Draft Technical Report

GROUND WATER MODELING STUDIES AT IN 

SITU LEACHING FACILITIES AND 

EVALUATION OF DOSES AND RISKS TO OFFSITE RECEPTORS FROM CONTAMINATED 

GROUND WATER

REVISION 2

U.S. Environmental Protection Agency

Office of Radiation and Indoor Air

Radiation Protection Division 

1200 Pennsylvania Avenue

Washington, DC 20460

EPA-402-D-14-002
Draft Technical Report

GROUND WATER MODELING STUDIES AT IN SITU LEACHING 

FACILITIES AND EVALUATION OF DOSES AND RISKS TO OFF-SITE 

RECEPTORS FROM CONTAMINATED GROUND WATER

REVISION 2

Contract Number EP-D-10-042

Work Assignment 2-06, Task 2

Prepared by:

S. Cohen & Associates

1608 Spring Hill Road, Suite 400

Vienna, VA 22182-2241

Prepared for:

U.S. Environmental Protection Agency

Office of Radiation and Indoor Air

1200 Pennsylvania Avenue, N.W.

Washington, DC 20460

Kenneth Czyscinski

Work Assignment Manager

December 2012
i

Contents

List of Acronyms and Abbreviations...............................................................................................x

Executive Summary.........................................................................................................................1

1.0 Introduction...................................................................................................................... 1-1

2.0 Ground Water Modeling of In-Situ Leaching Failure Scenarios..................................... 2-1

2.1 In-Situ Leaching Process ..................................................................................... 2-1

2.2 Failures during Operations................................................................................... 2-3

2.3 Failures after Shutdown ....................................................................................... 2-5

2.4 Ground Water Model Development..................................................................... 2-6

2.4.1 Basic Aspects of Computer (Numerical) Modeling................................. 2-7

2.4.2 Conceptual Model Development ............................................................. 2-8

2.4.3 Computer Code Selection ........................................................................ 2-9

2.5 Representative ISL Facility Development and General Modeling Approach ... 2-10

2.6 Development of Scenarios................................................................................. 2-13

2.6.1 Spills and Leaks..................................................................................... 2-14

2.6.2 Excursion Scenarios............................................................................... 2-30

3.0 Pathway Dose and Risk Conversion Factors................................................................... 3-1

3.1 Pathway Dose and Risk Models .......................................................................... 3-3

3.1.1 Ingestion of Drinking Water.................................................................... 3-3

3.1.2 Inadvertent Ingestion of Soil.................................................................... 3-3

3.1.3 Ingestion of Vegetables............................................................................ 3-4

3.1.4 Ingestion of Milk...................................................................................... 3-5

3.1.5 Ingestion of Meat ..................................................................................... 3-6

3.1.6 Pathway Dose and Risk Factors............................................................... 3-6

3.1.7 Implementation ........................................................................................ 3-7

3.2 Input Parameters .................................................................................................. 3-7

3.2.1 Age Groups.............................................................................................. 3-7

3.2.2 Dose and Risk Conversion Coefficients.................................................. 3-9

3.2.3 Ingestion of Drinking Water.................................................................. 3-15

3.2.4 Inadvertent Ingestion of Soil.................................................................. 3-18

3.2.5 Ingestion of Vegetables.......................................................................... 3-20

3.2.6 Ingestion of Milk.................................................................................... 3-23

3.2.7 Ingestion of Meat ................................................................................... 3-27

3.3 Pathway Dose and Risk Conversion Factors..................................................... 3-29

3.4 Native American Exposures .............................................................................. 3-35

3.4.1 Ingestion of Drinking Water.................................................................. 3-35

3.4.2 Ingestion of Vegetables.......................................................................... 3-36

3.4.3 Ingestion of Meat ................................................................................... 3-37

3.4.4 Exposure in Sweat Lodge ...................................................................... 3-38

3.4.5 Native American Pathway Dose and Risk Conversion Factors............. 3-45

3.5 Exposure Pathways Not Analyzed in Detail...................................................... 3-47

3.5.1 External Exposure to Contaminated Ground ......................................... 3-48

3.5.2 Exposure to Indoor Radon ..................................................................... 3-48
ii

3.5.3 Swimming Pool/Hot Tub Exposures ..................................................... 3-50

3.5.4 Exposures Due to Hydroponics and/or Aquaculture ............................. 3-50

3.5.5 Embryo and Fetus Exposure .................................................................. 3-51

3.5.6 Infant Consumption of Formula/Milk.................................................... 3-52

4.0 Dose and Risk Assessment .............................................................................................. 4-1

4.1 Introduction.......................................................................................................... 4-1

4.2 Selection of Lixiviant Concentrations and Kds.................................................... 4-1

4.2.1 Lixiviant Concentration ........................................................................... 4-1

4.2.2 Selection of Kd Values............................................................................. 4-3

4.2.3 Recommendations for Kd and Radionuclide Source Term...................... 4-6

4.3 Dose and Risk Calculations................................................................................. 4-7

4.3.1 Limiting Doses and Risks........................................................................ 4-7

4.3.2 Doses and Risks from Excursion Scenarios............................................. 4-7

4.3.3 Doses and Risks from Surface Leak Scenarios...................................... 4-13

4.4 Risks to Non-standard Receptors....................................................................... 4-15

4.5 Non-Radiological Risks..................................................................................... 4-17

5.0 Summary and Conclusions .............................................................................................. 5-1

5.1 Ground Water Modeling Studies......................................................................... 5-1

5.2 Pathway Dose and Risk Conversion Factors....................................................... 5-2

5.3 Dose/Risk Assessment for Modeled Scenarios.................................................... 5-3

6.0 References........................................................................................................................ 6-1
iii

List of Tables

Table ES-1: Calculated Total Pathway Dose Conversion Factors...............................................4

Table ES-2: Calculated Total Pathway Risk Conversion Factors................................................4

Table ES-3: Typical Pathway Contributions to the Adult PDCF and PRCF...............................5

Table ES-4: Calculated Pathway Dose Conversion.....................................................................6

Table ES-5: Excursion Scenario Maximum Doses and Risks – Mean Adult..............................6

Table ES-6: Excursion Scenario Doses and Risks to Various Receptors....................................7

Table ES-7: Surface Leak Scenario Doses and Risks..................................................................7

Table 2-1: Representative ISL Parameter Ranges............................................................... 2-13

Table 2-2: Catastrophic Leak Failure Scenarios ................................................................. 2-19

Table 2-3: Long-Term/Low Volume Leak Scenarios......................................................... 2-22

Table 2-4: Short-Term/High-Volume Leak Scenarios........................................................ 2-28

Table 2-5: Series 1 – 5-Spot Injection at a Spacing of 250 feet – Receptor Well at 528 

feet...................................................................................................................... 2-37

Table 2-6: Series 2 – 5-Spot Injection at a Spacing of 50 feet – Receptor Well at 528 

feet...................................................................................................................... 2-38

Table 2-7: Series 3 – 7-Spot Injection at a Spacing of 250 feet – Receptor Well at 528 

feet...................................................................................................................... 2-41

Table 2-8: Series 4 – 7-Spot Injection at a Spacing of 50 feet – Receptor Well at 528 

feet...................................................................................................................... 2-44

Table 2-9: Series 5 – 5-Spot Injection\Pumping Rates Dependent Upon Hydraulic 

Conductivity....................................................................................................... 2-46

Table 2-10: Series 6 – 5-Spot 20-Foot Thick Mined Interval ............................................... 2-49

Table 2-11: Series 7 – Twenty-five 5-Spot Pumping/Injection Cells................................... 2-52

Table 2-12: Abandoned Borehole Simulations..................................................................... 2-54

Table 2-13: Confining Bed Discontinuity Simulations......................................................... 2-58

Table 3-1: Age Groups Used in the Analysis........................................................................ 3-9

Table 3-2: Radionuclide-Specific Ingestion Dose and Risk Coefficients........................... 3-11

Table 3-3: FGR 13 Drinking Water Cancer Morbidity / Mortality Risk Ratio................... 3-13

Table 3-4: Risk Coefficient Uncertainty ............................................................................. 3-14

Table 3-5: Age-Dependent Weight Normalized Water Consumption Rates ...................... 3-15

Table 3-6: Body Weight Distributions................................................................................ 3-16

Table 3-7: Soil + Dust Ingestion ......................................................................................... 3-19

Table 3-8: Element-Specific Input Parameters for Lognormal Distribution....................... 3-20

Table 3-9: Age-Dependent Weight Normalized Vegetable Consumption Rates................ 3-22

Table 3-10: Age-Dependent Weight Normalized Milk Consumption Rates ........................ 3-26

Table 3-11: Age-Dependent Weight Normalized Meat Consumption Rates........................ 3-28

Table 3-12: Calculated Total Pathway Dose Conversion Factors......................................... 3-29
iv

Table 3-13: Calculated Total Pathway Risk Conversion Factors.......................................... 3-30

Table 3-14: Typical Pathway Contributions to the Adult PDCF and PRCF......................... 3-31

Table 3-15: U-238+P Pathway Contributions to the Adult PDCF and PRCF ...................... 3-32

Table 3-16: Typical Pathway Contributions to the Teen PDCF and PRCF.......................... 3-33

Table 3-17: Typical Pathway Contributions to the Child PDCF and PRCF......................... 3-34

Table 3-18: Typical Pathway Contributions to the Infant PDCF and PRCF ........................ 3-34

Table 3-19: Radionuclide-Specific Inhalation Dose and Risk Coefficients.......................... 3-39

Table 3-20: Radionuclide-Specific Submersion Dose and Risk Coefficients....................... 3-40

Table 3-21: Other Parameters Used to Evaluate the Sweat Lodge Pathway......................... 3-41

Table 3-22: Air Moisture Content with Heat Indices of 130° F and 180° F ......................... 3-44

Table 3-23: Calculated Native American Total Pathway Dose and Risk Conversion 

Factors................................................................................................................ 3-46

Table 3-24: Native American Pathway Contributions to the PDCF ..................................... 3-46

Table 3-25: Native American Pathway Contributions to the PRCF...................................... 3-47

Table 3-26: External Exposure to Contaminated Ground Screening Study Results............. 3-48

Table 3-27: Data Used to Evaluate the PDCF for Home Radon Exposure........................... 3-49

Table 3-28: PDCF for Home Radon Exposure...................................................................... 3-50

Table 3-29: Water Immersion Screening Calculation........................................................... 3-50

Table 3-30: Dose Coefficients to the Offspring from Chronic Intake by the Mother........... 3-51

Table 3-31: PDCF to the Offspring from Chronic Intake by the Mother.............................. 3-52

Table 3-32: Dose and Risk Coefficients from Breast Milk Consumption ............................ 3-53

Table 3-33: PDCF/PRCF for Infant Milk Consumption ....................................................... 3-54

Table 4-1: Representative Concentrations in Uranium Alkaline ISR Lixiviants.................. 4-1

Table 4-2: Highest Observed Concentrations in Pregnant Lixiviants based on a 

Survey of Licensing Documents.......................................................................... 4-2

Table 4-3: Estimated Range of Kd Values for Lead as a Function of Soil pH, and 

Equilibrium Lead Concentrations........................................................................ 4-4

Table 4-4: Estimated Range of Kd Values for Uranium based on pH................................... 4-5

Table 4-5: Radium Kd Values by Soil Type.......................................................................... 4-5

Table 4-6: Summary of Kd Values and Lixiviant Concentrations Used in Dose and 

Risk Calculations................................................................................................. 4-6

Table 4-7: Radionuclide Well Limiting Concentrations....................................................... 4-7

Table 4-8: ISL Site Hydraulic Data....................................................................................... 4-8

Table 4-9: Excursion Scenario Maximum Doses and Risks – Mean Adult........................ 4-11

Table 4-10: Surface Leak Scenario Doses and Risks – Mean Adult..................................... 4-14

Table 4-11: Excursion Scenario Non-standard Receptor Doses and Risksa......................... 4-16

Table 4-12: Leak Scenario Non-standard Receptor Doses and Risksa.................................. 4-16

Table 4-13: Highest Contaminant Levels in Pregnant Lixiviant (mg/L) .............................. 4-18

Table 4-14: Scenario Maximum Contaminant Levels Versus Limit..................................... 4-18
v

Table 5-1: Typical Pathway Contributions to the Adult PDCF and PRCF........................... 5-2

Table 5-2: Typical Pathway Contributions to the PDCF and PRCF for U-234 or U-238..... 5-2

Table 5-3: Radionuclide Source Term Limiting Concentrations – Receptor Well 

at 528 ft ................................................................................................................ 5-3

Table 5-4: Summary of Excursion Runs............................................................................... 5-4

Table 5-5: Number of Simulations Resulting in Various Dose Levels as Function of 

Conductivity × Gradient Product......................................................................... 5-5

Table 5-6: Excursion Scenario Maximum Doses and Risks................................................. 5-6

Table 5-7: Summary of Dose Rate for Leak Scenarios – Adult Male Exposed to U nat 

at Receptor Well 328 ft Down-gradient............................................................... 5-6

Table 5-8: Comparison of Excursion Scenario Non-standard Receptor Doses and Risks 

Relative to 90th Percentile Adult.......................................................................... 5-7
vi

List of Figures

Figure 1-1: In-situ Uranium Recovery – Process Flow Diagram........................................... 1-2

Figure 2-1: Idealized Schematic Cross Section to Illustrate Ore-Zone Geology and 

Lixiviant Migration from an Injection Well to a Production Well (NRC 

2009b) .................................................................................................................. 2-1

Figure 2-2: Schematic Diagram of a Wellfield Showing Typical Injection/Production 

Well Patterns, Monitoring Wells, Manifold Buildings, and Pipelines (NRC 

2009b) .................................................................................................................. 2-2

Figure 2-3: Plan View of the Model Grid for Leak Scenarios.............................................. 2-15

Figure 2-4: Cross Sectional View of the Model Grid for Leak Scenarios............................ 2-16

Figure 2-5: Relative Concentrations for L10 at 140 Days.................................................... 2-23

Figure 2-6: Relative Concentrations for L11 at 140 Days.................................................... 2-24

Figure 2-7: Relative Concentrations for L13 at 877 Days.................................................... 2-25

Figure 2-8: Relative Concentrations for L15 at 877 Days.................................................... 2-26

Figure 2-9: Maximum Relative Concentrations versus Time for All Leak Simulations...... 2-29

Figure 2-10: Plan View of the Model Grid for Excursion Scenarios (Series 1 through 7) .... 2-31

Figure 2-11: Cross Section View of the Model Grid for Excursion Scenarios 

(Series 1 through 7)............................................................................................ 2-32

Figure 2-12: Plan View of Pumping\Injection Well Configurations and Receptor 

Locations for Excursion Scenarios (Series 1, 2, 5 and 6).................................. 2-33

Figure 2-13: Plan View of Pumping\Injection Well Configurations and Receptor 

Locations for Excursion Scenarios (Series 3 and 4).......................................... 2-34

Figure 2-14: Example of Breakthrough Curves at Receptor Locations.................................. 2-35

Figure 2-15: Plan View of the Twenty-five 5-spot Pumping/Injection Well 

Configurations and Receptor Locations for Excursion Scenario (Series 7) ...... 2-51

Figure 2-16: Plan View of the Model Grid for the Abandoned Borehole and 

Discontinuous Confining Bed Excursion Simulations ...................................... 2-55

Figure 2-17: Cross-Sectional View of the Model Grid for the Abandoned Borehole and 

Discontinuous Confining Bed Excursion Simulations ...................................... 2-56

Figure 2-18: Maximum Relative Concentrations versus Time for All Excursion 

Simulations ........................................................................................................ 2-60

Figure 2-19: Effect of Well Spacing at Constant Hydraulic Conductivity of 100 ft/day

and Constant Hydraulic Gradient of 0.001. ....................................................... 2-61

Figure 2-20: Effect of Well Spacing at Constant Hydraulic Conductivity of 100 ft/day

and Hydraulic Gradients of 0.1(Runs 1a, 2a) or 0.001 (Runs 1c, 2c)................ 2-61

Figure 2-21: Effect of Well Spacing at Constant Hydraulic Conductivity of 100 ft/day

and Hydraulic Gradients of 0.01(Runs 1b, 2b) and 0.001(Runs 1c, 2c)............ 2-62

Figure 2-22: Effect of Well Spacing on Concentration as a Function of Hydraulic 

Conductivity at a Constant Hydraulic Gradient of 0.1. ..................................... 2-62
vii

Figure 2-23: Effect of Well Spacing on Concentration as a Function of Hydraulic 

Conductivity at a Constant Hydraulic Gradient of 0.01. ................................... 2-63

Figure 2-24: Relative Concentrations as a Function of Hydraulic Conductivities 

and Gradients..................................................................................................... 2-63

Figure 2-25: Relative Concentrations as a Function of Hydraulic Gradient 

(Run 1d - 0.1, Run 1g - 0.01) and Hydraulic Conductivity of 1 ft/day.............. 2-64

Figure 2-26: Relative Concentrations as a Function of Hydraulic Gradient 

(Run 1f - 0.1, Run 1i – 0.01) and Hydraulic Conductivity of 100 ft/day .......... 2-64

Figure 2-27: Correlation of Relative Concentration to Pumping Rate ................................... 2-65

Figure 2-28: Correlation of Relative Concentration to Hydraulic Gradient........................... 2-66

Figure 2-29: Correlation of Relative Concentration to Hydraulic Conductivity (ft/day)....... 2-67

Figure 2-30: Correlation of Relative Concentration to Hydraulic Gradient times 

Hydraulic Conductivity...................................................................................... 2-68

Figure 2-31: Correlation of Relative Concentration to Well Spacing .................................... 2-69

Figure 2-32: Correlation of Relative Concentration to Pumping Array ................................. 2-70

Figure 2-33: Correlation of Relative Concentration to Model Layer..................................... 2-71

Figure 3-1: Exposure Pathways Analyzed.............................................................................. 3-2

Figure 3-2: Uranium Decay Series ....................................................................................... 3-10

Figure 3-3: Distribution of Age-Dependent Weight Normalized Water Consumption 

Rates................................................................................................................... 3-16

Figure 3-4: Distribution of Age-Dependent Water Consumption Rates Compared to 

Regulatory Guide 1.109 Recommended Maximum Rates ................................ 3-17

Figure 3-5: Distribution of Age-Dependent Water Consumption Rates Compared to the 

RESRAD Distribution ....................................................................................... 3-18

Figure 3-6: Distribution of Irrigation Water Application Rate............................................. 3-19

Figure 3-7: Distribution of Vegetable Yields....................................................................... 3-22

Figure 3-8: Distribution of Age-Dependent Weight Normalized 

Vegetable Consumption Rates........................................................................... 3-23

Figure 3-9: Distribution of Fodder (Dry) Yield.................................................................... 3-25

Figure 3-10: Distribution of Age-Dependent Weight Normalized Milk Consumption 

Rates................................................................................................................... 3-26

Figure 3-11: Distribution of Age-Dependent Weight Normalized Meat Consumption 

Rates................................................................................................................... 3-29

Figure 3-12: Cumulative Distributions of Pathway Contribution to the Total Adult U238+P PCDF/PRCF ........................................................................................... 3-33

Figure 3-13: Native American Drinking Water Rate Within the 

Exposure Factors Handbook Distribution......................................................... 3-36

Figure 3-14: Native American Vegetable Consumption Within the 

Exposure Factors Handbook Distribution......................................................... 3-37

Figure 3-15: Native American Meat Consumption Within the 

Exposure Factors Handbook Distribution......................................................... 3-38
viii

Figure 4-1: Cumulative Distribution of ISL Site Hydraulic Data .......................................... 4-9

Figure 4-2: Excursion Scenario 6E Peak Dose Arrival Time, Kd = 0.4 ml/g ....................... 4-10

Figure 4-3: Excursion Scenario Dose Results versus Time.................................................. 4-10

Figure 4-4: Excursion Scenario Dose Results versus Hydraulic Data.................................. 4-11

Figure 4-5: Excursion Scenario Risk Results versus Time................................................... 4-12

Figure 4-6: Excursion Scenario 6E Peak Dose Arrival Time, Kd = 4.0 ml/g ....................... 4-13

Figure 4-7: Surface Leak Scenario Risk Results versus Time.............................................. 4-14

Figure 4-8: Surface Leak Scenario Risk Results versus Time.............................................. 4-14

Figure 4-9: 28 Day Surface Leak Scenario Doses from Uranium........................................ 4-15
ix

Appendices

Appendix A: Verification of Codes

Appendix B: Concentration Breakthrough Curves for Leak Scenarios Runs L-1 through L-24

Appendix C: Concentration Breakthrough Curves for Excursion Runs 1a through 7g

Appendix D: Concentration Breakthrough Curves for Abandoned Boreholes and Discontinuous 

Confining Unit Runs AB-R1, AB-R2, AB-R3, CBD-R1, and CBD-R2
x

LIST OF ACRONYMS AND ABBREVIATIONS

AMG algebraic multi-grid

ATSDR Agency for Toxic Substances and Disease Registry

Bq/kg Becquerel per kilogram

CC Change Control

CCA Compliance Certification Application

CFR Code of Federal Regulations

cm3/g cubic centimeter per gram

CNWRA Center for Nuclear Waste Regulatory Analyses

COPC contaminant of potential concern

CRA Compliance Recertification Application

CTUIR Confederated Tribes of the Umatilla Indian Reservation

CVS Concurrent Versions System

DD Design Document

DDREF dose and dose-rate effectiveness factor

DF Decontamination Factor

DFSL Sweat Lodge Evaporation Decontamination Factor

DOE U.S. Department of Energy

DW drinking water

EFH Exposure Factors Handbook 

EPA U.S. Environmental Protection Agency

FGR 13 Federal Guidance Report No. 13

ft/day foot per day

g/cm3gram per cubic centimeter

g/day gram per day

GCGCD Goliad County Groundwater Conservation District 

GM geometric mean

gpm gallons per minute

GSD geometric standard deviation

I&C Installation & Checkout

ICRP International Commission on Radiological Protection

ID Implementation Document

ISCORS Interagency Steering Committee on Radiation Standards

ISL in-situ leaching 
xi

ISR in-situ recovery

Kd distribution coefficient

kg/m2kilogram per square meter

kg/yr kilogram per year

L liter

LCF latent cancer fatality

LET linear energy transfer

LMG Linked algebraic Multi-Grid solver

m meter

m

3

cubic meter

MSL Sweat Lodge Air Moisture Content

mg/L milligram per liter

ml/g milliliter per gram

mrem millirem

NAS National Academy of Sciences

NCRP National Council on Radiation Protection and Measurements

NRC U.S. Nuclear Regulatory Commission

NWS National Weather Service

PABC Performance Assessment Baseline Calculation

PCG Preconditioned Conjugate Gradient

pCi picocurie

PDCF pathway dose conversion factor

pH measure of the acidity or alkalinity of a solution

PHA public health assessment 

PRCF pathway risk conversion factor

psig pounds per square inch, gauge

QA Quality Assurance

RBE relative biological effectiveness

RCRA Resource Conservation and Recovery Act

RD Requirements Document

Rf retardation factor

SAB Science Advisory Board (EPA)

SC&A S. Cohen and Associates

SCMS Software Configuration Management Plan
xii

SIP Strongly Implicit Procedure

SPR Software Problem Report

Sv sievert

UCL upper control limits

UIC underground injection control

UM User’s Manual

UMTRCA Uranium Mill Tailings Radiation Control Act

U nat natural uranium

USCB U.S. Census Bureau

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey

VD Validation Document

VMS Versions Management System

VVP Verification and Validation Plan

WA work assignment

WDEQ Wyoming Department of Environmental Quality

WIPP Waste Isolation Pilot Plant

WSDOH Washington State Department of Health

μCi/ml microcurie per milliliter
ES-1

EXECUTIVE SUMMARY

EPA is proposing revisions to 40 CFR 192 to create a new subsection dealing specifically with 

in-situ mining of uranium. Since 40 CFR 192 was promulgated in 1983, there has been a shift in 

uranium recovery from conventional milling to in-situ leaching (ISL). In the ISL process,1

chemical solutions are pumped underground through an array of wells into the ore body, where 

the uranium is dissolved in place. The uranium-rich solutions are pumped to the surface, where 

the uranium is extracted. The solutions are then chemically refortified and pumped back into the 

ore body to recover additional uranium. Several mechanisms can be postulated by which 

contaminants can leak from the ISL site and migrate to off-site wells. This report describes the 

ground water modeling studies and the calculated doses and risks to receptors residing downgradient from a hypothetical ISL site who obtain water for drinking and agricultural purposes 

from a contaminated well on their property.

The steps involved in modeling doses and risks to receptors from contaminated ground water

contaminated with radionuclides are as follows:

(1) Based on an assumed initial unit source concentration Ci for the i

th radionuclide (1 mg/L), 

calculate the relative concentration in the ground water at a down-gradient receptor well 

(CR). Convert to units of pCi/m3based on specific activities.

(2) Develop pathway dose and risk conversion factors for all components of the ingestion 

pathway, such as drinking water. For example, based on an annual drinking water 

consumption IngWat, (m3/yr) and a dose conversion factor DCIng (mrem per pCi) from 

EPA’s Federal Guidance Report 13, the pathway dose conversion factor PDCFwat

(mrem/yr per pCi/m3) is:

PCDFwat = Ingwat × DCing

(3) Multiply the relative down-gradient receptor well concentration (CR) by the ratio of the 

actual source concentration, CS, to the unit source concentration (CS/Ci ) to obtain the 

actual down-gradient concentration, CW. Then multiply CW by the pathway dose 

conversion factor to obtain the annual dose, Ewat (mrem/yr) to an individual from 

drinking contaminated water from the receptor well:

Ewat = Cw×PDCFwat

The ground water modeling studies used the computer codes MODFLOW-2000 and MT3D. For 

all ground water simulations, an arbitrary source term concentration of 1 mg/L was assumed and 

concentrations at down-gradient receptor wells were calculated relative to this source term. The 

simulations were based on a hypotheticqal ISL site rather than a specific site and input 

parameters were selected according 

 

1

In-situ leaching (ISL) is also referred to as in-situ recovery (ISR).
ES-2

Scenarios evaluated included excursions from wellfields which remain contained in the orebearing aquifer, catastrophic surface leaks such as that which might occur from a break in 

process piping, and slow surface leaks. For the surface leak scenarios, the leakage was assumed 

to reach an aquifer overlying the ore-bearing aquifer. Excursions from the ore zone to an 

overlying aquifer through an abandoned borehole and or through a natural pathway (e.g., a 

discontinuity in the intervening aquitard) between the ore-bearing aquifer and an overlying 

aquifer were also modeled. 

A total of 63 excursion simulations involving transport of contaminants within the ore-bearing 

aquifer were run, of which 49 were unique simulations and the remaining 14 duplicated other 

model runs. Receptor wells for the leak scenarios were assumed to be located 328 ft, 656 ft, 

1,640 ft and 3,280 ft down-gradient, while the excursion scenarios set the receptor well distances 

at down-gradient distances of 528 ft, 856 ft, 1,840 ft and 3,480 ft. The excursion scenarios

involved operating an injection/extraction pattern(s) for 3 years and then shutting down the 

injection wells, but continuing to remove fluids from the extraction wells. The simulations were 

designed to evaluate the sensitivity of various physical and hydrogeologic properties to the 

relative down-gradient concentrations at receptor wells. Variables examined in the excursion 

simulations included:

 Well spacings (50, 150, and 250 ft)

 Hydraulic conductivity (1, 10, and 100 ft/day)

 Hydraulic gradient (0.001, 0.01, and 0.1 ft/ft)

 Pumping pattern (5-spot, multiple 5-spot, and 7-spot)

 Injection rates (7, 50, 150, and 500 gpm)

 Ore zone thickness (20 and 70 ft)

In all scenarios, the extraction rate was 2% greater than the injection rate during operation.

It is difficult to develop general conclusions from these excursion simulations because results of 

comparisons designed into the modeling runs are at times counter-intuitive. In spite of this 

difficulty, some conclusions are provided below, but the reader is cautioned to read the full text 

for complete understanding as to the limitations of these conclusions:

 As expected, steeper hydraulic gradients result in shorter travel times. Furthermore, since 

the pumping/injection wells are altering the regional hydraulic gradients, the arrival times 

are not linearly scaled. 

 An increase in hydraulic conductivity causes the contaminant plume to become more 

elongated and leads to lower relative peak concentrations.

 At higher regional gradients, wider well spacings provide better capture of the lixiviant. 

At lower regional hydraulic gradients, however, better capture can be maintained at 

smaller well spacings.

 The 7-spot well pattern results in lower relative peak concentrations for all of the runs as 

compared to a 5-spot pattern. 

 The effect of pumping/injection on hydraulic gradients is strongly affected by the 

transmissivity (i.e., hydraulic conductivity multiplied by thickness) of the geologic units. 
ES-3

The lower transmissivity results in shorter times to peak arrivals at the low and high 

gradients and a longer time at the medium gradient. These relationships are all related to 

how the regional and localized gradients created by the pumping/injection interact to 

form a capture zone. It also illustrates the complexity and need to understand the sitespecific geology and flow system, since the effects of the interactions are not always 

intuitive.

 Comparison of a single 5-spot pattern with a multiple (25) 5-spot pattern shows that 

relative concentrations at down-gradient receptor wells are lower with the multiple 5-spot 

pattern. These results are explained by the larger capture zone that is created by the array

of pumping/extraction wells.

Transport from the mined aquifer to an overlying aquifer through an abandoned borehole that 

was not properly cemented was also evaluated. This excursion scenario can result in significant 

down-gradient leakage. This emphasizes the need to carefully cement, test and inspect 

abandoned boreholes to insure their integrity. 

As the first step in the dose/risk assessment, probabilistic pathway dose and risk conversion 

factors (PDCFs and PRCFs) were developed for the ingestion exposure pathway. For most of the 

scenarios considered here, ingestion is the only significant pathway. PDCFs/PRCFs relate the 

dose/risk received by an individual who utilizes the contaminated well water to the radionuclide 

concentration in the well water (e.g., millirem/year per picocurie/m3). Radionuclide specific 

pathway dose/risk conversion factors were calculated for U-234, U-238, Th-230 Ra-226, and 

Pb-210 based on conversion factors from FGR-13 and its supporting documents. Pathways 

evaluated included ingestion of milk, meat, water, contaminated soil, and vegetables. The basic 

mathematical models used to calculate the dose and risk from the ingestion pathways for this 

analysis were obtained from the Nuclear Regulatory Commission’s (NRC’s) Regulatory Guide 

1.109. Although the numerical values for many of the parameters given in Regulatory Guide 

1.109 have been updated since its publication more than 30 years ago, the basic mathematical 

models remain valid and form the basis for many of today’s computer programs used to calculate 

radiological impacts. While the Regulatory Guide 1.109 models form the basis for many of 

today’s computer programs, those computer programs often used more refined models to better

reflect reality. When appropriate, these refined models have been used in this analysis.

Probabilistic PDCFs and PRCFs were generated for four age groups (Infant, Child, Teen and 

Adult) using Excel spreadsheets and Crystal Ball to execute Monte Carlo calculations. Table 

ES-1 and Table ES-2 summarize these factors. Mean pathway dose conversion factors were 

lowest for adults and increased for younger people.

 
ES-4

Table ES-1: Calculated Total Pathway Dose Conversion Factors

Adult Teen Child Infant

Nuclide Mean PDCF (mrem/yr / pCi/m3)

Pb-210+P 6.28E-03 7.33E-03 1.10E-02 3.89E-02

Ra-226+P 1.30E-02 1.98E-02 2.62E-02 7.06E-02

Th-230 6.02E-04 3.62E-04 2.89E-04 4.27E-03

U-234 1.61E-04 1.53E-04 1.33E-04 4.19E-04

U-238+P 1.55E-04 1.47E-04 1.40E-04 4.24E-04

Nuclide Median PDCF (mrem/yr / pCi/m3)

Pb-210+P 5.16E-03 5.17E-03 6.91E-03 3.77E-02

Ra-226+P 1.01E-02 1.39E-02 1.65E-02 5.76E-02

Th-230 5.30E-04 2.76E-04 2.54E-04 4.37E-03

U-234 1.40E-04 1.15E-04 1.01E-04 4.11E-04

U-238+P 1.35E-04 1.10E-04 1.06E-04 4.16E-04

Nuclide 90th Percentile PDCF (mrem/yr / pCi/m3)

Pb-210+P 1.06E-02 1.31E-02 1.40E-02 6.31E-02

Ra-226+P 2.23E-02 3.55E-02 4.28E-02 1.14E-01

Th-230 1.09E-03 7.43E-04 5.09E-04 7.28E-03

U-234 2.77E-04 2.75E-04 1.93E-04 6.97E-04

U-238+P 2.68E-04 2.64E-04 2.02E-04 7.06E-04

Table ES-2: Calculated Total Pathway Risk Conversion Factors

Adult Teen Child Infant

Nuclide Mean PRCF (LCF/yr / pCi/m3)

Pb-210+P 1.12E-09 1.62E-09 2.90E-09 4.33E-09

Ra-226+P 2.25E-09 3.83E-09 7.25E-09 7.60E-09

Th-230 3.07E-11 4.52E-11 4.69E-11 1.31E-10

U-234 2.21E-11 4.22E-11 6.92E-11 1.13E-10

U-238+P 2.28E-11 4.91E-11 9.00E-11 1.54E-10

Nuclide Median PRCF (LCF/yr / pCi/m3)

Pb-210+P 9.24E-10 1.14E-09 1.82E-09 4.20E-09

Ra-226+P 1.73E-09 2.69E-09 4.56E-09 6.23E-09

Th-230 2.69E-11 3.44E-11 4.13E-11 1.34E-10

U-234 1.91E-11 3.16E-11 5.23E-11 1.11E-10

U-238+P 1.98E-11 3.67E-11 6.80E-11 1.51E-10
ES-5

Table ES-2: Calculated Total Pathway Risk Conversion Factors

Adult Teen Child Infant

Nuclide 90th Percentile PRCF (LCF/yr / pCi/m3)

Pb-210+P 1.89E-09 2.88E-09 3.69E-09 7.00E-09

Ra-226+P 3.86E-09 6.88E-09 1.19E-08 1.23E-08

Th-230 5.60E-11 9.27E-11 8.27E-11 2.22E-10

U-234 3.79E-11 7.58E-11 1.00E-10 1.89E-10

U-238+P 3.92E-11 8.80E-11 1.30E-10 2.56E-10

LCF = latent cancer fatality

Comparing Table ES-1 to Table ES-2 shows that the dose-to-risk relationship varies by a little 

over an order of magnitude, from about 3×10-8to 6×10-7latent cancer fatality per millirem, 

depending on the radionuclide and age group. 

The relative contributions for the modeled ingestion pathways for an adult are summarized in 

Table ES-3.

Table ES-3: Typical Pathway Contributions to the Adult PDCF and PRCF

Pathway – Adult Pb-210+P Ra-226+P Th-230 U-234 U-238+P

Ingestion of Drinking Water 78.3% 51.2% 82.8% 76.2% 76.2%

Inadvertent Ingestion of Soil 0.1% 0.2% 0.3% 0.3% 0.3%

Ingestion of 

Vegetables

Leaf Deposition 13.5% 8.8% 14.3% 13.1% 13.1%

Root Uptake 2.3% 26.5% 2.1% 2.9% 2.9%

Ingestion of Meat

Cattle Drinking 0.3% 0.2% 0.0% 0.3% 0.3%

Leaf Deposition 1.6% 1.3% 0.2% 1.5% 1.5%

Root Uptake 0.3% 1.1% 0.0% 0.3% 0.3%

Soil Ingestion 0.1% 0.3% 0.0% 0.3% 0.3%

Ingestion of Milk

Cow Drinking 0.3% 0.7% 0.0% 0.4% 0.4%

Leaf Deposition 2.2% 4.8% 0.0% 2.8% 2.8%

Root Uptake 0.5% 4.1% 0.0% 0.6% 0.6%

Soil Ingestion 0.5% 1.0% 0.2% 1.2% 1.2%

The water ingestion pathway dominates for all of the radionuclides.

Deterministic PDCFs and PRCFs were also developed for Native Americans whose lifestyle was

significantly different from the standard adult receptor. Insufficient data on intake parameters 

were available to do these analyses probabilistically. In addition to the ingestion pathways 

evaluated for the standard receptors, exposures from sweat lodge rituals were included. Added 

pathways involved submersion in a steam vapor cloud and inhalation of contaminated steam. 

Results for PDCFs for mean adults and Native Americans are compared in Table ES-4. The 

PDCFs for Native Americans are about 2 to 3 times higher than the mean for standard adult 

receptors for Ra-226+P, Th-230, U-234, and U-238+P. For Pb-210+P, the PDCF for the mean 

adult is higher than for the Native American.
ES-6

Table ES-4: Calculated Pathway Dose Conversion

Factors for Native Americans and Adults (mean)

Nuclide Native American PDCF 

(mrem/yr / pCi/m3)

Adult (mean) PDCF

(mrem/yr / pCi/m3)

Pb-210+P 5.10E-03 6.28E-03

Ra-226+P 2.68E-02 1.30E-02

Th-230 1.94E-03 6.02E-04

U-234 4.14E-04 1.61E-04

U-238+P 3.70E-04 1.55E-04

Doses and risks were calculated for many of the excursion simulations within the ore-bearing 

aquifer using expected concentrations in the leaking lixiviant. In some cases, the excursion 

simulations used combinations of hydraulic parameters that were outside the ranges for operating 

ISL sites. This was done to evaluate the sensitivity of various ground water modeling parameters 

to variations in model output. However, dose and risk assessments were limited to parameter 

ranges expected at operating facilities. Based on available site information, a cumulative 

distribution function of hydraulic gradient × hydraulic conductivity (as a surrogate for ground 

water velocity) was developed. From this distribution function, it was determined that all values 

of the conductivity/gradient product were <0.13 ft/day. Of the 29 unique excursion simulations 

that met the conductivity × gradient cutoff of 0.13 ft/day, the dose from uranium was >15

mrem/yr in 17 simulations. The highest estimated dose from uranium at a receptor well 528 ft 

down-gradient was 10,072 mrem/yr, while the lowest estimated dose was 1.69E-12 mrem/yr. 

The annual uranium dose at 528 ft down-gradient was less than 15 mrem for all of the scenarios 

with a hydraulic conductivity of 1 ft/day and a hydraulic gradient of 0.001.

Table ES-5 shows the maximum calculated dose for various radionuclides from all 37 excursion 

scenarios that were analyzed for the mean adult. Clearly, uranium and Ra-226 (+ progeny) are 

the significant contributors to dose and risk. The contribution from Th-230 is one to two orders 

of magnitude lower.

Table ES-5: Excursion Scenario Maximum Doses and Risks –

Mean Adult

Nuclide Dose (mrem/yr) Risk (LCF/yr)

U nat 1.0E+04 1.4E-03

Th-230 2.4E+02 1.2E-05

Ra-226+P 2.8E+04 4.8E-03

Table ES-6 summarizes doses and risks to various receptors from excursion scenarios. From this 

table, it is apparent that the Mean Infant is the recipient of the largest calculated doses and risks. 

The Mean Infant doses and risks are about a factor of three to eight times larger than the standard 

receptor doses and risks. The dose and risk ratios of the other non-standard receptors to the 

standard receptor are less than for the Mean Infant. For example, the Native American has a 

calculated uranium risk that is 3.1 times greater than the standard receptor’s mean uranium risk. 

The maximum doses and risks in Table ES-6 are for particular combinations of wellfield design 

parameters and hydrogeologic parameters. However, as described above, various combinations 
ES-7

of wellfield design and hydrogeologic parameters based on actual ISL facilities can result in 

annual doses of less than 15 mrem.

Table ES-6: Excursion Scenario Doses and Risks to 

Various Receptors

Receptor Nuclide

Maximum

Dose 

(mrem/yr)

Risk 

(LCF/yr)

90th Percentile Adult

U nat 1.7E+04 2.5E-03

Th-230 4.4E+02 2.2E-05

Ra-226+P 4.7E+04 8.2E-03

Mean Teenager

U nat 9.6E+03 2.9E-03

Th-230 1.5E+02 1.8E-05

Ra-226+P 4.2E+04 8.2E-03

Mean Child

U nat 8.7E+03 5.1E-03

Th-230 1.2E+02 1.9E-05

Ra-226+P 5.6E+04 1.5E-02

Mean Infant

U nat 2.7E+04 8.5E-03

Th-230 1.7E+03 5.3E-05

Ra-226+P 1.5E+05 1.6E-02

Native American

U nat 2.5E+04 4.3E-03

Th-230 7.8E+02 4.0E-05

Ra-226+P 5.7E+04 1.0E-02

a – Doses for Ra-226 include progeny

Three surface leakage scenarios were evaluated: (1) catastrophic spills ranging from 100,000 to 

200,000 gallons, (2) a slow leak of 1 to 2 gpm for a period of 3 years, and (3) leaks varying from 

1 to 40 gpm over a 28-day period. The highest doses were incurred for scenarios involving a 

slow leak over a 3-year period, while the lowest doses resulted from a 1 gpm surface leak over a 

28-day period. For all scenarios, the annual doses to a mean adult from U nat were greater than 

15 mrem. Results are summarized in Table ES-7.

Table ES-7: Surface Leak Scenario Doses and Risks

Dose (mrem/yr) Risk (LCF/yr)

Minimum Maximum Minimum Maximum

U nat 3.2E+01 1.7E+03 4.5E-06 2.4E-04

Th-230 7.6E-01 4.0E+01 3.9E-08 2.0E-06

Ra-226 + P 8.7E+01 4.6E+03 1.5E-05 7.9E-04

The scenarios examined focus on failures of the ISR operations that are possible but unlikely if 

the operations are carefully monitored by the operators and the regulatory authorities. These 

failure scenarios contain some conservative assumptions that are typical of risk assessments and 

may not apply in any specific situation because of population distribution differences and the 

magnitude and duration of the exposure scenarios examined versus an actual occurrence. Results

of these calculations point to the need for a rigorous regulatory regime applied to ISR operations. 
ES-8

Rigorous monitoring of the ISR operations would minimize the potential for these failure 

scenarios and minimize exposures if they were to happen. 
1-1

1.0 INTRODUCTION 

In 1983, the U.S. Environmental Protection Agency (EPA) promulgated regulations at 40 CFR 

Part 192 – Health and Environmental Protection Standards for Uranium and Thorium Mill 

Tailings in response to the statutory requirements of the Uranium Mill Tailings Radiation 

Control Act (UMTRCA) of 1978 and the Atomic Energy Act of 1954 as amended. At the time 

40 CFR 192 was promulgated, uranium recovery from ore was based almost exclusively on the 

conventional milling process, where a few pounds of uranium were recovered for each ton of ore 

mined and processed (milled). The residues from the milling process (the tailings) were 

accumulated in large piles on the surface at the mill site. Concern that these tailings piles would 

be a continuing source of radiation exposure unless properly reclaimed was the driving force 

behind the passage of UMTRCA. Virtually no attention was directed to other uranium recovery

operations, such as heap leaching and in-situ leaching, since at that time, the major 

environmental risk was perceived to come from the conventional uranium mill tailings piles.

EPA’s Office of Air and Radiation (ORIA) is currently reviewing 40 CFR Part 192 to determine 

what, if any, revisions are needed to the regulations to bring them up to date. In support of 

EPA’s effort, ORIA requested SC&A, Inc. (SC&A) to perform a series of studies and analyses 

that evaluate the potential impacts to individuals living near an operating ISL uranium recovery

facility, 

The regulations under review by EPA establish standards for protection of the public health, 

safety, and environment from radiological and non-radiological hazards associated with uranium 

and thorium ore processing and their associated wastes. The cross-media standards apply to 

pollution emissions and site restoration. The existing 40 CFR Part 192 is utilized by the U.S. 

Nuclear Regulatory Commission (NRC) and its Agreement States,2and the U.S. Department of 

Energy (DOE) in their oversight of uranium extraction facility licensing, operations, sites, and 

wastes. UMTRCA requires EPA to develop health and environmental standards for both Title I 

inactive mill sites administered by the DOE and Title II and future NRC-licensed sites. For 

future NRC-licensed sites, the standards shall be “... consistent with the standards required under 

subtitle C of the Solid Waste Disposal Act,3as amended, which are applicable to such hazards.” 4

Since 40 CFR 192 was promulgated, there has been a shift in uranium recovery from 

conventional milling to in-situ leaching (ISL) where, in a sense, a portion of the milling process 

is conducted underground within the ore body. In the ISL process,5chemical solutions are 

pumped underground through an array of wells into the ore body, where the uranium is dissolved 

in place. A process flow diagram for a ISL facility is shown in Figure 1-1. The uranium-rich 

solutions are pumped to the surface, where the uranium is extracted. The solutions are then 

chemically refortified and pumped back into the ore body to recover additional uranium.

 

2 There are currently 37 Agreement States that are responsible through the state radiation control directors and 

staff, under authority of Section 274 of the Atomic Energy Act of 1954, as amended, to regulate certain uses of 

radioactive materials within the state.

3 Now known as the Resource Conservation and Recovery Act (RCRA). 

4 U.S.C. Title 42, Chapter 23, Section 2022.

5

In-situ leaching (ISL) is also referred to as in-situ recovery (ISR).
1-2

Figure 1-1: In-situ Uranium Recovery – Process Flow Diagram

ORIA requested advice from the EPA Science Advisory Board (SAB) related to the Agency’s 

review of 40 CFR Part 192 with regard to ISL facilities. SC&A provided a separate report 

summarizing relevant background information and statistical modeling approaches to assist EPA 

in defining the technical issues to be considered by the SAB (SC&A 2011a). Additionally, 

SC&A performed several other technical ISL studies in support of ORIA’s review of Part 192. 

For example, under Task 4 of Work Assignment (WA) 1-11 (Contract EP-D-10-042), SC&A 

was required to collect information pertinent to site characteristics of existing heap and in-situ 

leaching operations, including ground water quality and chemistry before and after leaching. 

This information was summarized in a report entitled Modeling of Heap Leach and In-Situ 

Leaching Operations (SC&A 2011b). This report was revised under Task 2 of WA 2-06 (SC&A 

2011c) (Contract EP-D-10-042). In addition, under WA 4-17, Task 6 (Contract No. EP-D-05-

002), SC&A prepared a database of ground water information as described in Database 

Summary Analysis Report: Uranium Mills and In-Situ Leach Facilities (SC&A 2008). These 

reports provided background information used to develop the current report. 

This report presents the results of ground water modeling studies on possible releases from 

hypothetical ISL faculties based on a range of release scenarios. The studies were designed to 

demonstrate whether or under what conditions leakage of various types from an ISL site to a 

down-gradient receptor well can result in hazardous situations. The studies do not address 
1-3

specific sites. The modeling studies considered the effects of a range of site and hydrologic 

variables on releases to a down-gradient receptor well. The intent was to systematically examine 

how changing these parameters alters the concentrations of various potentially hazardous 

constituents (mainly radionuclides) at down-gradient well relative to the concentration in the ore 

zone. As such, the ground water modeling studies did not consider retardation effects, such as 

radiological decay or other removal mechanisms. The relative concentrations at the receptor 

wells were subsequently converted to specific concentrations by taking into account the 

concentrations of specific elements at the point of release (based on reported values from actual 

ISL sites). Retardation was considered only to the extent that it staggered the arrival times of the 

different radionuclides at the receptor well. These specific down-gradient concentrations were 

then used to calculate doses and risks using a stochastic, ingestion-based biosphere model. 

Unlike the ground water modeling, the dose and risk modeling was limited to those combinations 

of hydraulic parameters that are representative of actual ISL sites. Ingestion of well water and 

products contaminated by well water are typically the dominant exposure pathways.

This report is not intended to provide a template as to how ground water modeling studies should 

be conducted at specific sites to support specific regulatory requirements, but rather its purpose 

is to scope the magnitude of potential problems that may be caused by mining solutions escaping 

from ISL wellfields. As such, in some instances assumptions were made that might result is 

lower radionuclide concentrations at the receptor well and/or lower doses/risks. This approach 

was acceptable here, since higher concentrations and/or doses/risks would only serve to make the 

potential problem worse. This is the converse to a typical site specific ground water modeling 

study, which would tend to make assumptions that result in maximizing the receptor well 

concentrations and/or doses/risks.

This report is divided into six chapters. Following this introductory chapter, Chapter 2 describes 

the ground water modeling studies, which employed the computer codes MODFLOW-2000 and 

MT3D. Scenarios evaluated included excursions from wellfields, which remain in the orebearing aquifer; catastrophic surface leaks, such as those which might occur from a break in 

process liquor piping; and slow surface leaks. For the surface leak scenarios, the leakage was 

assumed to reach an aquifer overlying the ore-bearing aquifer. Leaks from the ore zone to an 

overlying aquifer through an abandoned borehole and or through a natural pathway between the 

ore-bearing aquifer and an overlying aquifer were also modeled. One of the key objectives of the 

ground water modeling studies was to evaluate the range of possible releases that might occur

under various hydrologic regimes. For example, the impact of hydraulic conductivity and 

hydraulic gradient were each examined over a range covering two orders of magnitude. While 

this approach was useful in delineating the sensitivity of each variable, unrealistic combinations 

of parameters could result. For example, in studying the effect of hydraulic conductivity, the 

procedure is to select a reasonable value of hydraulic gradient (based on reported data) and vary 

the conductivity over the range selected for evaluation. In this way one obtains a clear picture of 

the effect of releases as a function of hydraulic conductivity at a fixed gradient. While the 

generic effects are clearly delineated, the combinations of conductivity and gradient in some 

simulations may exceed actual site values. This possibility is addressed in the risk assessments 

by limiting the combinations of hydraulic gradient and hydraulic conductivity to those reported 

for actual ISL sites. The ground water modeling assumed a constant source term of 1 mg/L and 

did not consider radionuclide-specific retardation.
1-4

In Chapter 3 of this report, we describe the development of probabilistic pathway dose and risk 

conversion factors (PDCFs and PRCFs). Radionuclide-specific dose and risk conversion factors 

were based on Federal Guidance Report 13 (Eckerman et al. 1999) and information supporting 

that document. The PDCFs and PRCFs were developed for standard receptors and for nonstandard receptors (specifically Native Americans and young children). As noted above, the 

ingestion pathways are the primary exposure pathways considered, except for Native Americans, 

where exposure during sweat lodge rituals is also included as an exposure pathway. Discussion 

of other exposure pathways is provided to indicate that their contribution to total radiation 

exposure is minimal. 

In Chapter 4, doses and risks for various scenarios are developed, based on: (1) source terms for 

ISL sites reported in the literature, (2) documented values of distribution coefficients (Kds), (3) 

ground water dispersion, as documented in Chapter 2, and (4) the PDCFs and PRCFs developed 

in Chapter 3. Since the radionuclides of interest have long half-lives (i.e., U-234, U-238, Th-230, 

and Ra-226), ground water retardation delays the time to peak dose, but not its magnitude. Doses 

and risks are calculated for both standard and non-standard receptors. The standard receptor was 

assumed to be an adult individual who represents the 50th percentile dose/risk. Non-standard 

receptors include adult individuals at the 90th percentile, teenagers, children, infants, and Native 

Americans.

Summary and conclusions are presented in Chapter 5, followed by the last chapter which 

provides a list of references used to develop this report.
2-1

2.0 GROUND WATER MODELING OF IN-SITU LEACHING FAILURE 

SCENARIOS

2.1 In-Situ Leaching Process

During ISL operations, chemicals such as sodium carbonate/bicarbonate, ammonia, sulfuric acid, 

gaseous oxygen, and hydrogen peroxide are added to the ground water to produce a concentrated 

oxygen-rich leaching solution called the lixiviant. The lixiviant is injected into the production 

zone to mobilize (dissolve) uranium from the underground formation, and this mobilized 

uranium is pumped back to the surface for extraction at a processing plant (Figure 2-1).

Figure 2-1: Idealized Schematic Cross Section to Illustrate Ore-Zone Geology and 

Lixiviant Migration from an Injection Well to a Production Well (NRC 2009b)

The most common injection/pumping patterns are 5- and 7-spot (NRC 2003). The shape of the 

mineralized ore body and surface topography, however, may give rise to other patterns (NRC 

1997). A typical 5-spot pattern contains 4 injection wells and 1 recovery well. The dimensions of 

the pattern vary depending on the mineralized zone, but the injection wells are generally between 

40 to 150 ft apart. In order to effectively recover the uranium and also to complete the ground 

water restoration, the wells are often completed so that they can be used as either injection or 

recovery wells. During mining operations, a slightly greater volume of water will be recovered 

from the mineralized zone aquifer than injected in order to create a cone of depression or a flow 

gradient towards the recovery wells. A typical well arrangement using 5- and 7-spot patterns is 

shown in Figure 2-2.
2-2

Figure 2-2: Schematic Diagram of a Wellfield Showing Typical Injection/Production 

Well Patterns, Monitoring Wells, Manifold Buildings, and Pipelines (NRC 2009b)

Ore body size and geometry will also influence the number of wells in a wellfield. For example, 

at the Crow Butte ISL facilities in Dawes County, Nebraska, the number of injection and 

production wells varied from about 190 in the first wellfield (MU-1) to about 900 in later 

wellfields (MU-5 and MU-6) (NRC 1998). Three types of wells are predominant at uranium ISL

facilities:

 Injection wells for introducing solutions into the uranium mineralization

 Production wells for extracting uranium-enriched solutions

 Monitoring wells for assessing ongoing operations

Deep injection wells permitted by the EPA or state and approved by NRC may also be drilled for 

liquid waste disposal. Injection and production wells are connected to manifolds in a nearby

header house.

Commercial-scale uranium ISL facilities usually have more than one wellfield. For example, the 

Crow Butte facility in Dawes County, Nebraska, has constructed 10 wellfields since 1991 (CBR 

2007). The locations and boundaries for each wellfield are adjusted as more detailed data on the 
2-3

subsurface stratigraphy and uranium mineralization distribution are collected during wellfield 

construction.

General information on ISL facilities is included in NRC 2009b.

2.2 Failures during Operations

Potential releases that may occur during operations involve the following scenarios: (1) spills 

and leaks on the surface and subsequent transport to the ground water, and (2) excursions beyond 

the injection/production wells or into other non-mined geologic units above and/or below the 

mined unit.

During operations, ISL operations may affect the ground water quality near the wellfields when 

lixiviant moves from the production zone to beyond the boundaries of the wellfield. This 

unintended spread, either horizontally or vertically, of recovery solutions beyond the production 

zone is known as an excursion. An excursion can be caused by:

 Improper water balance between injection and recovery rates

 Undetected high permeability strata or geologic faults

 Improperly abandoned exploration drill holes

 Discontinuity within the confining layers

 Poor well integrity, such as a cracked well casing or leaking joints between casing 

sections

 Hydrofracturing of the ore zone or surrounding units

NRC license and underground injection control (UIC) permit conditions6require that licensees 

conduct periodic tests to protect against excursions. These include, but are not limited to:

 Conducting pump tests for each wellfield prior to operations within the wellfield to 

evaluate the confinement of the production horizon.

 Continued wellfield characterization to identify geologic features (e.g., thinning 

confining layers, fractures, high flow zones) that might result in excursions.

 Mechanical integrity testing of each well to check for leaks or cracks in the casing. An 

excursion that moves laterally from the production zone is a horizontal excursion. 

Vertical excursions occur where barren or pregnant lixiviant migrates into other aquifers 

above or below the production zone.

Operators must maintain ground water monitoring programs to detect both vertical and 

horizontal excursions, and must have operating procedures to analyze an excursion and 

determine how to remediate it. Monitoring wells are sampled at least every 2 weeks during 

 

6 Lixiviant injection wells are classified as Type III injection wells and must meet the requirements set forth in 

40 CFR 144.
2-4

wellfield operations to verify that ISL solutions are contained within the operating wellfield

(NRC 2003). Geochemical excursion indicators are identified based on wellfield pre-operational 

baseline water quality.

The spacing of horizontal excursion monitoring wells is based on site-specific conditions, but 

typically they are spaced about 90–150 m [300–500 ft] apart and screened in the production zone 

(NRC 1997 and 2003; Mackin et al. 2001; EIA 1995). The distance between monitoring wells 

and the distance of monitoring wells from the wellfield are typically similar (NRC 1997 and 

2006). The specific location and spacing of the monitoring wells is established on a site-by-site 

basis by license condition. These are often modified according to site-specific hydrogeologic 

characteristics, such as the extent of the confining layer, hydraulic gradient, and aquifer 

transmissivity. Well placement may also be modified as the licensee gains experience detecting, 

recovering, and remediating these excursions. NRC licenses also include requirements to 

establish monitoring wells in overlying and, as appropriate, in underlying aquifers to detect 

vertical excursions. Although uranium deposits are typically located in hydrogeologic units 

bounded above and below by adequately confining units, the possibility of vertical contaminant 

transport must be considered. Historically, these monitoring wells are more widely spaced than 

those within the host aquifer, although underlying aquifer monitoring wells may not be required 

under some circumstances (Mackin et al. 2001). Frequency of vertical monitoring wells at 

licensed ISL facilities has been (1) 1 monitoring well per 4 acres of wellfield in the first 

overlying aquifer, (2) 1 monitoring well per 8 acres in each higher aquifer, and (3) 1 monitoring 

well per 4 to 8 acres in the underlying aquifer (Mackin et al. 2001). These monitoring wells are 

typically sampled every 2 weeks during operations.

An excursion is defined to occur when two or more excursion indicators in a monitoring well 

exceed their upper control limits (UCLs) (NRC 2003).

7

 Alternatively, since the advent of 

performance-based licensing, procedures to identify excursions can be imposed through sitespecific license conditions. For example, an excursion may be defined to occur when one 

excursion indicator is exceeded in a monitoring well by a certain percentage. If an excursion is 

detected, the licensee takes several steps including notifying NRC and confirming the excursion 

through additional and more frequent sampling (NRC 2003, Chapter 8). As described in NRC 

guidance (NRC 2003, Section 5.7.8.3), licensees typically recover from horizontal excursions by

adjusting the flow rates of the nearby injection and production wells to increase process bleed in 

the area of the excursion. To address vertical excursions, licensees may adjust injection and 

production flow rates in the area of the excursion and pump directly from the affected 

monitoring wells or from other wells drilled for that purpose. Vertical excursions are more 

difficult to recover from, persisting for years in some cases. If an excursion cannot be recovered, 

 

7

“Upper control limits are concentrations for excursion indicator constituents [e.g., chloride, total dissolved 

solids or bicarbonate] that provide early warning that leaching solutions are moving away from the well fields and 

that groundwater outside the monitor well ring may be threatened. Excursion indicator constituents should be 

parameters that are strong indicators of the in situ leach process and that are not significantly attenuated by 

geochemical reactions in the aquifers. If possible, the chosen parameters should be easily analyzed to allow timely 

data reporting. The upper control limit concentrations of the chosen excursion indicators should be set high enough 

that false positives (false alarms from natural fluctuations in water chemistry) are not a frequent problem, but not so 

high that significant ground-water quality degradation could occur by the time an excursion is identified. A 

minimum of three excursion indicators should be proposed” (NRC 2003).
2-5

the licensee may be required to stop injection of lixiviant into a wellfield (NRC 2003, 

Section 5.7.8.3).

2.3 Failures after Shutdown 

Prior to implementing post-closure monitoring, aquifer restoration activities are conducted. The 

purpose of aquifer restoration is to return wellfield water quality parameters to the standards in 

10 CFR 40, Appendix A, Criterion 5(B)(5) or another standard approved by NRC. Ground water

adjacent to the exempted portion of the aquifer, however, must still be protected. Prior to 

wellfield operations, applicants and licensees must determine baseline ground water quality for 

the production zone (NRC 2003). In their applications, applicants or licensees identify the NRCaccepted list of constituents to be sampled. Operators may identify other constituents, or remove 

constituents, as long as a basis for the constituent(s) is provided and approved by NRC. State and 

other federal agencies with jurisdiction over ground water could also specify constituents, which 

may or may not be included in the NRC-accepted list. In this case, the applicant would be 

accountable to the subject state or federal agency for characterizing and restoring these 

constituents. To determine baseline water quality conditions prior to wellfield operations, 

applicants or licensees collect at least four sets of samples, spaced sufficiently in time to 

establish seasonal variability, and analyze the samples for the identified constituent (NRC 2003). 

An NRC-acceptable set of samples should include all wellfield perimeter monitoring wells and 

all upper and lower monitoring wells. Additionally, the applicant or licensee should sample at 

least one production/injection well per acre in the wellfield, or enough production/injection wells 

to provide an adequate statistical population if less than one well per acre is used. NRC verifies 

the accuracy of baseline water quality data by ensuring that the applicant’s or licensee’s 

procedures include (1) acceptable sample collection methods, (2) a set of sampled parameters 

that is appropriate for the site and ISL extraction method, and (3) collection of sample sets that 

are sufficient to represent natural spatial and temporal variations in water quality.

After uranium recovery has ended, the ground water in the wellfield contains constituents that 

were mobilized by the lixiviant. Licensees usually begin aquifer restoration in each wellfield

soon after the uranium recovery operations end (NRC 2008). Aquifer restoration criteria for the 

site-specific baseline constituents are determined either on a well-by-well or wellfield-bywellfield basis. NRC licensees are required to return water quality parameters to the standards in 

10 CFR Part 40, Appendix A, Criterion 5B(5) or to another standard approved in their NRC 

license (NRC 2009a). Aquifer restoration programs typically use a combination of methods,

including (1) ground water transfer, (2) ground water sweep, (3) reverse osmosis with permeate 

injection, (4) ground water recirculation, and (5) stabilization monitoring (EIA 1995; Mackin 

et al. 2001; Davis and Curtis 2007). NRC allows licensees the flexibility to select the restoration 

methods to be used for each wellfield (NRC 2003). The EPA or state authorized to implement 

the EPA underground injection control program reviews any aquifer restoration plans for 

compliance with the applicable terms and conditions of the UIC permit requirements. NRC staff 

review any aquifer restoration plans for compliance with the NRC license to protect human 

health, safety, and the environment.
2-6

2.4 Ground Water Model Development

To investigate potential impacts caused by ISL facilities on ground water, the following activities 

were performed as described in this report: (1) development of a representative ISL facility, 

(2) implementation of a deterministic modeling methodology, (3) definition of release scenarios, 

(4) performance of a sensitivity analysis, and (5) calculation of risks based upon potential doses 

to receptors for selected simulations.

Ground water flow and contaminant transport modeling are frequently conducted at ISL facilities 

to evaluate possible effects of proposed and ongoing mining. For instance, a three-dimensional 

ground water flow and contaminant transport model was constructed to evaluate the ISL facility

in Goliad County, Texas (DBS&A 2007). This modeling was performed for the Goliad County

Groundwater Conservation District (GCGCD) and the major objectives of the modeling study 

were to (1) evaluate the practicality of controlling injection fluid excursions from escaping 

downdip, (2) evaluate the practicality of controlling injection fluid excursions from escaping 

vertically into non-mined aquifer zones, and (3) determine the amount of bleed water required to 

control or eliminate such excursions. ADD NRDC 2012

The modeling at the Goliad site is particularly relevant to this current task for the following 

reasons: (1) several of the modeling objectives are very similar, (2) the modeling approach that 

was taken is analogous to the approach undertaken here, and (3) the computer codes that were 

used are identical to those used for the modeling in this report. Furthermore, as presented below, 

the modeling conclusions have implications with respect to the occurrence of potential for 

excursions during active mining, the placement and depths of monitoring wells, sampling 

frequencies, and post-closure monitoring timeframes. As described in DBS&A 2007:

Simulation results indicate that capture of injected fluids within the mined zone 

with 1 percent bleed water is feasible, although the simulation results are very 

sensitive to well placement, selected injection and pumping rates, and hydraulic 

conductivity of the aquifer. In some cases, an increase of even 20 feet in the well 

spacing caused the simulated bleed water to increase by about 6 percent.

In addition, the nature of the hydraulic groundwater flow field that may develop 

due to mining leads to the formation of long, low-velocity travel paths in certain 

areas of the injection-capture system; impacted groundwater within or near these 

travel paths may not be extracted during the life of the mining operation if a 

specific approach is not designed and implemented to account for these aquifer 

regions. Most of the injected fluid is extracted at capture wells within a time 

frame of about 3 years or less for the scenarios evaluated. At the cessation of ISL 

mining, monitoring locations should be selected carefully in order to identify 

potential groundwater impacts in the vicinity of these longer, low-velocity 

pathways. In addition, groundwater monitoring should be continued for an 

extended period of time.

The simulation results also indicate that ISL fluids can, and likely would, migrate 

vertically between aquifer layers. The ISL scenario evaluated assumes that 
2-7

injection and pumping would occur in the uppermost layer of the Evangeline 

aquifer (Goliad Sand), which has a saturated thickness of approximately 105 feet 

in the model. ISL fluids migrate vertically downward in the vicinity of the 

injection well. Once these fluids enter the model layer below the mined layer, they 

are not recaptured by the pumping wells, but rather migrate with the ambient 

groundwater flow velocity of the deeper aquifer layer.

2.4.1 Basic Aspects of Computer (Numerical) Modeling

The flow and transport modeling performed for this analysis uses finite-difference techniques, 

which require that the ground water system be divided (‘discretized’) into finite-sized blocks or 

‘cells.’ Each cell is assigned unique hydraulic properties depending on the available field data 

and the goals for the analysis. In this way, complex features of the ground water system can be 

accommodated in the model. The time represented by the modeling effort must also be divided 

into discrete periods or ‘time steps.’ These steps must be short enough to provide an accurate 

solution, but not so short that they require an excessive number of calculations to run a 

simulation. The finite-difference method also requires that values for head be assigned at flow 

boundaries (referred to as ‘boundary conditions’), as well as for the initial time period of the 

simulation (referred to as ‘initial conditions’). This is a requirement for producing a unique 

solution with any numerical method that depends on iteration, as does the finite-difference 

method. Models were applied that simulate ground water flow and chemical transport.

The three-dimensional computer model for analyzing ground water flow generates a flow field 

(array of head values) representing average conditions in the model area. The flow model is used 

as the basis for the transport model.

The chemical transport model evaluates how the average flow field, along with other transport 

parameters, affects chemical movement in ground water and plume development from lixiviant 

sources. The chemical transport model simulates the expansion of the plume, both during the 

active leaching activities time as well as the post-closure stage.

After assigning material properties and initial and boundary conditions, the finite-difference 

equations for flow are solved to produce a mathematically ‘approximate,’ but scientifically

reliable, value of the average ground water head (potentiometric surface elevation) within each 

cell. Models that use the finite-difference numerical techniques allow rapid analysis of complex, 

time-dependent ground water systems.

Numerical models are operated by a computer code or program. The code is a generalized set of 

steps, to which specific field conditions, such as initial and boundary conditions, are imposed. 

Because computer codes are generic in nature and must be adapted to actual field conditions, a 

clear understanding of the existing physical system (a conceptual model) is required.

It is important to establish why the model is being created, and to properly design the model 

simulations to sufficiently address the objectives. The model development for each of the failure 

scenarios followed the steps detailed below:
2-8

(1) Developing a conceptual model to guide creation of model attributes

(2) Selecting an appropriate computer code(s) for the analysis

(3) Establishing the time period represented by the model and the duration of subdivisions of 

this period (time steps) required for modeling

(4) Selecting a suitable model domain and determining the dimensional (horizontal and 

vertical) limits of the analysis

(5) Establishing the model structure by determining the number of model layers and the grid 

spacing requirements for the flow analysis

(6) Incorporating hydraulic boundaries and features, including the shape and characteristics 

of constant-head boundaries, precipitation/recharge, and pumping/injection

(7) Assigning hydraulic conductivity values

(8) Specifying initial head values (ground water surface elevation)

(9) Evaluating and assigning appropriate model computational characteristics; for example, 

solution method, iteration limits, and convergence criteria, to enhance model stability, 

computational efficiency, and solution accuracy

(10)Evaluating the sensitivity of model results to changes in model parameters

(11)Establishing the model structure, including determining the number of model layers and 

the grid spacing requirements for the transport analysis

(12)Assigning the characteristics of chemical sources (e.g., leaks, spills) consisting of 

dimensions, locations, concentrations, and time dependency

(13)Assigning transport parameters, including the dispersivities and porosities

(14)Conducting chemical transport simulations and exporting the observed concentrations at 

pre-specified locations

(15)Processing the data within Excel spreadsheets

2.4.2 Conceptual Model Development

The general components of the conceptual model that serve as the basis for the construction of 

the ground water flow and contaminant transport models are described below. This conceptual 

model summarizes the theoretical understanding of the primary conditions that affect ground 

water flow and chemical transport and fate. More detailed descriptions (e.g., pumping/extraction 

well configurations) are presented within discussions of the respective failure scenarios. 

As contaminant plumes move down-gradient from the source area, they tend to spread laterally

and vertically, thereby lowering the average contaminant concentration as the plume expands. 

The shape taken by an individual plume varies depending primarily on the nature of the geologic 

materials making up the aquifer, but also secondarily on the rate of ground water flow.

In fine-grained unconsolidated sediments, such as sands and silts, plumes tend to spread out 

laterally in a fan shape as they move down-gradient. This process is called dispersion. Vertical 
2-9

flow also occurs and is controlled by the uniformity of the sediments, as well as the vertical 

hydraulic gradient. When all the aquifer materials are of essentially the same size and are wellrounded, vertical flow can easily take place, assuming a vertical hydraulic gradient exists. Finegrained layers of sediments such as clays and silts in an otherwise coarse-grained aquifer prevent 

or retard downward (or upward) vertical flow. Ground water flowing at a moderate to fast rate 

tends to minimize both horizontal and vertical dispersion, while slower flow (normally in finegrained materials) allows greater dispersion. All of these processes, however, will be 

complicated by the effects caused by the injection and withdrawal of ground water.

Contaminant plumes extend down-gradient from the source area over time until a steady state 

condition is reached, based on the rate of contaminant flux to the ground water and the degree of 

chemical degradation/sorption taking place in the aquifer. Contaminant concentrations decline as 

down-gradient flow occurs, because processes such as dispersion, adsorption, and chemical 

transformation are constantly taking place in the aquifer. The length of a plume will depend on 

(1) how rapidly these processes work, (2) the rate of ground water flow, (3) the rate of chemical 

releases to the aquifer, (4) chemical interactions between the ground water and aquifer matrix, 

and (5) other environmental factors, such as temperature and the basic chemistry of the ground 

water. Ultimately, even with a constant source of contamination to the aquifer, any plume will 

reach a point beyond which it can no longer expand and will more or less stabilize. This 

stabilization, or steady state condition, occurs when degradation and/or sorption processes in the 

aquifer remove as much contaminant mass as is being released to the aquifer in the source area.

If the source of contamination is cut off, for example by pump and treat extraction wells, a 

reduction in chemical concentrations will occur down-gradient of the source area and will be 

especially noticeable along the axis of the plume. Over time, the reduction in plume 

concentrations will be propagated farther down-gradient consistent with the hydraulic 

conductivity of the aquifer. Subsequently, the plume will begin to contract in areal extent.

In the case of ISL facilities, the contaminants are mobilized by the lixiviant, which is often 

injected into units that are straddled above and below by aquifers that are used as drinking 

sources. In fact, at least one ISL facility injects lixiviant into the same aquifer as that used as a 

nearby drinking water source (Rice 2006).

2.4.3 Computer Code Selection 

The computer codes that were used for this analysis are MODFLOW-2000 and MT3D-MS. 

MODFLOW-2000, the U.S. Geological Survey (USGS) finite-difference ground water flow 

model, is a popular and widely used computer code (Harbaugh et al. 2000). Ground water flow 

within the aquifer is simulated using a block-centered finite-difference approach. Layers can be 

simulated as confined, unconfined, or a combination of confined and unconfined. Flow 

associated with external stresses, such as wells, areal recharge, evapotranspiration, drains, and 

streams, can also be simulated.

The modular three-dimensional (3-D) transport model referred to as MT3D-MS was originally 

developed by Zheng and Wang (1999) at S.S. Papadopulos & Associates, Inc., and subsequently 

documented for the Robert S. Kerr Environmental Research Laboratory of the EPA. In the past 
2-10

several years, various versions of the MT3D code have been commonly used in contaminant 

transport modeling and remediation assessment studies. MT3D-MS does not explicitly simulate 

geochemical reactions, but can be used to simulate changes in concentrations of miscible 

contaminants in ground water considering advection, dispersion, diffusion and some aggregate 

chemical reactions (i.e., distribution coefficient), with various types of boundary conditions and 

external sources and/or sinks. The basic chemical reactions included in the model are 

equilibrium-controlled or rate-limited linear or non-linear sorption, and first-order irreversible or

reversible kinetic reactions. Somewhat more sophisticated, multispecies chemical reactions can 

be simulated by add-on reaction packages.

MODFLOW2000 and MT3D-MS are commonly applied at ISL facilities to evaluate how the 

average flow field, together with other transport parameters, affects chemical movement in 

ground water and plume development from lixiviant sources. The chemical transport model is 

often used to simulate the expansion of the plume, both during the active leaching activities time 

as well as the post-closure stage.

The pre- and post-processing of data input/output for these codes was performed with 

Groundwater Vistas (Rumbaugh and Rumbaugh 1998).

2.5 Representative ISL Facility Development and General Modeling Approach

Prior to discussing the model input and parameter ranges, it is important that the overall goal of 

the modeling be reiterated. The central question to be addressed by the modeling is whether 

health-based standards may be exceeded under representative injection\extraction configurations 

and realistic parameter combinations. To meet this objective, the model does not need to 

simulate flow processes at the interstitial pore level (e.g., heterogeneity, dispersivity) or 

geochemical processes at the molecular level (e.g., kinetics and thermodynamics). Instead 

simplifying assumptions are made in the modeling to adequately capture the overall effects of 

very complex processes affecting ground water flow and contaminant transport. 

An important aspect in constructing the model was to ensure that the assumptions were not so 

conservative as to render the results unproductive by routinely exceeding the health-based 

standards. In some instances, assumptions are made that may underestimate the predicted 

downgradient plume concentrations. This approach is justified because even under the 

nonconservative assumptions, the health-based standards are still exceeded in some cases. It is 

important to keep in mind that this modeling does not replace the need for site-specific modeling, 

nor is it intended to provide a framework upon which to perform site-specific modeling.

In deciding whether to perform deterministic or stochastic modeling, several factors were 

considered, including (1) the ability to meet the overall objectives, (2) difficulty in setting up and 

explaining the model (e.g., treatment of correlation, defining parameter distributions), 

(3) complexities of running the model (e.g., demonstrating statistical convergence); and (4) effort 

required to interpret the results. After evaluating these considerations, a deterministic approach

was selected, since it could meet the objectives, is simpler to set-up and explain, and the 

interpretation of the results is more straightforward.
2-11

A thorough review of readily available ISL facility information indicates that there are several 

important factors that need to be considered in evaluating their potential impacts on ground water

and must be conceptualized in the numerical model. These common elements provide a 

framework from which the sensitivity analyses for each of the scenarios was conducted. Those 

attributes that are specific to each scenario (e.g., model domain and grid spacings) will be 

presented under each of the respective sections.

As part of the deterministic approach, the sensitivity of the results to the most uncertain 

parameter values was assessed. These parameters include hydraulic gradients and conductivities, 

leak, pumping and injection rates, and durations. All of the concentrations are calculated as 

relative concentrations assuming a 1-mg/L source term and are adjusted to actual concentrations 

during the dose assessment (Section 4). Ranges for the representative model input parameters are 

presented in Table 2-1 and discussed below. 

The regional hydraulic gradient is an estimated parameter based upon a literature review of 

natural gradients under nonstressed conditions. For a set value of hydraulic conductivity and 

effective porosity, a higher hydraulic gradient results in a faster ground water velocity, less 

dispersion and higher doses. There may be cases where pumping at the ISL facility creates 

hydraulic gradients that are greater than those estimated for the modeling. Therefore, as part of 

the sensitivity analysis, the range of hydraulic gradients was increased to 0.1 ft/ft.

One of the common strategies at all of the ISL facilities is to better understand the most probable 

fate and transport of uranium and other constituents during and after ISL operations. To achieve 

this goal, mathematical modeling of chemical reaction kinetic equations or equilibrium 

thermodynamic equations are often used to describe chemical interactions among dissolved 

chemical species, the dissolution of immobile solid phases, or the formation and precipitation of 

new, immobile solid phases. EPA recognizes the importance of understanding the geochemical 

processes and has entered into a corporative agreement with the U.S. Geological Survey under a 

Regional Applied Research Effort (RARE). The main objective of this work is to provide a 

predictive model that describes the ground water flow and geochemical changes along with 

longer-term transport of dissolved constituents during and after the uranium ISL mining process. 

To accomplish this goal, analysis of the lithology, ore and ground water chemistry is being 

characterized at the Dewey Burdock uranium project site. This site is located approximately

65 miles southeast of Rapid City, South Dakota, which is one of the areas being considered for 

in-situ leaching (ISL) of uranium. The available ground water flow and transport data will be 

input into MODFLOW and MT3D-MS. More quantitative reactive transport modeling will also 

be conducted using PHT3D, which couples MODFLOW to PHREEQC (an advanced 

thermodynamically based geochemical code).

To meet the objectives of the current ISL modeling, however, a more simplistic approach is 

taken to simulate the geochemical behavior. In this method, the net effect from all of the 

geochemical processes are expressed as a distribution coefficient (Kd). The distribution 

coefficient is subsequently used to estimate the amount of retardation that each of the 

contaminants would experience along the travel path. Since all of the key radionuclides have 

long half lives, the amount of retardation does not significantly affect the peak doses. Therefore, 
2-12

retardation and radioactive decay are not explicitly simulated in the modeling. Arrival times (i.e., 

breakthrough curves) are corrected for retardation as part of the risk assessment presented in 

Section 4.

Areal recharge impacts ground water flow and contaminant transport in several ways, including 

(1) altering the horizontal and vertical hydraulic gradients, (2) affecting the geochemistry, and 

(3) depressing the plume deeper into the aquifer as a function of distance. Since the analyses 

were performed over a range of hydraulic gradients, the effects of recharge on the gradients are 

implicitly considered. Any recharge water infiltrating to the depths being mined by the ISL 

facilities will take sufficiently long that the system will approach equilibrium conditions. 

Furthermore, any transient effects of recharge on geochemistry and contaminant mobility are 

expected to be minimal compared to the geochemical mobilization properties of the lixivant. The 

prevailing geochemical conditions within the mined unit and aquifers are assumed to result in 

low sorption properties for the contaminants (Section 4.2.2).

Although areal recharge may depress the plume, these effects would not significantly affect the 

peak dose and are therefore ignored. For these reasons, no areal recharge was explicitly assigned 

in the modeling.

With respect to hydrostratigraphy, the major sandstone roll-front uranium deposits found in 

Wyoming, South Dakota, Nebraska, New Mexico, and Texas have similar depositional histories, 

which have resulted in similar rock compositions. Typically, the roll-front deposits consist of 

sandstones of fluvial origin that are generally interbedded between siltstones and mudstones 

(NRC 2009a). Sandstones generally have moderate hydraulic conductivities, and the modeling 

assumes a range from 1 to 100 ft/day (Nicot et al. 2010). To simplify the analysis, the hydraulic 

conductivities in each of the simulations is assumed to be homogeneous and isotropic. 

Heterogeneity and preferential pathways from the simulations are addressed in several ways. 

First, the hydraulic conductivities are sampled over several orders of magnitude and should 

adequately bracket the potential impacts of heterogeneities on contaminant arrival times. Second, 

as discussed in greater detail below, the dispersivities have been set to the high end of the 

expected range. This assumption spreads out the plume to better simulate the effects of potential 

heterogeneities. Finally, although discrete features and lower dispersivities could lead to releases 

greater than those predicted, the dose limits were already exceeded (see Section 4.3) without 

their consideration, thereby removing the need to further evaluate their potential impacts.

Dispersivity is a geometric property of a porous medium which determines the dispersion 

characteristics of the medium by relating the components of pore velocity to the dispersion 

coefficient. The amount of dispersion is scale dependent and describes the degree to which the 

plume spreads out and elongates along the travel path. Longitudinal dispersivity is often assigned 

a value of about 10% of the travel distance. The transverse dispersivity is about 10% of the 

longitudinal and the vertical dispersivity is about 10% of the transverse. The nearest receptor for 

the dose assessment is assumed to be about 328 feet downgradient from the ISL facility. The 

dispersivities assigned in the modeling are held constant for all simulations and are 65 ft, 6.5 ft 

and 0.65 ft, for the longitudinal, transverse and vertical, respectively. Although these values are 

somewhat higher than would be expected, they are reflective of potential heterogeneities that are 

not captured in the homogeneous and isotropic assumptions.
2-13

The effective porosity describes the amount of interconnected pore space. A constant value of 

20% was used for all of the simulations. Although it is likely that the porosity and 

permeability/connectivity of the aquifer will change as the uranium is extracted from the 

production areas, these impacts will be greatest within the mined areas, rather than those areas 

where downgradient excursions are occurring. The possibility that changes in porosity and 

permeability could cause greater excursions is captured by the simulations where the high range 

of hydraulic conductivities is selected. 

Pumping of wells in the vicinity of the ISL facility may affect hydraulic gradients as well as the 

amount of dilution. As discussed above, higher gradients have been investigated to capture 

potential pumping effects. To compensate for dilution effects, the dispersivity is set to a 

relatively high value and the plume is assumed to be completely mixed within the entire 

thickness of the aquifer. Although this assumption may underestimate peak doses, health-based 

standards are still exceeded in a number of scenarios.

Since long-term average injection/extraction rates are assumed, all of the flow simulations are 

performed to steady-state solutions controlled by constant head values. Head values can change 

for many reasons and over various time scales as a result of severe, short-term weather 

disturbances, seasonal variations, or longer-term variations most likely due to human activities. 

Long-term variations (year to decade time scales) could occur in the arid, western portion of the 

United States where there could be increased competition for water resources from aquifers. It is 

recognized that changes in the boundary conditions could increase or decrease the probability

and severity of excursions away from an ISL site. It is unlikely, however, that the releases would 

fall outside of the range of those predicted. Furthermore, the transport analysis is conducted for a 

time period that is sufficiently long to allow for contaminant concentrations to peak at the nearest 

receptor well.

Table 2-1: Representative ISL Parameter Ranges

Model Input Parameter Potential Range of Values References

Regional Hydraulic Gradient (ft/ft) 0.001–0.01 Estimated parameter

Recharge 0.09-0.15 Chowdhury and Mace 2007

Effective Porosity (%) 5–30 Freeze and Cherry 1979

Dispersivity (ft)

 Longitudinal

 Transverse

 Vertical 

10–1000 

1–100 .

0.1–10

L =0.1(plume 

length)

T = 0.1(L) 

v = 0.1(T)

Gelhar et al. 1992

Injection Fluid Conc. (milligram per L) 1 Normalized concentration

Hydraulic Conductivities (ft/day)

Over- and underlying aquifers

Sandstone (mined interval)

 

10-100

 1–10

Nicot et al. 2010

Injection/Extraction Well Spacing (ft) 40–150 NRC 2009b

Range of Injection Rates (gpm) 20–200 NRC 2009b

Operating Life of ISL Well Pattern (yrs) 1–3 NRC 2009b

2.6 Development of Scenarios
2-14

Two scenarios are investigated that are associated with the active phase of the mining activities; 

(1) spills and leaks on the surface and subsequent transport to the ground water, and 

(2) excursions beyond the injection/production wells or into other non-mined geologic units 

above the mined unit. The general question that the flow and transport modeling will assist in 

addressing is: What concentrations of contaminants could reach nearby domestic wells and over 

what timeframes? The modeling does not make any assumptions with respect to whether best 

management practices are being followed, and simply relies on published data for parameter 

ranges and distributions. In many cases, it is likely that a leak would be detected and remedied 

before any contamination reaches nearby receptors.

2.6.1 Spills and Leaks

During ISL operations and aquifer restoration, barren and pregnant uranium-bearing process

solutions are moved through pipelines to and from the wellfield and among different surface

facilities (e.g., processing circuit, evaporation ponds). To investigate the potential effects of a 

pipeline rupture or failure, the Spills and Leaks scenario investigated three general types of spills 

and leaks, including (1) catastrophic failure (e.g., break of a pipe or breach of an evaporation 

pond), (2) long-term/low-volume leak of an underground pipe or well transferring or 

withdrawing lixiviant, and (3) short-term/high-volume leak of an underground pipe or well 

transferring or withdrawing lixiviant.

The model grid is identical for all of the spills and leaks simulations and consists of a domain 

that has an area of 1 square mile (Figure 2-3). It is assumed that ground water is flowing due 

south and the model grid is oriented perpendicular to ground water flow. Constant head 

boundaries are assigned along the northern and southern boundaries in order to simulate the 

regional gradient. Boundaries on the east and west of the model domain are oriented 

perpendicular to ground water flow (i.e., hydraulic divide) and, therefore, ground water does not 

enter or exit the model along these boundaries (i.e., no-flow boundary conditions). This 

boundary configuration makes the solutions to the flow problem more unique than would be 

obtained if constant head boundaries surrounded the entire domain. The base of the model is also 

represented by a no-flow boundary condition.

The aquifer is assigned a thickness of 130 ft that is discretized into 10 layers (Figure 2-4).

Horizontal grid spacing is on 10-ft centers in the vicinity of the spill, and is gradually increased 

to a spacing of 250 ft at the model boundaries. The vertical grid spacing ranges from 2 ft at the 

top of the model to 50 ft at the base. The smaller grid spacing in the area of the contaminant 

source reduces numerical dispersion, provides better mass balance and allows for more rapid 

numerical convergence of the solver.
2-15

5280 feet

5

2

8

0 fe

et

Receptor Locations

Constant Head Boundaries

Constant Head Boundaries

Figure 2-3: Plan View of the Model Grid for Leak Scenarios. 
2-16

South Cross-Section Along Column 31 NorthThickness (feet)

1

15

4

2

3

10

20

25

35

5

Spill/Leak

Figure 2-4: Cross Sectional View of the Model Grid for Leak Scenarios.

All fluid entering the model does so as recharge through a 100-ft2area. The rationale for this 

assumption is that leaks from even small releases are likely to spread out at least 10 ft as the fluid 

migrates through the unsaturated zone.

The minerals within the vadose zone may have significantly different chemical properties than 

those of the aquifer leading to different solute retardation behavior than assumed for the 

underlying aquifer. In the case where there is greater sorption in the vadose zone than in the 
2-17

underlying aquifer, receptor dose could be significantly decreased. In some cases, the 

contaminants may diffuse into the finer grained matrix, precipitate and/or be adsorbed. This 

could result in the vadose zone being a potential long-term source of contaminants that could 

leach into the ground water. None of these processes are explicitly simulated in the model and 

flow through the unsaturated zone is assumed to be instantaneous. This assumption is made 

because the focus of the modeling is on cases where the unsaturated zone does not inhibit the 

migration of contaminants or act as a long-term contaminant sink.

Hydraulic conductivities are assigned homogeneous and isotropic properties. Since contaminant 

concentrations at receptor locations are averaged across the entire aquifer to simulate pumping 

effects, this assumption will not bias the results. Hydraulic conductivities are assigned values of 

1, 10 and 100 ft/day to represent the transmissive properties of the aquifer. These values are 

within those ranges presented for overlying aquifers in the south Texas uranium province (Nicot 

et al. 2010), and are at the high end of the range for lithologic units within the United States 

where ISL mining is practical.

Potential receptors are assumed to be located at 328 ft (100 m), 656 ft (200 m), 1,640 ft (500 m) 

and 3,280 ft (1,000 m). In the discussion that follows, all of the relative peak arrival times and 

concentrations cited are for the potential receptor located at 328 ft down-gradient of the release. 

Breakthrough curves (i.e., concentrations as a function of time and distance) for all of the 

potential receptors are provided in Appendix B.

All source fluid concentrations are assigned a relative concentration of 1 mg/L. As part of the 

dose assessment (Section 4), the relative concentrations arriving at the receptor well(s) are 

corrected to actual concentrations of the source fluid, and dose calculations are conducted. For 

example, if the injection fluid concentration of uranium is 3 mg/L and a relative concentration of 

0.01 mg/L reaches a receptor, this would result in a concentration of 0.03 mg/L for the dose 

calculation. The breakthrough curves for all of the leak simulations are included in Appendix B.

It should be noted that for all the modeling runs discussed in Chapter 2, no retardation is 

considered. Retardation effects are considered in Chapter 4. In the absence of significant 

radioactive decay, Retardation only affects the time of the peak dose and not its magnitude.

Catastrophic Leak Failures

As described in NRC 2009b, the Wyoming Department of Environmental Quality (WDEQ)

identified more than 80 spills at the Smith-Ranch Highland site during commercial operations 

from 1988 to 2007. This is the largest NRC-licensed ISL uranium recovery facility. The size of 

the spills at Smith Ranch-Highland has ranged from a 50- to 100-gallon spill in February 2004 to 

a 198,500-gal spill of injection fluid in June 2007. The spills most commonly involved injection 

fluids containing 0.5 to 3.0 mg/L uranium, although spills of production fluids containing 10.0 to

152 mg/L uranium also have occurred. These spills have been caused predominantly by the 

failure of joints, flanges, and unions of pipelines and at wellheads. The large June 2007 spill at 

Smith Ranch–Highland was the apparent result of a failed fitting.
2-18

Nine simulations were conducted (L1 through L9) to simulate the potential effects of a 

catastrophic failure (Table 2-2). Leak rates for this scenario range from 100,000 to 200,000 and 

introduction to the ground water takes place over a 7-day period. Although the largest reported 

leak is 200,000 gallons and most of that water was discharged to surface water, it is still 

conceivable that it could take 7 days to detect and remedy a similar release. Since the fluid is 

released over 7 days, the rate is about 19 gallons per minute (gpm). Other release rates (i.e., 9.5 

and 14 gpm) are also investigated. The catastrophic failure simulations also investigate the 

effects of hydraulic gradient and conductivities.
2-19

Table 2-2: Catastrophic Leak Failure Scenarios

CATASTROPHIC LEAK FAILURES

General Assumptions – All Runs Run L1 L2 L3 L4 L5 L6 L7 L8 L9

Receptor 

Locations (ft)

328, 656, 1,640, 

3,280

Regional Hydraulic 

Gradient 0.1 0.01 0.001 0.01 0.01 0.01 0.01 0.01 0.01

Simulation 

Time (yr) 53 Effective Porosity (%) 20 20 20 20 20 20 20 20 20

Leak Time 

(day) 7

Dispersivity (ft)

Longitudinal

Transverse

Vertical

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

Model Area (ft2)

5,280 (ft) × 5,280 (ft) 

= 1 mi2

Leaking Fluid Conc. 

(milligram per L) 1 1 1 1 1 1 1 1 1

Leak Area (ft2) 100 Hydraulic 

Conductivities (ft/day) 10 10 10 1 10 100 1 10 100

Model Layers 10 Distribution coefficients 

(ml/g)

Rf1

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Minimum/ 

Maximum Grid 

Spacing (ft) 

10/250 Leakage (gallons) 150,000 150,000 150,000 100,000 100,000 100,000 200,000 200,000 200,000

All Layers Homogenous

Time until Peak Arrival 

(days) 3.96×101

3.61×1024.52×1033.60×1033.61×1023.96×1013.60×1033.61×1023.96×101

Relative Peak 

Concentration 8.75×10-3

6.17×10-37.18×10-34.15×10-34.25×10-34.89×10-37.94×10-38.01×10-39.48×10-3

1 Rf = retardation factor calculated in Section 4
2-20

Simulations L1 through L3 were focused on evaluating the effects of hydraulic gradients on the 

release concentrations and arrival times. The leak rate is 14 gpm (150,000 gallons) and the 

hydraulic gradients for L1, L2 and L3 are 0.1, 0.01 and 0.001, respectively. Hydraulic 

conductivities for these three runs are all set to the median value investigated (i.e., 10 ft/day). As 

shown in Appendix B and presented in Table 2-2, the peak arrival times for simulations L1 

through L3 are 40, 361 and 452 days, respectively. Since all parameters are constant except for 

hydraulic gradients, the model predictions are as expected and the steeper hydraulic gradients 

result in shorter travel times. Furthermore, since the leak volume is creating a recharge mound 

and affecting the hydraulic gradients, the arrival times are not linearly scaled (i.e., a factor of 10 

increase in gradient does not result in a factor of 10 reduction in travel time). Relative peak 

concentrations are 8.75×10-3, 6.17×10-3and 7.18×10-3. The shape and longevity of the recharge 

mound also affect the contaminant concentrations and lead to an apparent discrepancy in the 

arrival concentrations in which the maximum relative concentrations observed for a hydraulic 

gradient of 0.01 are less than those observed under a gradient of 0.001.

Simulations L4 through L6 were designed to investigate the sensitivity of the results to hydraulic 

conductivities (Table 2-2). The assigned values for hydraulic conductivities are 1, 10 and 

100 ft/day, respectively. A hydraulic gradient of 0.01 ft/ft and a leak rate of 9.5 gpm are common 

to all three simulations. The arrival times for simulations L4 through L6 are 3,600, 361 and 

39.6 days, respectively (Table 2-2). Since ground water velocities are linearly related to 

hydraulic conductivities, there is almost a factor of 10 difference in the peak arrival times. As 

was previously observed in simulations L1 through L3, the relationship is not exactly linear, 

because the recharge causes some mounding and subsequently alters the hydraulic gradients as a 

function of the hydraulic conductivities.

Relative peak concentrations for simulations L4 through L6 are 4.15×10-3, 4.25×10-3and 

4.89×10-3. As indicated by the results, the peak concentrations are slightly higher for the higher 

hydraulic conductivities and are related to the greater dispersive effects at the lower hydraulic 

conductivities, as observed by the wider breakthrough curves presented in Appendix B.

Simulations L7 through L9 are identical to L4 through L6 except that the leak volume has been 

increased to 200,000 gallons (Table 2-2). The arrival times for simulations L7 through L9 are 

3,600, 361 and 39.6 days, respectively. The arrival times are identical to those observed for 

simulations L4 though L6 and indicate that the additional 100,000 gallons added as recharge do 

not appreciably alter the hydraulic gradients. The relative peak concentrations, however, are 

about a factor of 2 higher, which is consistent with the increase of twice the mass of contaminant 

introduced to the system.

Long-Term/Low-Volume Leak(s)

To evaluate the potential effects of a longer-term leak (3 years) at a lower volume release rate 

(i.e., 1 to 2 gpm), nine simulations were conducted (L10 through L18). All of the parameters are 

identical to the previous leak simulations except the leak volume and timeframes (Table 2-3). 

The 3-year timeframe was selected, because this is the upper range of the times that individual 

cells (e.g., in a 5-spot pattern) are mined. It is postulated that underground piping would be either 

moved or inspected within this timeframe. A 1- to 2-gallon leak is assumed because this volume 
2-21

is sufficiently small to where a long-term leak would possibly go either undetected or 

uncorrected.

Simulations L10 through L12 are designed to evaluate the effects of hydraulic gradients on the 

release concentrations and arrival times. The leak rate is 1.5 gpm for 3 years, and the hydraulic 

gradients for L10, L11 and L12 are 0.1, 0.01 and 0.001, respectively. Hydraulic conductivities 

for these three runs are all set to the median value investigated (i.e., 10 ft/day). The peak arrival 

times for simulations L10 through L12 are 353, 1,180 and 4,950 days, respectively (Table 2-3). 

As has been previously observed, the steeper hydraulic gradients accelerate the peak arrivals, but 

do not behave linearly. As the hydraulic gradients flatten, the leak has a more pronounced impact 

on the arrival times. For example, the peak arrival for a gradient of 0.01 ft/ft is approximately 3 

times longer than for a peak arrival at a gradient of 0.1 ft/ft, and is about 4 times slower than the 

peak arrival at a gradient of 0.001 ft/ft.

Relative peak concentrations for simulations L10 through L12 are 1.11×10-2, 6.87×10-2and 

9.19×10-2, respectively (Table 2-3). As shown in the Appendix B charts, the leading edge of the 

breakthrough curve for peak arrivals under a gradient of 0.1 ft/ft is very steep, with the maximum 

concentration maintained for about 800 days. The breakthrough curves for runs L11 and L12 

exhibit more traditional behavior and are depicted by a more gradual arrival, a defined peak, and 

gradual tapering off. The difference in this behavior can be explained, because the very steep 

hydraulic gradient leads to a compact plume with very small concentration gradients. 

Furthermore, the plume that forms under the higher gradients (Figure 2-5) becomes far less 

elongated than at lower gradients (Figure 2-6), and the mass is distributed over a larger area, 

which results in lower relative peak concentrations. 

In runs L13 through L15, the hydraulic gradient is set to 0.01 ft/ft and the leak rate is 1 gpm 

(Table 2-3). The hydraulic conductivity, however, is assigned a value of either 1 ft/day (L13), 

10 ft/day (L14) or 100 ft/day (L15). The peak arrival times are 4,160, 1,180 and 451 days, 

respectively. As expected, the lower hydraulic conductivities lead to longer travel times.

The relative peak concentrations for simulations L13 through L15 are 6.02×10-2, 4.77×10-2and 

5.92×10-3, respectively (Table 2-3). The peak concentrations are higher at lower hydraulic 

conductivities, because although the same amount of mass has entered the system, the plume 

created by the lower hydraulic conductivity remains more compact and is less elongated, as 

shown in Figure 2-7 and Figure 2-8.
2-22

Table 2-3: Long-Term/Low Volume Leak Scenarios

LONG-TERM/LOW VOLUME LEAK FAILURES

General Assumptions – All Runs Run L10 L11 L12 L13 L14 L15 L16 L17 L18

Receptor 

Locations (ft)

328, 656, 1,640, 

3,280

Regional Hydraulic 

Gradient 0.1 0.01 0.001 0.01 0.01 0.01 0.01 0.01 0.01

Simulation 

Time (yr) 53 Effective Porosity (%) 20 20 20 20 20 20 20 20 20

Leak Time (yr) 3

Dispersivity (ft)

Longitudinal

Transverse

Vertical

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

Model Area (ft2)

5,280 (ft) × 5,280 (ft) 

= 1 mi2

Leaking Fluid Conc. 

(milligram per L) 1 1 1 1 1 1 1 1 1

Leak Area 

(pipe - in2)

28 Hydraulic 

Conductivities (ft/day) 10 10 10 1 10 100 1 10 100

Model Layers 10 Distribution coefficients 

(ml/g)

Rf1

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Minimum/ 

Maximum Grid 

Spacing (ft) 

10/250 Leak Rates 

(gallons/min) 1.5 1.5 1.5 1 1 1 2 2 2

All Layers Homogenous

Time until Peak Arrival 

(days) 3.53×102

1.18×1034.95×1034.16×1031.18×1034.51×1024.27×1031.18×1032.95×102

Relative Peak 

Concentration 1.11×10-2

6.87×10-29.19×10-26.12×10-24.77×10-25.92×10-31.04×10-18.80×10-21.18×10-2

1 Rf = retardation factor calculated in Section 4
2-23

Figure 2-5: Relative Concentrations for L10 at 140 Days
2-24

Figure 2-6: Relative Concentrations for L11 at 140 Days
2-25

Figure 2-7: Relative Concentrations for L13 at 877 Days
2-26

Figure 2-8: Relative Concentrations for L15 at 877 Days
2-27

Simulations L16 through L18 were conducted to investigate the sensitivity of the results to leak 

rates (Table 2-3). These runs are paired with simulations L13 through L15 and, with the 

exception of leak rate, all of the input parameters for these runs are identical for each pair (i.e., 

L16 and L13, L17 and L14, and L15 and L18). The relative peak arrival time for L16 

(4,270 days) is longer than L13 (4,160 days), which may seem counterintuitive since L16 should 

have steeper gradients and faster velocities. An inspection of the output files, however, reveals 

that the additional volume of water (2 vs. 1 gpm) does increase the velocity, but the increase in 

mass redistributes the center of mass leading to a longer time before the peak concentration is 

reached. At a hydraulic conductivity of 10 ft/day (L14 and L17), the relative peak arrival times 

(1,180 days) are virtually the same. The relative peak concentrations, however, vary by a factor 

of a little less than 2, with the peak concentration of L14 being 4.77×10-2, and 8.80×10-2for L18. 

These relationships indicate that the changes caused to the flow field by the higher injection rates 

are balanced by the additional mass. The differences observed between runs L18 and L15 are 

more pronounced, with the peak arrival time for L18 being 295 days versus the 451 days for 

L15; and the relative peak concentrations are 1.18×10-2and 5.92×10-3for runs L18 and L15, 

respectively. These results indicate that, since the plume at higher hydraulic conductivities is 

more elongated and there is less movement of the plume in the transverse direction relative to the 

longitudinal, the concentration gradients within the plume are less pronounced and lead to a 

greater differentiation in travel times and relative concentrations.

Short-Term/High-Volume Leak(s)

The final leak scenario pertains to shorter-term, higher-volume leaks and involves six 

simulations (L19 through L24) (Table 2-4). In these simulations, the release time has been 

decreased to 28 days. All of the parameters are identical with the exception of the leak rates, 

which vary from 1 gpm to 40 gpm. Although it is very unlikely that 20 and 40 gpm leaks would 

continue undetected for 28 days, these leak rates are included to provide an upper bound on the 

sensitivity analysis. The hydraulic conductivity is set to 10 ft/day and the hydraulic gradient is 

0.01 ft/ft.

The peak arrival times for all six simulations are identical at 382 days, thus indicating that any

changes to the hydraulic gradients are balanced by the increased introduction of mass. The leak 

rates for runs L19 and L20 are 1 and 2 gpm, respectively. The relative peak concentration for 

L19 is 2.00×10-3, which is approximately one-half of the peak relative concentration for L20 of 

3.90×10-3. This nearly linear relationship between the amount of mass released and the relative 

peak concentrations is maintained for runs L21 (3 gpm) and L22 (4 gpm) in which the relative 

peak concentrations are 5.70×10-3 and 7.40×10-3, respectively. For the higher concentrations, 

however, the linear relationship is not as apparent and relative peak concentrations for L23 

(20 gpm) and L24 (40 gpm) are 3.07×10-2 and 5.38×10-2, respectively. All the peak arrival 

breakthrough curves are presented in Appendix B.
2-28

Table 2-4: Short-Term/High-Volume Leak Scenarios

SHORT-TERM/ HIGH VOLUME LEAK SCENARIOS

General Assumptions – All Runs Run L19 L20 L21 L22 L23 L24

Receptor 

Locations (ft)

328, 656, 1,640, 

3,280

Regional Hydraulic 

Gradient 0.01 0.01 0.01 0.01 0.01 0.01

Simulation 

Time (yr) 53 Effective Porosity (%) 20 20 20 20 20 20

Leak Time 

(days) 28

Dispersivity (ft)

Longitudinal

Transverse

Vertical

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

Model Area (ft2)

5,280 (ft) × 5,280 (ft)

= 1 mi2

Leaking Fluid Conc. 

(milligram per L) 1 1 1 1 1 1

Leak Area 

(pipe - in2)

28 Hydraulic 

Conductivities (ft/day) 10 10 10 10 10 10

Model Layers 10 Distribution coefficients 

(ml/g)

Rf1

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Rf 

Specified in 

Spreadsheet

Minimum/ 

Maximum Grid 

Spacing (ft) 

10/250 Leak Rates 

(gallons/min) 1 2 3 4 20 40

All Layers Homogenous

Time until Peak Arrival 

(days) 3.82×102

3.82×1023.82×1023.82×1023.82×1023.82×102

Relative Peak 

Concentration 2×10-3

3.9×10-35.7×10-37.4×10-33.07×10-25.38×10-2

1 Rf = retardation factor calculated in Section 4
2-29

Summary and Conclusions of Leak Scenarios

A total of three leak and/or spill scenarios were conducted to investigate the potential impacts to 

nearby receptors. Nine simulations were performed to evaluate catastrophic leaks. Longterm/low-volume releases were also simulated with nine simulations, and short-term/highvolume releases were simulated with six simulations. The sensitivity analysis was focused on 

those parameters that are most uncertain. These parameters include hydraulic gradients and 

conductivities, leak rates, and durations. All of the concentrations are calculated as relative 

concentrations assuming a 1-mg/L source term and will be adjusted to actual concentrations 

during the dose assessment (Section 4).

All of the relative peak concentrations versus peak arrival times are shown in Figure 2-9. The 

highest relative concentrations are associated with L12, L16 and L17. All three of these 

simulations are associated with the long-term/low-volume leaks. Runs L12 and L16 both have 

low ground water velocities. In the case of run L12, this is due to the low gradient (0.001 ft/ft), 

whereas the slow ground water velocity for run L16 is because of the low hydraulic conductivity 

(1 ft/day). The input for simulation L17 is identical to L16 except that the hydraulic conductivity 

in L17 is set to 10 ft/day. This explains the shorter peak arrival time.

Figure 2-9: Maximum Relative Concentrations versus Time for All Leak Simulations
2-30

In all cases where the hydraulic conductivity was set to 100 ft/day and/or the hydraulic gradient 

was specified as 0.1 ft/ft, the relative concentrations did not exceed 0.012 and the peak arrival 

times were less than 452 days. These results are explained by the fact that under more rapid 

velocities, the plume becomes more elongated and distributes the mass over a larger volume.

2.6.2 Excursion Scenarios

To investigate the potential effects of excursions beyond the active injection/production wells or 

into other non-mined geologic units above and/or below the mined unit, three excursion 

scenarios were developed: (1) injection fluid excursions downgradient within the same 

lithologic unit, (2) fluid excursions into overlying units through abandoned boreholes, and 

(3) fluid excursions into overlying units through discontinuous aquitard(s). The model runs 

performed for Scenario 1 are divided into 7 series with each series consisting of 9 simulations for 

a total of 63 simulations. Three simulations were conducted to investigate Scenario 2, and two 

abandoned borehole simulations were performed (Scenario 3). Summaries of all of the 

simulations are provided in Tables 2-5 through 2-11.

Of the 63 simulations, 54 involve either 5- or 7-spot injection\pumping well configurations 

(Figure 2-2). The remaining 9 simulate multiple 5-spot well configurations. As discussed in 

Section 2.6.2.7, these multiple 5-spot simulations results in longer transport times and lower 

peak concentrations.

The results from the single 5- and 7-spot scenarios, however, are more representative of the 

mining practices for extracting uranium from isolated outliers and uranium-rich stringers. 

The areal extent of the model grid is identical for all of the excursion simulations and consists of 

an area that covers 6.25 mi2(Figure 2-10). Horizontal grid spacing is on 10-ft centers in the 

vicinity of the spill, and is gradually increased to a spacing of 1,000 ft at the model boundaries.

The model grid for all of the scenarios is divided into nine layers (Figure 2-11). The vertical grid 

spacing, however, is different for Series 1 through 6 of Scenario 1 than it is for Series 7 of 

Scenario 1 and for Scenarios 2 and 3. For Series 1 through 6, all the layers are a uniform 75 ft 

thick (Figure 2-11). For the 7th series of Scenario 1 and Scenarios 2 and 3, variable layer 

thicknesses are assigned and the mined interval thickness is reduced from 75 to 20 ft thick 

(Figure 2-11).
2-31

5 10 15 20 253035 404550 556065 707580 859095100105110115120125 130 135 140 145

145

140

135

130

125

120

115

110

105

100

95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

5

500 feet

13300 feet

Constant Head Boundaries

Constant Head Boundaries

Figure 2-10: Plan View of the Model Grid for Excursion Scenarios (Series 1 through 7) 
2-32

South Cross-Section Along Column 73 North

75

Thickness (feet)

75 

75 

Series 6 -7 

Mined Interval

75 

75 

75 

75 

75 

75 

50

Series 1 -5 

100

75

130

75

20

75 

100

75 

50

Figure 2-11: Cross Section View of the Model Grid for Excursion Scenarios 

(Series 1 through 7) 

It is assumed that ground water is flowing due south and the model grid is oriented perpendicular 

to ground water flow. Constant head boundaries are assigned along the northern and southern 

boundaries in order to simulate the regional gradient (Figure 2-10). Boundaries on the east and 

west of the model domain are oriented perpendicular to ground water flow (i.e., hydraulic divide) 

and, therefore, ground water does not enter or exit the model along these boundaries (i.e., noflow boundary conditions). This boundary configuration makes the solutions to the flow problem 

more unique than would be obtained if constant head boundaries surrounded the entire domain. 

The base of the model is also represented by a no-flow boundary condition.

Since the most common injection/pumping patterns are 5- and 7-spot configurations, both types 

of arrangements have been simulated (Figure 2-12 and Figure 2-13, respectively). The extraction 

well is always assumed to be in the center with the injection wells along the periphery of the 

pattern. Although the injection is only assumed to occur for 3 years, the extraction wells remain 

on for the entire simulation and remove between 1% to 3% more than the volume of water being 

injected. If the specified pumping rate is 153 gpm, then each of the four injection wells (5-spot 

pattern) will be injecting at a rate of 37.5 gpm.
2-33

Figure 2-12: Plan View of Pumping\Injection Well Configurations and Receptor 

Locations for Excursion Scenarios (Series 1, 2, 5 and 6)
2-34

Figure 2-13: Plan View of Pumping\Injection Well Configurations and Receptor 

Locations for Excursion Scenarios (Series 3 and 4) 
2-35

Potential receptors are assumed to be located at 528 ft, 856 ft, 1,840 ft and 3,480 ft downgradient. In the discussion that follows, all of the relative peak arrival times and concentrations 

cited are for the potential receptor located at 528 ft down-gradient of the release (Figure 2-14). 

Breakthrough curves (i.e., concentrations as a function of time and distance) for all of the 

potential receptors are provided in Appendix C. These curves are based on a retardation factor 

of 1. Radionuclide-specific retardation effects are considered in Chapter 4.

Figure 2-14: Example of Breakthrough Curves at Receptor Locations 

All source fluid concentrations are assigned a relative concentration of 1 mg/L. As part of the 

dose assessment (Section 4), the relative concentrations arriving at the receptor well(s) are 

corrected to hypothesized concentrations of the source fluid and dose calculations are conducted.

2.6.2.1 Series 1 – 5-Spot 250-ft Spacing

The first series of simulations for Scenario 1 not only investigated the sensitivity of the results to 

hydraulic conductivities, gradients and extraction/injection rates, but also allowed an evaluation 

of the sensitivity to injection well spacing when compared to Series 2 (Section 2.6.2.2) 

counterpart simulations (Table 2-5 and Table 2-6). Since an injection/extraction well spacing of

250 ft is on the upper bound of well spacings typically used, this value was assigned in order to 

accentuate the differences in the results when compared against smaller spacings. Of the nine 

series of simulations, two series (1 and 3) assumed this upper bound on the well spacing. For the 
2-36

remaining series, the wells were spaced at more commonly used intervals between 50 and 150 ft 

apart.

The only difference among model runs 1a, 1b and 1c is that the hydraulic gradients are specified 

as either 0.1, 0.01, or 0.001, respectively.

As shown in Appendix C and presented in Table 2-5, the peak arrival times for simulations 1a 

through 1c are 193, 1,080 and 1,600 days, respectively. Since all parameters are constant except 

for hydraulic gradients, the model predictions are as expected, and the steeper hydraulic 

gradients result in shorter travel times. Furthermore, since the pumping/injection wells are 

altering the regional hydraulic gradients, the arrival times are not linearly scaled (i.e., a factor of 

10 increase in gradient does not result in a factor of 10 reduction in travel time).

Relative peak concentrations for runs 1a, 1b and 1c are 6.94×10-3, 6.54×10-2and 9.32×10-2 mg/L

at a receptor well at a distance of 528 ft. The difference in these results can be explained by the 

fact that a steeper hydraulic gradient leads to a more elongated plume in which the mass is 

distributed over a larger down-gradient area, resulting in lower relative peak concentrations. 

Thus, the peak arrival concentration of run 1a is lower than run 1b, and relative concentrations of 

1b are less than run 1c.

Simulations 1d, 1e and 1f are designed to investigate the impact that hydraulic conductivity has 

on the peak arrival times and relative peak concentrations. Hydraulic conductivities are 1, 10 and 

100 ft/day, which coincide with the peak arrival times of 1,700, 1,100 and 193 days, 

respectively. As expected, the longer arrival times are associated with the lower hydraulic 

conductivities. Also of interest is that the peak arrival times of runs peak arrivals times of 1b and 

1e are very close (1,080 and 1,100, respectively) and runs 1c and 1d are also similar (1,600 and 

1,700, respectively). These similarities are related to the fact that velocities will be controlled by

the product of hydraulic conductivity multiplied by the hydraulic gradient. For runs 1b and 1e, 

this product is 1.0, and for runs 1c and 1d the product is 0.1.

Relative peak concentrations for runs 1d, 1e and 1f are 2.14×10-1, 6.55×10-2and 6.94×10-3 mg/L. 

As has been observed for other simulations, an increase in hydraulic conductivity causes the 

plume to become more dispersed and leads to lower relative peak concentrations (Table 2-5).
2-37

Table 2-5: Series 1 – 5-Spot Injection at a Spacing of 250 feet – Receptor Well at 528 feet

SERIES 1 – 5-SPOT INJECTION AT 250 FOOT SPACING 

General Assumptions – All Runs Run 1a 1b 1c 1d 1e 1f 1g 1h 1i

Receptor 

Locations (ft) 528, 856, 1,840, 3,480 Regional Hydraulic Gradient 0.1 0.01 0.001 0.1 0.1 0.1 0.01 0.01 0.01

Simulation 

Time (yr) 53 Effective Porosity (%) 20 20 20 20 20 20 20 20 20

Well 

Configuration

5-Spot (pumping from 

center well)

Dispersivity (ft)

Longitudinal

Transverse

Vertical

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

Well Spacing 

(ft) 250 Injection Fluid Conc. (milligram per L) 1 1 1 1 1 1 1 1 1

Injection Time 

(yr) 3

Hydraulic Conductivities 

(ft/day) 100 100 100 1 10 100 1 10 100

Model Area 

(ft2)

13,300 (ft) × 13,300 (ft)

= 6.25 mi2 Distribution coefficients 

(ml/g)

Rf1Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Model Layers 9

Injection/Extraction Well 

Spacing (ft)

5-spot –

250

5-spot -

250

5-spot -

250

5-spot -

250

5-spot -

250

5-spot -

250

5-spot -

250

5-spot -

250

5-spot -

250

Minimum/ 

Maximum Grid 

Spacing (ft) 

10/1000 Injection Rate (gpm) 150 150 150 150 150 150 150 150 150

All Layers Homogenous Pumping Rate (gpm) 153 153 153 153 153 153 153 153 153

Operating Life of ISL 

Well Pattern (yrs) 3 3 3 3 3 3 3 3 3

Time until Peak Arrival 

(days) 1.93×102

1.08×1031.60×1031.70×1031.1×1031.93×1022.6×1041.60×1031.08×103

Relative Peak 

Concentration 6.94×10-3

6.54×10-29.32×10-22.14×10-16.55×10-26.94×10-31.90×10-49.27×10-26.54×10-2

1 Rf = retardation factor calculated in Section 4
2-38

Table 2-6: Series 2 – 5-Spot Injection at a Spacing of 50 feet – Receptor Well at 528 feet

SERIES 2 – 5-SPOT INJECTION AT 50 FOOT SPACING

General Assumptions – All Runs Run 2a 2b 2c 2d 2e 2f 2g 2h 2i

Receptor 

Locations (ft) 528, 856, 1,840, 3,480 Regional Hydraulic Gradient 0.1 0.01 0.001 0.1 0.1 0.1 0.01 0.01 0.01

Simulation Time 

(yr) 53 Effective Porosity (%) 20 20 20 20 20 20 20 20 20

Well 

Configuration 

5-Spot (pumping from 

center well)

Dispersivity (ft)

Longitudinal

Transverse

Vertical

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

Well Spacing 

(ft) 50 Injection Fluid Conc. (milligram per L) 1 1 1 1 1 1 1 1 1

Injection Time 

(yr) 3

Hydraulic Conductivities 

(ft/day) 100 100 100 1 10 100 1 10 100

Model Area (ft2)

13,300 (ft) × 13,300 (ft)

= 6.25 mi2

Distribution coefficients 

(ml/g)

Rf1Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Model Layers 9

Injection/Extraction Well 

Spacing (ft) 5-spot - 50 5-spot - 50 5-spot - 50 5-spot - 50 5-spot - 50 5-spot - 50 5-spot - 50 5-spot - 50 5-spot - 50

Minimum/ 

Maximum Grid 

Spacing (ft) 

10/1000 Injection Rate (gpm) 150 150 150 150 150 150 150 150 150

All Layers Homogenous Pumping Rate (gpm) 153 153 153 153 153 153 153 153 153

Operating Life of ISL 

Well Pattern (yrs) 3 3 3 3 3 3 3 3 3

Time until Peak Arrival 

(days) 1.32×102

1.05×1031.60×1031.52×1031.05×1031.32×1028.22×1031.60×1031.05×103

Relative Peak 

Concentration 9.62×10-3

2.35×10-28.75×10-31.18×10-22.35×10-29.62×10-33.39×10-68.22×10-32.35×10-2

1 Rf = retardation factor calculated in Section 4
2-39

The hydraulic gradient assigned to simulations 1g, 1h and 1i is 0.01 ft/ft. The effect that the 

regional hydraulic gradient will have on the results is evaluated by comparing simulation output 

from these runs to results obtained from runs 1d, 1e and 1f, which are assigned a regional 

gradient of 0.1 ft/ft.

The peak arrival times for simulations 1g, 1h and 1i are 26,000, 1,600 and 1,080 days, 

respectively. The arrival time for run 1g is about 11 times that of run 1d (1,700 days), which is 

close to what would be expected, since the hydraulic gradient is 10 times lower in run 1g than in 

run 1d. This same relationship for the peak arrival times for 1h and 1i, however, are not 

maintained when compared to runs 1e and 1f, respectively. This is primarily due to the fact that 

the injection rates are held constant across all the simulations and the relative effects on 

hydraulic gradients will be much greater at lower hydraulic conductivities. 

Relative peak concentrations for runs 1g, 1h and 1i are 1.80×10-4, 9.27×10-2and 6.54×10-2 mg/L. 

These results indicate that with flatter gradients (i.e., 0.01), the capture zone for the pumping 

wells becomes more pronounced laterally and more of the injectant is captured by the extraction 

wells. The inter-relationship between the hydraulic conductivities, regional gradient and 

pumping rates is complex, but in general, there is an internal consistency to the results. For 

instance, the relative concentrations for runs 1b and 1e are very similar and, upon inspection of 

the data files, it is evident that the product of hydraulic conductivity and gradient is 1.0 for both 

simulations. This same relationship is observed for runs 1c and 1h where the product is equal to 

0.1.

2.6.2.2 Series 2 – 5-Spot 50-ft Spacing

The input parameters to all of the Series 2 simulations are identical to those performed in Series 

1 with the exception that the spacing between the injection wells is decreased from 250 ft to 50 ft 

(Table 2-6).

Relative peak arrival times for runs 2a, 2b and 2c are 132, 1,050 and 1,600 days, respectively. 

These values are similar to the peak arrival times for simulations 1a through 1c, which are 193, 

1,080 and 1,600 days. The fact that run pairs 2b:1b and 2c:1c are so close indicates that the 

hydraulic gradients beyond the capture zone are very similar and any excursions will migrate at 

very similar rates. The peak arrival times for runs 2a and 1a, however, show a greater divergence 

in which the smaller well spacing and higher hydraulic gradient (0.1) results in a shorter travel 

time (i.e., 132 versus 193 days). This is because the larger well spacing affects (flattens) the 

hydraulic gradient over a larger area and achieves a better degree of capture, as is confirmed by 

comparing the relative peak concentrations.

Relative peak concentrations for runs 2a, 2b and 2c are 9.62×10-3, 2.35×10-2and 8.75×10-3mg/L. 

The peak concentration for run 2a is somewhat higher than that observed for run 1a (i.e., 

6.94×10-3), indicating that at higher regional gradients, the wider well spacing provides better 

capture of the lixiviant. At lower regional hydraulic gradients, however, better capture can be 

maintained at smaller well spacings, as evidenced by the lower peak concentrations observed in 

runs 2b and 2c when compared to their counterparts.
2-40

Simulations 2d, 2e and 2f are designed to investigate the sensitivity of hydraulic conductivity

(i.e., 1, 10 and 100). Relative peak arrival times for these runs are 1,520, 1,050 and 132 days, 

respectively. These results indicate somewhat shorter times than those predicted with the larger 

well spacings. Peak arrival times of simulations 1d, 1e and 1f are 1,700, 1,100 and 193 days, 

respectively. These results indicate that the larger well spacing tends to level out the hydraulic 

gradients to a greater degree, thus resulting in longer travel times.

Relative peak concentrations for runs 2d, 2e and 2f are 1.18×10-2, 2.35×10-2 and 9.62×10-3 mg/L, 

and the relative peak concentrations for runs 1d, 1e and 1f are 2.14×10-1, 6.55×10-2and 

6.94×10-3 mg/L. The lower concentrations of 2d and 2e than 1d and 1e indicate that the closer 

well spacing leads to better capture and lower excursion concentrations at lower hydraulic 

conductivities.

The hydraulic gradient assigned to simulations 2g, 2h and 2i is 0.01 ft/ft and the hydraulic 

conductivities are 1, 10 and 100, respectively. Peak arrival times for these simulations are 8,220, 

1,600 and 1,050 days. The peak arrival times for counterpart simulations 1g, 1h and 1i are 

26,000, 1,600 and 1,080 days, respectively. Based upon these results, the hydraulic gradients are 

most impacted by well spacing at the lower hydraulic conductivities. This is because at higher 

hydraulic conductivities, the impacts of injection/withdrawal are in closer proximity to the 

pumping/injection well(s). Therefore, a wider well spacing will tend to spread out the effects of 

pumping over a larger area, although this does not necessarily mean that the capture of the 

lixiviant is greater at a larger well spacing, as is evidenced by the relative peak concentrations.

Relative peak concentrations for runs 2g, 2h and 2i are 3.39×10-6, 8.22×10-3and 2.35×10-2mg/L, 

and relative peak concentrations for runs 1g, 1h and 1i are 1.80×10-4, 9.27×10-2and 

6.54×10-2 mg/L. These results indicate that under moderate hydraulic gradients and at lower 

hydraulic conductivities, concentrations of the excursions will be lower. At the higher hydraulic 

conductivity (i.e., 100 ft/day), however, and smaller well spacing (run 2i), the excursion reaches 

the receptor at higher concentrations as compared to the larger well spacing (run 1i). Although 

the larger well spacing flattens out the gradient and slows the migration more relative to the 

smaller well spacing, the capture zone for the smaller well spacing does not extend as far in the 

lateral directions (perpendicular to flow) as the larger well spacing. Furthermore, there tends to 

be more lixiviant that escapes between the wells at the larger spacing.

2.6.2.3 Series 3 – 7-Spot 250-ft Spacing

The input parameters to all of the Series 3 simulations are identical to those performed in 

Series 1 with the exception that the extraction and injection wells are in a 7-spot well 

configuration (Figure 2-13).

Relative peak arrival times for runs 3a, 3b and 3c are 235, 1,100 and 1,900 days, respectively

(Table 2-7). These values are very similar to the peak arrival times for simulations 1a through 1c,

which are 193, 1,080 and 1,600 days. These results indicate that the 7-spot well configuration 

does not significantly alter the travel times as compared to the 5-spot well configuration over a 

range of hydraulic gradients (i.e., 0.1, 0.01 and 0.001) and at a hydraulic conductivity of 100 

ft/day.
2-41

Table 2-7: Series 3 – 7-Spot Injection at a Spacing of 250 feet – Receptor Well at 528 feet

SERIES 3 – 7-SPOT INJECTION AT 250 FOOT SPACING

General Assumptions – All Runs Run 3a 3b 3c 3d 3e 3f 3g 3h 3i

Receptor 

Locations (ft) 528, 856, 1840, 3480 Regional Hydraulic Gradient 0.1 0.01 0.001 0.1 0.1 0.1 0.01 0.01 0.01

Simulation 

Time (yr) 53 Effective Porosity (%) 20 20 20 20 20 20 20 20 20

Well 

Configuration 

7-spot (pumping from

center well)

Dispersivity (ft)

Longitudinal

Transverse

Vertical

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

Well Spacing 

(ft) 250 Injection Fluid Conc. (milligram per L) 1 1 1 1 1 1 1 1 1

Injection Time 

(yr) 3

Hydraulic Conductivities 

(ft/day) 100 100 100 1 10 100 1 10 100

Model Area 

(ft2)

13,300 (ft) × 13,300 (ft)

= 6.25 mi2

Distribution coefficients 

(ml/g)

Rf1Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Model Layers 9

Injection/Extraction Well 

Spacing (ft) 7-spot - 250 7-spot - 250 7-spot - 250 7-spot - 250 7-spot - 250 7-spot - 250 7-spot - 250 7-spot - 250 7-spot - 250

Minimum/ 

Maximum Grid 

Spacing (ft) 

10/1000 Injection Rate (gpm) 150 150 150 150 150 150 150 150 150

All Layers Homogenous Pumping Rate (gpm) 153 153 153 153 153 153 153 153 153

Operating Life of ISL 

Well Pattern (yrs) 3 3 3 3 3 3 3 3 3

Time until Peak Arrival 

(days) 2.35×102

1.1×1031.90×1031.97×1031.1×1032.35×1024.00×1041.90×1031.10×103

Relative Peak 

Concentration 2.78×10-3

3.53×10-26.79×10

-2

1.41×10-13.53×10-22.78×10-31.06×10-46.75×10-23.53×10-2

1 Rf = retardation factor calculated in Section 4
2-42

Relative peak concentrations for runs 3a, 3b and 3c are 2.78×10-3, 3.53×10-2 and 6.79×10-2 mg/L, 

and relative peak concentrations for runs 1a, 1b and 1c are 6.94×10-3, 6.54×10-2and 

9.32×10-2 mg/L. Within each series, the flatter regional hydraulic gradients result in higher 

concentrations due to the plumes being less elongated and the mass more confined. The 7-spot 

well configuration, however, results in lower relative peak concentrations for all of the runs. This 

is because the additional pumping/extraction wells allow more overlap of the capture zone(s). In 

a 7-spot pattern, the distance from an injection to an extraction well is 250 ft, while for a 5-spot 

pattern it is 176 ft.

Hydraulic conductivities for runs 3d, 3e and 3f are 1, 10 and 100 ft/day, which coincide with 

relative peak arrival times of 1,970, 1,100 and 235 days, respectively. Relative peak arrivals for 

simulations 1d, 1e and 1f are 1,700, 1,100 and 193 days. Therefore, the peak arrival times for 

both sets of simulations are relatively similar over a range of hydraulic conductivities.

Relative peak concentrations for runs 3d, 3e and 3f are 1.41×10-1, 3.53×10-2 and 2.78×10-3 mg/L,

and relative peak concentrations for runs 1d, 1e and 1f are 2.14×10-1, 6.55×10-2and 

6.94×10-3 mg/L. Lower concentrations are correlated to the higher hydraulic conductivities. As 

was previously observed for runs 3a-c and 1a-c, the 7-spot configuration results in lower relative 

peak concentrations for all of the simulations. 

The hydraulic gradient assigned to simulations 3g, 3h and 3i is 0.01 ft/ft and the hydraulic 

conductivities are 1, 10 and 100, respectively. Peak arrival times for these simulations are 

40,000, 1,900 and 1,100 days. The peak arrival times for counterpart simulations 1g, 1h and 1i 

are 26,000, 1,600 and 1,080 days, respectively. Based upon these results, the travel times at the 

higher hydraulic conductivities are very similar for the hydraulic conductivity of 1 ft/day (run 3g 

and 1g), however, the time until peak arrival for the 7-spot well configuration is considerably

longer. This observation is primarily due to the effect that the additional wells have on flattening 

out the hydraulic gradient.

Relative peak concentrations for runs 3g, 3h and 3i are 1.06×10-4, 6.75×10-2 and 3.53×10-2 mg/L, 

and relative peak concentrations for runs 1g, 1h and 1i are 1.80×10-4, 9.27×10-2and 

6.54×10-2 mg/L. These results indicate that under moderate hydraulic gradients and at lower 

hydraulic conductivities, concentrations of the excursions will be lower. Furthermore, the 

relative peak concentrations for all of the 7-spot well configurations are lower than those for the 

analogous 5-spot well simulations.

2.6.2.4 Series 4 – 7-Spot 50-ft Spacing

The input parameters to all of the Series 4 simulations are identical to those performed in 

Series 3 with the exception that the extraction and injection wells are spaced at 50 ft instead of at 

250 ft.

Relative peak arrival times for runs 4a, 4b and 4c are 186, 1,100 and 1,450 days, respectively

(Table 2-8). For comparable simulations in Series 3, the relative peak arrival times for runs 3a, 

3b and 3c are 235, 1,100 and 1,900 days, respectively (Table 2-7). These results indicate that at 

the high (4c:3c) and low (4a:3a) hydraulic gradients, the relative peak arrival times are shorter 
2-43

for the more narrowly spaced wells. This relationship was also observed when the output for 

Series 1 and 2 was compared (Section 2.6.2.2) and is caused by greater flattening of the 

hydraulic gradient over a larger area with the wider well spacing. At the moderate gradient 

(0.01 ft/ft), however, the peak arrival times for runs 3b and 4b are essentially the same. This is 

because at the moderate gradient, there is a balance between the regional gradient and the 

gradients caused by the pumping/injection in which the net gradients that result are not as 

sensitive to the well spacing.

Relative peak concentrations for runs 4a, 4b and 4c are 1.25×10-2, 4.03×10-3and 9.01×10-3 mg/L,

and relative peak concentrations for runs 3a, 3b and 3c are 2.78×10-3, 3.53×10-2 and 

6.79×10-2 mg/L. The peak concentration for run 4a is somewhat higher than that observed for run 

3a, indicating that at higher regional gradients, the wider well spacing provides better capture of 

the lixiviant. At lower regional hydraulic gradients, however, better capture can be maintained at 

smaller well spacings, as evidenced by the lower peak concentrations observed in runs 4b and 4c 

when compared to their counterparts. This same relationship was observed when Series 1a, b, 

and c was compared to Series 2a, b, and c (Section 2.6.2.2).

Hydraulic conductivities for runs 4d, 4e and 4f are 1, 10 and 100 ft/day, which coincide with 

relative peak arrival times for runs of 1,390, 1,020 and 186 days, respectively. Relative peak 

arrival times for runs 3d, 3e and 3f are 1,970, 1,100 and 235 days. As is expected, the longer 

arrival times are associated with the lower hydraulic conductivities. The peak arrival times of 4e 

and 3e are very close (1,020 and 1,100, respectively). For the remaining simulations, however, 

the results are similar, but do not compare as well. Due to the hydraulic gradients not being 

affected over as large an area, the smaller well spacing results in shorter peak arrival times.

Relative peak concentrations for runs 4d, 4e and 4f are 3.07×10-2, 4.03×10-2and 1.25×10-2 mg/L,

and relative peak concentrations for runs 3d, 3e and 3f are 1.41×10-1, 3.53×10-2 and 

2.78×10-3 mg/L. With exception of the 4e:3e run comparison, the closer well spacing leads to 

lower relative peak concentrations. The closer well spacing also leads to better capture, in 

general, as evidenced by the similar release concentrations among runs 4d, 4e and 4f.

The hydraulic gradient for runs 4g, 4h, and 4i is 0.01 ft/ft and the hydraulic conductivities are 1, 

10 and 100 ft/day, respectively. Relative peak arrival times for runs 4g, 4h and 4i are 7,840, 

1,450 and 1,100 days, respectively (Table 2-8). For comparable simulations in Series 3, the 

relative peak arrival times for runs 3g, 3h and 3i are 40,000, 1,900 and 1,100 days. At the lower 

hydraulic conductivities (4g and 4h), the closer well spacing results in shorter arrival times. For a 

hydraulic conductivity of 100 ft/day, the arrival times (4i:3i) are identical.
2-44

Table 2-8: Series 4 – 7-Spot Injection at a Spacing of 50 feet – Receptor Well at 528 feet

SERIES 4 – 7-SPOT INJECTION AT 50 FOOT SPACING

General Assumptions – All Runs Run 4a 4b 4c 4d 4e 4f 4g 4h 4i

Receptor 

Locations (ft) 528, 856, 1,840, 3,480 Regional Hydraulic Gradient 0.1 0.01 0.001 0.1 0.1 0.1 0.01 0.01 0.01

Simulation 

Time (yr) 53 Effective Porosity (%) 20 20 20 20 20 20 20 20 20

Well 

Configuration 

7-Spot (pumping from 

center well)

Dispersivity (ft)

Longitudinal

Transverse

Vertical

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

Well Spacing 

(ft) 50 Injection Fluid Conc. (milligram per L) 1 1 1 1 1 1 1 1 1

Injection Time 

(yr) 3

Hydraulic Conductivities 

(ft/day) 100 100 100 1 10 100 1 10 100

Model Area 

(ft2)

13,300 (ft) × 13,300 (ft)

= 6.25 mi2

Distribution coefficients 

(ml/g)

Rf1Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Model Layers 9

Injection/Extraction Well 

Spacing (ft) 7-spot - 50 7-spot - 50 7-spot - 50 7-spot - 50 7-spot - 50 7-spot - 50 7-spot - 50 7-spot - 50 7-spot - 50

Minimum/ 

Maximum Grid 

Spacing (ft) 

10/1000 Injection Rate (gpm) 150 150 150 150 150 150 150 150 150

All Layers Homogenous Pumping Rate (gpm) 153 153 153 153 153 153 153 153 153

Operating Life of ISL 

Well Pattern (yrs) 3 3 3 3 3 3 3 3 3

Time until Peak Arrival 

(days) 1.86×102

1.10×1031.45×1031.39×1031.02×1031.86×1027.84×1031.45×1031.10×103

Relative Peak 

Concentration 1.25×10-2

4.03×10-39.01×10-33.07×10-24.03×10-21.25×10-22.14×10-68.94×10-34.03×10-2

1 Rf = retardation factor calculated in Section 4
2-45

Relative peak concentrations for runs 4g, 4h and 4i are 2.14×10-6, 8.94×10-3and 4.03×10-2 mg/L. 

These outcomes indicate that capture of the injectant is more effective at the lower hydraulic 

conductivities and results in lower relative peak concentrations. A comparison against 

simulations 4d, 4e and 4f indicates that the steeper hydraulic gradient results in larger releases 

because the lateral capture is not as effective.

Relative peak concentrations for runs 4g, 4h and 4i are lower than those observed for

runs 3g, 3h and 3i, which are 1.06×10-4, 6.75×10-2and 3.53×10-2 mg/L. These results support 

earlier findings indicating that the closer well spacing captures more of the injectant.

2.6.2.5 Series 5 – 5-Spot Injection/Pumping Rates Dependent Upon Hydraulic Conductivity

The injection/pumping rates specified in all of the simulations performed in Series 1 through 4 

are 150 gpm (injection) and 153 gpm (pumping). Although these rates are the same, in actuality, 

the hydraulic conductivities would be considered in determining the amount of water that is 

pumped and injected. This correlation was not factored into the Series 1 through 4 simulations 

because it would make it very difficult to isolate the effects of the other parameters (e.g., 

hydraulic gradients, well configuration and spacing). For Series 5, however, the 

injection/pumping rates have been adjusted to more realistically reflect the hydraulic 

conductivity of the system.

To estimate pumping/injection rates at hydraulic conductivities of 1, 10 and 100 ft/day, a 

constant head boundary was set to an elevation representative of the approximate pumping level 

in each of the wells. MODFLOW output files provide the amount of water removed by the 

constant head boundary, and this value was subsequently used as input for the pumping/injection 

rates. The specified rates for hydraulic conductivities of 1, 10 and 100 ft/day are 7.15/7, 51/50 

and 510/500 gpm, respectively (Table 2-9).

In addition to the change in the pumping/extraction rates, the well spacing for the Series 5 

simulations is set to 150 ft, as opposed to the 50 or 250 spacings assigned in the earlier 

simulations.
2-46

Table 2-9: Series 5 – 5-Spot Injection\Pumping Rates Dependent Upon Hydraulic Conductivity

SERIES 5 – 5-SPOT INJECTION/PUMPING RATES DEPENDENT UPON HYDRAULIC CONDUCTIVITY 

General Assumptions - All Runs Run 5a 5b 5c 5d 5e 5f 5g 5h 5i

Receptor 

Locations (ft) 528, 856, 1840, 3480 Regional Hydraulic Gradient 0.1 0.01 0.001 0.01 0.01 0.01 0.001 0.001 0.001

Simulation 

Time (yr) 53 Effective Porosity (%) 20 20 20 20 20 20 20 20 20

Well 

Configuration 

5-Spot (pumping from 

center well)

Dispersivity (ft)

Longitudinal

Transverse

Vertical

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

Well Spacing 

(ft) 150 Injection Fluid Conc. (milligram per L) 1 1 1 1 1 1 1 1 1

Injection Time 

(yr) 3

Hydraulic Conductivities 

(ft/day) 10 10 10 1 10 100 1 10 100

Model Area 

(ft2)

13,300 (ft) × 13,300 (ft)

= 6.25 mi2

Distribution coefficients 

(ml/g)

Rf1Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Model Layers 9

Injection/Extraction Well 

Spacing (ft) 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150

Minimum/ 

Maximum Grid 

Spacing (ft) 

10/1000 Injection Rate (gpm) 50 50 50 7 50 500 7 50 500

All Layers Homogenous Pumping Rate (gpm) 51 51 51 7.15 51 510 7.15 51 510

Operating Life of ISL 

Well Pattern (yrs) 3 3 3 3 3 3 3 3 3

Time until Peak Arrival 

(days) 1.10×103

1.52×1031.8×1041.01×1041.52×1031.05×1031.28×1041.8×1041.10×103

Relative Peak 

Concentration 3.76×10-2

6.79×10-28.24×10-41.53×10-26.79×10-21.01×10-11.05×10-16 8.24×10-41.08×10-1

1 Rf = retardation factor calculated in Section 4
2-47

The hydraulic gradients for runs 5a, 5b and 5c are 0.1, 0.01 and 0.001 ft/ft. All of the hydraulic 

conductivities are set to 10 ft/day with an accompanying pumping/injection rate of 51/50 gpm. 

Relative peak arrival times for runs 5a, 5b and 5c are 1,100, 1,520 and 18,000 days, respectively

(Table 2-9). Since so many parameters have been changed from those assigned to simulations in 

the previous series, it is difficult to draw inter-series comparisons. The arrival time of 

18,000 days, however, is one of the longest times observed.

Relative peak concentrations for runs 5a, 5b and 5c are 3.76×10-2, 6.79×10-2and 8.24×10-4 mg/L

and indicate that the capture zone is most effective at the lowest hydraulic gradient of 0.001 ft/ft 

(run 5c). Results for runs 5a and 5b indicate that although the capture is more effective at a 

gradient of 0.01 than 0.1 ft/ft, the injectant becomes more widely dispersed in the down-gradient 

direction under the 0.1 ft/ft gradient due to the higher velocities.

Runs 5d, 5e and 5f are all performed with a hydraulic gradient of 0.01 ft/ft; hydraulic 

conductivities of 1, 10 and 100 ft/day; and with the pumping/injection rates varying as a function 

of the hydraulic conductivity. Relative peak arrival times for runs 5d, 5e and 5f are 10,100, 1,520 

and 1,050 days. The arrival time for 5d is almost 10 times that of 5a, which is expected since the 

hydraulic gradient is decreased by a factor of 10. This result also reflects the fact that the 

pumping rate for 5d of 7.15 gpm was similarly scaled to a hydraulic conductivity of 1, as was the 

pumping rate of 50 gpm to a hydraulic conductivity of 10 ft/day. 

Relative peak concentrations for runs 5d, 5e and 5f are 1.53×10-2, 6.79×10-2and 1.01×10-1 mg/L. 

The concentrations increase as a function of increasing pumping/injection rates and hydraulic 

conductivities and indicate that capture is more complete at the lower hydraulic conductivities. 

These results also suggest that the bleed rate (i.e., difference between pumping and injection 

rates) should be increased as a function of hydraulic conductivity in order to increase the capture 

zone at hydraulic conductivites.

The input for runs 5g, 5h and 5i are identical to that for runs 5d, 5e and 5f, except that the

hydraulic gradient is set to 0.001 ft/ft instead of 0.01 ft/ft. The peak arrival times for runs 5g, 5h 

and 5i are 12,800, 18,000 and 1,100 days. With the exception of runs 5h and 5e, the arrival times 

for the 5g-e and 5i-f pairs are very similar. This suggests that the hydraulic gradients created by 

the pumping/injection wells are large enough to overwhelm the differences in the regional 

hydraulic gradients. With respect to run 5h, it appears that the long peak arrival time observed is 

due to the very low relative peak concentration (i.e., 1.05×10-16) in which numerical dispersion 

may be a significant contributor.

Relative peak concentrations for runs 5g, 5h and 5i are 1.05×10-16, 8.24×10-4and 1.08×10-1 mg/L. 

These results, when compared against those obtained for runs 5d, 5e and 5f, indicate that the 

flatter gradients allow better capture and that at the conductivity of 100 ft/day, the injection/ 

pumping is large enough to overwhelm the differences in regional gradients.
2-48

2.6.2.6 Series 6 – 5-Spot 20-ft Thick Mined Interval

All of the simulations performed in Series 6 are identical to those conducted for Series 5 except 

that the model layers vary in thickness and the mined interval was reduced from a thickness of 

70 ft to 20 ft (Figure 2-11).

The relative peak arrival times for run 6a, 6b and 6c are 1,700, 1,100 and 20,600 days, 

respectively (Table 2-10). As described in Section 2.6.2.5, the relative peak arrival times for runs 

5a, 5b and 5c are 1,100, 1,520 and 18,000 days. The effect of pumping/injection on hydraulic 

gradients is strongly affected by the transmissivity (i.e., hydraulic conductivity multiplied by

thickness) of the geologic units. The lower transmissivity results in shorter times to peak arrivals 

at the low and high gradients (runs 6a and 6c) and a longer time at the medium gradient (run 6b). 

These relationships are all related to how the regional and localized gradients created by the 

pumping/injection interact to form a capture zone. It also illustrates the complexity and need to 

understand the geology and flow system, since the effects of the interactions are not always 

intuitive.

Relative peak concentrations for runs 6a, 6b and 6c are 3.31×10-1, 4.75×10-1 and 1.30×10-3 mg/L

and, as was observed with runs 5a, 5b and 5c, indicate that the capture zone is most effective at 

the lowest hydraulic gradient (0.001 ft/ft). All of the relative concentrations are approximately an 

order of magnitude higher than the relative peak concentrations for runs 5a, 5b and 5c (i.e.,

3.76×10-2, 6.79×10-2 and 8.24×10-4 mg/L). The higher concentration at the lower transmissivity

is due to the injectant being concentrated within a smaller volume.

Runs 6d, 6e and 6f are all performed with a hydraulic gradient of 0.01 ft/ft; hydraulic 

conductivities of 1, 10 and 100 ft/day; and with the pumping/injection rates varying as a function 

of the hydraulic conductivity. Relative peak arrival times for runs 6d, 6e and 6f are 9,930, 1,520 

and 1,050 days. The times are very similar for runs 6d-5d and essentially identical for runs 6e-5e 

and 6f-5f. Therefore, for the same regional gradient, the peak arrival times are insensitive to the 

transmissivity. 

Relative peak concentrations for runs 6d, 6e and 6f are 1.06×10-1, 4.75×10-1and 6.45×10-1 mg/L, 

and, as was observed with the 6a, b, and c versus 5a, b, and c series, are all considerably higher 

than their counterparts where the relative peak concentrations for runs 5d, 5e and 5f are 

1.53×10-2, 6.79×10-2and 1.01×10-1mg/L. These results demonstrate that the lower 

transmissivities will result in higher relative concentrations.

The peak arrival times for runs 6g, 6h and 6i are 288,000, 20,700 and 1,210 days. The very long 

peak arrival time for run 6g and low peak concentration (i.e., 7.40×10-5) indicate that capture at 

the lowest hydraulic conductivity is essentially complete. The relative peak arrival times for 5h 

and 5i (i.e., 18,000 and 1,100 days) are very close to those observed for runs 6h and 6i, which 

indicate that similar hydraulic gradients are being created for both sets of simulations.
2-49

Table 2-10: Series 6 – 5-Spot 20-Foot Thick Mined Interval

SERIES 6 – 5-SPOT – 20-FOOT THICK MINED INTERVAL

General Assumptions - All Runs Run 6a 6b 6c 6d 6e 6f 6g 6h 6i

Receptor 

Locations (ft) 528, 856, 1,840, 3,480 Regional Hydraulic Gradient 0.1 0.01 0.001 0.01 0.01 0.01 0.001 0.001 0.001

Simulation 

Time (yr) 53 Effective Porosity (%) 20 20 20 20 20 20 20 20 20

Well 

Configuration 

5-Spot (pumping from 

center well)

Dispersivity (ft)

Longitudinal

Transverse

Vertical

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

Well Spacing 

(ft) 150 Injection Fluid Conc. (milligram per L) 1 1 1 1 1 1 1 1 1

Injection Time 

(yr) 3

Hydraulic Conductivities 

(ft/day) 10 10 10 1 10 100 1 10 100

Model Area 

(ft2)

13,300 (ft) × 13,300 (ft)

= 6.25 mi2

Distribution coefficients 

(ml/g)

Rf1Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Model Layers

9

The thickness of layer 5 is 

decreased to 20 ft thick and 

has the monitoring/ 

injection/pumping wells.

Injection/Extraction Well 

Spacing (ft) 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150

Minimum/ 

Maximum Grid 

Spacing (ft) 

10/1000 Injection Rate (gpm) 50 50 50 7 50 500 7 50 500

All Layers Homogenous Pumping Rate (gpm) 51 51 51 7.15 51 510 7.15 51 510

Operating Life of ISL 

Well Pattern (yrs) 3 3 3 3 3 3 3 3 3

Time until Peak Arrival 

(days) 1.70×103

1.10×1032.06×1049.93×1031.52×1031.05×1032.88×1052.07×1041.21×103

Relative Peak 

Concentration 3.31×10-1

4.75×10-11.30×10-31.06×10-14.75×10-16.45×10-17.40×10-51.30×10-36.26×10-1

1 Rf = retardation factor calculated in Section 4
2-50

Relative peak concentrations for runs 6g, 6h and 6i are 7.40×10-5, 1.30×10-3and 6.26×10-1 mg/L. 

These results, when compared against those obtained for runs 5g, 5h and 5i (i.e., 1.05×10-16, 

8.24×10-4and 1.08×10-1), are all higher and, as was observed between the 6d, e, and f and 5d, e, 

and f comparisons, the lower transmissivities result in higher relative concentrations.

2.6.2.7 Series 7 – Twenty-Five 5-Spot Pumping/Injection Cells

To evaluate the potential cumulative effects of multiple pumping/injection cells, the simulations 

in Series 7 consist of twenty-five 5-spot injection cells (Figure 2-15). With the exception of the 

multiple cells, all of the other input parameters for Series 7 are identical to those for Series 6, 

where only one 5-spot pattern was modeled (Table 2-11).

The only difference among model runs 7a, 7b and 7c is that the hydraulic gradients are specified 

as 0.1, 0.01 or 0.001, respectively. The relative peak arrival times for runs 7a, 7b, and 7c are 

1,090, 4,830 and 1,280 days, while the relative peak arrival times for runs 6a, 6b and 6c are 

1,700, 1,100 and 20,600 days. The observed differences are associated with the degree to which 

the multiple cells affect the hydraulic gradients as compared to the single pumping/injection cell. 

At the highest gradient (runs 7a and 6a), both simulations impact the hydraulic gradients to a 

relatively similar degree. At the moderate gradient, however, the multiple cells are more 

effective at flattening the hydraulic gradients, which is reflected in the longer peak arrival times 

in run 7b as compared to run 6b. For the runs with the relatively flat regional gradients (7c and 

6c), the impact of the pumping/injection for the multiple cells in run 7c overwhelms the effects 

of the regional gradient, as expressed by the very long travel time in run 6c.

Relative peak concentrations for runs 7a, 7b and 7c are 3.12×10-1, 6.61×10-4and 2.70×10-4 mg/L, 

and the relative peak concentrations for runs 6a, 6b and 6c are 3.31×10-1, 4.75×10-1and 

1.30×10-3 mg/L. Except for the simulations with the steep regional gradient (7a and 6a), the peak 

relative concentrations with the multiple cells are orders of magnitude lower than the single cell 

simulations. These results indicate that at lower hydraulic gradients, the majority of the injectant 

that is not recaptured is lost from the injection wells that are located to the furthest east and west 

and not very much is lost from the line of injection wells located in between. Under the steeper 

hydraulic gradient, however, the capture is not complete along the entire line of wells running 

east-west and, therefore, the relative peak concentrations for runs 7a and 6a are at similarly 

elevated levels.

Runs 7d, 7e and 7f are all performed with a hydraulic gradient of 0.01 ft/ft, and hydraulic 

conductivities of 1, 10 and 100 ft/day. Relative peak arrival times for runs 7d, 7e and 7f are 

48,700, 4,800 and 1,100 days. At the lower hydraulic conductivities, these results indicate slower 

velocities than the comparable runs in Series 6 (6d and 6e), which are 9,930, and 1,520 days and 

indicate the formation of flatter gradients formed by the array of pumping/injection wells. At the 

hydraulic conductivity of 100 ft/day, however, the arrival time for run 7f is nearly the same as 

that for run 6f (1,050 days), indicating that the gradients are less affected by the 

pumping/injection at higher hydraulic conductivities.
2-51

528 ft

856 ft

1840 ft

3480 ft

500 feet

Pumping

Injection Injection

Injection Injection

Figure 2-15: Plan View of the Twenty-five 5-spot Pumping/Injection Well Configurations 

and Receptor Locations for Excursion Scenario (Series 7) 
2-52

Table 2-11: Series 7 – Twenty-five 5-Spot Pumping/Injection Cells

SERIES 7 – TWENTY-FIVE (25) 5-SPOT PUMPING/INJECTION CELLS

General Assumptions - All Runs Run 7a 7b 7c 7d 7e 7f 7g 7h 7i

Receptor 

Locations (ft) 528, 856, 1,840, 3,480 Regional Hydraulic Gradient 0.1 0.01 0.001 0.01 0.01 0.01 0.001 0.001 0.001

Simulation 

Time (yr) 53 Effective Porosity (%) 20 20 20 20 20 20 20 20 20

Well 

Configuration 

5-Spot (pumping from 

center well)

Dispersivity (ft)

Longitudinal

Transverse

Vertical

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

Well Spacing 

(ft) 150 Injection Fluid Conc.(milligram per L) 1 1 1 1 1 1 1 1 1

Injection Time 

(yr) 3

Hydraulic Conductivities 

(ft/day) 10 10 10 1 10 100 1 10 100

Model Area 

(ft2)

13,300 (ft) × 13,300 (ft)

= 6.25 mi2

Distribution coefficients 

(ml/g)

Rf1Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Rf Specified 

in 

Spreadsheet

Model Layers

9

The thickness of layer 5 is 

decreased to 20 ft thick and 

has the monitoring/ 

injection/pumping wells.

Injection/Extraction Well 

Spacing (ft) 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150 5-spot - 150

Minimum/ 

Maximum Grid 

Spacing (ft) 

10/1000 Injection Rate (gpm) 50 50 50 7 50 500 7 50 500

All Layers Homogenous Pumping Rate (gpm) 51 51 51 7.15 51 510 7.15 51 510

Operating Life of ISL 

Well Pattern (yrs) 3 3 3 3 3 3 3 3 3

Time until Peak Arrival 

(days) 1.09×103

4.83×1031.28×1034.87×1044.80×1031.10×1036.63×1031.28×1031.10×103

Relative Peak 

Concentration 3.12×10-1

6.61×10-42.70×10-42.35×10-56.61×10-41.10×10-25.25×10-52.70×10-44.50×10-4

1 Rf = retardation factor calculated in Section 4
2-53

Relative peak concentrations for runs 7d, 7e and 7f are 2.35×10-5, 6.61×10-4and 1.10×10-2 mg/L

and are all significantly lower than for their Series 6 counterparts, where relative peak 

concentrations for runs 6d, 6e and 6f are 1.06×10-1, 4.75×10-1 and 6.45×10-1mg/L. These results 

indicate that, not only is the degree of capture a function of the hydraulic conductivity, but also 

that a significant amount of the injectant that is not captured is from the injection into the wells 

located farthest to the east and to the west. This conclusion is based on the fact that the receptor 

well(s) are placed on the plume centerline and, therefore, in the case with the single injection 

well, the concentration will be impacted by any uncaptured releases from the wells to the east, 

west and between the pumping and injection wells. For the multiple injection array, however, the 

receptor well is farther from the injection wells to the east and west, and therefore, the injectant

concentrations detected at the well are not as high.

The model input parameters for runs 7g, 7h and 7i are identical to those assigned in runs 7d, 7e 

and 7f except that the hydraulic gradient was changed from 0.01 to 0.001 ft/ft. The peak arrival 

times for runs 7g, 7h and 7i are 6,630, 1,280 and 1,100 days and, except at the highest hydraulic 

conductivity, are much shorter than for runs 6g, 6h and 6i, which are 288,000, 20,700 and 

1,210 days. These results indicate that at the low and moderate hydraulic conductivities (1 and 

10 ft/day) and flat gradients (0.001), the multiple well configuration does not flatten the 

hydraulic gradients as much as the single pumping/injection cell configuration.

Relative peak concentrations for runs 7g, 7h and 7i are 5.25×10

-5

, 2.70×10-4and 4.50×10-4 mg/L. 

These values are all lower than the relative peak concentrations for runs 6g, 6h and 6i, which are 

7.40×10-5, 1.30×10-3 and 6.26×10-1 mg/L. These results are explained by the larger capture zone 

that is created by the array of pumping/extraction wells.

While simulations with a single 5- or 7-spot pattern are useful in understanding the interactions 

and sensitivity to various modeling parameters, this modeling approach produces conservative 

results (i.e., high relative concentration at the receptor well). In general, modeling of multiple 

injection/extraction patterns, which are a closer approximation to a full-scale wellfield, results in 

lower relative concentrations.

2.6.2.8 Abandoned Borehole Pathway

Several modeling simulations were performed to investigate the potential impacts on overlying 

aquifers of an exploratory borehole that penetrates into the mined unit (Table 2-12). The major 

conceptual components of the modeled system are a 60-ft thick low-conductivity confining unit 

(1×10-6ft/day) that separates a 30-ft thick mined interval from an overlying aquifer with an 

assigned thickness of either 50 or 100 ft, depending upon the simulation. The abandoned 

borehole hydraulically connects the overlying aquifer to the mined unit through a 1-ft2high 

hydraulic conductivity damaged rock zone created as the borehole was cored.

To simulate this system, a 5-layer model was constructed that covers approximately a 1-mi2area

(Figure 2-16). Grid spacing in the horizontal direction ranges from 1 to 50 ft and in the vertical 

direction from 20 to 100 ft (Figure 2-17). The aquifer and mined unit are represented by single 

layers and are separated by an aquitard that is divided into 3 layers. 
2-54

Table 2-12: Abandoned Borehole Simulations

ABANDONED BOREHOLE SIMULATIONS

General Assumptions - All Runs Run AB-R1 AB-R2 AB-R3

Receptor Locations (ft) 528, 856, 1,840 Regional Hydraulic Gradient 0.01 0.01 0.01

Simulation Time (yr) 53 Effective Porosity (%) 20 20 20

Well Configuration 5-Spot (pumping from 

center well)

Dispersivity (ft)

Longitudinal

Transverse

Vertical

65

6.5

0.65

65

6.5

0.65

65

6.5

0.65

Well Spacing (ft) 150 Injection Fluid Conc.

(milligram per L) 1 1 1

Injection Time (yr) 3

Upper Aquifer 1 Hydraulic 

Conductivity (ft/day) 100 10 100

Model Area (ft2)

5,280 (ft) × 5,280 (ft) 

= 1 mi2 Upper Aquifer Thickness (ft) 100 100 50

Model Layers

5

The thickness of the mined 

interval (layer 5) is 30 ft and 

has the monitoring/

injection/pumping wells.

Mined Interval Hydraulic 

Conductivity (ft/day) 10 10 10

Minimum/ Maximum 

Grid Spacing (ft) 1/50 Retardation Factor 1 1 1

All Layers

Confining units 

separate upper aquifer 

from production zone

Injection/Extraction Well 

Spacing (ft) 5-spot - 100 5-spot - 100 5-spot - 100

Disturbed Rock Zone 

Around Borehole (ft2)

1 Injection Rate (gpm) 50 50 50

Pumping Rate (gpm) 51 51 51

Operating Life of ISL Well 

Pattern (yrs) 3 3 3

Time until Peak Arrival (days) 2.98×1034.15×1032.06×103

Relative Peak Concentration 5.81×10-15.12×10-18.00×10-1
2-55

5200 feet

5

2

8

0 fe

et

Constant Head Boundaries

Receptor Wells

Constant Head Boundaries

Figure 2-16: Plan View of the Model Grid for the Abandoned Borehole and Discontinuous 

Confining Bed Excursion Simulations
2-56

West East

20

Thickness (Feet)

100

30

20

20

Figure 2-17: Cross-Sectional View of the Model Grid for the Abandoned Borehole and 

Discontinuous Confining Bed Excursion Simulations 

Three model simulations were performed to investigate the effects of aquifer thickness and 

aquifer conductivity. It is recognized that the results would be influenced by many parameters 

(e.g., size and permeability of the damaged rock zone), but the primary objective was to assess 

whether, under a reasonable set of assumptions, vertical migration via a borehole could result in 

significant releases. It should be kept in mind, however, that for this type of release to occur, 

there has to be an upward vertical gradient and the damaged rock zone has to be of sufficient 

hydraulic conductivity to allow vertical movement.

The peak arrival time for the base case abandoned borehole simulation (AB-R1) is 2,980 days 

and the peak concentration is 5.81×10

-1

. This is one of the most significant releases predicted 

across all of the potential failure scenarios (i.e., leaks and excursions).

The effect that a lower aquifer hydraulic conductivity has on the results is evaluated in run 

AB-R2 where the hydraulic conductivity is lowered from 100 to 10 ft/day. The peak arrival time 

for this simulation (AB-R2) is 4,150 days and the peak concentration is 5.12×10-1. The travel 

time has increased from the base case because of the lower ground water velocities. The peak
2-57

concentrations, however, remain very similar. In previous excursion simulations, the higher 

hydraulic conductivities typically had lower peak concentrations. In this comparison, however, 

the higher hydraulic conductivity has a higher release. This relationship occurs because the 

vertical gradient is affected by the hydraulic conductivity of the overlying aquifer. At higher 

hydraulic conductivities, the vertical gradient allows more injectant to enter the aquifer via the 

borehole. The amount of water entering the upper aquifer also impacts the horizontal gradients 

within the aquifer.

The impact of the reduction in aquifer thickness from 100 to 50 ft is investigated in run AB-R3. 

The peak arrival time for this simulation is 2,060 days and the peak concentration is 8.00×10-1. 

The shorter peak arrival time can be explained by the steeper hydraulic gradients in the aquifer, 

which are due to the water flowing up the borehole encountering a lower transmissivity

(hydraulic conductivity multiplied by thickness). The higher relative concentration occurs 

because the amount of injectant entering the aquifer is distributed over a smaller vertical area (or 

volume). Breakthrough curves are included in Appendix D. 

2.6.2.9 Confining Bed Discontinuity

To investigate the potential effects of a discontinuous confining unit between the mined aquifer 

and the overlying aquifer, two simulations were performed (Table). For both of these 

simulations, the model grid, domain and input parameters are identical to those of the abandoned 

borehole base case (AB-R1), except that the abandoned borehole is replaced by an area of either 

10 or 100 ft2, where the hydraulic conductivity is high (100 ft/day). Conceptually, this area is 

representative of an erosional or depositional surface that has been filled with the material from 

the overlying aquifer (e.g., a sand lens).

For run CBD-R1, this area of discontinuity is specified as 100 ft2. The peak arrival time for this 

simulation is 11,900 days and the peak concentration is 5.09×10-1. This longer arrival time from 

that observed for the abandoned borehole simulations occurs because the high transmissivity

zone (i.e., damaged rock zone) does not extend through the entire aquifer. Therefore, injectant 

entering the aquifer must do so only at the base, and it takes longer for the injectant to mix with 

the water in the aquifer to reach a peak concentration. The peak concentration, however, is very

similar to those observed in runs AB-R1 and AB-R2.

The area of discontinuity was decreased from 100 ft2to 25 ft2in run CBD-R2. The peak arrival 

time for this simulation is 45,700 days and the peak concentration is 4.73×10-1. The longer peak 

arrival time for the smaller area is related to the smaller amount of injectant that is migrating 

vertically upward. The effect of the smaller area is also reflected in the lower relative peak 

concentration. Breakthrough curves are included in Appendix D. Since the potential releases 

through the discontinuous layers result in uranium concentrations that are almost the same as 

those observed in the wellfield, there was no need to calculate the health-based standards for 

these scenarios.

Furthermore, although this analysis was focused upon overlying aquifers, similar results would 

be obtained from potential excursions to underlying aquifers.
2-58

Table 2-13: Confining Bed Discontinuity Simulations

CONFINING BED DISCONTINUITY SIMULATIONS

General Assumptions-All Runs Run CBD-R1 CBD-R2

Receptor Locations (ft) 528, 856, 1,840 Regional Hydraulic Gradient 0.01 0.01

Simulation Time (yr) 53 Effective Porosity (%) 20 20

Well Configuration 5-Spot (pumping 

from center well)

Dispersivity (ft)

Longitudinal

Transverse

Vertical

65

6.5

0.65

65

6.5

0.65

Well Spacing (ft) 150 Injection Fluid 

Conc.(milligram per L) 1 1

Injection Time (yr) 3

Upper Aquifer 1 Hydraulic 

Conductivity (ft/day) 100 100

Model Area (ft2)

5,280 (ft) × 5,280 (ft) 

= 1 mi2 Upper Aquifer Thickness (ft) 100 100

Model Layers

5

The thickness of the mined 

interval (layer 5) is 30 ft

and has the monitoring/ 

injection/pumping wells.

Mined Interval Hydraulic 

Conductivity (ft/day) 10 10

Minimum/ Maximum 

Grid Spacing (ft) 1/50 Area of Confining Bed Discontinuity (ft2)

100 25

All Layers

Confining units 

separate upper aquifer 

from production zone

Retardation Factor 1 1

Injection/Extraction Well 

Spacing (ft) 5-spot - 100 5-spot - 100

Injection Rate (gpm) 50 50

Pumping Rate (gpm) 51 51

Operating Life of ISL Well 

Pattern (yrs) 3 3

Time until Peak Arrival (days) 1.19×1044.57×104

Relative Peak Concentration 5.09×10-14.73×10-1

2.6.2.10 Summary and Conclusions of Excursion Scenarios

Three excursion scenarios were developed to investigate the potential impacts to nearby 

receptors. Forty-nine (49) unique simulations were performed to evaluate excursions within the 

mined unit beyond the pumping/injection wells. Potential flow up an abandoned borehole was 

investigated with three simulations, and two simulations were conducted to evaluate potential 

migration through a discontinuous aquitard.

The sensitivity analysis was focused on those parameters that are most uncertain and for which 

the results are most sensitive. These parameters include hydraulic gradients and conductivities, 

pumping/injection well spacing, aquifer thickness and size of the discontinuity. All of the 

concentrations are calculated as relative concentrations and are adjusted to actual concentrations 

during the dose assessment (Chapter 4).

All of the relative peak concentrations versus peak arrival times are shown in Figure 2-18. The 

highest relative concentrations are associated with the abandoned borehole, the discontinuous 
2-59

confining unit, and Series 6 simulations. The mined interval in the Series 6 simulations is 

reduced to a thickness of 20 ft, as compared to 70 ft in the other excursion simulations within the 

mined interval.

Several graphs have been constructed that show the effect of well spacing at a hydraulic 

conductivity of 100 ft/day and a gradient of 0.001 ft/ft (Figures 2-19 and 2-21). As depicted in 

the figures, the relative concentrations are always higher at the larger well spacing because of the 

injectant escaping between the wells. This relationship is maintained over a range of hydraulic 

conductivities, as shown in Figures 2-22 and 2-23.

Relative concentrations as a function of hydraulic conductivities and gradients are shown in 

Figures 2-24 through 2-26. As depicted in the figures, the relative concentration is generally 

higher for those simulations with lower hydraulic conductivities and gradients. This relationship 

occurs because at the higher concentrations and gradients, the injectant reaches the receptor more 

quickly, and at lower concentrations, the injectant mass is distributed over a larger area, which 

decreases the concentration within the plume.

A statistical analysis was conducted to assess whether there are correlations among any of the 

parameters investigated. The results of this analysis are shown in Figures 2-27 through 2-33 and 

demonstrate that there is very little correlation among the parameters tested. In each of these 

figures, the relative concentrations are plotted both on a linear and a logarithmic scale. The plots 

were initially prepared using a linear concentration scale and exhibited no significant 

correlations. However, in numerous runs, the relative concentrations were below 0.1 mg/L. To 

expand the delineation of data in this region, the scatter plots were redone using a logarithmic 

scale for the ordinate.data. Again, very little correlation between concentration and the various 

model parameters was noted.

The higher ratios of advection to dispersion resulted in higher peak concentrations. Furthermore, 

an important result regarding the interplay of the local and regional gradients is that the steeper 

the regional gradient the narrower the capture zone and the greater the contaminant releases.
2-60

Figure 2-18: Maximum Relative Concentrations versus Time for All Excursion Simulations
2-61

Figure 2-19: Effect of Well Spacing at Constant Hydraulic Conductivity of 100 ft/day and 

Constant Hydraulic Gradient of 0.001.

Figure 2-20: Effect of Well Spacing at Constant Hydraulic Conductivity of 100 ft/day and 

Hydraulic Gradients of 0.1(Runs 1a, 2a) or 0.001 (Runs 1c, 2c).
2-62

Figure 2-21: Effect of Well Spacing at Constant Hydraulic Conductivity of 100 ft/day and 

Hydraulic Gradients of 0.01(Runs 1b, 2b) and 0.001(Runs 1c, 2c).

Figure 2-22: Effect of Well Spacing on Concentration as a Function of Hydraulic 

Conductivity at a Constant Hydraulic Gradient of 0.1.
2-63

Figure 2-23: Effect of Well Spacing on Concentration as a Function of Hydraulic 

Conductivity at a Constant Hydraulic Gradient of 0.01.

Figure 2-24: Relative Concentrations as a Function of Hydraulic Conductivities 

and Gradients
2-64

Figure 2-25: Relative Concentrations as a Function of Hydraulic Gradient 

(Run 1d - 0.1, Run 1g - 0.01) and Hydraulic Conductivity of 1 ft/day

Figure 2-26: Relative Concentrations as a Function of Hydraulic Gradient 

(Run 1f - 0.1, Run 1i – 0.01) and Hydraulic Conductivity of 100 ft/day
2-65

Figure 2-27: Correlation of Relative Concentration to Pumping Rate
2-66

Figure 2-28: Correlation of Relative Concentration to Hydraulic Gradient
2-67

Figure 2-29: Correlation of Relative Concentration to Hydraulic Conductivity (ft/day)
2-68

Figure 2-30: Correlation of Relative Concentration to Hydraulic Gradient times 

Hydraulic Conductivity
2-69

Figure 2-31: Correlation of Relative Concentration to Well Spacing
2-70

Figure 2-32: Correlation of Relative Concentration to Pumping Array
2-71

Figure 2-33: Correlation of Relative Concentration to Model Layer
3-1

3.0 PATHWAY DOSE AND RISK CONVERSION FACTORS

To calculate doses and risks to individuals who use water from a well that is contaminated with 

radioactivity from the ISL facility, pathway dose and risk conversion factors (PDCFs and 

PRCFs) were developed and used.8 PDCFs/PRCFs are defined as the dose/risk received by an 

individual (e.g., millirem/year or latent cancer fatality [LCF]/year) divided by the radionuclide 

concentration in the well water (e.g., pCi/m3). Because of linearity, the annual dose/risk can be

calculated by multiplying the calculated radionuclide concentration in ground water by the 

corresponding PDCF/PRCF. PDCFs/PRCFs include dose and risk coefficients due to the intake 

of radionuclides into the human body. The radionuclide-specific dose and risk coefficients used 

in calculating the PDCFs/PRCFs were obtained from Federal Guidance Report No. 13 (FGR 13) 

(Eckerman et al. 1999 and EPA 2002; see Section 3.2.2 of this report). PDCFs/PRCFs also 

include factors that describe the movement and uptake of the radionuclide within the biosphere 

(e.g., irrigation rates, plant and animal bioaccumulation factors, weathering and other removal 

mechanisms, etc.), as well as human ingestion rates. As described in Section 3.2, for this 

analysis, ranges of values were selected for each of these factors and the PDCF/PRCF model was 

implemented in spreadsheet form to allow calculation of doses via the ingestion pathway from 

the use of well water for drinking and irrigation. The total PDCFs/PRCFs are composed of the 

following individual ingestion pathways:

 Ingestion of Drinking Water

 Inadvertent Ingestion of Soil

 Ingestion of Vegetables

 Ingestion of Milk

 Ingestion of Meat

The general equations for calculating the total PDCFs/PRCFs are shown below:

   

P

Ing P P

P

Total P PDCF PDCF DC Ing f (b,r)

3-1

   

P

Ing P P

P

Total P PRCF PRCF RC Ing f (b,r)

where DCIng and RDIng are the dose and risk coefficients from FGR 13, IngP is the exposure 

pathway human ingestion rate, and f(b,r)P is a pathway-specific function that describes the 

buildup (b) and removal (r) (if any) of the radionuclide in water, soil, vegetables, milk, or meat. 

For example, the direct consumption of well water, f(b,r)P is set to 1.

Figure 3-1 shows the exposure pathways that were considered in this analysis, while Section 3.1

describes the specific mathematical equations (including the form of f(b,r)P) for each exposure 

pathway that was analyzed in detail. In addition to the pathways that were analyzed in detail, 

there were a number of exposure pathways that were considered, but not analyzed, these are 

discussed in Section 3.5 and are shown in grey in Figure 3-1.

 

8

Pathway dose/risk conversion factors are sometimes referred to as biosphere dose/risk conversion factors.
3-2

Figure 3-1: Exposure Pathways Analyzed

Figure 3-1 also shows that there are three sources of radioactivity at an ISL facility: the Plant, 

where uranium is removed from the lixiviant; the Lixiviant; and Evaporation (or Holding) Ponds. 

This analysis only considers exposures due to the Lixiviant source, because (as indicated in 

Figure 3-1) the Plant is regulated under 10 CFR Part 40, by the Nuclear Regulatory Commission 

(or an Agreement State), and Evaporation Ponds are regulated by the EPA under 40 CFR Part 61, 

Subpart W. Finally, Figure 3-1 shows that lixiviant surface spills and ground water containing 

lixiviant from an excursion could flow to a surface water body. While these pathways exist, they

were not analyzed in this report because the addition dilution provided by the surface water body 

would result in lower exposures to an individual than exposures from the ground water to well 

pathway.

PDCFs and PRCFs were developed for the following longer-lived radionuclides in the U-238 

decay series: U-238, U-234, Th-230, Ra-226, and Pb-210 (see Section 3.2.2).
3-3

3.1 Pathway Dose and Risk Models

The basic mathematical models used to calculate the dose and risk from the ingestion pathways 

for this analysis were obtained from the NRC’s Regulatory Guide 1.109 (NRC 1977). Although 

the numerical values for many of the parameters given in Regulatory Guide 1.109 have been 

updated in the 30-plus years since its publication, the basic mathematical models remain valid 

and form the basis for many of today’s computer programs used to calculate radiological 

impacts, including CAP88 (Trinity 2007), GENII (PNL 1988), and RESRAD (ANL 2001). 

While the Regulatory Guide 1.109 models form the basis for many of today’s computer 

programs, those computer programs often have refined the models to more accurately reflect 

reality. When appropriate, those refined models have been used in this analysis, and are pointed 

out in the following discussion.

This section presents the mathematical models that were used to calculate the PDCFs/PRCFs, 

while Section 3.2 presents and discusses the values that were assumed for each of the parameters 

used in the models.

3.1.1 Ingestion of Drinking Water

The annual effective dose (EWat,A, mrem/yr) and risk (RWat,A, LCF/yr) to a human of age group A

9

from the consumption of unfiltered drinking water is given by:

Wat A W Wat A DCIng A E C Ing ,  , ,

3-2

RWat,A  CW IngWat,A RCIng,A,DW

where CW is the radionuclide concentration in the well from which the water is taken (pCi/m3), 

IngWat,A is the water consumption rate for an individual of age group A (m3/yr), DCIng,A is the 

ingestion dose coefficient for age group A (mrem/pCi), and RCIng,A,DW is the drinking water risk 

coefficient for age group A (LCF/pCi).

3.1.2 Inadvertent Ingestion of Soil

Soil can be inadvertently ingested by humans. The annual effective dose (ESoil,A, mrem/yr) and 

risk (RSoil,A, LCF/yr) to a human of age group A from the inadvertent ingestion of soil is given by:

Soil A Soil Soil A DCIng A E C Ing ,  , ,

3-3

Soil A Soil Soil A RCIng A Diet R C Ing ,  , , ,

where CSoil is the radionuclide concentration in the soil (pCi/kg), IngSoil,A is the inadvertent soil 

ingestion rate for an individual of age group A (kg/yr), DCIng,A is the ingestion dose coefficient 

 

9 Age Group A is a generic designator for various age groups for which PDCFs and PRCFs are calculated 

as described in detail in Section 3.2.1. 
3-4

for age group A (mrem/pCi), and RCIng,A,Diet is the dietary ingestion risk coefficient for age group 

A (LCF/pCi).10

The radionuclide concentration in the surface soil (CSoil), which is available to an individual for 

inadvertent ingestion, is given by:

 

W

R

t

Soil C

P

e

C Irr

R b

( )

1

( )

 

 







 

3-4

where Irr is the irrigation rate (m/yr), λ is the radionuclide decay constant (yr-1), λR is the 

removal rate from soil (yr

-1

), tb is the period of time soil is irrigated with contaminated water 

(yr), and P is the effective “surface density” for soil, (kg/m2).

Although Regulatory Guide 1.109 does not calculate exposures from soil ingestion, equation 3-4

is based on the soil buildup portion of Regulatory Guide 1.109 equation A-8 (NRC 1977). In 

addition to radiological decay, equation 3-4 includes removal from the soil by physical processes 

(λR), which is not included in Regulatory Guide 1.109 equation A-8. This term is intended to 

model the leaching of radionuclide from the surface layer to soil depths were the radionuclides 

are no longer available for uptake by humans, plants, and/or animals. This approach is used in 

models such as Peterson (1983), PATHWAY (Whicker and Kirchner 1987), GENII (Napier et al. 

1988), Abbott and Rood (1993), RESRAD (Yu et al. 2002), and CAP88 (Trinity 2007).

3.1.3 Ingestion of Vegetables

The annual effective dose (EVeg,A, mrem/yr) and risk (RVeg,A, LCF/yr) to a human of age group A

from the consumption of vegetables grown with unfiltered irrigation water is given by:

Veg A Veg Veg Veg A DCIng A E C DD Ing ,  , ,

3-5

Veg A Veg Veg Veg A RCIng A Diet R C DD Ing ,  , , ,

where CVeg is the radionuclide concentration in vegetables (pCi/kg), IngCrop,A is the vegetable 

ingestion rate for an individual of age group A (kg/yr), DDVeg is the fraction of radioactivity

retained on leafy vegetables and produce after washing, DCIng,A is the ingestion dose coefficient 

for age group A (mrem/pCi), and RCIng,A,Diet is the dietary ingestion risk coefficient for age group 

A (LCF/pCi).

According to Regulatory Guide 1.109, the radionuclide concentration in vegetation results from

deposition onto the plant foliage and from uptake from the soil of activity deposited on the 

ground. The equation that models the radionuclide concentration in vegetation (CVeg) is:

 

10 FGR 13 lists a single dose conversion factor for ingestion of each radionuclide, while separate risk 

conversion factors are cited for drinking water (DW) and dietary (Diet) ingestion.
3-5

    

W

R

t

v v

v w

t

v

Veg C

P

B B e

Y

r e

C Irr

w v R b



















 









   

( )

1

( )

1

( ) / ( )

   

   

3-6

where rv is the fraction of deposited activity retained on vegetables (dimensionless), λw is the 

removal rate constant for physical removal by weathering (yr

-1

), tv is the length of the vegetable 

growing season (yrs), Yv is the vegetable crop productivity or yield (kg/m2), Bv is the elementspecific vegetable uptake factor from soil (pCi/kg vegetables per pCi/kg soil), and Bv

 represents 

the net effect of all resuspension processes (NCRP 1999).

In equation 3-6, the first term in brackets relates to the concentration derived from direct foliar 

deposition during the growing season, while the second term relates to root uptake of 

radionuclide contamination from soil and reflects the long-term deposition during operation of 

the uranium recovery facility.

Equation 3-6 is based on Regulatory Guide 1.109, equation A-8 (NRC 1977), except that two 

additional features have been included in the root uptake term. First, in addition to radiological 

decay, equation 3-6 includes removal due to removal from the soil (λR), which was described in 

Section 3.1.2. Second, as stated by the National Council on Radiation Protection and 

Measurements (NCRP) in Report No. 129 (NCRP 1999, page 92):

In the case of contamination by resuspended soil, the mechanisms can be quite 

complex. Not only can airborne resuspension and subsequent redeposit 

contaminate the vegetation, but also phenomena such as rain splash, saltation 

and mechanical disturbances during harvest. Thus, B'v must also be determined 

empirically for the particular site or type of site and the type of vegetation.

To include this effect, Regulatory Guide 1.109, equation A-8 has been modified to include a 

transfer factor representing the net effect of all resuspension processes ( Bv

), as recommended by 

the NCRP Report No. 129, equation 5.2 (NCRP 1999).

3.1.4 Ingestion of Milk

The annual effective dose (EMk,A, mrem/yr) and risk (RMk,A, LCF/yr) to a human of age group A

from the consumption of milk from a cow that ingests contaminated well water, fodder grown 

with unfiltered irrigation water, and contaminated soil is given by:

Mk A Mk Mk A DCIng A E C Ing ,  , ,

3-7

Mk A Mk Mk A RCIng A Diet R C Ing ,  , , ,

where CMk is the radionuclide concentration in milk (pCi/L) and IngMk,A is the individual milk 

consumption rate (L/yr). The CMk term is calculated using the following equation:

  Mk Mk Mk W W Mk F Fod Mk SoilCSoil C CF Ing C Ing C Ing  ,  ,  , 3-8
3-6

where CFMk is the element-specific cow’s intake to milk transfer coefficient (pCi/L milk per 

pCi/day intake), CFod is the radionuclide concentration in the animal’s fodder (pCi/kg) (see 

equation 3-9), IngMk,Fod is the consumption rate of fodder by the cow (kg fresh weight of 

fodder/day), IngMk,W is the consumption rate of water by the cow (m3/day), CSoil is the 

radionuclide concentration in the soil (Bq/kg) (see equation 3-4), and IngMk,Soil is the 

consumption rate of soil by the cow (kg/day). The radionuclide concentration in animal fodder is 

given by:

    

W

R

t

f f

f w

t

f

Fod C

P

B B e

Y

r e

C Irr

w f R b



















 









   

( )

1

( )

1

( ) / ( )

   

   

3-9

where rf is the fraction of deposited activity retained on fodder (dimensionless), λw is the removal 

rate constant for physical removal by weathering (yr

-1

), tf is the length of the fodder growing 

season (yrs), Yf is the fodder productivity or yield (kg/m2), Bf is the element-specific 

concentration fodder uptake factor from soil (pCi/kg fodder / pCi/kg soil), and Bf

 represents the 

net effect of all resuspension processes (NCRP 1999).

3.1.5 Ingestion of Meat

The annual effective dose (EMt,A, mrem/yr) and risk (RMt,A, LCF/yr) to a human of age group A

from the consumption of meat from cattle that ingests contaminated well water, fodder grown 

with unfiltered irrigation water, and contaminated soil is given by:

Mt A Mt Mt A DCIng A E C Ing ,  , ,

3-10

Mt A Mt Mt A RCIng A Diet R C Ing ,  , , ,

where CMt is the radionuclide concentration in meat (pCi/kg) and IngMt,A is the individual 

consumption rate of the animal product (kg/yr). The CMt term is calculated using the following 

equation:

  Mt Mt Mt W W Mt F Fod Mt Soil CSoil C CF Ing C Ing C Ing  ,  ,  , 3-11

where CFMt is the element-specific cattle intake to meat transfer coefficient (pCi/kg meat per 

pCi/day intake), CFod is the radionuclide concentration in the animal’s fodder (pCi/kg fresh 

weight of fodder) (see equation 3-9), IngMt,Fod is the consumption rate of fodder by cattle (kg 

fresh weight of fodder/day), IngMt,W is the consumption rate of water by cattle (m3/day), CSoil is 

the radionuclide concentration in the soil (pCi/kg) (see equation 3-4), and IngMt,Soil is the 

consumption rate of soil by cattle (kg/day).

3.1.6 Pathway Dose and Risk Factors

The pathway dose and risk factors are calculated by dividing the above derived exposure 

equations by the radionuclide concentration in the well from which the water is taken and then 

summing over all of the exposure pathways, as shown below:
3-7

   

P W

P A

P

Total A P A

C

E

PDCF PDCF ,

, ,

3-12

   

P W

P A

P

Total A P A

C

R

PRCF PRCF ,

, ,

where P identifies the exposure pathway (i.e., drinking water, soil ingestion, vegetable ingestion, 

ingestion of milk, and ingestion of meat), and A identifies the age group (i.e., infant, child, teen, 

and adult).

3.1.7 Implementation

In order to calculate the PDCFs and PRCFs, the above equations were programmed into an 

Excel® spreadsheet. To propagate the uncertainty in the value for many of the parameters used 

in the above equations, the Excel add-in Crystal Ball was used to solve for the PDCFs and 

PRCFs using a Monte Carlo simulation. Parameter uncertainty is due to both natural variation 

between individuals (e.g., drinking water, vegetable, milk, and meat consumption rates [IngDW,A, 

IngVeg,A, IngMk,A, and IngMt,A], etc.) and uncertainty as to the “true” value for the parameter (e.g., 

vegetable, fodder, milk, and meat transfer coefficients [Bv, Bf, CFmilk, and CFmeat], etc.). Instead 

of selecting a single value to represent each parameter, the Crystal Ball Monte Carlo simulation 

will randomly sample from a range of values for each parameter and solve for the PDCFs and 

PRCFs, and then it will repeat the entire process until it has calculated a range of PDCFs and 

PRCFs. From this range of values, mean, median, 90th percentile, and other measures can be 

selected. For this analysis, the Monte Carlo simulation repeated the calculations 1,000 times.

3.2 Input Parameters

This section describes how the values for the various input parameters were selected and 

identifies those parameters for which parameter distributions were specified.

3.2.1 Age Groups

As shown above in Section 3.1 and in keeping with Regulatory Guide 1.109, the PDCFs and 

PRCFs have been calculated for four age groups—infant, child, teen, and adult. In order to 

calculate the age-specific PDCFs and PRCFs, it was necessary to use age-specific dose and risk 

conversion factors and exposure factors (e.g., water and food consumption rates). Fortunately, 

the source documents for these parameters present age-dependent data. Unfortunately, the 

manner in which the data are grouped by age is not uniform across each source document, or 

even for different parameters within a single source document.

The age-dependent parameters used in this analysis were obtained from two primary sources: 

Federal Guidance Report No. 13 CD Supplement (EPA 2002) for dose and risk coefficients, and 

the Exposure Factors Handbook (EFH) (EPA 2011b) for usage factors.
3-8

The dose and risk coefficients used in this report were developed based on the recommendations 

of the International Commission on Radiological Protection (ICRP). Starting in the mid 1980s, 

the ICRP began developing age-dependent dose coefficients for members of the public (ICRP 

2006). For example, in Publication 72 (ICRP 1995), the ICRP has indicated that the following 

six age-specific dose coefficients are appropriate for calculating doses for the indicated age 

ranges:

ICRP Age Group Applicable Age Range

3 months from 0 to 1 year

1 year from 1 year to 2 years

5 year more than 2 years to 7 years

10 year more than 7 years to 12 years

15 year more than 12 years to 17 years

Adult more than 17 years

However, in Publication 101 (ICRP 2006), the ICRP recommended:

... that the annual dose for the representative person should be defined by three age 

categories. These categories are 0–5 years (infant), 6–15 years (child), and 16–70 years 

(adult). The shorter time period is selected for the infant age category, when dosimetric 

characteristics are changing most rapidly, to avoid any unwarranted reduction in the 

importance attached to doses to younger age groups. Use of these three age categories is 

sufficient to characterize the radiological impact of a source and to ensure consideration 

of younger, more sensitive populations. For practical implementation of this 

recommendation, dose coefficients and habit data for a 1-year-old (infant), a 10-year-old 

(child), and an adult should be used to represent the three age categories. (ICRP 2006)

The age grouping recommendations from ICRP Publication 101 are summarized in the following 

table.

Age category (years) Name of age category Dose coefficient and habit data to be used

0–5 Infant 1 year old

6–15 Child 10 year old

16–70 Adult Adult

Unlike the dose and risk coefficients, the exposure factors used in this study were taken from the 

EPA’s EFH (EPA 2011b). Many of the exposure factors presented in the Handbook are broken 

down by age, and the age grouping (at least for children) is that recommended by the EPA (EPA 

2005). EPA 2005, Table 4, recommends the following 10 age groups for children under the age 

of 21 years:

Children <1 Year Children >1 Year

Birth to <1 month 1 to <2 years

1 to <3 months 2 to <3 years

3 to <6 months 3 to <6 years

6 to <12 months 6 to <11 years

11 to <16 years

16 to <21 years
3-9

Table 3-1 presents the age groups for the various parameters from each source document mapped 

into the four age groups used in this analysis. The four age groups used in this analysis assume 

that the population is made up of infants, children, teenagers, and adults (NRC 1977). In Table 

3-1, the parameter (or distribution) values used in this analysis are shown in bold.

Table 3-1: Age Groups Used in the Analysis

Age 

Group

FGR 13* Exposure Factors Handbook*

Risk 

Coefficients

Dose 

Coefficients

Water 

Consumption Soil and Dust

Vegetables, 

Milk and 

Meat

Body Weight

Infant 0 to 5 yr** 100 days

1 yr

Birth to <1 mo

1 to <3 mos

3 to <6 mos

6 to <12 mos

6 wks to <1 yr Birth to 1 yr

Birth to <1 mo

1 to <3 mos

3 to <6 mos

6 to <12 mos

Child 5 to 15 5

10

1 to <2

2 to <3

3 to <6

6 to <11

1 to <6

3 to <6

1 to 2

3 to 5

6 to 12

1 to <2

2 to <3

3 to <6

6 to <11

Teen 15 to 25 15 11 to <16

16 to <18 6 to 21 13 to 19 11 to <1616 to <21

Adult 25 to 70 20

18 to <21

≥21

≥65

6 to 21 20 to 49

≥50

21 to <30

30 to <40

40 to <50

50 to <60

60 to <70

70 to <80

≥80

* Items in bold were selected for use in this analysis.

** For Child, Teen, and Adult, all ages are given in years.

Although different groupings could be made, based on the analysis performed by the ICRP 

(ICRP 2006), it is not anticipated that any alternative age grouping would have a significant 

effect on the calculated doses and risks.

3.2.2 Dose and Risk Conversion Coefficients

Naturally occurring uranium found in the ground contains (by weight) 99.3% U-238, 0.7% 

U-235, and a trace amount of U-234. In terms of the amount of radioactivity, natural uranium 

contains approximately 48.6 % U-238, and 49.2 % U-234, and 2.2 % U-235. Because U-235 

composes such a small portion of the radioactivity in natural uranium and because the U-235 

ingestion dose and risk conversion coefficients are very similar to the U-234 and U-238 

coefficients, U-235 has not been included in this analysis. In addition to being radioactive, 

uranium is hazardous from the standpoint of chemical toxicity. The main chemical effect 

associated with exposure to uranium is irreversible kidney damage. Although this section focuses 

on uranium’s radioactive toxicity, its chemical toxicity risk is discussed in Section 4.5, along 

with the chemical toxicity risk of various other metals found in the ore.

As shown in Figure 3-2, the uranium radioactive decay series contains five radionuclides with 

half-lives of over a year. They are U-238 (4.5×109years), U-234 (2.4×105years), Th-230 

(7.7×104years), Ra-226 (1,600 years), and Pb-210 (22.3 years). This analysis focuses on these 
3-10

five radionuclides. If other shorter-lived radionuclides are initially present in the lixiviant, they

will decay before they reach the receptor well. However, the longer-lived radionuclides will 

decay as they travel to the receptor well, so the shorter-lived radionuclides will be present at the 

well. This analysis addresses the in-growth of the shorter-lived radionuclides by including the 

dose and risk coefficients of the short-lived progeny with the parent’s coefficients. For example, 

the Ra-226 ingestion dose and risk coefficients used in this analysis include the contributions 

from Po-210, Bi-210, Pb-210, Bi-214, and Pb-214 [the FGR 13 CD Supplement (EPA 2002)

does not provide ingestion dose or risk factors for Pa-234m, Rn-222, Po-218, or Po-214, likely

because Rn-222 is a noble gas, and the two polonium isotopes and protactinium half-lives are too 

short for them to enter the food chain, as shown in Figure 3-2].

Figure 3-2: Uranium Decay Series

The dose and risk coefficients used in this analysis were obtained from FGR 13 (Eckerman et al. 

1999). FGR 13 does not actually present age-specific risk coefficients or any dose coefficients. 

Instead, it presents risk coefficients that are representative of the U.S. population. However, in 

order to calculate the FGR 13 risk coefficients, it was necessary to calculate age-specific dose 

and risk coefficients. Those age-specific dose and risk coefficients are included in data files 

supplied with the FGR 13 CD Supplement (EPA 2002):11 FGR13ING.GBD contains the dose 

coefficients due to ingestion of radioactive material and FRG13ING.RBS contains both the 

drinking water and dietary ingestion risk coefficients. The dose conversion factors use the latest 

methods and models from the ICRP and are analogous to the dose factors contained in ICRP 

Publication 72 (ICRP 1995). The dose and risk coefficients from FGR 13 that were used in this 

 

11

 FGR13ING.GBD and FGR13ING.RBS, containing age-dependent ingestion dose and risk factors, are also 

supplied with the CAP88 and GENII computer programs.
3-11

analysis are shown in Table 3-2. In Table 3-2, the Ra-226 ingestion dose and risk factors that 

include the contribution from the short-lived progeny are shown as Ra-226+P.

Table 3-2: Radionuclide-Specific Ingestion Dose 

and Risk Coefficients

Nuclide

Mortality Coefficient – Drinking (LC/Sv)

Infant Child Teen Adult

0- 5 5- 15 15- 25 25- 70

Po-210 2.56E-07 1.02E-07 4.58E-08 2.47E-08

Bi-210 1.71E-09 6.93E-10 1.92E-10 3.30E-11

Pb-210 1.25E-07 6.92E-08 3.08E-08 8.78E-09

Pb-210+P 3.83E-07 1.72E-07 7.68E-08 3.35E-08

Bi-214 3.55E-11 1.62E-11 9.51E-12 1.86E-12

Pb-214 6.51E-11 2.80E-11 1.34E-11 2.61E-12

Ra-226 3.92E-08 2.59E-08 1.97E-08 3.07E-09

Ra-226+P 4.22E-07 1.98E-07 9.65E-08 3.66E-08

Th-230 1.27E-08 5.02E-09 2.73E-09 1.07E-09

U-234 9.99E-09 4.59E-09 2.05E-09 6.60E-10

Th-234 4.48E-09 1.81E-09 5.19E-10 7.78E-11

U-238 9.06E-09 4.16E-09 1.86E-09 6.05E-10

U-238+P 1.35E-08 5.97E-09 2.38E-09 6.83E-10

Nuclide

Mortality Coefficient – Dietary (LCF/Sv)

Infant Child Teen Adult

0- 5 5- 15 15- 25 25- 70

Po-210 2.52E-07 1.03E-07 4.6E-08 2.61E-08

Bi-210 1.71E-09 6.95E-10 1.95E-10 3.74E-11

Pb-210 1.24E-07 6.93E-08 3.10E-08 9.38E-09

Pb-210+P 3.78E-07 1.73E-07 7.72E-08 3.55E-08

Bi-214 3.54E-11 1.62E-11 9.5E-12 2.18E-12

Pb-214 6.48E-11 2.80E-11 1.34E-11 3.00E-12

Ra-226 3.86E-08 2.59E-08 1.99E-08 3.31E-09

Ra-226+P 4.16E-07 1.99E-07 9.71E-08 3.88E-08

Th-230 1.20E-08 5.03E-09 2.73E-09 1.18E-09

U-234 9.94E-09 4.60E-09 2.06E-09 7.30E-10

Th-234 4.49E-09 1.81E-09 5.25E-10 8.90E-11

U-238 9.01E-09 4.17E-09 1.87E-09 6.68E-10

U-238+P 1.35E-08 5.98E-09 2.40E-09 7.57E-10
3-12

Table 3-2: Radionuclide-Specific Ingestion Dose 

and Risk Coefficients

Nuclide

Effective Dose Coefficient (Sv/Bq)

Infant Child Teen Adult

100 days 5 yrs 15 yrs 20 yrs

Po-210 2.60E-05 4.37E-06 1.57E-06 1.21E-06

Bi-210 1.50E-08 4.84E-09 1.63E-09 1.31E-09

Pb-210 8.31E-06 2.18E-06 1.92E-06 6.96E-07

Pb-210+P 3.43E-05 6.55E-06 3.49E-06 1.91E-06

Bi-214 1.37E-09 3.66E-10 1.42E-10 1.12E-10

Pb-214 2.17E-09 5.21E-10 2.03E-10 1.39E-10

Ra-226 4.65E-06 6.16E-07 1.52E-06 2.80E-07

Ra-226+P 3.90E-05 7.18E-06 5.01E-06 2.19E-06

Th-230 4.13E-06 3.09E-07 2.19E-07 2.14E-07

U-234 3.69E-07 8.84E-08 7.45E-08 4.95E-08

Th-234 3.99E-08 1.26E-08 4.23E-09 3.40E-09

U-238 3.34E-07 8.01E-08 6.71E-08 4.45E-08

U-238+P 3.74E-07 9.27E-08 7.13E-08 4.79E-08

Source: FGR 13 CD Supplement (EPA 2002)

The dose factors from Table 3-2 were converted to the dose coefficients for ingestion 

(DCIng,A, mrem/pCi) by multiplying by 0.037×105(Bq/pCi)(mrem/Sv), while the risk factors 

from Table 3-2 were converted to the risk coefficients for ingestion (RCIng,A,DW and RCIng,A,Diet, 

LCF/pCi) by multiplying by 0.037 (Bq/pCi).

Mortality Versus Morbidity Risk Coefficients

For risks due to non-radiological sources, the EPA typically bases its rulemaking on cancer 

morbidity risk, e.g., the National Contingency Plan (55 Federal Register 8665-8865, March 8, 

1990). However, for risks due to radiological sources, the EPA has traditionally used cancer 

mortality as the basis for their rulemaking, e.g., the Radiation Protection Standards for Yucca 

Mountain (73 Federal Register 61256- 61289, October 15, 2008). A brief comparison of cancer 

morbidity to mortality risks is presented here.

Based on analysis by the EPA (EPA 1999a), the Interagency Steering Committee on Radiation 

Standards (ISCORS 2002) recommended a cancer morbidity risk coefficient of 8×10-4per rem 

(8×10-6per sievert) and a cancer mortality risk coefficient of 6×10-4per rem (6×10-6per sievert). 

Thus, the morbidity to mortality risk ratio is 1.33. Alternatively, the National Academy of 

Sciences uses a ratio of 1.5 for total cancer incidence to fatal cancer incidence. Depending upon 

exposure pathways, radionuclide, total inventory and site characteristics, the ratio of 1.5 could 

be off by a factor of 2. (NAS 1995, page 51)

Finally, Table 3-3 shows the morbidity to mortality risk ratio based on the FGR 13 drinking 

water risk coefficients. As Table 3-3 shows, the ratio ranges from 1.30 for the adult Ra-226+P 

risk coefficients to 1.70 for the infant U-238+P risk coefficients.
3-13

Table 3-3: FGR 13 Drinking Water Cancer 

Morbidity / Mortality Risk Ratio

Nuclide

Morbidity / Mortality Risk Ratio

Infant Child Teen Adult

0- 5 5- 15 15- 25 25- 70

Po-210 1.43 1.42 1.38 1.30

Bi-210 1.81 1.80 1.80 1.77

Pb-210 1.42 1.41 1.36 1.30

Pb-210+P 1.43 1.42 1.37 1.30

Bi-214 1.23 1.23 1.17 1.17

Pb-214 1.44 1.42 1.30 1.29

Ra-226 1.55 1.49 1.42 1.38

Ra-226+P 1.44 1.43 1.38 1.31

Th-230 1.56 1.56 1.46 1.39

U-234 1.64 1.61 1.53 1.44

Th-234 1.81 1.81 1.80 1.80

U-238 1.64 1.61 1.52 1.43

U-238+P 1.70 1.67 1.58 1.47

Dose/Risk Coefficient Uncertainty

In FGR 13, the dose and risk coefficients are presented as single values, rather than as a 

distribution or range of values. However, in order to calculate the FGR 13 dose and risk 

coefficients, numerous assumptions had to be made regarding biokinetic, dosimetric, and 

radiogenic cancer risk models. Different values could have been assumed regarding these models 

and/or parameter values, which would have resulted in different values for the dose and risk 

coefficients. FGR 13, Table 2.4 and Appendix D, briefly describe the uncertainty in the dose and 

risk coefficients that result from these assumptions. Pawel et al. (2007) provide additional insight 

into the uncertainty associated with the FGR 13 risk coefficients. As Pawel et al. (2007) 

explains:

Assigned levels of uncertainty were based on sensitivity analyses in which various 

combinations of plausible biokinetic and dosimetric models and radiogenic 

cancer risk models were used to generate alternative risk coefficients. 

Uncertainties relating to the validity of the linear-no-threshold hypothesis were 

not addressed in the analysis because this is not feasible.

The uncertainty in a risk coefficient was viewed as the net result of uncertainties 

in the following main components of the derivation: biokinetic models describing 

the biological behavior of ingested or inhaled radionuclides; specific energies 

that relate emissions from source organs to energy deposition in target organs; 

risk model coefficients representing the risk of cancer per unit absorbed dose to 

sensitive tissues from low-LET radiation at high dose and high dose rate; tissue-
3-14

specific dose and dose rate effectiveness factor (DDREF); and tissue-specific 

high-dose relative biological effectiveness (RBE).

Although this analysis will use the FGR 13 dose and risk coefficients as single values, the 

following discussion is given to provide some understanding of the implication of this 

assumption.

The results of the Pawel et al. (2007) uncertainty analysis were assigned to one of five 

“uncertainty categories” (A through E), depending on the ratio of the 5th to the 95th percentiles of 

the calculated risk coefficients. For the five radionuclides of interest in this analysis, Table 3-4

presents the results of the Pawel et al. (2007) uncertainty analysis.

Table 3-4: Risk Coefficient Uncertainty

Nuclide Uncertainty Category Category Range*

Pb-210 C 35 < Q95/Q5 < 50

Ra-226 C 35 < Q95/Q5 < 50

Th-230 D 50 < Q95/Q5 < 150

U-234 C 35 < Q95/Q5 < 50

U-238 B 15 < Q95/Q5 < 35

* Q5 and Q95 are the 5% and 95% sample quantiles of the risk 

coefficients generated by combining plausible variations of the 

biokinetic, dosimetric, and risk models.

Table 3-4 shows that for the five radionuclides of interest, U-238 has the least uncertainty (at 

between a factor of 15 to 35), while Th-230 has the most (at between a factor of 50 to 150). For 

several radionuclides, Pawel et al. (2007) provides a brief discussion of the basis of the 

uncertainty. As it happens, these brief discussions were provided for two of the five 

radionuclides of interest for this analysis, and they have been reproduced below.

Regarding the uncertainty of the Ra-226 ingestion risk factors, Pawel et al. (2007) states: 

Risk and dose models contribute ~90% and 10% of uncertainty, respectively. GI 

uptake and systemic biokinetics reasonably well established. Important cancer 

sites include bone, for which the risk model is highly uncertain, and colon, for 

which risk model is moderately uncertain.

Regarding the uncertainty of the U-234 ingestion risk factors, Pawel et al. (2007) states: 

Risk and dose models contribute ~90% and 10% of uncertainty, respectively. GI 

uptake and most of the important aspects of systemic biokinetics reasonably well 

known. Important cancer sites include colon, for which risk model is moderately 

well established, and bone, for which risk model is poorly established.

Although it is not possible to incorporate this risk coefficient uncertainty into the present 

analysis, the following two examples demonstrate the potential impact of this uncertainty.
3-15

If it is assumed that the FGR 13 U-238 dietary risk coefficient of 1.51×10-9Sv-1is halfway

between the 5th to the 95th percentiles, and that Q95/Q5 is equal to 15, then the average U-238 risk 

coefficient has a range from 3.90×10-10 to 5.85×10-9Sv-1, rather than the FGR 13 value.

Likewise, if it is assumed that the FGR 13 Th-230 dietary risk coefficient of 2.16×10-9Sv-1is 

halfway between the 5th to the 95th percentiles, and that Q95/Q5 is equal to 150, then the average 

Th-230 risk coefficient has a range from 1.76×10-10 to 2.65×10-8Sv-1, rather than the FGR 13 

value.

3.2.3 Ingestion of Drinking Water

As shown by equation 3-2, the only parameters necessary to calculate the PDCFWat,A and 

PRCFWat,A are the age-dependent drinking water consumption rates. The age-dependent drinking 

water consumption rates were obtained by multiplying the weight normalized water consumption 

rate provided in EFH, Table 3-41 (EPA 2011b), and shown in Table 3-5, by the age-specific 

body weight, as shown in Table 3-6.

Table 3-5: Age-Dependent Weight Normalized Water 

Consumption Rates

Percentile

Water Intake – mL/kg-day

Infant Child Teen Adult

3 to <6 mos 3 to 6 yrs 11 to 16 yrs ≥21 yrs

10% 8 7 4 6

25% 50 14 7 12

50% 95 24 13 19

75% 132 37 20 29

90% 163 52 33 41

95% 186 63 44 50

99% 238 96 66 70

Source: EPA 2011b, Table 3-41

In order to demonstrate that the water consumption rate data from the EFH was entered into and 

used correctly by Crystal Ball, Figure 3-3 shows the distributions of the four age-dependent 

water consumption rates calculated by the Crystal Ball Monte Carlo simulation for this analysis 

and compares them to the Table 3-5 EFH distributions. As shown, there is very good agreement 

between the EFH distributions and the distributions used in this analysis.
3-16

Figure 3-3: Distribution of Age-Dependent Weight Normalized 

Water Consumption Rates

As indicated above, the daily weight normalized water consumption rates from Table 3-5 were 

converted to the individual water ingestion rate (IngWat, m3/yr) by multiplying by 365.25×10-6

(day/yr)(m3/mL), and then multiplying by the individual’s body weight. Table 3-6 presents the 

age-dependent body weight distributions that were obtained from the EFH, Table 8-3 (EPA 

2011b).

Table 3-6: Body Weight Distributions

Percentile

Body Weight – kg

Infant Child Teen Adult

3 to <6 mos 3 to <6 yrs 11 to <16 yrs 30 to <40 yrs

5% 5.7 13.5 34.0 53.5

10% 6.1 14.4 37.2 57.4

15% 6.3 14.9 40.6 60.1

25% 6.7 15.8 45.0 66.1

50% 7.3 17.8 54.2 77.9

75% 8.0 20.3 65.0 92.4

85% 8.4 22.0 73.0 101.0

90% 8.7 23.6 79.3 107.0

95% 9.1 26.2 88.8 118.0

Source: EPA 2011b, Table 8-3

Figure 3-4 shows the Crystal Ball (CB) calculated combined body weight and drinking water 

consumption distributions for all four age groups, and compares them to the Regulatory Guide 

1.109, Table E-5 maximum exposed recommended age dependent annual drinking water rates. 
3-17

Notice that the R.G. 1.109 recommended maximum rate for adults corresponds to 2 liters per 

day, which is commonly used in risk assessments. Also, notice that all of the R.G. 1.109 

recommended maximum rates falls within the CB calculated distributions. 

Figure 3-4: Distribution of Age-Dependent Water Consumption Rates 

Compared to Regulatory Guide 1.109 Recommended Maximum Rates

Figure 3-5 compares the CB calculated combined adult body weight and drinking water 

consumption distribution to the drinking water distribution recommended in the RESRAD 

documentation (NUREG/CR-6697, Attachment C, Section 5.2). For a cumulative probability of 

less than 20%, the RESRAD distribution is larger than the CB distribution, at 50% cumulative 

probability the CB value is 33% larger than the RESRAD value, while at 93.3% cumulative 

probability the maximum difference of 66% between the CB and RESRAD values was 

calculated. 
3-18

Figure 3-5: Distribution of Age-Dependent Water Consumption Rates 

Compared to the RESRAD Distribution

Based on Figure 3-4 and Figure 3-5 it is concluded that the Crystal Ball calculated combined 

EFH body weight distributions and weight-normalized water consumption rate distributions are 

consistent with both Regulatory Guide 1.109 and RESRAD water consumption rates. It is 

reasonable to assume that similar results would be obtained for other distributions that result 

from the coupling of variables.

3.2.4 Inadvertent Ingestion of Soil

As shown in Section 3.1.2, in order to calculate the PDCFSoil,A and PRCFSoil,A, it is necessary to 

know:

 The irrigation rate (Irr)

 The removal rate from soil (λw)

 The duration period for irrigation (tb)

 The effective “surface density” for soil (P)

 The removal rate constant for physical removal by weathering (λw)

 Age-dependent inadvertent soil ingestion rates (IngSoil,A)

The irrigation rate (Irr) was obtained from the 2002 Census of Agriculture Farm and Ranch 

Irrigation Survey (USDA 2004), which presented the area of farmland irrigated and the volume 

of irrigation water used for each state. Irrigation rates (m/yr) were calculated for each state, and 

the cumulative distribution shown in Figure 3-6 was developed and used in this analysis. As a 

check of the 2002 census data, NUREG/CR-5512 (SNL 1999) was consulted. Table 6.18 of 

NUREG/CR-5512, Volume 3, contains data from the 1992 Census of Agriculture for 27 states. 
3-19

Figure 3-6 contains the cumulative distribution of the NUREG/CR-5512 data, which shows very

good agreement with the 2002 Census of Agriculture cumulative distribution.

Figure 3-6: Distribution of Irrigation Water Application Rate

The removal rate from soil (λR) and the duration period for irrigation (tb) were taken from the 

CAP88 Users Manual (Trinity 2007) as a constant 0.02 (yr

-1

) and 100 yrs, respectively. Based on 

NUREG/CR-6697, Appendix C, Section 3.1 (ANL 2000), the effective “surface density” for soil 

(P) was assumed to have a normal distribution, with a mean and standard deviation of 1.52 and 

0.23 g/cm3, and minimum and maximum values of 0.83 and 2.21 g/cm3.

The inadvertent soil ingestion rates were modeled as a triangular distribution, with minimum, 

most likely, and maximum values for each age group, as shown in Table 3-7. The most likely

soil ingestion rates in Table 3-7 were obtained from the general population central tendency

values in EFH (EPA 2011b), Table 5-1 for soil plus dust ingestion. Likewise, the maximum 

Child soil ingestion rate was obtained from the child general population upper percentile value in 

EFH (EPA 2011b), Table 5-1 for soil plus dust ingestion. The minimum and the other age group 

maximum values were assumed.

Table 3-7: Soil + Dust Ingestion

Age Group Soil Ingestion Rate (mg/day)

Minimum Most Likely* Maximum

Infant 0 60 200

Child 0 100 200*

Teen 0 50 200

Adult 0 50 200

* EPA 2011b, Table 5-1
3-20

3.2.5 Ingestion of Vegetables

As shown in Section 3.1.3, in order to calculate the PDCFVeg,A and PRCFVeg,A, it is necessary to 

know:

 The fraction of radioactivity retained on leafy vegetables and produce after washing 

(DDVeg)

 The fraction of deposited activity retained on vegetables (rv)

 The removal rate constant for physical removal by weathering (λw)

 The length of the vegetable growing season (tv)

 The vegetable productivity or yield (Yv)

 The element-specific vegetable uptake factor from soil (Bv)

 The net effect of all resuspension processes (B'v)

 Age-dependent vegetable ingestion rates (IngVeg,A)

The following terms have already been defined in Section 3.2.4:

 The irrigation rate (Irr)

 The removal rate from soil (λw)

 The duration period for irrigation (tb)

 The effective “surface density” for soil (P)

 The removal rate constant for physical removal by weathering (λw)

The geometric means and standard deviations of the lognormal distributions for the elementspecific vegetable uptake factor from soil (Bv) used in this analysis were taken from NCRP 1999, 

and are shown in Table 3-8.

Table 3-8: Element-Specific Input Parameters for Lognormal Distribution

Element

Bv

Vegetable Uptake 

(pCi/g vegetables 

per pCi/g soil)

Bf

Fodder Uptake 

(pCi/g fodder per 

pCi/g soil)

CFMt

Meat Transfer 

(pCi/kg meat per 

pCi/day intake)

CFMk

Milk Transfer

(pCi/L milk per 

pCi/day intake)

GM GSD GM GSD GM GSD GM GSD

Pb 4E-03 2.5 0.09 2.5 8E-04 2.0 3E-04 2.5

Ra 0.04 2.5 0.2 2.5 1E-03 2.0 1E-03 1.6

Th 1E-03 2.5 1E-03 2.5 1E-04 2.8 5E-06 2.5

U 2E-03 2.5 0.01 2.5 8E-04 2.0 4E-04 1.8

Note: GM is the geometric mean, and GSD is the geometric standard deviation

Source: NCRP (1999) 

The fraction of deposited activity retained on vegetables (rv) was assumed to be a uniform 

distribution with a minimum of 0.1 and a maximum of 0.6, as recommended for leafy and other 

vegetables by NUREG/CR-5512, Volume 3, Table 6.76 (SNL 1999). The fraction of 

radioactivity retained on leafy vegetables and produce after washing (DDVeg) was taken as 0.5, as 

specified in Section 10.2 of the CAP88 Users Manual (Trinity 2007).
3-21

The removal rate constant for physical removal by weathering (λw) was assumed to be a 

triangular distribution with minimum, maximum, and most likely values of 5.1, 84, and 18 yr

-1

,

respectively, as recommended by NUREG/CR-6697, Appendix C, Section 6.6 (ANL 2000). It is 

noted that the most likely value has a weathering half-life of 14 days, which is identical to the 

value recommended in CAP88, Section 10 (Trinity 2007); Regulatory Guide 1.109, Table E-15 

(NRC 1977); and NUREG/CR-5512, Volume 3, Table 6.30 (SNL 1999).

As described in Section 3.1.3, equation 3-6 includes a term to account for the net effect of all 

resuspension processes (B'v) on the radionuclide concentration in vegetables. For this analysis, a 

lognormal distribution with a mean of 0.001 and a standard deviation of 2.2 was assumed for B'v, 

as recommended by NCRP 1999, Table 5.7, for vegetables grown in a heavily vegetated rural

area.

For this analysis, the length of the vegetable growing season (tv) was assumed to be 60 days, as 

recommended in the CAP88 Users Manual (Trinity 2007).

The vegetable productivity or yield (Yv) was obtained from the U.S. Department of Agriculture’s 

(USDA’s) 2010 vegetable summary (USDA 2011). In the 2010 vegetable summary, the USDA 

presents the area harvested and production values for 21 different vegetables grown in 2008, 

2009, and 2010. The vegetables included in the USDA report were artichokes, asparagus, snap 

beans, broccoli, cabbage, carrots, cauliflower, celery, sweet corn, cucumbers, garlic, head 

lettuce, leaf lettuce, romaine lettuce, onions, bell peppers, chili peppers, pumpkins, spinach, 

squash, and tomatoes. The data from the USDA report were converted into a cumulative 

distribution of the vegetable yield, as shown in Figure 3-7. This is the vegetable yield (Yv) 

distribution that was used for this analysis. For comparison, Figure 3-7 also presents the 

cumulative distribution of vegetable yield for non-leafy vegetables obtained from NUREG/CR6697, Appendix C, Section 6.5 (ANL 2000), and shows that the NUREG/CR-6697 yield is 

substantially lower than the USDA’s yield. For example, at the 50th percentile, the NUREG/CR6697 yield is 1.75 kg/m2

, while the USDA’s yield is 3.61 kg/m2. Of course, the NUREG/CR6697 yield is for non-leafy vegetables, which tend to have lower yields than leafy vegetables 

included in the USDA’s yield distribution. However, since the yield is being used in equation 3-6

to calculate the vegetable radionuclide concentration due to leaf deposition, it is believed to be 

correct to include leafy vegetables in the yield cumulative distribution. Finally, Figure 3-7 shows 

that the head lettuce (as a representative of leafy vegetables) has a yield of about 4.1 kg/m2.
3-22

Figure 3-7: Distribution of Vegetable Yields

The age-dependent vegetable consumption rates (IngVeg,A) were obtained by multiplying the 

weight normalized vegetable consumption rates provided in EFH, Table 9-4 (EPA 2011b) and 

shown in Table 3-9, by the age-specific body weight, as shown in Table 3-6.

Table 3-9: Age-Dependent Weight 

Normalized Vegetable Consumption Rates

Percentile

Consumption Rate (g/kg-day)

Infant Child Teen Adult

<1 yr 3-5 yrs 13-19 yrs 20-49 yrs

1% 0 0.1 0 0.1

5% 0.1 0.6 0.3 0.4

10% 0.4 1.5 0.5 0.7

25% 2.6 2.3 1.1 1.3

50% 5.5 4.2 1.8 2.2

75% 10.1 7.2 3 3.3

90% 14.5 10.6 4.3 4.9

95% 18.1 13.4 5.5 5.9

99% 22.7 21.4 8.9 8.6

100% 36.1 30.3 20 18.3

Source: EPA 2011b, Table 9-4

In order to demonstrate that the vegetable consumption rate data from the EFH was entered into 

and used correctly by Crystal Ball, Figure 3-8 shows the distributions of the four age-dependent 

vegetable consumption rates calculated by the Crystal Ball Monte Carlo simulation for this 
3-23

analysis and compares them to the Table 3-9 EFH distributions. As shown, there is very good 

agreement between the EFH distributions and the distributions used in this analysis.

Figure 3-8: Distribution of Age-Dependent Weight Normalized 

Vegetable Consumption Rates

As stated above, the Table 3-6 body weight distributions and the Table 3-9 vegetable 

consumption distributions were used together to calculate the vegetable consumption rates 

(IngVeg,A) used in this analysis.

3.2.6 Ingestion of Milk

As shown in Section 3.1.4, in order to calculate the PDCFMk,A and PRCFMk,A, it is necessary to 

know:

 The element-specific cow’s intake to milk transfer coefficient (CFMk)

 The consumption rate of water by the cow (IngMk,W)

 The consumption rate of fodder by the cow (IngMk,Fod)

 The fraction of deposited activity retained on fodder (rf)

 The length of the fodder growing season (tf)

 The fodder productivity or yield (Yf)

 The element-specific fodder uptake factor from soil (Bf)

 The net effect of all resuspension processes on fodder (B'f)

 The ingestion rate of soil by the cow (IngMk,Soil)

 The age-dependent milk ingestion rates (IngMk,A)
3-24

The following terms used to calculate the dose and risk due to milk ingestion have already been 

defined in Section 3.2.4:

 The irrigation rate (Irr)

 The removal rate from soil (λw)

 The duration period for irrigation (tb)

 The effective “surface density” for soil (P)

 The removal rate constant for physical removal by weathering (λw)

The element-specific cow’s intake-to-milk transfer coefficient (CFMk) and the element-specific 

fodder uptake factor from soil (Bf) are shown in Table 3-8, and were taken from NCRP 1999, 

Appendix D.

As discussed in Section 3.2.5, NCRP Report No. 129 (NCRP 1999) includes a term to account 

for the net effect of all resuspension processes on the radionuclide concentration of fodder (B'f). 

For this analysis, a lognormal distribution with a mean of 0.05 and a standard deviation of 1.4 

was assumed for the (B'f), as recommended by NCRP 1999, Table 5.7, for fodder grown in a 

heavily vegetated rural area.

The consumption rates of water (IngMk,W) and fodder (IngMk,Fod) by cows were based on 

information provided by the National Academy of Sciences’ National Research Council (NAS 

2001) and New Mexico State University (Looper and Waldner 2002). Fodder consumption rates 

are a function of the weight of the animal and how much milk the cow produces. Looper and 

Waldner (2002) present dry matter intakes for a typical sized dairy cow for various milk 

production rates, which range from 42 to 60 lbs/day. Thus, for this analysis, the consumption rate 

of fodder by dairy cows (IngMk,Fod) was specified as a uniform distribution, with minimum and 

maximum values of 19.1 and 27.2 kg/day, respectively.

NAS 2001 also indicates that cattle water consumption rates are dependent on the dry matter 

(i.e., fodder) intake, temperature, milk production, and the amount of dietary sodium. Looper and 

Waldner (2002) indicate a range of dairy cow water consumption rates between 18.4 and 35.6 

gallons/day. Thus, for this analysis, the consumption rate of water by cattle (IngMt,W) was 

specified as a uniform distribution, with minimum and maximum values of 69.7 and 

134.8 L/day.

The ingestion rate of soil by the cow (IngMk,Soil) was based on NCRP 1999, Section 5.2.3, which 

states, “estimates in the literature indicate that on average an animal on pasture all year long will 

ingest an amount of soil equivalent to about six percent of its total dry matter intake.”

The fraction of deposited activity retained on fodder (rf) was assumed to have a triangular 

distribution, with minimum, maximum, and most likely values of 0.06, 0.95, and 0.67, 

respectively, as recommended in NUREG/CR-6697, Attachment C, Section 6.7.

The length of the fodder growing season (tf) was assumed to be 30 days, as recommended in 

Section 10 of the CAP88 Users Manual (Trinity 2007).
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The fodder productivity or yield (Yf) was obtained from the U.S. Department of Agriculture’s 

2007 Census of Agriculture (USDA 2009). In the census, the USDA presents area and quantity

harvested in each of the 50 states for 6 types of animal feed: forage (36.0%), hay (32.0%), 

alfalfa-hay (15.5%), other-tame-hay (12.5%), wild-hay (2.1%), and small-grain-hay (1.8%). 

From these data, it was possible to calculate the yields and to construct a cumulative distribution 

of the yield, as shown in Figure 3-9. This is the fodder yield (Yf) distribution that was used for 

this analysis. For comparison, CAP88 Users Manual (Trinity 2007, Section 10) and Regulatory

Guide 1.109 (NRC 1977, Table E-7) recommend an agricultural productivity by unit area for the 

grass-cow-milk-man pathway of 0.28 and 0.7 kg/m2, respectively. These are equivalent to the 1.6 

and 76.4 percentiles in Figure 3-9.

Figure 3-9: Distribution of Fodder (Dry) Yield

The age-dependent milk consumption rates (IngMk,A) were obtained by multiplying the weight 

normalized milk consumption rates from EFH, Table 11-3 (EPA 2011b), and shown in Table 

3-10, by the age-specific body weight, as shown in Table 3-6.
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Table 3-10: Age-Dependent Weight 

Normalized Milk Consumption Rates

Percentile

Consumption Rate (g/kg-day)

Infant Child Teen Adult

<1 yr 3–5 yrs 13-19 yrs 20-49 yrs

1% 0 0.9 0.1 0

5% 0 4.5 0.4 0.2

10% 0 8.3 0.6 0.4

25% 1.2 13.6 1.6 1

50% 6.4 20.7 4 2.4

75% 11.5 32 7.6 4.7

90% 19.6 41.9 12.3 8.1

95% 43.2 51.1 16.4 10.3

99% 83.1 68.2 24.9 17.1

100% 163.9 154.5 45.0 52.7

Source: EPA 2011b, Table 11-3

In order to demonstrate that the milk consumption rate data from the EFH was entered into and 

used correctly by Crystal Ball, Figure 3-10 shows the distributions of the four age-dependent 

milk consumption rates calculated by the Crystal Ball Monte Carlo simulation for this analysis 

and compares them to the Table 3-10 EFH distributions. As shown, there is very good agreement 

between the EFH distributions and the distributions used here.

Figure 3-10: Distribution of Age-Dependent Weight Normalized 

Milk Consumption Rates
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As stated above, the Table 3-10 and Figure 3-10 milk consumption rates are presented as a 

function of the body weight of the individual consuming the milk. Therefore, Table 3-6 presents 

the age-dependent body weight distributions that were obtained from the EFH (EPA 2011b), 

Table 8-3. The Table 3-6 body weight distributions and the Table 3-10 milk consumption 

distributions were used together to calculate the milk consumption rates used in this analysis.

3.2.7 Ingestion of Meat

As shown in Section 3.1.5, in order to calculate the PDCFMt,A and PRCFMt,A, it is necessary to 

know:

 The cattle intake-to-meat transfer coefficient (CFMt)

 The consumption rate of water by cattle (IngMt,W)

 The consumption rate of fodder by cattle (IngMt,Fod)

 The ingestion rate of soil by cattle (IngMt,Soil)

 Age-dependent meat ingestion rates (IngMt,A)

The following terms used to calculate the dose and risk due to meat ingestion have already been 

defined in Section 3.2.6:

 The fraction of deposited activity retained on fodder (rf)

 The length of the fodder growing season (tf)

 The fodder productivity or yield (Yf)

 The element-specific fodder uptake factor from soil (Bf)

 The net effect of all resuspension processes on fodder ( Bf

 )

The following terms used to calculate the dose and risk due to meat ingestion have already been 

defined in Section 3.2.4:

 The irrigation rate (Irr)

 The removal rate from soil (λw)

 The duration period for irrigation (tb)

 The effective “surface density” for soil (P)

 The removal rate constant for physical removal by weathering (λw)

The element-specific cattle intake-to-meat transfer coefficient (CFMt) is shown in Table 3-8, and 

was taken from NCRP 1999, Appendix D.

The consumption rates of water (IngMt,W) and fodder (IngMt,Fod) by cattle were based on 

information provided by the National Research Council (NAS 2000) and the Oklahoma State 

University (Lalman 2004). Fodder consumption rates are a function of the weight of the animal, 

the nutrition content of the feed, and how much the animal is growing. Table 5 of Lalman (2004) 

presents dry matter intakes for various sized animals and various growth rates, which range from 

13.7 to 27.4 lbs/day. Thus, for this analysis, the consumption rate of fodder by cattle (IngMt,Fod) 
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was specified as a uniform distribution, with minimum and maximum values of 6.2 and 12.4 

kg/day, respectively.

NAS 2000 indicates that cattle water consumption rates are dependent on the dry matter (i.e., 

fodder) intake, temperature, precipitation, and the amount of dietary salt. Table 6-1 of NAS 2000 

indicates a range of “finishing” cattle water consumption rates between 22.7 and 78.0 L/day. 

Thus, for this analysis, the consumption rate of water by cattle (IngMt,W) was specified as a 

uniform distribution, with minimum and maximum values of 22.7 and 78.0 L/day.

The ingestion rate of soil by cattle (IngMt,Soil) was based on NCRP 1999, Section 5.2.3, which 

states, “estimates in the literature indicate that on average an animal on pasture all year long will 

ingest an amount of soil equivalent to about six percent of its total dry matter intake.”

The age-dependent meat consumption rates (IngMt,A) were obtained by multiplying the weight 

normalized meat consumption rates from EFH, Table 11-3 (EPA 2011b), and shown in Table 

3-11, by the age-specific body weight, as shown in Table 3-6.

Table 3-11: Age-Dependent Weight Normalized Meat 

Consumption Rates

Percentile

Consumption Rate (g/kg-day)

Infant Child Teen Adult

<1 yr 3–5 yrs 13–19 yrs 20–49 yrs

1% 0 0 0 0

5% 0 0.7 0.3 0.3

10% 0 1.4 0.6 0.5

25% 0 2.1 1 1

50% 0 3.3 1.7 1.6

75% 1.7 5 2.7 2.4

90% 3.6 7.6 3.8 3.4

95% 5.4 8.5 4.7 4.1

99% 9.3 12.4 6.8 5.7

100% 18.7 19.5 13.5 12

Source: EPA 2011b, Table 11-3

In order to demonstrate that the meat consumption rate data from the EFH was entered into and 

used correctly by Crystal Ball, Figure 3-11 shows the distributions of the four age-dependent 

meat consumption rates calculated by the Crystal Ball Monte Carlo simulation for this analysis 

and compares them to the Table 3-11 EFH distributions. As shown, there is very good agreement 

between the EFH distributions and the distributions used in this analysis.
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Figure 3-11: Distribution of Age-Dependent Weight Normalized 

Meat Consumption Rates

As stated above, the Table 3-11 and Figure 3-11 meat consumption rates are presented as a 

function of the body weight of the individual consuming the meat. Therefore, Table 3-6 presents 

the age-dependent body weight distributions that were obtained from the EFH (EPA 2011b), 

Table 8-3. The Table 3-6 body weight distributions and the Table 3-11 meat consumption 

distributions were used together to calculate the meat consumption rates used in this analysis.

3.3 Pathway Dose and Risk Conversion Factors

As described in Section 3.1.7, the Excel add-in Crystal Ball was used to solve for the PDCFs and 

PRCFs using Monte Carlo simulations. To solve for the PDCFs and PRCFs, the Monte Carlo 

simulations were performed 1,000 times, and the resultant mean, median, and 90th percentile 

PDCFs were calculated as shown in Table 3-12. The resultant PRCFs are shown in Table 3-13.

Table 3-12: Calculated Total Pathway Dose Conversion Factors

Adult Teen Child Infant

Nuclide Mean PDCF (mrem/yr / pCi/m3)

Pb-210+P 6.28E-03 7.33E-03 1.10E-02 3.89E-02

Ra-226+P 1.30E-02 1.98E-02 2.62E-02 7.06E-02

Th-230 6.02E-04 3.62E-04 2.89E-04 4.27E-03

U-234 1.61E-04 1.53E-04 1.33E-04 4.19E-04

U-238+P 1.55E-04 1.47E-04 1.40E-04 4.24E-04
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Table 3-12: Calculated Total Pathway Dose Conversion Factors

Adult Teen Child Infant

Nuclide Median PDCF (mrem/yr / pCi/m3)

Pb-210+P 5.16E-03 5.17E-03 6.91E-03 3.77E-02

Ra-226+P 1.01E-02 1.39E-02 1.65E-02 5.76E-02

Th-230 5.30E-04 2.76E-04 2.54E-04 4.37E-03

U-234 1.40E-04 1.15E-04 1.01E-04 4.11E-04

U-238+P 1.35E-04 1.10E-04 1.06E-04 4.16E-04

Nuclide 90th Percentile PDCF (mrem/yr / pCi/m3)

Pb-210+P 1.06E-02 1.31E-02 1.40E-02 6.31E-02

Ra-226+P 2.23E-02 3.55E-02 4.28E-02 1.14E-01

Th-230 1.09E-03 7.43E-04 5.09E-04 7.28E-03

U-234 2.77E-04 2.75E-04 1.93E-04 6.97E-04

U-238+P 2.68E-04 2.64E-04 2.02E-04 7.06E-04

Table 3-13: Calculated Total Pathway Risk Conversion Factors

Adult Teen Child Infant

Nuclide Mean PRCF (LCF/yr / pCi/m3)

Pb-210+P 1.12E-09 1.62E-09 2.90E-09 4.33E-09

Ra-226+P 2.25E-09 3.83E-09 7.25E-09 7.60E-09

Th-230 3.07E-11 4.52E-11 4.69E-11 1.31E-10

U-234 2.21E-11 4.22E-11 6.92E-11 1.13E-10

U-238+P 2.28E-11 4.91E-11 9.00E-11 1.54E-10

Nuclide Median PRCF (LCF/yr / pCi/m3)

Pb-210+P 9.24E-10 1.14E-09 1.82E-09 4.20E-09

Ra-226+P 1.73E-09 2.69E-09 4.56E-09 6.23E-09

Th-230 2.69E-11 3.44E-11 4.13E-11 1.34E-10

U-234 1.91E-11 3.16E-11 5.23E-11 1.11E-10

U-238+P 1.98E-11 3.67E-11 6.80E-11 1.51E-10

Nuclide 90th Percentile PRCF (LCF/yr / pCi/m3)

Pb-210+P 1.89E-09 2.88E-09 3.69E-09 7.00E-09

Ra-226+P 3.86E-09 6.88E-09 1.19E-08 1.23E-08

Th-230 5.60E-11 9.27E-11 8.27E-11 2.22E-10

U-234 3.79E-11 7.58E-11 1.00E-10 1.89E-10

U-238+P 3.92E-11 8.80E-11 1.30E-10 2.56E-10

LCF = latent cancer fatality

Comparing Table 3-12 to Table 3-13 shows that the dose-to-risk relationship varies by a little 

over an order of magnitude, from about 3×10-8to 6×10-7latent cancer fatality per millirem, 

depending on the radionuclide and the age group. This is consistent with the dose-to-risk 

relationship for the FGR 13 dose and risk coefficients shown in Table 3-2. Furthermore, the 

Interagency Steering Committee on Radiation Standards (ISCORS 2002) recommends a nominal 
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cancer mortality dose-to-risk factor of 6×10-7per millirem, which is at the high end of the range, 

based on the Table 3-12 PDCFs and the Table 3-13 PRCFs. However, ISCORS 2002 points out 

that the nominal conversion factor varies from radionuclide to radionuclide and may

overestimate the risk by about an order of magnitude for some of bone-seeking radionuclides. 

Since radium and uranium are bone-seekers, the Table 3-12 PDCFs and Table 3-13 PRCFs are 

consistent with the findings in ISCORS 2002.

“Typical” contributions to the adult, teen, child, and infant total PDCFs and PRCFs from each 

exposure pathway are presented in Table 3-14, Table 3-16, Table 3-17, and Table 3-18, 

respectively. The contributions shown on these tables were calculated deterministically using the 

mean or 50th percentile parameter values, so for any 1 of the 1,000 Monte Carlo trials, any 1 of 

the pathways would be expected to contribute more or less to the dose/risk than is shown. 

Nonetheless, the following tables provide a good indication as to which pathways are the major 

contributors and which are only minor contributors.

Table 3-14: Typical Pathway Contributions to the Adult PDCF and PRCF

Pathway – Adult Pb-210 Ra-226+P Th-230 U-234 U-238

Ingestion of Drinking Water 78.3% 51.2% 82.8% 76.2% 76.2%

Inadvertent Ingestion of Soil 0.1% 0.2% 0.3% 0.3% 0.3%

Ingestion of 

Vegetables

Leaf Deposition 13.5% 8.8% 14.3% 13.1% 13.1%

Root Uptake 2.3% 26.5% 2.1% 2.9% 2.9%

Ingestion of Meat

Cattle Drinking 0.3% 0.2% 0.0% 0.3% 0.3%

Leaf Deposition 1.6% 1.3% 0.2% 1.5% 1.5%

Root Uptake 0.3% 1.1% 0.0% 0.3% 0.3%

Soil Ingestion 0.1% 0.3% 0.0% 0.3% 0.3%

Ingestion of Milk

Cow Drinking 0.3% 0.7% 0.0% 0.4% 0.4%

Leaf Deposition 2.2% 4.8% 0.0% 2.8% 2.8%

Root Uptake 0.5% 4.1% 0.0% 0.6% 0.6%

Soil Ingestion 0.5% 1.0% 0.2% 1.2% 1.2%

For the adult PDCFs and PRCFs, Table 3-14 indicates that the drinking water pathway is the 

major contributor for all radionuclides. Ingestion of vegetables is the second most important 

contributor for all of the radionuclides. For Ra-226, the root uptake portion of the vegetable 

ingestion pathway is significant, due to radium’s high concentration soil uptake factor for 

vegetables (Bv), as indicated in Table 3-8. For the other radionuclides, the leaf deposition portion 

of the ingestion pathways is always the higher contributor. For all radionuclides, the milk and 

meat pathways are only minor contributors to the adult total PDCFs and PRCFs. Finally, the 

inadvertent ingestion of soil is a very minor contributor to the adult total PDCFs and PRCFs for 

all radionuclides.

The percentage contributions shown in Table 3-14 are only typical, and will vary for each of the 

1000 realizations that was calculated. Table 3-15 shows how this variation in each pathway’s 

contribution to the total adult U-238 PDCF and PRCF. For each pathway, Table 3-15 shows the 

typical contribution to the total PDCF/PRCF from Table 3-14, as well as the contributions at the 
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10th, 50th, and 90th percentiles of the PDCF/PRCF, and the range of the contribution over all 1000 

realizations.

Table 3-15: U-238+P Pathway Contributions to the Adult PDCF and PRCF

Pathway – Adult Typical 

(Table 3-14)

Percentile Range

10th 50th 90th Minimum Maximum

Ingestion of Drinking Water 76.2% 86.0% 95.0% 60.0% 0.3% 99.1%

Inadvertent Ingestion of Soil 0.3% 0.5% 0.2% 0.4% 0.0% 5.5%

Ingestion of 

Vegetables

Leaf Deposition 13.1% 5.3% 1.3% 10.8% 0.1% 79.2%

Root Uptake 2.9% 0.4% 2.1% 5.7% 0.0% 68.9%

Ingestion of Meat

Cattle Drinking 0.3% 0.1% 0.1% 0.4% 0.0% 8.8%

Leaf Deposition 1.5% 0.4% 0.3% 0.8% 0.0% 52.8%

Root Uptake 0.3% 0.2% 0.1% 1.8% 0.0% 14.9%

Soil Ingestion 0.3% 0.2% 0.1% 1.4% 0.0% 7.1%

Ingestion of Milk

Cow Drinking 0.4% 0.6% 0.0% 1.6% 0.0% 10.3%

Leaf Deposition 2.8% 3.7% 0.2% 2.2% 0.0% 67.1%

Root Uptake 0.6% 1.9% 0.1% 5.0% 0.0% 23.8%

Soil Ingestion 1.2% 0.6% 0.4% 9.9% 0.0% 49.6%

The range values shown in Table 3-15 may be misleading, since they may represent a single 

Monte Carlo realization. Alternatively, Figure 3-12 shows the cumulative distribution of each 

exposure pathway’s contribution to the total adult U-238 PDCF/PRCF. For example, Figure 3-12

shows that for 50% of the realizations the drinking water exposure pathway contributes less than 

77% and for 50% of the realizations it contributes more than 77% to the total PDCF/PRCF. 

Similarly, Figure 3-12 shows that for 80% of the realizations the leaf deposition component of 

the vegetable ingestion pathway contributes 21% or less to the total PDCF/PRCF. Finally, Figure 

3-12 shows that for only 19% of the realizations did the drinking water pathway contribute less 

than 50% to the total PDCF/PRCF.
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Figure 3-12: Cumulative Distributions of Pathway Contribution to the 

Total Adult U-238+P PCDF/PRCF

Although Table 3-15 and Figure 3-12 are specific for the U-238+P adult PDCF/PRCF it is 

reasonable to assume that the pathway contributions for the other radionuclides and age groups 

will behave similarly. 

Table 3-16: Typical Pathway Contributions to the Teen PDCF and PRCF

Pathway – Teen Pb-210 Ra-226+P Th-230 U-234 U-238

Ingestion of Drinking Water 71.5% 41.4% 79.6% 68.1% 68.1%

Inadvertent Ingestion of Soil 0.3% 0.3% 0.6% 0.5% 0.5%

Ingestion of 

Vegetables

Leaf Deposition 14.7% 8.5% 16.4% 14.0% 14.0%

Root Uptake 2.5% 25.6% 2.4% 3.1% 3.1%

Ingestion of Meat

Cattle Drinking 0.4% 0.3% 0.1% 0.4% 0.4%

Leaf Deposition 2.2% 1.6% 0.3% 2.1% 2.1%

Root Uptake 0.5% 1.4% 0.1% 0.4% 0.4%

Soil Ingestion 0.2% 0.3% 0.1% 0.4% 0.4%

Ingestion of Milk

Cow Drinking 0.7% 1.3% 0.0% 0.9% 0.9%

Leaf Deposition 4.9% 9.4% 0.1% 6.2% 6.2%

Root Uptake 1.1% 8.0% 0.0% 1.3% 1.3%

Soil Ingestion 1.2% 1.9% 0.4% 2.6% 2.6%

The pathway contributions to the teen total PDCFs and PRCFs are similar to the adult pathway

contributions. Specifically, drinking water is the major contributor for all radionuclides. 

Ingestion of vegetables is the second most important contributor for all radionuclides, and 
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inadvertent ingestion of soil is a very minor contributor for all pathways, as are the cattle/cow 

drinking water and soil ingestion for the meat and milk pathways.

The milk pathway (and to a lesser extent, the meat pathway) starts to become more of a 

contributor to the teen PDCFs/PRCFs than it was for the adult. For example, for Ra-226, the 

milk pathway contributes about 20% to the teen total PDCF/PRCF, but only about 11% to the 

adult total PDCF/PRCF. Similar relative increases are observed for the other radionuclides.

Table 3-17: Typical Pathway Contributions to the Child PDCF and PRCF

Pathway – Child Pb-210 Ra-226+P Th-230 U-234 U-238

Ingestion of Drinking Water 59.9% 28.1% 74.1% 54.2% 54.2%

Inadvertent Ingestion of Soil 0.7% 0.7% 1.9% 1.4% 1.4%

Ingestion of 

Vegetables

Leaf Deposition 15.6% 7.3% 19.3% 14.1% 14.1%

Root Uptake 2.6% 22.0% 2.9% 3.1% 3.1%

Ingestion of Meat

Cattle Drinking 0.3% 0.2% 0.1% 0.3% 0.3%

Leaf Deposition 2.0% 1.1% 0.3% 1.8% 1.8%

Root Uptake 0.4% 1.0% 0.1% 0.4% 0.4%

Soil Ingestion 0.2% 0.2% 0.1% 0.4% 0.4%

Ingestion of Milk

Cow Drinking 1.6% 2.5% 0.0% 1.9% 1.9%

Leaf Deposition 11.4% 17.9% 0.2% 13.8% 13.8%

Root Uptake 2.5% 15.3% 0.0% 2.9% 2.9%

Soil Ingestion 2.9% 3.7% 1.0% 5.7% 5.7%

The contributions to the child total PDCFs and PRCFs from the milk pathway illustrate the 

growing importance of this pathway. For Ra-226, it is the dominant pathway, exceeding both 

ingestion of drinking water and vegetables. For the other radionuclides (except Th-230), the 

drinking water pathway remains the most significant pathway, but the milk pathway is next in 

importance. For Th-230, the milk pathway continues to be a minor contributor to the child total 

PDCF/PRCF due to its very low milk uptake (CFmilk) and pasture uptake (Bp) factors, as shown 

in Table 3-8.

Table 3-18: Typical Pathway Contributions to the Infant PDCF and PRCF

Pathway – Infant Pb-210 Ra-226+P Th-230 U-234 U-238

Ingestion of Drinking Water 88.6% 58.9% 87.6% 81.2% 81.2%

Inadvertent Ingestion of Soil 0.4% 1.2% 1.8% 1.6% 1.6%

Ingestion of 

Vegetables

Leaf Deposition 7.6% 6.1% 9.0% 8.4% 8.4%

Root Uptake 1.3% 18.2% 1.3% 1.9% 1.9%

Ingestion of Meat

Cattle Drinking 0.0% 0.0% 0.0% 0.0% 0.0%

Leaf Deposition 0.0% 0.0% 0.0% 0.0% 0.0%

Root Uptake 0.0% 0.0% 0.0% 0.0% 0.0%

Soil Ingestion 0.0% 0.0% 0.0% 0.0% 0.0%
3-35

Table 3-18: Typical Pathway Contributions to the Infant PDCF and PRCF

Pathway – Infant Pb-210 Ra-226+P Th-230 U-234 U-238

Ingestion of Milk

Cow Drinking 0.2% 1.0% 0.0% 0.5% 0.5%

Leaf Deposition 1.3% 7.1% 0.1% 3.9% 3.9%

Root Uptake 0.3% 6.1% 0.0% 0.8% 0.8%

Soil Ingestion 0.3% 1.5% 0.2% 1.6% 1.6%

For the infant total PDCFs and PRCFs, Table 3-18 indicates that the ingestion of drinking water 

is the major contributor for all five radionuclides; the ingestion of vegetables is the second most 

important contributor after drinking water. The inadvertent soil ingestion and meat pathways 

continue to be minor contributors to total PDCFs and PRCFs.

3.4 Native American Exposures

For the five ingestion pathways, the equations described in Section 3.1 for the PDCF and PRCF 

calculations also apply to the Native American; the only difference is the parameter values for 

the various individual consumption rates (i.e., IngWat,A, IngSoil,A, IngVeg,A, IngMk,A, and IngMt,A). A

detailed literature search was unable to locate usage rates that were specific to Native Americans

living in Texas, Colorado, and/or Wyoming (the locations most likely to support ISL facilities). 

Thus, the analysis used the Native American usage rates, which were obtained from a number of 

sources focusing on tribes in the Pacific Northwest, including DOE 1997, WSDOH 2003, Harris

and Harper 2004, Rittmann 2004, PNNL 2006, Harper et al. 2007, and Harper and Ranco 2009.

3.4.1 Ingestion of Drinking Water

According to Harper and Ranco (2009, Section 8.3.1), the Native American drinking water 

consumption rate depends on where the individual lives within the United States. For those 

Native Americans that live in cool and wet climates (e.g., in Maine), the conventional water 

consumption rate of 2 L/day (EPA 1991) should be used. For Native Americans living in areas 

with a hot, arid climate (e.g., the Columbia basin), a higher water consumption rate of 3 L/day

was recommended. For this analysis, the higher water consumption rate of 3 L/day was used. 

Figure 3-13 compares this Native American water consumption rate to the water consumption 

distribution given in Table 3-5 and distribution from the EFH, Table 3-11 (EPA 2011b).
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Figure 3-13: Native American Drinking Water Rate Within the 

Exposure Factors Handbook Distribution

As Figure 3-13 demonstrates, the annual Native American drinking water consumption assumed 

for this analysis is at about the 85th percentile of the EFH distribution.

3.4.2 Ingestion of Vegetables

DOE (1997, Table 5.7) gives the Native American subsistence resident consumption rate of 

fruits and vegetables as 660 grams per day, with a range of 200 to 800. Rittmann (2004, 

Table A4) indicates that the breakdown of the 660 g/day is 6.6% leafy vegetables, 30.2% grains, 

31.4% fruit, and 31.8% other vegetables. For this analysis, it was assumed that the entire intake 

of 660 g/day was of vegetables irrigated with contaminated well water. Figure 3-14 compares 

this Native American vegetable consumption rate to the vegetable consumption distribution used 

to calculate the PDCFs and PRCFs, which (as explained in Section 3.2.5) is a combination of the 

body weight distribution (Table 3-6) and vegetable consumption distributions (Table 3-9) from 

the EFH (EPA 2011b).
3-37

Figure 3-14: Native American Vegetable Consumption Within the 

Exposure Factors Handbook Distribution

As Figure 3-14 demonstrates, the annual Native American vegetable consumption assumed in 

this analysis is at about the 95th percentile of the EFH distribution.

Terry (2011) indicated that gardening and/or full-scale production of fruits and vegetables on the 

Navajo Reservation is virtually nonexistent. He also indicated that fruit and vegetable 

consumption (even from outside locales) is probably also lower among residents of the Navajo 

Reservation than for the U.S. population as a whole. Unfortunately, Terry (2011) did not provide 

any numerical estimates of the Navajo vegetable consumption that could be used in this analysis, 

other than to eliminate the pathway all together.

3.4.3 Ingestion of Meat

Rittmann (2004, Table A4) provides the Native American beef consumption rate as 34 kg/yr and 

another 70 kg/yr for game, broken down to 22 kg/y deer, 32 kg/y wild birds, and 16 kg/y wild 

bird eggs. These values are consistent with Table 5.7 of DOE (1997), which gives the following 

consumption rates: 150 g/day (55 kg/yr) animal protein, 18 g/day (7 kg/yr) upland birds, 

70 g/day (26 kg/yr) waterfowl, and 45 g/day (16 kg/yr) wild bird eggs. The beef and deer 

consumption rates from Rittmann (2004) are equivalent to the animal protein rates from DOE 

(1997). Since game would not be expected to consume well water or fodder that was irrigated, 

the beef consumption rate of 34 kg/yr was used in this analysis. However, it is recognized that 

use of the meat consumption rate for northwestern Native Americans (such as those given in 

Rittmann 2004 and DOE 1997) may under estimate the meat consumption of southwestern 

Native Americans, due to the large amount of fish consumed by northwestern Native Americans.
3-38

Terry (2011) indicated that meat consumption by residents of the Navajo Reservation, 

particularly lamb, mutton, and their internal organs, is much higher than for the United States 

population as a whole. He noted that sheep on the Navajo Nation are not fed in feedlots or 

farmyards, but rely almost exclusively on grazing, and that ingestion of (contaminated) soil by

grazing livestock is probably much higher than in other locales, owing to the semi-arid climate 

and overgrazing. Unfortunately, Terry (2011) did not provide any numerical estimates of the 

Navajo meat consumption that could be used in this analysis.

Although both Harper and Ranco (2009) and Harper et al. (2007) provide data on the Native 

American consumption of game, neither provides any data regarding the consumption of beef by 

Native Americans, so they were not used in this analysis.

Figure 3-15 compares this Native American beef consumption rate to the meat consumption 

distribution used to calculate the PDCFs and PRCFs, which (as explained in Section 3.2.7) is a 

combination of the body weight distributions and meat consumption distributions from the EFH

(EPA 2011b).

Figure 3-15: Native American Meat Consumption Within the 

Exposure Factors Handbook Distribution

Figure 3-15 shows that the Native American beef consumption rate is only at about the 

35th percentile of the meat consumption distribution, based on EFH (EPA 2011b) data.

3.4.4 Exposure in Sweat Lodge

The Sweat Lodge Ceremony is a traditional Native American custom. Based on tribal 

descriptions, PNNL 2006 (page 3-56) stated that between 0.5 and 3 hours/day could be spent 
3-39

sweat bathing, with the inside of the sweat lodge kept at 60°–80° Centigrade (145°–180°

Fahrenheit). For sweat lodge exposure, three sub-pathways were evaluated; inhalation of 

airborne radionuclides, external exposure due to submersion in the airborne radionuclides, and 

drinking additional water to make up for water lost while in the sweat lodge. The annual 

individual effective dose (ESL,NA, mrem/yr) and risk (RSL,NA, LCF/yr) to a Native American from 

time spent in the sweat lodge is given by:

  











  NA Inh  Sub SL SL

SL

SL

SL NA W Wat NA SL Ing B DC DC D N

DF

M

E C Ing DC



, , ,

3-13

  











  NA Inh  Sub SL SL

SL

SL

SL NA W Wat NA SL Ing DW B RC RC D N

DF

M

R C Ing RC



, , , ,

where Cw is the radionuclide concentration in the well from which the water is taken 

(pCi/m3

water), IngWat,NA,SL is the water consumed to make up for water lost while inside the sweat 

lodge (m3/sweat), DCIng is the dose coefficient for ingestion (mrem/pCi), MSL is the moisture 

content of the air inside the sweat lodge (kg/m3

air), δ is the density of water (kg/ m

3

water), DFSL is 

the evaporation decontamination factor (see below), BNA is the Native American’s breathing rate 

while inside the sweat lodge (m3

air/sec), DCInh is the dose coefficient for inhalation (mrem/pCi),

DCSub is the dose coefficient for submersion within a cloud (mrem/hr per pCi/m3), DSL is the 

sweat duration (hr/sweat), NSL is the number of sweats (sweats/yr), RCIng,DW is the risk coefficient 

for ingestion (LCF/pCi), RCInh is the risk coefficient for inhalation (LCF/pCi), and RCSub is the 

risk coefficient for submersion within a cloud (LCF/hr per pCi/m3).

As with the ingestion dose and risk coefficients, the inhalation and submersion dose and risk 

coefficients were obtained from FGR 13. The inhalation dose and risk coefficients used in this 

analysis are shown in Table 3-19, while the submersion dose and risk coefficients are shown in 

Table 3-20.

Table 3-19: Radionuclide-Specific Inhalation 

Dose and Risk Coefficients

Nuclide

Mortality Coefficient (Bq-1)

Infant Child Teen Adult

0- 5 5- 15 15- 25 25- 70

Po-210 2.08E-06 1.13E-06 4.56E-07 2.20E-07

Bi-210 6.61E-08 3.56E-08 1.41E-08 6.83E-09

Pb-210 2.67E-06 1.23E-06 3.51E-07 2.64E-07

Bi-214 3.67E-09 2.37E-09 1.09E-09 4.85E-10

Pb-214 4.68E-09 3.00E-09 1.48E-09 6.12E-10

Ra-226 4.42E-06 2.17E-06 7.59E-07 4.53E-07

Ra-226+P 9.24E-06 4.57E-06 1.58E-06 9.45E-07

Th-230 4.36E-06 2.15E-06 1.03E-06 5.06E-07

U-234 4.37E-06 2.14E-06 7.49E-07 4.47E-07

Th-234 4.68E-09 2.26E-09 8.88E-10 3.76E-10

U-238 3.77E-06 1.82E-06 6.27E-07 3.81E-07
3-40

Table 3-19: Radionuclide-Specific Inhalation 

Dose and Risk Coefficients

Nuclide

Effective Dose Coefficient (Sv/Bq)

Infant Child Teen Adult

100 days 5 yrs 15 yrs 20 yrs

Po-210 1.78E-05 8.62E-06 5.12E-06 4.27E-06

Bi-210 5.60E-07 2.72E-07 1.59E-07 1.33E-07

Pb-210 1.83E-05 1.15E-05 5.83E-06 5.61E-06

Pb-210+P 3.67E-05 2.04E-05 1.11E-05 1.00E-05

Bi-214 9.24E-08 3.26E-08 1.84E-08 1.54E-08

Pb-214 6.92E-08 2.81E-08 1.53E-08 1.47E-08

Ra-226 3.35E-05 1.89E-05 1.04E-05 9.51E-06

Ra-226+P 7.03E-05 3.93E-05 2.15E-05 1.95E-05

Th-230 2.07E-04 1.41E-04 1.00E-04 1.02E-04

U-234 3.31E-05 1.87E-05 1.03E-05 9.40E-06

Th-234 4.08E-08 1.69E-08 9.07E-09 7.69E-09

U-238 2.85E-05 1.61E-05 8.69E-06 8.04E-06

U-238+P 2.85E-05 1.61E-05 8.70E-06 8.05E-06

Source: FGR 13 CD Supplement (EPA 2002)

Table 3-20: Radionuclide-Specific Submersion 

Dose and Risk Coefficients

Nuclide

Mortality Coefficient (m3/Bq-s) Effective Dose 

Coefficient 

(Sv/s)/(Bq/m3)

Infant Child Teen Adult

0- 5 5- 15 15- 25 25- 70

Po-210 4.63E-20 4.55E-20 3.48E-20 1.33E-20 3.89E-19

Bi-210 8.08E-18 7.43E-18 5.94E-18 2.63E-18 2.58E-16

Pb-210 4.65E-18 4.58E-18 3.49E-18 1.31E-18 4.51E-17

Pb-210+P 1.28E-17 1.21E-17 9.46E-18 3.95E-18 3.03E-16

Po-214 4.54E-19 4.46E-19 3.41E-19 1.31E-19 3.81E-18

Bi-214 8.65E-15 8.50E-15 6.51E-15 2.50E-15 7.25E-14

Pb-214 1.27E-15 1.25E-15 9.58E-16 3.67E-16 1.10E-14

Po-218 4.99E-20 4.91E-20 3.75E-20 1.44E-20 4.21E-19

Rn-222 2.10E-18 2.07E-18 1.58E-18 6.06E-19 1.78E-17

Ra-226 3.29E-17 3.23E-17 2.47E-17 9.46E-18 2.84E-16

Ra-226+P 9.97E-15 9.79E-15 7.5E-15 2.88E-15 8.41E-14

Th-230 1.64E-18 1.61E-18 1.23E-18 4.64E-19 1.49E-17

U-234 6.22E-19 6.11E-19 4.69E-19 1.71E-19 6.13E-18

Pa-234 1.04E-14 1.02E-14 7.8E-15 2.99E-15 8.73E-14

Th-234 3.27E-17 3.22E-17 2.46E-17 9.36E-18 2.95E-16

U-238 2.27E-19 2.23E-19 1.72E-19 5.91E-20 2.51E-18

U-238+P 1.04E-14 1.02E-14 7.83E-15 3.00E-15 8.76E-14

As with the Section 3.3 pathway conversion factors, the Native American PDCFs and PRCFs for 

Ra-226 included the contribution from its short-lived progeny, which are shown in both Table 

3-19 and Table 3-20 as Ra-226+P. Table 3-20 also includes external dose and risk coefficients 

for Rn-222 and the two very short-lived polonium isotopes, Po-214 and Po-218. Because Rn-222 
3-41

is a noble gas and does not remain within the body, and because the dose coefficients for Po-214 

and Po-218 are so small relative to the Ra-226 dose coefficient, these three radionuclides do not 

significantly contribute to either the ingestion or inhalation exposure pathways. However, as the 

Table 3-20 external dose coefficients show, they may make a contribution to the external 

pathway dose and risk. Notice also, that while Pa-234 and Th-234 make very small contributions 

to the internal dose and risk, as compared to their parent U-238, their contribution (particularly 

Pa-234) is a significant contributor to the U-238 dose and risk for the external exposure pathway.

Table 3-21 presents the values for the other parameters used in the evaluation of Native 

American exposures due to the sweat lodge pathway. The source and rationale for selection of 

each of the other parameter values are discussed following Table 3-21.

Table 3-21: Other Parameters Used to 

Evaluate the Sweat Lodge Pathway

Parameter Value Units

Moisture content 0.026 kg/m3

Number of sweats 365 sweats/yr-1

Sweat duration 1 hr/sweat

Rehydration water 1 L/sweat

Breathing rate 1.25 m

3

/hr

Decontamination factor 10 ―

DOE 1997 (Table 5.7), Harris and Harper 2004 (Table 3), Rittmann 2004 (Table A17), and 

Harper et al. 2007 (Table B.2) all agree that the number of sweats and the sweat duration should 

be 365 sweats/yr and 1 hr/sweat, respectively. 

The WSDOH 2003 (Section 3.2), Harris and Harper 2004 (Table 3), Harper et al. 2007 

(Section 3.6), and Harper and Ranco 2009 (Section 8.3.1) all agree that 1 liter of water is needed 

to make up for water lost while inside the sweat lodge (IngWat,NA,SL).

Although Harper and Ranco 2009 (Appendix 1) recommends a Native American breathing rate 

of 25 m3/day, DOE 1997 (Table 5.7), WSDOH 2003 (Table 3.2.2), Rittmann 2004 (Table A10), 

and Harper et al. 2007 (Table B.1) all recommend that 30 m3/day be used as the adult Native 

American breathing rate. Thus, for this analysis, a Native American breathing rate of 30 m

3

/day

was assumed.

Many previous sweat lodge exposure analyses (e.g., DOE 1997, WSDOH 2003, ATSDR 2010) 

have based their values for the last two parameters, the sweat lodge air moisture content (MSL)

and the sweat lodge evaporation decontamination factor (DFSL), on a methodology developed by

Rodney S. Skeen, PhD, Confederated Tribes of the Umatilla Indian Reservation (CTUIR),

Department of Science and Engineering, and presented in Harper et al. 2007 and elsewhere. 

Recently, the Skeen methodology has come under criticism. For example, in the River Corridor 

Baseline Risk Assessment (DOE 2010), the Hanford risk assessors expressed concern with using 

the Skeen methodology:
3-42

There is no physical basis provided in Harris and Harper (...) to correlate 

respirable aerosol concentrations with saturated water vapor, and exposure 

concentrations of nonvolatiles in sweat lodge air calculated in this manner are 

considered physically implausible. (page 3-71)

The Harris and Harper (...) equation for calculating air-phase EPCs [exposure 

point concentrations] for nonvolatile analytes (Equation 3-2) calculates the 

concentration of a nonvolatile COPC [contaminant of potential concern] in air as 

a function of the concentration of water vapor produced by the volatilization of 

water poured over hot rocks in a sweat lodge. Because nonvolatile contaminants 

have no vapor pressure, Equation 3-2 does not have a common physical basis 

with volatile chemicals. (page 6-77)

Furthermore, on page O-60, Rittmann (1999) said the following about the Skeen methodology as 

utilized by the Columbia River Comprehensive Impact Assessment (DOE 1997):

..., the sweat lodge temperature is 60° C (140° F) with a relative humidity of 

100%, which means the air in the sauna is 20% water vapor. The sudden 20% 

reduction in oxygen concentration would certainly result in labored breathing, 

possibly fainting. The exposure to high temperature at saturated conditions for 

one hour would lead to skin burns over most of the body.

The Agency for Toxic Substances and Disease Registry (ATSDR) prepared a public health 

assessment (PHA) for the Midnite Mine site, and included an evaluation of sweat lodge 

exposures that utilized Skeen’s methodology. Among the comments received on the ATSDR 

Midnite Mine PHA was the following (ATSDR 2010, page E-23):

Yet another flaw in the sweat lodge models relates to the assumption that 

nonvolatile chemicals in water brought into the sweat lodge will be released to 

the air. During use of a sweat lodge, the PHA assumes that water is poured over 

heated rocks to generate steam or water vapor via the evaporation process. 

Evaporation is defined as a process whereby atoms or molecules in a liquid state 

gain sufficient energy to enter the gaseous state. For evaporation to occur, the 

atoms or molecules of a chemical component in a liquid solution must be heated 

to a level that reaches the boiling point for that chemical. Although the heated 

rocks in the sweat lodge could be hot enough to evaporate the water, they would 

not be hot enough to vaporize the manganese and other metals present in the 

water. The boiling points for most of the metals are extremely high. For example, 

the boiling point for elemental manganese is 2,061° C (3,742° F). Chemical 

components with such high boiling points will remain in the solution (via 

processes of distillation and condensation) or will form a precipitate. Therefore, 

the assumption that the water vapor inside the sweat lodge contains metals at the 

same concentrations as in the water poured on the rocks is flawed.

WSDOH (2003, Section 5.0.2) had this to say regarding Skeen’s methodology for calculating the 

sweat lodge airborne concentration:
3-43

The operating assumption is that 100% of the contaminants in the groundwater 

(used as the source of steam for the sweat lodge) will become airborne and 

remain available for inhalation. Uranium and plutonium compounds have a 

higher melting point than the temperature observed in a sweat lodge and must be 

entrained in the water transitioning to steam to be available for inhalation. Of 

those contaminant particles in the air, it is likely that the deposition rate will be 

higher than that of water vapor and would also serve to decrease the average air 

concentration. In addition, it is likely that a fraction of the contaminants will fail 

to become entrained in the water and become airborne, further reducing the air 

concentrations from those used in the calculation. The sweat lodge calculations 

are therefore considered a worst case estimate of the potential exposure to 

contaminants. Until data are available on the potential air concentration in a 

similar environment, the current model is considered the appropriate method for 

estimating exposure.

For this analysis, the Skeen methodology was set aside and the airborne radionuclide 

concentrations were calculated based on realistically conservative assumptions regarding the 

sweat lodge air moisture content (MSL) and the sweat lodge evaporation decontamination factor 

(DFSL).

Sweat Lodge Air Moisture Content (MSL)

The first of Skeen’s assumptions that was challenged is that the air within the sweat lodge is at 

100% humidity. While this is clearly a conservative assumption, it leads to conditions inside the 

sweat lodge that would make the sweat lodge uninhabitable. That is, at sweat lodge temperatures 

(i.e., between 60° C and 80° C), the humidity must be kept down to nearly zero in order to 

prevent scalding of the skin on contact with the air moisture. When the humidity approaches 

100%, a much lower temperature of around 40° C (104° F) is necessary to prevent scalding. This 

analysis utilized a humidity level that, while conservative, is consistent with a habitable sweat

lodge.

The heat index combines air temperature and relative humidity in an attempt to determine the 

human-perceived equivalent temperature, i.e., how hot it feels. When the heat index is above 

80° F, the National Weather Service (NWS) will issue a caution, and the NWS considers a heat 

index above 130° F to be extremely dangerous, with the possibility of heatstroke highly likely. 

At the above sweat lodge temperature range, the assumption that the air is saturated (i.e., 100% 

humidity) would result in a heat index well above 500° F, which is unreasonable. Thus, the 

humidity within a sweat lodge must be considerably less than 100%.

For different temperatures, Table 3-22 shows what the moisture content of the air would be when 

the heat index is at 130° F. The maximum air moisture content (0.028 kg/m3) is achieved at the 

lowest temperature (100° F). Because the Sweat Lodge Ceremony pushes the individuals beyond 

their normal limits, Table 3-22 also shows what the air moisture content would be when the heat 

index is at 180° F, significantly higher than the extremely dangerous level. With a heat index of 

180° F, the moisture content of the air is 0.026 kg/m3or less in the sweat lodge temperature 

range (i.e., 145° to 180° F).
3-44

Table 3-22: Air Moisture Content with Heat 

Indices of 130° F and 180° F

Temp 

(° F)

Heat Index: 130° F Heat Index: 180° F

RH Moisture 

(kg/m3)

RH Moisture 

(kg/m3)

100 60.4 0.028 92.3 0.042

110 36.0 0.022 64.1 0.039

120 20.1 0.016 44.1 0.035

130 11.1 0.011 30.2 0.031

140 6.65 0.0086 21.1 0.027

145 5.40 0.0079 17.8 0.026

150 4.50 0.0074 15.3 0.025

155 3.85 0.0071 13.2 0.024

160 3.41 0.0071 11.6 0.024

165 3.10 0.0072 10.3 0.024

170 2.87 0.0074 9.3 0.024

175 2.72 0.0078 8.4 0.024

180 2.62 0.0084 7.7 0.025

For this analysis, the moisture content of the air inside the sweat lodge (MSL) was assumed to be 

0.026 kg/m3, since that is the maximum over the 145° F to 180° F temperature range when the 

heat index is 180° F, and it is near the maximum over the 100° F to 189° F when the heat index 

is 130° F.

Sweat Lodge Evaporation Decontamination Factor (DFSL)

The second of Skeen’s assumptions that was challenged is that the concentration of nonvolatiles 

in the sweat lodge airborne moisture is the same as the nonvolatile ground water concentration. 

Since evaporation is often used to remove impurities from water, this assumption is very 

conservative. For example, providers of distilled water first evaporate the water and then recondense it, leaving behind virtually all of the impurities. Also, the NRC (1985, Section 2.19.1) 

recommends that, depending on its use, an evaporator can remove 99% to 99.9% of contamination from the re-condensed water, resulting in decontamination factors of 100 to 1,000. While 

pouring water onto hot stones in a sweat lodge is a far cry from a distiller or evaporator, it 

nonetheless results in the evaporation of the water.

From Exposure Scenarios and Unit Dose Factors for the Hanford Immobilized Low-Activity 

Tank Waste Performance Assessment (Rittmann 1999), page O-57:

Note that only a small fraction of the radioactivity in the evaporating water 

becomes airborne. In Airborne Release Fractions/Rates and Respirable Fractions

for Nonreactor Nuclear Facilities, Volume 1 (DOE-HDBK-3010-94) data for the 

sudden depressurization of superheated aqueous solutions is presented in Table 

3-5 for various initial pressures and volumes. With a source volume of 0.35 L at a 

pressure of 60 psig, the respirable release fraction is 0.006. A somewhat larger 
3-45

value of 0.01 will be used to represent the resuspension from pouring water on 

hot rocks in the sweat lodge, and for the spray of a shower. (The only exception to 

this is tritium. Since it is assumed to be oxidized, 100% of the tritium becomes 

airborne.)

In the 1970s, the Oak Ridge National Laboratory performed a survey to evaluate the 

effectiveness of evaporation as a treatment method for reducing releases of radioactive effluents 

to the environment from nuclear power plants. The principal emphasis was placed upon data 

concerning the ratio of the feed to condensate concentrations, or decontamination factor (DF). 

For nonvolatile contaminants, Godbee (1973, page 2) found that:

An average system DF of 103to 104can be expected under routine operating 

conditions for nonvolatile radioactive contaminants treated in evaporators.

Godbee (1973) noted that the evaporation DF could be reduced by entrainment, which is liquid 

suspended in the vapor as fine droplets that are carried along with the rising vapor stream.

NUREG-1140 (NRC 1991), Section 2.3.1.2, recommends a release fraction be used when 

evaluating nuclear fuel cycle facility accidents. Regarding the release of nonvolatile compounds 

from non-flammable liquids (e.g., water), NUREG-1140 (page 77) states:

Nonvolatile compounds in nonflammable liquids are assigned a release fraction 

of 0.001. Several studies have measured releases in these circumstances. In 

general, release of these compounds can be expected to be small until the liquid is 

dried. After drying release fractions generally remain small because the material 

normally cakes on the substrate or binds into particles too large to be respirable.

It is recognized that none of the above examples exactly represents the pouring of water onto hot 

stones in a sweat lodge. However, taken together, they provide convincing evidence that only a 

small portion of nonvolatile contaminants are released during the evaporation of water. Although 

the above discussion suggests that a value of either 100 or 1,000 could be used, for this analysis, 

a sweat lodge evaporation decontamination factor (DFSL) of 10 was assumed. By using this low 

value for the DFSL, one acknowledges that a significant fraction of nonvolatile contaminants 

could become airborne, while still recognizing that, for water poured onto hot stones, the 

behavior of nonvolatile contaminants within the water is poorly understood.

3.4.5 Native American Pathway Dose and Risk Conversion Factors

Using the above described models and parameter values, the calculated Native American PDCFs 

and PRCFs are shown in Table 3-23.
3-46

Table 3-23: Calculated Native American Total 

Pathway Dose and Risk Conversion Factors

Nuclide PDCF 

(mrem/yr / pCi/m3)

PRCF 

(LCF/yr / pCi/m3)

Pb-210+P 5.10E-03 6.63E-10

Ra-226+P 2.68E-02 4.86E-09

Th-230 1.94E-03 9.86E-11

U-234 4.14E-04 7.08E-11

U-238+P 3.70E-04 6.36E-11

Comparing the Native American PDCFs and PRCFs to the adult total PDCFs and PRCFs given 

in Table 3-12 and Table 3-13, respectively, shows that the Native American PDCFs and PRCFs 

are greater than the 90th percentile adult total PDCFs and PRCFs. As explained below in the 

discussion of the pathway contributions to the Native American PDCFs/PRCFs, this is due to the 

larger drinking water and vegetable consumption rates that were assumed for the Native 

American.

Table 3-24 presents the breakdown by exposure pathway of the Native American PDCFs, while 

Table 3-25 does the same for the Native American PRCFs. Because the Section 3.3 PDCFs and 

PRCFs only included ingestion pathways, the relative pathway contributions to the total PDCF 

are the same as to the total PRCF. However, since the Native American PDCFs/PRCFs include 

the inhalation and ingestion pathways, and because the dose-to-risk relationship differs between 

the inhalation and ingestion pathways, it was necessary to present the Native American pathway

contributions on separate tables.

Table 3-24: Native American Pathway Contributions to the PDCF

Pathway – Adult Pb-210+P Ra-226+P Th-230 U-234 U-238+P

Ingestion of Drinking Water 55.3% 33.0% 44.7% 48.5% 48.8%

Inadvertent Ingestion of Soil 0.1% 0.2% 0.2% 0.3% 0.3%

Ingestion of 

Vegetables

Leaf Deposition 18.1% 10.8% 14.6% 15.9% 16.0%

Root Uptake 3.0% 32.4% 2.2% 3.5% 3.6%

Ingestion of Meat

Cattle Drinking 0.1% 0.1% 0.0% 0.1% 0.1%

Leaf Deposition 0.4% 0.3% 0.0% 0.4% 0.4%

Root Uptake 0.1% 0.3% 0.0% 0.1% 0.1%

Soil Ingestion 0.0% 0.1% 0.0% 0.1% 0.1%

Ingestion of Milk

Cow Drinking 0.3% 0.7% 0.0% 0.4% 0.4%

Leaf Deposition 2.4% 4.9% 0.0% 2.9% 2.9%

Root Uptake 0.5% 4.2% 0.0% 0.6% 0.6%

Soil Ingestion 0.6% 1.0% 0.1% 1.2% 1.2%

Sweat Lodge

Inhalation 0.5% 1.2% 23.1% 10.0% 9.6%

Submersion 0.0% 0.0% 0.0% 0.0% 0.0%

Drinking Water 18.4% 11.0% 14.9% 16.2% 16.3%
3-47

Table 3-24 shows that the drinking water pathway is the major contributor to the PDCF for four 

of the five radionuclides. For Ra-226+P, the vegetable consumption pathway (particularly the 

root uptake sub-pathway) is the major contributor to the PDCF. This is due to radium’s large 

concentration soil uptake factor for vegetables (Bv), as indicated in Table 3-8. The sweat lodge 

contribution (particularly the rehydrate drinking water sub-pathway) to the PDCF is significant 

for all five radionuclides. For Th-230, U-234, and U-238, the sweat lodge inhalation subpathway is also a contributor. As with the Section 3.3 PDCFs, the inadvertent ingest of soil, 

meat, and milk pathways are small contributors to the Native American total PDCF.

Table 3-25: Native American Pathway Contributions to the PRCF

Pathway – Adult Pb-210+P Ra-226+P Th-230 U-234 U-238+P

Ingestion of Drinking Water 53.6% 30.5% 44.0% 37.8% 38.6%

Inadvertent Ingestion of Soil 0.1% 0.2% 0.3% 0.2% 0.2%

Ingestion of 

Vegetables

Leaf Deposition 18.7% 10.8% 15.9% 13.7% 13.9%

Root Uptake 3.1% 32.3% 2.4% 3.0% 3.1%

Ingestion of Meat

Cattle Drinking 0.1% 0.1% 0.0% 0.1% 0.1%

Leaf Deposition 0.4% 0.3% 0.0% 0.3% 0.3%

Root Uptake 0.1% 0.3% 0.0% 0.1% 0.1%

Soil Ingestion 0.0% 0.1% 0.0% 0.1% 0.1%

Ingestion of Milk

Cow Drinking 0.4% 0.7% 0.0% 0.3% 0.3%

Leaf Deposition 2.5% 4.8% 0.0% 2.5% 2.5%

Root Uptake 0.6% 4.1% 0.0% 0.5% 0.5%

Soil Ingestion 0.6% 1.0% 0.1% 1.0% 1.0%

Sweat Lodge

Inhalation 1.8% 4.9% 22.6% 27.8% 26.4%

Submersion 0.0% 0.0% 0.0% 0.0% 0.0%

Drinking Water 17.9% 10.2% 14.7% 12.6% 12.9%

Table 3-25 shows similar results for the pathway contributions to the Native American total 

PRCFs as the Table 3-24 PDCFs, except that the contribution from the sweat lodge inhalation 

sub-pathway has more than doubled. The reason for this is that on a per-Curie basis, the 

inhalation risk coefficients (Table 3-19) are a factor of 28 (Ra-226) to 612 (U-234) times larger 

than the ingestion risk coefficients (Table 3-2).

3.5 Exposure Pathways Not Analyzed in Detail

This section describes scoping calculations, which were performed for three potential exposure 

pathways: (1) radon in the home due to off-gas from well water, (2) exposure to the fetus while 

in the womb, and (3) infant exposure from the consumption of breast milk. Because these 

scoping calculations showed that they did not contribute significantly to the PDCFs, the three 

exposure pathways discussed in this section were not analyzed in detail.
3-48

3.5.1 External Exposure to Contaminated Ground

A screening calculation was performed to determine the impact from external exposure to soil relative to 

the soil ingestion pathway. It was assumed that an individual would spend 136.4 minutes per day 

outdoors at their residence (EPA 2011b, Table 16-22) (SNL 1999, Volume 3, Table 6.7 gives a slightly 

larger value of 158.5 min/day [40.2 24-hr days/year] as the mean time spent outdoors at a residence). As 

Table 3-26 shows, it was determined that relative to the soil ingestion pathway, the external exposure 

pathway can be a significant contributor to the dose, primarily due to the contributions from the daughter 

products (e.g., Bi-210, Bi-214, Pb-214, Th-234). However, based on Table 3-14, the external exposure 

pathway could contribute only about 1.3% to the Ra-226+P overall PDCFs/PRCFs, and much less for the 

other radionuclide.

Table 3-26: External Exposure to 

Contaminated Ground Screening Study Results

Nuclide

Soil Dose (Sv/yr)/(Bq/g)

External Ingestion

Po-210 1.26E-09 2.21E-05

Bi-210 1.40E-07 2.39E-08

Pb-210 5.07E-08 1.27E-05

Pb-210+P 1.92E-07 3.48E-05

Bi-214 2.39E-04 2.05E-09

Pb-214 3.18E-05 2.54E-09

Ra-226 7.46E-07 5.11E-06

Ra-226+P 2.71E-04 4.00E-05

Th-230 2.74E-08 3.91E-06

U-234 8.80E-09 9.04E-07

Th-234 5.45E-07 6.21E-08

U-238 2.04E-09 8.13E-07

U-238+P 5.47E-07 8.75E-07

3.5.2 Exposure to Indoor Radon

If the well water contains radium-226, then it will also contain its decay product radon-222 (see 

Figure 3-2). Radon-222 is a noble gas, and once the water is in the home, the radon will escape 

and become airborne within the home. Individuals within the home will be exposed to radon and 

its short-lived daughter products (i.e., Po-218, Pb-214, Bi-214, and Po-214; see Figure 3-2) via 

two exposure pathways; inhalation and submersion. The amount of radon airborne in the home 

depends upon the amount of water brought into the home, how much of the radon escapes from 

the well water, and the home’s air turnover rate. The following equation was used to calculate 

the exposure to radon that is released from well water brought into the home.
3-49

  h f Inh Sub

h h

Wat h o g

Radon f E B K DC DC

V

U S f

PDCF  





3-14

where PDCFRadon is the PDCF due to radon in a home where well water is used (mrem/yr 

per pCi/m3

water), UWat is the per capita water usage rate (m3

/day per person), Sh is the number of 

people living in the home (people), fo-g is the radon off-gas fraction, λh is the home’s air turnover 

rate (day-1), Vh is the volume of the home (m3), fh is the fraction of time spent in the home, Ef is 

the radon daughter product equilibrium fraction, B is the breathing rate (m3/day), DCInh is the 

radon daughter products dose coefficient for inhalation (mrem/pCi), K is a units conversion 

factor (day/year), and DCSub is the dose coefficient for radon submersion within the home

(mrem/hr per pCi/m3). The value of each of the parameters used in equation 3-14 has been 

tabulated in Table 3-27.

Table 3-27: Data Used to Evaluate the PDCF for Home Radon Exposure

Parameter Units Value Source

Per capita water usage rate UWat (m3/day per person) 0.26 EPA 2009

Number of people living in the home Sh (people) 2.58 USCB 2011

Radon off-gas fraction fo-g None 0.90 NAS 1999a, page 91

Home’s air turnover rate λh (day-1) 10.8 4.32 EPA 2011b, Table 19-1

Volume of the home Vh (m3) 492 154 EPA 2011b, Table 19-1

Fraction f time spent in the home 

(residence)

fh None 0.66 0.99 EPA 2011b, Table 16-1

Breathing rate B (m3/day) 16.3 24.6 EPA 2011b, Table 5-1

Radon daughter product equilibrium 

fraction

Ef None 0.4 NAS 1999b, Figure B-12

Radon daughter products dose 

coefficient for inhalation

DCInh (mrem/pCi) 1.55E-04 FGR 13 CD Supplement, 

EPA 2002

Dose coefficient for radon 

submersion

DCSub (mrem/yr per pCi/m3) 2.07E-06 FGR 13 CD Supplement, 

EPA 2002

For the parameters whose values were obtained from the EFH (EPA 2011b), two values are 

presented in Table 3-27; the first value was used to calculate an estimate of the mean exposure, 

while the second value was used to calculate an upper estimate of the exposure. The radon offgas fraction (fo-g) was used to calculate how much radon escapes from the water into the air. The 

value for the off-gas fraction (90%) was taken from NAS 1999a, and is specifically for radon 

escaping in the shower, although it has been applied to all water for this scoping calculation. 

Other researchers have published a radon off-gas fraction closer to 70% (Hopke 2006).

The results of the radon exposure pathway PDCF scoping calculation are shown in Table 3-28. 

Comparing the radon PDCFs from Table 3-28 to the Ra-226+P PDCFs from Table 3-12 shows 

that the contributions from the radon exposure pathways are small (i.e., two to three orders of 

magnitude lower).
3-50

Table 3-28: PDCF for Home Radon Exposure

Exposure 

Pathway

Radon PDCF (mrem/yr per pCi/m3)

Mean Upper

Inhalation 3.10E-05 5.63E-04

Submersion 1.74E-10 2.09E-09

Total 3.10E-05 5.63E-04

3.5.3 Swimming Pool/Hot Tub Exposures

A screening calculation was performed to determine the impact from external exposure to water, 

such as by swimming or a hot tub. For this screening calculation it was assumed that an 

individual would spend 60 minutes per month in either a swimming pool or hot tub (EPA 2011b, 

Table 16-57, Overall, 50th Percentile). As Table 3-29 shows, it was determined that the external 

exposure pathways are not a significant contributor to the dose (relative to drinking water). 

Based on these results, the water immersion exposure pathways were not analyzed in detail in 

this analysis. 

Table 3-29: Water Immersion 

Screening Calculation

Nuclide

Water Dose (Sv/yr)/(Bq/m3)

Immersion Drinking

Po-210 3.90E-17 6.54E-07

Bi-210 2.73E-15 7.08E-10

Pb-210 5.66E-15 3.76E-07

Pb-210+P 8.43E-15 1.03E-06

Bi-214 7.17E-12 6.05E-11

Pb-214 1.12E-12 7.51E-11

Ra-226 3.00E-14 1.51E-07

Ra-226+P 8.33E-12 1.18E-06

Th-230 1.70E-15 1.16E-07

U-234 7.56E-16 2.68E-08

Th-234 3.30E-14 1.84E-09

U-238 3.43E-16 2.41E-08

U-238+P 3.33E-14 2.59E-08

3.5.4 Exposures Due to Hydroponics and/or Aquaculture

Appendix V of the Background Information Document (BID) prepared by EPA for its 40 CFR 

197 rulemaking (EPA 2001) addressed the impact of contaminated ground water on hydroponics 

farming (i.e., the science of growing plants without soil). After reviewing the literature on 

hydroponics, the BID concluded: given that hydroponically-grown vegetables would not be 

subject to the buildup of radionuclides in soil, it is reasonable to conclude that they would have 
3-51

lower radionuclide concentrations than vegetables grown in soil. (EPA 2001, page 8-46) It is 

reasonable to make a similar conclusion for this analysis. Also, as shown in Table 3-13 through 

3-16, with the exception of Ra-226, the root uptake component contributions to the total 

PDCFs/PRCFs are small (e.g., less than 4%). For these reasons, a detailed assessment of the 

hydroponics exposure pathway has not been included in this analysis.

Likewise, Appendix V of the Background Information Document (BID) prepared by EPA for its 

40 CFR 197 rulemaking (EPA 2001) also addressed the impact of contaminated ground water on 

aquaculture (i.e., fish farming). After reviewing the literature on hydroponics, the BID found 

that: In arid areas, fish farming is usually conducted in large tanks filled with ground water that 

is continually filtered and aerated. Food, in the form of commercial pelletized floating feed, is 

introduced into the tanks daily. The extensive literature on concentration factors for 

radionuclides in freshwater fish is not considered applicable to the unique conditions of 

aquaculture. Uptake is limited to direct sorption of radionuclides in the water. (EPA 2001, page 

8-46) For these reasons, a detailed assessment of the aquaculture exposure pathway has not been 

included in this analysis.

3.5.5 Embryo and Fetus Exposure

While in the womb, an embryo/fetus receives nourishment from its mother. If the mother ingests 

and/or inhales radioactivity during her pregnancy, then some of that radioactivity may be 

delivered to the fetus. This phenomenon has been studied by the ICRP, and their results and 

recommendations have been published in Publication 88 (ICRP 2001).

In Publication 88 new biokinetic and dosimetric models for calculating doses to 

the developing embryo and fetus are developed .... The models [which are 

developed] take account of transfer of radionuclides across the placenta, 

distribution and retention of radionuclides in fetal tissues, growth of the fetus, and 

photon irradiation from radionuclides in the placenta and maternal tissues. ... 

Intake scenarios comprising single or continuous maternal intakes are taken into 

account in the compilation of effective dose coefficients following ingestion or 

inhalation of the radionuclides considered. (ICRP 2001)

Table 3-30 gives the ICRP Publication 88 effective dose coefficients from conception to birth (in 

utero), as well as the effective dose coefficients from birth to age 70 years (post natal) due to 

exposures while in the womb.

Table 3-30: Dose Coefficients to the Offspring from 

Chronic Intake by the Mother

Nuclide Time of Intake 

(weeks)

Dose Coefficient (Sv/Bq)

In Utero Post Natal

Pb-210 -260 2.4E-08 5.1E-08

-52 3.0E-08 7.0E-08

0 1.1E-08 1.3E-07

Ra-226 -260 1.4E-09 5.0E-11

-52 2.9E-09 8.3E-11

0 2.9E-07 2.8E-08
3-52

Table 3-30: Dose Coefficients to the Offspring from 

Chronic Intake by the Mother

Nuclide Time of Intake 

(weeks)

Dose Coefficient (Sv/Bq)

In Utero Post Natal

Th-230 -260 1.9E-10 7.5E-10

-52 2.2E-10 7.7E-10

0 7.6E-10 7.8E-09

U-234 -260 7.6E-10 1.8E-10

-52 9.9E-10 2.1E-10

0 1.1E-08 4.1E-09

U-238 -260 6.8E-10 1.6E-10

-52 8.8E-10 2.0E-10

0 9.5E-09 3.8E-09

Source: ICRP 2001

The Table 3-30 dose coefficients are for chronic intake of radioactivity for three different time 

periods: (1) for 5 years (260 weeks) up to (but not after) conception, (2) for 1 year (52 weeks) 

up to (but not after) conception, and (3) during the pregnancy, starting from conception.

The first and third sets of dose coefficients were combined with the water consumption rate for 

non-pregnant and pregnant women (i.e., 1,243 and 1,318 mL/day, respectively) from EFH (EPA 

2011b), Table 3-76. This model assumes that the woman drinks water from the contaminated 

well for 5 years prior to conception, and continues to drink from the well throughout her 

pregnancy. The results of this scoping calculation are shown in Table 3-31. Comparing the 

PDCFs from Table 3-31 to the PDCFs from Table 3-12 shows that the contribution to the total 

exposure from the chronic intake of radioactivity by the mother during pregnancy is small.

Table 3-31: PDCF to the Offspring from 

Chronic Intake by the Mother

Nuclide PDCF (mrem / pCi/m3

)

In Utero Post Natal Total

Pb-210 2.14E-04 5.82E-04 7.96E-04

Ra-226 3.58E-04 3.39E-05 3.92E-04

Th-230 2.50E-06 1.56E-05 1.81E-05

U-234 1.95E-05 6.41E-06 2.59E-05

U-238 1.70E-05 5.88E-06 2.29E-05

3.5.6 Infant Consumption of Formula/Milk

Infants receive their nourishment from formula, breast milk, or cows’ milk. Furthermore, 

formula is available in three forms; powder, liquid concentrate (which requires dilution), and 

premixed ready-to-feed. Currently, 74.6% of American infants have been breastfed at least once, 

with 35% of infants exclusively breastfed at 3 months, 44.3% receiving some breastfeeding at 

6 months, and 23.8% receiving breastfeeding at 1 year (CDC 2011). Consumption of cows’ milk 

is not recommended for infants under the age of 1 year; however, that recommendation is often 

ignored and cows’ milk is introduced earlier. EFH (EPA 2011b), Table 3-72, shows that 5% of 

infants consumed cows' milk at 6 months, increasing to 25% at 9 months and to 79% at 

12 months.
3-53

Except for premixed ready-to-feed formula, each of these means of feeding could result in 

exposure to an infant if the well water is contaminated. For powder or liquid concentrate 

formula, well water is mixed with the formula and fed directly to the infant. Section 3.1.1

presents the methodology used to calculate exposures due to water consumption, and this same 

methodology was used to calculate exposure from powder or liquid concentrate formula 

consumption. Table 3-5 and Figure 3-3 show the infant’s water consumption distribution, which 

was obtained from EFH (EPA 2011b), Table 3-41. On Figure 3-3, the weight normalized infant’s 

water consumption is significantly larger than the water consumption for the other age groups, 

reflecting the fact that water is used to prepare formula, which is the bulk of an infant’s 

nourishment.

The methodology used to calculate exposures from the cows’ milk pathway was described in 

Section 3.1.4. Table 3-10 and Figure 3-10 show the infant’s cows’ milk consumption 

distribution, which was obtained from EFH (EPA 2011b), Table 11-3. On Figure 3-10, the 

weight normalized infant’s milk consumption is significantly smaller than the child’s milk 

consumption; this reflects the fact that cows’ milk is not recommended for infants under the age 

of 1 year.

Thus, the infant’s exposures from consumption of formula and cows’ milk have been included in 

the PDCFs that are presented in Section 3.3. However, the infant’s exposure from breast milk 

consumption has not been addressed. The ICRP has studied this exposure pathway, and their 

results and recommendations have been documented in Publication 95 (ICRP 2004). In

Publication 95, the ICRP adapted existing biokinetic models for the female adult to include 

transfer of radioactivity to milk. The fraction of the mother’s radioactivity intake that reaches the 

breast milk was then combined with the infant’s ingestion dose coefficient to arrive at a dose 

coefficient that relates the amount of radioactivity ingested by the mother to the dose received by 

the breastfeeding infant (i.e., Sv to the infant/Bq intake by the mother). Table 3-32 shows the 

fraction of mother’s radioactivity intake that reaches the breast milk and the breastfeeding infant 

dose coefficient obtained from ICRP Publication 95, as well as the risk coefficient, which was 

calculated in the same manner as the dose coefficient.

Table 3-32: Dose and Risk Coefficients from Breast Milk Consumption

Nuclide Fraction of Mother’s 

Intake to Breast Milk*

Dose Coefficient 

(Sv/Bq)*

Risk Coefficient 

(LCF/Bq)

Pb-210 2.5E-02 2.2E-07 9.6E-09

Ra-226 5.9E-03 2.8E-08 2.5E-09

Th-230 2.4E-05 9.8E-11 3.0E-13

U-234 9.7E-04 3.6E-10 9.7E-12

U-238 9.7E-04 3.3E-10 1.3E-11

*ICRP 2004

Table 3-33 presents a comparison of the doses and risks received by infants consuming cows’ 

milk, formula, and breast milk. For cows’ milk and formula, it was assumed that the infant 

consumed 778 mL/day (EPA 2011b, Table 3-74), and for breast milk, it was assumed that the 

mother consumed 1,806 mL/day of water (EPA 2011b, Table 3-76). The radioactivity in the 

cows’ milk was calculated using the methodology from Section 3.1.4 and the parameter values 
3-54

from Section 3.2.6. In addition to consuming contaminated well water, the mother was assumed 

to consume contaminated vegetables, cows’ milk, and meat, as described in Sections 3.1 and 3.2. 

Table 3-33: PDCF/PRCF for Infant Milk Consumption

Infant Milk Ingestion – Dose (mrem/yr / pCi/m3)

Pb-210 Ra-226 Th-230 U-234 U-238

Cows’ Milk 1.28E-02 1.76E-01 1.04E-04 3.52E-04 3.53E-04

Formula 3.61E-02 4.10E-02 4.34E-03 3.88E-04 3.93E-04

Breast Milk 6.59E-04 1.22E-04 2.80E-07 1.10E-06 1.01E-06

Infant Milk Ingestion – Risk (LCF/yr / pCi/m3)

Pb-210 Ra-226 Th230 U-234 U-238

Cows’ Milk 1.43E-04 1.91E-03 3.20E-07 9.53E-06 1.28E-05

Formula 4.02E-04 4.44E-04 1.34E-05 1.05E-05 1.42E-05

Breast Milk 7.35E-06 1.32E-06 8.61E-10 2.99E-08 3.66E-08

As Table 3-33 shows, the infant’s exposure due to consuming breast milk is small compared to 

the exposure due to either formula or cows’ milk consumption. This is expected, since the 

mother’s body only allows a fraction of the radioactivity entering her body to reach her breast 

milk (Table 3-32), where it can be consumed by the infant, whereas for formula consumption, all 

radioactivity in the well water is consumed by the infant.
4-1

4.0 DOSE AND RISK ASSESSMENT

4.1 Introduction

Chapter 3 described the methodology for calculating probabilistic pathway dose and risk 

conversion factors, while Chapter 2 described the ground water modeling for excursion and 

leakage scenarios. The ground water modeling was based on an initial source concentration of 

1 mg/L for a generic species, and concentrations were calculated at down-gradient receptor wells 

located various distances from the source assuming no retardation. In this chapter, we evaluate 

expected doses and risks assuming source term strengths based on available measurements from 

the literature and include the effects of radionuclide-specific retardation factors.

4.2 Selection of Lixiviant Concentrations and Kds 

To be conservative, we have generally selected low-end Kds and high-end lixiviant 

concentrations for calculating doses and risks.

4.2.1 Lixiviant Concentration

Table 4-1 summarizes reported ranges of concentrations of various species in ISL/ISR lixiviants 

(PNNL 2010). 

Table 4-1: Representative Concentrations in Uranium Alkaline ISR Lixiviants

Constituent Wyoming 

Site(a)

Texas 

Site(a)

Kingsville Dome 

Site, Texas(b)

Typical 

Chemistry(c)

Typical Lixiviant 

Chemistry Range(d)

Calcium 138 273 560 100–350 ≤ 20–500

Magnesium 42 82 92 10–50 ≤ 3–100

Sodium 365 1,007 800 500–1,600 ≤ 400–6,000

Potassium 12 26.5 31 25–250 ≤ 15–300

Carbonate NR NR NR 0–500 ≤ 0.5–2,500

Bicarbonate NR 579 619 800–1,500 ≤ 400–5,000

Chloride 140 1,009 919 250–1,800 ≤ 200–5,000

Sulfate 229 1,181 1,660 100–1,200 ≤ 400–5,000

Silica 24.6 NR 23.5 25–50 NR

pH (standard units) 6.7 6.71 6.82 7–9 ≤ 6.5–10.5

Total dissolved solids 1,713 4,186 4,640 1,500–5,500 ≤ 1,650–12,000

Alkalinity (as CaCO3) 620 NR 507 NR NR

Arsenic NR NR 0.016 NR NR

Iron NR NR 0.02 NR NR

Manganese NR NR 1.7 NR NR

Molybdenum NR NR 32 NR NR

Radium-226 (pCi/L) NR NR 293 500 NR

Selenium NR NR 0.104 NR NR

Uranium 18.2 28.6 29.0 50–250 ≤ 0.008–424

Vanadium NR NR <0.01 NR ≤ 0.006–56

All units are mg/L unless otherwise noted.

NR = Not reported

(a) Deutsch et al. 1985

(b) Schramke et al. 2009 (c) Pelizza 2008

(d) NRC 2009b
4-2

Additional information on lixiviant concentrations is included in Table 4-2: (CNWRA 2001). 

These are the maximum quoted concentrations based on a survey of licensing documents.

Table 4-2: Highest Observed Concentrations in Pregnant Lixiviants 

based on a Survey of Licensing Documents 

Element/Isotope Concentration

Ra-226 3,400 pCi/L

Lead 0.01 mg/L

Uranium 250 mg/L

Based on this information, lixiviant concentrations were established as follows.

Lead Concentration

The maximum amount of lead reported in lixiviants is 0.01 mg/L (Table 4-2). 

Uranium Concentration

The maximum amount of uranium reported in lixiviants is 424 mg/L (Table 4-1). This value can 

be traced to NRC 1989, but that reference is silent on the source of the data, noting only that the 

“values represent the concentration ranges that could be found in barren lixiviant and pregnant 

lixiviant and would include the concentrations normally found in ‘injection fluids’” (NRC 1989,

Table 3.4.01). Use of 424 mg/L as an upper bound on the uranium concentration thus lacks 

credibility. The values in Column 5 of Table 4-1 are from Pelizza 2008, but Pelizza also does not 

provide the source of the data. It likely came from NUREG-1508 (NRC 1997), which also lists 

the range for uranium from 50 to 250 mg/L. According to Table 2.1 of NUREG-1508, these data 

were obtained from HRI 1993 and are based on test data and operational licensing experience. 

This is presumably from the same source as cited in Table 4-2 above. CNWRA 2001 cites 

60 mg/L as a typical concentration for pregnant lixiviant. Another source of information is spill 

incidents in 1999 at the Smith Ranch involving injection fluids with a natural uranium 

concentration of 2.7E-06 μCi/ml or 3.9 mg/L, and extraction fluids with a concentration of 5.3E05 μCi/ml or 77 mg/L (NRC 2000). Additionally, NRC reported that since June 1997, uranium 

releases in spills at the Smith Ranch-Highland Uranium Project ranged from 0.7 to 152 mg/L,

with about 70% of the releases below 10 mg/L (NRC 2007). Based on this information, we have 

selected a uranium concentration of 150 mg/L as a reasonable upper-end value for calculating 

doses and risks.

Radium Concentration 

The highest observed Ra-226 concentration in lixiviants is 3,400 pC/L (Table 4-2). Based on the 

specific activity of 1 Ci/g for Ra-226, this is equivalent to 3.4E-06 mg/L. This value is based on 

the Ra-226 concentration in production fluids at the Smith Ranch Facility in Converse County, 

Wyoming (NRC 2000). The Ra-226 concentration in injection fluids was similar (i.e., 

3,300 pCi/L); radium is not removed when uranium is extracted from the pregnant lixiviant and 

is returned underground with the injection solutions.
4-3

Thorium Concentration

As noted by the NRC in its Standard Review Plan for ISLs (NRC 2003, Section 5.7.8.3):

.....many licensees have decided not to sample for Th-230; Th-230 is a daughter 

product from the decay of uranium-238, and studies have shown that it is 

mobilized by bicarbonate-laden leaching solutions. However, studies have also 

shown that after restoration, thorium in the ground-water will not remain in 

solution, because the chemistry of thorium causes it to precipitate and chemically 

react with the rock matrix (Hem, 1985). As a result of its low solubility in natural 

waters, thorium is found in only trace concentrations. Additionally, chemical tests 

for thorium are expensive, and are not commonly included in water analyses at in 

situ leach facilities. This example concerning Th-230 demonstrates an acceptable 

technical basis for excluding Th-230 from the list of sampled constituents.

In the absence of detailed geochemical modeling to support the immobility of thorium in ground

water, we have selected a value of 640 pCi/L (3.05E-05 mg/L) for the Th-230 source term. This 

value was measured at the Irigaray Solution Mining Project in the post-leaching ground water

prior to restoration (NRC 1978, Table 5.1).

4.2.2 Selection of Kd Values

Selection of Kds was based on selecting low-end values to conservatively establish the time to 

reach peak dose at a down-gradient well. It should be noted that the Kd value affects the time at 

which the peak dose occurs, but not its magnitude in the absence of significant radioactive decay. 

Considerations in selecting the Kd values are described below.

Lead Kd

EPA 1999b provides a thorough survey of the adsorption behavior of lead in soil studies and 

identifies the factors that influence the behavior of lead. The pH of the aqueous phase had an 

important effect on lead sorption, which increases over the pH range of 4–11. Table 4-3

summarizes lead Kd values as a function of pH and aqueous lead concentrations from EPA 

1999b. It is probable that in the ISL environment, lead will derive from the uranium series decay

chain and will be present in low concentrations.
4-4

Table 4-3: Estimated Range of Kd Values for Lead as a Function of Soil pH, and 

Equilibrium Lead Concentrations

Equilibrium Lead Concentration 

(micro g/l) Kd (ml/g) Soil pH

4.0–6.3 6.4–8.7 8.8–11.0

0.1–0.9 Minimum 940 4,360 11,520

Maximum 8,650 23,270 44,580

1.0–9.9 Minimum 420 1,950 5,160

Maximum 4,000 10,760 20,620

10–99.9 Minimum 190 900 2,380

Maximum 1,850 4,970 9,530

100–200 Minimum 150 710 1,880

Maximum 860 2,300 4,410

Based on this information, we selected a minimum Kd for 10 μg/L of lead in solution of 900 ml/g 

for pH 6.4–8.7. This pH range is consistent with observed values at ISL facilities (Davis and 

Curtis 2007, Table 3 and 4).

With a Kd of 900 ml/g for lead and conservative hydraulic data from existing ISL facilities (see 

Table 4-8 and Figure 4-1), it would take the isotopes of lead initially present in the ground water

approximately 260,000 years to reach the nearest well located at a distance of 528 ft from the 

facility. Since the half-life of Pb-210 is 22.3 years, any Pb-210 contamination initially present in 

the lixiviant would completely decay away before it could arrive at the receptor well. 

Furthermore, as described in Section 3.2.2, any in-growth of Pb-210 from the decay of Ra-226 

has been accounted for in this analysis by adding the Pb-210 dose and risk factors (as well as the 

dose and risk factors for other short-lived Ra-226 progeny) to the Ra-226 dose and risk factors. 

Thus, the Ra-226 doses and risks presented in this report include the contribution from Pb-210

and the other Ra-226 short-lived progeny.

Uranium Kd

Aqueous uranium and its complexes sorb onto clays, organics, and iron oxides (EPA 1999b). 

Uranium sorption by soils generally reaches a maximum in the pH range from pH 5 to 8 (EPA 

1999b). Higher ionic-strength solutions or the presence of carbonate ions tend to decrease 

uranium(VI) sorption. Uranium can also be attenuated in ground water through co-precipitation 

reactions with metal oxyhydroxides, such as iron hydroxide.

Table 4-4 provides a uranium Kd look-up table from EPA 2004. The general trend in uranium Kd

values as a function of pH is that adsorption is low at pH values of 3 or less, increases rapidly 

from pH of 3 to 5, reaches a maximum between pH of 5 and 8, and then decreases with 

increasing pH greater than 8. The decrease in absorption at high pH is actually related to the

presence of dissolved carbonate. At near- and above-neutral pH conditions, dissolved U(VI) 

forms strong anionic uranyl-carbonato complexes with dissolved carbonate, making it less likely 

to adsorb to the surface-charged soil minerals (EPA 2004).
4-5

Table 4-4: Estimated Range of Kd Values for Uranium based on pH

Kd

(ml/g)

pH

3 4 5 6 7 8 9 10

Minimum <1 0.4 25 100 63 0.4 <1 <1

Maximum 32 5,000 160,000 1,000,000 630,000 250,000 7,900 5

Source: EPA 1999b

As documented in EPA 1999b (Section J.40):

Under oxidizing conditions at pH values greater than 6, their derived Kd values 

were approximately 100 ml/g. At high concentrations of dissolved carbonate, and 

pH values greater than 6, the Kd values for uranium decrease considerably.

Based on these considerations, we have selected a value of 0.4 ml/g (the minimum value at pH 8 

in Table 4-4).

Radium Kd

Radium, an alkaline earth element, is generally relatively immobile, but can be mobilized under 

some conditions. Radium-226 and radium-228 are present in uranium roll-front deposits, because 

of the decay of uranium-238 and thorium-232, respectively. Ground water radium concentrations 

commonly are elevated in the ore zone relative to the background levels present immediately upgradient and down-gradient of the ore (Hoy 2006). In general, radium adsorption on mineral 

surfaces increases with increasing pH. For iron oxides, the increase in adsorption begins around 

pH of 6 to 8 and reaches a maximum of around 10 or less (EPA 2004). Radium can be attenuated 

by adsorption onto clays. Radium is also strongly adsorbed to mineral oxides, especially at nearneutral and alkaline pH conditions (EPA 2004).

Compared to most other radionuclides, very limited data are available on radium sorption, 

particularly Kd values. Moreover, EPA (2004) states that any data indicating high radium 

adsorption on geologic materials should be viewed carefully, as (Ba, Ra) S04 co-precipitation 

may have occurred during the measurements. However, from an attenuation standpoint, the 

amount of radium that is removed from ground water by adsorption versus precipitation is 

largely irrelevant. EPA (2004) presents ranges of Kd values by soil type, as shown in Table 4-5. 

As noted by EPA (2004), “radium is readily adsorbed to clays and mineral oxides present in 

soils, especially at near neutral and alkaline pH conditions.”

Table 4-5: Radium Kd Values by Soil Type

Soil Type Kd Values (ml/g)

Geometric Mean Number of Observations Range

Sand 500 3 57 – 21,000

Silt 36,000 3 1,262 – 530,000

Clay 9,100 8 696 – 56,000

Organic 2,400 1 None Listed

 Source: EPA 2004
4-6

As can be seen from Table 4-5, a minimum Kd value of 57 ml/g is cited for radium in sand. NRC 

recommends a value of 100 ml/g for alkaline (cementitious) environments (NRC 1998). This 

seems more relevant to alkaline ISL environments and has been selected for calculating 

retardation factors here.

Thorium Kd

NRC (1998) recommended a Kd value of 500 ml/g for thorium in a Type III environment, which 

is equivalent to highly weathered cement. EPA (1999b, Table 5-15) quotes a minimum value of 

20 ml/g for Th concentrations of <10-9 M (2.3E-07 g/L) and pH of 8 to 10. For the pH range of 5 

to 8, EPA quotes a minimum value of 1,700 ml/g. For the analyses presented here, the 

intermediate value of 500 ml/g was selected.

4.2.3 Recommendations for Kd and Radionuclide Source Term

Recommended Kd values and source concentrations for radionuclides are summarized in Table 

4-6. It is recognized that, if leakage is from an injection well as contrasted to an extraction well, 

the concentrations, particularly uranium, will be lower than cited in Table 4-6. If lixiviant 

concentrations at actual sites are different than those assumed in Table 4-6, the results can be 

scaled linearly from those used here.

Table 4-6: Summary of Kd Values and Lixiviant 

Concentrations Used in Dose and Risk Calculations

Radionuclide Kd (ml/g) Lixiviant Concentration 

(mg/L)

U nat 0.4 150

Th-230 500 3.05E-05

Ra-226 100 3.4E-06

Pb-210 900 0.01

Radionuclide-specific retardation factors may be calculated using the following equation:

Ri =

t

b d

p

ρ K

1

i  4-1

Ri = Retardation factor of radionuclide i in the saturated zone ―

ρb = Saturated zone soil bulk density (g/cm3)

di

K = Radionuclide i distribution coefficient in the saturated zone (cm3

/g)

pt = Total porosity of the saturated zone (dimensionless) ―

Radionuclide-specific retardation factors were calculated using equation 4-1, the above Kds, a 

soil bulk density (ρb) of 2 g/cc, and a total porosity (pt) of 0.3. 
4-7

4.3 Dose and Risk Calculations

4.3.1 Limiting Doses and Risks

For illustrative purposes, limiting concentrations at the receptor well are derived for an assumed 

dose limit of 15 mrem/yr and a lifetime risk limit of 10-4LCF

12, or assuming a 70-year life 

expectancy, an annual risk limit of 1.4 × 10-6LCF/yr. The limiting radionuclide concentrations 

shown in Table 4-7 were derived using the adult, mean pathway dose and risk conversion factors 

(PDCFs and PRCFs) derived in Chapter 3. Based on the approach taken for the CAP88 computer 

program, the adult PDCFs and PRCFs were used to calculate the Table 4-7 limiting 

concentrations. The limiting concentrations for Ra-226 in Table 4-7 are shown both with and 

without progeny. Limits for one daughter product, Pb-210 with a 22.3-year half-life, are also 

shown separately. 

Table 4-7: Radionuclide Well Limiting Concentrations

Nuclide 15 mrem/yr Dose Limit 10-4 LCF Lifetime Risk Limit

(pCi/L) (mg/L) (pCi/L) (mg/L)

U-238+P 97 2.8E-01 63 1.8E-01

U-234 93 1.5E-05 65 1.0E-05

U-natural 95 1.4E-01 64 9.4E-02

Th-230 25 1.2E-06 47 2.2E-06

Ra-226 1.2 1.2E-09 0.6 6.3E-10

Ra-226 + P 2.4 3.1E-11 1.3 1.7E-11

Pb-210+P 97 2.8E-01 63 1.8E-01

 U-238+P included progeny:Th-234

 Ra-226+P included progeny:Pb-214, Bi-214, Pb-210, Bi-210, Po-210

 Pb-210+P included progeny: Bi-210, Po-210

As Table 4-7 demonstrates, a lifetime risk limit of 10-4LCF is slightly more restrictive than a 

dose limit of 15 mrem/yr for uranium, Ra-226, and Pb-210, while the dose limit is slightly more 

restrictive for Th-230. However, for all of the radionuclides considered, both dose and risk 

limiting concentrations are within a factor of two.

4.3.2 Doses and Risks from Excursion Scenarios

As described in Section 2.6.2, 63 excursion scenarios were analyzed in which some lixiviant 

leaked down-gradient from various injection/extraction pumping patterns. The ground water

modeling studies described in Chapter 2 included broad ranges of selected hydraulic parameters 

to assist in evaluating the sensitivity of receptor well concentrations to parameter changes. While 

the selected ranges for individual parameters were based on literature reviews, some 

combinations of hydraulic parameters may not exist in actual licensed ISL facilities. For 

example, ground water velocity is defined as the product of hydraulic conductivity and hydraulic 

 

12 A lifetime cancer morbidity risk limit of 10-4 was specified in the National Contingency Plan (55 Federal 

Register 8665-8865, March 8, 1990). In this example the cancer morbidity risk limit is being applied to the risk of 

latent cancer fatalities (LCF). For the radionuclides analyzed in this study, the relationship between cancer morbidity 

and mortality is discussed in Section 3.2.2.
4-8

gradient divided by the effective porosity. In some of the modeling scenarios, both high 

conductivity and high gradient were included, which could result in unrealistically high 

velocities. To focus on doses and risks from those scenarios that are most representative of actual 

ISL conditions, we obtained data from several proposed and operating ISL facilities, as 

summarized in Table 4-8. From these data, we constructed the cumulative distribution for the 

product of hydraulic gradient times hydraulic conductivity function shown in Figure 4-1. It can 

be seen that all of the values fall below a limit of 0.13 (ft/day). Based on this limit, we developed 

doses and risks for those modeled excursion scenarios not exceeding this limit.

Table 4-8: ISL Site Hydraulic Data

Site / Identifier

Hydraulic 

Conductivity 

(ft/day)

Hydraulic 

Gradient Reference

Crow Butte, NE 9.11 0.0004 Crow Butte 1995 

(Table 2.7-6 and p.2-13) 

Goliad County, TX 3.5 0.0009 DBS&A 2007 

(Table 1 and Sec. 2.5)

Irigaray, WY Major 1.55 0.033 NRC 1978 (p. 2-16)

Minor 0.896 0.033

Kingsville Dome, TX PBL-1 24.48 0.005 Rice 2006 

PBL-2 14.112 0.0009 (Tables A-2, A-3, A-4)

PBL-3 20.16 0.005

PBL-5 12.672 0.0009

Lost Creek, WY Multi-well 1.8 to 4.4 0.003 Lost Creek 2007 

Single well 4.4 to 11.7 0.006 (Tables 2.7-7 and 2.7-9)

Moore Ranch, Campbell County WY 5.36 0.004 NRC 2009a 

(Sec. 3.5.2.3)

Nichols Ranch, WY A sand 0.5 0.0033 NRC 2009c 

F sand 0.6 0.005 (Sec. 3.5.2.3)

North Butte, WY A sand 8.79 0.015 PRI 2006 

B and C sand 8.43 0.0061 (Table 10.1 and p. 10-13) 
4-9

Figure 4-1: Cumulative Distribution of ISL Site Hydraulic Data

Thirty-seven (37) analyzed excursion scenarios met Figure 4-1 hydraulic data criteria and were 

included in the dose and risk analysis. Figure 4-2 shows the dose to an adult from uranium for 

scenario 6E at each of the four assumed down-gradient receptor well locations. From Figure 4-2,

it is apparent that as the down-gradient distance to the well increases, two things occur; (1) the 

dose to the receptor decreases, and (2) the time after the release when the peak dose occurs 

increases. As an example, from Figure 4-2, the dose to an adult from a well at 528 ft is about 

7,600 mrem/yr and occurs at about 20 years, whereas the dose to an adult from a well at 3,480 ft

is about 610 mrem/yr and occurs at about 124 years. Unless otherwise specified, all of the 

remaining doses and risks discussed in this section are based on the assumption that the receptor 

well is located at a distance of 528 ft down-gradient.

Figure 4-2 also illustrates an important factor regarding wellfield restoration. If lixiviant has 

escaped from the wellfield during operations, it will not arrive at a well 528-ft away for at least

20 years, depending on the actual Kd. Thus, the peak uranium dose at the down-gradient well is 

likely to occur long after restoration of the wellfield has been completed, based on current 

practices. 
4-10

Figure 4-2: Excursion Scenario 6E Peak Dose Arrival Time, Kd = 0.4 ml/g

Figure 4-3 shows the adult mean doses at the time of maximum concentration that were 

calculated for each scenario and radionuclide. (Note that some of the 37 scenarios and 

radionuclide maximum doses were lower than the 0.01 mrem/yr cutoff and are not shown in 

Figure 4-3.) All of the doses shown in Figure 4-3 are for a receptor located at the nearest well, at 

a distance of 528 ft. In Figure 4-3 and all subsequent tables and figures in Chapter 4, radiological 

decay is not explicitly included; however, whether specifically noted or not, contribution of the 

progeny to the dose/risk is included as described in Section 3.2.2.

Figure 4-3: Excursion Scenario Dose Results versus Time
4-11

In Figure 4-3, the receptor doses due to uranium occur first, due to the assumed low value of the 

uranium Kd. Also, the uranium doses are received in two distinct groupings—the first around 

30 years and the second around 120 years. This grouping tendency is due to the fact that the 

product of the hydraulic gradient and hydraulic conductivity for 36 of the 37 scenarios analyzed 

was either 0.01 or 0.1 ft/day (see Figure 4-4). This same pattern is shown in Figure 4-3 for the 

Th-230 and Ra-226 (+ progeny) doses. Because each radionuclide arrives at the receptor well at 

a different time, due to their different Kd values, it is not necessary to sum the exposure from 

different radionuclides. Table 4-9 shows the maximum calculated dose for each radionuclide 

from all 37 excursion scenarios that were analyzed. Clearly, uranium and Ra-226 (+ progeny) are 

the significant contributors to dose and risk.

Table 4-9: Excursion Scenario Maximum Doses and Risks –

Mean Adult

Nuclide Dose (mrem/yr) Risk (LCF/yr)

U nat 1.0E+04 1.4E-03

Th-230 2.4E+02 1.2E-05

Ra-226+ P 2.8E+04 4.8E-03

Figure 4-4: Excursion Scenario Dose Results versus Hydraulic Data

Figure 4-4 shows the same dose results as Figure 4-3, except as a function of their hydraulic 

data, instead of as a function of time. As indicated above, Figure 4-4 shows that 36 of the 37 

excursion scenarios analyzed have the product of their hydraulic gradient and hydraulic 

conductivity equal to either 0.01 or 0.1 ft/day; in one scenario, the product was 0.001 ft/day. 

Figure 4-1 shows that a hydraulic data product of 0.1 ft/day or more occurs in only 10% of the 

actual ISL sites, whereas a hydraulic data product of 0.01 ft/day or less occurs in about 43% of 

the actual ISL sites. Thus, although very high doses were calculated for some of the excursion 
4-12

scenarios, many of the scenarios with high doses are at hypothetical sites with hydraulic 

properties that occur very infrequently at actual ISL sites.

Figure 4-5 is similar to Figure 4-3, except that it shows the calculated risks. The above 

discussion of the dose results also applies to the risk results, and is not repeated.

Figure 4-5: Excursion Scenario Risk Results versus Time

When viewing Figure 4-5, it should be remembered that a lifetime risk limit of 10-4LCF 

corresponds to an annual risk of about 1.4×10-6LCF/yr.

Figure 4-2 shows the annual doses that result at the various assumed well locations due to the 

passing of the uranium contamination plume. In developing Figure 4-2, it was assumed that the 

Kd for uranium was 0.4 ml/g, as described in Section 4.2.2. However, Section 4.2.2 also pointed 

out that there is a great deal of variability in the possible uranium Kd, with Table 4-4 showing 

that potentially the uranium Kd could be in the range of 100 to 1,000,000 ml/g.

Figure 4-6 shows the effect on the dose by arbitrarily increasing the uranium Kd by a order of 

magnitude from 0.4 to 4 ml/g. When comparing the Figure 4-6 doses to the Figure 4-2 doses, 

notice that the only thing that changes is the time when the plume arrives at each well location, 

but that the magnitude of the doses received remains unaffected by the choice of the uranium Kd. 

This effect is due to the very long half-life of uranium-238 (i.e., 4.47×109years), which results in 

virtually no additional radiological decay during its longer travel time to the receptor wells. 

Because Th-230 and Ra-226 also have long half-lives (i.e., 75,380 and 1,600 years, respectively), 

the doses due to Th-230 and Ra-226 would likewise be unaffected by changes to their Kd values.
4-13

Figure 4-6: Excursion Scenario 6E Peak Dose Arrival Time, Kd = 4.0 ml/g

4.3.3 Doses and Risks from Surface Leak Scenarios

As described in Chapter 2, 24 scenarios were analyzed that postulated the spill of lixiviant onto 

the ground, with subsequent leakage into the ground water and transport to an offsite receptor 

well. As explained in Chapter 2, three types of surface leak scenarios were analyzed: 

(1) catastrophic spills ranging from 100,000 to 200,000 gallons, (2) a slow leak of 1 to 2 gpm for

a period of 3 years, and (3) leaks varying from 1 to 45 gpm over a 28-day period. Figure 4-7 and 

Figure 4-8 show the calculated doses and risks for all of the 24 leak scenarios, respectively, and 

Table 4-10 shows the maximum and minimum dose and risks from Figure 4-7 and Figure 4-8. 

All of the doses and risks shown are for a receptor located at the nearest well at a distance of 

328 ft. Notice on Figure 4-7 and Figure 4-8 that the first uranium doses are received earlier for 

some of the leak scenarios than for any of the excursion scenarios. This is due to the fact that for 

three leak scenarios, the product of their hydraulic gradient and hydraulic conductivity was equal 

to 1.0 ft/day. This higher hydraulic data product was felt to be reasonable for the leak scenarios, 

because it is believed that the leak could travel in regions near the ground surface that have both 

a high gradient and a high conductivity (UT 2010). Thus, for the surface leak scenarios, the 

uranium contamination plume could arrive at a receptor well very soon after the spill occurs 

(e.g., within 2 to 3 years).
4-14

Figure 4-7: Surface Leak Scenario Risk Results versus Time

Figure 4-8: Surface Leak Scenario Risk Results versus Time

Table 4-10: Surface Leak Scenario Doses and Risks – Mean Adult

Dose (mrem/yr) Risk (LCF/yr)

Minimum Maximum Minimum Maximum

U nat 3.2E+01 1.7E+03 4.5E-06 2.4E-04

Th-230 7.6E-01 4.0E+01 3.9E-08 2.0E-06

Ra-226 + P 8.7E+01 4.6E+03 1.5E-05 7.9E-04
4-15

Leak scenarios 19 through 24 assumed that leaks of different magnitudes occurred for 28 days. 

Figure 4-9 shows the doses due to uranium as a function of the various leak rates at each of the 

four assumed receptor well locations. As Figure 4-9 shows, there is almost a linear relationship 

between the amount of leaking lixiviant and the doses received at the receptor wells.

Figure 4-9: 28 Day Surface Leak Scenario Doses from Uranium 

4.4 Risks to Non-standard Receptors

All of the doses and risks discussed in Section 4.3 were based on the receptor being an adult with 

mean usage rates, i.e., the adult mean PDCFs and PRCFs from Tables 3-10 and 3-11,

respectively. This was the approach taken in CAP88 to demonstrate compliance with 40 CFR 

Part 61, Subpart H. However, it is recognized that there may be individuals living near the ISL 

facility who are not adults, or who have usage rates that significantly deviate from the mean. To 

address those individuals, this section describes the results of calculations that have been 

performed for non-standard dose and risk receptors.

In Chapter 3, PDCFs and PRCFs were developed for a number of non-standard receptors, 

including:

 90th Percentile Adult – an individual who has increased usage rates, such that his/her 

PDCFs and PRCFs are at the 90th percentile, as calculated from the usage rate 

distributions described in Chapter 3. Only 10% of adults would be expected to have 

larger PDCFs and PRCFs than this individual.

 Mean Teenager – an individual who is between the age of about 13 and 19 years old, with 

mean usage rates.

 Mean Child – an individual who is between the age of about 1 and 12 years old, with 

mean usage rates.

 Mean Infant – an individual who is less than 1-year old, with mean usage rates.
4-16

 Native American – an adult individual with exposure pathways and usage rates that are 

typical for a Native American (e.g., exposure during a Sweat Lodge Ceremony).

Refer to Section 3.4 for more information and discussion of the Native American exposure 

pathways and usage rates, and to Section 3.2 for more information regarding the other nonstandard receptors. The Native American PDCFs and PRCFs are given in Table 3-20, while 

Tables 3-10 and 3-11 give the PDCFs and PRCFs for the other non-standard receptors, 

respectively.

Table 4-11 and Table 4-12 show the calculated doses and risks to the non-standard receptors for 

the excursion and leak scenarios, respectively. Notice that for the excursion scenarios, only the 

maximum dose and risks are presented in Table 4-11, while in Table 4-12, both the maximum 

and minimum doses and risks are presented. This was done because the minimum doses and 

risks for some of the excursion scenarios are negligibly small. For Table 4-11 and Table 4-12, it 

is apparent that the Mean Infant is the recipient of the largest calculated doses and risks.

Table 4-11: Excursion Scenario Non-standard Receptor Doses and Risksa

Receptor Nuclide Maximum

Dose (mrem/yr) Risk (LCF/yr)

90th Percentile Adult

U nat 1.7E+04 2.5E-03

Th-230 4.4E+02 2.2E-05

Ra-226 4.7E+04 8.2E-03

Mean Teenager

U nat 9.6E+03 2.9E-03

Th-230 1.5E+02 1.8E-05

Ra-226 4.2E+04 8.2E-03

Mean Child

U nat 8.7E+03 5.1E-03

Th-230 1.2E+02 1.9E-05

Ra-226 5.6E+04 1.5E-02

Mean Infant

U nat 2.7E+04 8.5E-03

Th-230 1.7E+03 5.3E-05

Ra-226 1.5E+05 1.6E-02

Native American

U nat 2.5E+04 4.3E-03

Th-230 7.8E+02 4.0E-05

Ra-226 5.7E+04 1.0E-02

 a – Doses for Ra-226 include progeny

Table 4-12: Leak Scenario Non-standard Receptor Doses and Risksa

Receptor Nuclide Dose (mrem/yr) Risk (LCF/yr)

Minimum Maximum Minimum Maximum

90th Percentile Adult

U nat 5.4E+01 2.9E+03 7.7E-06 4.1E-04

Th-230 1.4E+00 7.2E+01 7.0E-08 3.7E-06

Ra-226 1.5E+02 7.8E+03 2.6E-05 1.4E-03

Mean Teenager

U nat 3.0E+01 1.6E+03 9.1E-06 4.8E-04

Th-230 4.5E-01 2.4E+01 5.7E-08 3.0E-06

Ra-226 1.3E+02 7.0E+03 2.6E-05 1.3E-03

Mean Child

U nat 2.7E+01 1.4E+03 1.6E-05 8.4E-04

Th-230 3.6E-01 1.9E+01 5.9E-08 3.1E-06

Ra-226 1.7E+02 9.2E+03 4.8E-05 2.6E-03

Mean Infant U nat 8.4E+01 4.4E+03 2.7E-05 1.4E-03
4-17

Table 4-12: Leak Scenario Non-standard Receptor Doses and Risksa

Receptor Nuclide Dose (mrem/yr) Risk (LCF/yr)

Minimum Maximum Minimum Maximum

Th-230 5.4E+00 2.8E+02 1.6E-07 8.7E-06

Ra-226 4.7E+02 2.5E+04 5.1E-05 2.7E-03

Native American

U nat 7.8E+01 4.1E+03 1.3E-05 7.1E-04

Th-230 2.4E+00 1.3E+02 1.2E-07 6.5E-06

Ra-226 1.8E+02 9.4E+03 3.2E-05 1.7E-03

 a – Doses for Ra-226 include progeny

The doses and risks to the non-standard receptors in Table 4-11 and Table 4-12 can be compared 

to the dose and risks presented in Table 4-9 for the excursion scenarios and Table 4-10 for the 

leak scenarios. This comparison shows that the Mean Infant doses and risks are about a factor of 

three to eight times larger than the standard receptor doses and risks. The dose and risk ratios of 

the other non-standard receptors to the standard receptor are less than for the Mean Infant. For 

example, the Native American has a calculated maximum uranium risk that is three times greater 

than the standard receptor’s uranium risk for both excursion and leak scenarios. Also, the Native 

American’s maximum Ra-226 dose is two times greater than the standard receptor’s Ra-226 

dose.

Finally, in Chapter 3, PDCFs and PRCFs were developed for a number of potential receptors 

who have not been included in this analysis, e.g., 90th Percentile Infant. If it is desired to include 

any of these individuals, that can be accomplished by simply multiplying the results shown in 

Table 4-11 and/or Table 4-12 by the ratio of the PDCFs or PRCFs, whichever is appropriate.

4.5 Non-Radiological Risks

In addition to radiological risks, the lixiviant used to leach the uranium from the ore will contain 

other potentially hazardous constituents. A screening calculation has been performed in order to 

obtain an indication as to how hazardous these other constituents in the lixiviant may be to an 

individual located at the nearest receptor well. The screening calculation consisted of comparing 

the well water concentration of the potentially hazardous constituents to the Safe Drinking Water 

Act concentration limits for that constituent.

Maximum lixiviant concentrations were obtained from NUREG/CR-6733 (CNWRA 2001, 

Table 4-7), with additional lixiviant concentrations from NUREG/CR-6970 for Highlands and 

Crow Butte (Davis and Curtis, 2007, Tables 3 and 5), as well as Smith Ranch Mine Unit B 

(MUB) (Power Resources 2004, Table 4), and Christensen MU2 and MU3 (COGEMA, 2008, 

MU2 Table 5.1 and MU3 Table 5.1). Table 4-13 shows all of the lixiviant concentrations at the 

end of mining that were collected.

Table 4-13 also provides the maximum concentration limit (MCL) for each of the potentially

hazardous constituents. An MCL is the legal threshold limit on the amount of a substance that is 

allowed in public water systems under the Safe Drinking Water Act. When an MCL is 

unavailable, the Lifetime Health Advisory (HA) concentration is provided. The Lifetime HA is 

the concentration of a chemical in drinking water that is not expected to cause any adverse 

noncarcinogenic effects for a lifetime of exposure. Both the MCLs and the Lifetime HAs were 
4-18

obtained from the 2011 Edition of the Drinking Water Standards and Health Advisories (EPA 

2011a).

Table 4-13: Highest Contaminant Levels in Pregnant Lixiviant (mg/L)

Contaminant NUREG/C

R-6733

Crow 

Butte Highland Smith Ranch

Christensen 

MU2

Christensen

MU3 Limit Type

Ammonium N.P. 0.37 0.1 0.52 0.52 1.14 30 Life-time

Arsenic 0.3 0.002 0.001 0.008 0.12 0.02 0.01 MCL

Barium 0.6 0.1 0.1 0.1 0.1 0.1 2 MCL

Boron 0.2 0.93 0.1 0.1 0.1 0.1 6 Life-time

Cadmium 0.01 0.006 0.01 0.01 0.01 0.01 0.005 MCL

Chloride 1800 204 4.7 232 122.9 155.4 4 MCL

Chromium 0.03 <0.03 0.05 0.1 0.05 0.05 0.1 MCL

Copper 0.04 0.017 0.01 0.01 0.01 0.01 1.3 MCL

Fluoride 1 0.69 0.2 0.1 0.1 0.1 4 MCL

Lead 0.01 0.031 0.05 0.1 0.05 0.05 0.015 MCL

Manganese 6 0.11 0.03 0.9 0.66 0.69 0.3 Life-time

Mercury <0.0001 0.001 0.001 0 0.001 0.001 0.002 MCL

Molybdenum 62 0.069 0.1 0.1 0.1 0.1 0.04 Life-time

Nickel 0.09 0.034 0.05 0.07 0.12 0.05 0.1 Life-time

Nitrate 1 0.05 0 0.3 0.22 0.1 1 MCL

Nitrite N.P. 0.01 0 0.1 0.12 0.1 10 MCL

Selenium 5 0.003 0.001 0.806 6.33 4.34 0.05 MCL

Uranium 250 0.092 0.05 22.3 11.75 15.58 0.03 MCL

Zinc N.P. 0.036 0.01 0.11 0.05 0.01 2 Life-time

For each potentially hazardous constituent, Table 4-14 first shows the maximum lixiviant 

concentration (mg/L) from Table 4-13, and then shows the MCL (or Lifetime HA) divided by the 

maximum concentration. If the quotient is 1 or greater than that constituent is not at a hazardous 

concentration in the ore zone or at the nearest receptor well.

Finally, as stated previously (Section 4.3.2) there were 37 excursion scenarios that met the 

hydraulic data criteria and were included in the dose and risk analysis. Each potentially

hazardous constituent concentration was compared to each analyzed scenario’s dilution 

coefficient at the receptor well. For each constituent Table 4-14 shows the number of scenarios 

that have dilution coefficients which would reduce the concentration at the receptor well to 

below the MCL (or Lifetime HA). For example, there are 11 analyzed scenarios resulting in dilution 

coefficients that reduce the maximum molybdenum concentration at the receptor well to below the 

molybdenum Lifetime HA. The final column of Table 4-14 simply shows the percentage of 

scenarios that have constituent concentrations above the MCL (or Lifetime HA).

Table 4-14: Scenario Maximum Contaminant Levels Versus Limit

Contaminant Maximum 

(mg/L) Source Limit Maximum

Scenarios 

Below Limit

at Receptor 

Well

Scenarios 

Above Limit

at Receptor 

Well

Nitrite 0.12 Christensen MU2 83.3 37 0.0%

Copper 0.04 NUREG/CR-6733 32.5 37 0.0%

Ammonium 1.14 Christensen MU3 26.3 37 0.0%
4-19

Table 4-14: Scenario Maximum Contaminant Levels Versus Limit

Contaminant Maximum 

(mg/L) Source Limit Maximum

Scenarios 

Below Limit

at Receptor 

Well

Scenarios 

Above Limit

at Receptor 

Well

Zinc 0.11 Smith Ranch 18.2 37 0.0%

Boron 0.93 Crow Butte 6.45 37 0.0%

Fluoride 1 NUREG/CR-6733 4.00 37 0.0%

Barium 0.6 NUREG/CR-6733 3.33 37 0.0%

Mercury 0.001 Multiple Sources 2.00 37 0.0%

Chromium 0.1 Smith Ranch 1.00 37 0.0%

Nitrate 1 NUREG/CR-6733 1.00 37 0.0%

Nickel 0.12 Christensen MU2 0.83 37 0.0%

Cadmium 0.01 Multiple Sources 0.50 36 2.7%

Lead 0.1 Smith Ranch 0.15 33 10.8%

Manganese 6 NUREG/CR-6733 0.050 24 35.1%

Arsenic 0.3 NUREG/CR-6733 0.033 24 35.1%

Selenium 6.33 Christensen MU2 0.0079 17 54.1%

Chloride 1800 NUREG/CR-6733 0.0022 17 54.1%

Molybdenum 62 NUREG/CR-6733 0.00065 11 70.3%

Uranium 250 NUREG/CR-6733 0.00012 7 81.1%

From Table 4-14 it can be seen that there are about five (5) lixiviant constituents (in addition to 

uranium), which could have concentrations significantly above the MCL (or Lifetime HA). Also, 

there are three (3) lixiviant constituents (i.e., nickel, cadmium, and lead), which have receptor 

well concentration which are borderline hazardous. Finally, there are 10 lixiviant constituents 

which are unlikely to be at hazardous concentrations, even within the ore zone.
5-1

5.0 SUMMARY AND CONCLUSIONS

5.1 Ground Water Modeling Studies

Ground water modeling studies, described in Chapter 3, considered scenarios involving 

excursions of contaminants from an ore-bearing aquifer and surface spills or leakage from 

process components. Simulations of excursions within the ore zone aquifer evaluated the 

sensitivity of the relative concentrations at down-gradient receptor wells to selected parameters. 

Variables examined in these excursion simulations included:

 Well spacing (50, 150, and 250 ft)

 Hydraulic conductivity (1, 10, and 100 ft/day)

 Hydraulic gradient (0.001, 0.01, and 0.1 ft/ft)

 Pumping pattern (5-spot, multiple 5-spot, and 7-spot)

 Injection rates (7, 50, 150, and 500 gpm)

 Ore zone thickness (20 and 70 ft)

It is difficult to develop broad, general conclusions from these excursion simulations, because 

results of comparisons designed into the modeling runs are at times counter-intuitive. In spite of 

this difficulty, some conclusions are provided below. However, the reader is cautioned to read 

the full text in Chapter 3 to understand the limitations of these conclusions.

 As expected, steeper hydraulic gradients result in shorter travel times. Furthermore, since 

the pumping/injection wells are altering the regional hydraulic gradients, the arrival times 

are not linearly scaled.

 An increase in hydraulic conductivity causes the contaminant plume to become more 

dispersed and leads to lower relative peak concentrations.

 At higher regional gradients, wider well spacings provide better capture of the lixiviant. 

At lower regional hydraulic gradients, however, better capture can be maintained at 

smaller well spacings.

 The 7-spot well configuration results in lower relative peak concentrations for all of the 

runs, as compared to a 5-spot pattern.

 The effect of pumping/injection on hydraulic gradients is strongly affected by the 

transmissivity (i.e., hydraulic conductivity multiplied by thickness) of the geologic units. 

The lower transmissivity results in shorter times to peak arrivals at the low and high 

gradients, and a longer time at the medium gradient. These relationships are all related to 

how the regional and localized gradients created by the pumping/injection interact to 

form a capture zone. This also illustrates the complexity and need to understand the sitespecific geology and flow system, since the effects of the interactions are not always 

intuitive.

 Comparison of a single 5-spot pattern with a multiple (25) 5-spot pattern shows that 

relative concentrations at down-gradient receptor wells are lower with the multiple 5-spot 

pattern. These results are explained by the larger capture zone that is created by the array

of pumping/extraction wells.
5-2

As described in Chapter 3, 24 scenarios were analyzed, which postulated the spill of lixiviant 

onto the ground, with subsequent leakage into the ground water and transport to an offsite 

receptor well. The scenarios included (1) catastrophic spills ranging from 100,000 to 

200,000 gallons, (2) a slow leak of 1 to 2 gpm for period of 3 years, and (3) leaks varying from 

1 to 45 gpm over a 28-day period.

Transport from the mined aquifer to an overlying aquifer through an abandoned borehole that 

was not properly cemented was also evaluated. This excursion scenario can result in significant 

down-gradient leakage. This emphasizes the need to carefully cement and inspect abandoned 

boreholes to insure their integrity.

5.2 Pathway Dose and Risk Conversion Factors

In Chapter 2, probabilistic dose and risk pathway conversion factors were developed for the 

ingestion pathway for four age groups (infants, children, teens and adults) and five radionuclides 

of importance in the ISL process (Ra-226+P, Pb-210, Th-230, U-234, and U-238). The 

individual radionuclide-specific dose and risk conversion factors were based on FGR 13 and 

supporting documentation. Contributions of the various components of the ingestion pathway to 

dose and risk for adults are summarized in Table 5-1. Except for Ra-226, drinking water 

accounts for about three-quarters of the total ingestion dose/risk. With Ra-226, ingestion of 

vegetables is more significant than for the other radionuclides, accounting for about 35% of the 

total ingestion dose/risk.

Table 5-1: Typical Pathway Contributions to the Adult PDCF and PRCF

Pathway – Adult Pb-210+P Ra-226+P Th-230 U-234 U-238+P

Ingestion of Drinking Water 78.3% 51.2% 82.8% 76.2% 76.2%

Inadvertent Ingestion of Soil 0.1% 0.2% 0.3% 0.3% 0.3%

Ingestion of Vegetables 15.7% 35.3% 16.4% 16.1% 16.1%

Ingestion of Meat 2.3% 2.9% 0.3% 2.4% 2.4%

Ingestion of Milk 3.5% 10.5% 0.2% 5.0% 5.0%

Table 5-2 summarizes the pathway contributions from uranium exposure for each age group. The 

shift from the dominance of the milk pathway to the drinking water pathway with increasing age 

from child to adult is apparent.

Table 5-2: Typical Pathway Contributions to the PDCF and PRCF for 

U-234 or U-238

Pathway – Adult Adult Teen Child Infant

Ingestion of Drinking Water 76.2% 68.1% 54.2% 81.2%

Inadvertent Ingestion of Soil 0.3% 0.5% 1.4% 1.6%

Ingestion of Vegetables 16.1% 17.2% 17.3% 10.2%

Ingestion of Meat 2.4% 3.4% 2.8% 0.0%

Ingestion of Milk 5.0% 10.9% 24.3% 6.9%

The pathway analysis for Native Americans was expanded from that of a standard receptor to 

address exposures associated with sweat lodge rituals. This included additional drinking water 

consumption, submersion in a cloud of contaminated vapor, and inhalation of contaminated 
5-3

vapor. The deterministic dose conversion factor for a Native American for natural uranium (i.e., 

U-234+U-238) is 4.05E-04 mrem/yr per pCi/m3, as compared to the 90th percentile dose 

conversion factor for a standard adult receptor of 2.73E-04 mrem/yr per pCi/m3.

5.3 Dose/Risk Assessment for Modeled Scenarios

Doses and risks are calculated in Chapter 4 for some of the ground water flow and transport 

scenarios modeled in Chapter 3. All modeling results in Chapter 3 excluded effects of retardation 

and assumed a source term of 1 mg/L. In Chapter 4, source terms were selected based on 

operating experience at ISL facilities, and included effects of retardation at down-gradient wells. 

Distribution coefficients used to calculate the retardation factors were selected as minimum 

reasonable values to reduce the travel time from the source to the receptor. As described in 

Chapter 3, some combinations of hydrogeologic parameters, such as hydraulic conductivity and 

hydraulic gradient, may lie outside the range expected at operating ISL sites, but were included 

to examine the sensitivity of the modeling results to a range of parameters. In Chapter 4, doses 

and risks were calculated only for those excursion scenarios with combinations of hydraulic 

conductivity and hydraulic gradient that had been reported at operating ISL sites (i.e., gradient × 

conductivity <0.13 ft/day). The product of hydraulic conductivity times hydraulic gradient was 

used as a surrogate for ground water velocity (i.e., hydraulic conductivity × gradient ÷ effective 

porosity). An empirical cumulative distribution function was developed from the site data and 

served to limit the range of parameters for which doses and risks for excursion scenarios were 

calculated.

Prior to calculating doses and risks for the selected modeling scenarios, scoping calculations 

were performed to illustrate the allowable radionuclide source term concentrations for 

representative dose and risk limits. Limiting concentrations were derived for an assumed dose 

limit of 15 mrem/yr and a lifetime risk limit of 10-4latent cancer fatalities (LCF), or, assuming a 

70-year life expectancy, an annual risk limit of 1.4 × 10-6LCF/yr. The limiting radionuclide 

concentrations shown in Table 5-3 were calculated using the adult mean pathway dose and risk 

conversion factors (PDCFs and PRCFs) derived in Chapter 2. The distance from the source to the 

receptor well was 528 ft.

Table 5-3: Radionuclide Source Term Limiting Concentrations –

Receptor Well at 528 ft

Nuclide 15 mrem/yr Dose Limit 10-4 LCF Lifetime Risk Limit

(pCi/L) (mg/L) (pCi/L) (mg/L)

U-238 97 2.8E-01 63 1.8E-01

U-234 93 1.5E-05 65 1.0E-05

U-natural 95 1.4E-01 64 9.4E-02

Th-230 25 1.2E-06 47 2.2E-06

Ra-226 1.2 1.2E-09 0.6 6.3E-10

Ra-226+P 2.4 3.1E-11 1.3 1.7E-11

Pb-210 97 2.8E-01 63 1.8E-01

As Table 5-3 demonstrates, a lifetime risk limit of 10-4LCF is slightly more restrictive than a 

dose limit of 15 mrem/yr for all the radionuclides except Th-230. However, for all of the 

radionuclides considered, dose- and risk-limiting concentrations are within a factor of two.
5-4

Of the 31 unique excursion simulations (37 total) within the ore-bearing aquifer that met the

conductivity × gradient cutoff of 0.13 ft/day, the dose from uranium was <15mrem/yr in 13

simulations. The highest estimated dose from uranium at a receptor well 528 ft down-gradient 

was 10,072 mrem/yr for Run 6i, while the lowest estimated dose was 1.69E-12 mrem/yr for 

Run 5g. Table 5-4 summarizes these excursion simulations. Doses from uranium (U-234 + 

U-238) are included in the last column for those simulations with a conductivity × gradient 

product <0.13 ft/day. Runs where the uranium dose was less than 15 mrem/yr are highlighted in 

yellow.

Table 5-4: Summary of Excursion Runs

Run Hydraulic 

Gradient

Hydraulic 

Conductivity 

(ft/day)

Well 

Spacing 

(ft)

Pumping 

Array

Pumping 

Rate 

(gpm)

Max. Relative 

Concentration at 

528 ft

Ore Zone

Thickness

(ft)

U nat

Dosea

(mrem/yr)

1a 0.1 100 250 5-spot 153 6.94E-03 75

1b 0.01 100 250 5-spot 153 6.54E-02 75

1c 0.001 100 250 5-spot 153 9.32E-02 75 1.50E+03

1d 0.1 1 250 5-spot 153 2.14E-01 75 3.44E+03

1e 0.1 10 250 5-spot 153 6.55E-02 75

1f 0.1 100 250 5-spot 153 6.94E-03 75

1g 0.01 1 250 5-spot 153 1.80E-04 75 2.90E+00

1h 0.01 10 250 5-spot 153 9.27E-02 75 1.49E+03

1i 0.01 100 250 5-spot 153 6.54E-02 75

2a 0.1 100 50 5-spot 153 9.62E-03 75

2b 0.01 100 50 5-spot 153 2.35E-02 75

2c 0.001 100 50 5-spot 153 8.75E-03 75 1.41E+02

2d 0.1 1 50 5-spot 153 1.18E-02 75 1.90E+02

2e 0.1 10 50 5-spot 153 2.35E-02 75

2f 0.1 100 50 5-spot 153 9.62E-03 75

2g 0.01 1 50 5-spot 153 3.39E-06 75 5.45E-02

2h 0.01 10 50 5-spot 153 8.22E-03 75 1.32E+02

2i 0.01 100 50 5-spot 153 2.35E-02 75

3a 0.1 100 250 7-spot 153 2.78E-03 75

3b 0.01 100 250 7-spot 153 3.53E-02 75

3c 0.001 100 250 7-spot 153 6.79E-02 75 1.09E+03

3d 0.1 1 250 7-spot 153 1.41E-01 75 2.27E+03

3e 0.1 10 250 7-spot 153 3.53E-02 75

3f 0.1 100 250 7-spot 153 2.78E-03 75

3g 0.01 1 250 7-spot 153 4.46E-05 75 1.70E+00

3h 0.01 10 250 7-spot 153 6.75E-02 75 1.09E+03

3i 0.01 100 250 7-spot 153 3.53E-02 75

4a 0.1 100 50 7-spot 153 1.25E-02 75

4b 0.01 100 50 7-spot 153 4.03E-02 75

4c 0.001 100 50 7-spot 153 9.01E-03 75 1.45E+02

4d 0.1 1 50 7-spot 153 3.07E-02 75 4.94E+02

4e 0.1 10 50 7-spot 153 4.03E-02 75

4f 0.1 100 50 7-spot 153 1.25E-02 75

4g 0.01 1 50 7-spot 153 2.14E-06 75 3.44E-02

4h 0.01 10 50 7-spot 153 8.94E-03 75 1.44E+02

4i 0.01 100 50 7-spot 153 4.03E-02 75

5a 0.1 10 150 5-spot 51 3.76E-02 75

5b 0.01 10 150 5-spot 51 6.79E-02 75 1.09E+03

5c 0.001 10 150 5-spot 51 8.24E-04 75 1.33E+01
5-5

Table 5-4: Summary of Excursion Runs

Run Hydraulic 

Gradient

Hydraulic 

Conductivity 

(ft/day)

Well 

Spacing 

(ft)

Pumping 

Array

Pumping 

Rate 

(gpm)

Max. Relative 

Concentration at 

528 ft

Ore Zone

Thickness

(ft)

U nat

Dosea

(mrem/yr)

5d 0.01 1 150 5-spot 7.15 1.53E-02 75 2.46E+02

5e 0.01 10 150 5-spot 51 6.79E-02 75 1.09E+03

5f 0.01 100 150 5-spot 510 1.01E-01 75

5g 0.001 1 150 5-spot 7.15 1.05E-16 75 1.69E-12

5h 0.001 10 150 5-spot 51 8.24E-04 75 1.33E+01

5i 0.001 100 150 5-spot 510 1.08E-01 75 1.74E+03

6a 0.1 10 150 5-spot 51 3.31E-01 20

6b 0.01 10 150 5-spot 51 4.75E-01 20 7.64E+03

6c 0.001 10 150 5-spot 51 1.30E-03 20 2.09E+01

6d 0.01 1 150 5-spot 7.15 1.06E-01 20 1.70E+03

6e 0.01 10 150 5-spot 51 4.75E-01 20 7.64E+03

6f 0.01 100 150 5-spot 510 6.45E-01 20

6g 0.001 1 150 5-spot 7.15 3.07E-13 20 1.19E+00

6h 0.001 10 150 5-spot 51 1.30E-03 20 2.09E+01

6i 0.001 100 150 5-spot 510 6.26E-01 20 1.01E+04

7a 0.1 10 150 25 × 5-spot 51 3.12E-01 20

7b 0.01 10 150 25 × 5-spot 51 6.61E-04 20 1.06E+01

7c 0.001 10 150 25 × 5-spot 51 2.70E-04 20 4.34E+00

7d 0.01 1 150 25 × 5-spot 7.15 8.11E-06 20 3.78E-01

7e 0.01 10 150 25 × 5-spot 51 6.61E-04 20 1.06E+01

7f 0.01 100 150 25 × 5-spot 510 1.10E-02 20

7g 0.001 1 150 25 × 5-spot 7.15 5.25E-05 20 8.44E-01

7h 0.001 10 150 25 × 5-spot 51 2.70E-04 20 4.34E+00

7i 0.001 100 150 25 × 5-spot 510 4.50E-04 20 7.24E+00

a – Dose at receptor well 528 ft down-gradient for simulations where hydraulic conductivity × gradient <0.13 ft/day

A breakdown of the unique simulations resulting in doses above and below 15 mrem/yr as a 

function of the conductivity × gradient product is presented in Table 5-5. It is clear that for most 

of the simulations with a dose of less than 15 mrem/yr, the product of hydraulic conductivity and 

gradient is 0.01 or less. However, in 10% of the cases, a conductivity × gradient product of 0.01 

resulted in doses greater than 15 mrem/yr.

Table 5-5: Number of Simulations Resulting in Various Dose Levels as Function of 

Conductivity × Gradient Product 

Conductivity (K) × Gradient (i) Number of Simulations

Less Than 15 mrem/yr Greater Than 15 mrem/yr

K × i = 0.1 2 16

K × I = 0.01 7 3

K × I = 0.001 3 0

To evaluate the effects of retardation on ground water transport, distribution coefficients were 

selected from the literature based on reasonable minimum values to reduce the travel time from 

the source wellfield to a down-gradient receptor well. Minimum peak travel times to a receptor 

well 528 ft down-gradient are about 20 years for uranium, about 3,000 years for radium and 

about 15,000 years for thorium. The significance is that, if any lixiviant escapes undetected from 
5-6

the ring of monitor wells surrounding a wellfield, an appreciable amount of time will elapse 

before the radionuclide arrives at the receptor well. The elapsed time will exceed the monitoring 

times at any current ISL facilities.

Table 5-6 (which repeats Table 4-9) shows the maximum calculated dose for each radionuclide 

from all excursion scenarios that were analyzed for dose and risk as shown in Table 5-4. These 

values are mean adult exposures for a receptor well 528 ft down-gradient.

Table 5-6: Excursion Scenario Maximum Doses and Risks

Nuclide Dose (mrem/yr) Risk (LCF/yr)

U nat 1.0E+04 1.4E-03

Th-230 2.4E+02 1.2E-05

Ra-226+P 2.8E+04 4.8E-03

As described in Chapter 4 (Section 4.3.3), three surface leakage scenarios were evaluated: 

(1) catastrophic spills ranging from 100,000 to 200,000 gallons, (2) a slow leak of 1 to 2 gpm for 

period of 3 years, and (3) leaks varying from 1 to 40 gpm over a 28-day period. The highest 

doses were incurred for scenarios involving a slow leak over a 3-year period, while the lowest 

doses resulted from a 1-gpm surface leak over a 28-day period. In all cases, the mean annual 

doses to an adult from U nat were greater than 15 mrem. Results for all leakage scenarios are 

summarized in Table 5-7. These results emphasize the importance of detecting small leaks 

through adequate instrumentation and frequent inspections of process piping. Automatic shut-off 

controls should be used to minimize surface spills.

Table 5-7: Summary of Dose Rate for Leak Scenarios – Adult Male Exposed to U nat

at Receptor Well 328 ft Down-gradient

Leak Scenario Minimum Dose Rate (mrem/yr) Maximum Dose Rate (mrem/yr)

Surface Spill (100,000–200,000 gal.) 6.7E+01 (Run L4) 1.5E+02 (Run L9)

Slow Leak (1–2 gpm) – 3 Years 9.5E+01 (Run L15) 1.7E+03 (Run L16)

Variable Leak (1–40 gpm) – 28 days 3.2E+01 (Run L19) 8.7E+02 (Run L24)

Doses and risks to non-standard receptors are compared to those for 90th percentile adults 

(relative dose/risk = 1.00) in Table 5-8. The basis for comparison is the excursion scenario, 

which occurs within the ore-bearing aquifer and where the receptor obtains water from a well 

528 ft down-gradient. Doses and risks for the Mean Infant and Native American were greater 

than for the 90th percentile adult receptor for all radionuclides evaluated. Relative doses for the 

mean teenager and the mean child (except for Ra-226+P) were lower than for the 90th percentile 

adult. For example, doses and risks to Native Americans were greater by factors of about 1.2 to 

1.8 than for the 90th percentile adult. Clearly, doses to several of the non-standard receptors do 

not fall within a reasonable upper limit for the standard receptor doses.
5-7

Table 5-8: Comparison of Excursion Scenario Non-standard Receptor Doses and 

Risks Relative to 90th Percentile Adult

Receptor Nuclide Maximum

Relative Dose Relative Risk 

90th Percentile Adult

U nat 1.00 1.00

Th-230 1.00 1.00

Ra-226+P 1.00 1.00

Mean Teenager

U nat 0.55 1.18

Th-230 0.33 0.81

Ra-226+P 0.89 0.99

Mean Child

U nat 0.50 2.06

Th-230 0.27 0.84

Ra-226+P 1.17 1.88

Mean Infant

U nat 1.55 3.46

Th-230 3.92 2.34

Ra-226+P 3.17 1.97

Native American

U nat 1.44 1.74

Th-230 1.78 1.76

Ra-226+P 1.20 1.26
6-1

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