-round on fresh forage even though the weather conditions for the 
glacial transition climate may not permit it. However, this assumption is conservative and it simplifies calculations 

6.5.5.2 Modeling Output 
The outcome of the BDCF statistical calculation consists of 150 results of individual model 
realizations for each radionuclide for every irrigation period. The corresponding means and 
standard deviations for the evolved climate are listed in Table 12 for the first and the sixth 
irrigation period. To evaluate the degree of radionuclide buildup in soil, the buildup factors were 
calculated for each radionuclide. The buildup factor is a ratio of the mean value of period 6 
(longest) to period 1 (no prior irrigation). Buildup factors are listed in Table 12. For those 
radionuclides whose buildup factor was greater than 1.15, BDCFs were also calculated for the 
remaining irrigation periods, i.e., periods 2 through 5. Their means and standard deviations are 
listed in Table 13. 

Table 12. Summary Results of Biosphere Dose Conversion Factor Modeling for the Evolved Climate for 
the First and Sixth Irrigation Periods and their Corresponding Buildup Factors 
Radionuclide 
BDCF, 1st Irrigation Period 
rem/y per pCi/L 
BDCF, 6th Irrigation Period 
rem/y per pCi/L 
BuildupFactor cMean a 
Standard 
Deviation a Time, y b Mean a 
Standard 
Deviation a 
14C 5.11E-5 1.41E-4 14 5.11E-5 1.41E-4 1.00 
63Ni 6.60E-7 3.67E-7 208 9.72E-7 7.63E-7 1.47 
90Sr 1.41E-4 7.36E-5 26 2.23E-4 2.15E-4 1.58 
99Tc 3.00E-6 1.89E-6 5 2.99E-6 1.95E-6 1.00 
129I 2.81E-4 1.46E-4 5 2.82E-4 1.46E-4 1.00 
137Cs 1.13E-4 1.43E-4 71 2.81E-4 1.52E-4 2.49 
210Pb 6.24E-3 3.84E-3 53 6.38E-3 3.87E-3 1.02 
226Ra 1.01E-3 5.23E-4 1005 1.08E-2 6.88E-3 10.7 
227Ac 1.43E-2 7.55E-3 54 1.49E-2 7.56E-3 1.04 
229Th 3.74E-3 2.01E-3 5848 2.71E-2 6.57E-3 7.25 
230Th 5.66E-4 3.03E-4 8108 2.25E-2 1.35E-2 39.8 
231Pa 1.07E-2 5.66E-3 1432 2.84E-2 7.69E-3 2.65 
232U 1.34E-3 6.99E-4 62 1.80E-3 7.11E-4 1.34 
233U 2.96E-4 1.55E-4 93 3.19E-4 1.55E-4 1.08 
234U 2.90E-4 1.52E-4 93 3.12E-4 1.52E-4 1.08 
236U 2.75E-4 1.44E-4 93 2.95E-4 1.44E-4 1.07 
238U 2.65E-4 1.39E-4 93 2.92E-4 1.39E-4 1.10 
237Np 5.37E-3 2.82E-3 14 5.42E-3 2.82E-3 1.01 
238Pu 3.27E-3 1.72E-3 196 3.38E-3 1.72E-3 1.03 
239Pu 3.63E-3 1.91E-3 1423 4.59E-3 1.94E-3 1.26 
240Pu 3.62E-3 1.91E-3 1341 4.53E-3 1.93E-3 1.25 
242Pu 3.37E-3 1.78E-3 1455 4.29E-3 1.80E-3 1.27 
241Am 3.69E-3 1.95E-3 915 4.39E-3 1.98E-3 1.19 
243Am 3.69E-3 1.94E-3 3983 9.05E-3 2.38E-3 2.45 
NOTES: aThe values were taken from GENII-S runs (see Attachment IV). 
bTime for 6th irrigation period from Table 3 
c Ratio of mean BDCF for 6th irrigation period to mean BDCF for 1st (no prior irrigation) period 

ANL-MGR-MD-000009 REV 01 73 December 2000 
Table 13. Summary Results of Biosphere Dose Conversion Factor Modeling for the Evolved Climate for Radionuclides with Buildup Factor 
Greater than 1.15 
Radionuclide 
Biosphere Dose Conversion Factors for Prior Irrigation Periods 
rem/y per pCi/L 
1 2 3 4 5 6 
Mean STD Mean STD Mean STD Mean STD Mean STD Mean STD 
63Ni 6.60E-7 3.69E-7 7.22E-7 4.08E-7 7.85E-7 4.75E-7 8.47E-7 5.62E-7 9.10E-7 6.60E-7 9.72E-7 7.63E-7 
90Sr 1.41E-4 7.36E-5 1.60E-4 9.34E-5 1.75E-4 1.17E-4 1.90E-4 1.47E-4 2.07E-4 1.81E-4 2.23E-4 2.15E-4 
137Cs 1.13E-4 1.43E-4 1.46E-4 1.44E-4 1.80E-4 1.45E-4 2.13E-4 1.47E-4 2.47E-4 1.49E-4 2.81E-4 1.52E-4 
226Ra 1.01E-3 5.23E-4 2.85E-3 1.28E-3 4.85E-3 2.65E-3 6.84E-3 4.05E-3 8.81E-3 5.41E-3 1.08E-2 6.88E-3 
229Th 3.74E-3 2.01E-3 8.39E-3 2.45E-3 1.30E-2 3.30E-3 1.77E-2 4.34E-3 2.24E-2 5.43E-3 2.71E-2 6.57E-3 
230Th 5.66E-4 3.03E-4 3.10E-3 1.40E-3 7.52E-3 2.66E-3 1.25E-2 7.11E-3 1.75E-2 1.03E-2 2.25E-2 1.35E-2 
231Pa 1.07E-2 5.66E-3 1.37E-2 5.79E-3 1.73E-2 6.07E-3 2.10E-2 6.52E-3 2.46E-2 7.05E-3 2.84E-2 7.69E-3 
232U 1.34E-3 6.99E-4 1.41E-7 7.00E-4 1.51E-3 7.02E-4 1.60E-3 7.05E-4 1.70E-3 7.07E-4 1.80E-3 7.11E-4 
239Pu 3.63E-3 1.91E-3 3.82E-3 1.91E-3 4.01E-3 1.92E-3 4.20E-3 1.92E-3 4.40E-3 1.93E-3 4.59E-3 1.94E-3 
240Pu 3.62E-3 1.91E-3 3.80E-3 1.91E-3 3.98E-3 1.92E-3 4.16E-3 1.92E-3 4.34E-3 1.93E-3 4.53E-3 1.93E-3 
242Pu 3.37E-3 1.78E-3 3.55E-3 1.78E-3 3.74E-3 1.78E-3 3.92E-3 1.79E-3 4.10E-3 1.80E-3 4.29E-3 1.80E-3 
241Am 3.69E-3 1.95E-3 3.83E-3 1.95E-3 3.97E-3 1.95E-3 4.11E-3 1.96E-3 4.25E-3 1.97E-3 4.39E-3 1.98E-3 
243Am 3.69E-3 1.94E-3 4.76E-3 1.98E-3 5.83E-3 2.04E-3 6.90E-3 2.23E-3 7.98E-3 2.26E-3 9.05E-3 2.38E-3 
Source: The values were taken from GENII-S runs (see Attachment IV). 

BDCFs for the evolved climate tend to increase with the duration of prior irrigation, similar to 
the BDCFs for the current climate. Out of the 24 radionuclides analyzed, 11 do not show 
significant radionuclide buildup in soil and their BDCFs remain relatively unchanged as the 
irrigation duration lengthens. The remaining 13 radionuclides show various degrees of buildup 
up to the maximum buildup factor value of 39.8 for 230Th. Similar to the current climate case, 
radionuclides showing the greatest degree of buildup need very long times of continuous 
irrigation, on the order of thousands of years, to reach an equilibrium activity concentration in 
soil. However, if soil erosion is factored in, the equilibrium condition is obtained much sooner 
because of the additional removal mechanism and the corresponding equilibrium level of activity 
concentration in soil is much lower. 
Comparison of the BDCFs for the current and the evolved climate is presented in Table 14 for 
the first and the sixth irrigation period. The BDCFs for the evolved climate are up to 10 percent 
lower than the BDCFs for the current climate, mainly due to the decreased irrigation rate. For 
the sixth irrigation period the difference is greater. The BDCFs for the evolved climate may be 
up to about 60 percent lower than the BDCFs for the current climate. The difference is the 
greatest for radionuclides such as isotopes of thorium that build up slowly in soil. 
Table 14. Comparison of the BDCFs (in rem/yr per pCi/L) for the Current and Evolved Climate 
Radionuclide 
1st Irrigation Period 6th Irrigation Period 
BDCF for 
Current 
Climate a 
BDCF for 
Evolved 
Climate b 
Evolved / 
Current 
BDCF ratio 
BDCF for 
Current 
Climate a 
BDCF for 
Evolved 
Climate b 
Evolved / 
Current 
BDCF ratio 
14C 5.19E-05 5.11E-05 0.99 5.19E-05 5.11E-05 0.99 
63Ni 7.22E-07 6.60E-07 0.91 1.16E-06 9.72E-07 0.84 
90Sr 1.55E-04 1.41E-04 0.91 2.57E-04 2.23E-04 0.87 
99Tc 3.34E-06 3.00E-06 0.90 3.39E-06 2.99E-06 0.88 
129I 3.10E-04 2.81E-04 0.91 3.11E-04 2.82E-04 0.91 
137Cs 1.26E-04 1.13E-04 0.90 4.54E-04 2.81E-04 0.62 
210Pb 6.67E-03 6.24E-03 0.94 6.86E-03 6.38E-03 0.93 
226Ra 1.10E-03 1.01E-03 0.92 1.81E-02 1.08E-02 0.60 
227Ac 1.54E-02 1.43E-02 0.93 1.64E-02 1.49E-02 0.91 
229Th 4.01E-03 3.74E-03 0.93 4.91E-02 2.71E-02 0.55 
230Th 6.05E-04 5.66E-04 0.94 3.97E-02 2.25E-02 0.57 
231Pa 1.15E-02 1.07E-02 0.93 4.55E-02 2.84E-02 0.62 
232U 1.45E-03 1.34E-03 0.92 2.34E-03 1.80E-03 0.77 
233U 3.19E-04 2.96E-04 0.93 3.62E-04 3.19E-04 0.88 
234U 3.13E-04 2.90E-04 0.93 3.52E-04 3.12E-04 0.89 
236U 2.97E-04 2.75E-04 0.93 3.33E-04 2.95E-04 0.89 
238U 2.86E-04 2.65E-04 0.93 3.36E-04 2.92E-04 0.87 
237Np 5.76E-03 5.37E-03 0.93 5.84E-03 5.42E-03 0.93 
238Pu 3.50E-03 3.27E-03 0.93 3.73E-03 3.38E-03 0.91 
239Pu 3.88E-03 3.63E-03 0.94 5.78E-03 4.59E-03 0.79 
240Pu 3.88E-03 3.62E-03 0.93 5.66E-03 4.53E-03 0.80 

Table 14. Comparison of the BDCFs (in rem/yr. per pCi/L) for the Current and Evolved Climate (Continued) 
1st Irrigation Period 6th Irrigation Period 
BDCF for 
Current 
BDCF for 
Evolved 
Evolved / 
Current 
BDCF for 
Current 
BDCF for 
Evolved 
Evolved / 
Current 
Radionuclide Climate a Climate b BDCF ratio Climate a Climate b BDCF ratio 
242Pu 3.61E-03 3.37E-03 0.93 5.41E-03 4.29E-03 0.79 
241Am 3.96E-03 3.69E-03 0.93 5.28E-03 4.39E-03 0.83 
243Am 3.95E-03 3.69E-03 0.93 1.44E-02 9.05E-03 0.63 
NOTES: aDeveloped from Tables 8 
bDeveloped from Table 11 
6.6 UNCERTAINTY ANALYSIS 
As with any modeling effort, uncertainty is inherent to the biosphere model. This means that the 
modeling results carry uncertainty resulting from both the uncertainties in the model itself as 
well as uncertainties in model parameters. Uncertainty analysis assists in interpreting the results 
of the BDCF development. The objective of the uncertainty analysis was to determine how the 
uncertainty in model parameters affects the model results. 
6.6.1 Sources of Uncertainty 
All assessments based on model calculations are inherently uncertain. This uncertainty arises 
from several factors, including: (1) the ability to adequately model the physical processes 
involved, (2) the degree to which exposure scenarios adequately represent individuals in the 
desired critical group, (3) the level of knowledge available to estimate appropriate values for 
parameters in the model, and (4) natural variability in various quantities used to estimate 
parameter values. 
Uncertainty in the model refers to uncertainty regarding abstracting a real system (in this case the 
biosphere  its components and the radionuclide transport between the components of the 
biosphere, including humans) and its evolution into a form that can be mathematically modeled. 
Model uncertainty results from limitations in the ability to mathematically represent a complex 
system and its behavior. This uncertainty, in the case of complex systems, may be difficult to 
quantify in a rigorous numerical fashion. However, it is usually possible to bound the effects of 
model uncertainties by making credible assumptions about the selection of likely processes and 
their conceptual representations. The compartment model selected to represent radionuclide 
behavior in the biosphere is an example of bounding analysis. The submodels of such system, 
which represent individual processes occurring in the biosphere, can be characterized as 
reasonably conservative i.e., realistic, yet unlikely to underestimate their modeling outcome. 
Such conservative conceptual models are commonly used for demonstrating compliance with the 
regulatory standards. 
Parameter uncertainty represents uncertainty in the data, parameters, and coefficients used in 
mathematical models and in the supporting computer codes, such as GENII-S, in the case of 
biosphere modeling. Parameter uncertainty originates from a number of sources including 
insufficient information, or lack of site-specific information, to accurately determine values of 

parameters and coefficients used in the biosphere model. Another source of uncertainty is 
associated with the temporal and spatial heterogeneity of the biosphere system. However, lack 
of knowledge about an input parameter typically contributes more to the uncertainty of the 
parameter values than does the natural variability of that parameter. Contribution of parameter 
uncertainty to the overall uncertainty of the modeling outcome can be more readily quantified 
than model uncertainty. This analysis does not attempt to quantify the relative contribution of 
model uncertainty to the overall DCF uncertainty. 
6.6.2 Biosphere Dose Conversion Factor Uncertainty 
As noted before, uncertainties are inherent in any modeling of a real system, such as the 
biosphere. These uncertainties include data uncertainty and model uncertainty. Data or 
parameter uncertainty is more readily quantified than model uncertainty. In this assessment the 
treatment of uncertainties is limited to parameter uncertainty. 
The results of BDCF calculations consist of 150 outcomes of individual model simulation. 
These BDCF values can be represented as a probability distribution function. The shape of the 
probability distribution function, the range of values within the predetermined confidence limits, 
and the associated statistics characterize the spread of BDCF values generated by the model runs. 
Distributions of BDCF values are related to probability distribution of the key uncertain 
parameters. Representation of the parameter values by their probability distribution functions 
have their origin in either the uncertainty on the best estimate of the parameters value or in the 
variability of the parameter in the sample. The examples of the latter include locally-produced 
food consumption rates which were obtained in the regional survey and thus represent variability 
of consumption rates in the surveyed population. On the other hand, soil-to-plant or animal 
food-to-animal product transfer scaling factors represent uncertainty in the best estimate of the 
parameter values. For the conditions of no prior irrigation, drinking water and leafy vegetable 
consumption rates are the key parameters contributing to the BDCF variance for almost all 
radionuclides. Therefore the distribution of BDCF values reflects predominantly variability in 
the consumption rates. 
6.7 PATHWAY ANALYSIS 
BDCFs include contributions from various radiological pathways through the biosphere, as 
described in Section 6.2.1. The evaluation of the degree to which various pathways contribute to 
BDCFs was the subject of an independent assessment. An analysis was first conducted 
separately for the current and evolved climates, including consideration of radionuclide buildup 
in soil. Then the results of each pathway analysis for the two climates were compared. 
To determine the contributions of different exposure pathways to the BDCF values, a single 
GENII-S deterministic assessment was performed for each radionuclide. Deterministic GENII-S 
runs were carried out simultaneously with the stochastic runs using input values listed in the 
column labeled Best Estimate of Tables 7 and 10, for the current and evolved climates, 
respectively. The method of obtaining pathway BDCFs is decribed in Attachment VI of this 
report. 

6.7.1 Pathway Analysis for the Current Climate 
The results of pathway analysis for the current climate and no prior irrigation are summarized in 
Table 15. An Excel software routine is documented in Attachment VI. For all radionuclides, 
except 14C and 137Cs, consumption of drinking water is the dominant pathway accounting for 55 
to 69 percent of the BDCF, depending on the radionuclide. Leafy vegetables consumption ranks 
second, contributing from 23 to 27 percent, depending on the radionuclide. Together, 
consumption of drinking water and leafy vegetables account for 82 to 96 percent of the BDCF 
values for these radionuclides. The remaining consumption pathways account for up to a few 
percent of the BDCFs. Inhalation and external exposure pathways are not significant, and 
neither of them contributes more than a few tenths of a percent for all radionuclides except 137Cs 
(7.7%), and 226Ra (2.5%). For 14C consumption of fish is by far the most important pathway 
(93 percent of the BDCF). Pathway contributions to BDCF for 137Cs include 23 percent from 
consumption of drinking water, 12 percent from leafy vegetables, 12 percent from meat, 
36 percent from fish, and 8 percent from external exposure. 
Contribution of the specific pathways to the BDCFs is affected by radionuclide buildup in soil. 
This is because some pathways depend on radionuclide concentration in soil. Among the 
pathways that are unaffected by radionuclide buildup in soil is consumption of drinking water, 
which is the major pathway for most radionuclides. Also, the contribution to BDCFs from 
consumption of fish and that fraction of the BDCF that results from activity deposition on a 
plants surfaces from irrigation water are unrelated to radionuclide concentration in soil. 
For many radionuclides, such as 99Tc, 129I, and 237Np, for which the effect of radionuclide 
buildup in soil is insignificant (see Table 12), pathway contributions to BDCFs remain virtually 
unchanged regardless of the previous irrigation practices. This, however, is not the case for 
radionuclides whose levels in soil increase with prolonged irrigation. 

ANL-MGR-MD-000009 REV 01 78 December 2000 
Table 15. Percent Pathway Contribution to Nominal Performance Biosphere Dose Conversion Factors for Current Climate and No Prior Irrigation 
Nuclide 
DrinkingWaterLeafy Vegetables 
Other Vegetables 
FruitCerealsMeatPoultry 
Cow MilkEggsFishSoil IngestionExternalExposureInhalation 
14C 3.0 0.9 0.6 0.8 0.1 0.4 0.2 0.2 1.1 92.8 0.0 0.0 0.0 
63Ni 59.0 23.4 1.7 1.6 0.1 2.5 0.0 7.8 0.1 3.7 0.0 0.0 0.0 
90Sr 59.7 26.9 3.0 1.9 0.2 5.1 0.0 0.7 0.3 2.3 0.0 0.0 0.0 
99Tc 55.3 27.9 3.5 1.8 0.2 0.1 0.0 8.2 2.4 0.7 0.0 0.0 0.0 
129I 61.1 23.6 1.8 1.7 0.2 3.6 0.0 4.1 2.3 1.6 0.0 0.0 0.0 
137Cs 28.8 11.5 0.9 0.9 0.1 12.3 0.2 1.5 0.1 36.0 0.0 7.7 0.0 
210Pb 60.6 23.6 1.8 1.5 0.1 0.2 0.0 0.1 0.7 11.3 0.0 0.0 0.0 
226Ra 65.3 25.4 1.9 1.6 0.2 0.5 0.0 0.5 0.0 2.0 0.0 2.5 0.0 
227Ac 68.3 26.5 1.9 1.7 0.2 0.0 0.0 0.0 0.0 1.1 0.0 0.0 0.2 
229Th 65.5 26.1 2.0 1.7 0.2 0.0 0.0 0.0 0.0 4.1 0.0 0.1 0.3 
230Th 66.2 25.6 2.0 1.7 0.2 0.0 0.0 0.0 0.0 4.1 0.0 0.0 0.3 
231Pa 68.7 26.8 2.0 1.8 0.2 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.1 
232U 67.2 26.5 2.0 1.7 0.2 0.2 0.1 0.3 0.8 0.4 0.0 0.3 0.3 
233U 67.8 26.2 2.0 1.7 0.2 0.2 0.1 0.3 0.8 0.4 0.0 0.0 0.2 
234U 67.8 26.2 2.0 1.7 0.2 0.2 0.1 0.3 0.8 0.4 0.0 0.0 0.2 
236U 67.3 26.7 2.0 1.7 0.2 0.2 0.1 0.3 0.8 0.4 0.0 0.0 0.2 
238U 67.3 26.6 2.0 1.8 0.2 0.2 0.1 0.3 0.8 0.4 0.0 0.1 0.2 
237Np 67.9 26.2 2.0 1.7 0.2 0.6 0.0 0.0 0.0 1.3 0.0 0.0 0.1 
238Pu 68.3 26.4 2.0 1.7 0.2 0.0 0.0 0.0 0.0 1.3 0.0 0.0 0.1 
239Pu 68.4 26.3 2.0 1.8 0.2 0.0 0.0 0.0 0.0 1.3 0.0 0.0 0.1 
240Pu 68.4 26.3 2.0 1.8 0.2 0.0 0.0 0.0 0.0 1.3 0.0 0.0 0.1 
242Pu 68.1 26.7 2.0 1.8 0.2 0.0 0.0 0.0 0.0 1.3 0.0 0.0 0.1 
241Am 68.3 26.5 2.0 1.7 0.2 0.0 0.0 0.0 0.0 1.3 0.0 0.0 0.1 
243Am 68.5 26.1 2.0 1.8 0.2 0.0 0.0 0.0 0.0 1.3 0.0 0.1 0.1 
NOTE: Pathway contributions were developed using software routine described in Attachment VI. 

The effect of radionuclide buildup in soil was evaluated by examining pathway contributions to 
BDCFs for selected radionuclides whose BDCFs increase with the duration of prior irrigation. It 
must be noted again that this analysis was carried out assuming constant level of activity 
concentration in water for the entire length of time needed to obtain steady-state conditions of 
radionuclide concentration is soil (thousands of years in many cases). In addition, other removal 
mechanisms, such as soil removal by erosion, were not factored in because they are considered 
in the abstraction process. The addition of soil removal from erosion would considerably shorten 
the time needed to obtain an equilibrium radionuclide concentration in soil and make the analysis 
of the BDCFs for very long periods of prior irrigation (such as those for 230Th) irrelevant. 
The graph shown in Figure 7 represents percentage pathway contributions to BDCF for 239Pu at 
different periods of prior irrigation. (Contributions to BDCF from consumption of all crop types 
and contributions from consumption of all animal products are portrayed as their respective 
category.) 239Pu is a radionuclide that shows a moderate degree of radionuclide buildup in soil 
and is a good representative of the group of radionuclides whose concentration levels in soil 
increase somewhat with irrigation time. The graph shows the decreasing importance of the water 
consumption pathway with irrigation time and growing inhalation and soil ingestion components. 
The pathway-specific BDCFs for 239Pu, as a function of irrigation duration, are shown in 
Figure 8. As for the previous graph, BDCFs from consumption of all crops as well as BDCFs 
from consumption of all animal foods were represented as two categories. The Figure shows that 
the pathway BDCFs for inhalation, soil ingestion, and external exposure increase with irrigation 
time, while BDCFs for the remaining pathways either remain constant (e.g. drinking water) or 
are not significantly affected by an increasing duration of prior irrigation. 
230Th is the radionuclide with the greatest degree of radionuclide buildup in soil. The graph 
shown in Figure 9 illustrates percentage pathway contributions for this radionuclide. The 
significance of water consumption decreases from being the major contributor for no prior 
irrigation condition to only about 10% for the second period, decreasing even more for the 
subsequent period. External exposure becomes a dominant pathway, with inhalation being the 
second greatest contributor. These two pathways together account for up to about 85% of the 
BDCF with the balance resulting mainly from crop consumption and soil ingestion. 
Analogous to the graph in Figure 8 for 239Pu, Figure 10 shows pathway BDCFs as a function of 
irrigation duration for 230Th. It must be noted that the time scales for Figures 8 and 10 are not 
the same. Irrigation periods are based on the effective removal constants, which are different for 
different radionuclides. In the case of 230Th, radionuclides are removed from soil very slowly, 
compared to other radionuclides and it would take many thousands of years of continuous 
irrigation for the equilibrium radionuclide concentration in soil to be reached. Therefore the 
results presented in Figures 7 through 10 must be put into perspective as representing a potential 
general trend rather than the actual expected conditions. 

0% 
Percentage of BDCF 
10% 
20% 
30% 
40% 
50% 
60% 
70% 
80% 
90% 
100% 
12345 
Time Step 
ion iExt. dose Inhalation Crop ingestAnmal product Soil Ing Water Fish 
Figure 7. 239Pu Pathway Contributions at Different Irrigation Periods (Time Steps) 
0 150 300 450 600 750 900 1050 1200 1350 1500 
(
BDCF (rem/yr)/(pCi/L) 
Anil l 
1.E-07 
1.E-06 
1.E-05 
1.E-04 
1.E-03 
1.E-02 
Irrigation T imeye ar) 
Ext. dose Inhale Crop maSoil Ing Water Fish Tota
Figure 8. 239Pu Pathway BDCFs as a Function of Irrigation Time 

0% 
10% 
20% 
30% 
40% 
50% 
60% 
70% 
80% 
90% 
100% 
Percentage of BDCF 
12345 
Time Step 
le AniilExt. dose InhaCrop mal So Ing Water Fish 
Figure 9. 230Th Pathway Contributions at Different Irrigation Periods (Time Steps) 
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 
(year) 
BDCF (rem/yr)/(pCi/L) 
Anil l 
1.E-06 
1.E-05 
1.E-04 
1.E-03 
1.E-02 
1.E-01 
Irrigation TimeExt. dose Inhale Crop maSoil Ing Water Fish Tota
Figure 10. 230Th Pathway BDCFs as a Function of Irrigation Time 

6.7.2 Pathway Analysis for the Evolved Climate 
As shown in Table 13, cooler and wetter conditions of the evolved climate result in a decrease in 
the magnitude of all BDCFs, as compared to the current climate. The decrease is not very 
significant, ranging from no more than 10 
ercents for the first irrigation period to up to 45 percent for the sixth irrigation period. Because 
the BDCFs for the conditions of no prior irrigation are not affected very much by the climate 
change, neither are the pathway BDCFs. Therefore the results of the pathway analysis conducted 
for the current climate and no prior irrigation (first period) remain valid. Because the climate 
change does not affect the drinking water consumption pathway BDCF, which was a dominant 
pathway for the current climate, the overall BDCF decrease is due to the decrease of other 
pathway BDCFs. This results in even greater contribution of water consumption pathway 
BDCFs to the evolved climate BDCFs than it was the case of the current climate. 
To analyze the combined effect on the BDCFs of prior irrigation and climate change, a graph 
was constructed that shows pathway BDCF ratios for the evolved and current climates for 239Pu. 
This radionuclide exhibits a moderate degree of buildup and it was identified as one of the key 
radionuclides in the TSPA-SR analysis. The graph is shown in Figure 11. The ratios of pathway 
BDCFs for the evolved and current climate are less than one except for the water consumption 
and fish consumption pathways, which do not depend on radionuclide concentration in soil. 
Ext.doseInhaleLeafyRootFruitGrainMeatPoultryMilkEggsSoilWaterFishTotal 
Ratio (wet/current) 
ii0.0 
0.2 
0.4 
0.6 
0.8 
1.0 
1.2 
Pathway 
Irrigaton Period 1 Irrigaton Period 6 
Figure 11. 239Pu Pathway BDCF Ratio for Evolved and Current Climates 

6.8 ALTERNATIVE MODELS 
The strategy governing conceptual description of the biosphere system is consistent with similar 
activities being persued by the international scientific community. Several issues were identified 
that are relevant to the model components and warrant consideration in this analysis. 
6.8.1 Alternative Exposure Scenarios 
BDCF development was carried out for a specific exposure scenario. An exposure scenario is 
defined as one possible combination of specified FEPs affecting the biosphere system that could 
lead to radiological consequences. There are several alternative exposure scenarios that could be 
considered for the current biosphere. The proposed rules and the guidance limit the assessment 
to the exposure scenario associated with an adult farming receptor group based on the behaviors 
and characteristics of Amargosa Valley residents. Other exposure scenarios that could be 
conceivable include the exposure conditions of a subsistence farmer receptor group, a residential 
receptor group, or a group consisting of non-adult receptors. 
The non-adult receptor group is not considered appropriate for the analysis of long-term 
performance of the potential repository. Considering the time scale of the assessment, it could 
be assumed that radioactive contamination of the biosphere due to releases from the repository is 
likely to remain constant over periods that are considerably longer that the human life span. It is 
then reasonable to calculate the annual dose averaged over the lifetime of the individuals, which 
means that it is not necessary to calculate doses to different age groups. This average can be 
adequately represented by the annual dose to an adult (ICRP 2000). 
6.8.2 Alternative Dosimetric Models 
The current assessment uses dosimetric models based in the conceptual approach recommended 
by the International Commission on Radiological Protection (ICRP) in Publication 26 
(ICRP 1977) and the dosimetric methods outlined in Publication 30 (ICRP 1979, ICRP 1980, 
ICRP 1981). By using such model the outcome of the biosphere model expressed in terms of 
TEDE is compatible with the format of the proposed performance standard (Dyer 1999, 
Section 113(b)) which uses the same methodology. 
Since the publication of the ICRP recommendations and models in the late 1970s and early 
1980s the Commission updated both its recommendations (ICRP 1991) as well as its dosimetric 
methods. Of particular significance is a set of publications concerning age-dependent doses to 
the members of the public from intakes of radionuclides. The summary document for this set, 
ICRP Publication 72 (ICRP 1996), provides a listing of dose coefficients (dose conversion 
factors) for inhalation and ingestion of radionuclides by age group. 
Dose conversion factors are a quintessence of dosimetric modeling, including metabolism of a 
specific radionuclide compound; possible deposition in various human organs and tissues; 
physical behavior, such as radioactive decay accompanied by the emission of ionizing radiation 
and production of radioactive progenies; irradiation of human organs and tissues by internally 
deposited radionuclides or radionuclides external to the human body; and the contribution of the 
radiation effects in the irradiated organs and tissues to the overall radiation effect. 

Dose conversion factors in ICRP Publication 30 and ICRP Publication 72 were developed using 
different approaches (e.g., they are based on different tissue weighting factor values) and can not 
be interchanged. For some radionuclides, DCFs may differ considerably between the two 
dosimetric systems. However, comparing DCF values may be misleading in some cases because 
of the different bases for their calculation. In addition, application of ICRP-72 DCFs does not 
yield results compatible with the concept of ICRP-30-based TEDE, which is used in th proposed 
regulations to define performance standards for the repository. 

7. CONCLUSIONS
The purpose of this analysis and model was to calculate BDCFs for the current and evolved 
climates. BDCFs for 24 radionuclides were developed for the receptor represented by the 
average member of the critical group. Analysis included evaluation of the general tendencies in 
all pathway BDCF values and individual pathway contributions following periods of prolonged 
irrigation with contaminated groundwater. BDCF development was accompanied by pathway, 
sensitivity, and uncertainty analyses. The resulting BDCFs are summarized in Tables 8 and 9 for 
the current climate, while the summary of the BDCFs for the evolved climate is presented in 
Tables 11 and 12. The results of biosphere modeling, abstracted into BDCF probability 
distribution functions, are used in the TSPA model to calculate doses for the nominal 
performance of the potential repository. 
The analysis included consideration of radionuclide buildup in soil due to prior irrigation with 
contaminated groundwater. First, the buildup factors were evaluated, by producing a ratio of the 
sixth irrigation period to the first irrigation period BDCF for each radionuclide. If the buildup 
exceeded 15%, BDCFs were calculated for all the six irrigation periods shown in Table 3. Over 
40% of radionuclides, including three of the four key radionuclides identified by the TSPA-SR 
analysis (99Tc, 129I, and 237Np), do not show significant (less than 15%) radionuclide buildup in 
soil. The analysis of the BDCFs for the remaining radionuclides indicated that the increase in 
radionuclide concentrations in soil tend to affect inhalation and external exposure pathway 
contributions to the BDCFs the most, with the water consumption and fish consumption 
contributions to the BDCFs essentially unchanged, and the remaining pathway BDCFs affected 
to a lesser degree. 
The comparison of the BDCFs for the current and evolved climate showed that BDCF values 
decrease for the cooler and wetter conditions of the evolved climate. The difference tends to be 
greater if the effect of radionuclide buildup in soil is included in the analysis. 
Pathway analysis confirmed the earlier findings (CRWMS M&O 2000o) that ingestion of water 
and leafy vegetables are the two dominant pathways, which together contribute over 90 percent 
to the BDCFs for most radionuclides. The drinking water and leafy vegetable consumption 
pathways contributed at least 55 and 23 percent, respectively, to the BDCF for all radionuclides 
except 14C and 137Cs. However, the importance of other pathways, such as inhalation and 
external exposure increases with radionuclide buildup in soil for those radionuclides that show 
significant buildup effect. 
The BDCFs developed in this AMR were obtained using current, updated inputs. If new input 
data become available, the BDCFs may be revised. The BDCF values presented in this report, as 
well as the conclusions from the pathway, and uncertainty analyses, differ from those developed 
previously (CRWMS M&O 2000a, CRWMS M&O 2000o) due to the differences in the input 
values. 

8. INPUTS AND REFERENCES
8.1 DOCUMENTS CITED 
CRWMS M&O 1998a. Software Qualification Report (SQR) GENII-S 1.485 Environmental 
Radiation Dosimetry Software System Version 1.485. CSCI: 30034 V1.4.8.5. DI: 30034-2003, 
Rev. 0. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.19980715.0029. 
CRWMS M&O 1998b. Yucca Mountain Site Characterization Project: Summary of 
Socioeconomic Data Analyses Conducted in Support of the Radiological Monitoring Program, 
April 1997 to April 1998. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.19980803.0064. 
CRWMS M&O 1998c. Biosphere. Chapter 9 of Total System Performance Assessment-
Viability Assessment (TSPA-VA) Analyses Technical Basis Document. B00000000-01717-4301-
00009 REV 01. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.19981008.0009. 
CRWMS M&O 1999. Dose Conversion Factor Analysis: Evaluation of GENII-S Dose 
Assessment Methods. ANL-MGR-MD-000002 REV 00. Las Vegas, Nevada: CRWMS M&O. 
ACC: MOL.19991207.0215. 
CRWMS M&O 2000a. Non-Disruptive Event Biosphere Dose Conversion Factors. 
ANL-MGR-MD-000009 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20000307.0383. 
CRWMS M&O 2000b. Total System Performance Assessment for the Site Recommendation. 
TDR-WIS-PA-000001 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20001005.0282. 
CRWMS M&O 2000c. Technical Work Plan for Biosphere Modeling and Expert Support. 
TWP-MGR-MD-000009 REV 0. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20001102.0378. 
CRWMS M&O 2000d. Cancelled 
CRWMS M&O 2000e. Environmental Transport Parameters Analysis. ANL-MGR-MD-
000007 REV 00 ICN 01. Las Vegas, Nevada: CRWMS M&O. Submit to RPC URN-0733 
CRWMS M&O 2000f. Transfer Coefficient Analysis. ANL-MGR-MD-000008 REV 00 
ICN 02. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20001016.0005. 
CRWMS M&O 2000g. Input Parameter Values for External and Inhalation Radiation Exposure 
Analysis. ANL-MGR-MD-000001 REV 01. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20001122.0005. 

CRWMS M&O 2000h. Identification of Ingestion Exposure Parameters. ANL-MGR-MD-
000006 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20000216.0104. 
CRWMS M&O 2000i. Calculation: Values and Consumption Rates of Locally Produced Food 
and Tap Water for the Receptor of Interest. CAL-MGR-MD-000005 REV 00. Las Vegas, 
Nevada: CRWMS M&O. ACC: MOL.20000922.0092. 
CRWMS M&O 2000j. Evaluate Soil/Radionuclide Removal by Erosion and Leaching. 
ANL NBS-MD-000009 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20000310.0057. 
CRWMS M&O 2000k. Inventory Abstraction. ANL-WIS-MD-000006 REV 00. Las Vegas, 
Nevada: CRWMS M&O. ACC: MOL.20000414.0643. 
CRWMS M&O 2000l. Identification of the Critical Group (Consumption of Locally Produced 
Food and Tap Water). ANL-MGR-MD-000005 REV 01. Las Vegas, Nevada: CRWMS M&O. 
ACC: Submit to RPC. URN-0732 
CRWMS M&O 2000m. The Development of Information Catalogued in REV00 of the YMP FEP 
Database. TDR-WIS-MD-000003 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20000705.0098. 
CRWMS M&O 2000n. Distribution Fitting to the Stochastic BDCF Data. ANL-NBS-MD-
000008 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20000517.0258; 
MOL.20000601.0753. 
CRWMS M&O 2000o. Yucca Mountain Site Description. TDR-CRW-GS-000001 REV 01 ICN 
01. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20001003.0111. 
CRWMS M&O 2000p. Non-Disruptive Event Biosphere Dose Conversion Factor Sensitivity 
Analysis. ANL-MGR-MD-000010 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20000420.0074. 
CRWMS M&O 2000q. Saturated Zone Flow and Transport Process Model Report. TDR-NBS-
HS-000001 REV 00 ICN 02. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20001102.0067. 
CRWMS M&O 2000r. Evaluation of the Applicability of Biosphere-Related Features, Events, 
and Processes (FEP). ANL-MGR-MD-000011 REV 00. Las Vegas, Nevada: CRWMS M&O. 
ACC: MOL.20000420.0075. 
CRWMS M&O 2000s. Disruptive Event Biosphere Dose Conversion Factor Analysis. 
ANL MGR-MD-000003 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20000303.0216. 

CRWMS M&O 2000t. Disruptive Event Biosphere Dose Conversion Factor Sensitivity 
Analysis. ANL-MGR-MD-000004 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20000418.0826. 
DOE (U.S. Department of Energy) 1997. The 1997 Biosphere Food Consumption Survey 
Summary Findings and Technical Documentation. Washington, D.C.: U.S. Department of 
Energy, Office of Civilian Radioactive Waste Management. ACC: MOL.19981021.0301. 
DOE (U.S. Department of Energy) 2000. Quality Assurance Requirements and Description. 
DOE/RW-0333P, Rev. 10. Washington, D.C.: U.S. Department of Energy, Office of Civilian 
Radioactive Waste Management. ACC: MOL.20000427.0422. 
Doorenbos, J. and Pruitt, W.O. 1977. Crop Water Requirements. FAO Irrigation and Drainage 
Paper 24. Rome, Italy: Food and Agriculture Organization of the United Nations. TIC: 
245199. 
Eckerman, K.F. and Ryman, J.C. 1993. External Exposure to Radionuclides in Air, Water, and 
Soil, Exposure-to-Dose Coefficients for General Application, Based on the 1987 Federal 
Radiation Protection Guidance. EPA 402-R-93-081. Federal Guidance Report No. 12. 
Washington, D.C.: U.S. Environmental Protection Agency, Office of Radiation and Indoor Air. 
TIC: 225472. 
Eckerman, K.F.; Wolbarst, A.B.; and Richardson, A.C.B. 1988. Limiting Values of Radionuclide 
Intake and Air Concentration and Dose Conversion Factors for Inhalation, Submersion, and 
Ingestion. EPA 520/1-88-020. Federal Guidance Report No. 11. Washington, D.C.: U.S. 
Environmental Protection Agency. TIC: 203350. 
Hogan, E. L., ed. 1988. Plant Care: Vegetables. Sunset Western Garden Book. pp. 194-197. 
Menlo Park, California: Lane Publishing Co. TIC: 243490. 
ICRP (International Commission on Radiological Protection) 1977. Recommendations of the 
International Commission on Radiological Protection. ICRP Publication 26. New York, New 
York: Pergamon Press. TIC: 221568. 
ICRP (International Commission on Radiological Protection) 1979. Limits for Intakes of 
Radionuclides by Workers. Volume 2, No. 3/4 of Annals of the ICRP. Sowby, F.D., ed. ICRP 
Publication 30 Part 1. New York, New York: Pergamon Press. TIC: 4939. 
ICRP (International Commission on Radiological Protection) 1980. Limits for Intakes of 
Radionuclides by Workers. Volume 4, No. 3/4 of Annals of the ICRP. ICRP Publication 30 
Part 2. New York, New York: Pergamon Press. TIC: 4941. 
ICRP (International Commission on Radiological Protection) 1981. Limits for Intakes of 
Radionuclides by Workers. Volume 6, No. 2/3 of Annals of the ICRP. ICRP Publication 30 
Part 3, Including Addendum to Parts 1 and 2. New York, New York: Pergamon Press. 
TIC: 4943. 

ICRP (International Commission on Radiological Protection) 1991. 1990 Recommendations of 
the International Commission on Radiological Protection. Volume 21, No. 1-3 of Annals of the 
ICRP. ICRP Publication 60. New York, New York: Pergamon Press. TIC: 235864. 
ICRP (International Commission on Radiological Protection) 1996. Age-Dependent Doses to 
Members of the Public from Intake of Radionuclides: Part 5 Compilation of Ingestion and 
Inhalation Dose Coefficients. Volume 26, No. 1 of Annals of the ICRP. Smith, H., ed. ICRP 
Publication 72. New York, New York: Pergamon Press. TIC: 235870. 
ICRP (International Commission on Radiological Protection) 2000. Radiation Protection 
Recommendations as Applied to the Disposal of Long-lived Solid Radioactive Waste. Annals of 
the ICRP. ICRP Publication 81. New York, New York: Pergamon Press. On Order Library 
Tracking Number-249031 
LaPlante, P.A. and Poor, K. 1997. Information and Analyses to Support Selection of Critical 
Groups and Reference Biospheres for Yucca Mountain Exposure Scenarios. CNWRA 97-009. 
San Antonio, Texas: Center for Nuclear Waste Regulatory Analyses. TIC: 236454. 
Leigh, C.D.; Thompson, B.M.; Campbell, J.E.; Longsine, D.E.; Kennedy, R.A.; and Napier, B.A. 
1993. User's Guide for GENII-S: A Code for Statistical and Deterministic Simulations of 
Radiation Doses to Humans from Radionuclides in the Environment. SAND91-0561. 
Albuquerque, New Mexico: Sandia National Laboratories. TIC: 231133. 
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Equations. Managing Nitrogen for Groundwater Quality and Farm Profitability, Proceedings 
of a Symposium Sponsored By Division A-5 of the American Society of Agronomy, Anaheim, 
California, 1988. Follett, R.F.; Keeney, D.R.; Cruse, R.M., eds. 333-338. Madison, Wisconsin: 
Soil Science Society of America. TIC: 238376. 
Napier, B.A.; Peloquin, R.A.; Strenge, D.L.; and Ramsdell, J.V. 1988. Conceptual 
Representation. Volume 1 of GENII - The Hanford Environmental Radiation Dosimetry 
Software System. PNL-6584. Richland, Washington: Pacific Northwest Laboratory. 
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Report). Richland, Washington: Pacific Northwest National Laboratory. TIC: 247141. 
NRC (U.S. Nuclear Regulatory Commission) 2000. Issue Resolution Status Report Key 
Technical Issue: Total System Performance Assessment and Integration. Rev. 2. Washington, 
D.C.: U.S. Nuclear Regulatory Commission. TIC: 247614. 
Rittmann, P.D. 1993. Verification Tests for the July 1993 Revision to the GENII Radionuclide 
and Dose Increment Libraries. WHC-SD-WM-TI-596. Richland, Washington: Westinghouse 
Hanford Company. TIC: 233965. 

Till, J.E. and Meyer, H.R. 1983. Radiological Assessment, A Textbook on Environmental Dose
Analysis. NUREG/CR-3332. Washington, D.C.: U.S. Nuclear Regulatory Commission.
TIC: 223809.
USGS (U.S. Geological Survey) 2000. Future Climate Analysis. ANL-NBS-GS-000008
REV 00. Denver, Colorado: U.S. Geological Survey. ACC: MOL.20000629.0907.
Washington Agricultural Statistics Service 1992. Spokane County, Washington. Washington,
D.C.: National Agricultural Statistics Service, U.S. Department of Agriculture. Accessed
9/12/2000. TIC: 248846. http://www.nass.usda.gov/wa/counties/cnty063.htm
Washington Agricultural Statistics Service 1999. 1999 Annual Bulletin: Usual Planting &
Harvesting Dates, Washington. Washington, D.C.: National Agricultural Statistics Service,
U.S. Department of Agriculture. Accessed 8/23/00. TIC: 248851. 
http://www.nass.usda.gov/wa/annual99/plant99.htm 
Western Regional Climate Center 1997. Spokane, Washington: Normals, Means, and 
Extremes. Reno, Nevada: Western Regional Climate Center, Desert Research Institute. 
Accessed 8/23/00. TIC: 248857. http://www.wrcc.dri.edu/cgi-bin/clilcd.pl?wa24157 
8.2 DATA CITED 
GS000100001221.001. EarthInfo, Inc. Western US Meteorologic Station Weather Data NCDC 
Summary of Day (West 1) and NCDC Summary of Day (West 2). Submittal date: 
01/25/2000. 
MO0002RIB00068.000. Ingestion Exposure Parameter Values. Submittal date: 02/03/2000. 
MO0004RIB00085.000. Soil and Radionuclide Removal by Erosion and Leaching. Submittal 
date: 04/06/2000. 
MO0007SPADMM05.002. Distributions, Mean, Minimum, and Maximum Consumption Levels 
of Locally Produced Food by Type and Tap Water for the Amargosa Valley Receptor of Interest. 
Submittal date: 07/31/2000. 
MO0010MWDPBD09.006. Biosphere Dose Conversion Factors for Nominal Performance. 
Submittal date: 10/05/2000. 
MO0010SPAAAM01.014. Input Parameter Values for External and Inhalation Radiation 
Exposure Analysis. Submittal date: 10/02/2000. 
MO0010SPAPET07.004. Environmental Transport Parameters. Submittal date: 10/03/2000. 
Submit to RPC URN-0659 
MO0010SPAPTC08.005. Transfer Coefficients. Submittal date: 10/03/2000. Submit to RPC 
URN-0660 

MO9912RIB00066.000. Parameter Values for Internal and External Dose Conversion Factors. 
Submittal date: 12/03/99. 
MO9912SPASUB02.001. Dose Coefficients for Air Submersion and Exposure to Contaminated 
Soil. Submittal date: 12/10/1999. 
8.3 CORRESPONDENCE CITED 
Dyer, J.R. 1999. Revised Interim Guidance Pending Issuance of New U.S. Nuclear Regulatory 
Commission (NRC) Regulations (Revision 01, July 22, 1999), for Yucca Mountain, Nevada. 
Letter from J.R. Dyer (DOE/YMSCO) to D.R. Wilkins (CRWMS M&O), September 3, 1999, 
OL&RC:SB-1714, with enclosure, Interim Guidance Pending Issuance of New NRC 
Regulations for Yucca Mountain (Revision 01).. ACC: MOL.19990910.0079. 
Harris, M.W. 1997. Recommendation of Models to be Used in the Biosphere Modeling Effort. 
Letter from M.W. Harris (CRWMS M&O) to W.R. Dixon (DOE/YMSCO), July 8, 1997, 
LV.ESR.CHT.07/97-125, with attachment. ACC: MOL.19971124.0033 MOL.19971124.0034. 
8.4 CODES, STANDARDS, AND REGULATIONS 
10 CFR 20. Energy: Standards for Protection Against Radiation. Readily available. 
64 FR 46976. Environmental Radiation Protection Standards for Yucca Mountain, Nevada. 
Readily available. 
64 FR 67054. Office of Civilian Radioactive Waste Management; General Guidelines for the 
Recommendation of Sites for Nuclear Waste Repositories; Yucca Mountain Site Suitability 
Guidelines. Readily available. 
64 FR 8640. Disposal of High-Level Radioactive Wastes in a Proposed Geologic Repository at 
Yucca Mountain, Nevada. Readily available. 
8.5 PROCEDURES 
AP-2.1Q, Rev. 1, ICN 0. Indoctrination and Training of Personnel. Washington, D.C.: 
U.S. Department of Energy, Office of Civilian Radioactive Waste Management. ACC: 
MOL.20000629.0909. 
AP-2.2Q, Rev. 0, ICN 0. Establishment and Verification of Required Education and Experience 
of Personnel. Washington, D.C.: U.S. Department of Energy, Office of Civilian Radioactive 
Waste Management. ACC: MOL.19990701.0618. 
AP-2.14Q, Rev. 1, ICN 0. Review of Technical Products. Washington, D.C.: U.S. Department 
of Energy, Office of Civilian Radioactive Waste Management. ACC: MOL.20001211.0001. 

AP-2.21Q, Rev. 0, ICN 0. Quality Determination 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-3.4Q, Rev. 2, ICN 0. Level 03 Change Control. Washington, D.C.: U.S. Department of 
Energy, Office of Civilian Radioactive Waste Management. ACC: MOL.20001213.0064. 
AP-3.10Q, Rev. 2, ICN 3, Analyses and Models. Washington, D.C.: U.S. Department of 
Energy, Office of Civilian Radioactive Waste Management. ACC: MOL.20000918.0282. 
AP-3.14Q, Rev. 0, ICN 2. Transmittal of Input. Washington, D.C.: U.S. Department of Energy, 
Office of Civilian Radioactive Waste Management. ACC: MOL.20001122.0001. 
AP-3.15Q, Rev. 2, ICN 0. Managing Technical Product Inputs. Washington, D.C.: 
U.S. Department of Energy, Office of Civilian Radioactive Waste Management. ACC: 
MOL.20001109.0051. 
AP-3.17Q, Rev. 1, ICN 0. Impact Reviews. Washington, D.C.: U.S. Department of Energy, 
Office of Civilian Radioactive Waste Management. ACC: MOL.20001211.0003. 
AP-6.1Q, Rev. 5, ICN 0. Controlled Documents. Washington, D.C.: U.S. Department of 
Energy, Office of Civilian Radioactive Waste Management. ACC: MOL.20000608.0009. 
AP-17.1Q, Rev. 1, ICN 3. Record Source Responsibilities for Inclusionary Records. 
Washington, D.C.: U.S. Department of Energy, Office of Civilian Radioactive Waste 
Management. ACC: MOL.20001120.0166. 
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-SIII.2Q. Rev. 0, ICN 3. Qualification of Unqualified Data and the Documentation of 
Rationale for Accepted Data. Washington, D.C.: U.S. Department of Energy, Office of Civilian 
Radioactive Waste Management. ACC: MOL.20001002.0152. 
AP-SIII.3Q. Rev. 0, ICN 3. Submittal and Incorporation of Data to the Technical Data 
Management System. Washington, D.C.: U.S. Department of Energy, Office of Civilian 
Radioactive Waste Management. ACC: MOL.20000418.0808. 
AP-SIII.4Q. Rev. 0, ICN 1. Development, Review, Online Placement, and Maintenance of 
Individual Reference Information Data Base Items. Washington, D.C.: U.S. Department of 
Energy, Office of Civilian Radioactive Waste Management. ACC: MOL.19991214.0631. 
AP-SV.1Q. Rev. 0, ICN 2. Control of the Electronic Management of Data. Washington, D.C.: 
U.S. Department of Energy, Office of Civilian Radioactive Waste Management. ACC: 
MOL.20000831.0065. 

NLP-2-0. Rev. 5, ICN 1. Determination of Importance Evaluations. Las Vegas, Nevada: 
Civilian Radioactive Waste Management System, Management and Operating Contractor. ACC: 
MOL.20000713.0360. 
QAP-2-3. Rev. 10. Classification of Permanent Items. Las Vegas, Nevada: Civilian 
Radioactive Waste Management System, Management and Operating Contractor. ACC: 
MOL.19990316.0006. 
8.6 SOFTWARE CITED 
Sandia National Laboratories 1998. Software Code: GENII-S V1.4.8.5 . V1.4.8.5. 30034 
V1.4.8.5. 

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ATTACHMENT I
MATHEMATICAL MODELS USED BY GENII-S

ATTACHMENT I 
MATHEMATICAL MODELS USED BY GENII-S 
This attachment describes selected models used in the GENII-S computer code, which are 
important from the perspective of mathematical biosphere modeling for performance assessment. 
Some components of the models are omitted in order to maintain focus on the main model 
features. For example, the radioactive decay factor is not included in the equations presented in 
this section, while GENII-S considers the decay and in-growth of radionuclides during transport 
through environmental media. 
I.1 ENVIRONMENTAL TRANSPORT OF RADIONUCLIDES 
The exposure scenarios under consideration are discussed in Section 6.2. In the case of the 
groundwater contamination scenario, radionuclide migration in the biosphere, and the subsequent 
human exposure, which is initiated when contaminated water is used for domestic and 
agricultural needs. The subsequent use of contaminated land, combined with the natural 
processes, causes radionuclide migration through the environment. As a result, radionuclides 
become redistributed from the initial source of contamination to other components of the 
biosphere, such as soil, air, plants, animals, and, eventually, humans. 
I.1.1 Radionuclide Concentration in Soil 
The soil model used in GENII-S is relatively simple. Although the code considers two soil 
strata: surface soil and deep soil, only contamination present in the surface soil layer is subject 
to resuspension and transfer to food products. The radionuclide concentration in the top layer of 
soil is governed by a conservation equation, where the rate of change in radionuclide 
concentration in a volume of soil is equal to the rate of activity addition from irrigation minus the 
rate of activity removal. GENII-S considers three mechanisms of potential radionuclide removal 
from the soil: radioactive decay, plant uptake, and leaching into the deeper soil layer where 
activity becomes unavailable to plants. A fourth mechanism of potential radionuclide removal, 
not incorporated into the GENII-S computer code, is physical loss of soil (i.e., erosion by wind 
and water). This process is modeled outside of the GENII-S code. 
Primary calculations of radionuclide concentration in soil in GENII-S are based on an irrigation 
period of one year. It is assumed that radioactivity is initially distributed throughout the upper 
soil layer, which is where the plants roots are assumed to be located. Radionuclide activity in 
soil per unit area, Cs, following irrigation with contaminated water is calculated using the 
following relationship (adapted from Napier et al. 1988, p. 4.57-4.58): 
4.25 CI -.lt )
w
C =(1 - e (Eq. I-1) 
s .l 

where 
Cs  radionuclide activity concentration in soil per unit area (Bq m-2) 
Cw  activity concentration of radionuclide in water (Bq L-1) 
I  irrigation rate (in y-1) 
.l  leaching rate (y-1) 
t  exposure time (equal to one year) 
-2
25.4 L in-1 m  unit conversion factor (number of liters in one inch of water applied 
over one m2 of soil surface 
For irrigation periods longer than one year, GENII-S factors in removal of radionuclides from 
soil by harvest. Harvest removal calculations are based on radionuclide concentration in a plant 
and the plants yield and are carried out cyclically for the assumed duration of prior irrigation 
with contaminated water. 
I.1.2 Radionuclide Concentration in Air 
The concentration of radionuclides in air, Ca, resulting from soil resuspension is calculated, from 
the definition of the resuspension factor, using the following equation (Napier et al. 1988, 
p. 4.63):
C = C  M (Eq. I-2) 
as 
where 
Ca  radionuclide concentration in air (Bq m-3) 
M  resuspension factor (m-1) 
I.1.3 Radionuclide Concentration in Plants 
Four categories of edible plants are considered in GENII-S: leafy vegetables, other (root) 
vegetables, fruit, and grain. There are two main mechanisms of radionuclide transfer to a plant: 
direct deposition on plant surfaces and the root uptake. Deposition on plant surfaces may result 
from irrigation with contaminated water and from resuspension of contaminated soil. 
In order to evaluate radionuclide deposition onto leaf surfaces, the deposition rates for irrigation 
and soil resuspension have to be calculated separately. The leaf deposition rate from irrigation, 
DRir , is calculated as follows (adapted from Napier et al. 1988, p. 4.57): 
4.25 12 CI
w
DRir = (Eq. I-3) 
ID 

where 
-1)
DRir  leaf deposition rate from irrigation (Bq m-2 yID - irrigation duration (months) 
-2
25.4 l in-1 m - unit conversion factor
12 months y-- unit conversion factor
The leaf deposition rate from resuspension, DRrs, is calculated as follows (adapted from Napier 
et al. 1988, p. 4.57): 

DR = 10 154.3 7 Ca vd (Eq. I-4) 
rs 
where 
-1)
DRrs - leaf deposition rate from resuspension (Bq m-2 y
vd - deposition velocity (m s-1)
3.154  107 s y-1 - unit conversion factor
Activity concentration in a plant, following leaf deposition of airborne radionuclides, Cp,d, is 
calculated as follows (adapted from Napier et al. 1988, p. 4.67): 
C 
d p 
(DR r + DR r )T (1 - e -.w tg )
irw rsa
= (Eq. I-5) 
, 365 . B
w 
where 
Cp,d,  activity concentration in a plant (Bq kg-1) 
.w  weathering constant (d-1) 
tg  growing time (days) 
rw  irrigation interception fraction (dimensionless) 
ra  air interception fraction (dimensionless) 
T  translocation factor (dimensionless) 
365 d y-1  unit conversion factor 
Translocation factor, T, descries the fraction of radionuclide transferred from plant surfaces to 
edible parts of the plant. Irrigation interception fraction, rw, and the air interception fraction, ra 
represent the fraction of initial deposition retained on the plant. 
The air interception fraction, ra, was calculated as follows (adopted from Napier et al. 1988, 
p. 4.69):
r = 0.1 - e -DW B a (Eq. I-6) 
a 

where 
a  empirical factor (m2 kg-1dry mass) 
B  biomass (kg wet mass m-2) 
DW  plant dry-to-wet weight ratio (kg dry mass kg-1wet mass) 
Empirical factor, a, is equal to 3.6 for root vegetables and fruit and 2.9 for leafy vegetables, grain 
and grasses (Napier et al. 1988, p. 4.69). 
Activity concentration in a plant which results from radionuclide uptake by a plant root system, 
Cp,r , is calculated using the following equation (adapted from Napier et al. 1988, p. 4.67): 
C
s
C = F DW (Eq. I-7) 
r p s. p
, . 
s 
where 
Cp,r  activity concentration in a plant from root uptake (Bq kg-1) 
. s  surface soil density (kg m-2) 
Fs. p  radionuclide soil-to-plant transfer factor 
(Bq kg-1dry plant per Bq kg-1dry soil) 
Total activity concentration in a plant, Cp , is the sum of deposition on plant surfaces and root 
uptake contributions (adapted from Napier et al. 1988, , p. 4.68): 
C = Cd p + C (Eq. I-8) 
p , r p 
, 
where 
Cp  total activity concentration in a plant (Bq kg-1) 
Activity concentration of 14C in plants was calculated using a different method (Napier 
et al. 1988, p. 4.86). It was assumed that the specific activity of 14C in an environmental 
medium, such as a plant, was the same as that of the contaminating medium. The fractional 
content of carbon in a plant was then used to compute the concentration of 14C in the food 
product under consideration. The following equation was used to determine 14C activity 
concentration in plants (Napier et al. 1988, p. 4.86): 
4.25  12 CI 1.0 
w 
C = FC (Eq. I-9) 
p p
ID . . 01.0 
ls 
where 
0.1  the assumed uptake of 10 
ercents of plant carbon from soil 
0.01  the average fraction of soil that is carbon 

FCp  fraction of carbon in a plant 
The assumption of uptake of 10 percent of plant carbon from the soil is conservative because 
plants acquire almost all of their carbon from the air (Napier et al. 1988, p. 4.86). 
I.1.4 Radionuclide Concentration in Animal Products 
Radionuclide transfer to animal products results from the ingestion of contaminated feed and 
contaminated water by the animal. Animal products considered include meat (beef and pork), 
milk, poultry, and eggs. Determination of radionuclide concentrations in animal products begins 
with the calculation of radionuclide concentrations in fresh forage and stored feed, Cf , using 
formulas similar to those described in Section I.1.3, but with parameter values characteristic of 
crops grown for animal consumption. Once the concentrations of radionuclides in fresh forage 
and stored feed were determined, the daily radionuclide intake by the animal is calculated which 
is then converted to the radionuclide concentration in animal product, Cap, using the following 
formula (adapted from Napier et al. 1988, p. 4.70-4.72): 
C =(C CRw a + C f CRa, f ) F (Eq. I-10) 
ap w , ad .ap 
where 
Cap  radionuclide concentration in animal product (Bq kg-1 or Bq L-1) 
CRa,w  animal consumption rate of water (L d-1) 
Cf  activity concentration in fresh forage or stored feed (Bq kg-1) 
CRa,f  animal consumption rate of fresh forage or stored feed (kg d-1) 
Fad.ap  radionuclide transfer factor from animal diet to animal product 
(d kg-1 or d L-1) 
The concentration of 14C in animal products is calculated using the following formula 
(Napier et al. 1988, p. 4.89): 
C f CRa, f + CR C 
w w a 
C = , FC (Eq. I-11) 
ap ap
FCf CRa, f + CR FC 
w w a 
, 
where 
FCap  fraction of carbon in animal product 
FCf  fraction of carbon in animal feed 
I.1.5 Radionuclide Concentration in Aquatic Food 
The concentration of radionuclides in fish, Cf, is calculated using the following formula: 
Cf = C BF (Eq. I-12) 
w 

where 
BF  bioaccumulation factor in fish (Bq kg-1 per Bq L-1) 
I.2 DOSE ASSESSMENT 
Radiation doses to humans may result from internal intake of radionuclides by inhalation or 
ingestion or from external exposure to radionuclides present in the environment. Dose 
assessment in GENII-S is carried out by considering radionuclide concentrations in 
environmental media, factoring in human exposure conditions, and performing the conversion of 
exposure to dose. For internal exposure, radionuclide activity intake is calculated by combining 
the radioactivity concentration in environmental media (e.g., food, soil, air, and water) with the 
amount of environmental medium taken into the body. Then, using dosimetric models, 
radionuclide intake is converted into dose. To assess exposure from external sources, GENII-S 
uses dose coefficients that convert radionuclide concentrations in environmental media to doses 
for the duration of exposure. 
Dose calculations performed by GENII-S are based on methods developed in ICRP-30 
(ICRP 1979, ICRP 1980, ICRP 1981). The code calculates incremental organ dose equivalents 
for each year following an initial radionuclide intake. Committed dose equivalent (50-year organ 
dose following an intake) is then assembled from incremental dose equivalents to each organ 
over the commitment period. Committed effective dose equivalent is then calculated by 
producing a sum of the organ dose equivalents weighted by organ/tissue weighting factors. 
Conceptually, calculation of doses from internally deposited radionuclides, Dint, can be 
considered as if the following relationship were used (adapted from Napier et al. 1988, pp. 4.63, 
4.69, 4.72): 
D
int = IN  DCF (Eq. I-13) 
where 
Dint  dose from annual radionuclide intake (Sv) 
IN  annual radionuclide intake by inhalation or by ingestion (Bq) 
DCFin  dose conversion factor for internal radionuclide intake by 
inhalation or ingestion (Sv Bq-1) 
Dose calculated as above is expressed in terms of committed effective dose equivalent (CEDE). 
To convert dose expressed in sieverts to rems, dose in sieverts should be multiplied by 100. 
Annual intake, IN, is a product of activity concentration in a medium and the amount of this 
medium taken internally over one year. For annual intake with food, activity concentration in a 
food product is multiplied by the annual consumption rate for this food; for annual intake with 
water, activity concentration in water gets combined with the annual consumption rate of water; 
for inadvertent soil ingestion, activity concentration in soil is multiplied by the amount of soil 
ingested annually. To calculate intake by inhalation, the activity concentration in air is 

multiplied by the breathing rate and the amount of time a person is exposed to a given activity 
concentration in air. 
GENII-S calculates the radiation doses from external exposures by considering radionuclide 
concentrations in soil or air and the duration of exposure to soil and air and combining them with 
dose coefficients to calculate radiation doses. For example, annual doses from external 
exposure, Dext, to radionuclides in soil were calculated using the following relationship (adapted 
from Napier et al. 1988, pp. 4.84): 
D
ext = Cv s Text DCFsoil (Eq. I-14) 
, 
where 
Dext  annual dose from external exposure (Sv)
Cs,v  activity concentration in soil per unit volume (Bq m-3)
Text  duration of external exposure to contaminated soil (h)
DCFsoil  dose coefficient for exposure to soil contaminated to a depth of 15 cm 
from FGR 12, (MO9912SPASUB02.001) (Eckerman and Ryman 
1993) (Sv s-1 per Bq m-3) 
Dose calculated as above is expressed in terms of effective dose equivalent (EDE). 
Doses from exposure to internally deposited radionuclides are combined with doses from 
external irradiation to produce total all-pathway doses. 
D
tot = Dint + Dext (Eq. I-15) 
where 
Dtot  total dose (Sv) 
Total dose, calculated using Eq. I-15 is expressed in terms of total effective dose equivalent 
(TEDE). 

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ATTACHMENT II
BIOSPHERE MODEL VALIDATION

ATTACHMENT II 
BIOSPHERE MODEL VALIDATION 
II.1 INTRODUCTION 
The biosphere system is the link between the geosphere system and the receptor and it is 
represented by the biosphere conceptual model. Typical scope of biosphere modeling includes 
the migration and accumulation of radionuclides in the biosphere and the evaluation of the 
potential radiological impacts on a human receptor. The specific case of the biosphere modeling 
for the potential repository at Yucca Mountain, described herein, differs from the conventional 
approach. The biosphere conceptual model in this case is limited to consideration of biosphere 
transport, and human intake and exposure, resulting from a unit of activity concentration present 
at the contamination source, such as groundwater. The model does not include evaluation of 
radiological impact or assessment of doses; it provides dose factors, called biosphere dose 
conversion factors (BDCFs), that enable such evaluations or assessments that are carried out in 
the TSPA model. 
The biosphere system can be conceived as a set of specific biotic and abiotic components of the 
accessible environment and the relationships between these components. Typically, construction 
of the conceptual model of the biosphere is based on developing assumptions and hypotheses 
regarding these characteristics. This is followed by constructing a logical and comprehensive 
framework that combines those assumptions and hypotheses with relevant scientific 
understanding to enable calculations of radiological impact. The attention, therefore, is focused 
on the characteristics of the environment that are important from the perspective of contributing 
to BDCFs for exposure scenarios under consideration. The biosphere conceptual model 
constructed for the potential repository is representative of a reference biosphere system 
delineated by the proposed rules and the interim guidance (64 FR 46976, Section III.B.6; 
64 FR 8640, 63.115; Dyer 1999, Section 115). 
II.1.1 Description of Conceptual Model 
The biosphere conceptual model for the region around Yucca Mountain consists of assumptions, 
simplifications, and idealizations that describe the essential aspects of the biosphere in the 
vicinity of Yucca Mountain. The model is used to evaluate the transport of radionuclides 
released from the source of contamination throughout the biosphere to the human receptor. The 
receptor of interest is discussed in Section II.1.2.1. 
The biosphere conceptual model provides a mechanism for the evaluation of BDCFs from 
selected pathways to a defined receptor. The release scenario addressed here is that of pumping 
contaminated groundwater to be used in a hypothetical farming community under undisturbed, or 
nominal, repository performance. 
The biosphere conceptual model includes the following components: surface soil above the 
lower bounds of the plant root zone, surface water, the atmosphere, flora and fauna. In other 
words, the lowest boundary of the biosphere conceptual model is at the bottom of the plant root 
zone, and it includes all biological components that may be a part of a potential pathway of 
radionuclides to humans. The biosphere conceptual model does not include processes related to 

atmospheric transport and dispersion of airborne radionuclides. The biosphere model does 
consider airborne radioactivity resulting from resuspension of contaminated soil. Therefore the 
contribution from inhalation of resuspended contaminated soil is included in the BDCFs. 
The primary function of the biosphere conceptual model is to support calculations of BDCFs. 
The BDCFs account for: (1) environmental transport of radionuclides by converting 
radionuclide concentrations at the source, such as groundwater, to concentrations in relevant 
biosphere media such as plants and animals; and (2) radionuclide uptake via, and external 
exposure to, environmental media and resulting dose factors. BDCFs are therefore functions of 
environmental transfer factors, exposure factors and dosimetric factors. Environmental transfer 
factors convert radionuclide concentrations in water to concentrations in a biosphere medium 
(e.g., plants, meat, milk, eggs). Exposure factors include parameters such as ingestion rate and 
exposure time (e.g., how much beef a receptor eats per year, how much time a receptor spends 
outdoors). Dosimetric factors convert internal and external exposures to radiation to doses. 
They account for the biological effectiveness of various types of radiation and the different 
sensitivities of various body tissues to radiation. Dosimetric factors are specific to each 
radionuclide and the mechanism by which they expose the receptor (e.g., direct radiation, 
ingestion, and inhalation). 
The objective of the biosphere modeling effort is to determine the values of BDCFs for those 
radionuclides expected to enter the biosphere, considering all of the important exposure 
pathways. Radionuclide-specific BDCFs quantify radionuclide transport, intake and external 
exposure, and the resulting doses per unit of activity concentration at the source. They are 
calculated for a specific receptor of interest and they combine contributions from various 
exposure pathways, such as ingestion of food, water, inadvertent ingestion of soil, inhalation, and 
exposure to radionuclides external to the body. 
BDCFs are multipliers, or factors, used in the TSPA model to convert radionuclide concentration 
at the source of contamination into radiation dose that a human receptor would receive from 
exposure pathways under consideration. For the groundwater contamination scenario, annual 
doses are estimated by multiplying radionuclide concentration in groundwater by the appropriate 
BDCFs. 
DCFs are expressed in terms of annual dose, i.e., TEDE or CEDE, per unit of activity 
concentration at the source of contamination. BDCFs are independent of the actual activity 
concentration in groundwater. Calculations of radionuclide concentrations in groundwater are 
developed in the TSPA code. They are based on the mass flux break-through curves, which are 
summarized in the Saturated Zone Flow and Transport PMR (CRWMS M&O 2000q). 
II.1.1.1 Model Development 
The biosphere system at Yucca Mountain is represented by the reference biosphere. A starting 
point for biosphere conceptual model development is the requirement that [f]eatures, events, 
and processes that describe the reference biosphere shall be consistent with present knowledge of 
the conditions in the region surrounding the Yucca Mountain site (64 FR 8640, 
Section 63.115(a)(1), Dyer 1999, Section 115). To meet this requirement, any conceptual model 
of the biosphere around Yucca Mountain must be based on FEPs that are reflective of current 

conditions. A list of FEPs that have been identified as reflective of current conditions considered 
applicable to the conduct of performance assessment for Yucca Mountain has been developed. 
FEPs that are considered applicable to biosphere modeling are shown in Table II-1. 
Table II-1. Features, Events, and Processes Applicable to Biosphere 
FEP NAME YMP FEP DATABASE NUMBER 
Erosion/denudation 1.2.07.01.00 
Deposition 1.2.07.02.00 
Climate change, global 1.3.01.00.00 
Wells 1.4.07.02.00 
Soil type 2.3.02.01.00 
Radionuclide accumulation in soils 2.3.02.02.00 
Soil and sediment transport 2.3.02.03.00 
Precipitation 2.3.11.01.00 
Surface runoff and flooding 2.3.11.02.00 
Biosphere characteristics 2.3.13.01.00 
Biosphere transport 2.3.13.02.00 
Human characteristics (physiology, metabolism) 2.4.01.00.00 
Diet and fluid intake 2.4.03.00.00 
Human lifestyle 2.4.04.01.00 
Dwelling 2.4.07.00.00 
Agricultural land use and irrigation 2.4.09.01.00 
Animal farms and fisheries 2.4.09.02.00 
Drinking water, foodstuffs and drugs, contaminant 3.3.01.00.00 
Plant uptake 3.3.02.01.00 
Animal uptake 3.3.02.02.00 
Bioaccumulation 3.3.02.03.00 
Ingestion 3.3.04.01.00 
Inhalation 3.3.04.02.00 
External exposure 3.3.04.03.00 
Radiation doses 3.3.05.01.00 
For the groundwater contamination scenario, the entry point for contaminants present in 
groundwater into the biosphere is a groundwater well head. It is assumed that the human 
receptors water supply is extracted from a groundwater well that is similar to wells that 
currently exist. 
II.1.1.2 Conceptual Model of the Biosphere 
As discussed in the previous sections, the attributes of the current biosphere model are based on 
a set of FEPs that conform to the proposed rules. These attributes, while sometimes appearing to 
be abstract, reflect the elements of a semi-arid environment in the Yucca Mountain vicinity and 
the possible processes leading to radionuclide transport in this environment. Generally, the 
reference biosphere, where the hypothetical farming community exists, is located 
geohydrologically downgradient from the potential repository. Therefore, it is conceivable that 

in the future radionuclides may be present in the groundwater at the well head used by the 
receptor at the location approximately 20 km south from the potential repository. 
For the groundwater contamination scenario, a farmer is assumed to use contaminated 
groundwater for irrigation and consumption. Contaminated groundwater used for irrigation 
causes contamination of soil, and, subsequently, contamination of edible crops, as well as animal 
feed. Contaminated animal feed results in contamination of animal food products, such as milk, 
meat and eggs. In addition, small particles of soil contaminated by irrigation may become 
resuspended. Resuspended contamination, if deposited on the crops, adds to the contamination 
caused by irrigation with contaminated water. A person may also inhale resuspended 
contamination. Contaminated soil is a source of external irradiation to a person from 
radionuclides emitting penetrating radiation, which are either present in soil or deposited on the 
soil surface. 
Figure 2, of this document, presents an illustrative, but not comprehensive, block diagram of the 
biosphere conceptual model for the groundwater contamination scenario. The figure shows 
important mechanisms of radionuclide migration in the biosphere from the source of 
contamination to the human receptor for the primary pathways relevant to the groundwater 
scenario. Important parameters controlling radionuclide transport in the biosphere are also 
identified in the diagrams. 
Ingestion, inhalation and external exposure are the major exposure pathways included in the 
conceptual model of the biosphere. The ingestion pathway includes drinking of contaminated 
water, consumption of locally produced crops that have been irrigated with contaminated 
groundwater, consumption of meat and dairy products from livestock that have been sustained on 
contaminated water, and ingestion of contaminated soil. The biosphere conceptual model allows 
for the assumption that livestock and poultry are sustained with some quantity of locally grown 
feed (e.g., pasture and seasonally harvested alfalfa). Thus, these animals are exposed to the 
radionuclides by consuming the radionuclides present in, or on, the plant tissues. Alfalfa is the 
predominant crop produced by the Amargosa Valley community and alfalfa and forage grasses 
comprise a major proportion of Nye County agricultural land (CRWMS M&O 1998b). Another 
component of the ingestion pathway is the inadvertent ingestion of soil. 
The primary inhalation pathway is through breathing of resuspended soil during outdoor 
activities such as farming and recreation. The factors that determine the degree of exposure 
through dust resuspension is the annual average concentration of suspended particles in air and 
the amount of time exposed annually to that concentration. 
The external pathway occurs as a result of direct exposure to the radiation emitted by radioactive 
materials external to the body (e.g., those present in the soil or on the soil surface). Duration of 
exposures depends on the activity concentration in soil and the amount of time spent indoors and 
outdoors. 
The magnitude of exposure from a number of pathways described above depends on the 
radionuclide concentration in soil. The dynamics of the radionuclide concentration in the top 
layer of soil are governed by a conservation equation where the rate of change in radionuclide 
concentration in a volume of soil is equal to the quantity flowing in from irrigation minus the 

amount being removed. Mechanisms of potential radionuclide removal from the soil include 
radioactive decay, plant uptake, leaching into the deeper soil layer and physical loss of soil 
during crop harvesting. Continual land use with the attendant tilling may accelerate the erosion 
process. 
Countering the removal of radionuclides from the soil is the continual addition of radionuclides 
from irrigation. For a given set of parameters, continual irrigation may cause an increase in the 
radionuclide concentration of the soil until a steady-state equilibrium is reached. Because all 
primary exposure pathways (i.e., ingestion, inhalation, and external exposure) are dependent in 
some way on the radionuclide concentration in soil, the BDCFs will increase as a function of 
continuing irrigation until the radionuclide concentration equilibrium is reached in soil. 
II.1.2 Exposure Scenario 
An exposure scenario establishes the circumstances of human exposure to radionuclides present 
in the biosphere. The groundwater contamination scenario includes the conditions of 
undisturbed performance as well as some disruptive events and processes that may lead to 
groundwater contamination, such as seismic events and human intrusion. Each exposure 
scenario would represent a different combination of FEPs and therefore would have different 
exposure pathways. 
The exposure scenario was defined in a two-step process. First, the geosphere-biosphere 
interface was defined. The radionuclide entry point to the biosphere is a well head. Second, the 
conditions that would lead to radionuclide intake and external exposure were determined and the 
applicable exposure pathways were identified based on the site-specific environment and 
assumptions about the human receptor group. For all exposure scenarios, present-day practices 
by a farming community were considered, and commercial and industrial activities were 
excluded. Exclusion of activities other than farming was due to the fact that farming activities 
involve more exposure pathways than other known activities of the region, and that the large 
demand for groundwater for irrigation associated with farming increases the likelihood of 
contamination and uptake by humans. 
II.1.2.1 Groundwater Contamination Scenario 
The groundwater contamination scenario is used to evaluate the radiological consequences of 
both an undisturbed potential repository system performance as well as the consequences of 
selected disruptive processes and events. The latter include potential consequences of 
earthquakes and igneous intrusions as well as consequences following a stylized human intrusion 
event. Human intrusion is assumed to result in breaching of the potential repositorys geologic 
and engineered barriers. The event involves drilling a borehole through a degraded waste 
container into the aquifer underlying the Yucca Mountain site. Water infiltration through the 
borehole enables radionuclide transport to the saturated zone. Radionuclides may enter the 
biosphere when the groundwater is used by humans. For this scenario, a groundwater well head 
is considered the source of drinking water; irrigation; animal watering and domestic uses, 
including gardening. 

The groundwater contamination scenario is based on proposed rules (64 FR 8640, 
Section 63.115; 64 FR 46976, Section 197.21) and interim guidance (Dyer 1999, Section 115) 
that assumes that the affected hypothetical farming community is located in the Amargosa 
Valley region 20 km from the potential repository at Yucca Mountain. This location represents 
the area closest to the potential repository, downgradient for radionuclide releases in the 
groundwater, where the depth of the water table is accessible for agricultural practices. 
Contaminated well water is the only way that radionuclides from the potential repository reach 
this hypothetical farming community. 
The receptor of interest is an average member of the critical group. The critical group is 
representative of individuals from a hypothetical farming community whose behavioral 
characteristics will result in the highest exposures among the individuals in the community. The 
average member of the critical group is an adult who lives year-round at this location, uses a well 
as the primary water source, and otherwise has habits (e.g., consumption of locally-produced 
foods) that are similar to those of the current population of the Amargosa Valley. 
The routes taken by radionuclides through the biosphere from the source to a person are called 
exposure pathways. The analysis considered pathways that are typical for a hypothetical farming 
community. Farming activities usually involve more exposure pathways than other human 
activities in the Yucca Mountain region, including ingestion through consumption of 
contaminated water, crops and animal products; inhalation and direct exposure from surface 
contamination intensified by the significant outdoor activity of a farming lifestyle. 
The following exposure pathways were considered for groundwater contamination scenario: 
 
Consumption of tap water 
 
Consumption of locally produced leafy vegetables 
 
Consumption of other (root) locally produced vegetables 
 
Consumption of locally produced fruit 
 
Consumption of locally produced grain 
 
Consumption of locally produced meat (beef and pork) 
 
Consumption of locally produced poultry 
 
Consumption of locally produced milk 
 
Consumption of locally produced eggs 
 
Consumption of fish 
 
Inadvertent soil ingestion 
 
Inhalation of resuspended particulate matter 
 
External exposure to contaminated soil. 
Pathways related to sediments and surface water were not considered because the current 
environment in the Yucca Mountain region lacks these features. 
The contribution to a BDCF from a specific pathway depends on the amount of contamination 
the human receptor comes in contact with, either through intake or through external exposure. 
Radioactivity intake may occur through inhalation or ingestion. The magnitude of the BDCF 
contribution from ingestion depends on the activity concentration in consumed products and the 

rate of consumption of these products. The magnitude of inhalation contribution to the BDCF 
(e.g., from breathing resuspended soil and dust during outdoor activities, such as gardening and 
recreation) is influenced by the amount of resuspended particulate matter in the air, breathing 
rate, as well as the amount of time a person is exposed to a given concentration of radioactivity 
in the air. BDCF component from the external exposure pathway results from exposure to 
radiation sources that are external to the body, such as contaminated soil. For this pathway, the 
contribution depends on the amount of time a person is exposed to activity in the soil. 
II.2 BIOSPHERE MODEL VALIDATION 
This attachment discusses the Biosphere Model Validation which was performed and originally 
presented in Evaluation of the Applicability of Biosphere-related Features, Events, and 
Processes (FEP) (CRWMS M&O 2000r). A model validation discussion will not be included in 
the revised document. An update to the model validation due to changes in input parameters is 
discussed in Section II.3. 
II.2.1 YMP Biosphere Model Validation Activity 
The validation activity documented here applies to all uses and applications of the YMP 
biosphere model, including the development of BDCFs. A BDCF is a multiplier used to convert 
a radionuclide concentration at the geosphere/biosphere interface into a dose that a human would 
receive from all pathways. BDCFs are expressed in units of annual dose per unit concentration 
in soil or water. 
Application of the biosphere model to develop BDCFs is documented in two AMRs: the BDCFs 
for groundwater contamination (a non-disruptive event) were originally developed in Non-
Disruptive Event Biosphere Dose Conversion Factors (CRWMS M&O 2000a), and BDCFs for 
an extursive igneous disruptive event (a disruptive event) were originally developed in 
Disruptive Event Biosphere Dose Conversion Factor Analysis (CRWMS M&O 2000s). The 
biosphere model was also used to perform sensitivity analyses for groundwater BDCFs in Non-
Disruptive Event Biosphere Dose Conversion Factor Sensitivity Analysis (CRWMS 
M&O 2000o), and for the disruptive event BDCFs in Disruptive Event Biosphere Dose 
Conversion Factor Sensitivity Analysis (CRWMS M&O 2000t). In addition, the model was used 
to support calculation of BDCF for an alternative receptor (CRWMS M&O 2000o). 
II.2.2 Approach to Model Validation 
AP-3.10Q, Analyses and Models identifies model validation as a process to determine and 
document the adequacy of the scientific bases (i.e., confidence) for a model and to demonstrate 
the model is appropriate and adequate for its intended use. Validation may be accomplished by 
different means and to different degrees, depending on the exact nature and complexity of the 
phenomenon, process or system being modeled. For a simple system, the actual outcome, as 
reflected in data from laboratory experiments, field experiments or observations of natural or 
man-made analogs, may be compared with the predictions of the model. If such data are not 
available to support validation of the model, AP-3.10Q, Analyses and Models suggests alternate 
approaches including: 

 
Peer review or review by international collaborations. 
 
Technical review through publication in open literature. 
 
Review of model calibration parameters for reasonableness or consistency in 
explanation of relevant data. 
 
Comparison of analysis results with results from alternative conceptual models. 
 
Calibration and corroboration within experimental data sets. 
 
Comparison of analysis results with data attained during Performance Confirmation 
studies. 
For the conditions being predicted with the YMP biosphere model (future exposure of humans to 
radioactive materials that may be released from the repository) direct observation of an actual 
outcome may never be possible. Accordingly, validation of the biosphere conceptual model as 
implemented using the GENII-S computer software was conducted using a combination of the 
alternative approaches suggested by AP-3.10Q, Analyses and Models. 
II.2.3 Validation Method 
The YMP biosphere model is a synthesis of the biosphere conceptual model and a generic 
mathematical model and submodels that are executed by the GENII-S computer code. The 
conceptual model considered in this validation was that of a farming community, located 
approximately 20 km south of the potential repository. The general climatic conditions are those 
of an arid/semi-arid environment. The individuals living in this community have a lifestyle 
consistent with present day behaviors and obtain a portion of the food and water they consume 
from local sources. The objective of this validation effort was to enhance confidence that the 
YMP biosphere model has an adequate scientific basis and is appropriate and adequate for the 
basic biosphere concept and this intended use. A validation process was developed, with 
predetermined validation criteria, to provide a high degree of confidence that: 
 
The GENII-S code, as installed, is operating correctly and gives results consistent with 
the inputs. 
 
The BDCFs produced using GENII-S and the biosphere model are reasonable when 
compared with results of other calculations and conceptual models, and 
 
The YMP biosphere pathways were assessed and parameterized in a technically 
defensible manner. 
A detailed presentation of the results of the validation method is provided in Section II.2.4. 
II.2.3.1 Segment 1: Software Qualification 
The GENII-S code qualification is one segment of the YMP biosphere model validation. As part 
of qualification process in the Software Qualification Report (SQR) GENII-S 1.485 (CRWMS 

M&O 1998a) validation criteria were established for comparison of the GENII-S output with the 
results published in the software documentation or the results of hand calculations. Similar 
criterion are used to support model validation in this AMR. 
Criterion 1.1: For test cases with numerical results, the GENII-S and expected (hand 
calculation or published) results agree within 5%. 
Criterion 1.2: For test cases with graphical output, actual and expected results agree (based 
on visual comparison). 
Six validation test cases were executed as part of the software qualification discussed in the SQR 
(CRWMS M&O 1998a). Five were the sample cases (including both deterministic and 
stochastic versions) provided with the GENII-S software package. The sample case results 
published in Users Guide for GENII-S: A Code for Statistical and Deterministic Simulations of 
Radiation Doses to Humans from Radionuclides in the Environment (Leigh et al. 1993) were 
used as the basis for comparison with results of the validation test runs. The sixth validation test 
case was an independent case specifically designed by the YMP staff to exercise all the pathways 
of interest. Hand calculations of the independent test case were done using the equations from 
the GENII-S mathematical model. 
Each of the five sample test cases provided with the software was run in both stochastic and 
deterministic modes. The independent test case was run only in the deterministic mode. The 
results of each sample case were compared with the results published in Leigh et al. (1993). The 
results of the independent test case were compared with the hand-calculated doses. 
For each of the six test cases, the numerical values produced by GENII-S fell within 5% of the 
published or hand-calculated value. It is concluded that validation criterion 1.1 was met. 
For each test case, graphical outputs were consistent with the expected results. It is concluded 
that validation criterion 1.2 was met. 
Meeting Criteria 1.1 and 1.2 demonstrates that the code was installed properly and is operating 
correctly. 
II.2.3.2 Segment 2: Comparison of the YMP BDCFs with Results of Other GENII-S 
Calculations and Conceptual Models 
The YMP BDCFs produced using the YMP current biosphere model, as presented in Non-
Disruptive Event Biosphere Dose Conversion Factors (CRWMS M&O 2000a) was compared 
and reconciled with results of other GENII-S calculations and conceptual models (LaPlante and 
Poor 1997 and CRWMS M&O 1998c). Most features of the alternative models selected for 
comparison are very similar to the YMP biosphere model. However, the alternative calculations 
reflect the professional judgement of different analysts regarding the GENII-S input settings and 
parameter values that best represent the YMP biosphere features. Thus, this segment 
corresponds to one of the alternative validation approaches specified in AP-3.10Q, Analyses and 
Models. 

This validation segment helps assure that no significant deficiencies have been made in 
describing the YMP biosphere or in implementing the model using GENII-S. If the YMP 
BDCFs are shown to be consistent with results of other modeling efforts, additional confidence 
is gained in the appropriateness and adequacy of the YMP biosphere model and in the accuracy 
of its application. Selection of analyses for comparison was based on similarity of the pathways 
modeled and the documentation of the analysis inputs, both of which were necessary in order to 
compare and reconcile the results. 
Validation Criterion 2.1 was established for comparison of the YMP BDCFs with results of other 
calculations and conceptual models. 
Criterion 2.1: For radionuclides in groundwater, differences between the YMP BDCFs 
and the values inferred from other analyses can be explained by 
differences in the pathway assumptions and values of input parameters 
used for the different analyses. 
If values agree within about a factor of three, then they will be considered to be entirely 
consistent and no additional effort will be made to reconcile the differences. If the difference is 
greater than about a factor of three but less than a factor of ten, the values will be considered to 
be somewhat consistent, but no effort was made to explain the difference in terms of the values 
of the inputs used. If the difference is greater than a factor of ten, alternative calculations will be 
done to test the effect of different input parameter values and assumptions. 
II.2.3.3 Segment 3: Independent Review of the Biosphere Model 
The third segment of the validation process was an independent review of the model by a 
qualified technical expert. The review was conducted to enhance confidence that the model has 
adequate scientific basis and is appropriate and adequate for its intended use as described in 
Section II.2.3 of this document. Certain reviewer qualification criteria were deemed essential for 
the review to be credible, meaningful and constructive. Accordingly, it was determined that the 
independent reviewer must: 
 
Have had no prior involvement in the development of the YMP biosphere conceptual 
model. 
 
Be independent from the organization conducting the YMP biosphere modeling effort. 
 
Have broad experience in environmental dose assessment and biosphere model 
development. 
 
Possess detailed knowledge of the GENII-S code, its uses and limitations. 
The following validation Criteria 3.1  3.4 were established for this independent review of the 
biosphere modeling effort. 
Criterion 3.1: In the judgement of the independent reviewer, the pathways considered in 
the biosphere model and the manner in which they are applied is 

consistent with current environmental conditions in the Amargosa Valley 
and with the FEPs of interest. 
Criterion 3.2: In the judgement of the independent reviewer, the logic and analysis 
methods used to select values for the GENII-S input parameters are 
reasonable. 
Criterion 3.3: In the judgement of the independent reviewer, the references and data 
sources cited by the YMP analysts are current and defensible. 
Criterion 3.4: In the judgement of the independent reviewer, the values and ranges of the 
GENII-S input parameters used to develop BDCF are reasonable for the 
environmental conditions implicit in the biosphere conceptual model. 
II.2.4 Validation Results 
II.2.4.1 Comparison of the YMP BDCFs with Results of Other GENII-S Calculations 
and Conceptual Models 
DOE guidance (Dyer 1999) and the nature of the YMP physical environment limit the possible 
processes by which radionuclides from the potential repository may enter the biosphere and the 
pathways through which humans may be exposed. As a result of the DOE guidance (Dyer 
1999), no alternative conceptual models were identified. Comparison of the YMP BDCFs with 
results of other GENII-S calculations for similar pathways and radionuclides is intended to 
enhance confidence in the YMP biosphere model and the integrity of the BDCF calculation 
process. 
As a basis for this comparison, alternative calculations involving the same dose pathways and 
some of the same radionuclides were identified. The first of these calculations is documented in 
Information and Analyses to Support Selection of Critical Groups and Reference Biospheres for 
Yucca Mountain Exposure Scenarios (LaPlante and Poor 1997), prepared for the U.S. Nuclear 
Regulatory Commission by the Center for Nuclear Waste Regulatory Analyses (CNWRA). The 
second set of calculations is documented in the TSPA-VA (CRWMS M&O 1998c). Although 
this second analysis was prepared within the CRWMS M&O, it represents a different set of 
biosphere calculations. 
The criteria that apply to the validation segments address the overall consistency of the YMP 
BDCFs with results of other calculations. Substantial variation (for example, an order of 
magnitude or more) may be observed between different environmental dose calculation results 
that are fundamentally consistent in their conceptual treatment of an issue. When the same 
calculational tool is used and the values for the input variables are documented, the effects of 
different inputs can be taken into account and the differences reconciled. 
Full and exact agreement between the YMP BDCFs as presented in CRWMS M&O (2000q), and 
the two other sets of calculations, reported in LaPlante and Poor (1997) and in CRWMS 
M&O (1998c) was not expected for all radionuclides. Whether or not the validation criteria were 
met was determined by the total weight of evidence presented by the alternative calculation 
results and not by any single BDCF comparison. 

The following sections compare the BDCF values for groundwater contamination (CRWMS 
M&O 2000a) directly with the corresponding results of the alternative calculations. Values that 
agreed within about a factor of three were considered to be entirely consistent and no additional 
effort was made to reconcile the differences. If the difference was greater than about a factor of 
three but less than a factor of ten, the values were considered to be somewhat consistent, but no 
effort was made to explain the difference in terms of the values of the inputs used. If the 
difference was greater than a factor of ten, alternative calculations were done to test the effect of 
different input parameter values and assumptions. 
II.2.4.2 Comparison of Groundwater BDCFs 
Table II-2 presents the BDCF values for groundwater (CRWMS M&O 2000a, Table 4) which 
are identified in the table heading as YMP BDCF, the corresponding values from CRWMS 
M&O (1998b), identified in the table heading as TSPA-VA, and the ratio of the two values, 
YMP: TSPA-VA. The two sets of radionuclides considered in this table are those that have the 
potential to reach the biosphere, based on the referenced documents. 
Table II-2 shows that the groundwater YMP BDCFs agree very well with the TSPA-VA values. 
The greatest observed difference is a factor of 1.44 and, for most radionuclides the agreement is 
much better. The TSPA-VA did not provide a BDCF value for one radionuclide (232U). This 
comparison strongly supports the finding that the validation criterion 2.1 was met. 
Table II-2. Comparison of YMP BDCFs with TSPA-VA BDCFs 
Radionuclide YMP BDCF1,2 TSPA-VA1,3 
YMP BDCF:TSPA-VA 
Ratio 
227Ac 1.81E+01 1.75E+01 1.03 
241Am 4.65E+00 4.50E+00 1.03 
243Am 4.64E+00 4.48E+00 1.04 
14C 4.06E-03 2.81E-03 1.44 
129I 3.61E-01 4.79E-01 0.75 
237Np 6.76E+00 6.57E+00 1.03 
238Pu 4.11E+00 3.97E+00 1.04 
239Pu 4.57E+00 4.41E+00 1.04 
240Pu 4.56E+00 4.41E+00 1.03 
99Tc 4.02E-03 3.14E-03 1.28 
229Th 4.59E+00 4.45E+00 1.03 
232U 1.71E+00 4 4 
233U 3.77E-01 3.65E-01 1.03 
234U 3.70E-01 3.58E-01 1.03 
236U 3.51E-01 3.40E-01 1.03 
238U 3.39E-01 3.28E-01 1.03 
NOTE: 1All values in units of mrem/y per pCi/l 
2CRWMS M&O 2000a 
3CRWMS M&O 1998c 
4BDCF Value for this radionuclide not included in TSPA-VA document (CRWMS M&O 1998c). 
Table II-3 presents the BDCF values for groundwater (CRWMS M&O 2000a, Table 4) which 
are identified in the table heading as YMP BDCF, the corresponding values from LaPlante and 
ANL-MGR-MD-000009 REV 01 II-14 December 2000 

Poor (1997), and the ratio of the two values, YMP: LaPlante and Poor ratio. The two sets of 
radionuclides considered in this table are those that have the potential to reach the biosphere, 
based on the referenced documents. 
Table II-3 shows that except for the plutonium isotopes, the groundwater YMP BDCF values 
agree with the values in LaPlante and Poor (1997) within a factor of about eight or less. The 
difference in the plutonium BDCF was examined to determine which input values might be 
responsible. The input pathway parameter values provided in LaPlante and Poor (1997) do not 
provide an obvious reason for the difference. The relevant parameter values used in the two 
analyses are not significantly different. 
Table II-3. Comparison of YMP BDCFs with LaPlante and Poor BDCFs 
Radionuclide YMP BDCF1,2 LaPlante and Poor 1,3 
YMP BDCF: LaPlante 
and Poor Ratio 
227Ac 1.81E+01 3.1E+01 0.58 
241Am 4.65E+00 7.9E+00 0.59 
243Am 4.64E+00 4 4 
14C 4.06E-03 1.9E-02 0.21 
129I 3.61E-01 3.1E+00 0.12 
237Np 6.76E+00 1.3E+01 0.52 
238Pu 4.11E+00 4 4 
239Pu 4.57E+00 1.1E-01 42 
240Pu 4.56E+00 1.1E-01 42 
99Tc 4.02E-03 8.4E-03 0.48 
229Th 4.59E+00 8.1E+00 0.57 
232U 1.71E+00 2.4E-01 7.1 
233U 3.77E-01 6.1E-02 6.2 
234U 3.70E-01 6.1E-02 6.1 
236U 3.51E-01 5.7E-02 6.2 
238U 3.39E-01 7.2E-02 4.7 
NOTE: 1All values in units of mrem/y per pCi/l 
2CRWMS M&O 2000a 
3LaPlante and Poor 1997 
4BDCF Value for this radionuclide not included in LaPlante and Poor (1997) 
Because the difference between the plutonium BDCF could not be understood from differences 
in the input parameters, the pathway contributions to the plutonium BDCF was examined to see 
if additional confidence in the YMP BDCF value could be gained. The Non-Disruptive Event 
Biosphere Dose Conversion Factor Sensitivity Analysis (CRWMS M&O 2000o) showed that 
more than half (61%) of the BDCF value was due to consumption of drinking water. The BDCF 
values were calculated using a fixed groundwater consumption rate of 752.8 l/y (CRWMS 
M&O 2000a). By applying the highest Plutonium-239 ingestion dose conversion factor (9.56E-7 
Sv/Bq or 3.54E-3 mrem/pCi) from Federal Guidance Report 11 (Eckerman et al. 1988) to that 
groundwater consumption rate, a BDCF of 2.66 mrem/yr per pCi/l was calculated. Because 
groundwater consumption contributes 61% of the BDCF value, a BDCF value of about 5 mrem/y 
per pCi/l can be inferred. This value is very close to the YMP BDCF value for Plutonium-239 
and suggests that the LaPlante and Poor (1997) results may have been calculated using lower 

values of dose per unit intake (dose conversion factors) for plutonium isotopes. LaPlante and 
Poor (1997) do not specify the dose per unit intake values used in their calculation. 
Conclusion: Comparison of the YMP groundwater BDCF (CRWMS M&O 2000a) with the 
corresponding TSPA-VA values (CRWMS M&O 1998c) strongly supports a finding that 
validation criterion 2.1 was met. The comparison of the YMP groundwater BDCF (CRWMS 
M&O 2000a) and the LaPlante and Poor (1997) values shows fair agreement as discussed. 
Based on the weight of evidence presented by these comparisons, it was concluded that 
validation criterion 2.1 was met. 
II.2.4.3 Independent Review of the Biosphere Model 
The independent reviewer selected was Mr. Bruce Napier of Pacific Northwest National 
Laboratory, principal architect of the GENII computer code. Mr. Napier is a nationally known 
expert in environmental dose assessment. In addition to developing GENII and collaborating in 
the creation of GENII-S, he has directed or participated in several other major environmental 
dose modeling efforts. He is currently in the process of completing the next generation of 
stochastic environmental exposure, dose, and risk computer codes for radionuclides for the 
U.S. Environmental Protection Agency (EPA). His experience and qualifications include: 
 
Technical Integrator and Chief Scientist for the Hanford Environmental Dose 
Reconstruction project. 
 
Principal investigator on the U.S./Russia Joint Coordinating Committee on Radiation 
Effects Research Projects on reconstruction of dose to the public around the Russian 
Mayak (Chelyabinsk-65) nuclear materials production site in Siberia. 
 
U. S. Chair of the U.S./Belarus and U.S./Ukraine Bi-National Advisory Committees on 
Chernobyl Studies for the U.S. National Cancer Institute. 
 
Consultant to the International Atomic Energy Agency (IAEA) and a participant in the 
IAEAs Cooperative Research Program on Biosphere Modeling and Assessment 
(BIOMASS). 
 
Member of EPA Science Advisory Board. 
 
Member of National Council on Radiation Protection and Measurements (NCRP) 
Scientific Committee 64 on Radionuclides in the Environment, Task Group 7 on 
Contaminated Soil as a Source of Radioactive Exposure. 
The review was conducted by Mr. Napier in February-March of 2000 (Napier 2000) using the 
most recent final and draft documents that describe the characteristics of the YMP biosphere and 
the associated receptor of interest. Those references and his findings with regard to the adequacy 
of the model are documented in a letter report (Napier 2000). Based on the information provided 
to him, he stated that, with minor exceptions: 

 The critical group consists of a farming community with members consuming locally-
produced food as a substantial part of their diet. This combination is reasonable, 
appropriate for the surroundings, and justifiable. The pathways considered in the 
biosphere model and the manner in which they are applied is consistent with current 
environmental conditions in the Amargosa Valley and with the FEP of interest. 
Criterion 3.1 was judged to be met. 
 
The logic and analysis methods used to select values for the GENII-S input parameters 
for the resident farmer scenario are sound. Criterion 3.2 was judged to be met. 
 
The references and data sources cited by the YMP analysts are current and credible. 
The parameters selected are well-described and traceable. Criterion 3.3 was judged to 
be met. 
 
The approach to selecting values and ranges for the input parameters is sound. The 
documentation is complete and relatively easily followed. The values and ranges of the 
GENII-S input parameters used to develop BDCF are reasonable for the environmental 
conditions implicit in the biosphere conceptual model. Criterion 3.4 was judged to be 
met. 
Mr. Napier concluded that the conceptual model of the biosphere, as laid out in the 
documents reviewed, is reasonable and in keeping with both the draft regulatory requirements 
and the actual physical setting. The biosphere conceptual model is clear, appropriate, and well 
documented. The mean or central values of the BDCF estimated are reasonable and 
appropriate. In addition to the above conclusion, Mr. Napier offered a number of suggestions 
and insights regarding stochastic environmental dose modeling and specific biosphere model 
parameters (Napier 2000). 
The results of the independent review indicate that the model is appropriate and adequate for the 
intended use. 
II.3 BIOSPHERE MODEL VALIDATION UPDATE 
As shown in previous sections, the Biosphere model was validated. However with the revision 
of the related AMRs, the validation needs to be revisited to ensure that the validation is still 
valid. AP-3.10Q, Analyses and Models, does not provide any direction as to when a validated 
model is no longer valid. To ensure that the validation is still sufficient, the revised YMP 
BDCFs were compared with the results of other modeling efforts to ensure that the criteria 2.1 
and 2.2 are still being satisfied. 
II.3.1 Software Qualification 
The GENII-S code was qualified and has not been changed so the original qualification is still 
valid. The original criteria 1.1 and 1.2 are still being met. 

II.3.2 Comparison of the Revised YMP BDCFs with Other Modeling Efforts 
When comparing the groundwater BDCFs with the TSPA-VA BDCFs for groundwater (Table II4), 
except for Carbon-14 the BDCFs were within a factor of three and agree very well with the 
TSPA-VA values. The Carbon-14 BDCF is higher because an incorrect leaching factor was 
found to have been used in earlier calculation of the Carbon-14 BDCF. This comparison 
supports the finding that the validation criteria 2.1 continues to be met. 
Table II-4. Comparison of Revised YMP BDCFs with TSPA-VA BDCFs 
Radionuclide Revised YMP BDCF1,2 TSPA-VA1,3 
YMP BDCF:TSPA-VA 
Ratio 
227Ac 1.54E+01 1.75E+01 0.88 
241Am 3.96E+00 4.50E+00 0.88 
243Am 3.95E+00 4.48E+00 0.88 
14C 5.19E-02 2.81E-03 18 
129I 3.10E-01 4.79E-01 0.65 
237Np 5.76E+00 6.57E+00 0.88 
238Pu 3.50E+00 3.97E+00 0.88 
239Pu 3.88E+00 4.41E+00 0.88 
240Pu 3.88E+00 4.41E+00 0.88 
99Tc 3.34E-03 3.14E-03 1.1 
229Th 4.01E+00 4.45E+00 0.90 
232U 1.45E+00 4 4 
233U 3.19E-01 3.65E-01 0.87 
234U 3.13E-01 3.58E-01 0.87 
236U 2.97E-01 3.40E-01 0.87 
238U 2.86E-01 3.28E-01 0.87 
NOTE: 1All values in units of mrem/y per pCi/L 
2Table 8 of this document 
3CRWMS M&O 1998 
4BDCF Value for this radionuclide not included in TSPA-VA document (CRWMS M&O 1998). 
Table II-4 shows that the revised groundwater YMP BDCF agree very well with the TSPA-VA 
values. The greatest observed difference is a factor of 18 for Carbon-14. For the other 
radionuclides the agreement is very good. The TSPA-VA did not provide a BDCF value for one 
radionuclide (Uranium-232). This comparison strongly supports the finding that the validation 
criterion 2.1 is still being met. 
Table II-5 presents the Revised YMP BDCF values for groundwater from Table 8 of this 
document which are identified in the table heading as YMP BDCF, the corresponding values 
from LaPlante and Poor (1997), and the ratio of the two values, YMP: LaPlante and Poor ratio. 
The two sets of radionuclides considered in this table are those that have the potential to reach 
the biosphere, based on the referenced documents. 
Table II-5 shows that except for the plutonium isotopes, the groundwater YMP BDCF values 
agree with the values in LaPlante and Poor (1997) within a factor of about six or less. The 
difference in the plutonium BDCF was examined previously as discussed in Section II.2.4.2 to 
determine which input values might be responsible. This earlier discussion is still applicable to 

the current ratio input pathway parameter values. The relevant parameter values used in the two 
analyses are not significantly different. 
Table II-5. Comparison of Revised YMP BDCFs with LaPlante and Poor BDCFs 
Radionuclide Revised YMP BDCF1,2 LaPlante and Poor 1,3 
YMP BDCF: LaPlante 
and Poor Ratio 
227Ac 1.54E+01 3.1E+01 0.50 
241Am 3.96E+00 7.9E+00 0.50 
243Am 3.95E+00 4 4 
14C 5.19E-02 1.9E-02 2.7 
129I 3.10E-01 3.1E+00 0.10 
237Np 5.76E+00 1.3E+01 0.44 
238Pu 3.50E+00 4 4 
239Pu 3.88E+00 1.1E-01 35 
240Pu 3.88E+00 1.1E-01 35 
99Tc 3.34E-03 8.4E-03 0.40 
229Th 4.01E+00 8.1E+00 0.50 
232U 1.45E+00 2.4E-01 6.0 
233U 3.19E-01 6.1E-02 5.2 
234U 3.13E-01 6.1E-02 5.1 
236U 2.97E-01 5.7E-02 5.2 
238U 2.86E-01 7.2E-02 4.0 
NOTE 12Table 8 of this document 
All values in units of mrem/y per pCi/L 
3LaPlante and Poor 1997
4BDCF Value for this radionuclide not included in LaPlante and Poor (1997).
In the original validation the difference between the plutonium BDCF could not be understood 
from differences in the input parameters. The pathway contributions to the plutonium BDCF 
were examined to see if additional confidence in the YMP BDCF value could be gained (see 
Section II.2.4.2). This prior discussion is still valid. 
Conclusion: Comparison of the revised YMP groundwater BDCF with the corresponding 
TSPA-VA values (CRWMS M&O 1998c) strongly supports a finding that validation criterion 
2.1 continues to be met. The comparison of the revised YMP groundwater BDCF and the 
LaPlante and Poor (1997) values shows fair agreement as discussed. Based on the weight of 
evidence presented by these comparisons, it is concluded that validation criterion 2.1 continues 
to be met. 
The model validation holds for the evolved climate because the ratio of the evolved to current 
climate BDCFs ranged from 0.90 to 0.99 (see Table 13 of this document) for the 1st irrigation 
period. This difference is insignificant and therefore the analysis and its concludions applicable 
to the revised current climate BDCFs also applies to the evolved climate BDCFs. 
II.3.4 Independent Reviewer 
The independent reviewer, Bruce Napier, concluded that all criteria, 3-1 through 3-4, had been 
met in the original model validation. For the validation update the independent reviewer was not 

contacted since primarily only the input parameters had changed and the comparison of the 
revised BDCFs with both the TSPA-VA (CRWMS M&O 1998c) and LaPlante and Poor (1997) 
data are good as shown in Tables II-4 and II-5. The other items included in the independent 
review have had minimum changes. In addition, the data being used for generating the BDCFs is 
qualified data. 

ATTACHMENT III 
INGESTION EXPOSURE PARAMETERS 
FOR NOMINAL CASE AND EVALUATION OF CLIMATE CHANGE 

ATTACHMENT III 
INGESTION EXPOSURE PARAMETERS 
FOR NOMINAL CASE AND EVALUATION OF CLIMATE CHANGE 
The data set (RIB item) Ingestion Exposure Parameter Values (MO0002RIB00068.000) contains 
values for thirteen ingestion exposure pathway parameters including: irrigation water source, 
drinking water treatment, crop interception fraction, water contaminated fraction, irrigation water 
contamination fraction, irrigation time, irrigation rate, aquatic food consideration, food yield, 
grow time, holdup time, storage time, dietary fraction. These parameters were developed for the 
current climate conditions for the Yucca Mountain (Amargosa Valley). They are necessary to 
calculate radionuclide-specific BDCFs. Interim guidance requires evaluation of natural climate 
change in the region surrounding the Yucca Mountain site (Dyer 1999, Section 114(k)). 
Therefore, a new set of selected ingestion exposure parameters was developed to address a 
cooler/wetter weather condition, using Spokane, Washington as an analogous site. 
In the process, several inconsistencies were found in the AMR Identification of Ingestion 
Exposure Parameters REV 00 (CRWMS M&O 2000h) which was used to develop RIB item 
MO0002RIB00068.000. They are as follows: 
1. 
The food groupings were incorrect (CRWMS M&O 2000h, Table 2). Crops such as 
tomatoes, cucumbers and corn were categorized as leafy vegetables, which is 
inconsistent with the 1997 Biosphere Food Consumption Survey (DOE 1997) and 
other references, such as Till and Meyer (1983, p. 5-50). 
2. 
Corn for grain was treated similarly to sweet corn, which has a shorter growing time 
(CRWMS M&O 2000h, Table 1). 
3. 
Two harvests each year for corn were considered, with planting dates conflicting with 
growing times (CRWMS M&O 2000h, Table 1). 
4. 
Some crop yields were developed using different crop types than those used to develop 
other parameters. For example, irrigation rate for fresh feed was based on alfalfa, 
while crop yield for fresh feed was based on alfalfa and other hays. 
5. 
Only one-harvest yield was considered for some vegetables, which can potentially be 
harvested twice (CRWMS M&O 2000h, Table 1 and Table 6). 
To develop a new set of data for Spokane, WA, the current data set had to be corrected for the 
above inconsistencies. The new data set could then be based on the same crop grouping. 
III.1 DATA SET FOR AMARGOSA VALLEY 
III.1.1 Selection of Input Data Set 
To correct the inconsistencies listed above new values for growing time, irrigation time, 
irrigation rate and crop yield were developed based on the data listed in CRWMS M&O (2000h, 
Table 2). These data, as shown in Table III-1 below, include growing time, irrigation rate, 

annual crop evapotranspiration, precipitation and annual irrigation rate for the crop of interest. 
Crops, such as tomatoes, cucumbers, peppers, snap beans, and peas, previously categorized as 
leafy vegetables, were not used in this analysis. Corn data were also corrected by using one 
harvest per year. Growing time of 140 days for grain corn was selected for warm desert climate 
as suggested by Doorenbos and Pruitt (1977, p.42). The four growing stage periods (number of 
days needed for crop development) for corn were 25/40/45/30. Planting time was kept at the 
same date as that for spring corn used in the AMR. Irrigation rate was then calculated using the 
methods provided in Tables 3 through 5 of the AMR (CRWMS M&O 2000h). The new data for 
grain corn are also listed in Table III-1 
Table III-1. Summary of Selected Ingestion Exposure Parameters 
Crop Crop Type 
Growing 
Time 
(day) 
IrrigationTime 
(mo/yr) 
Annual 
ET 
(in/yr) 
Precipitation 
(in/yr) 
Annual 
Irrigation(in/yr) 
Annual 
Crop Yield 1 
(kg/m2) 
Spinach Leafy vegetable 45 3.0 22.9 0.8 28 4.4 
Lettuce Leafy vegetable 68 4.5 38.5 1.2 43 4.8 
Carrots Root vegetable 70 4.6 46.7 1.1 52 7.8-9.8 
Potatoes Root vegetable 98 3.2 42.1 0.7 47 4.1 
Melons Fruit 88 2.9 39.8 0.4 45 1.9 
Grapes Fruit 184 6.0 N/A N/A 30 1.6-2.3 
Wheat Grain 244 8.0 53.3 3.4 56 0.3-0.7 
Corn Stored feed for 
poultry and eggs 
140 4.6 70.1 0.8 75 0.6-0.8 
Alfalfa Fresh feed for 46-135 12 92.7 4.0 95 1.0-1.2 
beef and milk 
Source: CRWMS M&O 2000h.
Note: 1 The values for spinach, lettuce, and carrots yields were doubled because of the double harvest.
The value of the crop yield for a specific crop was used in the calculation only if the same crop 
was also considered for growing time, irrigation time and irrigation rate. Most of the crop yields 
were taken from (CRWMS M&O 2000h, Table 6), but their values were doubled for two-harvest 
crops (spinach, lettuce, and carrots) to become annual crop yields. Other crop yields were taken 
from (CRWMS M&O 2000h, Section 6.7). Since fresh feed was determined to be the only feed 
for beef cattle and milk cows, as recommended in (CRWMS M&O 2000h, Section 6.11), alfalfa 
was selected as a representative crop, and other hay was not included. All crop yields are shown 
in Table III-1. 
III.1.2 Ingestion Exposure Parameters for Amargosa Valley 
Based on data listed in Table III-1, the four ingestion exposure parameters for eight crop 
categories were developed as listed in Table III-2. Due to limited data available, if there were 
two values available for one parameter within the same crop type, a uniform distribution was 
assumed between the minimum and maximum values. Otherwise, a fixed value was used for the 
parameter. The only exception was growing time for forage. The same distribution and values 
were used as the ones developed previously (CRWMS M&O 2000h, Section 6.8). 

Table III-2. Summary of Developed Ingestion Parameter Values for Amargosa Valley 
Parameter Distribution 
Reasonable 
Estimate 
Minimum 
Value 
Maximum 
Value 
Growing Time (day): 
Leafy vegetables uniform 57 45 68 
Root vegetables uniform 84 70 98 
Fruit uniform 136 88 184 
Grain Fixed 244 244 244 
Fresh feed for beef Triangular 47 46 135 
Stored feed for poultry Fixed 140 140 140 
Fresh feed for milk Triangular 47 46 135 
Stored feed for eggs Fixed 140 140 140 
Irrigation Time (mo/yr): 
Leafy vegetables uniform 3.8 3.0 4.5 
Root vegetables uniform 3.9 3.2 4.6 
Fruit uniform 4.5 2.9 6.0 
Grain Fixed 8.0 8.0 8.0 
Fresh feed for beef Fixed 12 12 12 
Stored feed for poultry Fixed 4.6 4.6 4.6 
Fresh feed for milk Fixed 12 12 12 
Stored feed for eggs Fixed 4.6 4.6 4.6 
Irrigation Rate (in/yr) 
Leafy vegetables uniform 36 28 43 
Root vegetables uniform 50 47 52 
Fruit uniform 38 30 45 
Grain Fixed 56 56 56 
Fresh feed for beef Fixed 95 95 95 
Stored feed for poultry Fixed 75 75 75 
Fresh feed for milk Fixed 95 95 95 
Stored feed for eggs Fixed 75 75 75 
Crop Yield (kg/m2) 
Leafy vegetables uniform 4.6 4.4 4.8 
Root vegetables uniform 7.0 4.1 9.8 
Fruit uniform 2.0 1.6 2.3 
Grain uniform 0.5 0.3 0.7 
Fresh feed for beef uniform 1.1 1.0 1.2 
Stored feed for poultry uniform 0.7 0.6 0.8 
Fresh feed for milk uniform 1.1 1.0 1.2 
Stored feed for eggs uniform 0.7 0.6 0.8 
NOTE: Parameter values developed from Table III-1 

III.2 DATA SET FOR SPOKANE, WA 
To evaluate the BDCFs change due to climate evolution, the impact of the cooler/wetter weather 
on all parameters was examined. It was determined that many ingestion exposure parameters, 
such as crop growing time, irrigation time, and irrigation rate, were affected by climate change. 
III.2.1 Crop Growing and Irrigation Requirements 
Crop irrigation requirements differ between the Spokane climate and the Amargosa Valley 
climate. In Amargosa Valley, irrigation is needed year-around, although irrigation rate is lower 
during the winter season. In Spokane, no irrigation is necessary between November and 
February, because the precipitation exceeds evapotranspiration (see Table III-4 for the 
precipitation data and Table III-5 for the evapotranspiration calculation). In addition, there are 
few crops growing during that period of time in Spokane. 
The same crops as those used for Amargosa Valley were evaluated for Spokane. The planting 
period and growing time are the important data, which are used to determine the irrigation time 
and irrigation rate. Two references were used as a source of information on planting periods and 
growing times: Usual Planting and Harvesting Dates for Washington State (Washington 
Agricultural Statistics Service 1999), and the Sunset Western Garden Book (Hogan 1988). 
Based on the Washington Agricultural Statistics Service, it was found that the growing times are 
longer in Spokane than those in Amargosa Valley for crops such as potatoes, corn for grain, and 
winter wheat. Therefore, growing times for other crops, including spinach, lettuce, carrots, and 
melons were based on the longest number of days to harvest listed in the Sunset Western Garden 
Book. 
Both grapes and alfalfa are multi-year crops. The selected growing time for grapes was 184 
days, conservatively assumed to be the same value as that used for Amargosa Valley (CRWMS 
M&O 2000h). Growing time for alfalfa was selected as 71 days, based on three harvests during 
the harvest period from May 15 to October 1 (Washington Agricultural Statistics Service 1999), 
assuming alfalfa starts to grow in early March in Spokane. The growing time and planting 
period for Spokane are shown in Table III-3. 
If the planting period was longer than the growing time, two harvests were assumed. Crops in 
this category include spinach, lettuce, and carrots, similar to the ones selected for Amargosa 
Valley (CRWMS M&O 2000h). Although there could be only one harvest each year for a 
particular crop in Spokane, it was nevertheless assumed that another crop could be planted on the 
same land during the available planting season in order to maximize farmland use. The use of 
two harvests in one year for the same crop simplifies calculation of growing period and other 
irrigation parameters. 
Planting date was assumed to fall in the middle of the planting period for each crop, which is the 
same approach as the one that was used previously (CRWMS M&O 2000h, Table 1). The 
irrigation period was then calculated based on the planting date(s) and the growing time. 
However, all irrigation periods were assumed to end on October 31, under the assumption of no 
irrigation between November and February, as discussed above. The planting dates, irrigation 
periods, and irrigation times for Spokane are listed in Table III-3. 

Table III-3. Crop Planting, Growing and Irrigation Schedule for Spokane 
Crop 
Growing 
Timea 
(day) 
PlantingPerioda 
PlantingDateb 
Irrigationperiodb 
IrrigationTimec 
(mo/yr) Notes 
Spinach 50 Apr.  May 4/6 and 5/26 4/6  7/14 3.3 
Lettuce 95 3/24  8/15 4/1 and 7/5 4/1  10/7 6.2 
Carrots 75 4/15  7/30 5/1 and 7/15 5/1  9/27 4.9 
Potatoes 168 3/15  5/15 4/16 4/16  9/30 5.5 
Melons 95 May  June 6/1 6/1 9/3 3.1 
Grapes 184 Perennial crop  5/1  10/31 6.0 
Wheat, winter 312 (191) 9/1  10/30 10/1 10/1-10/31 6.2 No irrigation Nov.-Feb. 
3/1  8/7 
Corn for grain 179 4/15  6/5 5/11 5/11 10/31 5.7 No irrigation after Oct. 
Alfalfa 71 Perennial crop  3/1  10/1 7.0 
NOTES: aWashington Agricultural Statistics Service (1992) or Hogan (1988) 
bDate and period were developed based on the growing time and plant period in the same table 
c Values were converted from the irrigation period in the same table 
Crop yields for Spokane were assumed to be the same as the ones for the Amargosa Valley, 
except for alfalfa, because it has only three harvests, and a shorter growing season in Spokane. 
An alfalfa yield of 0.5 kg/m2 for Spokane was taken from Washington Agricultural Statistics 
Service (1992). In addition, this reference confirmed that some crop yields for Spokane were 
very close to the ones for the Amargosa Valley. For example, the yields of winter wheat, 
potatoes, and lettuce for Spokane were 0.5 kg/m2, 4.6 kg/m2 and 2.2 kg/m2 (Washington 
Agricultural Statistics Service 1992) as compared with 0.3-0.7 kg/m2, 4.1 kg/m2 and 2.2 kg/m2, 
respectively, for the Amargosa Valley (CRWMS M&O 2000h, Section 6.7). 
III.2.2 Calculation of Reference Crop Evapotranspiration 
To calculate the monthly average reference crop evapotranspiration, meteorological data are 
needed. The meteorological station selected was WBAN 24157 in Spokane, WA. The 30 year 
average meteorological data, including temperature, possible sunshine, relative humidity, 
precipitation, and wind speed were taken from the Western Regional Climate Center (1997). 
These monthly data are listed in Table III-4. 
The temperature was converted to degrees Celsius (C). Average monthly relative humidity was 
calculated using the four provided values at six hour intervals through the day. Average monthly 
wind speed was converted to meters per second (m/s). 

Table III-4. Average Monthly Meteorological Data for Spokane, WA 
Month 
AverageMonthlyTemperature 
(C) 
AverageMonthlySunshine 
n/N 
AverageMonthly Relative 
Humidity(%) 
AverageMonthlyPrecipitation 
(in/mo) 
Average 
Monthly Wind 
Speed(m/s) 
January -2.7 0.28 83 1.98 3.9 
February 0.7 0.41 79 1.49 4.1 
March 3.7 0.55 69 1.49 4.3 
April 7.7 0.61 61 1.18 4.5 
May 12.2 0.65 58 1.41 4.1 
June 16.7 0.67 55 1.26 4.2 
July 20.4 0.80 45 0.67 3.8 
August 20.2 0.78 45 0.72 3.7 
September 14.9 0.72 53 0.73 3.7 
October 8.5 0.55 66 0.99 3.7 
November 1.7 0.29 83 2.15 3.9 
December -2.3 0.23 86 2.42 3.9 
Source: Western Regional Climate Center (1997) 
Reference crop evapotranspiration was calculated using the radiation method suggested by 
Doorenbos and Pruitt (1977, Section 1.2). The Jensen-Haise method, previously used to estimate 
reference crop evapotranspiration (CRWMS M&O 2000h), was considered less applicable to 
semiarid and subhumid climates (Martin et al. 1991, p. 334). The radiation method, selected 
from among the four methods shown in Doorenbos and Pruitt (1977, Section 1), was determined 
to be the best method based on the available meteorological data. The calculation formula can 
be expressed as: 
ET0 = c (W Rs) = c ET (Eq. III-1) 
where: 
ET0 - reference crop evapotranspiration in mm/day for the periods considered. 
c - adjustment factor which depends on mean relative humidity and wind speed 
from Figure 2 in the Doorenbos and Pruitt (1977, p.14). Three different 
relative humidity cases with wind speed of 2  5 m/s were used in the 
calculation: 
(II) RHmean = 40-55%: ET0 = 1.04 ET - 0.4 (Eq. III-2) 
(III) RHmean = 55-70%: ET0 = 1.00 ET - 0.6 (Eq. III-3) 
(IV) RHmean > 70%: ET0 = 0.87 ET - 0.4 (Eq. III-4) 
W - weighting factor which depends on temperature and altitude. Values of W can 
be obtained from Table 4 in the Doorenbos and Pruitt (1977, p.13) based on 
measured temperature and 718 m altitude for Spokane. 

Rs - solar radiation in units equivalent to evapotranspiration in mm/day, which can 
be calculated as: 
Rs = (0.25 + 0.5 n/N ) Ra (Eq. III-5) 
where: 
Ra - extra terrestrial radiation (mm/day) as a function of latitude, values of Ra can 
be obtained from Table III-3 in the Doorenbos and Pruitt (1977, p.12) based on 
48northern latitude for Spokane. 
n/N - ratio of actual measured bright sunshine to maximum possible sunshine, values 
of n/N can be obtained from average monthly possible sunshine measured in 
Spokane. 
ET - evapotranspiration value calculated from W Rs 
Table III-5. Reference Crop Evapotranspiration Calculation Using Radiation Method 
Month 
Ra Values for 
48Latitude a 
(mm/day) 
W Values for 
718 m and 
MonthlyTemperaturea 
Calculated 
ET Valuesb 
(mm/day) 
Conversion 
Formula a 
Calculated 
Daily ET0 
c 
(mm/day) 
AverageMonthly ET0 
d 
(in/mo) 
January 4.3 0.40 0.67 Eq. III-4 0.18 0.22 
February 6.6 0.44 1.32 Eq. III-4 0.75 0.83 
March 9.8 0.48 2.47 Eq. III-3 1.87 2.28 
April 13.0 0.54 3.90 Eq. III-3 3.30 3.89 
May 15.9 0.61 5.58 Eq. III-3 4.98 6.07 
June 17.2 0.66 6.64 Eq. III-3 6.04 7.13 
July 16.5 0.71 7.61 Eq. III-2 7.52 9.18 
August 14.3 0.71 6.50 Eq. III-2 6.36 7.76 
September 11.2 0.64 4.37 Eq. III-2 4.15 4.90 
October 7.8 0.55 2.25 Eq. III-3 1.65 2.02 
November 5.0 0.45 0.89 Eq. III-4 0.37 0.44 
December 3.7 0.40 0.54 Eq. III-4 0.07 0.09 
NOTES: aDoorenbos and Pruitt (1977) 
bCalculated using Eq. 1 (W x Rs formula ) 
c Calculated using Equations III-1, III-2, III-3, and III-4 
dUnits of mm per day were converted to units of inch per month by multiplying the value in mm per day by 
the number of days in a month and dividing it by 25.4. 
III.2.3 Calculation of Crop Irrigation Rate 
Annual evapotranspiration value (ET) for each crop can be calculated based on its growing time 
by using reference crop evapotranspiration (ET0). The calculation method was proposed by 
Doorenbos and Pruitt (1977, Section 2): 
ET =.kc Ti ETdi (Eq. III-6)
i 
i 

where: 
ET - crop annual evapotranspiration (inch/year) 
i - index of crop growing stage (i = 1, 2, 3, 4), or index of month for alfalfa and 
grapes 
kci - crop coefficient for the growing stage 
T - length of the growing stage (day/year) 
ETdi - daily average reference crop evapotranspiration for the growing stage 
(inch/day). 
Although the crop coefficients and stage lengths for all crops under consideration can be 
obtained from Doorenbos and Pruitt (1977, Section 2), the average recurrence interval of 
irrigation or significant rain for Spokane was unknown. This parameter is needed to determine 
crop coefficient for the first growing stage. Therefore, similar to the method used in the AMR 
(CRWMS M&O 2000h), one (1) was used for the crop coefficient for the first stage for all crops 
(except grapes and alfalfa, which will be explained later) as a conservative approach. It was 
recognized that once one (1) was used for crop coefficient of the first stage, the difference in 
crop coefficient values between stages was not very large (0.8 ~ 1.2). To simplify the calculation 
of crop annual evapotranspiration, it was assumed that the crop coefficient was equal to 1.1 for 
every stage of the plant growth. Therefore, Equation III-6 can be simplified as: 
ET =. kci  Ti  ETdi = 1.1 . Fj  TE 0 j (Eq. III-7) 
ij 
where: 
j - index of crop growing month 
Fj - fraction of the month that the crop is in its growing period (month/year) 
ET0i - average monthly evapotranspiration for reference crop (inch/month), calculated 
in Table III-5. 
The calculation of crop ET value consisted of summing reference crop ET0 values for the 
growing period, and then multiplying by 1.1. The crop coefficient for alfalfa was selected at a 
peak value of 1.05, as a conservative approach, at the humid and moderate wind conditions for 
Spokane (Doorenbos and Pruitt 1977, p.45). The crop coefficient for grapes varies on a monthly 
basis, instead of distinct growing stages. A humid moderate wind condition was selected with 
crop coefficients of 0.5/0.65/0.75/0.8/0.75/0.65 for May through October, respectively. 
Similar to crop annual evapotranspiration, crop annual precipitation was calculated by summing 
monthly precipitation during the crop growing period. 
AP =. Fj  PPj (Eq. III-8) 
j 
where: 
AP - annual precipitation for a crop (inch/year) 
PPj - monthly precipitation measured by local weather station (inch/month) 

The calculated annual evapotranspiration and precipitation values are listed in Table III-6. The 
annual irrigation rate was then calculated based on 6 inches per year of deep percolation, which 
is the same approach as used previously (CRWMS M&O 2000h, Section 6.5), that is: 
IR = ET  AP + DP (Eq. III-9) 
where: 
IR - crop annual irrigation rate (inch/year)
DP - deep percolation, or amount of overwatering (6 inch/year)
The calculated annual irrigation rates are shown in Table III-6. 
Table III-6. Calculated Annual Crop Evapotranspiration and Precipitation Values, and Annual Irrigation 
Rates 
Crop Crop Type 
Growing 
Timea 
(day) 
IrrigationTimea 
(mo/y) 
Annual 
ETb 
(in/yr) 
Precipitationc 
(in/yr) 
Annual 
Irrigationd 
(in/yr) 
Annual 
Crop Yielde 
(kg/m2) 
Spinach Leafy vegetable 50 3.3 22.7 4.0 25 4.4 
Lettuce Leafy vegetable 95 6.2 43.3 6.2 43 4.8 
Carrots Root vegetable 75 4.9 38.0 4.7 39 7.8-9.8 
Potatoes Root vegetable 168 5.5 40.7 5.4 41 4.1 
Melons Fruit 95 3.1 27.0 2.7 30 1.9 
Grapes Fruit 184 6.0 25.7 5.8 26 1.6-2.3 
Wheat Grain 191 6.2 35.6 7.2 34 0.3-0.7 
Corn Stored feed for 
poultry and eggs 
179 5.7 38.6 5.3 39 0.6-0.8 
Alfalfa Fresh feed for 71 7.0 43.3 7.5 42 0.5 
beef and milk 
NOTES: aGrowing time and irrigation time were taken from Table III-3 
bCrop annual ET were calculated using Eq. III-7 
c Crop annual precipitation rates were calculated using Eq. III-8 
dCrop annual irrigation rates were calculated using Eq. III-9 
eAnnual crop yields were taken from Table III-1, except alfalfa, see discussion in Section III.2.1 
III.2.4 Ingestion Exposure Parameters for Spokane 
Based on Table III-6, the four ingestion exposure parameters for eight crop categories were 
calculated for Spokane, WA, as listed in Table 7; by using the method similar to the one used to 
develop ingestion exposure parameters for Amargosa Valley, listed in Table III-2. 

Table III-7. Summary of Four Ingestion Parameters for Spokane, WA 
Parameter Distribution 
Reasonable 
Estimate 
Minimum 
Value 
Maximum 
Value 
Growing Time (day): 
Leafy vegetables Uniform 73 50 95 
Root vegetables Uniform 122 75 168 
Fruit Uniform 140 95 184 
Grain Fixed 191 191 191 
Fresh feed for beef Fixed 71 71 71 
Stored feed for poultry Fixed 179 179 179 
Fresh feed for milk Fixed 71 71 71 
Stored feed for eggs Fixed 179 179 179 
Irrigation Time (mo/yr): 
Leafy vegetables Uniform 4.8 3.3 6.2 
Root vegetables Uniform 5.2 4.9 5.5 
Fruit Uniform 4.6 3.1 6.0 
Grain Fixed 6.2 6.2 6.2 
Fresh feed for beef Fixed 7.0 7.0 7.0 
Stored feed for poultry Fixed 5.7 5.7 5.7 
Fresh feed for milk Fixed 7.0 7.0 7.0 
Stored feed for eggs Fixed 5.7 5.7 5.7 
Irrigation Rate (in/yr) 
Leafy vegetables Uniform 34 25 43 
Root vegetables Uniform 40 39 41 
Fruit Uniform 28 26 30 
Grain Fixed 34 34 34 
Fresh feed for beef Fixed 42 42 42 
Stored feed for poultry Fixed 39 39 39 
Fresh feed for milk Fixed 42 42 42 
Stored feed for eggs Fixed 39 39 39 
Crop Yield (kg/m2) 
Leafy vegetables Uniform 4.6 4.4 4.8 
Root vegetables Uniform 7.0 4.1 9.8 
Fruit Uniform 2.0 1.6 2.3 
Grain Uniform 0.5 0.3 0.7 
Fresh feed for beef Fixed 0.5 0.5 0.5 
Stored feed for poultry Uniform 0.7 0.6 0.8 
Fresh feed for milk Fixed 0.5 0.5 0.5 
Stored feed for eggs Uniform 0.7 0.6 0.8 
Source: Parameter values developed from Table III-6. 

III.3 DISCUSSION 
Comparing Table III-7 with Table III-2, the following conclusions can be drawn: 
 
Crop growing times in Spokane are longer than in Amargosa Valley due to cooler 
weather, except for winter wheat, which was assumed not to grow during the winter. 
 
Most crop irrigation times in Spokane are longer than in Amargosa Valley, except for 
alfalfa and wheat, which do not grow during the winter. 
 
Irrigation rates in Spokane are 20  60% lower than in Amargosa Valley, except for 
leafy vegetables, which have comparable irrigation rates. This is because the growing 
requirements of leafy vegetables, especially as related to temperature, are similar for 
both areas, which results from different planting seasons for leafy vegetables. 
 
Crop yields in both areas are the same, except for alfalfa due to a shorter growing season 
in Spokane. 

INTENTIONALLY LEFT BLANK

ATTACHMENT IV
FILES GENERATED IN MODEL RUNS

ATTACHMENT IV 
FILES GENERATED IN MODEL RUNS 
The following file name notation was used: 
File name: ABCDEFGH.* 
A N  nominal performance 
B C  current climate 
W  evolved (wetter and cooler) climate 
C C  average member of the critical group 
D 1-6  irrigation periods 
EFGH notation for radionuclide name 
e.g. CX14 for C-14
I129 for I-129
PU21 for Pu-241
* file extension (.inp and .flg are extensions for input files, while .out, .vec, .pti, and .txt are 
extensions for output files) 
The model input and output files can be found in the Model Warehouse Database, DTN: 
MO0010MWDPBD09.006. 
IV.1 GENII-S FILES FOR THE CURRENT CLIMATE 
NCC1AC27 FLG 712 09-14-00 5:13p 
NCC1AC27 INP 16,334 09-14-00 5:13p 
NCC1AC27 OUT 17,636 09-14-00 5:14p 
NCC1AC27 PTI 9,018 09-14-00 5:14p 
NCC1AC27 TXT 150,273 09-14-00 5:15p 
NCC1AC27 VEC 55,364 09-14-00 5:14p 
NCC1AM21 FLG 712 09-14-00 5:15p 
NCC1AM21 INP 16,334 09-14-00 5:16p 
NCC1AM21 OUT 17,764 09-14-00 5:16p 
NCC1AM21 PTI 9,018 09-14-00 5:16p 
NCC1AM21 TXT 150,273 09-14-00 5:17p 
NCC1AM21 VEC 55,364 09-14-00 5:16p 
NCC1AM23 FLG 712 09-14-00 5:17p 
NCC1AM23 INP 16,334 09-14-00 5:17p 
NCC1AM23 OUT 17,563 09-14-00 5:18p 
NCC1AM23 PTI 9,018 09-14-00 5:18p 
NCC1AM23 TXT 150,273 09-14-00 5:18p 
NCC1AM23 VEC 55,364 09-14-00 5:18p 

NCC1CS17 FLG 712 09-14-00 5:18p 
NCC1CS17 INP 16,334 09-14-00 5:19p 
NCC1CS17 OUT 16,079 09-14-00 5:19p 
NCC1CS17 PTI 9,018 09-14-00 5:19p 
NCC1CS17 TXT 150,273 09-14-00 5:20p 
NCC1CS17 VEC 55,364 09-14-00 5:19p 
NCC1CX14 FLG 710 09-14-00 5:20p 
NCC1CX14 INP 16,334 09-14-00 5:20p 
NCC1CX14 OUT 16,079 09-14-00 5:20p 
NCC1CX14 PTI 9,018 09-14-00 5:20p 
NCC1CX14 TXT 150,273 09-14-00 5:21p 
NCC1CX14 VEC 55,364 09-14-00 5:20p 
NCC1I129 FLG 712 09-14-00 5:21p 
NCC1I129 INP 16,334 09-14-00 5:21p 
NCC1I129 OUT 16,079 09-14-00 5:22p 
NCC1I129 PTI 9,018 09-14-00 5:22p 
NCC1I129 TXT 150,273 09-14-00 5:22p 
NCC1I129 VEC 55,364 09-14-00 5:22p 
NCC1NI63 FLG 711 09-14-00 5:22p 
NCC1NI63 INP 16,334 09-14-00 5:23p 
NCC1NI63 OUT 16,265 09-14-00 5:23p 
NCC1NI63 PTI 9,018 09-14-00 5:23p 
NCC1NI63 TXT 150,273 09-14-00 5:24p 
NCC1NI63 VEC 55,364 09-14-00 5:23p 
NCC1NP27 FLG 712 09-14-00 5:24p 
NCC1NP27 INP 16,334 09-14-00 5:24p 
NCC1NP27 OUT 17,618 09-14-00 5:24p 
NCC1NP27 PTI 9,018 09-14-00 5:24p 
NCC1NP27 TXT 150,273 09-14-00 5:25p 
NCC1NP27 VEC 55,364 09-14-00 5:24p 
NCC1PA21 FLG 712 09-14-00 5:26p 
NCC1PA21 INP 16,334 09-14-00 5:26p 
NCC1PA21 OUT 17,837 09-14-00 5:26p 
NCC1PA21 PTI 9,018 09-14-00 5:26p 
NCC1PA21 TXT 150,273 09-14-00 5:27p 
NCC1PA21 VEC 55,364 09-14-00 5:26p 
NCC1PB20 FLG 712 09-14-00 5:27p 
NCC1PB20 INP 16,334 09-14-00 5:27p 
NCC1PB20 OUT 17,819 09-14-00 5:28p 
NCC1PB20 PTI 9,018 09-14-00 5:28p 
NCC1PB20 TXT 150,273 09-14-00 5:28p 
NCC1PB20 VEC 55,364 09-14-00 5:28p 
NCC1PU20 FLG 712 09-14-00 5:29p 
NCC1PU20 INP 16,334 09-14-00 5:29p 
NCC1PU20 OUT 17,618 09-14-00 5:29p 
NCC1PU20 PTI 9,018 09-14-00 5:29p 
NCC1PU20 TXT 150,273 09-14-00 5:30p 
NCC1PU20 VEC 55,364 09-14-00 5:29p 
NCC1PU22 FLG 712 09-14-00 5:30p 
NCC1PU22 INP 16,334 09-14-00 5:30p 
NCC1PU22 OUT 17,636 09-14-00 5:30p 
NCC1PU22 PTI 9,018 09-14-00 5:30p 
NCC1PU22 TXT 150,273 09-14-00 5:31p 
NCC1PU22 VEC 55,364 09-14-00 5:30p 
NCC1PU28 FLG 712 09-14-00 5:31p 
NCC1PU28 INP 16,334 09-14-00 5:31p 
NCC1PU28 OUT 17,618 09-14-00 5:32p 

NCC1PU28 PTI 9,018 09-14-00 5:32p 
NCC1PU28 TXT 150,273 09-14-00 5:32p 
NCC1PU28 VEC 55,364 09-14-00 5:32p 
NCC1PU29 FLG 712 09-14-00 5:33p 
NCC1PU29 INP 16,334 09-14-00 5:33p 
NCC1PU29 OUT 16,265 09-14-00 5:33p 
NCC1PU29 PTI 9,018 09-14-00 5:33p 
NCC1PU29 TXT 150,273 09-14-00 5:33p 
NCC1PU29 VEC 55,364 09-14-00 5:33p 
NCC1RA26 FLG 712 09-14-00 5:34p 
NCC1RA26 INP 16,334 09-14-00 5:34p 
NCC1RA26 OUT 18,024 09-14-00 5:34p 
NCC1RA26 PTI 9,018 09-14-00 5:34p 
NCC1RA26 TXT 150,273 09-14-00 5:35p 
NCC1RA26 VEC 55,364 09-14-00 5:34p 
NCC1SR90 FLG 711 09-14-00 5:35p 
NCC1SR90 INP 16,334 09-14-00 5:35p 
NCC1SR90 OUT 17,490 09-14-00 5:35p 
NCC1SR90 PTI 9,018 09-14-00 5:35p 
NCC1SR90 TXT 150,273 09-14-00 5:36p 
NCC1SR90 VEC 55,364 09-14-00 5:35p 
NCC1TC99 FLG 711 09-14-00 5:36p 
NCC1TC99 INP 16,334 09-14-00 5:36p 
NCC1TC99 OUT 16,265 09-14-00 5:37p 
NCC1TC99 PTI 9,018 09-14-00 5:37p 
NCC1TC99 TXT 150,273 09-14-00 5:37p 
NCC1TC99 VEC 55,364 09-14-00 5:37p 
NCC1TH20 FLG 712 09-14-00 5:39p 
NCC1TH20 INP 16,334 09-14-00 5:39p 
NCC1TH20 OUT 18,097 09-14-00 5:40p 
NCC1TH20 PTI 9,018 09-14-00 5:40p 
NCC1TH20 TXT 150,273 09-14-00 5:41p 
NCC1TH20 VEC 55,364 09-14-00 5:40p 
NCC1TH29 FLG 712 09-14-00 5:41p 
NCC1TH29 INP 16,334 09-14-00 5:41p 
NCC1TH29 OUT 17,563 09-14-00 5:41p 
NCC1TH29 PTI 9,018 09-14-00 5:41p 
NCC1TH29 TXT 150,273 09-14-00 5:42p 
NCC1TH29 VEC 55,364 09-14-00 5:41p 
NCC1U232 FLG 712 09-14-00 5:42p 
NCC1U232 INP 16,334 09-14-00 5:43p 
NCC1U232 OUT 18,056 09-14-00 5:43p 
NCC1U232 PTI 9,018 09-14-00 5:43p 
NCC1U232 TXT 150,273 09-14-00 5:44p 
NCC1U232 VEC 55,364 09-14-00 5:43p 
NCC1U233 FLG 712 09-14-00 5:44p 
NCC1U233 INP 16,334 09-14-00 5:44p 
NCC1U233 OUT 17,764 09-14-00 5:44p 
NCC1U233 PTI 9,018 09-14-00 5:44p 
NCC1U233 TXT 150,273 09-14-00 5:45p 
NCC1U233 VEC 55,364 09-14-00 5:44p 
NCC1U234 FLG 712 09-14-00 5:45p 
NCC1U234 INP 16,334 09-14-00 5:45p 
NCC1U234 OUT 16,265 09-14-00 5:46p 
NCC1U234 PTI 9,018 09-14-00 5:46p 
NCC1U234 TXT 150,273 09-14-00 5:46p 
NCC1U234 VEC 55,364 09-14-00 5:46p 

NCC1U236 FLG 712 09-14-00 5:46p 
NCC1U236 INP 16,334 09-14-00 5:47p 
NCC1U236 OUT 16,265 09-14-00 5:47p 
NCC1U236 PTI 9,018 09-14-00 5:47p 
NCC1U236 TXT 150,273 09-14-00 5:47p 
NCC1U236 VEC 55,364 09-14-00 5:47p 
NCC1U238 FLG 712 09-14-00 5:47p 
NCC1U238 INP 16,334 09-14-00 5:48p 
NCC1U238 OUT 17,691 09-14-00 5:48p 
NCC1U238 PTI 9,018 09-14-00 5:48p 
NCC1U238 TXT 150,273 09-14-00 5:49p 
NCC1U238 VEC 55,364 09-14-00 5:48p 
NCC2AM21 FLG 712 09-19-00 12:16p 
NCC2AM21 INP 16,335 09-19-00 12:17p 
NCC2AM21 OUT 17,848 09-19-00 12:17p 
NCC2AM21 PTI 9,018 09-19-00 12:17p 
NCC2AM21 TXT 150,273 09-19-00 12:17p 
NCC2AM21 VEC 55,364 09-19-00 12:17p 
NCC2AM23 FLG 712 09-14-00 7:32p 
NCC2AM23 INP 16,336 09-14-00 7:32p 
NCC2AM23 OUT 17,647 09-14-00 7:33p 
NCC2AM23 PTI 9,018 09-14-00 7:33p 
NCC2AM23 TXT 150,273 09-14-00 7:34p 
NCC2AM23 VEC 55,364 09-14-00 7:33p 
NCC2CS17 FLG 712 09-14-00 8:39p 
NCC2CS17 INP 16,334 09-14-00 8:39p 
NCC2CS17 OUT 16,163 09-14-00 7:34p 
NCC2CS17 PTI 9,018 09-14-00 7:34p 
NCC2CS17 TXT 150,273 09-14-00 8:39p 
NCC2CS17 VEC 55,364 09-14-00 7:34p 
NCC2NI63 FLG 711 09-19-00 2:00p 
NCC2NI63 INP 16,335 09-19-00 2:00p 
NCC2NI63 OUT 16,349 09-19-00 10:47a 
NCC2NI63 PTI 9,018 09-19-00 10:47a 
NCC2NI63 TXT 150,273 09-19-00 10:48a 
NCC2NI63 VEC 55,364 09-19-00 10:47a 
NCC2PA21 FLG 712 09-14-00 7:35p 
NCC2PA21 INP 16,336 09-14-00 7:35p 
NCC2PA21 OUT 17,921 09-14-00 7:36p 
NCC2PA21 PTI 9,018 09-14-00 7:36p 
NCC2PA21 TXT 150,273 09-14-00 7:36p 
NCC2PA21 VEC 55,364 09-14-00 7:36p 
NCC2PU20 FLG 712 09-19-00 11:50a 
NCC2PU20 INP 16,336 09-19-00 11:51a 
NCC2PU20 OUT 17,702 09-19-00 11:51a 
NCC2PU20 PTI 9,018 09-19-00 11:51a 
NCC2PU20 TXT 150,273 09-19-00 11:51a 
NCC2PU20 VEC 55,364 09-19-00 11:51a 
NCC2PU22 FLG 712 09-19-00 12:01p 
NCC2PU22 INP 16,336 09-19-00 12:01p 
NCC2PU22 OUT 17,720 09-19-00 12:02p 
NCC2PU22 PTI 9,018 09-19-00 12:02p 
NCC2PU22 TXT 150,273 09-19-00 12:02p 
NCC2PU22 VEC 55,364 09-19-00 12:02p 
NCC2PU29 FLG 712 09-19-00 11:39a 
NCC2PU29 INP 16,336 09-19-00 11:39a 
NCC2PU29 OUT 16,349 09-19-00 11:40a 

NCC2PU29 PTI 9,018 09-19-00 11:40a 
NCC2PU29 TXT 150,273 09-19-00 11:40a 
NCC2PU29 VEC 55,364 09-19-00 11:40a 
NCC2RA26 FLG 712 09-14-00 8:39p 
NCC2RA26 INP 16,336 09-14-00 8:39p 
NCC2RA26 OUT 18,108 09-14-00 7:37p 
NCC2RA26 PTI 9,018 09-14-00 7:37p 
NCC2RA26 TXT 150,273 09-14-00 8:40p 
NCC2RA26 VEC 55,364 09-14-00 7:37p 
NCC2SR90 FLG 711 09-19-00 2:03p 
NCC2SR90 INP 16,334 09-19-00 2:03p 
NCC2SR90 OUT 17,574 09-19-00 11:00a 
NCC2SR90 PTI 9,018 09-19-00 11:00a 
NCC2SR90 TXT 150,273 09-19-00 11:00a 
NCC2SR90 VEC 55,364 09-19-00 11:00a 
NCC2TH20 FLG 712 09-14-00 7:38p 
NCC2TH20 INP 16,336 09-14-00 7:38p 
NCC2TH20 OUT 18,181 09-14-00 7:41p 
NCC2TH20 PTI 9,018 09-14-00 7:41p 
NCC2TH20 TXT 150,273 09-14-00 7:41p 
NCC2TH20 VEC 55,364 09-14-00 7:41p 
NCC2TH29 FLG 712 09-14-00 7:41p 
NCC2TH29 INP 16,336 09-14-00 7:41p 
NCC2TH29 OUT 17,647 09-14-00 7:42p 
NCC2TH29 PTI 9,018 09-14-00 7:42p 
NCC2TH29 TXT 150,273 09-14-00 7:43p 
NCC2TH29 VEC 55,364 09-14-00 7:42p 
NCC2U232 FLG 712 09-19-00 11:12a 
NCC2U232 INP 16,334 09-19-00 11:12a 
NCC2U232 OUT 18,140 09-19-00 11:13a 
NCC2U232 PTI 9,018 09-19-00 11:13a 
NCC2U232 TXT 150,273 09-19-00 11:13a 
NCC2U232 VEC 55,364 09-19-00 11:13a 
NCC2U238 FLG 712 09-19-00 11:25a 
NCC2U238 INP 16,334 09-19-00 11:25a 
NCC2U238 OUT 17,775 09-19-00 11:26a 
NCC2U238 PTI 9,018 09-19-00 11:26a 
NCC2U238 TXT 150,273 09-19-00 11:26a 
NCC2U238 VEC 55,364 09-19-00 11:26a 
NCC3AM21 FLG 712 09-19-00 12:18p 
NCC3AM21 INP 16,336 09-19-00 12:19p 
NCC3AM21 OUT 17,848 09-19-00 12:19p 
NCC3AM21 PTI 9,018 09-19-00 12:19p 
NCC3AM21 TXT 150,273 09-19-00 12:20p 
NCC3AM21 VEC 55,364 09-19-00 12:19p 
NCC3AM23 FLG 712 09-14-00 7:43p 
NCC3AM23 INP 16,336 09-14-00 7:43p 
NCC3AM23 OUT 17,647 09-14-00 7:44p 
NCC3AM23 PTI 9,018 09-14-00 7:44p 
NCC3AM23 TXT 150,273 09-14-00 7:45p 
NCC3AM23 VEC 55,364 09-14-00 7:44p 
NCC3CS17 FLG 712 09-14-00 7:45p 
NCC3CS17 INP 16,335 09-14-00 7:45p 
NCC3CS17 OUT 16,163 09-14-00 7:46p 
NCC3CS17 PTI 9,018 09-14-00 7:46p 
NCC3CS17 TXT 150,273 09-14-00 7:47p 
NCC3CS17 VEC 55,364 09-14-00 7:46p 

NCC3NI63 FLG 711 09-19-00 2:01p 
NCC3NI63 INP 16,335 09-19-00 2:01p 
NCC3NI63 OUT 16,349 09-19-00 10:50a 
NCC3NI63 PTI 9,018 09-19-00 10:50a 
NCC3NI63 TXT 150,273 09-19-00 10:50a 
NCC3NI63 VEC 55,364 09-19-00 10:50a 
NCC3PA21 FLG 712 09-14-00 7:47p 
NCC3PA21 INP 16,336 09-14-00 7:47p 
NCC3PA21 OUT 17,921 09-14-00 7:48p 
NCC3PA21 PTI 9,018 09-14-00 7:48p 
NCC3PA21 TXT 150,273 09-14-00 7:48p 
NCC3PA21 VEC 55,364 09-14-00 7:48p 
NCC3PU20 FLG 712 09-19-00 11:52a 
NCC3PU20 INP 16,336 09-19-00 11:53a 
NCC3PU20 OUT 17,702 09-19-00 11:53a 
NCC3PU20 PTI 9,018 09-19-00 11:53a 
NCC3PU20 TXT 150,273 09-19-00 11:54a 
NCC3PU20 VEC 55,364 09-19-00 11:53a 
NCC3PU22 FLG 712 09-19-00 12:03p 
NCC3PU22 INP 16,336 09-19-00 12:03p 
NCC3PU22 OUT 17,720 09-19-00 12:04p 
NCC3PU22 PTI 9,018 09-19-00 12:04p 
NCC3PU22 TXT 150,273 09-19-00 12:05p 
NCC3PU22 VEC 55,364 09-19-00 12:04p 
NCC3PU29 FLG 712 09-19-00 11:42a 
NCC3PU29 INP 16,336 09-19-00 11:42a 
NCC3PU29 OUT 16,349 09-19-00 11:43a 
NCC3PU29 PTI 9,018 09-19-00 11:43a 
NCC3PU29 TXT 150,273 09-19-00 11:43a 
NCC3PU29 VEC 55,364 09-19-00 11:43a 
NCC3RA26 FLG 712 09-14-00 7:49p 
NCC3RA26 INP 16,336 09-14-00 7:49p 
NCC3RA26 OUT 18,108 09-14-00 7:50p 
NCC3RA26 PTI 9,018 09-14-00 7:50p 
NCC3RA26 TXT 150,273 09-14-00 7:50p 
NCC3RA26 VEC 55,364 09-14-00 7:50p 
NCC3SR90 FLG 711 09-19-00 2:04p 
NCC3SR90 INP 16,334 09-19-00 2:04p 
NCC3SR90 OUT 17,574 09-19-00 11:02a 
NCC3SR90 PTI 9,018 09-19-00 11:02a 
NCC3SR90 TXT 150,273 09-19-00 11:03a 
NCC3SR90 VEC 55,364 09-19-00 11:02a 
NCC3TH20 FLG 712 09-14-00 7:50p 
NCC3TH20 INP 16,337 09-14-00 7:51p 
NCC3TH20 OUT 18,181 09-14-00 7:55p 
NCC3TH20 PTI 9,018 09-14-00 7:55p 
NCC3TH20 TXT 150,273 09-14-00 7:56p 
NCC3TH20 VEC 55,364 09-14-00 7:55p 
NCC3TH29 FLG 712 09-14-00 7:56p 
NCC3TH29 INP 16,337 09-14-00 7:56p 
NCC3TH29 OUT 17,647 09-14-00 7:58p 
NCC3TH29 PTI 9,018 09-14-00 7:58p 
NCC3TH29 TXT 150,273 09-14-00 7:58p 
NCC3TH29 VEC 55,364 09-14-00 7:58p 
NCC3U232 FLG 712 09-19-00 11:15a 
NCC3U232 INP 16,335 09-19-00 11:15a 
NCC3U232 OUT 18,140 09-19-00 11:16a 

NCC3U232 PTI 9,018 09-19-00 11:16a 
NCC3U232 TXT 150,273 09-19-00 11:17a 
NCC3U232 VEC 55,364 09-19-00 11:16a 
NCC3U238 FLG 712 09-19-00 11:28a 
NCC3U238 INP 16,335 09-19-00 11:31a 
NCC3U238 OUT 17,775 09-19-00 11:31a 
NCC3U238 PTI 9,018 09-19-00 11:31a 
NCC3U238 TXT 150,273 09-19-00 11:32a 
NCC3U238 VEC 55,364 09-19-00 11:31a 
NCC4AM21 FLG 712 09-19-00 12:21p 
NCC4AM21 INP 16,336 09-19-00 12:21p 
NCC4AM21 OUT 17,848 09-19-00 12:22p 
NCC4AM21 PTI 9,018 09-19-00 12:22p 
NCC4AM21 TXT 150,273 09-19-00 12:22p 
NCC4AM21 VEC 55,364 09-19-00 12:22p 
NCC4AM23 FLG 712 09-14-00 7:58p 
NCC4AM23 INP 16,337 09-14-00 7:59p 
NCC4AM23 OUT 17,647 09-14-00 8:00p 
NCC4AM23 PTI 9,018 09-14-00 8:00p 
NCC4AM23 TXT 150,273 09-14-00 8:01p 
NCC4AM23 VEC 55,364 09-14-00 8:00p 
NCC4CS17 FLG 712 09-14-00 8:01p 
NCC4CS17 INP 16,335 09-14-00 8:01p 
NCC4CS17 OUT 16,163 09-14-00 8:01p 
NCC4CS17 PTI 9,018 09-14-00 8:01p 
NCC4CS17 TXT 150,273 09-14-00 8:02p 
NCC4CS17 VEC 55,364 09-14-00 8:01p 
NCC4NI63 FLG 711 09-19-00 2:01p 
NCC4NI63 INP 16,335 09-19-00 2:01p 
NCC4NI63 OUT 16,349 09-19-00 10:52a 
NCC4NI63 PTI 9,018 09-19-00 10:52a 
NCC4NI63 TXT 150,273 09-19-00 10:52a 
NCC4NI63 VEC 55,364 09-19-00 10:52a 
NCC4PA21 FLG 712 09-14-00 8:02p 
NCC4PA21 INP 16,336 09-14-00 8:02p 
NCC4PA21 OUT 17,921 09-14-00 8:04p 
NCC4PA21 PTI 9,018 09-14-00 8:04p 
NCC4PA21 TXT 150,273 09-14-00 8:04p 
NCC4PA21 VEC 55,364 09-14-00 8:04p 
NCC4PU20 FLG 712 09-19-00 11:55a 
NCC4PU20 INP 16,336 09-19-00 11:55a 
NCC4PU20 OUT 17,702 09-19-00 11:56a 
NCC4PU20 PTI 9,018 09-19-00 11:56a 
NCC4PU20 TXT 150,273 09-19-00 11:56a 
NCC4PU20 VEC 55,364 09-19-00 11:56a 
NCC4PU22 FLG 712 09-19-00 12:06p 
NCC4PU22 INP 16,336 09-19-00 12:06p 
NCC4PU22 OUT 17,720 09-19-00 12:07p 
NCC4PU22 PTI 9,018 09-19-00 12:07p 
NCC4PU22 TXT 150,273 09-19-00 12:07p 
NCC4PU22 VEC 55,364 09-19-00 12:07p 
NCC4PU29 FLG 712 09-19-00 11:44a 
NCC4PU29 INP 16,336 09-19-00 11:44a 
NCC4PU29 OUT 16,349 09-19-00 11:45a 
NCC4PU29 PTI 9,018 09-19-00 11:45a 
NCC4PU29 TXT 150,273 09-19-00 11:45a 
NCC4PU29 VEC 55,364 09-19-00 11:45a 

NCC4RA26 FLG 712 09-14-00 8:04p 
NCC4RA26 INP 16,336 09-14-00 8:04p 
NCC4RA26 OUT 18,108 09-14-00 8:05p 
NCC4RA26 PTI 9,018 09-14-00 8:05p 
NCC4RA26 TXT 150,273 09-14-00 8:06p 
NCC4RA26 VEC 55,364 09-14-00 8:05p 
NCC4SR90 FLG 711 09-19-00 11:04a 
NCC4SR90 INP 16,335 09-19-00 11:04a 
NCC4SR90 OUT 17,574 09-19-00 11:05a 
NCC4SR90 PTI 9,018 09-19-00 11:05a 
NCC4SR90 TXT 150,273 09-19-00 11:05a 
NCC4SR90 VEC 55,364 09-19-00 11:05a 
NCC4TH20 FLG 712 09-14-00 8:09p 
NCC4TH20 INP 16,337 09-14-00 8:09p 
NCC4TH20 OUT 18,181 09-14-00 8:17p 
NCC4TH20 PTI 9,018 09-14-00 8:17p 
NCC4TH20 TXT 150,273 09-14-00 8:18p 
NCC4TH20 VEC 55,364 09-14-00 8:17p 
NCC4TH29 FLG 712 09-14-00 8:40p 
NCC4TH29 INP 16,337 09-14-00 8:40p 
NCC4TH29 OUT 17,647 09-14-00 8:09p 
NCC4TH29 PTI 9,018 09-14-00 8:09p 
NCC4TH29 TXT 150,273 09-14-00 8:40p 
NCC4TH29 VEC 55,364 09-14-00 8:09p 
NCC4U232 FLG 712 09-19-00 11:18a 
NCC4U232 INP 16,335 09-19-00 11:19a 
NCC4U232 OUT 18,140 09-19-00 11:19a 
NCC4U232 PTI 9,018 09-19-00 11:19a 
NCC4U232 TXT 150,273 09-19-00 11:20a 
NCC4U232 VEC 55,364 09-19-00 11:19a 
NCC4U238 FLG 712 09-19-00 11:33a 
NCC4U238 INP 16,335 09-19-00 11:33a 
NCC4U238 OUT 17,775 09-19-00 11:33a 
NCC4U238 PTI 9,018 09-19-00 11:33a 
NCC4U238 TXT 150,273 09-19-00 11:34a 
NCC4U238 VEC 55,364 09-19-00 11:33a 
NCC5AM21 FLG 712 09-19-00 12:23p 
NCC5AM21 INP 16,336 09-19-00 12:23p 
NCC5AM21 OUT 17,848 09-19-00 12:25p 
NCC5AM21 PTI 9,018 09-19-00 12:25p 
NCC5AM21 TXT 150,273 09-19-00 12:25p 
NCC5AM21 VEC 55,364 09-19-00 12:25p 
NCC5AM23 FLG 712 09-14-00 8:18p 
NCC5AM23 INP 16,337 09-14-00 8:18p 
NCC5AM23 OUT 17,647 09-14-00 8:20p 
NCC5AM23 PTI 9,018 09-14-00 8:20p 
NCC5AM23 TXT 150,273 09-14-00 8:21p 
NCC5AM23 VEC 55,364 09-14-00 8:20p 
NCC5CS17 FLG 712 09-14-00 8:21p 
NCC5CS17 INP 16,335 09-14-00 8:21p 
NCC5CS17 OUT 16,163 09-14-00 8:21p 
NCC5CS17 PTI 9,018 09-14-00 8:21p 
NCC5CS17 TXT 150,273 09-14-00 8:22p 
NCC5CS17 VEC 55,364 09-14-00 8:21p 
NCC5NI63 FLG 711 09-19-00 2:02p 
NCC5NI63 INP 16,336 09-19-00 2:02p 
NCC5NI63 OUT 16,349 09-19-00 10:53a 

NCC5NI63 PTI 9,018 09-19-00 10:53a 
NCC5NI63 TXT 150,273 09-19-00 10:54a 
NCC5NI63 VEC 55,364 09-19-00 10:53a 
NCC5PA21 FLG 712 09-14-00 8:22p 
NCC5PA21 INP 16,336 09-14-00 8:22p 
NCC5PA21 OUT 17,921 09-14-00 8:24p 
NCC5PA21 PTI 9,018 09-14-00 8:24p 
NCC5PA21 TXT 150,273 09-14-00 8:25p 
NCC5PA21 VEC 55,364 09-14-00 8:24p 
NCC5PU20 FLG 712 09-19-00 11:57a 
NCC5PU20 INP 16,336 09-19-00 11:57a 
NCC5PU20 OUT 17,702 09-19-00 11:58a 
NCC5PU20 PTI 9,018 09-19-00 11:58a 
NCC5PU20 TXT 150,273 09-19-00 11:58a 
NCC5PU20 VEC 55,364 09-19-00 11:58a 
NCC5PU22 FLG 712 09-19-00 12:10p 
NCC5PU22 INP 16,336 09-19-00 12:10p 
NCC5PU22 OUT 17,720 09-19-00 12:11p 
NCC5PU22 PTI 9,018 09-19-00 12:11p 
NCC5PU22 TXT 150,273 09-19-00 12:14p 
NCC5PU22 VEC 55,364 09-19-00 12:11p 
NCC5PU29 FLG 712 09-19-00 11:47a 
NCC5PU29 INP 16,336 09-19-00 11:47a 
NCC5PU29 OUT 16,349 09-19-00 11:47a 
NCC5PU29 PTI 9,018 09-19-00 11:47a 
NCC5PU29 TXT 150,273 09-19-00 11:48a 
NCC5PU29 VEC 55,364 09-19-00 11:47a 
NCC5RA26 FLG 712 09-14-00 8:25p 
NCC5RA26 INP 16,336 09-14-00 8:25p 
NCC5RA26 OUT 18,108 09-14-00 8:26p 
NCC5RA26 PTI 9,018 09-14-00 8:26p 
NCC5RA26 TXT 150,273 09-14-00 8:27p 
NCC5RA26 VEC 55,364 09-14-00 8:26p 
NCC5SR90 FLG 711 09-19-00 11:06a 
NCC5SR90 INP 16,335 09-19-00 11:07a 
NCC5SR90 OUT 17,574 09-19-00 11:07a 
NCC5SR90 PTI 9,018 09-19-00 11:07a 
NCC5SR90 TXT 150,273 09-19-00 11:08a 
NCC5SR90 VEC 55,364 09-19-00 11:07a 
NCC5TH20 FLG 712 09-15-00 7:31a 
NCC5TH20 INP 16,337 09-15-00 7:31a 
NCC5TH20 OUT 18,181 09-14-00 9:43p 
NCC5TH20 PTI 9,018 09-14-00 9:43p 
NCC5TH20 TXT 150,273 09-15-00 7:31a 
NCC5TH20 VEC 55,364 09-14-00 9:43p 
NCC5TH29 FLG 712 09-14-00 8:27p 
NCC5TH29 INP 16,337 09-14-00 8:28p 
NCC5TH29 OUT 17,647 09-14-00 8:31p 
NCC5TH29 PTI 9,018 09-14-00 8:31p 
NCC5TH29 TXT 150,273 09-14-00 8:32p 
NCC5TH29 VEC 55,364 09-14-00 8:31p 
NCC5U232 FLG 712 09-19-00 11:21a 
NCC5U232 INP 16,335 09-19-00 11:21a 
NCC5U232 OUT 18,140 09-19-00 11:22a 
NCC5U232 PTI 9,018 09-19-00 11:22a 
NCC5U232 TXT 150,273 09-19-00 11:22a 
NCC5U232 VEC 55,364 09-19-00 11:22a 

NCC5U238 FLG 712 09-19-00 11:35a 
NCC5U238 INP 16,335 09-19-00 11:35a 
NCC5U238 OUT 17,775 09-19-00 11:36a 
NCC5U238 PTI 9,018 09-19-00 11:36a 
NCC5U238 TXT 150,273 09-19-00 11:36a 
NCC5U238 VEC 55,364 09-19-00 11:36a 
NCC6AC27 FLG 712 09-14-00 8:42p 
NCC6AC27 INP 16,335 09-14-00 8:43p 
NCC6AC27 OUT 17,720 09-14-00 8:43p 
NCC6AC27 PTI 9,018 09-14-00 8:43p 
NCC6AC27 TXT 150,273 09-14-00 8:44p 
NCC6AC27 VEC 55,364 09-14-00 8:43p 
NCC6AM21 FLG 712 09-14-00 8:44p 
NCC6AM21 INP 16,336 09-14-00 8:44p 
NCC6AM21 OUT 17,848 09-14-00 8:46p 
NCC6AM21 PTI 9,018 09-14-00 8:46p 
NCC6AM21 TXT 150,273 09-14-00 8:46p 
NCC6AM21 VEC 55,364 09-14-00 8:46p 
NCC6AM23 FLG 712 09-14-00 8:46p 
NCC6AM23 INP 16,337 09-14-00 8:47p 
NCC6AM23 OUT 17,647 09-14-00 8:50p 
NCC6AM23 PTI 9,018 09-14-00 8:50p 
NCC6AM23 TXT 150,273 09-14-00 8:51p 
NCC6AM23 VEC 55,364 09-14-00 8:50p 
NCC6CS17 FLG 712 09-14-00 8:51p 
NCC6CS17 INP 16,335 09-14-00 8:51p 
NCC6CS17 OUT 16,163 09-14-00 8:52p 
NCC6CS17 PTI 9,018 09-14-00 8:52p 
NCC6CS17 TXT 150,273 09-14-00 8:52p 
NCC6CS17 VEC 55,364 09-14-00 8:52p 
NCC6CX14 FLG 710 09-14-00 8:52p 
NCC6CX14 INP 16,335 09-14-00 8:52p 
NCC6CX14 OUT 16,163 09-14-00 8:53p 
NCC6CX14 PTI 9,018 09-14-00 8:53p 
NCC6CX14 TXT 150,273 09-14-00 8:53p 
NCC6CX14 VEC 55,364 09-14-00 8:53p 
NCC6I129 FLG 712 09-14-00 8:53p 
NCC6I129 INP 16,334 09-14-00 8:54p 
NCC6I129 OUT 16,163 09-14-00 8:54p 
NCC6I129 PTI 9,018 09-14-00 8:54p 
NCC6I129 TXT 150,273 09-14-00 8:54p 
NCC6I129 VEC 55,364 09-14-00 8:54p 
NCC6NI63 FLG 711 09-14-00 8:55p 
NCC6NI63 INP 16,336 09-14-00 8:55p 
NCC6NI63 OUT 16,349 09-14-00 8:55p 
NCC6NI63 PTI 9,018 09-14-00 8:55p 
NCC6NI63 TXT 150,273 09-14-00 8:56p 
NCC6NI63 VEC 55,364 09-14-00 8:55p 
NCC6NP27 FLG 712 09-14-00 8:56p 
NCC6NP27 INP 16,335 09-14-00 8:56p 
NCC6NP27 OUT 17,702 09-14-00 8:56p 
NCC6NP27 PTI 9,018 09-14-00 8:56p 
NCC6NP27 TXT 150,273 09-14-00 8:57p 
NCC6NP27 VEC 55,364 09-14-00 8:56p 
NCC6PA21 FLG 712 09-14-00 8:57p 
NCC6PA21 INP 16,337 09-14-00 8:57p 
NCC6PA21 OUT 17,921 09-14-00 9:00p 

NCC6PA21 PTI 9,018 09-14-00 9:00p 
NCC6PA21 TXT 150,273 09-14-00 9:01p 
NCC6PA21 VEC 55,364 09-14-00 9:00p 
NCC6PB20 FLG 712 09-14-00 9:01p 
NCC6PB20 INP 16,335 09-14-00 9:01p 
NCC6PB20 OUT 17,903 09-14-00 9:02p 
NCC6PB20 PTI 9,018 09-14-00 9:02p 
NCC6PB20 TXT 150,273 09-14-00 9:02p 
NCC6PB20 VEC 55,364 09-14-00 9:02p 
NCC6PU20 FLG 712 09-14-00 9:02p 
NCC6PU20 INP 16,337 09-14-00 9:03p 
NCC6PU20 OUT 17,702 09-14-00 9:03p 
NCC6PU20 PTI 9,018 09-14-00 9:03p 
NCC6PU20 TXT 150,273 09-14-00 9:04p 
NCC6PU20 VEC 55,364 09-14-00 9:03p 
NCC6PU22 FLG 712 09-14-00 9:04p 
NCC6PU22 INP 16,337 09-14-00 9:04p 
NCC6PU22 OUT 17,720 09-14-00 9:06p 
NCC6PU22 PTI 9,018 09-14-00 9:06p 
NCC6PU22 TXT 150,273 09-14-00 9:07p 
NCC6PU22 VEC 55,364 09-14-00 9:06p 
NCC6PU28 FLG 712 09-14-00 9:08p 
NCC6PU28 INP 16,336 09-14-00 9:08p 
NCC6PU28 OUT 17,702 09-14-00 9:08p 
NCC6PU28 PTI 9,018 09-14-00 9:08p 
NCC6PU28 TXT 150,273 09-14-00 9:09p 
NCC6PU28 VEC 55,364 09-14-00 9:08p 
NCC6PU29 FLG 712 09-14-00 9:09p 
NCC6PU29 INP 16,337 09-14-00 9:09p 
NCC6PU29 OUT 16,349 09-14-00 9:10p 
NCC6PU29 PTI 9,018 09-14-00 9:10p 
NCC6PU29 TXT 150,273 09-14-00 9:10p 
NCC6PU29 VEC 55,364 09-14-00 9:10p 
NCC6RA26 FLG 712 09-14-00 9:10p 
NCC6RA26 INP 16,337 09-14-00 9:11p 
NCC6RA26 OUT 18,108 09-14-00 9:12p 
NCC6RA26 PTI 9,018 09-14-00 9:12p 
NCC6RA26 TXT 150,273 09-14-00 9:13p 
NCC6RA26 VEC 55,364 09-14-00 9:12p 
NCC6SR90 FLG 711 09-14-00 9:14p 
NCC6SR90 INP 16,335 09-14-00 9:14p 
NCC6SR90 OUT 17,574 09-14-00 9:14p 
NCC6SR90 PTI 9,018 09-14-00 9:14p 
NCC6SR90 TXT 150,273 09-14-00 9:15p 
NCC6SR90 VEC 55,364 09-14-00 9:14p 
NCC6TC99 FLG 711 09-14-00 9:15p 
NCC6TC99 INP 16,334 09-14-00 9:15p 
NCC6TC99 OUT 16,349 09-14-00 9:15p 
NCC6TC99 PTI 9,018 09-14-00 9:15p 
NCC6TC99 TXT 150,273 09-14-00 9:16p 
NCC6TC99 VEC 55,364 09-14-00 9:15p 
NCC6TH20 FLG 712 09-15-00 7:31a 
NCC6TH20 INP 16,337 09-15-00 7:31a 
NCC6TH20 OUT 18,181 09-14-00 6:09p 
NCC6TH20 PTI 9,018 09-14-00 6:09p 
NCC6TH20 TXT 150,273 09-15-00 7:32a 
NCC6TH20 VEC 55,364 09-14-00 6:09p 

NCC6TH29 FLG 712 09-14-00 9:23p 
NCC6TH29 INP 16,337 09-14-00 9:23p 
NCC6TH29 OUT 17,647 09-14-00 9:29p 
NCC6TH29 PTI 9,018 09-14-00 9:29p 
NCC6TH29 TXT 150,273 09-14-00 9:29p 
NCC6TH29 VEC 55,364 09-14-00 9:29p 
NCC6U232 FLG 712 09-14-00 9:16p 
NCC6U232 INP 16,335 09-14-00 9:16p 
NCC6U232 OUT 18,140 09-14-00 9:17p 
NCC6U232 PTI 9,018 09-14-00 9:17p 
NCC6U232 TXT 150,273 09-14-00 9:18p 
NCC6U232 VEC 55,364 09-14-00 9:17p 
NCC6U233 FLG 712 09-14-00 9:18p 
NCC6U233 INP 16,335 09-14-00 9:18p 
NCC6U233 OUT 17,848 09-14-00 9:19p 
NCC6U233 PTI 9,018 09-14-00 9:19p 
NCC6U233 TXT 150,273 09-14-00 9:19p 
NCC6U233 VEC 55,364 09-14-00 9:19p 
NCC6U234 FLG 712 09-14-00 9:20p 
NCC6U234 INP 16,335 09-14-00 9:20p 
NCC6U234 OUT 16,349 09-14-00 9:20p 
NCC6U234 PTI 9,018 09-14-00 9:20p 
NCC6U234 TXT 150,273 09-14-00 9:20p 
NCC6U234 VEC 55,364 09-14-00 9:20p 
NCC6U236 FLG 712 09-14-00 9:21p 
NCC6U236 INP 16,335 09-14-00 9:21p 
NCC6U236 OUT 16,349 09-14-00 9:21p 
NCC6U236 PTI 9,018 09-14-00 9:21p 
NCC6U236 TXT 150,273 09-14-00 9:22p 
NCC6U236 VEC 55,364 09-14-00 9:21p 
NCC6U238 FLG 712 09-14-00 9:22p 
NCC6U238 INP 16,335 09-14-00 9:22p 
NCC6U238 OUT 17,775 09-14-00 9:23p 
NCC6U238 PTI 9,018 09-14-00 9:23p 
NCC6U238 TXT 150,273 09-14-00 9:23p 
NCC6U238 VEC 55,364 09-14-00 9:23p 
IV.2 GENII-S FILES FOR THE EVOLVED CLIMATE 
NWC1AC27 FLG 712 09-15-00 8:32a
NWC1AC27 INP 16,326 09-15-00 8:33a
NWC1AC27 OUT 17,636 09-15-00 8:33a
NWC1AC27 PTI 9,018 09-15-00 8:33a
NWC1AC27 TXT 140,841 09-15-00 8:34a
NWC1AC27 VEC 50,964 09-15-00 8:33a
NWC1AM21 FLG 712 09-15-00 8:34a
NWC1AM21 INP 16,326 09-15-00 8:34a
NWC1AM21 OUT 17,764 09-15-00 8:34a
NWC1AM21 PTI 9,018 09-15-00 8:34a
NWC1AM21 TXT 140,841 09-15-00 8:35a
NWC1AM21 VEC 50,964 09-15-00 8:34a
NWC1AM23 FLG 712 09-15-00 8:35a
NWC1AM23 INP 16,326 09-15-00 8:35a
NWC1AM23 OUT 17,563 09-15-00 8:35a
NWC1AM23 PTI 9,018 09-15-00 8:35a

NWC1AM23 TXT 140,841 09-15-00 8:36a 
NWC1AM23 VEC 50,964 09-15-00 8:35a 
NWC1CS17 FLG 712 09-15-00 8:36a 
NWC1CS17 INP 16,326 09-15-00 8:36a 
NWC1CS17 OUT 16,079 09-15-00 8:37a 
NWC1CS17 PTI 9,018 09-15-00 8:37a 
NWC1CS17 TXT 140,841 09-15-00 8:40a 
NWC1CS17 VEC 50,964 09-15-00 8:37a 
NWC1CX14 FLG 710 09-15-00 8:40a 
NWC1CX14 INP 16,326 09-15-00 8:40a 
NWC1CX14 OUT 16,079 09-15-00 8:41a 
NWC1CX14 PTI 9,018 09-15-00 8:41a 
NWC1CX14 TXT 140,841 09-15-00 8:43a 
NWC1CX14 VEC 50,964 09-15-00 8:41a 
NWC1I129 FLG 712 09-15-00 8:44a 
NWC1I129 INP 16,326 09-15-00 8:45a 
NWC1I129 OUT 16,079 09-15-00 8:45a 
NWC1I129 PTI 9,018 09-15-00 8:45a 
NWC1I129 TXT 140,841 09-15-00 8:46a 
NWC1I129 VEC 50,964 09-15-00 8:45a 
NWC1NI63 FLG 711 09-15-00 8:46a 
NWC1NI63 INP 16,326 09-15-00 8:46a 
NWC1NI63 OUT 16,265 09-15-00 8:46a 
NWC1NI63 PTI 9,018 09-15-00 8:46a 
NWC1NI63 TXT 140,841 09-15-00 8:47a 
NWC1NI63 VEC 50,964 09-15-00 8:46a 
NWC1NP27 FLG 712 09-15-00 8:47a 
NWC1NP27 INP 16,326 09-15-00 8:47a 
NWC1NP27 OUT 17,618 09-15-00 8:47a 
NWC1NP27 PTI 9,018 09-15-00 8:47a 
NWC1NP27 TXT 140,841 09-15-00 8:48a 
NWC1NP27 VEC 50,964 09-15-00 8:47a 
NWC1PA21 FLG 712 09-15-00 8:48a 
NWC1PA21 INP 16,326 09-15-00 8:48a 
NWC1PA21 OUT 17,837 09-15-00 8:49a 
NWC1PA21 PTI 9,018 09-15-00 8:49a 
NWC1PA21 TXT 140,841 09-15-00 8:49a 
NWC1PA21 VEC 50,964 09-15-00 8:49a 
NWC1PB20 FLG 712 09-15-00 8:49a 
NWC1PB20 INP 16,326 09-15-00 8:49a 
NWC1PB20 OUT 17,819 09-15-00 8:50a 
NWC1PB20 PTI 9,018 09-15-00 8:50a 
NWC1PB20 TXT 140,841 09-15-00 8:50a 
NWC1PB20 VEC 50,964 09-15-00 8:50a 
NWC1PU20 FLG 712 09-15-00 8:50a 
NWC1PU20 INP 16,326 09-15-00 8:51a 
NWC1PU20 OUT 17,618 09-15-00 8:51a 
NWC1PU20 PTI 9,018 09-15-00 8:51a 
NWC1PU20 TXT 140,841 09-15-00 8:51a 
NWC1PU20 VEC 50,964 09-15-00 8:51a 
NWC1PU22 FLG 712 09-15-00 8:52a 
NWC1PU22 INP 16,326 09-15-00 8:52a 
NWC1PU22 OUT 17,636 09-15-00 8:52a 
NWC1PU22 PTI 9,018 09-15-00 8:52a 
NWC1PU22 TXT 140,841 09-15-00 8:53a 
NWC1PU22 VEC 50,964 09-15-00 8:52a 
NWC1PU28 FLG 712 09-15-00 8:53a 

NWC1PU28 INP 16,326 09-15-00 8:53a 
NWC1PU28 OUT 17,618 09-15-00 8:53a 
NWC1PU28 PTI 9,018 09-15-00 8:53a 
NWC1PU28 TXT 140,841 09-15-00 8:54a 
NWC1PU28 VEC 50,964 09-15-00 8:53a 
NWC1PU29 FLG 712 09-15-00 8:54a 
NWC1PU29 INP 16,326 09-15-00 8:54a 
NWC1PU29 OUT 16,265 09-15-00 8:54a 
NWC1PU29 PTI 9,018 09-15-00 8:54a 
NWC1PU29 TXT 140,841 09-15-00 8:55a 
NWC1PU29 VEC 50,964 09-15-00 8:54a 
NWC1RA26 FLG 712 09-15-00 8:56a 
NWC1RA26 INP 16,326 09-15-00 8:56a 
NWC1RA26 OUT 18,024 09-15-00 8:57a 
NWC1RA26 PTI 9,018 09-15-00 8:57a 
NWC1RA26 TXT 140,841 09-15-00 8:57a 
NWC1RA26 VEC 50,964 09-15-00 8:57a 
NWC1SR90 FLG 711 09-15-00 8:58a 
NWC1SR90 INP 16,326 09-15-00 8:58a 
NWC1SR90 OUT 17,490 09-15-00 8:58a 
NWC1SR90 PTI 9,018 09-15-00 8:58a 
NWC1SR90 TXT 140,841 09-15-00 8:59a 
NWC1SR90 VEC 50,964 09-15-00 8:58a 
NWC1TC99 FLG 711 09-15-00 8:59a 
NWC1TC99 INP 16,326 09-15-00 8:59a 
NWC1TC99 OUT 16,265 09-15-00 8:59a 
NWC1TC99 PTI 9,018 09-15-00 8:59a 
NWC1TC99 TXT 140,841 09-15-00 9:00a 
NWC1TC99 VEC 50,964 09-15-00 8:59a 
NWC1TH20 FLG 712 09-15-00 9:00a 
NWC1TH20 INP 16,326 09-15-00 9:00a 
NWC1TH20 OUT 18,097 09-15-00 9:01a 
NWC1TH20 PTI 9,018 09-15-00 9:01a 
NWC1TH20 TXT 140,841 09-15-00 9:01a 
NWC1TH20 VEC 50,964 09-15-00 9:01a 
NWC1TH29 FLG 712 09-15-00 9:02a 
NWC1TH29 INP 16,326 09-15-00 9:02a 
NWC1TH29 OUT 17,563 09-15-00 9:02a 
NWC1TH29 PTI 9,018 09-15-00 9:02a 
NWC1TH29 TXT 140,841 09-15-00 9:03a 
NWC1TH29 VEC 50,964 09-15-00 9:02a 
NWC1U232 FLG 712 09-15-00 9:03a 
NWC1U232 INP 16,326 09-15-00 9:03a 
NWC1U232 OUT 18,056 09-15-00 9:03a 
NWC1U232 PTI 9,018 09-15-00 9:03a 
NWC1U232 TXT 140,841 09-15-00 9:05a 
NWC1U232 VEC 50,964 09-15-00 9:03a 
NWC1U233 FLG 712 09-15-00 9:05a 
NWC1U233 INP 16,326 09-15-00 9:05a 
NWC1U233 OUT 17,764 09-15-00 9:05a 
NWC1U233 PTI 9,018 09-15-00 9:05a 
NWC1U233 TXT 140,841 09-15-00 9:06a 
NWC1U233 VEC 50,964 09-15-00 9:05a 
NWC1U234 FLG 712 09-15-00 9:06a 
NWC1U234 INP 16,326 09-15-00 9:07a 
NWC1U234 OUT 16,265 09-15-00 9:07a 
NWC1U234 PTI 9,018 09-15-00 9:07a 

NWC1U234 TXT 140,841 09-15-00 9:08a 
NWC1U234 VEC 50,964 09-15-00 9:07a 
NWC1U236 FLG 712 09-15-00 9:14a 
NWC1U236 INP 16,326 09-15-00 9:14a 
NWC1U236 OUT 16,265 09-15-00 9:09a 
NWC1U236 PTI 9,018 09-15-00 9:09a 
NWC1U236 TXT 140,841 09-15-00 9:14a 
NWC1U236 VEC 50,964 09-15-00 9:09a 
NWC1U238 FLG 712 09-15-00 9:11a 
NWC1U238 INP 16,326 09-15-00 9:11a 
NWC1U238 OUT 17,691 09-15-00 9:11a 
NWC1U238 PTI 9,018 09-15-00 9:11a 
NWC1U238 TXT 140,841 09-15-00 9:12a 
NWC1U238 VEC 50,964 09-15-00 9:11a 
NWC2AM21 FLG 712 09-19-00 1:48p 
NWC2AM21 INP 16,327 09-19-00 1:49p 
NWC2AM21 OUT 17,848 09-19-00 1:49p 
NWC2AM21 PTI 9,018 09-19-00 1:49p 
NWC2AM21 TXT 140,841 09-19-00 1:50p 
NWC2AM21 VEC 50,964 09-19-00 1:49p 
NWC2AM23 FLG 712 09-15-00 9:20a 
NWC2AM23 INP 16,328 09-15-00 9:20a 
NWC2AM23 OUT 17,647 09-15-00 9:21a 
NWC2AM23 PTI 9,018 09-15-00 9:21a 
NWC2AM23 TXT 140,841 09-15-00 9:22a 
NWC2AM23 VEC 50,964 09-15-00 9:21a 
NWC2CS17 FLG 712 09-15-00 9:22a 
NWC2CS17 INP 16,326 09-15-00 9:22a 
NWC2CS17 OUT 16,163 09-15-00 9:22a 
NWC2CS17 PTI 9,018 09-15-00 9:22a 
NWC2CS17 TXT 140,841 09-15-00 9:23a 
NWC2CS17 VEC 50,964 09-15-00 9:22a 
NWC2NI63 FLG 711 09-19-00 12:31p 
NWC2NI63 INP 16,327 09-19-00 12:32p 
NWC2NI63 OUT 16,349 09-19-00 12:32p 
NWC2NI63 PTI 9,018 09-19-00 12:32p 
NWC2NI63 TXT 140,841 09-19-00 12:33p 
NWC2NI63 VEC 50,964 09-19-00 12:32p 
NWC2PA21 FLG 712 09-15-00 9:23a 
NWC2PA21 INP 16,328 09-15-00 9:23a 
NWC2PA21 OUT 17,921 09-15-00 9:24a 
NWC2PA21 PTI 9,018 09-15-00 9:24a 
NWC2PA21 TXT 140,841 09-15-00 9:24a 
NWC2PA21 VEC 50,964 09-15-00 9:24a 
NWC2PU20 FLG 712 09-19-00 1:26p 
NWC2PU20 INP 16,328 09-19-00 1:26p 
NWC2PU20 OUT 17,702 09-19-00 1:26p 
NWC2PU20 PTI 9,018 09-19-00 1:26p 
NWC2PU20 TXT 140,841 09-19-00 1:27p 
NWC2PU20 VEC 50,964 09-19-00 1:26p 
NWC2PU22 FLG 712 09-19-00 1:36p 
NWC2PU22 INP 16,328 09-19-00 1:36p 
NWC2PU22 OUT 17,720 09-19-00 1:37p 
NWC2PU22 PTI 9,018 09-19-00 1:37p 
NWC2PU22 TXT 140,841 09-19-00 1:37p 
NWC2PU22 VEC 50,964 09-19-00 1:37p 
NWC2PU29 FLG 712 09-19-00 1:14p 

NWC2PU29 INP 16,328 09-19-00 1:15p 
NWC2PU29 OUT 16,349 09-19-00 1:15p 
NWC2PU29 PTI 9,018 09-19-00 1:15p 
NWC2PU29 TXT 140,841 09-19-00 1:15p 
NWC2PU29 VEC 50,964 09-19-00 1:15p 
NWC2RA26 FLG 712 09-15-00 9:24a 
NWC2RA26 INP 16,328 09-15-00 9:24a 
NWC2RA26 OUT 18,108 09-15-00 9:25a 
NWC2RA26 PTI 9,018 09-15-00 9:25a 
NWC2RA26 TXT 140,841 09-15-00 9:26a 
NWC2RA26 VEC 50,964 09-15-00 9:25a 
NWC2SR90 FLG 711 09-19-00 12:43p 
NWC2SR90 INP 16,326 09-19-00 12:44p 
NWC2SR90 OUT 17,574 09-19-00 12:44p 
NWC2SR90 PTI 9,018 09-19-00 12:44p 
NWC2SR90 TXT 140,841 09-19-00 12:45p 
NWC2SR90 VEC 50,964 09-19-00 12:44p 
NWC2TH20 FLG 712 09-15-00 9:26a 
NWC2TH20 INP 16,328 09-15-00 9:26a 
NWC2TH20 OUT 18,181 09-15-00 9:28a 
NWC2TH20 PTI 9,018 09-15-00 9:28a 
NWC2TH20 TXT 140,841 09-15-00 9:29a 
NWC2TH20 VEC 50,964 09-15-00 9:28a 
NWC2TH29 FLG 712 09-15-00 9:29a 
NWC2TH29 INP 16,328 09-15-00 9:29a 
NWC2TH29 OUT 17,647 09-15-00 9:30a 
NWC2TH29 PTI 9,018 09-15-00 9:30a 
NWC2TH29 TXT 140,841 09-15-00 9:31a 
NWC2TH29 VEC 50,964 09-15-00 9:30a 
NWC2U232 FLG 712 09-19-00 1:01p 
NWC2U232 INP 16,326 09-19-00 1:03p 
NWC2U232 OUT 18,140 09-19-00 1:04p 
NWC2U232 PTI 9,018 09-19-00 1:04p 
NWC2U232 TXT 140,841 09-19-00 1:05p 
NWC2U232 VEC 50,964 09-19-00 1:04p 
NWC3AM21 FLG 712 09-19-00 1:51p 
NWC3AM21 INP 16,328 09-19-00 1:52p 
NWC3AM21 OUT 17,848 09-19-00 1:52p 
NWC3AM21 PTI 9,018 09-19-00 1:52p 
NWC3AM21 TXT 140,841 09-19-00 1:53p 
NWC3AM21 VEC 50,964 09-19-00 1:52p 
NWC3AM23 FLG 712 09-15-00 9:32a 
NWC3AM23 INP 16,328 09-15-00 9:32a 
NWC3AM23 OUT 17,647 09-15-00 9:34a 
NWC3AM23 PTI 9,018 09-15-00 9:34a 
NWC3AM23 TXT 140,841 09-15-00 9:34a 
NWC3AM23 VEC 50,964 09-15-00 9:34a 
NWC3CS17 FLG 712 09-15-00 9:34a 
NWC3CS17 INP 16,327 09-15-00 9:34a 
NWC3CS17 OUT 16,163 09-15-00 9:35a 
NWC3CS17 PTI 9,018 09-15-00 9:35a 
NWC3CS17 TXT 140,841 09-15-00 9:35a 
NWC3CS17 VEC 50,964 09-15-00 9:35a 
NWC3NI63 FLG 711 09-19-00 12:34p 
NWC3NI63 INP 16,327 09-19-00 12:34p 
NWC3NI63 OUT 16,349 09-19-00 12:35p 
NWC3NI63 PTI 9,018 09-19-00 12:35p 

NWC3NI63 TXT 140,841 09-19-00 12:35p 
NWC3NI63 VEC 50,964 09-19-00 12:35p 
NWC3PA21 FLG 712 09-15-00 9:35a 
NWC3PA21 INP 16,328 09-15-00 9:37a 
NWC3PA21 OUT 17,921 09-15-00 9:38a 
NWC3PA21 PTI 9,018 09-15-00 9:38a 
NWC3PA21 TXT 140,841 09-15-00 9:41a 
NWC3PA21 VEC 50,964 09-15-00 9:38a 
NWC3PU20 FLG 712 09-19-00 1:28p 
NWC3PU20 INP 16,328 09-19-00 1:28p 
NWC3PU20 OUT 17,702 09-19-00 1:29p 
NWC3PU20 PTI 9,018 09-19-00 1:29p 
NWC3PU20 TXT 140,841 09-19-00 1:29p 
NWC3PU20 VEC 50,964 09-19-00 1:29p 
NWC3PU22 FLG 712 09-19-00 1:38p 
NWC3PU22 INP 16,328 09-19-00 1:38p 
NWC3PU22 OUT 17,720 09-19-00 1:39p 
NWC3PU22 PTI 9,018 09-19-00 1:39p 
NWC3PU22 TXT 140,841 09-19-00 1:40p 
NWC3PU22 VEC 50,964 09-19-00 1:39p 
NWC3PU29 FLG 712 09-19-00 1:16p 
NWC3PU29 INP 16,328 09-19-00 1:16p 
NWC3PU29 OUT 16,349 09-19-00 1:17p 
NWC3PU29 PTI 9,018 09-19-00 1:17p 
NWC3PU29 TXT 140,841 09-19-00 1:17p 
NWC3PU29 VEC 50,964 09-19-00 1:17p 
NWC3RA26 FLG 712 09-15-00 9:41a 
NWC3RA26 INP 16,328 09-15-00 9:41a 
NWC3RA26 OUT 18,108 09-15-00 9:42a 
NWC3RA26 PTI 9,018 09-15-00 9:42a 
NWC3RA26 TXT 140,841 09-15-00 9:43a 
NWC3RA26 VEC 50,964 09-15-00 9:42a 
NWC3SR90 FLG 711 09-19-00 12:53p 
NWC3SR90 INP 16,326 09-19-00 12:53p 
NWC3SR90 OUT 17,574 09-19-00 12:54p 
NWC3SR90 PTI 9,018 09-19-00 12:54p 
NWC3SR90 TXT 140,841 09-19-00 12:54p 
NWC3SR90 VEC 50,964 09-19-00 12:54p 
NWC3TH20 FLG 712 09-15-00 9:43a 
NWC3TH20 INP 16,329 09-15-00 9:43a 
NWC3TH20 OUT 18,181 09-15-00 9:48a 
NWC3TH20 PTI 9,018 09-15-00 9:48a 
NWC3TH20 TXT 140,841 09-15-00 9:49a 
NWC3TH20 VEC 50,964 09-15-00 9:48a 
NWC3TH29 FLG 712 09-15-00 9:49a 
NWC3TH29 INP 16,329 09-15-00 9:49a 
NWC3TH29 OUT 17,647 09-15-00 9:50a 
NWC3TH29 PTI 9,018 09-15-00 9:50a 
NWC3TH29 TXT 140,841 09-15-00 9:51a 
NWC3TH29 VEC 50,964 09-15-00 9:50a 
NWC3U232 FLG 712 09-19-00 1:06p 
NWC3U232 INP 16,327 09-19-00 1:06p 
NWC3U232 OUT 18,140 09-19-00 1:07p 
NWC3U232 PTI 9,018 09-19-00 1:07p 
NWC3U232 TXT 140,841 09-19-00 1:07p 
NWC3U232 VEC 50,964 09-19-00 1:07p 
NWC4AM21 FLG 712 09-19-00 1:54p 

NWC4AM21 INP 16,328 09-19-00 1:54p 
NWC4AM21 OUT 17,848 09-19-00 1:55p 
NWC4AM21 PTI 9,018 09-19-00 1:55p 
NWC4AM21 TXT 140,841 09-19-00 1:55p 
NWC4AM21 VEC 50,964 09-19-00 1:55p 
NWC4AM23 FLG 712 09-15-00 9:52a 
NWC4AM23 INP 16,329 09-15-00 9:53a 
NWC4AM23 OUT 17,647 09-15-00 9:54a 
NWC4AM23 PTI 9,018 09-15-00 9:54a 
NWC4AM23 TXT 140,841 09-15-00 9:55a 
NWC4AM23 VEC 50,964 09-15-00 9:54a 
NWC4CS17 FLG 712 09-15-00 9:55a 
NWC4CS17 INP 16,327 09-15-00 9:55a 
NWC4CS17 OUT 16,163 09-15-00 9:55a 
NWC4CS17 PTI 9,018 09-15-00 9:55a 
NWC4CS17 TXT 140,841 09-15-00 9:56a 
NWC4CS17 VEC 50,964 09-15-00 9:55a 
NWC4NI63 FLG 711 09-19-00 12:37p 
NWC4NI63 INP 16,327 09-19-00 12:37p 
NWC4NI63 OUT 16,349 09-19-00 12:37p 
NWC4NI63 PTI 9,018 09-19-00 12:37p 
NWC4NI63 TXT 140,841 09-19-00 12:38p 
NWC4NI63 VEC 50,964 09-19-00 12:37p 
NWC4PA21 FLG 712 09-15-00 9:56a 
NWC4PA21 INP 16,328 09-15-00 9:56a 
NWC4PA21 OUT 17,921 09-15-00 9:57a 
NWC4PA21 PTI 9,018 09-15-00 9:57a 
NWC4PA21 TXT 140,841 09-15-00 9:58a 
NWC4PA21 VEC 50,964 09-15-00 9:57a 
NWC4PU20 FLG 712 09-19-00 1:30p 
NWC4PU20 INP 16,328 09-19-00 1:30p 
NWC4PU20 OUT 17,702 09-19-00 1:31p 
NWC4PU20 PTI 9,018 09-19-00 1:31p 
NWC4PU20 TXT 140,841 09-19-00 1:31p 
NWC4PU20 VEC 50,964 09-19-00 1:31p 
NWC4PU22 FLG 712 09-19-00 1:41p 
NWC4PU22 INP 16,328 09-19-00 1:41p 
NWC4PU22 OUT 17,720 09-19-00 1:42p 
NWC4PU22 PTI 9,018 09-19-00 1:42p 
NWC4PU22 TXT 140,841 09-19-00 1:43p 
NWC4PU22 VEC 50,964 09-19-00 1:42p 
NWC4PU29 FLG 712 09-19-00 1:19p 
NWC4PU29 INP 16,328 09-19-00 1:19p 
NWC4PU29 OUT 16,349 09-19-00 1:19p 
NWC4PU29 PTI 9,018 09-19-00 1:19p 
NWC4PU29 TXT 140,841 09-19-00 1:20p 
NWC4PU29 VEC 50,964 09-19-00 1:19p 
NWC4RA26 FLG 712 09-15-00 9:58a 
NWC4RA26 INP 16,328 09-15-00 9:58a 
NWC4RA26 OUT 18,108 09-15-00 9:59a 
NWC4RA26 PTI 9,018 09-15-00 9:59a 
NWC4RA26 TXT 140,841 09-15-00 10:00a 
NWC4RA26 VEC 50,964 09-15-00 9:59a 
NWC4SR90 FLG 711 09-19-00 12:55p 
NWC4SR90 INP 16,327 09-19-00 12:56p 
NWC4SR90 OUT 17,574 09-19-00 12:56p 
NWC4SR90 PTI 9,018 09-19-00 12:56p 

NWC4SR90 TXT 140,841 09-19-00 12:56p 
NWC4SR90 VEC 50,964 09-19-00 12:56p 
NWC4TH20 FLG 712 09-15-00 10:00a 
NWC4TH20 INP 16,329 09-15-00 10:00a 
NWC4TH20 OUT 18,181 09-15-00 10:08a 
NWC4TH20 PTI 9,018 09-15-00 10:08a 
NWC4TH20 TXT 140,841 09-15-00 10:08a 
NWC4TH20 VEC 50,964 09-15-00 10:08a 
NWC4TH29 FLG 712 09-15-00 10:09a 
NWC4TH29 INP 16,329 09-15-00 10:09a 
NWC4TH29 OUT 17,647 09-15-00 10:11a 
NWC4TH29 PTI 9,018 09-15-00 10:11a 
NWC4TH29 TXT 140,841 09-15-00 10:11a 
NWC4TH29 VEC 50,964 09-15-00 10:11a 
NWC4U232 FLG 712 09-19-00 1:08p 
NWC4U232 INP 16,327 09-19-00 1:08p 
NWC4U232 OUT 18,140 09-19-00 1:09p 
NWC4U232 PTI 9,018 09-19-00 1:09p 
NWC4U232 TXT 140,841 09-19-00 1:09p 
NWC4U232 VEC 50,964 09-19-00 1:09p 
NWC5AM21 FLG 712 09-19-00 1:56p 
NWC5AM21 INP 16,328 09-19-00 1:57p 
NWC5AM21 OUT 17,848 09-19-00 1:58p 
NWC5AM21 PTI 9,018 09-19-00 1:58p 
NWC5AM21 TXT 140,841 09-19-00 1:59p 
NWC5AM21 VEC 50,964 09-19-00 1:58p 
NWC5AM23 FLG 712 09-15-00 10:13a 
NWC5AM23 INP 16,329 09-15-00 10:13a 
NWC5AM23 OUT 17,647 09-15-00 10:15a 
NWC5AM23 PTI 9,018 09-15-00 10:15a 
NWC5AM23 TXT 140,841 09-15-00 10:16a 
NWC5AM23 VEC 50,964 09-15-00 10:15a 
NWC5CS17 FLG 712 09-15-00 10:16a 
NWC5CS17 INP 16,327 09-15-00 10:16a 
NWC5CS17 OUT 16,163 09-15-00 10:16a 
NWC5CS17 PTI 9,018 09-15-00 10:16a 
NWC5CS17 TXT 140,841 09-15-00 10:17a 
NWC5CS17 VEC 50,964 09-15-00 10:16a 
NWC5NI63 FLG 711 09-19-00 12:39p 
NWC5NI63 INP 16,328 09-19-00 12:39p 
NWC5NI63 OUT 16,349 09-19-00 12:40p 
NWC5NI63 PTI 9,018 09-19-00 12:40p 
NWC5NI63 TXT 140,841 09-19-00 12:40p 
NWC5NI63 VEC 50,964 09-19-00 12:40p 
NWC5PA21 FLG 712 09-15-00 10:17a 
NWC5PA21 INP 16,328 09-15-00 10:17a 
NWC5PA21 OUT 17,921 09-15-00 10:19a 
NWC5PA21 PTI 9,018 09-15-00 10:19a 
NWC5PA21 TXT 140,841 09-15-00 10:19a 
NWC5PA21 VEC 50,964 09-15-00 10:19a 
NWC5PU20 FLG 712 09-19-00 1:32p 
NWC5PU20 INP 16,328 09-19-00 1:32p 
NWC5PU20 OUT 17,702 09-19-00 1:33p 
NWC5PU20 PTI 9,018 09-19-00 1:33p 
NWC5PU20 TXT 140,841 09-19-00 1:34p 
NWC5PU20 VEC 50,964 09-19-00 1:33p 
NWC5PU22 FLG 712 09-19-00 1:43p 

NWC5PU22 INP 16,328 09-19-00 1:44p 
NWC5PU22 OUT 17,720 09-19-00 1:45p 
NWC5PU22 PTI 9,018 09-19-00 1:45p 
NWC5PU22 TXT 140,841 09-19-00 1:45p 
NWC5PU22 VEC 50,964 09-19-00 1:45p 
NWC5PU29 FLG 712 09-19-00 1:22p 
NWC5PU29 INP 16,328 09-19-00 1:22p 
NWC5PU29 OUT 16,349 09-19-00 1:23p 
NWC5PU29 PTI 9,018 09-19-00 1:23p 
NWC5PU29 TXT 140,841 09-19-00 1:23p 
NWC5PU29 VEC 50,964 09-19-00 1:23p 
NWC5RA26 FLG 712 09-15-00 10:20a 
NWC5RA26 INP 16,328 09-15-00 10:20a 
NWC5RA26 OUT 18,108 09-15-00 10:21a 
NWC5RA26 PTI 9,018 09-15-00 10:21a 
NWC5RA26 TXT 140,841 09-15-00 10:22a 
NWC5RA26 VEC 50,964 09-15-00 10:21a 
NWC5SR90 FLG 711 09-19-00 12:57p 
NWC5SR90 INP 16,327 09-19-00 12:57p 
NWC5SR90 OUT 17,574 09-19-00 12:58p 
NWC5SR90 PTI 9,018 09-19-00 12:58p 
NWC5SR90 TXT 140,841 09-19-00 12:58p 
NWC5SR90 VEC 50,964 09-19-00 12:58p 
NWC5TH20 FLG 712 09-15-00 10:26a 
NWC5TH20 INP 16,329 09-15-00 10:26a 
NWC5TH20 OUT 18,181 09-15-00 10:38a 
NWC5TH20 PTI 9,018 09-15-00 10:38a 
NWC5TH20 TXT 140,841 09-15-00 10:39a 
NWC5TH20 VEC 50,964 09-15-00 10:38a 
NWC5TH29 FLG 712 09-15-00 10:22a 
NWC5TH29 INP 16,329 09-15-00 10:22a 
NWC5TH29 OUT 17,647 09-15-00 10:25a 
NWC5TH29 PTI 9,018 09-15-00 10:25a 
NWC5TH29 TXT 140,841 09-15-00 10:26a 
NWC5TH29 VEC 50,964 09-15-00 10:25a 
NWC5U232 FLG 712 09-19-00 1:11p 
NWC5U232 INP 16,327 09-19-00 1:11p 
NWC5U232 OUT 18,140 09-19-00 1:12p 
NWC5U232 PTI 9,018 09-19-00 1:12p 
NWC5U232 TXT 140,841 09-19-00 1:12p 
NWC5U232 VEC 50,964 09-19-00 1:12p 
NWC6AC27 FLG 712 09-15-00 10:41a 
NWC6AC27 INP 16,327 09-15-00 10:41a 
NWC6AC27 OUT 17,720 09-15-00 10:41a 
NWC6AC27 PTI 9,018 09-15-00 10:41a 
NWC6AC27 TXT 140,841 09-15-00 10:42a 
NWC6AC27 VEC 50,964 09-15-00 10:41a 
NWC6AM21 FLG 712 09-15-00 10:42a 
NWC6AM21 INP 16,328 09-15-00 10:42a 
NWC6AM21 OUT 17,848 09-15-00 10:43a 
NWC6AM21 PTI 9,018 09-15-00 10:43a 
NWC6AM21 TXT 140,841 09-15-00 10:44a 
NWC6AM21 VEC 50,964 09-15-00 10:43a 
NWC6AM23 FLG 712 09-15-00 10:44a 
NWC6AM23 INP 16,329 09-15-00 10:44a 
NWC6AM23 OUT 17,647 09-15-00 10:48a 
NWC6AM23 PTI 9,018 09-15-00 10:48a 

NWC6AM23 TXT 140,841 09-15-00 10:48a 
NWC6AM23 VEC 50,964 09-15-00 10:48a 
NWC6CS17 FLG 712 09-15-00 10:48a 
NWC6CS17 INP 16,327 09-15-00 10:49a 
NWC6CS17 OUT 16,163 09-15-00 10:49a 
NWC6CS17 PTI 9,018 09-15-00 10:49a 
NWC6CS17 TXT 140,841 09-15-00 10:49a 
NWC6CS17 VEC 50,964 09-15-00 10:49a 
NWC6CX14 FLG 710 09-15-00 10:49a 
NWC6CX14 INP 16,327 09-15-00 10:50a 
NWC6CX14 OUT 16,163 09-15-00 10:50a 
NWC6CX14 PTI 9,018 09-15-00 10:50a 
NWC6CX14 TXT 140,841 09-15-00 10:50a 
NWC6CX14 VEC 50,964 09-15-00 10:50a 
NWC6I129 FLG 712 09-15-00 10:50a 
NWC6I129 INP 16,326 09-15-00 10:51a 
NWC6I129 OUT 16,163 09-15-00 10:51a 
NWC6I129 PTI 9,018 09-15-00 10:51a 
NWC6I129 TXT 140,841 09-15-00 10:52a 
NWC6I129 VEC 50,964 09-15-00 10:51a 
NWC6NI63 FLG 711 09-15-00 10:52a 
NWC6NI63 INP 16,328 09-15-00 10:52a 
NWC6NI63 OUT 16,349 09-15-00 10:52a 
NWC6NI63 PTI 9,018 09-15-00 10:52a 
NWC6NI63 TXT 140,841 09-15-00 10:53a 
NWC6NI63 VEC 50,964 09-15-00 10:52a 
NWC6NP27 FLG 712 09-15-00 10:53a 
NWC6NP27 INP 16,327 09-15-00 10:53a 
NWC6NP27 OUT 17,702 09-15-00 10:53a 
NWC6NP27 PTI 9,018 09-15-00 10:53a 
NWC6NP27 TXT 140,841 09-15-00 10:54a 
NWC6NP27 VEC 50,964 09-15-00 10:53a 
NWC6PA21 FLG 712 09-15-00 10:54a 
NWC6PA21 INP 16,329 09-15-00 10:54a 
NWC6PA21 OUT 17,921 09-15-00 10:57a 
NWC6PA21 PTI 9,018 09-15-00 10:57a 
NWC6PA21 TXT 140,841 09-15-00 10:58a 
NWC6PA21 VEC 50,964 09-15-00 10:57a 
NWC6PB20 FLG 712 09-15-00 10:58a 
NWC6PB20 INP 16,327 09-15-00 10:58a 
NWC6PB20 OUT 17,903 09-15-00 10:58a 
NWC6PB20 PTI 9,018 09-15-00 10:58a 
NWC6PB20 TXT 140,841 09-15-00 10:59a 
NWC6PB20 VEC 50,964 09-15-00 10:58a 
NWC6PU20 FLG 712 09-15-00 10:59a 
NWC6PU20 INP 16,329 09-15-00 10:59a 
NWC6PU20 OUT 17,702 09-15-00 11:00a 
NWC6PU20 PTI 9,018 09-15-00 11:00a 
NWC6PU20 TXT 140,841 09-15-00 11:00a 
NWC6PU20 VEC 50,964 09-15-00 11:00a 
NWC6PU22 FLG 712 09-15-00 11:00a 
NWC6PU22 INP 16,329 09-15-00 11:01a 
NWC6PU22 OUT 17,720 09-15-00 11:02a 
NWC6PU22 PTI 9,018 09-15-00 11:02a 
NWC6PU22 TXT 140,841 09-15-00 11:03a 
NWC6PU22 VEC 50,964 09-15-00 11:02a 
NWC6PU28 FLG 712 09-15-00 11:03a 

NWC6PU28 INP 16,328 09-15-00 11:03a 
NWC6PU28 OUT 17,702 09-15-00 11:04a 
NWC6PU28 PTI 9,018 09-15-00 11:04a 
NWC6PU28 TXT 140,841 09-15-00 11:04a 
NWC6PU28 VEC 50,964 09-15-00 11:04a 
NWC6PU29 FLG 712 09-15-00 11:04a 
NWC6PU29 INP 16,329 09-15-00 11:05a 
NWC6PU29 OUT 16,349 09-15-00 11:05a 
NWC6PU29 PTI 9,018 09-15-00 11:05a 
NWC6PU29 TXT 140,841 09-15-00 11:07a 
NWC6PU29 VEC 50,964 09-15-00 11:05a 
NWC6RA26 FLG 712 09-15-00 11:07a 
NWC6RA26 INP 16,329 09-15-00 11:07a 
NWC6RA26 OUT 18,108 09-15-00 11:09a 
NWC6RA26 PTI 9,018 09-15-00 11:09a 
NWC6RA26 TXT 140,841 09-15-00 11:27a 
NWC6RA26 VEC 50,964 09-15-00 11:09a 
NWC6SR90 FLG 711 09-15-00 11:27a 
NWC6SR90 INP 16,327 09-15-00 11:27a 
NWC6SR90 OUT 17,574 09-15-00 11:28a 
NWC6SR90 PTI 9,018 09-15-00 11:28a 
NWC6SR90 TXT 140,841 09-15-00 11:28a 
NWC6SR90 VEC 50,964 09-15-00 11:28a 
NWC6TC99 FLG 711 09-15-00 11:28a 
NWC6TC99 INP 16,326 09-15-00 11:29a 
NWC6TC99 OUT 16,349 09-15-00 11:29a 
NWC6TC99 PTI 9,018 09-15-00 11:29a 
NWC6TC99 TXT 140,841 09-15-00 11:29a 
NWC6TC99 VEC 50,964 09-15-00 11:29a 
NWC6TH20 FLG 712 09-15-00 11:38a 
NWC6TH20 INP 16,329 09-15-00 11:38a 
NWC6TH20 OUT 18,181 09-15-00 11:57a 
NWC6TH20 PTI 9,018 09-15-00 11:57a 
NWC6TH20 TXT 140,841 09-15-00 12:36p 
NWC6TH20 VEC 50,964 09-15-00 11:57a 
NWC6TH29 FLG 712 09-15-00 12:36p 
NWC6TH29 INP 16,329 09-15-00 12:37p 
NWC6TH29 OUT 17,647 09-15-00 12:42p 
NWC6TH29 PTI 9,018 09-15-00 12:42p 
NWC6TH29 TXT 140,841 09-15-00 12:43p 
NWC6TH29 VEC 50,964 09-15-00 12:42p 
NWC6U232 FLG 712 09-15-00 11:30a 
NWC6U232 INP 16,327 09-15-00 11:30a 
NWC6U232 OUT 18,140 09-15-00 11:31a 
NWC6U232 PTI 9,018 09-15-00 11:31a 
NWC6U232 TXT 140,841 09-15-00 11:31a 
NWC6U232 VEC 50,964 09-15-00 11:31a 
NWC6U233 FLG 712 09-15-00 11:31a 
NWC6U233 INP 16,327 09-15-00 11:31a 
NWC6U233 OUT 17,848 09-15-00 11:32a 
NWC6U233 PTI 9,018 09-15-00 11:32a 
NWC6U233 TXT 140,841 09-15-00 11:32a 
NWC6U233 VEC 50,964 09-15-00 11:32a 
NWC6U234 FLG 712 09-15-00 11:32a 
NWC6U234 INP 16,327 09-15-00 11:33a 
NWC6U234 OUT 16,349 09-15-00 11:33a 
NWC6U234 PTI 9,018 09-15-00 11:33a 

NWC6U234 TXT 140,841 09-15-00 11:33a 
NWC6U234 VEC 50,964 09-15-00 11:33a 
NWC6U236 FLG 712 09-15-00 11:34a 
NWC6U236 INP 16,327 09-15-00 11:34a 
NWC6U236 OUT 16,349 09-15-00 11:34a 
NWC6U236 PTI 9,018 09-15-00 11:34a 
NWC6U236 TXT 140,841 09-15-00 11:34a 
NWC6U236 VEC 50,964 09-15-00 11:34a 
NWC6U238 FLG 712 09-15-00 11:35a 
NWC6U238 INP 16,327 09-15-00 11:35a 
NWC6U238 OUT 17,775 09-15-00 11:35a 
NWC6U238 PTI 9,018 09-15-00 11:35a 
NWC6U238 TXT 140,841 09-15-00 11:36a 
NWC6U238 VEC 50,964 09-15-00 11:35a 

INTENTIONALLY LEFT BLANK

ATTACHMENT V
DETERMINATION OF PRIOR IRRIGATION PERIODS

ATTACHMENT V
DETERMINATION OF PRIOR IRRIGATION PERIODS
The prior irrigation periods were calculated using standard functions of Microsoft Excel version 
97 SR-2. The calculations are documented in this attachment. 
To address the effect of radionuclide buildup in soil, it was necessary to determine whether 
buildup caused any significant change in the BDCFs. For those radionuclides where buildup did 
not occur to any significant degree, the periods of irrigation that were used for the calculation 
were of no consequence. However, for those radionuclides where buildup was shown to be 
significant, an approach was required that would allow the BDCFs to be determined for an 
arbitrary previous irrigation period. The technique adopted to determine the functional 
relationship between the value of the BDCF and the duration of previous irrigation was that of 
curve fitting (CRWMS M&O 2000n) with subsequent use of the fitted curve for interpolation or 
extrapolation. To accomplish this, the BDCFs were calculated for several periods of prior 
irrigation times. (The period of prior irrigation represents the number of years that the land has 
been irrigated with contaminated water before the intake occurs.) 
The technique of curve fitting does not require that BDCFs be calculated for any specific time. 
The only requirement is that the dependent variable (BDCFs) is calculated over the range of the 
variable parameter of interest (irrigation periods). Considering this, the values of irrigation 
periods were calculated such that the series of BDCFs would be approximately equally spaced 
between the value for the first period and the asymptotic value after an infinitely long period of 
previous irrigation. Assuming the constant addition rate of radionuclide to soil via irrigation, 
BDCFs are proportional to the function f: 
f = 1 - e-.t (Eq. V-1) 
where . is the effective removal constant for each radionuclide in soil, and t is the duration of 
previous irrigation. The effective removal constant was approximated by the sum of the 
radioactive decay constant and the leaching factor. The range between the BDCF for no prior 
irrigation (f = 0) and the equilibrium value of the BDCF for an individual radionuclide (f = 1) 
was arbitrarily divided into the six equal intervals to obtain a sufficient number of data points for 
the future curve fitting. Then the irrigation time periods were calculated based on the effective 
removal rate of radionuclides from the top 15 cm of soil using the following formula: 
. 1 . 
.. 
t = 
ln ... 1 - f . (Eq. V-2)
. 
where f is the fraction of the BDCF at equilibrium. For the six irrigation time periods, f is equal 
to 1/6, 2/6, 3/6, 4/6, and 5/6 for the second through the sixth irrigation period, respectively. The 
first time period corresponds to no prior irrigation with contaminated water. 

The Excel spreadsheet that was used to calculate irrigation periods is shown below. 
Calculation of irrigation periods 
BDCF is proportional to 1-exp(lambda * t)
where lambda is the effective removal constant, t is duration of prior irrigation
The effective removal constant is the sum of the radioactive decay constant and the leacing factor.
0.17 0.33 0.50 0.67 0.83 <----- Equally spaced intervals (one sixths) 
f = 1-exp(-lambda*t) <----- Solve for t 
f - fraction of equilibrium BDCF equal to 1/6, 2/6, 3/6, 4//6, and 5/6 for the six irrigation periods 
<----- Equation is used to calculate irrigation periods 
t = ln(1/(1-f))/lambda 
Removal constant, 1/y Irrigation periods, years 
Radionuclide Rad.decay Leaching Effective 
Carbon-14 1.21E-04 1.32E-01 1.32E-01 1 3 5 8 14 
Nickel-63 7.22E-03 1.69E-03 8.91E-03 20 46 78 123 201 
Strontium-90 2.38E-02 4.47E-02 6.85E-02 3 6 10 16 26 
Technetium-99 3.25E-06 2.77E+00 2.77E+00 0 0 0 0 1 
Iodine-129 4.41E-08 5.92E-01 5.92E-01 0 1 1 2 3 
Cesium-137 2.31E-02 2.42E-03 2.55E-02 7 16 27 43 70 
Lead-210 3.11E-02 2.51E-03 3.36E-02 5 12 21 33 53 
Radium-226 4.33E-04 1.35E-03 1.78E-03 102 227 389 616 1005 
Actinium-227 3.18E-02 1.50E-03 3.33E-02 5 12 21 33 54 
Thorium-229 9.44E-05 2.12E-04 3.06E-04 595 1323 2262 3586 5848 
Thorium-230 9.00E-06 2.12E-04 2.21E-04 825 1835 3136 4971 8108 
Protactinium-231 2.12E-05 1.23E-03 1.25E-03 146 324 554 878 1432 
Uranium-232 9.63E-03 1.93E-02 2.89E-02 6 14 24 38 62 
Uranium-233 4.37E-06 1.93E-02 1.93E-02 9 21 36 57 93 
Uranium-234 2.83E-06 1.93E-02 1.93E-02 9 21 36 57 93 
Uranium-236 2.96E-08 1.93E-02 1.93E-02 9 21 36 57 93 
Uranium-238 1.55E-10 1.93E-02 1.93E-02 9 21 36 57 93 
Neptunium-237 3.24E-07 1.32E-01 1.32E-01 1 3 5 8 14 
Plutonium-238 7.90E-03 1.23E-03 9.13E-03 20 44 76 120 196 
Plutonium-239 2.88E-05 1.23E-03 1.26E-03 145 322 551 873 1423 
Plutonium-240 1.06E-04 1.23E-03 1.34E-03 136 304 519 822 1341 
Plutonium-242 1.84E-06 1.23E-03 1.23E-03 148 329 563 892 1455 
Americium-241 1.60E-03 3.56E-04 1.96E-03 93 207 354 561 915 
Americium-243 9.39E-05 3.56E-04 4.50E-04 405 901 1541 2442 3983 
Figure V-1. The Image of the Excel Spreadsheet Showing Calculation of Irrigation Periods 

ATTACHMENT VI
DETERMINATION OF PATHWAY CONTRIBUTION

ATTACHMENT VI
DETERMINATION OF PATHWAY CONTRIBUTION
A software routine, Pathway Contribution REV 0, built into a Microsoft Excel version 97 SR-2 
spreadsheet, was used to automate the determination of contribution to the BDCF by each 
pathway. 
PATHWAY CONTRIBUTION, REV. 0 
Set-up and Operation of Software Routine: 
An example of the application is provided to assist the user in understanding the organization 
and function of the routine used to determine the contribution to the BDCF by each pathway. 
Figure VI-1 is a representation of the spreadsheet for 99Tc, under the current climate with no 
prior irrigation. The spreadsheet has been divided into three areas (i.e., A, B and C) for ease of 
explanation. 
All data imported from the GENII-S output files is obtained by blocking and copying the desired 
data and then pasting it into the appropriate section of the spreadsheet. Inserting the data into the 
spreadsheet is accomplished by using the Paste command and the Text to columns submenu 
item under Data from the tool bar to arrange the data into individual cells. 
Area A is a table imported directly from the GENII-S output file which shows the 
contributions to the annual committed effective dose equivalent from internal exposure by 
inhalation and ingestion and the external exposure. GENII-S calculates internal doses by 
multiplying the committed dose equivalent to each organ times the weighting factor for that 
organ, then summing the weighted organ dose equivalents. The sum of the internal dose 
(committed effective dose equivalent) from annual intake and the external dose (effective dose 
equivalent) from annual exposure yields the annual total effective dose equivalent. 
The upper portion of area B is a table imported directly from the GENII-S output file. This 
table contains the committed dose equivalent to each organ from each individual pathway. The 
totals at the bottom of the organ columns are the same as the values shown in the committed dose 
equivalent column from the table shown in area A. In order to determine the contribution by 
pathway to the annual total effective dose equivalent it is necessary to calculate the weighted 
dose equivalent to each organ from each pathway. This is done by multiplying the values in the 
upper table by each organs weighting factor. The results are contained in the table in the lower 
portion of area B. The total contribution for each pathway is obtained by summing the 
weighted organ committed dose equivalents. The results are found in the lower right corner of 
area B under the column titled Total. 
In area C the total percentage contributions to the BDCF are presented. The percentage 
contributions from each pathway were calculated by dividing the pathway contribution by the 
annual total effective dose equivalent and multiplying the result by 100. 

Confirmation of Correct Operation:
The individual calculations have been spot checked with hand calculations to ensure that correct 
results are being produced. Also, as a spot check for each use of the routine, the total from the 
lower right hand corner of area B is compared to the Internal Effective Dose Equivalent 
value from the bottom right hand corner of area A. These values should be almost the same. 

ANL-MGR-MD-000009 REV 01 VI-5 November 2000 
DC-BDCFs: Average 1x, Tc-99 Committed Dose Equivalent by Exposure Pathway 
A Committed Weighted Pathway Lung Stomach S Int. UL Int. LL Int. Bone Su R Marro Testes Ovaries Muscle Thyroid Liver 
Dose Weighting Dose B 
Organ Equivalent Factors Equivalent Inhale 1.10E-10 2.90E-11 4.50E-13 1.40E-12 3.60E-12 3.10E-13 3.10E-13 2.60E-13 2.60E-13 2.60E-13 6.90E-12 3.50E-13 
Leaf Veg 8.90E-08 9.60E-06 1.70E-07 5.80E-07 1.50E-06 1.00E-07 1.00E-07 8.90E-08 8.90E-08 8.90E-08 2.20E-06 1.20E-07 
Gonads 3.20E-07 2.50E-01 7.90E-08 Oth. Veg 1.10E-08 1.20E-06 2.00E-08 7.00E-08 1.90E-07 1.30E-08 1.30E-08 1.10E-08 1.10E-08 1.10E-08 2.70E-07 1.50E-08 
Breast 3.20E-07 1.50E-01 4.70E-08 Fruit 5.80E-09 6.30E-07 1.10E-08 3.80E-08 1.00E-07 6.80E-09 6.80E-09 5.80E-09 5.80E-09 5.80E-09 1.50E-07 8.00E-09 
R Marrow 3.70E-07 1.20E-01 4.50E-08 Cereals 5.80E-10 6.30E-08 1.10E-09 3.80E-09 1.00E-08 6.80E-10 6.80E-10 5.80E-10 5.80E-10 5.80E-10 1.50E-08 8.00E-10 
Lung 3.20E-07 1.20E-01 3.80E-08 Meat 2.80E-10 3.00E-08 5.30E-10 1.80E-09 4.90E-09 3.30E-10 3.30E-10 2.80E-10 2.80E-10 2.80E-10 7.10E-09 3.80E-10 
Thyroid 8.00E-06 3.00E-02 2.40E-07 Poultry 8.90E-12 9.60E-10 1.70E-11 5.80E-11 1.50E-10 1.00E-11 1.00E-11 8.90E-12 8.90E-12 8.90E-12 2.20E-10 1.20E-11 
Bone Sur 3.70E-07 3.00E-02 1.10E-08 Cow Milk 2.60E-08 2.80E-06 4.90E-08 1.70E-07 4.50E-07 3.00E-08 3.00E-08 2.60E-08 2.60E-08 2.60E-08 6.50E-07 3.50E-08 
Stomach 3.40E-05 6.00E-02 2.00E-06 Eggs 7.50E-09 8.10E-07 1.40E-08 4.90E-08 1.30E-07 8.80E-09 8.80E-09 7.50E-09 7.50E-09 7.50E-09 1.90E-07 1.00E-08 
LL Int. 5.50E-06 6.00E-02 3.30E-07 Soil Ing 1.20E-11 1.30E-09 2.30E-11 7.70E-11 2.10E-10 1.40E-11 1.40E-11 1.20E-11 1.20E-11 1.20E-11 3.00E-10 1.60E-11 
UL Int. 2.10E-06 6.00E-02 1.20E-07 Water 1.70E-07 1.90E-05 3.30E-07 1.10E-06 3.10E-06 2.10E-07 2.10E-07 1.70E-07 1.70E-07 1.70E-07 4.40E-06 2.40E-07 
S Int. 6.00E-07 6.00E-02 3.60E-08 Fish 2.20E-09 2.40E-07 4.20E-09 1.40E-08 3.80E-08 2.60E-09 2.60E-09 2.20E-09 2.20E-09 2.20E-09 5.60E-08 3.00E-09 
Liver 4.30E-07 6.00E-02 2.60E-08 
Total 3.20E-07 3.40E-05 6.00E-07 2.10E-06 5.50E-06 3.70E-07 3.70E-07 3.20E-07 3.20E-07 3.20E-07 8.00E-06 4.30E-07 
Internal Effective Dose Equivale 3.00E-06 
External Dose 1.20E-10 Weighting 1.20E-01 6.00E-02 6.00E-02 6.00E-02 6.00E-02 3.00E-02 1.20E-01 2.50E-01 1.50E-01 3.00E-02 6.00E-02 
Annual Effective Dose Equivalen 3.00E-06 CEDE 
Total 
Inhale 1.32E-11 1.74E-12 2.70E-14 8.40E-14 2.16E-13 9.30E-15 3.72E-14 6.50E-14 0.00E+00 3.90E-14 2.07E-13 2.10E-14 1.56E-11 
Leaf Veg 1.07E-08 5.76E-07 1.02E-08 3.48E-08 9.00E-08 3.00E-09 1.20E-08 2.23E-08 0.00E+00 1.34E-08 6.60E-08 7.20E-09 8.45E-07 
Oth. Veg 1.32E-09 7.20E-08 1.20E-09 4.20E-09 1.14E-08 3.90E-10 1.56E-09 2.75E-09 0.00E+00 1.65E-09 8.10E-09 9.00E-10 1.05E-07 
Fruit 6.96E-10 3.78E-08 6.60E-10 2.28E-09 6.00E-09 2.04E-10 8.16E-10 1.45E-09 0.00E+00 8.70E-10 4.50E-09 4.80E-10 5.58E-08 
Cereals 6.96E-11 3.78E-09 6.60E-11 2.28E-10 6.00E-10 2.04E-11 8.16E-11 1.45E-10 0.00E+00 8.70E-11 4.50E-10 4.80E-11 5.58E-09 
Meat 3.36E-11 1.80E-09 3.18E-11 1.08E-10 2.94E-10 9.90E-12 3.96E-11 7.00E-11 0.00E+00 4.20E-11 2.13E-10 2.28E-11 2.66E-09 
Poultry 1.07E-12 5.76E-11 1.02E-12 3.48E-12 9.00E-12 3.00E-13 1.20E-12 2.23E-12 0.00E+00 1.34E-12 6.60E-12 7.20E-13 8.45E-11 
pathway rem/yr mrem/yr % Cow Milk 3.12E-09 1.68E-07 2.94E-09 1.02E-08 2.70E-08 9.00E-10 3.60E-09 6.50E-09 0.00E+00 3.90E-09 1.95E-08 2.10E-09 2.48E-07 
Inhale 1.56E-11 1.56E-08 C 0.0% Eggs 9.00E-10 4.86E-08 8.40E-10 2.94E-09 7.80E-09 2.64E-10 1.06E-09 1.88E-09 0.00E+00 1.13E-09 5.70E-09 6.00E-10 7.17E-08 
Leaf Veg 8.45E-07 8.45E-04 27.9% Soil Ing 1.44E-12 7.80E-11 1.38E-12 4.62E-12 1.26E-11 4.20E-13 1.68E-12 3.00E-12 0.00E+00 1.80E-12 9.00E-12 9.60E-13 1.15E-10 
Oth. Veg 1.05E-07 1.05E-04 3.5% Water 2.04E-08 1.14E-06 1.98E-08 6.60E-08 1.86E-07 6.30E-09 2.52E-08 4.25E-08 0.00E+00 2.55E-08 1.32E-07 1.44E-08 1.68E-06 
Fruit 5.58E-08 5.58E-05 1.8% Fish 2.64E-10 1.44E-08 2.52E-10 8.40E-10 2.28E-09 7.80E-11 3.12E-10 5.50E-10 0.00E+00 3.30E-10 1.68E-09 1.80E-10 2.12E-08 
Cereals 5.58E-09 5.58E-06 0.2% 
Meat 2.66E-09 2.66E-06 0.1% Total 3.84E-08 2.04E-06 3.60E-08 1.26E-07 3.30E-07 1.11E-08 4.44E-08 8.00E-08 0.00E+00 4.80E-08 2.40E-07 2.58E-08 3.02E-06 
Poultry 8.45E-11 8.45E-08 0.0% 
Cow Milk 2.48E-07 2.48E-04 8.2% 
Eggs 7.17E-08 7.17E-05 2.4% 
Soil Ing 1.15E-10 1.15E-07 0.0% 
Water 1.68E-06 1.68E-03 55.3% 
Fish 2.12E-08 2.12E-05 0.7% 
Ext. dose 1.20E-10 1.20E-07 0.0% 
Total 3.03E-06 3.03E-03 100% 
Figure VI-1. The Image of Excel Spreadsheet Showing Calculation of Pathway Contributions to BDCFs 

INTENTIONALLY LEFT BLANK