Processes, Properties, and 

Conditions Controlling In Situ 

Bioremediation of Uranium in 

Shallow, Alluvial Aquifers

Office of Nuclear Regulatory Research 

NUREG/CR-7014

PNNL-19026

Processes, Properties, and

Conditions Controlling In Situ

Bioremediation of Uranium in

Shallow, Alluvial Aquifers

Manuscript Completed: March 2010 

Date Published: July 2010 

Prepared by

S.B. Yabusaki, Y. Fang, S.R. Waichler, and P.E. Long 

Pacific Northwest National Laboratory

902 Battelle Boulevard

Richland, WA 99352

M. Fuhrmann, NRC Project Manager

NRC Job Code N6648

Office of Nuclear Regulatory Research 

NUREG/CR-7014

PNNL-19026

iii

Abstract 

Uranium can be removed from groundwater by adding an electron donor to the subsurface that stimulates 

growth of native bacteria, generating conditions that result in precipitation of uranium. The long-term 

efficacy of this technology is unproven. Numerical modeling results for uranium bioremediation in a 

shallow, alluvial aquifer are provided to establish a broad framework for understanding processes 

associated with bioremediation of uranium and to bound conditions under which bioremediation could 

succeed in the long-term and conditions under which it is likely to fail. The models are benchmarked 

against experiments conducted at the Rifle, Colorado site. Sensitivity analysis of model parameters were 

conducted, examining: alternatives to the acetate electron donor (lactate and ethanol), oxygen and nitrate 

terminal electron acceptors, multiphase flow, density and gas entrapment processes, and hypothetical 

flood events. Sensitivity of simulated aqueous U(VI) concentrations to process model parameters 

suggest that groundwater flow rate, uranium bioreduction rate, and sulfate bioreduction parameters exert 

the most impact on bioremediation effectiveness. The simulated scenarios are used to assess potential 

performance issues for site conditions and other bioremediation approaches. 

v

Table of Contents 

Contents..................................................................................................................v 

List of Figures..........................................................................................................vii 

List of Tables............................................................................................................ix 

Executive Summary....................................................................................................xi 

Acknowledgements....................................................................................................xiii 

1 Introduction ........................................................................................................................................ 1.1

1.1 Purpose and Background ........................................................................................................... 1.1

1.2 Key Results ................................................................................................................................ 1.2

1.3 Report Scope .............................................................................................................................. 1.4

2 Bioremediation Studies at the Old Rifle IFRC site ............................................................................ 2.1

2.1 Site Description .......................................................................................................................... 2.1

2.1.1 Site Conditions ...................................................................................................................................... 2.3

2.2 Bioremediation at the Rifle IFRC site ........................................................................................ 2.4

2.3 Reactive Transport Modeling Studies ........................................................................................ 2.6

2.3.1 Biologically Mediated Reactions, Yabusaki et al. (2007) ..................................................................... 2.6

2.3.2 Abiotic Reaction Processes, Fang et al. (2009) .................................................................................... 2.7

2.4 Conceptual Process Model ......................................................................................................... 2.9

3 Model Specification ........................................................................................................................... 3.1

3.1 Hydrologic Model ...................................................................................................................... 3.1

3.1.1 Gas Entrapment .................................................................................................................................... 3.4

3.1.2 Transport .............................................................................................................................................. 3.4

3.1.3 Solution Procedure ............................................................................................................................... 3.5

3.1.4 Hydrologic Modeling Parameters ........................................................................................................ 3.6

3.2 Biogeochemical Model .............................................................................................................. 3.7

3.2.1 Biologically Mediated Reactions .......................................................................................................... 3.7

3.2.2 Abiotic Reactions .................................................................................................................................. 3.9

3.2.3 Biogeochemical Model Calibration .................................................................................................... 3.14

3.3 Numerical Model Specification ............................................................................................... 3.15

3.4 Sensitivity Analyses ................................................................................................................. 3.16

3.4.1 Baseline .............................................................................................................................................. 3.17

3.4.2 Parameter Selection ............................................................................................................................ 3.19

3.4.3 Hydrologic Model ............................................................................................................................... 3.19

3.4.4 Biologically Mediated Reaction Model............................................................................................... 3.21

3.4.5 Geochemical Reaction Model ............................................................................................................. 3.23

4 Modeling Scenarios ........................................................................................................................... 4.1

4.1 Additional Electron Acceptors ................................................................................................... 4.1

4.2 Additional Electron Donors ....................................................................................................... 4.3

4.3 Two-Dimensional Baseline ........................................................................................................ 4.6

4.3.1 Hydrologic Conditions .......................................................................................................................... 4.6

4.3.2 Injectate Density Effects ..................................................................................................................... 4.13

4.3.3 Seasonal Water Table Fluctuation ...................................................................................................... 4.18

4.3.4 Flood Case Scenario ........................................................................................................................... 4.23

5 Summary and Conclusions................................................................................................................. 5.1

5.1 One-Dimensional Reactive Transport ........................................................................................ 5.2

5.1.1 Sensitivity Analyses ............................................................................................................................... 5.2

5.1.2 Terminal Electron Accepting Processes (TEAPs) ................................................................................. 5.3
vi

5.1.3 Electron Donors .................................................................................................................................... 5.4

5.2 Two-Dimensional Variably Saturated Flow and Reactive Transport ........................................ 5.5

5.2.1 Injectate Density ................................................................................................................................... 5.5

5.2.2 Gas Entrapment .................................................................................................................................... 5.6

5.2.3 Flood Events ......................................................................................................................................... 5.7

5.3 Closing Comments and Next Steps for Reactive Transport Modeling of Uranium 

Bioremediation ....................................................................................................................................... 5.8

6 References .......................................................................................................................................... 6.1

 Appendix A.......................................................................................................A.1 
 vii 

Table of Figures 

Figure 2.1. Excavated exposure of subsurface above water table (left) and typical lithology 

(right) from Rifle Site. ............................................................................................................. 2.2

Figure 2.2. Rifle IFRC site with the location of the well layout (inset) for the 2007 Winchester / 

2008 Big Rusty Experiments circled. ...................................................................................... 2.3

Figure 2.3. Uranium aqueous speciation assuming Rifle groundwater chemistry. ........................ 2.8

Figure 3.1. One-dimensional and two-dimensional modeling domains. Red vertical lines indicate 

upgradient (U-wells), injection gallery, and downgradient (D-wells) well locations. The 

one-dimensional model domain is 11.5 m long with the upgradient boundary at the injection 

gallery (X=28 m). Ponding at the ground surface for the flood scenario in the twodimensional model domain is between X=20 and X=40 m. .................................................. 3.16

Figure 3.2. Simulated and observed acetate (left) and U(VI) (right) in monitoring well row 1 (2.5 

m downgradient), row 2 (5.0 m downgradient), and row 3 (8.5 m downgradient). ............... 3.18

Figure 3.3. Temporal aqueous Fe(II) (left) and sulfate (right) at 2.5m (top), 5.0m (center) and 8.5 

m (bottom) downgradient from injection gallery ................................................................... 3.18

Figure 3.4. Aqueous uranium sensitivity to flow rates 2.5 m (diamonds), 5.0 m (squares), and 8.5 

m (triangles) downgradient from the injection gallery. ......................................................... 3.20

Figure 3.5. Aqueous uranium sensitivity to porosity 2.5 m (diamonds), 5.0 m (squares), and 8.5 

m (triangles) downgradient from the injection gallery. ......................................................... 3.21

Figure 3.6. Aqueous uranium sensitivity to U(VI) bioreduction rate 2.5 m (diamonds), 5.0 m 

(squares), and 8.5 m (triangles) downgradient from the injection gallery. ............................ 3.22

Figure 3.7. Aqueous uranium sensitivity to sulfate bioreduction rate 2.5 m (diamonds), 5.0 m 

(squares), and 8.5 m (triangles) downgradient from the injection gallery. ............................ 3.22

Figure 3.8. Aqueous uranium sensitivity to pH 2.5 m (diamonds), 5.0 m (squares), and 8.5 m 

(triangles) downgradient from the injection gallery. ............................................................. 3.23

Figure 3.9. Aqueous uranium sensitivity to site density for uranium surface complexation 2.5 m 

(diamonds), 5.0 m (squares), and 8.5 m (triangles) downgradient from the injection gallery.3.24

Figure 3.10. Aqueous uranium sensitivity to initial U(VI) concentration 2.5 m (diamonds), 5.0 m 

(squares), and 8.5 m (triangles) downgradient from the injection gallery. ............................ 3.25

Figure 4.1. Dissolved oxygen (top) and aqueous U(VI) (bottom) during acetate biostimulation in 

downgradient monitoring row 1 (2.5 m), row 2 (5.0 m), and row 3 (8.5 m). .......................... 4.2

Figure 4.2. Nitrate (top) and U(VI) (bottom) during acetate biostimulation in downgradient 

monitoring row 1 (2.5 m), row 2 (5.0 m), and row 3 (8.5 m). ................................................. 4.3

Figure 4.3. Lactate electron donor simulation: lactate (top) and U(VI) (bottom) concentrations in 

downgradient monitoring rows 1 (2.5 m), 2 (5.0 m), and 3 (8.5 m) ........................................ 4.5

Figure 4.4. Ethanol electron donor simulation: ethanol (top) and U(VI) (bottom) concentrations 

in downgradient monitoring rows 1 (2.5 m), 2 (5.0 m), and 3 (8.5 m). ................................... 4.6

Figure 4.5. Rifle IFRC water table elevation during seasonal rise and fall. ................................... 4.7

Figure 4.6. Comparison of simulated bromide concentrations with field observations in the 

downgradient monitoring well rows 2.5 m (D-02g and D-03), 5.0 m (D-06g and D-07), and 

8.5 m (D-10g and D-11) downgradient from the injection gallery wells. The ―g‖ designation 

for wells refers to the deepest sampling depth in the well. Note that soon after the increase 

in acetate concentrations on day 38 (see Table 3.10), uneven metering of amendment to the 
viii

injection wells and changes in the groundwater flow direction resulted in some of the 

bromide not being accounted for in the downgradient monitoring wells. ............................... 4.8

Figure 4.7. Simulated spatial distributions of acetate 20, 30, 40, and 50 days after start of 

biostimulation. Note that the injection gallery is located at X=28 m and the last row of 

monitoring wells is at X=36.5 m. ............................................................................................ 4.9

Figure 4.8. Simulated baseline aqueous U(VI) concentration distributions for day 20 to 100 of the 

biostimulation. ....................................................................................................................... 4.10

Figure 4.9. Simulated baseline UO2(s) concentration distributions for day 20 to 100 of the 

biostimulation. ....................................................................................................................... 4.10

Figure 4.10. Simulated baseline aqueous Fe(II) concentration distributions for day 20 to 100 of 

the biostimulation. ................................................................................................................. 4.11

Figure 4.11. Simulated baseline aqueous sulfate concentration distributions for day 20 to 100 of 

the biostimulation. ................................................................................................................. 4.12

Figure 4.12. Simulated baseline FeS(s) concentration distributions for day 20 to 100 of the 

biostimulation. ....................................................................................................................... 4.12

Figure 4.13. Acetate (top) and bromide (bottom) concentration as a function of depth on October 

1, 2003. .................................................................................................................................. 4.13

Figure 4.14. Density (top) and viscosity (bottom) variation with sodium acetate (blue) and 

sodium bromide (red) in solution. .......................................................................................... 4.14

Figure 4.15. Simulated 2003 field experiment illustrating the potential for acetate stratification.4.14

Figure 4.16. Cross-well mixing scheme with standard (left) and reversed (right) pumping 

directions. Two 2-channel peristaltic pumps are run throughout the period of injection with 

daily reversals. Cross-well mixing consists of two tubes, one to deep and one to shallow 

depths. .................................................................................................................................... 4.15

Figure 4.17. Simulated acetate concentrations at 0 to 120 days after the initiation of 

biostimulation on July 20, 2008. Note that the injection gallery is located at X=28 m and the 

last row of monitoring wells is at X=36.5 m. ........................................................................ 4.16

Figure 4.18. Simulated variation of acetate concentrations with depth after 30 and 60 days of the 

baseline (corresponding to times with injection of 50 and 150 mM acetate, respectively), and 

after 60 days that included a hypothetical 300 mM acetate injection from day 38. ............... 4.17

Figure 4.19. May 2002 to January 2004 Rifle water table elevation, U(VI) concentrations, and 

dissolved oxygen in upgradient wells B-01, B-02, and B-03. ............................................... 4.18

Figure 4.20. Simulated dissolved oxygen and aqueous saturation for six dates in 2008: April 15, 

June 3, July 17, September 11, November 9, and December 21. Note that the 2008 Big 

Rusty experimental plot lies between X=125 and X=140. .................................................... 4.19

Figure 4.21. Simulated dissolved oxygen after 10 days from the start of biostimulation. ............ 4.20

Figure 4.22. Comparison of simulated acetate after 10 days of biostimulation for the baseline 

(top) and the gas entrapment case (bottom). .......................................................................... 4.20

Figure 4.23. Comparison of simulated aqueous U(VI) after 10 days of biostimulation for the 

baseline (top) and the gas entrapment case (bottom) ............................................................. 4.21

Figure 4.24. Comparison of simulated aqueous Fe(II) after 10 days of biostimulation for the 

baseline (top) and the gas entrapment case (bottom) ............................................................. 4.22

Figure 4.25. Simulated sulfate after 10 days of biostimulation with gas entrapment. .................. 4.22

Figure 4.26. Simulated aqueous saturations during the 7 day flood and 21 days following the 

flood. ...................................................................................................................................... 4.25
 ix 

Figure 4.27. Simulated hydraulic head during the 7 day flood and subsequent 21 days. ............. 4.25

Figure 4.28. Simulated flood water tracer during the 7 day flood and the following 21 days. ..... 4.26

Figure 4.29. Simulated aqueous oxygen during the 7-day flood and the following 21 days. ....... 4.26

Figure 4.30. Comparison of baseline and flood scenario at day 5 for U(VI), acetate, UO2(s), and 

aqueous sulfide. ..................................................................................................................... 4.27

Figure 4.31. Comparison of injected tracer at 20, 30, 40, and 50 days after start of biostimulation 

for baseline (left) and flood scenario (right). ......................................................................... 4.28

Figure 4.32. Simulated dissolved oxygen concentrations at 20, 30, 40, 50, 60, 70, 80, and 100 

days after initiation of acetate biostimulation. ....................................................................... 4.29

Figure 4.33. Comparison of simulated acetate concentrations at 20, 30, 40, and 50 days after start 

of biostimulation for baseline (left) and flood (right) scenarios. ........................................... 4.29

Figure 4.34. Comparison of simulated aqueous U(VI) concentrations at 20, 30, 40, and 50 days 

after start of biostimulation for baseline (left) and flood (right) scenarios. ........................... 4.30

Figure 4.35. Comparison of simulated UO2(s) concentrations at 20, 30, 40, and 50 days after start 

of biostimulation for baseline (left) and flood (right) scenarios. ........................................... 4.30

Figure 4.36. Comparison of simulated aqueous Fe(II) concentrations at 20, 30, 40, and 50 days 

after start of biostimulation for baseline (left) and flood (right) scenario. ............................. 4.31

Figure 4.37. Comparison of simulated aqueous sulfate concentrations at 60, 70, 80, and 100 days 

after start of biostimulation for baseline (left) and flood (right) scenario. ............................. 4.32

Figure A.1. Time-dependent aqueous uranium sensitivity to flow rate at 2.5 m (row 1), 5.0 m 

(row 2), and 8.5 m (row 3). ..................................................................................................... A.1

Figure A.2. Time-dependent aqueous uranium sensitivity to porosity at 2.5 m (row 1), 5.0 m 

(row 2), and 8.5 m (row 3). ..................................................................................................... A.1

Figure A.3. Time-dependent aqueous uranium sensitivity to dispersivity at 2.5 m (row 1), 5.0 m 

(row 2), and 8.5 m (row 3) ...................................................................................................... A.2

Figure A.4. Time-dependent aqueous uranium sensitivity to initial SRB biomass at 2.5 m (row 

1), 5.0 m (row 2), and 8.5 m (row 3). ..................................................................................... A.2

Figure A.5. Time-dependent aqueous uranium sensitivity to initial SRB biomass decay at 2.5 m 

(row 1), 5.0 m (row 2), and 8.5 m (row 3). ............................................................................. A.3

Figure A.6. Time-dependent aqueous uranium sensitivity to uranium bioreduction rate at 2.5 m 

(row 1), 5.0 m (row 2), and 8.5 m (row 3). ............................................................................. A.3

Figure A.7. Time-dependent aqueous uranium sensitivity to sulfate bioreduction rate at 2.5 m 

(row 1), 5.0 m (row 2), and 8.5 m (row 3). ............................................................................. A.4

Figure A.8. Time-dependent aqueous uranium sensitivity to initial aqueous uranium 

concentration at 2.5 m (row 1), 5.0 m (row 2), and 8.5 m (row 3). ........................................ A.4

Figure A.9. Time-dependent aqueous uranium sensitivity to uranium surface complexation site 

density at 2.5 m (row 1), 5.0 m (row 2), and 8.5 m (row 3). .................................................. A.5

Figure A.10. Time-dependent aqueous uranium sensitivity to pH at 2.5 m (row 1), 5.0 m (row 

2), and 8.5 m (row 3). ............................................................................................................. A.5
x

Tables 

Table 2.1. Hydrologic parameters and water chemistry for the Rifle IFRC alluvial aquifer. ......... 2.4

Table 3.1. Parameters for hydrologic modeling and transport ........................................................ 3.7

Table 3.2. U(VI) surface complexation reactions. ....................................................................... 3.10

Table 3.3. Uranium species and their formation constants. .......................................................... 3.11

Table 3.4. Fe2+ surface complexation reactions. ........................................................................... 3.11

Table 3.5. Modeled mineral reactions. .......................................................................................... 3.12

Table 3.6. Equilibrium reactions in the reaction network. ............................................................ 3.13

Table 3.7. Values for rate law parameters. .................................................................................... 3.14

Table 3.8. 2008 Big Rusty schedule of amendment concentrations. ............................................. 3.15

Table 3.9. Perturbation of model parameters for sensitivity analyses. .......................................... 3.19
xi

Processes, Properties and Conditions Controlling In Situ

Bioremediation of Uranium in Shallow, Alluvial Aquifers 

Executive Summary 

Proposed use of bioremediation for in situ treatment of uranium plumes in shallow alluvial 

aquifers drives the need to develop the understanding and the ability to assess such proposals and 

guide their implementation. To a large degree, the efficacy of uranium immobilization via 

engineered biostimulation will be site-specific. Hydrologic and biogeochemical processes, in the 

context of the site conditions and contaminant disposition, will control the uranium behavior. 

In this study, numerical modeling results for uranium bioremediation in a shallow, alluvial 

aquifer are provided to establish a broad framework for understanding processes associated with 

bioremediation of uranium and to bound conditions under which bioremediation could succeed 

in the long-term and conditions under which it is likely to fail. 

The baseline process models and parameterizations are based on work conducted at the Rifle 

Integrated Field Research Challenge (IFRC) site, supported by the Climate and Environmental 

Sciences Division of the Office of Biological and Environmental Research at the U.S. 

Department of Energy Office of Science. At this former uranium mill tailings site, laboratorysupported field studies have demonstrated that indigenous dissimilatory metal-reducing bacteria, 

stimulated using acetate as an electron donor, can reduce soluble hexavalent uranium [U(VI)] to 

immobile solid-phase U(IV). This biologically mediated reduction of U(VI) can decrease 

concentrations of U(VI) in the shallow unconfined aquifer below relevant standards. 

Groundwater concentrations of U(VI) are lowest during the initial 30 to 40 days of 

biostimulation when iron reduction dominates. This is followed by a transition to dominantly

sulfate-reducing conditions, which is accompanied by enrichment in sulfate-reducing bacteria 

and increasing concentrations of U(VI) in groundwater. 

Observations from field biostimulation experiments have been successfully reproduced using 

numerical simulations of biogeochemical reactive transport based on the conceptual model that 

emerged from this research. The current study extends the Rifle IFRC modeling baseline to 

consider process model parameter sensitivity analyses; lactate and ethanol electron donors; 

oxygen and nitrate terminal electron acceptors; multiphase flow, density and gas entrapment 

processes; and hypothetical flood scenarios. These simulations are used to illustrate and develop 

insights for potential issues in the 1) site-specific characterization of processes, properties, and 

conditions controlling uranium behavior and 2) site-specific design of in situ uranium 

bioremediation. 

Key results from field experiments and from modeling of uranium bioremediation in the shallow, 

alluvial aquifer are summarized with a focus on coupled process interactions. Sensitivity of the 

simulated aqueous U(VI) concentrations to process model parameters suggest that groundwater 
xii

flow rate, uranium bioreduction rate, and sulfate bioreduction parameters exert the most impact 

on bioremediation effectiveness. Flow rate affects the in situ concentration of injected 

amendments, the availability of reactants, and the transport of the electron donor and the aqueous 

reaction byproducts, which all play contributing roles in uranium bioremediation. The intrinsic 

uranium bioreduction rate has a direct impact on the magnitude of the aqueous uranium 

concentrations (i.e., higher intrinsic reaction rate results in higher uranium removal from 

groundwater). Once sulfate reducing bacteria begin to dominate the microbial community, 

higher sulfate bioreduction reaction rates lead to higher aqueous U(VI) concentrations. In this 

case, the increase in bicarbonate from acetate oxidation favors the formation of uraniumcarbonate species with less affinity for the mineral surfaces. 

Dissolved oxygen and nitrate are important oxidizers that can potentially degrade the 

performance of engineered bioremediation. To create redox conditions most conducive to 

uranium bioreduction and U(IV) stability, the simulations demonstrate that additional electron 

donor will be necessary to drive the terminal electron accepting processes (TEAPs) that will 

reduce/remove oxygen and nitrate in groundwater. The additional electron donor needed to 

achieve these conditions may be relatively small, but the TEAP kinetics needs to be 

characterized at a given site prior to full-scale deployment. 

A key issue for uranium bioremediation is the stability of solid-phase reduction products, 

including U(IV). Stability must be ensured either by natural conditions in the aquifer similar to 

those observed at the Rifle IFRC or by maintaining reducing conditions through periodic 

injections of electron donors. The injection frequency will depend on site-specific conditions 

which must be carefully evaluated for successful application of this technology. 

The modeling scenarios with lactate and ethanol identified how the stoichiometry of the TEAP 

reactions specific to these electron donors can result in increased consumption of electron donor 

and increased production of TEAP products. In cases where the amendment density is greater 

than the ambient groundwater density (e.g., for acetate, lactate), electron donor can potentially 

sink and accumulate near the aquifer bottom. This can be avoided with judicious design of 

electron donor concentrations and cross-well mixing. 

Wetting events (e.g., seasonal water table rise, recharge, flooding) that result in the saturation of 

previously unsaturated pores can trap gas with near-atmospheric compositions, leading to 

enhanced dissolved oxygen in groundwater. The modeling results show that flooding by surface 

ponding can significantly disrupt the distribution of electron donor and increase dissolved 

oxygen deep in an aquifer. The potential for interfering with bioremediation progress and/or 

reoxidizing reduced uranium suggests careful evaluation of the risk presented by flood events. 

Overall results from these numerical modeling studies illustrate their value for uranium 

bioremediation design and the importance of documenting site-specific processes and rates 

including disruptive events such as floods, before, during, and after bioremediation. 
xiii

Acknowledgments 

The authors would like to acknowledge Mike Wilkins and Signe Wurstner for their helpful 

comments and suggestions in the development of this document. This document benefited 

significantly from the thorough and thoughtful review provided by Mark Fuhrmann, Joe Kanney, 

Wendy Reed and Tom Nicholson. 
1.1

1 Introduction

1.1 Purpose and Background

Cleanup of uranium contamination in groundwater using bioremediation was first proposed in 

the early 1990’s by Derek Lovley and coworkers (Lovley et al., 1991). Their approach, based on 

laboratory experiments, was to use catalysis of dissimilatory metal reduction by microorganisms 

to reduce the soluble U(VI) form of uranium to the relatively insoluble U(IV) form. The concept 

is that amendment of acetate electron donor in situ results in the reduction of aqueous U(VI) to 

sparingly-soluble U(IV), in the form of uraninite (UO2) mineral. The uraninite becomes attached 

to solid surfaces in the aquifer sediments. Field experiments to test the efficacy of this process 

were not performed until 2002 (Anderson et al., 2003). Since those initial experiments, a 

significant amount of field-scale and lab-scale experimentation has been performed to investigate 

a broad range of processes associated with electron donor amendment and microbial U(VI) 

reduction (Vrionis et al., 2005; Wu et al., 2006). 

Remediation practitioners noted results from these field studies (Anderson et al., 2003; Vrionis et 

al., 2005; Wu et al., 2006) and the potential cost savings for clients if uranium bioremediation 

could be successfully applied. This led to applications proposing its use to regulatory agencies, 

including the U.S. Nuclear Regulatory Commission (NRC). To address this situation, the NRC 

first identified the overall technical basis for assessing uranium bioremediation (Long et al., 

2008), which included a description of controlling processes, design considerations, and 

performance indicators that should be a part of the monitoring and modeling approach. 

Underlying this seemingly straightforward bioremediation principle is the knowledge that 

uranium exhibits a wide range of behavior that is sensitive to site-specific processes, properties 

and conditions. Aqueous complexation of uranium with major ions (e.g., calcium, bicarbonate) 

can significantly alter uranium affinity for mineral surfaces and availability for bioreduction

(Brooks et al., 2003). These processes are in turn affected by the pH, carbonate chemistry 

including mineral reactions, and cation exchange processes. The biostimulation that 

accomplishes uranium bioreduction also introduces a dynamic component to the aforementioned 

behaviors: increases in pH, alkalinity, exchangeable cations, and highly reactive reduction 

products. Furthermore, the microbial community is also responding to the biostimulation and 

evolving in ways that may eventually impact the efficacy of uranium bioremediation. Mineral 

reactions induced by these changes can potentially provide a basis for long-term stabilization of 

uranium. This is important because the reduction of U(VI) is known to be reversible in 

laboratory experiments (Abdelouas et al., 1999; Komlos et al., 2008a; Komlos et al., 2008b; 

Moon et al., 2007; Moon et al., 2009; Sani et al., 2005; Senko et al., 2002), hence the oxidation 

of U(IV) could resolubilize uranium and possibly defeat the objective of bioremediation. 

However, if bioremediation of uranium can be successfully deployed, it is extremely attractive 

because extraction methods such as pump and treat can require unacceptably long time periods 

(i.e., decades) to satisfy the 30 μg/L Maximum Concentration Limit for drinking water (EPA, 
1.2

2000). Moreover, at many uranium-contaminated sites, groundwater concentrations of uranium 

are not naturally attenuating at rates predicted by modeling with constant uranium distribution 

coefficients, Kds (Yabusaki et al., 2008). Long time frames for natural attenuation are also not 

acceptable in most regulatory situations and hence a method that would accelerate attenuation of 

uranium and maintain low groundwater concentrations in the long term would be welcome as 

long as it could be shown to be protective of the public health and safety.

In the present study, numerical modeling results for uranium bioremediation in a shallow, 

alluvial aquifer are provided to establish a broad framework for understanding processes 

associated with the bioremediation of uranium and to bound conditions under which 

bioremediation could succeed in the long-term and conditions under which it is likely to fail. The 

baseline process models and parameterizations are based on work conducted at the Rifle 

Integrated Field Research Challenge site, supported by the U.S. Department of Energy, Office of 

Science, Biological and Environmental Research (BER), Environmental Remediation Sciences 

Program (ERSP). This former Uranium Mill Tailings Remedial Action (UMTRA) site in Rifle, 

Colorado has proven to be a useful test bed for field-scale electron donor amendment 

experiments for examining bioremediation of uranium in alluvial aquifers. Both field and 

modeling studies have been conducted in this research program and experimental results have 

been successfully reproduced using reactive transport models (Fang et al., 2009; Yabusaki et al., 

2007). 

While the modeling presented here is based on a specific uranium-contaminated site, the current 

study goes beyond the baseline for that site to consider model parameter sensitivity analyses; 

alternatives to the acetate electron donor (lactate and ethanol); oxygen and nitrate terminal 

electron acceptors; multiphase flow, density and gas entrapment processes; and hypothetical 

flood events. The simulated scenarios are used to assess potential performance issues for other 

site conditions and other bioremediation approaches. This document thus provides regulators and 

practitioners with information to better address potential issues in 1) site-specific 

characterization of processes, properties, and conditions controlling uranium behavior and 2) 

site-specific design of in situ uranium bioremediation. Furthermore, performance predictions for 

proposed bioremediation can be compared against those obtained for this site. Since the 

hypothetical scenarios include processes, properties and conditions that have not actually been 

tested under field conditions, prescriptive application of the results presented here would be 

inappropriate. Instead, numerical model outcomes in this report are best used to assess the 

completeness of processes used to predict bioremediation outcomes and to assess the relative 

impacts of uncertainty in specific parameters.

1.2 Key Results

Key results from field experiments and from modeling of uranium bioremediation in an alluvial 

aquifer are as follows:
1.3

• If bioremediation is designed to treat uranium transported via groundwater flow, the 

system must be able to immobilize uranium at a rate greater than or equal to the influx of 

uranium for its design life or until upgradient uranium is sufficiently depleted. Otherwise, 

concentrations of uranium in the treatment zone will eventually rebound to upgradient 

levels. Evidence for significant abiotic uranium reduction in the field has yet to be 

observed at the Rifle Site. Consequently, continual or pulsed electron donor amendment, 

application of slow-release electron donor, and/or alternative immobilization approaches

may be required.

• If an entire uranium plume is treated, continual electron donor amendment may not be as 

important. Instead, the rate of remobilization must be well understood. Under Rifle 

groundwater conditions, this rate may be low. Under more oxic conditions the 

remobilization rate is likely to be higher, although direct measurement of such rates under 

aquifer conditions are only now becoming available. 

• Biostimulation can significantly alter the local biogeochemistry in ways that affect the 

near-term and long-term abiotic controls on uranium behavior. In particular, increases in 

Fe(II), sulfide, and bicarbonate lead to solid-phase reactions that affect uranium surface 

complexation, uranium co-precipitation, and availability of electron donors for additional 

bioreduction. 

• In this study, parameter sensitivity analyses with our numerical process models suggest 

that groundwater flow rate, uranium bioreduction rate, and sulfate bioreduction 

parameters exert the most impact on bioremediation effectiveness. Additional numerical 

experiments are warranted to determine how these parameters interact and if the same 

conclusions would be derived from more extreme ranges of properties.

• The presence of oxygen and nitrate can complicate uranium bioremediation. They are 

both oxidants with respect to the Fe(II), U(IV), and sulfide bioreduction products 

associated with uranium bioremediation. In particular, the nitrite intermediate product in 

the nitrate terminal electron accepting process (TEAP) is a strong oxidant (Moon et al., 

2009), and must also be addressed for U(VI) bioreduction to be effective. Consequently, 

additional electron donor will be necessary to drive the TEAPs for oxygen, nitrate, and 

nitrite reduction/removal from groundwater to establish redox conditions most conducive 

to uranium bioreduction and U(IV) stability. The additional electron donor needed to 

achieve these conditions may be relatively small, but the TEAP kinetics needs to be 

characterized prior to full-scale deployment of a bioremediation design. 

• Lactate and ethanol are possible electron donors for uranium bioremediation (Shelobolina 

et al., 2008). In both cases, acetate is an intermediate oxidation byproduct of the TEAP 

reactions. This allows retention of the existing acetate oxidizing TEAP reactions and rate 

laws, supplemented with the necessary lactate and ethanol reactions to model the required 

sequence of reactions. In this study, the simulated results for ethanol and lactate are very 
1.4

similar but, in comparison to the acetate baseline, there is considerably more electron

donor consumption. The TEAP reactions specific to these electron donors also result in 

increased production of TEAP products (e.g., bicarbonate). 

• In cases where the amendment density is greater than the ambient groundwater density, 

there is potential for the delivery and mixing of the electron donor in the aquifer to be 

adversely impacted. Acetate and lactate are denser than groundwater, which can lead to 

higher concentrations of electron donor near the base of the aquifer. It was shown that 

the electron donor concentrations and cross-well mixing of amendments with 

groundwater are important considerations in the bioremediation design. Density impacts 

are minimized with increases in mixing, groundwater velocity, subsurface heterogeneity, 

and anisotropy.

• In this study, it was shown that wetting events (e.g., seasonal water table rise, recharge)

that saturate previously unsaturated pores may trap gas with atmospheric levels of 

oxygen. The oxygen gas can partition to the aqueous phase and diffuse through the 

groundwater. The magnitude of enhanced oxygenation depends on sediment texture, 

vadose zone soil gas composition, and the magnitude, frequency, and duration of water 

table fluctuations. Concerns arising from elevated oxygen near the water table include 1) 

re-oxidation and remobilization of reduced uranium are possible, 2) treatment capacity 

may be lost precisely where elevated groundwater uranium concentrations are commonly 

found (i.e., near the water table), 3) key microorganisms during bioremediation are not 

very tolerant of high oxygen levels, and 4) additional electron donor will be needed to 

achieve anaerobic conditions. 

• Because uranium-contaminated alluvial aquifers are commonly located on floodplains, 

the disruptive scenario studied was a one-week long flood. Hydrologic impacts can 

negatively impact amendment delivery and dissolved oxygen can be driven deep into 

alluvial aquifers at levels near saturation with atmospheric oxygen. The enhanced 

dissolved oxygen is an additional terminal electron acceptor that requires additional 

electron donor for removal. While the additional amendment required to remove 

dissolved oxygen is small compared to typical biostimulation concentrations, a postbiostimulation flood event could reoxidize and remobilize previously bioreduced 

uranium. Thus, it is important to document site-specific flood impacts, before, during, 

and after bioremediation. 

1.3 Report Scope

The main body of this report begins with Section 2.0 Bioremediation Studies at the Old Rifle 

IFRC site, which describes the Rifle IFRC site hydrology, geochemistry, and contaminant 

history and summarizes previous modeling studies at the site. This section includes a description 

of the conceptual model for uranium bioremediation at the site. The hydrologic model and 

biogeochemical reaction network are then described in Section 3.0, Model Specification. 
1.5

Included in this section is the description of the baseline specification of the modeled system 

including the sensitivity of aqueous uranium concentrations to physical, geochemical, and 

biological process model parameters. Section 4.0, Modeling Scenarios, begins with onedimensional simulations that investigate the impact of additional electron acceptors and electron 

donors on bioremediation. The two-dimensional scenarios target the impacts of densitydependent behavior of the injected amendment, seasonal water table fluctuations, and flooding 

events. The narrative part of the report ends with Section 5.0, Summary and Conclusions, which 

discusses results from the modeling scenarios with implications for deployment and research 

needs. This section provides a synopsis of the report, highlighting implications of the modeling 

results for shallow alluvial sites under consideration for in situ uranium bioremediation. 

2.1

2 Bioremediation Studies at the Old Rifle IFRC site 

2.1 Site Description

The Old Rifle IFRC site is a former Uranium Mill Tailings Remedial Action (UMTRA) site in 

Rifle, Colorado on a flood plain of the Colorado River. The flood plain is 750 m in length along 

the river shore and 250 m at the widest point. The unconfined aquifer is contaminated by residual 

uranium leached from now-removed mill tailings (Anderson et al., 2003; Vrionis et al., 2005), 

which percolated through a 4 m thick vadose zone to the water table, where it was transported 

laterally through the aquifer via groundwater flow. The uranium migrates through the subsurface 

as hexavalent U(VI), exceeding the UMTRA regulatory standard of 44 μg/L (EPA, 1998) 

throughout the experimental site, with maximum concentrations ca. 300 μg/L. Groundwater 

moves primarily in the topmost hydrostratigraphic unit of the unconfined aquifer, a sandy-gravel, 

gravelly-sand alluvium with an average saturated thickness of 2.4 m in the vicinity of the 

experimental plot. Grain size for the gravels range from pebbles to cobbles (Wentworth scale). 

This permeable layer is underlain by a relatively impermeable silty shale from the weathered 

Wasatch formation (DOE, 1999). The alluvium is approximately 6 m thick, including 2 m of 

clay fill, with the average water table occurring at 4 m depth (Figure 2.1) The clay fill was 

installed after the mill tailings and contaminated soil were removed during cleanup operations 

under the UMTRA program. 

The field plot for the current study was installed in 2007 in an untreated part of the Rifle IFRC 

site about 35 m southeast from the original 2002/2003 experimental field plot. The new plot 

comprises an injection gallery of 10 fully penetrating and screened wells, and 3 upgradient and 

12 downgradient monitoring wells (Figure 2.2). The 12 downgradient monitoring wells were 

arranged in 3 rows, 2.5, 5.0, and 8.5 m from the injection gallery, oriented to capture transport 

along the predominant groundwater flow direction. Two of the upgradient wells, U-01 and U02, were 4 m from the injection gallery, while the central upgradient well, U-03, was 8 m from 

the injection gallery. Despite seasonal water table dynamics that change the saturated thickness 

of the aquifer, the groundwater flows predominantly to the south-southwest toward the Colorado 

River. More detailed descriptions of the well network and injection system can be found in Fang 

et al. (2009) and more detailed descriptions of the site can be found in DOE (1999). 
2.2

Figure 2.1. Excavated exposure of subsurface above water table (left) and typical lithology 

(right) from Rifle Site.
2.3

Figure 2.2. Rifle IFRC site with the location of the well layout (inset) for the 2007 Winchester / 

2008 Big Rusty Experiments circled.

2.1.1 Site Conditions

This study began with the identification of a set of baseline process models and associated 

parameters from the current state of knowledge provided by the DOE ERSP Rifle IFRC project. 

In particular, we are using characterization data from the 2008 acetate biostimulation field 

experiment. Based on analyses of groundwater samples at the site, the aquifer is characterized 

by very low dissolved oxygen (<0.2 ppm), although higher measurements are observed near the 

water table and during elevated water table conditions in late spring. Nitrate concentrations are 

also very low (<0.2 ppm). The low nitrate concentrations are consistent with iron reducing 

conditions indicated by the presence of Fe(II) in solution and low Eh (<250 mV) at 

circumneutral pH. The parameters with observed ranges describing the hydrology and water 

chemistry at the site are summarized in Table 2.1. 
2.4

Table 2.1. Hydrologic parameters and water chemistry for the Rifle IFRC alluvial aquifer.

Hydrologic Parameters

Saturated Thickness 2.4 to 3.4 m

Hydraulic Conductivity 5 to 20 m/d

Porosity 0.15 to 0.35

Pore Velocity 0.3 to 0.5 m/d

Water Chemistry

Uranium ~0.7 – 1.5 μM

pH ~7.0 – 7.2

Alkalinity ~7-10 meq/L

Eh -150 to 240 mV

Dissolved Oxygen 3 to 20 μM

Fe(II) 15 to 50 μM

Nitrate 2 to 3 μM

Sulfate 8 to 11 mM

Sulfide 0.03 to 0.5 μM

2.2 Bioremediation at the Rifle IFRC site 

In 2002, a series of uranium bioremediation field experiments began at the Old Rifle UMTRA 

site in Western Colorado (subsequently referred to as the Rifle IFRC site). The principle of 

direct enzymatic reduction of mobile hexavalent uranium [U(VI)] that these field experiments 

were based on was identified more than 10 years earlier by (Lovley et al., 1991) and Gorby and 

Lovley (1992) who suggested that dissimilatory metal-reducing bacteria grown with acetate as an 

electron donor could be used to immobilize U(VI) as precipitated U(IV) mineral. In particular, 

the 2002 and 2003 biostimulation field experiments, which were performed in the same field plot 

at the Rifle IFRC site (Anderson et al., 2003; Vrionis et al., 2005), demonstrated that U(VI) 

concentrations in the shallow unconfined aquifer could be lowered below relevant standards by 

stimulating indigenous dissimilatory metal reducing bacteria with acetate as the electron donor. 

The removal of U(VI) from solution was coincident with a decrease in Fe(III) minerals, an 

accumulation of aqueous Fe(II), and an enrichment of members of the Geobacteraceae, which 

led to the attribution of uranium bioreduction to iron-reducing Geobacter species. The greatest 

enrichment of members of the Geobacteraceae in sediments was correlated to the highest ratio of 

U(IV) to total uranium. This was followed by a sulfate-reducing phase characterized by the 

simultaneous observance of sulfide, depleted Fe(III), and 16S rDNA sequences most closely 

related to members of the Desulfobacterales. The transition to dominant sulfate-reducing 

conditions, as evidenced by a decrease in sulfate concentrations after 45 days of acetate 

amendment in 2002, was accompanied by a partial rebound in U(VI) groundwater concentrations 

(Anderson et al., 2003). The field observations, including downgradient sediment cores with 

elevated U(IV), were consistent with previous laboratory studies where 1) the stimulation of 
2.5

acetate-oxidizing metal reducing bacteria (i.e., Geobacter sp.) concomitantly reduced and 

immobilized aqueous U(VI) (Finneran et al., 2002; Holmes et al., 2002), and 2) acetate-oxidizing 

sulfate reducing bacteria were not as effective at U(VI) immobilization (Lovley et al., 1993; 

Ortiz-Bernad et al., 2004). 

In the summer of 2007, the “Winchester” field experiment was designed to test the feasibility of 

proteomic sampling and analysis during acetate biostimulation ofdissimilatory metal-reducers, 

without initiating significant sulfate reduction. A relatively short 31-day biostimulation 

experiment was designed to maintain iron-reducing conditions and test the impact of a 7-day 

interruption of continuous acetate injection on the response of the system biogeochemistry. The 

initial mixing of amendment between injection wells was controlled by continuous cross-well 

mixing using peristaltic pumps to transfer liquid between injection wells at three depths in each 

of the 10 gallery wells.

Biostimulation clearly alters solid phase mineralogy during field experiments at the Rifle site. It 

is the biologically mediated transfer of electrons to solid-phase Fe(III) that provides the energy 

for cell maintenance, activity, and growth in Fe(III)-reducing bacteria (FeRB). Fe(III) mineral 

sources are abundant in Rifle sediments and Fe does not appear to be limiting based on column 

experiments using Rifle sediments in which Fe reduction continued throughout an experiment 

that ran for more than 200 days (Komlos et al., 2008b). During sulfate reduction (typically the 

dominant process after about 30 to 40 days), a combination of calcite and iron sulfide are thought 

to be volumetrically the most important precipitates. Lesser amounts of elemental sulfur and 

siderite have also been observed in sediment from column experiments and from the subsurface, 

post-biostimulation sampling. Reduced uranium [U(IV)] also becomes part of the solid phase, 

most likely as a low abundance surface coating or decoration on existing grains. It may also be 

incorporated into calcite. Laboratory experiments with relatively high concentrations of uranium 

have produced both cell-associated and extra-cellular nanoparticulate uraninite (e.g., Suzuki et 

al. 2005). While it is attractive to assume this as the form of U(IV) in the subsurface during in 

situ bioremediation and this is the from used in the modeling results reported in this document, it 

is challenging to actually determine the solid phase chemistry of U(IV) in sediments retrieved 

from the subsurface at the Rifle site. This is because of the low uranium concentrations in Rifle

sediments, which limit our ability to use spectroscopy or microscopy to analyze uranium phases 

or phases that incorporate uranium. This limitation is being addressed on Rifle sediments via 

column and in situ experiments. EXAFS and XANES obtained on such systems to date suggest 

that sorbed U(IV) may be more important than uraninite (Bargar et al., 2008). Factors governing 

the formation of sorbed U(IV) versus uraninite are not well understood and it is not known if 

sorbed U(IV) is cell-associated or adsorbed to mineral surfaces. Currently, rates of uraninite 

dissolution are being measured in situ at the Rifle site (Bargar et al., 2009a; Bargar et al., 2009b) 

and this work will likely be extended to include cell-associated U(IV) in the future. 
2.6

2.3 Reactive Transport Modeling Studies 

2.3.1 Biologically Mediated Reactions, Yabusaki et al. (2007)

A reactive transport modeling study by Yabusaki et al. (2007) analyzed data from the 2002 and 

2003 field experiments and characterized the principal flow, transport and biological processes 

controlling uranium mobility during the experiments. Data from the 2002 field experiment were 

used to identify the dominant transport and biological processes controlling uranium mobility 

during biostimulation, and determine field-scale parameters for these modeled processes. Under 

the very low dissolved oxygen and nitrate conditions in the Rifle IFRC groundwater, the 

biostimulation was assumed to quickly proceed to iron reduction with negligible consumption of 

electron donor by the oxygen and nitrate TEAPs. Microbial community composition and 

function from Anderson et al. (2003) provided insight on the differentiation of the metabolic 

capability associated with specific community members related to uranium immobilization. A 

simple process model conceptualization with three TEAPs and two distinct immobile microbial 

populations was chosen to build an understanding of the dominant behaviors of the bulk system 

observed in the field at the Rifle IFRC site. 

Acetate injection initially stimulates the growth of members of the Geobacteraceae, resulting in 

the reduction of Fe(III), concurrent with removal of U(VI) from groundwater. The second phase 

of the conceptual model of biologically mediated reactions is sulfate-reduction. In this case, the 

TEAP reaction for sulfate reduction was associated with immobile sulfate-reducing organisms. 

The stoichiometry in these irreversible reactions, which include the yield of an immobile 

biomass, are energetics-based (Rittmann and McCarty, 2001) under the assumption of a biomass 

molecular formula of C5H7O2N and an energy-transfer efficiency value of 0.6. In these three 

TEAP reactions, the biomass is nominally attributed to iron-reducing microbial communities that 

are known to be dominated by Geobacter spp. Goethite, one of the iron oxides identified at the 

site by Mössbauer spectroscopy (Komlos et al., 2008b), was used as an Fe(III) oxide terminal 

electron acceptor. Uranyl was used as the terminal electron acceptor in the uranium reduction 

reaction, while the ammonium ion, NH4

+ was considered to be non-limiting. The coupled process 

simulation approach was able to establish a quantitative characterization of the principal flow, 

transport, and reaction processes based on the 2002 field experiment that could be applied 

without modification to describe the 2003 field experiment. 

The decline of acetate concentrations coincided with the onset of sulfate reduction as evidenced 

by the concomitant decrease in sulfate concentrations and detection of sulfide. The eventual 

depletion of acetate occurs due to several factors: 1) in the postulated reaction stoichiometry, one 

mole of acetate reacts with 0.924 moles of sulfate [versus 4.8 moles of Fe(III) and 6.2 moles of 

U(VI) in the iron and uranium TEAPs, respectively], 2) sulfate concentrations are approximately 

three times the initial acetate concentrations in the groundwater, and 3) the sulfate bioreduction 

rate is relatively rapid. This results in an acetate-limited sulfate bioreduction reaction that 

significantly contributes to the nearly complete consumption of acetate toward the end of the 

biostimulation period. 
2.7

Vrionis et al. (2005) provided geochemical evidence suggesting that Fe(III), U(VI), and sulfate 

reduction could simultaneously occur during acetate biostimulation in the field. This observation 

was bolstered by the presence of members of both the Geobacteraceae and Desulfobacterales in 

the associated sediment sample. Accordingly, a rate law was developed for the Fe(III) TEAP that 

allowed the activity of FeRB to continue after the onset of sulfate reduction. In this case, the rise 

in aqueous U(VI) observed during sulfate reduction was attributed to the lower activity of FeRB

competing with sulfate-reducing bacteria (SRB) for the electron donor, acetate. Thus, the onset 

of sulfate reduction is not an intrinsic limit on the Fe(III) mineral that can be utilized as a 

terminal electron acceptor. In systems where acetate is not limiting, it is possible that the FeRB

would be able to utilize significantly more Fe(III) after the onset of sulfate reduction, albeit less 

efficiently. This behavior is consistent with observations of low concentration sulfate column 

experiments performed with sediments from the Rifle IFRC site (Moon et al., 2007). 

2.3.2 Abiotic Reaction Processes, Fang et al. (2009)

The Rifle biostimulation modeling study by Yabusaki et al. (2007), which focused on 

microbially-mediated TEAPs, allowed early progress under a simple conceptualization of the 

reaction network. Clearly, a combination of hydrologic, geochemical, and biological factors 

control the effectiveness of the uranium immobilization, making it a challenge to reliably predict 

important behaviors during and after bioremediation. An important consideration is the impact of 

changes in the geochemical environment induced by the acetate-mediated biostimulation. Of 

interest is the abiotic response (e.g., mineral precipitation and dissolution, aqueous and surface 

complexation) to the products of the TEAP reactions: bicarbonate, U(IV), Fe(II) and sulfide. Of 

the millimolar concentrations of Fe(II) and sulfide produced by the respective TEAPs during 

biostimulation, only micromolar concentrations are found in solution. Thus, nearly all of the 

bioreduced products of the biostimulation are strongly associated with the solid phases. These 

reduced phases may play an important role in long-term stabilization of immobilized uranium. 

The inclusion of abiotic chemistry in the modeled reaction network is also necessary to account 

for changes in solution chemistry (e.g., pH, alkalinity, and calcium) that nominally control 

uranium speciation and mobility. Uranium has a broad range of mobility that is dependent on 

the redox state of the dissolved uranium, ambient water chemistry, and the surface reactivity of 

the subsurface sediments (Curtis et al., 2004; Davis et al., 2006; Davis and Kent, 1990; Davis et 

al., 2004). 

Uranium Surface Complexation

Figure 2.3

. Surface complexation is an important process in attenuating the 

mobility of uranium in the subsurface environment (Moyes et al., 2000; Pabalan et al., 1996). 

Under the ambient pH and alkalinity found in the groundwater at the Rifle IFRC site, a 

significant fraction of the U(VI) (principally as uranyl and uranyl carbonate) would be expected 

to adsorb to the sediments via surface complexation. Zheng et al. (2003) and Fox et al. (2006) 

provided experimental evidence of the strong influence of calcium on the sorption of U(VI), 

through competition of the aqueous complex Ca2UO2(CO3)3 ( ) with the iron surface 

complexes. 
2.8

Figure 2.3. Uranium aqueous speciation assuming Rifle groundwater chemistry.

Phyllosilicate Iron Reduction. Until recently, it was commonly assumed that Fe(III)-oxides and 

oxyhydroxides were the principal terminal electron acceptor for iron reduction (Roden and 

Zachara, 1996). Under this conceptual process model, the biologically mediated reductive 

dissolution of Fe(III)-oxides liberates millimolar concentrations of Fe++, which must quickly be 

associated with the aquifer solids to agree with the observed micromolar Fe++ concentrations. 

This required a particularly strong Fe++ surface complexation model that had difficulty 

reproducing the aqueous Fe++ dynamics observed during the iron-reducing phase. The 

preferential bioreduction of phyllosilicate Fe(III) over Fe(III)-oxides was recently identified in 

Rifle IFRC laboratory studies by Komlos et al.(2008b). This finding permitted an important 

refinement to the conceptual model of processes and properties controlling uranium mobility 

during biostimulation. In this case, the higher rate of microbial reduction of phyllosilicate iron 

accounts for ~90% of the bioreduced iron, with the remainder attributed to the reduction of iron 

oxides and oxyhydroxides. In the phyllosilicate Fe(III) TEAP, the biogenic Fe(II) largely

remains in the layer silicate structure. With less Fe++ entering solution, the representation of Fe++

dynamics was significantly improved. Thus, the reactive transport model now accounts for 

parallel Fe(III) TEAPs for oxides/oxyhydroxides and phyllosilicates. 
2.9

Sulfate Reduction. The impact of the SRB becomes more noticeable after ~30 days of acetate 

amendment, when the abundance of this group increases sufficiently to collectively remove a 

large fraction of the ~9 millimolar sulfate present in the groundwater. At this point, the SRB are 

outcompeting the FeRB for acetate. For each mole of acetate oxidized in the sulfate TEAP 

reaction, approximately 1 mole of sulfide and 2 moles of bicarbonate are produced. Observed 

solution concentrations of sulfide and bicarbonate during sulfate reduction, while elevated, 

represent a very small fraction of these TEAP products. In the model, calcite, iron sulfide, and 

elemental sulfur are predicted to be the principal sinks for the bicarbonate and sulfide removed 

from solution. Over longer periods of biostimulation, the volume fraction of these secondary 

minerals could be sufficient to alter the aquifer permeability (Englert et al., 2009; Li et al., 2009). 

Higher abundance of these reduced oxidation state sulfur phases is correlated with longer-term 

post-biostimulation uranium removal. During sulfate bioreduction, the elevated bicarbonate 

concentrations and the net increase in pH from the mineral reactions are favorable conditions for 

uranium desorption. This behavior is accounted for in the uranium surface complexation model 

and is responsible, in part, for the higher (but still less than influent) uranium concentrations 

predicted during sulfate reduction. 

Mineral Reactions. Based on the background geochemical conditions and the products of the 

principal TEAP reactions, calcite, siderite, iron sulfide, and elemental sulfur were added to the 

reaction network with rates as a function of the saturation state. An important sulfide sink was 

the inclusion of the reaction of goethite with sulfide that produced elemental sulfur and Fe(II). 

2.4 Conceptual Process Model

The integration of the abiotic chemistry with the TEAPs in the modeling allows for more 

accurate representation of the impact of biostimulation products on uranium behavior. pH and 

alkalinity, for example, have strong impacts on uranium mobility; yet, the aqueous 

concentrations of bicarbonate and other TEAP reaction products (e.g., Fe(II), sulfide) are more 

than an order of magnitude smaller than what was produced. This underscores the importance of 

accurately representing the solid phase reactions (e.g., mineral reactions for carbonates, iron 

sulfides; iron and uranium surface complexation) that control the pH, Eh, alkalinity, and aqueous 

components of interest. Many of the secondary minerals that form during biostimulation can 

also contribute to longer-term post-biostimulation stabilization of reduced phases. 

The resulting conceptual process model has the FeRB and SRB simultaneously active during 

biostimulation with the acetate electron donor. Initially, the FeRB are much more active leading 

to relatively rapid reduction of the Fe(III) mineral terminal electron acceptor and the concomitant 

reduction of the trace concentrations of the U(VI) electron acceptor. Only a small fraction of the 

biogenic Fe(II) is found in solution; the bulk is reduced in place in the layer silicates or sorbed to 

mineral surfaces. The SRB activity is linked to their biomass, which is initially low. 

Compounded by a relatively slow intrinsic growth rate, conversion of the sulfate terminal 
2.10

electron acceptor during the first 4 to 5 weeks of biostimulation is negligible. In general, acetate 

consumption during this period is relatively low, ~0.5 mM or about 10-20% of what is available. 

This excess acetate allows the acetate electron donor to migrate further downgradient, resulting 

in a larger zone of metal reduction. 

As the SRB biomass steadily increases, the rate of the sulfate TEAP increases commensurately 

until sulfate reduction is the dominant biologically mediated process. High ambient sulfate 

concentrations (~8-10 mM), high stoichiometric acetate consumption (~8 times the FeRB 

stoichiometry), and now high sulfate TEAP reaction rates result in significant depletion of the 

acetate electron donor. Important consequences of the predominance of sulfate reduction include 

1) less acetate available for FeRB, 2) production of significant quantities of bicarbonate and 

sulfide, and 3) slight net increase in pH primarily from sulfide – Fe(III) mineral reactions. Each 

of these impacts can limit the net removal of U(VI) from the groundwater. In the Monod rate 

law, lower acetate concentrations will depress the TEAP rates including U(VI) bioreduction. 

Elevated bicarbonate concentrations lead to the formation of higher concentrations of aqueous 

U(VI)-carbonate complexes and the precipitation of carbonate minerals. Sulfide reacts with 

Fe(II) to precipitate iron sulfides. Sulfide reaction with Fe(III) minerals results in the formation 

of elemental sulfur and additional Fe(II). In the uranium surface complexation model, removal 

of U(VI) from groundwater via bioreduction, increase in alkalinity, and pH increase, all favor 

U(VI) desorption. U(VI) bioreduction is still occurring but the net impact of the sulfate 

reduction is a net increase in aqueous U(VI) concentrations compared to the earlier period when 

Fe(III) reduction was predominate. These U(VI) concentrations are still lower than the influent 

U(VI) entering the treatment zone. As long as FeRB are active with sufficient acetate and U(VI) 

available, uranium bioreduction will continue. Under these conditions and in the absence of 

other U(VI) sources, there will be a progressive removal of U(VI) from groundwater with 

distance from the injection gallery.
3.1

3 Model Specification 

3.1 Hydrologic Model

The variably saturated flow conditions at many shallow alluvial aquifers are amenable to a 

single-phase, constant-density Richard’s equation (Bear and Bahmat, 1991) modeling approach. 

However, for the conditions being investigated in this particular modeling study at the Rifle 

IFRC site, we use a fully coupled two-phase (liquid and gas) formulation with variable density. 

The active gas phase was necessary to account for the gas entrapment mechanism in the seasonal 

water table fluctuation and the flood scenarios, whereas the variable density formulation was 

necessary to account for amendment injectate that was denser than the groundwater. The 

STOMP simulator (White and Oostrom, 2006) that we use for the modeling of the Rifle variably 

saturated flow and gas entrapment in this study is based on Equations 3.1 to 3.4. 

The water mass conservation equation, shown in Equation 3.1, equates the time rate of change of 

water mass within a control volume with the flux of water mass crossing the control volume 

surface. In the STOMP simulator, water exists in the diffusive pore space as liquid water in the 

aqueous phase and water vapor in the gas phase. Water transport occurs by advection through the 

aqueous and gas phases and by diffusion-dispersion through the gas phase. Flow of fluid phases 

is computed from Darcy’s Law. Transport of phase components is computed from a modified 

form of Fick’s law, where a combined diffusion-dispersion coefficient replaces the classical 

Fickian diffusion coefficient. Equation 3.1 includes an osmotic flux term, which accounts for the 

flow of aqueous fluid by osmotic pressure for simulations with coupled salt transport. 

 ( ) ( ) wS

l

gl

w w

gl

w nD m

t

+∇−∇+∇−=  



 





∂

∂ ∑ ∑ = =

FJF

γ ,

γ γ

γ ,

γγγ ρω s (3.1)

The air mass conservation equation, shown in Equation 3.2 equates the time rate of change of the 

air mass within a control volume with the flux of air mass crossing the control volume surface. In 
3.2

the STOMP simulator, air exists in the diffusive pore space as a component of the gas phase and 

dissolved in the aqueous phase. Air transport occurs by advection and diffusion-dispersion 

through the aqueous and gas phases. Flow of fluid phases is computed from Darcy’s Law. 

Transport of phase components is computed from a modified form of Fick’s law, where a 

combined diffusion-dispersion coefficient replaces the classical Fickian diffusion coefficient.

 

 

 (3.2) 

diffusive dispersive offlux airfor phase γ

diffusive dispersive offlux waterfor phase γ

advective offlux airin phase γ

advective offlux waterin phase γ

osmotic aqueoustheofflux phase

a

w

a

γ

w

γ

= −

= −

=

=

=

γ

γ

J

J

F

F

FS

l
3.3

salt concentrat in ion liquid phase

salt diffusion coefficient for liquid phase

mole offraction air in phase

mole offraction water in phase

diffusion coefficient of air in phase

diffusion coefficient of water in phase

molecular weight of phase

molecular weight of air

molecular weight of water

phase for y tortuosit phase

unit gravitational direction vector

accelerati gravity ofon 

 of pressure phase

kinematic viscosity of phase

intrinsic permeability tensor

 fluid relative permeabili ofty phase

mass source rate of air

mass source rate of water

saturation of phase

for density phase

mass offraction air in phase

mass offraction water in phase

porosity diffusive

=

=

=

=

=

=

=

=

=

=

=

=

=

=

=

=

=

=

=

=

=

=

=

l

S

l

a

w

a

w

a

w

g

r

a

w

a

w

D

S

D

D

D

M

M

M

z

g

P

k

k

m

m

s

n

χ γ

χ γ

γ

γ

γ

τ γ

γ

μ γ

γ

γ

ρ γ

ω γ

ω γ

γ

γ

γ

γ

γ

γ

γ

γ

γ

γ

γ

γ

γ




3.4

3.1.1 Gas Entrapment

We use the hysteretic saturation function of Kaluarachchi and Parker (1992) to account for 

entrapped gas as a component in otherwise saturated pores. Gas entrapment during aqueousphase imbibition depends on the aqueous saturation and current saturation path. Gas effective 

residual saturations are computed using an empirical relationship developed by Land (1968) for 

aqueous-NAPL systems. In this simplified hysteretic model for aqueous-gas systems, gas can be 

trapped or free, where free gas refers to continuous volumes which advect freely and trapped gas 

refers to discontinuous ganglia of gas occluded within the aqueous phase. Occluded gas is 

assumed to be immobile. In hysteretic systems, the residual saturation is independent of 

capillary pressure. The effective trapped gas saturation is computed according to Equation 3.3, 

which recognizes that entrapped gas cannot exceed the gas present. Land’s parameter for gasaqueous interfaces, Lg, is 1

i

s g r

−1, where i

s g r

is the maximum effective residual gas saturation.

, for ~ min

ll > ss (3.3)

where tl g s = effective gas saturation trapped by aqueous phase

min

l s = minimum effective aqueous saturation

l s

~ = apparent aqueous saturation = gl tl + ss

m

ml

l

s

ss

s −

− = 1

l s = actual aqueous saturation

ms = actual irreducible aqueous saturation

g s = effective gas saturation

Lg = Land’s parameter for gas-aqueous interfaces 

 r g

i

s = maximum effective residual gas saturation.

3.1.2 Transport

Transport occurs through liquid and gas phases with Darcy’s Law calculation of the advective 

velocity. The solute conservation equation, shown in Equation 3.4, equates the time rate of

change of solute within a control volume with the flux of solute crossing the control volume 

surface. In the STOMP simulator, solute is partitioned among the fluid and solid phases 

through equilibrium and kinetic reactions. Solute transport occurs by advection and diffusiondispersion through the aqueous phase and gas phase. 

 

 

 

 

 

 

 

( ) ( ) 































−+

− − −+− = g

lg

l

lg

l

g s

sL

s

sL

s

s tl , ~ 11

~ 1

11

1 min min

min
3.5

∑( ) [ ] ∑( [( ) ]) = =

∇+−+∇−= ∇+

∂

∂

gl

i

hD

C

D

iCC

gl

i

i

CRmC CnsDns

t

C i i i

, γ ,

γγγγ γ

γ

γγ γ V   τ D

 (3.4)

γ

γ

τ γ

γ

μ γ

γ

γ

ρ γ

γ

γ

γ

γ

γ

γ

γ

γ

γ

γ

γ

γ

diffusion coefficient of air in phase

diffusion coefficient in icomponent of phase

phase for y tortuosit phase

unit gravitational direction vector

accelerati gravity ofon 

 of pressure phase

kinematic viscosity of phase

intrinsic permeability tensor

 fluid relative permeabili ofty phase

solute constantratedecay

mass source rate icomponent of 

saturation of phase

for density phase

porosity diffusive

concentrat in icomponent ofion phase

concentrat icomponent ofion 

Darcy offlux phase

=

=

=

=

=

=

=

=

=

=

=

=

=

=

=

=

=

h

C

g

r

C

C

D

i

i

D

D

z

g

P

k

k

R

m

s

n

C

C

i

i

i





V

3.1.3 Solution Procedure

The STOMP simulator has been developed by the Pacific Northwest National Laboratory to 

predict thermal and hydrological flow and reactive transport in variably saturated subsurface 

environments. It is a fully functional, multiphase, multi-component reservoir code used 

extensively in environmental and gas/CO2 modeling applications. STOMP solves nonlinear 
3.6

partial differential equations for the conservation equations, with reactive transport occurring 

over mobile phases. The nonlinear equations describing mass and/or energy conservation are 

discretized spatially on structured orthogonal grids using the integral finite difference approach, 

and temporally using first-order backward Euler differencing. The nonlinearities from the 

constitutive equations that relate the primary and secondary variables are resolved using NewtonRaphson iteration. The global implicit solution of the algebraic system of equations uses a 

preconditioned conjugate gradient sparse matrix solver (BiCGstab with block-jacobi ILU 

preconditioning). 

After solution of the flow equations, the hydrologic transport equations are solved using a TVD 

(total variation diminishing) scheme. The coupled hydrologic transport and mixed geochemical 

reaction systems are solved using an operator splitting approach with no iteration between the 

transport and reaction systems. The chemistry module ECKEChem (Equilibrium-ConservationKinetic-Equation Chemistry) solves mass balance equations, mass action equations, and kinetic 

equations simultaneously using the Newton-Raphson approach. The simulator has several 

different operational modes, i.e., different source code depending on the solved governing 

equations (e.g., water mass, air mass, dissolved-oil mass, oil mass, salt mass, thermal energy). 

3.1.4 Hydrologic Modeling Parameters

Liquid phase boundary conditions for the hydrologic model are based on observed upgradient 

and downgradient water levels. The gas phase boundary condition at the ground surface is 

atmospheric pressure. It should be noted that the elevation of the Rifle IFRC site is 5300 feet 

above mean sea level and the pressure is nominally 0.8224 atmospheres. This non-standard 

atmospheric pressure was taken into account for the calculation of dissolved oxygen in 

equilibrium with the gas phases. 

Bromide tracer was included in the acetate amendment that was released into the Rifle aquifer. 

The hydraulic conductivity, porosity, and dispersivity for the 2008 experiment have been 

calibrated based on observations of bromide tracer migration. The unsaturated flow parameters 

are based on the Brooks-Corey saturation functions (Brooks and Corey, 1966) and were 

developed for the Rifle aquifer materials. The hydrologic modeling parameters of the site are 

summarized in Table 3.1. Initial and boundary conditions are based on measured concentrations 

of chemical components and water quality (e.g., pH, Eh, alkalinity). Initial sorbed U(VI) 

partitioning is calculated by using the uranium surface complexation model to equilibrate with 

measured aqueous U(VI) concentrations. 
3.7

Table 3.1. Parameters for hydrologic modeling and transport 

Hydrologic Parameter Value

Air Entry Pressure Head 1.00925 cm 

Pore-Size Distribution Index 0.257979 

Residual Saturation 0.0712 

Saturated Hydraulic Conductivity 8 m/d

Effective Porosity 0.10 

Dispersivity 0.4 m 

3.2 Biogeochemical Model

3.2.1 Biologically Mediated Reactions

The biologically mediated reactions and rate laws are based on acetate as the electron donor, 

three principal terminal electron acceptors (Fe(III) mineral, U(VI), and sulfate), and two 

functional microbial groups (FeRB and SRB). 

For the FeRB (ostensibly Geobacter spp.), two parallel competing TEAP reactions are used to 

catalyze the conversion of Fe(III) mineral to Fe(II). The first uses goethite as the nominal Fe(III) 

oxide/oxyhydroxide terminal electron acceptor, whereas the second parallel reaction uses 

phyllosilicate Fe(III), identified by Komlos et al. (2008b) as the principal source for bioavailable 

Fe(III). 

0.125CH3COO- + FeOOH(s) + 1.875H+ = Fe++ + 1.5H2O + 0.25HCO3

-

 (3.5) 

Fe(III) oxide TEAP

0.125 CH3COO- + Fe(III)(ls) + 0.5H2O = 

Phyllosilicate Fe(III) TEAP

 0.25HCO3

- + 0.9 Fe(II)(ls) + 0.1 Fe++ + 1.125H+ (3.6)

 

Based on the low biomass yield for Geobacter reported by Mahadevan et al. (2006), catabolic 

stoichiometry is assumed for both the oxide and phyllosilicate Fe(III) TEAP reactions. 
3.8

For the bioreduction of U(VI) in groundwater, uranyl (UO2

++

) is used as the terminal electron 

acceptor in the TEAP reaction, which is also mediated by the FeRB microbial population (i.e., 

Geobacter spp.). 

0.1250CH3COO- + 0.3538H2O + 0.0113NH4

+

 + 0.3875UO2

++

= 

U(VI) TEAP

 0.0113BM_FeRB + 0.855H+ + 0.1938HCO3

- + 0.3875UO2(s) (3.7) 

Note that the abiotic uranium aqueous and surface complexation reaction model (described in

section 3.2.2) requires 22 U(VI) aqueous species in addition to uranyl. As uranyl is consumed in 

the biologically mediated TEAP reaction, all other U(VI) species will diminish due to the 

assumption that all aqueous complexation reactions for uranium are fast reactions. 

The TEAP reaction for sulfate reduction (Equation 3.8) is associated with an attached population 

of sulfate-reducing bacteria (SRB): 

 

Sulfate TEAP

0.125CH3COO- + 0.0057H+ + 0.0038NH4

+

 + 0.1155SO4

-- = 

 0.0038BM_SRB + 0.0114H2O + 0.231HCO3

- + 0.1155HS- (3.8) 

The stoichiometries in Equations 3.7 and 3.8 which include the yield of an immobile biomass of 

0.17 g cells/g acetate and 0.058 g cells/g acetate, respectively, are energetics-based (Rittmann 

and McCarty, 2001) under the assumption of a biomass molecular formula of C5H7O2N and an 

energy transfer efficiency value of 0.6.

Rate Law for Biologically Mediated Reactions. A recent conceptual model update has both 

functional microbial groups, FeRB and SRB, present and active during the initial period of 

biostimulation. In this case, the initial reaction rate of the sulfate TEAP is slow, reflecting the 

lower abundance and slower growth rate of the SRB. Sulfate reduction during this early period 

is negligible and the initial groundwater observations are dominated by elevated aqueous Fe(II) 

and conversion of aqueous U(VI) to insoluble U(IV) catalyzed by the FeRB. As SRB biomass 

increases, the bulk sulfate TEAP reaction rate increases and after ca. 30 days of acetate 

amendment, sulfate reduction becomes the dominant TEAP and consumer of acetate.

The kinetics of the microbially mediated TEAP reactions is of the Monod type with 

thermodynamic control for all the TEAP reactions. In conjunction with the four biologically 

mediated reduction reactions (i.e., phyllosilicate Fe(III), oxide Fe(III), U(VI), and sulfate), the 

acetate consumption rate, bio RC , can be identified as: 

 
3.9

(3.9) 

where NeA = number of terminal electron acceptors

sC = stoichiometric coefficient of acetate in the TEAP reaction

Cc = acetate concentration

B = biomass concentration of microorganism mediating the TEAP reaction

CeA = terminal electron acceptor concentration

μm,eA = acetate oxidation rate for the terminal electron acceptor

Ks,C = half-saturation coefficient for acetate

Ks,eA = half-saturation coefficient for the terminal electron acceptor

f(ΔGr) = 1 – exp[(ΔGr- ΔGmin)/RT]

ΔGr = free energy change of the corresponding TEAP reaction

ΔGmin = minimum free energy change required to drive ATP 

Synthesis

R = gas constant

T = absolute temperature.

In the updated model for the baseline system, the three TEAPs of interest (Fe(III), U(VI), and 

sulfate) are simultaneously active but only the sulfate TEAP reaction rate depends on the amount 

of biomass. In this case, the SRB biomass is initially low such that the sulfate TEAP, while 

active, is nearly imperceptible. As the SRB biomass slowly grows, the TEAP reaction rate 

increases commensurately. A first-order decay model (dB/dt = -B) is used to describe the loss of 

SRB biomass. Eventually, after 4 to 5 weeks, the net growth of SRB biomass is sufficient for 

sulfate reduction to become the dominant TEAP reaction and principal consumer of acetate. 

In the case of Fe(III)-oxide bioreduction, the terminal electron acceptor is a solid phase so we 

replace the second Monod term with the available surface hydroxyl site density in equivalent 

molarity. This is based on the assumption that the reactivity of the Fe(III) oxide/oxyhydroxide 

terminal electron acceptor is reduced by Fe(II) surface complexation (Liu et al., 2001; Urrutia et 

al., 1999). For phyllosilicate Fe(III) reduction, the second Monod term is eliminated and the rate 

law simplifies to second order kinetics limited by the bioavailability of phyllosilicate Fe(III). 

For the U(VI) TEAP, CeA in the Monod expression is total aqueous U(VI) without the calciumuranyl-carbonate complexes because they have been shown to be thermodynamically inhibited 

for bioreduction (Brooks et al., 2003) . 

3.2.2 Abiotic Reactions

In addition to the biologically mediated reactions, the modeled biogeochemical reaction network 

includes uranium aqueous and surface speciation reactions, Fe(II) aqueous and sorption 

reactions, mineral dissolution and precipitation and other aqueous complexation reactions. 

( )r

N

eA s eAeA

eA

CCs

C

m eA C

bio

C Gf CK

C

CK

C R Bs eA

∆ 















+ 















+ −= ∑ , ,

μ ,
3.10

Surface Complexation. The uranium surface complexation model (Table 3.2) was developed 

from batch experiments with Rifle sediments performed over a relevant range of pH, alkalinity, 

and uranium concentrations. The generalized composite surface complexation approach (Davis 

et al., 1998; Davis et al., 2004; Waite et al., 2000) that was used considered two sorption 

reactions with three different sites—0.01% very strong sites (>SSOH), 0.1% strong sites 

(>SOH), and 99.89% weak sites (>WOH)—which are defined by their relative binding strength 

for U(VI), assuming a total site density of 3.84 μmoles m-2 of hydroxyl groups and a surface area 

of 4.256 m2g

-1. Stability constants for the six surface complexation reactions were parameterized 

from batch experiment data. 

Table 3.2. U(VI) surface complexation reactions.

Surface Species

Reaction logK (estimated)

SSOH + UO2

2+ = SSOUO2+ + H+ 12.28

SOH + UO2

2+ = SOUO2+ + H+ 6.95

WOH + UO2

2+ = WOUO2+ + H+ 2.74

SSOH + UO2

2+ + H2O = SSOUOOH + 2H+ 0.033

SOH + UO2

2+ + H2O = SOUOOH + 2H+ -2.12

WOH + UO2

2+ + H2O = WOUOOH + 2H+ -5.01

SSOH denoting very strong binding sites: 0.01% of total sites

SOH denoting strong binding sites: 0.1% of total sites 

WOH denoting weak binding sites: 99.89% of total sites

The model system is based on 22 aqueous uranium complexation reactions (including Ca-UO2-

CO3 ternary complexes) (Table 3.3). The primary benefit from this level of detail in uranium 

modeling is to more accurately represent the sensitivity of U(VI) mobility to changes in 

alkalinity, pH, and uranium concentrations. These are all well-known abiotic consequences of in 

situ biostimulation. 
3.11

Table 3.3. Uranium species and their formation constants. 

Aqueous Species 

Reaction logK (I = 0) (a)

UO2

2+ + H2O = UO2OH+ + H+ -5.25

UO2

2+ + 2H2O = UO2(OH)2(aq) + 2H+ -12.15

UO2

2+ + 3H2O = UO2(OH)3- + 3H+ -20.25

UO2

2+ + 4H2O = UO2(OH)42- + 4H+ -32.40

2UO2

2+ + H2O = (UO2)2OH3+ + H+ -2.70

2UO2

2+ + 2H2O = (UO2)2(OH)22+ + 2H+ -5.62

3UO2

2+ + 4H2O = (UO2)3(OH)42+ + 4H+ -11.90

3UO2

2+ + 5H2O = (UO2)3(OH)5+ + 5H+ -15.55

3UO2

2+ + 7H2O = (UO2)3(OH)7- + 7H+ -32.20

4UO2

2+ + 7H2O = (UO2)4(OH)7+ + 7H+ -21.90

UO2

2+ + CO32- = UO2CO3(aq) 9.94

UO2

2+ + 2CO32- = UO2(CO3)22- 16.61

UO2

2+ + 3CO32- = UO2(CO3)34- 21.84

3UO2

2+ + 6CO32- = (UO2)3(CO3)66- 54.00

2UO2

2+ + CO32- + 3H2O = (UO2)2CO3(OH)3- + 3H+ -0.85

3UO2

2+ + CO3-- + 3H2O = (UO2)3CO3(OH)3+ + 3H+ 0.66

UO2

2+ + Cl- = UO2Cl+ 0.17

Ca2+ + UO2

2+ + 3CO32- = CaUO2(CO3)32- 25.40 (b)

2Ca2+ + UO2

2+ + 3CO32- = Ca2UO2(CO3)30

(aq) 30.55 (b)

UO2

2+ + 2Cl- = UO2Cl2(aq) -1.10

UO2

2+ + SO42- = UO2(SO4)aq 3.15

UO2

2+ + 2SO42- = UO2(SO4)22- 4.14

(a) Values from Guillaumont et al.(2003), unless otherwise indicated. 

(b) Bernhard et al.(2001). 

A general, non-electrostatic surface complexation approach (Table 3.4) similar to the uranium 

surface complexation model proposed by Davis et al. (2004) was used to model Fe(II) sorption. 

Table 3.4. Fe2+ surface complexation reactions.

Surface Species

Reaction logK (estimated)

>FeOH + H+ = >FeOH2

+ 7.47

>FeOH = >FeO- + H+ -9.51

>FeOH + Fe2+ = >FeOFe+ + H+ -5.00

>FeOH + Fe2+ + H2O = >FeOFeOH + 2H+ -11.96
3.12

This approach may be the most appropriate for complex field applications since it is difficult to 

characterize surface charging behavior of non-ideal natural mineral phases (Davis et al., 1998). 

Note that the abiotic reduction of adsorbed uranyl by adsorbed Fe(II) was not included in the 

reaction network. While there have been observations of abiotic uranium reduction in the 

literature (Liger et al., 1999; Regenspurg et al., 2009; Scott et al., 2005), they usually involve 

model minerals and/or electron shuttles (e.g., AQDS). Reduced natural sediments in the absence 

of microbial activity, apparently are recalcitrant to reduction of adsorbed U(VI) (Jeon et al., 

2005; Ortiz-Bernad et al., 2004). This is consistent with biostimulation events at the Rifle IFRC 

site where there has been no observation of abiotic reduction of U(VI). Furthermore, a recent 

field experiment at the Rifle IFRC site that specifically targeted abiotic U(VI) reduction via 

Fe(II) amendment also showed negligible U(VI) conversion. 

Mineral Reactions. As mentioned in Section 2.3.2 the TEAP reactions can generate 1-2 orders 

of magnitude more bicarbonate, Fe(II), and sulfide than is measured in solution. Thus, solid 

phase reactions are important for accurately representing the water chemistry, which ultimately 

controls uranium behavior. Table 3.5 includes all abiotic mineral reactions in the reaction 

network. These reactions also have relevance for pore space reduction via mineral precipitation. 

Table 3.5. Modeled mineral reactions.

Ca2+ + HCO3

- = Calcite(s) + H+

Fe2+ + HCO3

- = Siderite(s) + H+

Fe2+ + HS- = FeS(s) + H+

2FeOOH + HS- + 5H+ = 2Fe2+ + S0 + 4H2O 

The principal sink for excess carbonate is the precipitation of calcite, which is predicted to 

account for the bulk of secondary mineral volume fraction. Iron sulfides are predicted to form in 

significant amounts during the sulfate reduction phase; however, the Fe

++ that is available from 

the bioreduction of Fe(III) mineral is limiting. An important reaction that facilitates the removal 

of sulfide from solution and the production of FeS(s) is the abiotic oxidation of dissolved sulfide 

by goethite (Canfield, 1989; Pyzik and Sommer, 1981; Zopfi et al., 2008) (Table 3.5). In this 

reaction elemental sulfur is precipitated and additional Fe2+ is produced. This reaction is 

consistent with recent column studies of acetate biostimulation in Rifle sediments that have 

identified significant elemental sulfur abundance during sulfate reduction. The rate law used for 

this reaction is 

 R = k [FeOOH] [HS-

], (3.10) 

where k is the rate constant, [FeOOH] and [HS-] are species concentrations of FeOOH and HS-, 

respectively. All other mineral reactions use the following rate formulations that are a function 

of the saturation state (Hunter et al., 1998): 
3.13

 

 Ri = ki( ) Ωi −1 for Ωi ≥1

k−i[ ] Qi ( ) Ωi −1 for Ωi <1

 

  

  (3.11) 

 

where i represents the i-th mineral phase, Ri is the rate of the reaction, i k is the mineral 

precipitation rate constant [ML-3T-1], i k− is the mineral dissolution rate constant [T-1], Qi is the 

concentration of the i-th mineral phase [ML-3], and Ωi is the saturation index of the i-th mineral 

phase. 

In general, the abiotic, non-uranium, aqueous reactions involving the primary components of 

interest (H+, NH4

+

, Na+, K+, Ca2+, Mg2+, Fe2+, Cl-, HS-, CO3

2-, SO42-, CH3COO-

) are modeled as 

equilibrium reactions with thermodynamics from the EQ3/6 database (Table 3.6).

Table 3.6. Equilibrium reactions in the reaction network. 

Equilibrium Reactions

Reaction LogK

CH3COO- + H+ = CH3COOH 4.76

Ca2+ + H2O = CaOH+ + H+ -12.60

Ca2+ + CH3COO- = CaCH3COO+ 1.18

Ca2+ + CO3

2- + H+ = CaHCO3+ 11.33

Ca2+ + CO3

2- = CaCO3 3.15

Ca2+ + SO4

2- = CaSO4 2.31

CO3

2- + 2H+ = H2CO3 16.68

CO3

2- + H+ = HCO3- 10.33

Fe2+ + H2O = FeOH+ + H+ -9.50

CH3COO- + Fe2+ = FeCH3COO+ 1.82

Fe2+ + CO3

2- + H+ = FeHCO3+ 12.33

Fe2+ + CO3

2- = FeCO3(aq) 5.50

Fe2+ + 2CO3

2- = Fe(CO3)22- 7.10

Fe2+ + SO4

2- = FeSO4 2.25

Fe2+ + Cl- = FeCl+ 0.90

Fe2+ + 2HS- = Fe(HS)2(aq) 8.95

Fe2+ + 3HS- = Fe(HS)3

- 10.99

HS- + H+ = H2S(aq) 6.99

HS- = H+ + S2- -12.92

K+ + SO4

2- = KSO4- 0.85

Mg2+ + H2O = MgOH+ + H+ -11.79

CH3COO- + Mg2+ = MgCH3COO+ 1.14

CO3

2- + Mg2+ + H+ = MgHCO3+ 11.40

CO3

2- + Mg2+ = MgCO3(aq) 2.98
3.14

Mg2+ + SO4

2- = MgSO4(aq) 2.25

Na+ + CH3COO- = NaCH3COO -0.18

Na+ + CO3

2- + H+ = NaHCO3(aq) 10.08

CO3

2- + Na+ = NaCO3- 1.27

Na+ + SO4

2- = NaSO4- 0.70

NH4

+ = NH3(aq) + H+ -9.25

NH4

+ + SO42- = NH4SO4- 1.11

SO4

2- + H+ = HSO4- 1.99

H2O = OH- + H+ -14.00

3.2.3 Biogeochemical Model Calibration

The calibration of the biogeochemical reaction model is based on the estimation of rate law 

parameters for the biologically mediated reactions and the abiotic mineral reactions. A single set 

of these rate parameters has been determined to best represent all the large-scale field 

biostimulation experiments performed at the Rifle IFRC site (2002, 2003, 2007, and 2008). 

Using spatial and temporal distributions of measured acetate, Fe(II), sulfate, U(VI), and pH, 

calibration included reaction network parameters for the TEAPs, Fe(II) sorption, precipitation of 

carbonate minerals and iron sulfide, and dissolution of goethite by sulfide. A stepwise approach 

was used to calibrate reaction parameters in the transport modeling framework. The TEAPs were

used as a starting point. We assumed that during the early iron reduction period, acetate

consumption is attributed to the predominant FeRB population. The maximum Fe(III) and sulfate

bioreduction rates and the initial SRB biomass and decay rates were calibrated to match observed 

data. Fe(II) sorption, and secondary mineral reaction parameters were then fitted iteratively to 

match aqueous Fe(II), sulfide, alkalinity and pH. Uranium in the system is at trace concentrations 

compared to other species, so it does not significantly impact the major ion chemistry. Its full 

speciation was included in the reaction network once other reaction parameters were fixed. All 

calibrated parameters are summarized in Table 3.7. 

 

Table 3.7. Values for rate law parameters.

Parameter Value

Precipitation rate constant for calcite 1.644×10-7 Md-1

Dissolution rate constant for calcite 1.37×10-6 d-1

Precipitation rate constant for siderite 1.37×10-6 Md-1

Dissolution rate constant for siderite 1.37×10-7 d-1

Precipitation rate constant for FeS(s) 5.11×10-6 Md-1

Dissolution rate constant for FeS(s) 6.85×10-6 d-1

Dissolution rate constant of goethite by sulfide 20.8 d-1

Rate constant for phyllosilicate iron bioreduction 0.075 Md-1

Rate constant for FeOOH bioreduction 0.016 d-1

Half-Saturation for acetate 5.0×10-4 M
3.15

Initial amount of SRB 0.003 μM

Rate constant for sulfate bioreduction 90 d-1

Half-Saturation for sulfate bioreduction 10-5 M

Rate constant for U(VI) conversion by FeRB 2.5×10-6 Md-1

Half-Saturation for U(VI) conversion by FeRB 10-7 M

SRB decay rate 0.015 d-1

3.3 Numerical Model Specification

The baseline for this study is the 2008 biostimulation field experiment at the Rifle IFRC site

nicknamed Big Rusty. This was the second biostimulation experiment performed in this 

particular experimental plot (Figure 2.2). In this case, a fairly long and complex acetate injection 

took place (Table 3.8). The operation of the injection gallery was designed to achieve a 10:1 

dilution of the initial acetate and bromide injectate. 14 days of injecting groundwater mixed with 

acetate and bromide amendment targeting a field concentration of 5 mM were followed by 9 

days of groundwater injection without amendment, then followed by another 14 days of injection 

of the initial amendment, before finishing with 59 days of enhanced acetate injection targeting 15 

mM acetate field concentrations. 

Table 3.8. 2008 Big Rusty schedule of amendment concentrations. 

 

START

DAY

START

DATE

END

DAY

END

DATE

ACETATE

/BROMIDE

CONCENTRATION

IN TANK

0 7/20/2008 14 8/3/2008 50 mM / 13 mM

15 8/4/2008 23 8/12/2008 0 mM / 0 mM 

24 8/13/2008 37 8/26/2008 50 mM / 13 mM 

38 8/27/2008 53 9/11/2008 150 mM / 13 mM

57 9/15/2008 72 9/30/2008 150 mM / 13 mM

73 10/1/2008 89 10/16/2008 150 mM / 13 mM

90 10/17/2008 96 10/23/2008 150 mM / 13 mM

For the one-dimensional, saturated reactive transport simulations, the injection gallery was 

modeled as the upgradient boundary (Figure 3.1). At this boundary, the injectate was assumed to 

be completely mixed over the saturated thickness and injection gallery width. Boundary 

conditions for Fe(II), sulfate, and U(VI) were interpolated from field measurements at upgradient 

wells. The initial conditions for pH, carbonate, Ca2+, Mg2+, K+

 and Na+ are based on field 

measurements. Na+ was also included in the injectate as the cation component in the acetate and 

bromide salts. The downgradient domain was discretized with 120 uniform 0.1-m-long grid cells. 
3.16

The maximum time step for the simulation was 0.25 d, with automatic time step reduction and 

expansion based on numerical convergence criteria. The simulation period began with the initial

acetate biostimulation on July 20, 2008. 

Figure 3.1. One-dimensional and two-dimensional modeling domains. Red vertical lines 

indicate upgradient (U-wells), injection gallery, and downgradient (D-wells) well 

locations. The one-dimensional model domain is 11.5 m long with the upgradient 

boundary at the injection gallery (X=28 m). Ponding at the ground surface for the 

flood scenario in the two-dimensional model domain is between X=20 and X=40 m. 

For the baseline two-dimensional, variably saturated flow and reactive transport simulations, the 

vertical cross-sectional domain size is 70 m wide by 6.3 m deep (Figure 3.1). The lateral extent 

of the domain was significantly increased over the one-dimensional domain to accommodate the 

impact of the flood event on the boundary conditions. The grid size in the lateral dimension 

ranged from 0.25 m around the injection well to 5 m near the boundary far from the injection. 

The grid size in the vertical direction ranged from 0.1 m to 0.25 m. Other than the injection of 

acetate, sodium and bromide, which are treated as source terms in the saturated zone 28 m from 

the influent boundary, the initial and boundary conditions for other species are the same as those 

for the one-dimensional system. The simulation time step was 0.0001 hr with an acceleration 

factor of 1.25, and a maximum time step of 0.1 d. For the flood scenario, initial and upstream 

boundary conditions for dissolved oxygen were obtained from a simulation of oxygen transport 

that included bioreduction of oxygen using natural organic carbon as the electron donor. 

3.4 Sensitivity Analyses

Analyses were performed targeting the sensitivity of simulated aqueous uranium concentrations 

to parameters in the physical, microbiological, and geochemical process models from the 

baseline conceptualization. Assuming that the process models are valid, the identification of the 

most sensitive parameters can help guide where additional care and effort in characterization 

activities might be warranted. For the purpose of initially identifying the sensitivity of simulation 
3.17

results to model parameters, we used a one-dimensional model domain for the multicomponent 

biogeochemical reactive transport simulations. The first 52 days of the 2008 biostimulation 

experiment at the Rifle IFRC site were used as the baseline for analyzing and comparing the 

results of the scenarios for this study. Although biostimulation continued well beyond this time, 

accurate accounting of the acetate mass could not be maintained due to problems in the uniform 

metering of the acetate to the injection wells and changes in the groundwater flow direction. 

The baseline hydraulic conductivity, porosity, and dispersivity were calibrated with 

downgradient monitoring well observations of bromide tracer that were injected with the acetate 

electron donor. The set of baseline reaction modeling parameters were developed using 

observations from the 2002, 2003, 2007, and 2008 biostimulation field experiments. Initial and 

boundary conditions for hydrology and geochemistry were based on observations from 

upgradient and downgradient monitoring wells. 

3.4.1 Baseline

For the sensitivity analyses, the baseline for comparison is the application of the one-dimensional 

reactive transport model to the 2008 biostimulation experiment. During this experiment, water 

chemistry was monitored in 12 downgradient wells arranged in three rows of four wells each, 2.5 

m, 5.0 m, and 8.5 m from the injection well gallery (Figure 2.2). In Figure 3.2, simulated acetate 

and U(VI) are plotted with observations from the monitoring well network. The modeled 

acetate generally reflects the pulsed loading (Table 3.8) delivered to the injection wells and 

compares more favorably with the observations over the first 30 days than the following 30 days. 

With the initiation of biostimulation, the bioreduction of U(VI) immediately begins to lower 

U(VI) concentrations. U(VI) removal from groundwater continues to increase over the first 30 

days before leveling off and diminishing slightly in the next 30 days (Figure 3.2). Fe(II) 

gradually increases over the first 20 days, reflecting Fe(III) bioreduction, before slightly 

diminishing over the following 40 days (Figure 3.3). Sulfate bioreduction is not discernible until 

about 30 days into the biostimulation, after which sulfate concentrations begin to decrease. In 

general, the dynamics of aqueous Fe(II) and U(VI) are only subtly correlated with the variable 

acetate pulses. This moderated behavior is due, in part, to surface complexation processes, 

which provide a short-term restoring force for concentration changes in these aqueous 

components. U(VI) desorption accounts for the rise in U(VI) from day 40 to day 50, whereas the 

subsequent decrease in U(VI) reflects enhanced bioreduction resulting from the tripling of the 

acetate concentrations on day 38. Most of the sulfide becomes associated with the solid phase in 

subsidiary reactions with Fe2+ (precipitating FeS) and goethite (resulting in precipitation of 

elemental sulfur). The significance of the sulfate reduction phase is that aqueous U(VI)

concentrations increased during this period. 
3.18

Figure 3.2. Simulated and observed acetate (left) and U(VI) (right) in monitoring well row 1 

(2.5 m downgradient), row 2 (5.0 m downgradient), and row 3 (8.5 m downgradient). 

Figure 3.3. Temporal aqueous Fe(II) (left) and sulfate (right) at 2.5m (top), 5.0m (center) and 

8.5 m (bottom) downgradient from injection gallery 

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 20 40

time (d)

U(VI) uM

D-01

D-02

D-03

D-04

Model

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 20 40

time (d)

U(VI) uM

D-05

D-06

D-07

D-08

Model

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 20 40

time (d)

U(VI) uM

D-09

D-10

D-11

D-12

Model

0

1

2

3

4

5

6

7

8

0 20 40 60

time (d)

Acetate (mM)

D-01

D-02

D-03

D-04

Model

0

1

2

3

4

5

6

7

8

0 20 40 60

time (d)

Acetate (mM)

D-05

D-06

D-07

D-08

Model

0

1

2

3

4

5

6

7

8

0 20 40 60 time (d)

Acetate (mM)

D-09

D-10

D-11

D-12

Model

0

50

100

150

200

0 20 40 60

time (d)

Fe(II) (uM)

D-01

D-02

D-03

D-04

Model

0

50

100

150

200

0 20 40 60

time (d)

Fe(II) (uM)

D-05

D-06

D-07

D-08

Model

0.0

50.0

100.0

150.0

200.0

0 20 40 60

time (d)

Fe(II) (uM)

D-09

D-10

D-11

D-12

Model

0

2

4

6

8

10

12

0 20 40 60

time (d)

Sulfate (mM)

D-01

D-02

D-03

D-04

Model

0

2

4

6

8

10

12

0 20 40 60

time (d)

Sulfate (uM)

D-09

D-10

D-11

D-12

Model

0

2

4

6

8

10

12

0 20 40 60

time (d)

Sulfate (mM)

D-05

D-06

D-07

D-08

Model
3.19

3.4.2 Parameter Selection

The deviation of aqueous uranium behavior from the simulated baseline behavior was used as the 

indicator of sensitivity to a model parameter. Selected parameters (Table 3.9) were individually 

perturbed to identify sensitivity. The physical process modeling parameters tested were flow rate, 

porosity, and dispersivity; the microbiological process modeling parameters tested were the 

initial sulfate biomass, uranium bioreduction rate, sulfate bioreduction rate, and biomass decay 

rate; and geochemical process modeling parameters tested were initial aqueous uranium, surface 

complexation site density, and pH. In general, the sensitivity analyses involved adding and 

subtracting 15% from the calibrated baseline values. The exceptions were pH, porosity and 

dispersivity, which used end member values observed at the Rifle IFRC site. The complete set 

of plots for the sensitivity analyses can be found in Appendix A. Those that are reproduced in 

this section represent the most sensitive model parameters with respect to predicted aqueous 

uranium concentrations. 

Table 3.9. Perturbation of model parameters for sensitivity analyses.

Model Parameter Low Value High Value

Flow rate 0.067m3/m2/d 0.091 m3/m2/d

Porosity 0.1 0.1725

Dispersivity 0.4 m 0.70 m

Initial Sulfate Biomass 2.55x10-9 mol/L 3.45x10-9 mol/L

U bioreduction rate 2.125E-6 mol/d 2.875E-6 mol/d

Sulfate bioreduction rate 76.5 1/d 103.5 1/d

Biomass decay rate 0.01275 1/d 0.01725 1/d

Initial U(VI) 2.295E-6 mol/L 3.105E-6 mol/L

pH 6.8 7.4

Site density 3.264 μmol/m2 4.416 μmol/m2

3.4.3 Hydrologic Model

Results of the sensitivity analyses are shown in Figures 3.4 through 3.10. In the case of the 

U(VI) TEAP, the intrinsic rate has a direct impact on the magnitude of the aqueous uranium 

concentrations (i.e., higher intrinsic rate results in higher uranium removal from groundwater). 

Of the hydrologic parameters that were tested, the perturbations in the flow rate had the largest 

impacts on the predicted aqueous uranium concentrations (Figure 3.4). This is because the flow 

rate affects 1) the influx of groundwater, which controls the in situ concentration of injected 

amendments; 2) the supply of chemical components in the influent groundwater, which controls 

the availability of reactants; and 3) the transport of the electron donor and the aqueous 

byproducts of reactions. 
3.20

Figure 3.4. Aqueous uranium sensitivity to flow rates 2.5 m (diamonds), 5.0 m (squares), and 

8.5 m (triangles) downgradient from the injection gallery. 

For biostimulation in advection-dominant systems, the porosity affects the timing of acetate 

arrival at downgradient locations. While the impact of porosity perturbation on aqueous uranium 

concentrations is considerably smaller than that of the flow rate, it is instructive to understand the 

interplay of transport rates and TEAP reaction rates that determine the location and magnitude of 

the mobile TEAP reactants and products. For example, as advection decreases with higher 

porosity, there is increased residence time which equates to more time for acetate reaction. This 

can result in more TEAP products nearer the acetate injection point, which can be associated 

with increased rates of uranium bioreduction (Figure 3.5). Dispersivity (not shown here) was the 

least sensitive of the hydrologic model parameters tested with respect to the aqueous uranium 

concentrations. This reflects the advection-dominant nature of the reactive transport problem 

being simulated.

Aqueous Uranium Sensitivity: Flow

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low Flow

Row 2 Low Flow

Row 3 Low Flow

Row 1 High Flow

Row 2 High Flow

Row 3 High Flow
3.21

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0 10 20 30 40 50 60

Concentration (uM)

Time (d)

Aqueous Uranium Sensitivity: Porosity

Row 1 Low Porosity

Row 2 Low Porosity

Row 3 Low Porosity

Row 1 High Porosity

Row 2 High Porosity

Row 3 High Porosity

Figure 3.5. Aqueous uranium sensitivity to porosity 2.5 m (diamonds), 5.0 m (squares), and 8.5 

m (triangles) downgradient from the injection gallery.

3.4.4 Biologically Mediated Reaction Model

Of the sensitivity analyses performed with the biologically mediated reaction model parameters, 

the simulated aqueous uranium concentrations were most sensitive to the uranium bioreduction 

rate. This was reflected by a deviation that grows over time between the low and high rate cases 

(Figure 3.6). 

The deviation between the simulated aqueous uranium for low and high sulfate bioreduction 

cases, on the other hand, is nonexistent for the first 30 days, when Fe(III) reduction is dominant. 

Then as sulfate reduction becomes the dominant TEAP, the high and low sulfate bioreduction 

rate cases deviate significantly. The high rate result exhibits a rebound in simulated aqueous 

uranium concentrations that is 10 days earlier and larger than the low rate case (Figure 3.7). 

The simulated aqueous uranium concentrations were generally insensitive to the perturbation of 

the other parameters in the sulfate TEAP rate law: initial SRB biomass and decay rate of the 

SRB biomass. While these parameters are important to the timing of the succession of the 

sulfate TEAP, 15% perturbations were not sufficient to significantly affect aqueous U(VI) 

concentrations. 
3.22

 

Figure 3.6. Aqueous uranium sensitivity to U(VI) bioreduction rate 2.5 m (diamonds), 5.0 m 

(squares), and 8.5 m (triangles) downgradient from the injection gallery. 

Figure 3.7. Aqueous uranium sensitivity to sulfate bioreduction rate 2.5 m (diamonds), 5.0 m 

(squares), and 8.5 m (triangles) downgradient from the injection gallery. 

Aqueous Uranium Sensitivity: U Rate

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low U Rate

Row 2 Low U Rate

Row 3 Low U Rate

Row 1 High U Rate

Row 2 High U Rate

Row 3 High U Rate

Aqueous Uranium Sensitivity: SO4 Rate

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low SO4 Rate

Row 2 Low SO4 Rate

Row 3 Low SO4 Rate

Row 1 High SO4 Rate

Row 2 High SO4 Rate

Row 3 High SO4 Rate
3.23

3.4.5 Geochemical Reaction Model

The principal geochemical control on aqueous uranium concentrations is through the uranium 

surface complexation model. Of the geochemical conditions tested in the sensitivity analyses, 

the largest differences in aqueous uranium concentrations were seen between the end member 

cases with pHs of 6.8 and 7.4 (Figure 3.8). This is not a large pH range yet deviations between 

the cases are initially large and become smaller with time before getting large again after sulfate 

reduction becomes dominant. Deviations during sulfate reduction for simulated days 40 to 50, 

grow larger with distance from the injection gallery. Under ambient Rifle aquifer water 

chemistry, the uranium surface complexation model responds to higher pH (e.g., slight elevation 

due to sulfide-goethite reaction) with higher aqueous U(VI) concentrations. Since the sensitivity 

analysis perturbed the initial pH, the higher pH case is associated with higher initial aqueous 

uranium concentrations. During sulfate reduction, increases in bicarbonate and pH result in 

U(VI) desorption. The magnitude of this desorption event is sensitive to the initial pH: the high 

pH case results in lower aqueous uranium concentrations during sulfate reduction because there 

is less U(VI) partitioned to the solid phase. Conversely, the lower initial pH case results in 

higher adsorbed uranium concentrations that can partition to the aqueous phase during the 

desorption event associated with the height of sulfate reduction. 

Figure 3.8. Aqueous uranium sensitivity to pH 2.5 m (diamonds), 5.0 m (squares), and 8.5 m 

(triangles) downgradient from the injection gallery.

Aqueous Uranium Sensitivity: pH

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low pH

Row 2 Low pH

Row 3 Low pH

Row 1 High pH

Row 2 High pH

Row 3 High pH
3.24

A similar but smaller sensitivity is exhibited by the perturbation of uranium surface 

complexation site density (Figure 3.9). In this case the most significant deviation is in the initial 

aqueous uranium concentrations, where the low site density is associated with higher aqueous 

uranium concentrations. The two cases generally coalesce from day 20 to 40 before a very 

minor crossover, whereupon the high site densities have more uranium to desorb, elevating the 

aqueous uranium concentrations. 

Figure 3.9. Aqueous uranium sensitivity to site density for uranium surface complexation 2.5 m 

(diamonds), 5.0 m (squares), and 8.5 m (triangles) downgradient from the injection 

gallery.

As with the two previous geochemical conditions, the sensitivity to the initial aqueous uranium 

concentration is largest at time zero (Figure 3.10). However, unlike the other geochemical 

conditions, the system eventually responds to the influent uranium boundary condition and the 

high and low cases coalesce at day 30 in the first row, day 40 in the second row, and day 50 in 

the third row. 

Aqueous Uranium Sensitivity: Site Density

0.00E+00

2.00E-01

4.00E-01

6.00E-01

8.00E-01

1.00E+00

1.20E+00

1.40E+00

1.60E+00

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low Site Density

Row 2 Low Site Density

Row 3 Low Site Density

Row 1 High Site Density

Row 2 High Site Density

Row 3 High Site Density
3.25

Figure 3.10. Aqueous uranium sensitivity to initial U(VI) concentration 2.5 m (diamonds), 5.0 m 

(squares), and 8.5 m (triangles) downgradient from the injection gallery. 

Aqueous Uranium Sensitivity: Initial Uranium

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low Initial U

Row 2 Low Initial U

Row 3 Low Initial U

Row 1 High Initial U

Row 2 High Initial U

Row 3 High Initial U

4.1

4 Modeling Scenarios

4.1 Additional Electron Acceptors

The groundwater at the Rifle IFRC site is sufficiently low in dissolved oxygen (~0.2 mg/L) and 

nitrate (~0.2 mg/L) that these species are not included in the biogeochemical reaction network. 

However, other sites may have one or both of these components present in the groundwater. 

Accordingly, we added these two TEAPs to the simulation capability to address scenarios where 

oxygen and/or nitrate may be present. The stoichiometry of these reactions is based on the 

assumption of a biomass molecular formula of C5H7O2N, the presence of an O2-reducing 

microorganism, and an energy transfer efficiency value of 0.6 (Rittman and McCarty 2001). For 

acetate electron donor, the oxygen TEAP reaction is: 

0.125 CH3COO- + 0.02904 NH4

+ + 0.10481 O2 = 0.02904 BM_O2 + 0.00885H+ + 

0.08711H2O + 0.10481HCO3

- (4.1)

Similar assumptions can be made for the presence of nitrate-reducing bacteria such that the 

nitrate TEAP reaction is:

0.125 CH3COO- + 0.01329 NH4

+ + 0.36713 NO3- = 0.01329 BM_NO3 + 0.07185 H+ + 

 0.03986H2O + 0.18357HCO3

- + 0.36713NO2- (4.2) 

Nitrite, an intermediate product of nitrate reduction has been shown to be a more efficient

oxidizer of uraninite than nitrate (Wan et al., 2005). Accordingly, we also include the 

corresponding nitrite reaction: 

0.125 CH3COO-

 + 0.18565H+ + 0.1128H2O + 0.13832NO2

-

 = 0.00851BM_NO2 + 

 0.20747HCO3

-

 + 0.12981NH4+ (4.3) 

Since we have not calibrated the rates for these reactions at the Rifle IFRC site, we assumed a 

simple rate law

R=k[Ac][O2] 

R=k[Ac][NO3

-

] (4.4) 

R=k[Ac][NO2

-

] 

where k=2000/d/M. 
4.2

Figure 4.1 shows the hypothetical behavior of the oxygen and U(VI) TEAPs using the 2008 Big 

Rusty experiment baseline model under the assumption of a background level of dissolved 

oxygen near saturation with atmospheric oxygen, 250 umol/L. Dissolved oxygen is essentially 

depleted during each of the two 2-week periods of 5 mM acetate biostimulation. The rebound in 

oxygen after two weeks is due to the 9-day interruption in amendment delivery that separated the 

two 2-week biostimulation periods. As modeled in this scenario, the impact of the dissolved 

oxygen on U(VI) and other TEAP processes is minimal as they are indistinguishable from the 

baseline results. 

Figure 4.1. Dissolved oxygen (top) and aqueous U(VI) (bottom) during acetate biostimulation in 

downgradient monitoring row 1 (2.5 m), row 2 (5.0 m), and row 3 (8.5 m). 

Similarly, the hypothetical inclusion of the nitrate TEAP into the 2008 Big Rusty baseline model 

was performed with an initial nitrate concentration of 130 μmol/L, which has been observed in 

another alluvial aquifer of interest. In this case, the stoichiometric conversion of nitrate per mole 

of acetate consumed is 3 times that of oxygen (Equation 4.1 and 4.2). This results in a relatively 

faster depletion of nitrate (Figure 4.2). As with the dissolved oxygen case, the U(VI) and other 

TEAP processes are essentially identical to the baseline. 

In both cases, acetate was non-limiting as the removal of oxygen and nitrate was generally 

complete. The stoichiometry suggests that 0.3 mM acetate is necessary to remove oxygen, 

whereas 0.044 mM acetate is necessary to remove nitrate. While the overall impact of oxygen 

and nitrate to the uranium bioremediation appears to be minimal, the continuous influx of 

oxidizers into the treatment zone can be detrimental to the stability of immobilized uranium and 

must be addressed. 

0

50

100

150

200

250

300

0 10 20 30 40 50 60

time (d)

Oxygen (uM)

Row 1

Row 2

Row 3

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 20 40 60

time (d)

U(VI) uM

Row 1

Row 2

Row 3
4.3

Figure 4.2. Nitrate (top) and U(VI) (bottom) during acetate biostimulation in downgradient 

monitoring row 1 (2.5 m), row 2 (5.0 m), and row 3 (8.5 m).

4.2 Additional Electron Donors

At the Rifle IFRC site, only acetate has been used as the electron donor for large-scale field 

experiments. Lactate and ethanol are other possible electron donors that have been used at other 

sites (e.g., Wu et al. 2005). We added lactate and ethanol reactions for our primary TEAPs once 

again following the Rittman and McCarty approach described earlier. 

Lactate + sulfate: 

0.25 CH3CHOHCOO- + 0.01562 NH4

+ + 0.08595 SO4-- = 0.01562 BM_lac + 

 0.25 CH3COO- + 0.10157 H+ + 0.04686 H2O + 0.17190 HCO3

- + 0.08595 HS-(4.5) 

Lactate + uranium: 

0.25 CH3CHOHCOO- + 0.24084 H2O + 0.01994 NH4

+ + 0.30065 UO2++ = 

0.01994 BM_iron + 0.25 CH3COO- + 0.77156H+ + 0.15032 HCO3

- + 0.30065 UO2(s) (4.6) 

0

50

100

150

0 10 20 30 40 50 60

time (d)

Nitrate (uM)

Row 1

Row 2

Row 3

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 10 20 30 40 50 60

time (d)

U(VI) uM

Row 1

Row 2

Row 3
4.4

Lactate + phyllosilicate iron: 

0.25 CH3CHOHCOO- + Fe(III)(ls) + 0.5 H2O = 0.25 CH3COO- + 0.25 HCO3

- 

 + 1.25 H+ + 0.9 Fe(II)(ls) + 0.1 Fe++ (4.7) 

Ethanol + sulfate: 

0.25 CH3CH2OH + 0.0568HCO3

-

 + 0.0923SO4

-- = 0.25CH3COO-

 + 

 0.1009H+ + 0.267H2O + 0.0923HS- + 0.0114SO4RM_cell (4.8) 

Ethanol + uranium: 

0.25 CH3CH2OH + 0.2161 H2O + 0.47 UO2(CO3)2

-- = 0.25 CH3COO-

 + 

 0.0026 DRM3_cell + 0.2370 H+ + 0.9270 HCO3

-

 + 0.47 UO2(s) (4.9) 

Ethanol + phyllosilicate iron: 

CH3CH2OH + 4 Fe(III) + 7 H+ = CH3COO-

 + 3.6 Fe(II)(ls) + 0.4 Fe(II)(aq) + 7 H2O (4.10) 

Note that acetate is a product of all these reactions, which allows all the acetate baseline TEAP 

reactions to continue to be used in these analyses, albeit as subsidiary reactions dependent on 

lactate and ethanol oxidation. In the absence of rates for these TEAPs with sediments and 

groundwater from the Rifle IFRC site, we assume the same rate laws and parameters that were 

used in the baseline acetate-mediated TEAPs. In these simulations of the hypothetical inclusion 

of lactate and ethanol, we assumed the target electron donor concentrations were the same as the 

acetate in the baseline (5 mM and 15 mM). 

For the lactate electron donor simulation, lactate is seen to be depleted relatively quickly and the 

second 5 mM pulse is not evident (Figure 4.3). Lactate does reappear after the electron donor 

concentration was tripled (day 38) but is completely consumed prior to reaching the third row of 

monitoring wells. U(VI) is bioreduced more slowly, appears to be mildly responsive to the 

electron donor pulses, and does not have an obvious increase during sulfate reduction as is seen 

in the baseline. 
4.5

Figure 4.3. Lactate electron donor simulation: lactate (top) and U(VI) (bottom) concentrations 

in downgradient monitoring rows 1 (2.5 m), 2 (5.0 m), and 3 (8.5 m) 

Similar to the lactate simulation, the hypothetical biostimulation with ethanol leads to generally 

complete consumption of ethanol in 2 to 3 weeks before appearing again after day 38 when the 

electron donor loading was tripled (Figure 4.4). After an initial decline over the first 2 to 3 

weeks, the U(VI) in this case bottoms out and rebounds slightly. It should be mentioned that 

differences in the simulated TEAP behaviors from the baseline are strictly due to differences in 

reaction stoichiometries that are unique to each electron donor. This explanation is possible only 

because the same rate laws and parameters as the baseline are being used. 

Finally, it should be noted that these results are based on reactions from the literature with 

hypothetical rates. Thus, the simulated field behavior based on these reactions and rates have not 

been confirmed through field observations. We use these simulations to identify that other 

electron donor – electron acceptor – microorganism combinations are possible and the 

expectation is that the resulting behavior will differ from the acetate biostimulation behavior. 

0

1

2

3

4

5

6

0 20 40 60

time (d)

Lactate (mM)

Row 1

Row 2

Row 3

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 20 40 60

time (d)

U(VI) uM

Row 1

Row 2

Row 3
4.6

Figure 4.4. Ethanol electron donor simulation: ethanol (top) and U(VI) (bottom) concentrations 

in downgradient monitoring rows 1 (2.5 m), 2 (5.0 m), and 3 (8.5 m). 

4.3 Two-Dimensional Baseline

4.3.1 Hydrologic Conditions

While the 1-D simulations allowed us to make significant progress on the biogeochemical 

reaction network, there are important physical processes that require a minimum of two 

dimensions to resolve the behaviors of interest. These include density effects and gas entrapment 

during water table rise and fall. A baseline 2-D variably saturated flow simulation of the 2008 

Big Rusty experiment was performed to provide a basis for comparison against the hypothetical 

scenarios that were subsequently simulated. The 2-D baseline simulation included water table 

changes that reduced the saturated thickness by ~20% over the duration of the 2008 experiment 

(Figure 4.5). 

0

1

2

3

4

5

6

0 20 40 60

time (d)

Ethanol (mM)

Row 1

Row 2

Row 3

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 20 40 60

time (d)

U(VI) uM

Row 1

Row 2

Row 3
4.7

Figure 4.5. Rifle IFRC water table elevation during seasonal rise and fall.

The 2-D simulations generally capture the pulsed amendment release over the first 40-days of the 

Big Rusty field experiment. By accounting for the water table changes, these simulations are in 

better agreement with the observed concentrations than the 1-D simulations. As mentioned 

previously, we generally focus on the first part of the 2008 Big Rusty biostimulation experiment 

because groundwater flow direction changes and problems in the metering of the acetate evenly 

between the 10 injection wells resulted in significant discrepancies in injectate mass after day 38 

(Figure 4.6). 
4.8

Figure 4.6. Comparison of simulated bromide concentrations with field observations in the 

downgradient monitoring well rows 2.5 m (D-02g and D-03), 5.0 m (D-06g and D07), and 8.5 m (D-10g and D-11) downgradient from the injection gallery wells. The 

“g” designation for wells refers to the deepest sampling depth in the well. Note that 

soon after the increase in acetate concentrations on day 38 (see Table 3.10), uneven 

metering of amendment to the injection wells and changes in the groundwater flow 

direction resulted in some of the bromide not being accounted for in the 

downgradient monitoring wells.
4.9

The effects of the pulsed acetate injection with different concentrations can be seen in Figure 4.7. 

At 20 days, the initial 2-week 5 mM acetate pulse is reacting and has transported approximately 

5 m downgradient from the injection wells (X=28 m). On day 30, the second acetate pulse is in 

the 7th day of injection, while the center of mass of the first pulse is approximately 12 m 

downgradient. On day 40, the tripling of injected acetate targeting an in situ 15 mM 

concentration has been ongoing for 2 days. The zone of enhanced acetate is expanded by 

approximately 5 m at 50 days with nearly complete dispersal of the first pulse. This is consistent 

with the ~0.5 m/d pore velocity. The front of high acetate concentrations continues to move 

downgradient from 60 to 100 days but oxidation is limiting the migration. The sloping water 

table is discernible in these results and some of the acetate is diffusing into the vadose zone. 

Other than the small movement into the vadose zone, the acetate is generally well-mixed in the 

vertical. This is true for the other reactive components and downgradient distributions are 

similar to the 1-D baseline results. 

 

Figure 4.7. Simulated spatial distributions of acetate 20, 30, 40, and 50 days after start of 

biostimulation. Note that the injection gallery is located at X=28 m and the last row 

of monitoring wells is at X=36.5 m.

The lowest aqueous U(VI) concentrations are not associated with the highest acetate 

concentrations near the injection point but 7 m or more downgradient (Figure 4.8). This is due to 

the kinetics of the U(VI) TEAP reactions, whose time scales are slow relative to groundwater 

transport. In this case, a parcel of U(VI)-bearing groundwater is progressively depleted of U(VI) 

over time as it is being transported. That depletion is from the precipitation of U(IV) mineral, 

UO2(s). Conversely, the highest concentrations of the precipitated U(IV) are found near the 

injection point with slightly lower concentrations downgradient (Figure 4.9). This is because the 

highest U(VI) and acetate concentrations are found at the injection point, which results in the 

highest reaction rates for this TEAP rate law. 

mol/L mol/L
4.10

Figure 4.8. Simulated baseline aqueous U(VI) concentration distributions for day 20 to 100 of 

the biostimulation. 

Figure 4.9. Simulated baseline UO2(s) concentration distributions for day 20 to 100 of the 

biostimulation. 

The highest concentrations of aqueous Fe(II) are associated with the early 5 mM acetate 

biostimulation pulses prior to the dominance of sulfate reduction. In this case, it can be seen in 

Figure 4.10 that the peak Fe(II) concentrations are generated through 30 days of the acetate 

biostimulation pulses. As sulfate reduction becomes the dominant TEAP, sulfide from that 

reaction reacts with the Fe(II) to form iron sulfide, FeS. This results in decreased aqueous Fe(II) 

concentrations for the duration of the biostimulation period. The higher Fe(II) concentrations 

mol/L mol/L

mol/L mol/L
4.11

from the early biostimulation period continue to migrate downgradient ahead of the lower Fe(II) 

concentrations that are associated with sulfate reduction. 

 

Figure 4.10. Simulated baseline aqueous Fe(II) concentration distributions for day 20 to 100 of 

the biostimulation.

In the simulation, low levels of sulfate conversion are occurring early in the biostimulation

(Figure 4.11), but these changes would be imperceptible in the field given the typical variability 

in measured sulfate concentrations. The limited amount of sulfide generated during this time 

period reacts with Fe(II) to form minute amounts of FeS mineral (Figure 4.12). The dominance 

of the sulfate TEAP begins after ~30 days of acetate biostimulation, marked by a 1 to 2 mM 

decrease in sulfate concentrations near the injection point. In some respects, the decrease in 

previously elevated aqueous Fe(II) concentrations in Figure 4.10 is a more definitive indicator of 

sulfate reduction than the small amount of sulfate removal relative to the ambient ~9 mM sulfate 

concentrations. Similar to the biologically mediated U(VI) behavior, sulfate transported 

downgradient in a parcel of groundwater with acetate continues to undergo rate-limited reaction, 

resulting in the lowest concentrations of sulfate traveling downgradient from the injection point. 

This is most obvious from 50 to 100 days of the biostimulation when significant sulfate removal 

was occurring. Conversely, FeS begins to form near the acetate injection point and as the sulfate 

TEAP becomes dominant, FeS forms further downgradient. The highest FeS abundance, 

however, is always in the vicinity of the injection point, where the availability of Fe(II) and 

sulfide are the highest. As mentioned previously, the importance of the sulfate TEAP is because

1) acetate utilization can limit availablility to the FeRB responsible for U(VI) bioreduction, 2) 

increases in the alkalinity and pH via the bicarbonate and sulfide produced can result in U(VI) 

desorption, and 3) FeS can play an important role in controlling post-biostimulation uranium 

remobilization both as an abiotic redox control and as an electron donor for microorganisms that 

use dissolved oxygen as a terminal electron acceptor (e.g., oxidizers). 

mol/L mol/L
4.12

Figure 4.11. Simulated baseline aqueous sulfate concentration distributions for day 20 to 100 of 

the biostimulation.

Figure 4.12. Simulated baseline FeS(s) concentration distributions for day 20 to 100 of the 

biostimulation. 

mol/L 

mol/L mol/L

mol/L
4.13

4.3.2 Injectate Density Effects

In the 2003 Rifle field experiments, it became clear that the injectate concentrations were 

sufficiently high to result in density effects. In this case, the injected acetate concentrations were 

~300 mM and the bromide concentrations were ~10 mM. Figure 4.13 shows the vertical 

distribution of acetate and bromide concentrations at locations down the centerline of the field 

plot from 3.7 m upgradient, B-02, to M-03, M-08, and M-13, which are 3.7, 7.3, and 14.6 m 

downgradient, respectively. Where acetate and bromide were detected, the acetate concentrations 

near the bottom of the aquifer were about 3 times higher than the shallow concentrations. 

Figure 4.13. Acetate (top) and bromide (bottom) concentration as a function of depth on October 

1, 2003. 
4.14

To model this behavior, we use an equation of state that relates the fluid properties of density and 

viscosity to bromide and acetate concentration. This is based on 20°C data from the 2008-2009 

Online CRC Handbook of Chemistry and Physics (Figure 4.14). 

Figure 4.14. Density (top) and viscosity (bottom) variation with sodium acetate (blue) and 

sodium bromide (red) in solution. 

The initial field experiments in 2002 and 2003 assumed that the injection well configuration 

would deliver a vertically well-mixed distribution. Note that the “injection” is actually a slow 

metering of a few milliliters of amendment per minute into a well at 3 depths. Observations and 

the 2-D simulations clearly show that immediately downgradient from the injection gallery a 

vertical gradient of acetate concentrations is generated (Figure 4.15). 

Figure 4.15. Simulated 2003 field experiment illustrating the potential for acetate stratification.

Acetate mol/L
4.15

For this reason, subsequent large-scale experiments at the site used cross-well mixing with lower 

acetate concentrations and higher injection rates (Figure 4.16). Cross-well mixing between 

injection gallery wells (Hyndman et al., 2000) enhances mixing between injection wells without 

continuously modifying the natural gradient. The purpose of cross-well mixing is to spread the 

amendment between individual injection wells, creating a more uniformly dispersed source term 

along the injection gallery. It also disperses the amendment more evenly in the vertical 

direction, an important consideration since we know that the density contrast between 

groundwater and amendment resulted in higher concentrations of Br and acetate toward the 

bottom of the aquifer. Since the target acetate field concentrations in the 2008 Big Rusty 

experiment were the highest attempted at the site, we revisited the density calculations. 

Figure 4.16. Cross-well mixing scheme with standard (left) and reversed (right) pumping 

directions. Two 2-channel peristaltic pumps are run throughout the period of 

injection with daily reversals. Cross-well mixing consists of two tubes, one to deep 

and one to shallow depths. 

For the simulation of the density scenario, we use the 2008 Big Rusty biostimulation experiment 

baseline model parameterizations and the bromide/acetate loading schedule in Table 3.8. The 

variable loading allows density analyses for periods of low acetate (5 mM) amendment 

interrupted by a period of no amendment, followed by a prolonged period of high acetate (15 

mM) amendment. We assume complete mixing in the injection wells to represent the cross-well 

mixing. 

During the first 30 days of simulation, it is seen that the targeted 5 mM acetate concentrations in 

the field result in no obvious density effects (Figure 4.17). After the tripling of acetate 

concentrations on Day 38, there is some vertical variation in the simulated acetate concentrations 

for the Big Rusty experiment; however, these impacts are generally mild in comparison to the 

2003 field experiment (Figure 4.15). The largest vertical variation in acetate concentration is

seen on the lateral edges of individual pulses. 
4.16

Figure 4.17. Simulated acetate concentrations at 0 to 120 days after the initiation of 

biostimulation on July 20, 2008. Note that the injection gallery is located at X=28 m 

and the last row of monitoring wells is at X=36.5 m. 

In Figure 4.18, the vertical variation of simulated acetate at monitoring well rows 1, 2, and 3 

(2.5, 5.0, and 8.5 m downgradient from the injection wells) is clearly small, even during the 

period where acetate concentrations were tripled (from day 38). At 30 d, the first row is 

experiencing acetate released at ~24 d, ~17d for row 2, and ~9 d for row 3. Because of the 

schedule of injections, the first and third rows are experiencing concentrations from active 

injection while row 2 is experiencing concentrations from the acetate interruption. A 

hypothetical case where the enhanced acetate injection was doubled to 300 mM was also 

simulated, which begins to show some vertical variation in the concentrations (Figure 4.18). 
4.17

Figure 4.18. Simulated variation of acetate concentrations with depth after 30 and 60 days of the 

baseline (corresponding to times with injection of 50 and 150 mM acetate, 

respectively), and after 60 days that included a hypothetical 300 mM acetate 

injection from day 38. 

Acetate 30 d Acetate 30 d Acetate 60 d Acetate 60 d

Acetate 60 d, 2X after 38 d Acetate 60 d, 2X after 38 d
4.18

4.3.3 Seasonal Water Table Fluctuation

Figure 4.5 shows water table elevation dynamics at well X-2 at the Rifle IFRC site from April to 

December 2008. Since the saturated thickness of the alluvial aquifer under baseflow conditions 

is ~2.5 m, the 1.5 m rise during the spring is a significant event. Clearly, concentrations of 

injected amendments can be affected, but increases in U(VI) and dissolved oxygen have been 

associated with the seasonal rise and fall of the water table in late spring (Figure 4.19). 

Figure 4.19. May 2002 to January 2004 Rifle water table elevation, U(VI) concentrations, and 

dissolved oxygen in upgradient wells B-01, B-02, and B-03. 

The focus here is on the mechanism of gas entrapment and dissolution during the imbibition of 

previously unsaturated sediments when the water table rises. The conceptual process model has 

bubbles of pore gases being trapped as the pores fill with rising groundwater. Gas measurements 

just above the water table at the Rifle IFRC site show that these pore gases likely contain 

atmospheric levels of oxygen (~20% pO2). This is in contrast to the generally low levels of 

dissolved oxygen (~0.2 mg/L) in Rifle groundwater that are in equilibrium with pO2 levels 45 

times smaller than atmospheric. As the gas in these bubbles equilibrates with the groundwater, 

oxygen dissolves into solution following Henry’s Law. Initially this only affects the dissolved 

oxygen in the previously unsaturated sediments, but as the water table begins to recede from its 

peak, groundwater with elevated levels of dissolved oxygen migrates into the permanently 
4.19

saturated region of the aquifer. Furthermore, diffusion and macroscopic dispersion also enhance 

the migration of elevated dissolved oxygen levels deeper into the aquifer. We model this aquifervadose zone behavior using a 2-phase (active liquid and active gas phases) approach with 

temperature-corrected atmospheric oxygen as the gas phase boundary condition at the ground 

surface (top of the vadose zone). 

The impact of the entrapped gas can be seen in simulated liquid saturations that are less than 1.0 

in the zone between the initial and elevated water table in June and July 2008 (Figure 4.20). 

During the recession of the water table the oxygen concentrations are generally increased 

throughout the saturated zone. 

Figure 4.20. Simulated dissolved oxygen and aqueous saturation for six dates in 2008: April 15, 

June 3, July 17, September 11, November 9, and December 21. Note that the 2008 

Big Rusty experimental plot lies between X=125 and X=140. 

The simulated impact of the biostimulation, which began on July 20, 2008, on dissolved oxygen 

concentrations from gas entrapment is shown in Figure 4.21. The enhanced levels of dissolved 

oxygen are generally depleted in the saturated zone from the injection point downgradient to the 

leading edge of the acetate plume. 
4.20

Figure 4.21. Simulated dissolved oxygen after 10 days from the start of biostimulation.

Slightly more acetate is consumed in the presence of the elevated dissolved oxygen 

concentrations near the water table and this behavior is progressive, i.e., acetate continues to be 

oxidized as it is transported downgradient (Figure 4.22). The impact of including the gas 

entrapment process is enhanced depletion of the downgradient acetate plume near the water 

table. 

Figure 4.22. Comparison of simulated acetate after 10 days of biostimulation for the baseline 

(top) and the gas entrapment case (bottom). 

(M)
4.21

The additional consumption of acetate required to remove oxygen resulting from gas entrapment 

processes is seen to affect the spatial distribution of the terminal electron accepting processes. 

Aqueous U(VI) distributions (Figure 4.23) are shaped similarly to those of the acetate. This 

reflects the dominance of the U(VI) TEAP reaction over the uranium surface complexation 

reactions. 

Figure 4.23. Comparison of simulated aqueous U(VI) after 10 days of biostimulation for the 

baseline (top) and the gas entrapment case (bottom) 

Aqueous Fe(II) reflects the net impact from the Fe(III) TEAP, Fe2+ surface complexation, sulfide 

promoted goethite dissolution, and iron sulfide mineral reactions. Although less Fe(III) oxide is 

reacted by the FeRB under the seasonal gas entrapment scenario, this actually leads to a slightly 

higher rate of the sulfide-goethite reaction. In this reaction, two moles of Fe2+ are produced for 

every mole of sulfide reacted (Table 3.5). The net result is a small increase in aqueous Fe(II)

(Figure 4.24). The sulfate behavior is more similar to the U(VI) in that there is slightly less 

sulfate conversion than the baseline with the biggest difference at the top, downgradient end of 

the sulfate removal plume (Figure 4.25). 

(M)
4.22

Figure 4.24. Comparison of simulated aqueous Fe(II) after 10 days of biostimulation for the 

baseline (top) and the gas entrapment case (bottom) 

Figure 4.25. Simulated sulfate after 10 days of biostimulation with gas entrapment.

(M)

(M)
4.23

4.3.4 Flood Case Scenario

Some shallow, alluvial aquifers are periodically subject to enhanced recharge from flood events. 

Depending on the magnitude and duration of the flood event, the hydrologic properties of the 

sediments, floodwater chemistry, and geochemistry of variably saturated sediments, the impacts 

can include 

• mobilization of vadose zone uranium, 

• increase in the saturated thickness of the water table aquifer, 

• alteration in groundwater flow direction, and 

• increase in entrapped gas during water table rise. 

Mobilization of vadose zone uranium. Many shallow uranium waste sites (e.g., UMTRA, 

DOE legacy waste sites, etc.) are the result of planned and/or unplanned contamination from 

surface operations and facilities (Riley and Zachara, 1992). Under most environmental 

conditions, uranium introduced into the unsaturated soil zone from the ground surface will tend 

to persist relative to the saturated zone because 1) there is less water flow moving through the 

unsaturated zone and less water in the pores, 2) uranium associated with the larger pores may be 

bypassed [it might have been originally transported and deposited under a higher flow regime 

during active surface operations (e.g, waste pond)], and 3) most of the uranium is associated with 

the solid phase through geochemical reactions. Under these conditions, the uraniumcontaminated sediments in the vadose zone are a potential long-term source to the underlying 

groundwater. A key question is whether recharge-driven uranium transport in the vadose zone 

can significantly contribute to the uranium concentrations in the groundwater. 

The initial recharge-driven uranium front that migrates downward through the vadose zone to the 

water table is relatively slow, as sorption will significantly retard the transport of the uranium 

front through the vadose zone. One caveat for this interpretation is that once the uranium front 

breaks through to the water table, which may be the case below disposal facilities, aqueous 

uranium is transported through the vadose zone with the velocity of the pore water. If the 

contribution of recharge to the groundwater flow is small, the recharge-driven flux of uranium 

may only be significant if relatively high concentrations of vadose zone uranium are widespread. 

There are, however, other process mechanisms that could lead to leaching of uranium from the 

vadose zone into the groundwater. Episodic or event-based water table rise can also allow 

groundwater to temporarily flow through contaminated sediments that were previously 

unsaturated. At the Rifle IFRC site and other shallow uranium-contaminated sites, elevated 

uranium levels in groundwater have been associated with seasonal and episodic hydrologic 

events. Such observations are consistent with vadose zone uranium-contaminated sediments

(Yabusaki et al., 2008). Moreover they are also consistent with a mechanism by which the 

highest groundwater concentrations are located near the water table, especially when the water 
4.24

table is high. The magnitude of this behavior can be less obvious when sampling is based on 

wells with large screened depth intervals. 

Potentially, the most intrusive hydrologic event is a flood. This is because it can provide high 

recharge downward through the vadose zone, potentially raise the water table (thereby increasing 

the saturated thickness), change the flow direction and drive oxygenated pore water deep into the 

saturated aquifer. Increase in the aquifer saturated thickness can be problematic if a treatment 

zone for influent contaminated groundwater has been previously established for a smaller zone. 

In this case, a fraction of the groundwater may be flowing over the nominal top of the treatment 

zone. Furthermore, it is not uncommon for higher contaminant levels to be found near the water 

table. Changes in flow direction can similarly result in contaminated groundwater bypassing a 

treatment zone. 

The modeled flood scenario is designed to examine the impact of 1 foot of river water ponded on 

the ground surface for 1 week at the Rifle IFRC site. This simulation is an extension of the 2-D, 

2-phase, variably-saturated flow with gas entrapment used to assess biostimulation in the 

presence of elevated dissolved oxygen during the seasonal rise and fall of the Rifle water table in 

the spring. In this case, the recharge through the ground surface of the ponded floodwaters was 

applied locally to the Big Rusty field plot. It was necessary to significantly extend the modeling 

domain upgradient and downgradient by moving the boundaries out to where their impact on the 

area of interest would be minimized. This is because we do not know a priori how the ponded 

water will affect the saturated thickness near the original boundaries. 

Figure 4.26 illustrates how the ponded water boundary condition results in near-saturation of a 

large fraction of the vadose zone. In fact, it is the entrapped gas that prevents complete saturation 

of the vadose zone pore space below the recharge source. The “mounding” effect during the 7 

days of active ponding (Figure 4.27) overwhelms the regional flow and drives water flow down 

and laterally away from the recharge source. Floodwater tracer in Figure 4.28 shows that the 

base of the groundwater mound is initially from vadose zone pore water that has been pushed 

down ahead of the infiltrating floodwater. This causes the flood water to drape over the mound 

and move laterally resulting in higher concentrations of floodwater around the perimeter of the 

mound. With the removal of the surface ponding condition, the regional flow in the saturated 

zone is largely restored within 3 days. However, the oxygenated water that was recharged has 

been driven deep into the saturated zone during the flood (Figure 4.29). This zone of dissolved 

oxygen, near saturation with atmospheric oxygen, persists until the regional flow can advect it 

out of the domain. 
4.25

Figure 4.26. Simulated aqueous saturations during the 7 day flood and 21 days following the 

flood. 

Figure 4.27. Simulated hydraulic head during the 7 day flood and subsequent 21 days. 
4.26

Figure 4.28. Simulated flood water tracer during the 7 day flood and the following 21 days. 

Figure 4.29. Simulated aqueous oxygen during the 7-day flood and the following 21 days. 
4.27

In the first scenario, the biostimulation sequence from the baseline simulation was initiated at the 

beginning of the flood ponding event. While our interest was in the effects of elevated oxygen on 

biostimulation, in this scenario the principal impact of the flood event was on the distribution of 

acetate during active injection (Figure 4.30). After 5 days of simulated biostimulation, the 

acetate has been driven down towards the aquifer bottom and upgradient of the initial 

experimental plot. As compared to the fairly regular distribution in the baseline, the distribution 

of sulfide and UO2(s), which are products of the biostimulation, are seen to be heavily skewed to 

the bottom of the aquifer and away from the intended treatment zone location. Another 

consequence of flooding with dilute river water can be seen in the significant removal of U(VI) 

from solution in the vadose and saturated zones (Figure 4.30). This is actually a geochemical 

response to the low alkalinity river water resulting in enhanced uranium surface complexation. 

Figure 4.30. Comparison of baseline and flood scenario at day 5 for U(VI), acetate, UO2(s), and 

aqueous sulfide. 

Noting that the regional flow field is largely restored a few days after the ponding condition is 

removed (Figure 4.27), a second flood scenario was simulated, this time beginning acetate 

injection 1 day after the ponding condition was removed. The comparison of the bromide tracer 

behavior between the baseline and this second flood scenario (Figure 4.31) indicates that 1) 

regional groundwater flow and transport has largely been re-established and 2) the saturated 

thickness of the system is larger than the baseline. In general, the additional saturated thickness 

(M) (M)

(M) (M)
4.28

results in a more gradual concentration transition from the top of the injectate pulses up to the 

water table. 

Figure 4.31. Comparison of injected tracer at 20, 30, 40, and 50 days after start of biostimulation 

for baseline (left) and flood scenario (right). 

The key impact from the flood scenario is the presence of saturated dissolved oxygen levels in 

porewaters throughout the subsurface model domain. The oxygen TEAP is usually a slow 

background process dependent on natural organic carbon, which is typically more refractory 

compared to low molecular weight electron donors such as acetate. Figure 4.32 illustrates the 

clear link between the concentration of the acetate pulses and dissolved oxygen removal. Near 

complete removal of oxygen occurs during the period of highest (tripled) acetate concentrations 

(after day 38 of biostimulation). These simulations also show how the ambient water chemistry 

(with low dissolved oxygen) entering the model domain from the upgradient model boundary 

can help drive the system back to pre-flood conditions. 

mol/L mol/L
4.29

Figure 4.32. Simulated dissolved oxygen concentrations at 20, 30, 40, 50, 60, 70, 80, and 100 

days after initiation of acetate biostimulation. 

The acetate concentrations in the post-flood scenario simulation are slightly but consistently 

lower than the baseline concentrations. This is due to the larger saturated thickness and the 

presence of the dissolved oxygen TEAP, which increases acetate consumption compared to the 

baseline (Figure 4.33). In general, acetate consumption leads to a more noticeable separation of 

the initial 2-week amendment pulse than exhibited by the tracer. Over the first 50 days of the 

biostimulation, the initial acetate pulse has more vertical variability and migrates as a 

semicircular distribution of diminishing concentrations about a center of mass at the aquifer 

bottom. 

Figure 4.33. Comparison of simulated acetate concentrations at 20, 30, 40, and 50 days after 

start of biostimulation for baseline (left) and flood (right) scenarios. 

mol/L mol/L

mol/L mol/L
4.30

The U(VI) behavior in the post-biostimulation scenario is not significantly different from the 

baseline. In both cases, uranium is generally distributed in one zone downgradient from the 

injection; i.e., the 1-week interruption in acetate is not discernible in the simulation of the U(VI) 

removal or the precipitation of U(IV) mineral, uraninite (UO2). Slightly lower concentrations 

can be found in the post-flood scenario aqueous U(VI) (Figure 4.34) and UO2(s) (Figure 4.35) 

which also have larger vertical extent because of the increased saturated aquifer thickness. 

Figure 4.34. Comparison of simulated aqueous U(VI) concentrations at 20, 30, 40, and 50 days 

after start of biostimulation for baseline (left) and flood (right) scenarios.

Figure 4.35. Comparison of simulated UO2(s) concentrations at 20, 30, 40, and 50 days after 

start of biostimulation for baseline (left) and flood (right) scenarios.

mol/L mol/L

mol/L mol/L
4.31

The impact of the additional oxygen present in the system from the flood event is more evident 

in the behavior of the other TEAPs. Although slightly more acetate is consumed in the postflood biostimulation scenario than the baseline, aqueous Fe(II) concentrations are slightly less 

(Figure 4.36) compared to the baseline. 

Figure 4.36. Comparison of simulated aqueous Fe(II) concentrations at 20, 30, 40, and 50 days 

after start of biostimulation for baseline (left) and flood (right) scenario. 

The sulfate TEAP, which becomes dominant later in the biostimulation, also exhibits slightly 

less conversion at comparable times for the post-flood simulation (Figure 4.37). For the postflood scenario under the assumption of a simultaneous oxygen TEAP, the general observation is 

that even under dissolved oxygen concentrations at saturation with the atmosphere, a relatively 

small amount of acetate can return the aquifer to suboxic levels. Under these conditions, the 

other TEAP reactions, including U(VI) bioreduction, can proceed similarly to the baseline case. 

mol/L mol/L
4.32

Figure 4.37. Comparison of simulated aqueous sulfate concentrations at 60, 70, 80, and 100 days 

after start of biostimulation for baseline (left) and flood (right) scenario.

mol/L mol/L
5.1

5 Summary and Conclusions

At a number of subsurface sites including the Hanford Site and Uranium Mill Tailings Remedial 

Action (UMTRA) sites, uranium plumes persist in spite of decade and longer periods of 

continuously high groundwater flow rates. A potentially cost-effective engineering alternative is 

uranium bioremediation via in situ biostimulation of indigenous microorganisms. At the Old 

Rifle UMTRA site in western Colorado, the U.S. Department of Energy (DOE) has been 

conducting a comprehensive investigation (Integrated Field-Scale Research Challenge or IFRC) 

into the subsurface processes, properties, and conditions controlling uranium behavior before, 

during, and after amending groundwater with electron donor. In this case, the injection of acetate 

has been accompanied by significant microbially-mediated conversion of mobile uranium in the 

hexavalent oxidation state, U(VI), to the reduced oxidation state, U(IV), which is mineralassociated and thus, immobile. As a relatively new technology where the contaminant is not 

destroyed or extracted, the long-term performance of in situ uranium bioremediation is an open 

issue. Our understanding of uranium mobility through the systematic interplay between biology, 

geochemistry, and hydrology continues to benefit from ongoing DOE research. In particular, the 

Rifle IFRC project has developed and parameterized a detailed biogeochemical reaction model 

that captures observed trends in multiple component concentrations over a range of 

biostimulation conditions and sites (Fang et al., 2009). 

The multicomponent biogeochemical reactive transport model, calibrated with data from the 

2008 field experiment at the Rifle IFRC site, accounts for key terminal electron accepting 

processes (i.e., U(VI), Fe(III), sulfate) and abiotic reaction processes in the biostimulation field 

experiments. The model generally captures the observed dynamics in pH, U(VI), Fe(II), sulfate, 

and acetate. 

The approach taken by the present study is to use the detailed understanding of uranium 

bioremediation developed at the Old Rifle IFRC site as a framework to evaluate hypothetical 

scenarios of general interest at other sites. The biogeochemical conceptual model, which is based 

on laboratory studies and acetate biostimulation experiments performed at the Old Rifle IFRC 

site since 2002, holds that Fe(III)-reducing microorganisms, stimulated by acetate, can 

simultaneously use Fe(III) mineral and aqueous U(VI) as terminal electron acceptors. It is this 

concomitant bioreduction of aqueous U(VI) to U(IV) mineral by dissimilatory metal reducing 

bacteria that is the bioremediation principle for uranium removal from groundwater. After about 

30 days, acetate-oxidizing sulfate reducing microorganisms become enriched using sulfate as a 

terminal electron acceptor with Fe(III)-reducing microorganisms remaining active. The 

significance of the sulfate reduction phase is that 1) U(VI) groundwater concentrations increase 

from the low concentrations associated with the iron reduction phase, 2) there is a continuous 

influx of high sulfate (ca. 10 mM), which can generally maintain sulfate reducing conditions 

unless acetate concentrations in excess of the sulfate concentrations are used, and 3) longer-term 

post-biostimulation removal of uranium from groundwater is associated with long periods 

(~months) of active sulfate reduction during the biostimulation. 
5.2

The paradox of the sulfate TEAP, i.e., higher uranium concentrations than during Fe(III) 

bioreduction but longer-term post-biostimulation removal, is the subject of ongoing 

investigation. In this case, the abiotic geochemistry before, during, and after biostimulation must 

be considered. At the Rifle IFRC site, uranium surface complexation is the dominant abiotic 

uranium attenuation process and, under site conditions, is sensitive to changes in the alkalinity, 

pH, and major ions that result from acetate biostimulation. Consequently, a comprehensive 

representation of heterogeneous and homogeneous abiotic chemical reactions in equilibrium and 

kinetic forms was necessary to account for the bicarbonate, U(IV), Fe(II), and sulfide products of 

the biologically mediated reactions. 

We use the parameterized biogeochemical reaction model developed by the Rifle IFRC project 

as part of the simulation testbed for the targeted scenarios in the present study. The principal 

assumption is that extensions to the coupled-process simulation capability required to address 

these scenarios are valid. Since these extensions were invoked without observation at the Rifle 

IFRC site, the simulated results should be used to highlight potential issues for consideration in 

the design of in situ uranium bioremediation. 

5.1 One-Dimensional Reactive Transport

5.1.1 Sensitivity Analyses

The 2008 biostimulation experiment at the Old Rifle IFRC site was used as the baseline for 

analyzing and comparing the results of the scenarios for this study. For the purpose of initially 

identifying the sensitivity of simulation results to model parameters, we used a one-dimensional 

model domain for the multicomponent biogeochemical reactive transport simulations. Hydraulic 

conductivity, porosity, and dispersivity were calibrated with downgradient monitoring well 

observations of bromide tracer that was injected along with the acetate electron donor. Initial and 

boundary conditions for hydrology and geochemistry were based on observations from 

upgradient and downgradient monitoring wells. 

The deviation of aqueous uranium behavior from the observed baseline behavior was used as the 

indicator of sensitivity to a process model parameter. Selected parameters were individually 

perturbed to identify sensitivity. In this case, parameter sensitivity was manifested in three 

different forms: reactions, reaction rates, and rates of transport. In the current biogeochemical 

reaction network, geochemical adsorption and desorption of uranium is controlled by a set of 

equilibrium uranium complexation reactions that are affected by many factors but especially 

alkalinity, pH, uranium, surface site density and calcium. Reaction rates for the kinetic reactions 

(e.g., biologically mediated and mineral reactions) are based on rate laws that are dependent on 

concentrations of reactants and/or products. In the case of the U(VI) TEAP, the intrinsic rate has 

a direct impact on the magnitude of the aqueous uranium concentrations (i.e., higher intrinsic 

rate results in higher uranium removal from groundwater). 
5.3

Flow rates, porosity, and dispersivity affect transport: 1) the influx of groundwater, which 

controls the in situ concentration of injected amendments; 2) the supply of chemical components 

in the influent groundwater, which controls the availability of reactants; and 3) the transport of 

the electron donor and the aqueous byproducts of TEAP reactions. For biostimulation in 

advection-dominant systems, the primary impact of transport on aqueous uranium concentrations 

is the timing of acetate arrivals at downgradient locations. The oxidation of acetate coupled to 

various TEAP reactions (bioreduction of Fe(III) minerals, U(VI), and sulfate) occurs during 

transport through the aquifer. Since the TEAP reactions are nonequilibrium, the interplay of 

transport rates and reaction rates will determine the location and magnitude of the mobile TEAP 

reactants and products. As advection decreases (e.g., lower flow rate, higher porosity), there is 

later arrival of TEAP reaction products together with decreased rates of uranium bioreduction. 

This effect is due to the increased residence time and reaction time for acetate nearer the acetate 

injection point. This results in more sulfate reduction nearer the acetate injection point, which is 

associated with decreased rates of uranium bioreduction. 

5.1.2 Terminal Electron Accepting Processes (TEAPs) 

Three TEAPs have been identified as having roles that contribute to uranium bioremediation at 

the Rifle IFRC site: Fe(III) mineral, aqueous U(VI), and sulfate. The conceptual model 

(Anderson et al., 2003; Fang et al., 2009; Vrionis et al., 2005; Yabusaki et al., 2007) is that 

Geobacter spp., the principal acetate-oxidizing dissimilatory metal-reducing bacteria associated 

with U(VI) bioreduction at the Rifle IFRC site, initially become enriched in the presence of 

acetate and bioavailable Fe(III). The sulfate-reducing bacteria exhibit a slower growth rate but 

after ca. 30 days of biostimulation with acetate amendment they become the principal consumer 

of acetate leading to enrichment in sulfate-reducing bacteria and dominance in the microbial 

community. 

In the Rifle biostimulation experiments, significant sulfate reduction is associated with higher

aqueous U(VI) concentrations and, paradoxically, longer post-biostimulation uranium removal 

from groundwater. A possible link of extended sulfate reduction to post-biostimulation uranium 

removal is through the formation of iron sulfides and elemental sulfur. These mineral phases 

have been identified in some studies (Boonchayaanant et al., 2009; Wersin et al., 1994) in the 

abiotic reduction of aqueous U(VI) to immobile U(IV) mineral. They can also act as an electron 

donor for microbially-mediated reduction of U(VI) to U(IV) and/or dissolved oxygen to water. 

The latter can be an important mechanism in maintaining reducing conditions needed for the 

stabilization of previously reduced and immobilized U(IV) (Abdelouas et al., 1999) although 

some studies have shown reduced sulfur minerals did not prevent U(IV) reoxidation in the 

presence of Fe(III) (Sani et al., 2005). Currently, post-biostimulation reduction of U(VI) to 

U(IV) has not been directly observed at the Rifle site. This is consistent with other studies 

(Suzuki et al., 2005) where the reduction to U(IV) has not been found in direct association with 

iron sulfide minerals. Another post-biostimulation uranium attenuation mechanism is U(VI) 

sorption on to biopolymers (Choi and Park, 2005). In laboratory experiments with Rifle 
5.4

sediments and groundwater, N’Guessan et al. (2008) identified sorption to Mollicute species

from the phylum Firmicutes. These small bacteria have no cell wall and became significant in 

the microbial community only after cessation of acetate biostimulation. 

Oxygen and nitrate concentrations in the Rifle groundwater are sufficiently low that they are 

assumed to be negligible. At many sites, this assumption cannot be made and the presence of 

these oxidizers (Akob et al., 2008; Madden et al., 2009) is a concern for bioremediation 

technologies that rely on lowering the redox potential. Accordingly, biologically mediated 

reactions for the oxygen and nitrate TEAPs were incorporated in the simulation capability. Since 

these TEAPs and the associated acetate-oxidizing microorganisms have not been characterized at 

the Rifle IFRC site, hypothetical rate laws were used. For this scenario, dissolved oxygen at near 

saturation with atmospheric oxygen (8 mg/L, 250 uM) and nitrate (8 mg/L, 130 uM) were added 

to the initial and boundary condition water chemistry. Based on the reaction stoichiometry, each 

mole of acetate can potentially reduce 0.8 moles of DO or 3 moles of nitrate. The results, when 

compared to the baseline, suggest that ~0.3 mM of additional acetate would be consumed to 

nearly deplete the DO and nitrate. This small amount of acetate is primarily due to the relatively 

low concentrations of these terminal electron acceptors although the stoichiometric efficiency of 

bioconversion is also a factor. While this is a relatively small amount of acetate, there are 

potential complications that should be considered. One issue is the possible need to establish 

and/or maintain depleted levels of oxygen, nitrate, and nitrite prior to remediation taking place. 

This may include time for the microbial community to transition to the desired structure and 

function. Another issue is the possible need to keep oxygen, nitrate, and nitrite levels sufficiently 

low to maintain the stability of bioreduced (and immobilized) uranium. This may be true in the 

presence of a continuous influx of high DO and nitrate that would otherwise oxidize reduced 

phases, possibly leading to uranium remobilization.

5.1.3 Electron Donors

While the field studies at the Rifle IFRC site have relied exclusively on acetate biostimulation, 

there are other electron donors (and combinations) that have been employed at other sites (Istok 

et al., 2004; Wu et al., 2006). Lactate and ethanol were incorporated into this analysis by using 

reactions from the literature for our Fe(III) mineral, aqueous U(VI) and sulfate TEAPs and the 

same rate law that was used for acetate. Acetate is an intermediate oxidation byproduct of TEAP 

reactions for both additional electron donors, so we retained the existing TEAP reactions and rate 

laws and supplemented them with the necessary lactate and ethanol reactions to model the 

required sequence of reactions. It should be noted that there were no field experiments using 

these electron donors so there is no confirmation that the same microorganisms or functional 

groups were stimulated, which could significantly impact the rates. The simulated results for 

ethanol and lactate are very similar but, in comparison to the acetate baseline, there is 

considerably more electron donor consumption and, consequently, more bicarbonate production. 

In this case, these behaviors are due to the different TEAP reaction stoichiometries that are 

specific to these electron donors. 
5.5

Modeling of slow-release glucose-based, oil-based, or polylactate electron donors was not 

included in modeling scenarios for this report. These electron donors may have desirable 

properties, depending on specific applications, and may be included in future modeling efforts. 

However, it is important to note that the performance of these materials when used for uranium 

bioreduction is still dependent on fundamental electron transfer processes resulting in reduction 

of U(VI) and maintenance of conditions that limit remobilization of uranium. Issues noted in this 

report are therefore likely to apply to these electron donors albeit with different rates and process 

details. This underscores the importance of site- and process-specific information to ensure that 

bioremediation performs as predicted. 

5.2 Two-Dimensional Variably Saturated Flow and Reactive Transport

Two-dimensional simulations of variably saturated flow and biogeochemical reactive transport 

were performed in a vertical cross-section of the Rifle subsurface to evaluate several scenarios. 

In this case the baseline included the vadose zone above the water table and accounted for 

observed changes in the saturated thickness of the phreatic aquifer. In addition to incorporating 

the vadose zone, the two-dimensional modeling approach afforded the opportunity to investigate 

variable density on the migration of electron donor amendment, gas entrapment during seasonal 

water table fluctuation, and dissolved oxygen enhancement under a flood scenario. The twodimensional simulation results for the baseline were generally similar to the one-dimensional 

results; however, the ability to address water table dynamics improved the fidelity of the 

simulated concentrations with the observations. 

5.2.1 Injectate Density

Acetate and bromide are slightly denser than groundwater. In the 2003 biostimulation field 

experiment performed at the Rifle IFRC site, there was clear evidence that the higher density of 

the injected amendment led to higher acetate and bromide concentrations near the bottom of the 

aquifer. In the more recent 2007 and 2008 field biostimulation experiments at the Rifle IFRC 

site, this issue was addressed by injecting less concentrated solutions at higher rates to achieve 

similar in situ acetate concentrations. Furthermore, cross-well mixing was used to maintain 

horizontal and vertical uniformity in the amendment release from the 10 fully penetrating 

injection wells. In this scenario, the variable density simulations targeted the behavior of the 

denser injectate (acetate and bromide) as it migrated from the injection wells. During transport 

there is mixing with groundwater and the acetate is being consumed in TEAP reactions. The 

results of the baseline simulation showed negligible impact of injectate density on the delivery of 

acetate downgradient when the target in situ acetate concentration was ~5 mM. When the target 

in situ acetate concentration increased to ~15 mM later in the simulation, there was a small 

vertical gradient of acetate concentration that increased ~1 mM for each meter of depth. To 

identify a concentration where variable density would have a more noticeable impact, the target 

in situ acetate concentration was increased to 30 mM. This resulted in a vertical gradient over 3 
5.6

mM/m depth. The impact of the higher densities was more pronounced at the leading and 

trailing edges of a finite amendment pulse. These results are specific to acetate but users of 

denser amendment solutions should proceed cautiously. It should be noted that the impact of 

variable injectate density effects is affected by many factors including groundwater flow rates, 

dispersivity, heterogeneity, and anisotropy. 

5.2.2 Gas Entrapment

Gas entrapment occurs as unsaturated pores fill with liquid during wetting events and is part of 

the theory governing hysteretic pressure-saturation behavior in the vadose zone (Kaluarachchi 

and Parker, 1992). In this case, we are interested in the oxygen in the gas that is trapped as 

bubbles in pores that are becoming saturated. Above the water table in shallow alluvial 

subsurface systems, pore gas may contain near-atmospheric levels of oxygen. This situation 

nominally provides a mechanism for oxygen to partition to the aqueous phase and diffuse 

through the water table into the groundwater. Without an oxygen sink, this process would 

eventually result in the oxygenation of the saturated zone. The low dissolved oxygen (0.1 – 0.5 

mg/L) in the Rifle groundwater is assumed to be the result of microbial activity. Under these 

conditions, the relatively thin layer of elevated oxygen (1-2 mg/L) near the water table would be 

the result of a dynamic balance of groundwater flow rate, oxygen diffusion at the water table, 

and microbial reduction. 

The seasonal (spring to summer) hydrograph describing water table rise and fall at the Rifle 

IFRC site is associated with elevated oxygen levels near the water table. In this case, the water 

table can rise and fall over 1 m, which is large relative to the ~2.4 m saturated aquifer thickness 

under baseflow conditions. The simulation results illustrated how the rise of the suboxic 

groundwater into the vadose zone resulted in a small fraction of the submerged pore space being 

occupied by entrapped gas. The entrapped gas initially contains atmospheric levels of oxygen. 

The oxygen in the entrapped gas partitions to the liquid phase based on Henry’s Law leading to 

dissolved gas concentrations 8-9 mg/L in the newly saturated zone above the previous water 

table. As the water table continues to rise, this zone of enhanced dissolved oxygen increases 

vertically while the bulk of the original saturated zone remains low in dissolved oxygen. As the 

water table falls in the summer, enhanced dissolved oxygen descends into the top of the original 

saturated zone. For a given site, the magnitude of enhanced oxygenation due to changes in water 

table elevation will depend on many factors. These include sediment texture, vadose zone soil 

gas composition, and the magnitude, frequency, and duration of water table fluctuations. Some 

concerns of the elevated oxygen near the water table include 1) the possible reoxidation and 

remobilization of reduced uranium, 2) loss of treatment capacity precisely where elevated 

groundwater uranium concentrations are commonly found (i.e., near the water table), 3) the need 

for additional electron donor to achieve anaerobic conditions, and 4) key microorganisms during 

bioremediation are not very tolerant of high oxygen levels. 
5.7

5.2.3 Flood Events

Seasonal and episodic flood events can be an issue for uranium bioremediation in shallow 

alluvial aquifers. If recharge from surface flooding is sufficiently high, the deployment and 

efficacy of in situ uranium bioremediation could be disrupted. To examine the impact of such 

events, we simulated a flood at the Rifle IFRC site by ponding 0.3 m of Colorado River water for 

1 week over a 20 m wide section of the site. For the Rifle sediments, this boundary condition 

introduced a large volume of water that saturated the vadose zone beneath the ponded floodwater 

and displaced most of the saturated zone pore volume in the 70 m long domain in just a few 

days. The displacement of U(VI)-bearing porewaters and the introduction of low alkalinity river 

water, which significantly enhanced uranium adsorption, temporarily resulted in locally lower 

U(VI) concentrations. Since the bulk of uranium is associated with the solid phases under these 

geochemical conditions, U(VI) concentrations eventually rebounded as re-equilibration with 

solid phase uranium and ambient water chemistry occurred. During flood conditions, a mound of 

groundwater fed by the high recharge increased the saturated thickness, and significantly altered 

the groundwater flow magnitude and direction. If biostimulation began on the first day of the 

flood, as was simulated in one scenario, the delivery of acetate was driven downward and 

upgradient away from the intended treatment zone. 

Another complication from flooding is the potential for oxygen- and/or nitrate-laden water to 

enter the saturated zone. While the general issue is similar to the enhanced oxygenation of 

groundwater induced by water table fluctuation and gas entrapment, the magnitude and extent is 

much larger. After 7 days of flood conditions, simulated dissolved oxygen levels, approximating 

equilibrium with atmospheric oxygen, were present throughout the saturated zone. After the 

ponded boundary condition was removed, these oxygen levels persisted, even though the 

regional groundwater flow field was quickly re-established. A scenario where acetate 

biostimulation was begun 1 day after the ponded boundary condition was removed, illustrated 

that flow and transport were very similar to the baseline condition, indicating a rapid recovery of 

the hydrologic system to pre-flood conditions. Similar to the one-dimensional simulations, the 

additional oxygen in the system led to slight increases in acetate consumption, and minor 

decreases in rates of microbial reduction of U(VI) and other terminal electron acceptors. In 

general, the simulated bioreduction of oxygen via acetate biostimulation was able to locally 

create suboxic conditions, albeit with a front that moved downgradient slower than advection by 

groundwater. This was due to limitations imposed by the injected acetate mass and reaction 

kinetics. With the engineered oxygen bioreduction and the influx of the ambient suboxic 

groundwater at the upgradient boundary, the saturated aquifer was restored to suboxic conditions 

in about 100 days. There was also a small but noticeable depth-dependent distribution of TEAP 

reaction products near the top of the water table aquifer due to the presence of dissolved oxygen 

near the top of the increased-thickness saturated zone. 

For the conditions assumed, the flood-induced presence of highly oxygenated groundwater did 

not have a large overall impact on the subsequent biostimulation of the aquifer. Based on the 

earlier analysis, floodwaters with 8 mg/L nitrate would have even less of an impact. If, however, 
5.8

the flood event followed the biostimulation, it is possible that the oxygenated water could 

reoxidize and remobilize bioreduced uranium. While there have been laboratory studies that have 

identified such behaviors (Abdelouas et al., 1999; Komlos et al., 2007), these conditions have not 

been replicated in the field. The post-biostimulation oxidation of bioreduced phases in the Rifle 

aquifer is the subject of ongoing investigation ranging from microorganisms that oxidize FeS 

while reducing oxygen, to kinetically-limited uraninite oxidation and dissolution.

5.3 Closing Comments and Next Steps for Reactive Transport 

Modeling of Uranium Bioremediation

The value of modeling presented here lies not so much in its predictive ability, although that is 

important, but in the integration of processes in such a way that process interactions can be 

understood. Ideally, this will make it possible to avoid unintended consequences of a particular 

bioremediation design. In the past, there has been reticence to use numerical models for newly 

developed in situ remediation approaches largely to avoid contention about the model itself. 

However, the use of such models to gain insight into bioremediation is crucial to the assessment 

of likely uranium bioremediation outcomes. We have provided a modeling framework that 

successfully links the dominant processes and provides an overall picture of parameter 

sensitivities.

However, further model development is needed, especially for additional parameters and 

processes controlling long-term behavior of a bioremediated uranium plume. Specific areas for 

improvement include: 

• Slow-release amendments (including a range of alternate electron donors)

• Reoxidation rates for reduced phases (including uraninite)

• Post-biostimulation sorption by Mollicutes or other microbes living on dead biomass 

• Rate-limited process controls on long-term uranium immobilization

Long-term predictions based on numerical models are inherently uncertain. Reduction of 

uncertainty in these areas will help to reduce overall model uncertainty. Ultimately, long-term 

predictions will be tested by long-term monitoring of actual bioremediation projects, enabling 

further model improvement. 

The future experimental research and numerical modeling being carried out at the Rifle IFRC 

site and other uranium-contaminated aquifers will add to the pool of data available for future 

development of these models and will increase the process accuracy of these tools. In the future, 

combined microbial and abiotic geochemical processes will likely be built in to reactive transport 

models with baseline data for a wide range of electron donors and initial microbial community 
5.9

compositions. Fundamental rate data controlling reoxidation of U(IV) will also be included, 

enabling accurate assessment of long-term behavior of bioreduced alluvial aquifers. 

6.1

6 References 

Abdelouas, A., Lutze, W. and Nuttall, H.E., 1999. Oxidative dissolution of uraninite precipitated 

on Navajo sandstone. Journal of Contaminant Hydrology, 36(3-4): 353-375. 

Akob, D.M. et al., 2008. Functional diversity and electron donor dependence of microbial 

populations capable of U(VI) reduction in radionuclide-contaminated subsurface 

sediments. Applied and Environmental Microbiology, 74(10): 3159-3170. 

Anderson, R.T. et al., 2003. Stimulating the in situ activity of Geobacter species to remove 

uranium from the groundwater of a uranium-contaminated aquifer. Applied and 

Environmental Microbiology, 69(10): 5884-5891. 

Bargar, J. et al., 2009a. Mechanisms of Biogenic Uraninite Corrosion in Groundwater at the 

Rifle IFRC site, Abstract H33L-07., AGU, Fall Meet. . Eos Trans., San Francisco, CA. 

Bargar, J.R., Bernier-Latmani, R., Giammar, D.E. and Tebo, B.M., 2008. Biogenic Uraninite 

Nanoparticles and Their Importance for Uranium Remediation. Elements, 4(6): 407-412. 

Bargar, J.R. et al., 2009b. Corrosion of Biogenic Uraninite: Molecular- and Intermediate-scale 

Measurements, Paper No. 64-8, Geological Society of America GSA Annual Meeting

GSA Abstracts with Programs Portland, OR. 

Bear, J. and Bahmat, Y., 1991. Introduction to Modeling of Transport Phenomena in Porous 

Meda. Theory and Applications of Transport in Porous Media, 4. Kluwer Academic 

Publishers, Dordrecht, The Netherlands. 

Bernhard, G. et al., 2001. Uranyl(VI) carbonate complex formation: Validation of the 

Ca2UO2(CO3)(3)(aq.) species. Radiochimica Acta, 89(8): 511-518. 

Boonchayaanant, B., Gu, B., Wang, W., Ortiz, M.E. and Criddle, C.S., 2009. Can microbiallygenerated hydrogen sulfide account for the rates of U(VI) reduction by a sulfate-reducing 

bacterium? Biodegradation, 21(1): 81-95. 

Brooks, R.H. and Corey, A.T., 1966. Properties of porous media affecting fluid flow. Proc. 

ASCE Journal of Irrigation and Drainage Division, 93(2): 61-88. 

Brooks, S.C. et al., 2003. Inhihition of bacterial U(VI) reduction by calcium. Environmental 

Science & Technology, 37(9): 1850-1858. 

Canfield, D.E., 1989. Reactive Iron in Marine-Sediments. Geochimica Et Cosmochimica Acta, 

53(3): 619-632. 

Choi, J. and Park, J.W., 2005. Competitive adsorption of heavy metals and uranium on soil 

constituents and microorganism. Geosciences Journal, 9(1): 53-61. 

Curtis, G.P., Fox, P., Kohler, M. and Davis, J.A., 2004. Comparison of in situ uranium K-D 

values with a laboratory determined surface complexation model. Applied Geochemistry, 

19(10): 1643-1653. 

Davis, J.A., Coston, J.A., Kent, D.B. and Fuller, C.C., 1998. Application of the surface 

complexation concept to complex mineral assemblages. Environmental Science & 

Technology, 32(19): 2820-2828. 

Davis, J.A. et al., 2006. Processes affecting transport of uranium in a suboxic aquifer. Physics 

and Chemistry of the Earth, 31(10-14): 548-555. 

Davis, J.A. and Kent, D.B., 1990. Surface Complexation Modeling in Aqueous Geochemistry. 

Reviews in Mineralogy, 23: 177-260. 

Davis, J.A., Meece, D.E., Kohler, M. and Curtis, G.P., 2004. Approaches to surface 

complexation modeling of uranium(VI) adsorption on aquifer sediments. Geochimica Et 

Cosmochimica Acta, 68(18): 3621-3641. 
6.2

DOE, 1999. Final site observational work plan for the UMTRA project Old Rifle site GJO-99-

88-TAR, Grand Junction, Colo. 

Englert, A., Hubbard, S.S., Williams, K.H., Li, L. and Steefel, C.I., 2009. Feedbacks Between 

Hydrological Heterogeneity and Bioremediation Induced Biogeochemical 

Transformations. Environmental Science & Technology, 43(14): 5197-5204. 

EPA, 1998. U.S. Environmental Protection Agency Soil Cleanup Criteria in 40 CFR Part 192. 

EPA, 2000. U.S. Environmental Protection Agency National Primary Drinking Water 

Regulations; Radionuclides; Final Rule in 40 CFR Parts 9, 141, and 142 

Fang, Y., Yabusaki, S.B., Morrison, S.J., Amonette, J.P. and Long, P.E., 2009. Multicomponent 

reactive transport modeling of uranium bioremediation field experiments. Geochimica Et 

Cosmochimica Acta, 73(20): 6029-6051. 

Finneran, K.T., Anderson, R.T., Nevin, K.P. and Lovley, D.R., 2002. Potential for 

Bioremediation of uranium-contaminated aquifers with microbial U(VI) reduction. Soil 

& Sediment Contamination, 11(3): 339-357. 

Fox, P.M., Davis, J.A. and Zachara, J.M., 2006. The effect of calcium on aqueous uranium(VI) 

speciation and adsorption to ferrihydrite and quartz. Geochimica Et Cosmochimica Acta, 

70(6): 1379-1387. 

Gorby, Y.A. and Lovley, D.R., 1992. Enzymatic Uranium Precipitation. Environmental Science 

& Technology, 26(1): 205-207. 

Guillaumont, R. et al., 2003. Chemical Thermodynamics 5. Update on the Chemical 

Thermodynamics of Uranium, Neptunium, Plutonium, Americium, and Technetium. 

Nuclear Energy Agency. Elsevier, Amsterdam.

Holmes, D.E., Finneran, K.T., O'Neil, R.A. and Lovley, D.R., 2002. Enrichment of members of 

the family Geobacteraceae associated with stimulation of dissimilatory metal reduction in 

uranium-contaminated aquifer sediments. Applied and Environmental Microbiology, 

68(5): 2300-2306. 

Hunter, K.S., Wang, Y.F. and Van Cappellen, P., 1998. Kinetic modeling of microbially-driven 

redox chemistry of subsurface environments: coupling transport, microbial metabolism 

and geochemistry. Journal of Hydrology, 209(1-4): 53-80. 

Hyndman, D.W. et al., 2000. Hydraulic characterization and design of a full-scale biocurtain. 

Ground Water, 38(3): 462-474. 

Istok, J.D. et al., 2004. In situ bioreduction of technetium and uranium in a nitrate-contaminated 

aquifer. Environmental Science & Technology, 38(2): 468-475. 

Jeon, B.H., Dempsey, B.A., Burgos, W.D., Barnett, M.O. and Roden, E.E., 2005. Chemical 

reduction of U(VI) by Fe(II) at the solid-water interface using natural and synthetic 

Fe(III) oxides. Environmental Science & Technology, 39(15): 5642-5649. 

Kaluarachchi, J.J. and Parker, J.C., 1992. Multiphase flow with a simplified model for oil 

entrapment. Transport in Porous Media, 7(1): 1-14. 

Komlos, J., Kukkadapu, R.K., Zachara, J.M. and Jaffe, P.R., 2007. Biostimulation of iron 

reduction and subsequent oxidation of sediment containing Fe-silicates and Fe-oxides: 

Effect of redox cycling on Fe(III) bioreduction. Water Research, 41(13): 2996-3004. 

Komlos, J., Mishra, B., Lanzirotti, A., Myneni, S.C.B. and Jaffe, P.R., 2008a. Real-time 

speciation of uranium during active bioremediation and U(IV) reoxidation. Journal of 

Environmental Engineering-Asce, 134(2): 78-86. 
6.3

Komlos, J., Peacock, A., Kukkadapu, R.K. and Jaffe, P.R., 2008b. Long-term dynamics of 

uranium reduction/reoxidation under low sulfate conditions. Geochimica Et 

Cosmochimica Acta, 72(15): 3603-3615. 

Land, C.S., 1968. Calculation of imbibition relative permeability for two and three-phase flow 

from rock properties. Society of Petroleum Engineers Journal, 8(2): 149-&.

Li, L., Steefel, C.I., Williams, K.H., Wilkins, M.J. and Hubbard, S.S., 2009. Mineral 

Transformation and Biomass Accumulation Associated With Uranium Bioremediation at 

Rifle, Colorado. Environmental Science & Technology, 43(14): 5429-5435. 

Liger, E., Charlet, L. and Van Cappellen, P., 1999. Surface catalysis of uranium(VI) reduction by 

iron(II). Geochimica Et Cosmochimica Acta, 63(19-20): 2939-2955. 

Liu, C.X., Kota, S., Zachara, J.M., Fredrickson, J.K. and Brinkman, C.K., 2001. Kinetic analysis 

of the bacterial reduction of goethite. Environmental Science & Technology, 35(12): 

2482-2490. 

Long, P.E., Yabusaki, S.B., Meyer, P.D., Murray, C.J. and N’Guessan, A.L., 2008. Technical 

Basis for Assessing Uranium Bioremediation Performance. NUREG/CR-6973, U.S. 

Nuclear Regulatory Commission.

Lovley, D.R., Phillips, E.J.P., Gorby, Y.A. and Landa, E.R., 1991. Microbial Reduction of 

Uranium. Abstracts of Papers of the American Chemical Society, 202: 8-Geoc.

Lovley, D.R., Roden, E.E., Phillips, E.J.P. and Woodward, J.C., 1993. Enzymatic Iron and 

Uranium Reduction by Sulfate-Reducing Bacteria. Marine Geology, 113(1-2): 41-53. 

Madden, A.S. et al., 2009. Donor-dependent Extent of Uranium Reduction for Bioremediation of 

Contaminated Sediment Microcosms. Journal of Environmental Quality, 38(1): 53-60. 

Mahadevan, R. et al., 2006. Characterization of metabolism in the Fe(III)-reducing organism 

Geobacter sulfurreducens by constraint-based modeling. Applied and Environmental 

Microbiology, 72(2): 1558-1568. 

Moon, H.S., Komlos, J. and Jaffe, P.R., 2007. Uranium reoxidation in previously bioreduced 

sediment by dissolved oxygen and nitrate. Environmental Science & Technology, 41(13): 

4587-4592. 

Moon, H.S., Komlos, J. and Jaffé, P.R., 2009. Biogenic U(IV) oxidation by dissolved oxygen 

and nitrate in sediment after prolonged U(VI)/Fe(III)/SO42- reduction. Journal of 

Contaminant Hydrology, 105(1-2): 18-27. 

Moyes, L.N. et al., 2000. Uranium uptake from aqueous solution by interaction with goethite, 

lepidocrocite, muscovite, and mackinawite: An X-ray absorption spectroscopy study. 

Environmental Science & Technology, 34(6): 1062-1068. 

N'Guessan, A.L., Vrionis, H.A., Resch, C.T., Long, P.E. and Lovley, D.R., 2008. Sustained 

removal of uranium from contaminated groundwater following stimulation of 

dissimilatory metal reduction. Environmental Science & Technology, 42(8): 2999-3004. 

Ortiz-Bernad, I., Anderson, R.T., Vrionis, H.A. and Lovley, D.R., 2004. Resistance of solidphase U(VI) to microbial reduction during in situ bioremediation of uraniumcontaminated groundwater. Applied and Environmental Microbiology, 70(12): 7558-

7560. 

Pabalan, R.T., Bertetti, F.P., Prikryl, J.D. and Turner, D.R., 1996. Uranium(VI) sorption onto 

selected mineral surfaces: Key geochemical parameters. Abstracts of Papers of the 

American Chemical Society, 211: 55-Geoc.

Pyzik, A.J. and Sommer, S.E., 1981. Sedimentary Iron Monosulfides - Kinetics and Mechanism 

of Formation. Geochimica Et Cosmochimica Acta, 45(5): 687-698. 
6.4

Regenspurg, S., Schild, D., Schafer, T., Huber, F. and Malmstrom, M.E., 2009. Removal of 

uranium(VI) from the aqueous phase by iron(II) minerals in presence of bicarbonate. 

Applied Geochemistry, 24(9): 1617-1625. 

Riley, R.G. and Zachara, J.M., 1992. Chemical contaminants on DOE lands and selection of 

contaminant mixtures for subsurface research. DOE/ER-0547T, U.S. Department of 

Energy, Washington, D.C. 

Rittmann, B.E. and McCarty, P.L., 2001. Environmental biotechnology : principles and 

applications. McGraw-Hill series in water resources and environmental engineering. 

McGraw-Hill, Boston, xiv, 754 p. pp. 

Roden, E.E. and Zachara, J.M., 1996. Microbial reduction of crystalline iron(III) oxides: 

Influence of oxide surface area and potential for cell growth. Environmental Science & 

Technology, 30(5): 1618-1628. 

Sani, R.K., Peyton, B.M., Dohnalkova, A. and Amonette, J.E., 2005. Reoxidation of reduced 

uranium with iron(III) (hydr)oxides under sulfate-reducing conditions. Environmental 

Science & Technology, 39(7): 2059-2066. 

Scott, T.B., Allen, G.C., Heard, P.J. and Randell, M.G., 2005. Reduction of U(VI) to U(IV) on 

the surface of magnetite. Geochimica Et Cosmochimica Acta, 69(24): 5639-5646. 

Senko, J.M., Istok, J.D., Suflita, J.M. and Krumholz, L.R., 2002. In-situ evidence for uranium 

immobilization and remobilization. Environmental Science & Technology, 36(7): 1491-

1496. 

Shelobolina, E.S., Vrionis, H.A., Findlay, R.H. and Lovley, D.R., 2008. Geobacter 

uraniireducens sp nov., isolated from subsurface sediment undergoing uranium 

bioremediation. International Journal of Systematic and Evolutionary Microbiology, 58: 

1075-1078. 

Suzuki, Y., Kelly, S.D., Kemner, K.M. and Banfield, J.F., 2005. Direct microbial reduction and 

subsequent preservation of uranium in natural near-surface sediment. Applied and 

Environmental Microbiology, 71(4): 1790-1797. 

Urrutia, M.M., Roden, E.E. and Zachara, J.M., 1999. Influence of aqueous and solid-phase Fe(II) 

complexants on microbial reduction of crystalline iron(III) oxides. Environmental 

Science & Technology, 33(22): 4022-4028. 

Vrionis, H.A. et al., 2005. Microbiological and geochemical heterogeneity in an in situ uranium 

bioremediation field site. Applied and Environmental Microbiology, 71(10): 6308-6318. 

Waite, T.D., Davis, J.A., Fenton, B.R. and Payne, T.E., 2000. Approaches to modelling 

uranium(VI) adsorption on natural mineral assemblages. Radiochimica Acta, 88(9-11): 

687-693. 

Wan, J.M. et al., 2005. Reoxidation of bioreduced uranium under reducing conditions. 

Environmental Science & Technology, 39(16): 6162-6169. 

Wersin, P. et al., 1994. Interaction between aqueous uranium(VI) and sulfide minerals - 

spectroscopic evidence for sorption and reduction. Geochimica Et Cosmochimica Acta, 

58(13): 2829-2843. 

White, M.D. and Oostrom, M., 2006. STOMP Subsurface Transport Over Multiple Phases 

Version 4.0 User’s Guide, PNNL-15782, Pacific Northwest National Laboratory, 

Richland, Washington. 

Wu, W.M. et al., 2006. Pilot-scale in situ bioremedation of uranium in a highly contaminated 

aquifer. 2. Reduction of U(VI) and geochemical control of U(VI) bioavailability. 

Environmental Science & Technology, 40(12): 3986-3995. 
6.5

Yabusaki, S.B. et al., 2007. Uranium Removal from Groundwater via In Situ Biostimulation: 

Field-Scale Modeling of Transport and Biological Processes. Journal of Contaminant 

Hydrology, 93(1-4): 216-235. 

Yabusaki, S.B., Fang, Y.L. and Waichler, S.R., 2008. Building conceptual models of field-scale 

uranium reactive transport in a dynamic vadose zone-aquifer-river system. Water 

Resources Research, 44(12): 24. 

Zheng, Z.P., Tokunaga, T.K. and Wan, J.M., 2003. Influence of calcium carbonate on U(VI) 

sorption to soils. Environmental Science and Technology, 37: 5603-5608. 

Zopfi, J., Bttcher, M.E. and Jorgensen, B.B., 2008. Biogeochemistry of sulfur and iron in 

Thioploca-colonized surface sediments in the upwelling area off central chile. 

Geochimica Et Cosmochimica Acta, 72(3): 827-843. 

A.1

Appendix A

This appendix contains all simulation results for the sensitivity analyses. 

Figure A.1. Time-dependent aqueous uranium sensitivity to flow rate at 2.5 m (row 1), 5.0 m 

(row 2), and 8.5 m (row 3). 

Figure A.2. Time-dependent aqueous uranium sensitivity to porosity at 2.5 m (row 1), 5.0 m 

(row 2), and 8.5 m (row 3). 

Aqueous Uranium Sensitivity: Flow

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low Flow

Row 2 Low Flow

Row 3 Low Flow

Row 1 High Flow

Row 2 High Flow

Row 3 High Flow

Aqueous Uranium Sensitivity: Porosity

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low Porosity

Row 2 Low Porosity

Row 3 Low Porosity

Row 1 High Porosity

Row 2 High Porosity

Row 3 High Porosity
A.2

Figure A.3. Time-dependent aqueous uranium sensitivity to dispersivity at 2.5 m (row 1), 5.0 m 

(row 2), and 8.5 m (row 3) 

Figure A.4. Time-dependent aqueous uranium sensitivity to initial SRB biomass at 2.5 m (row 

1), 5.0 m (row 2), and 8.5 m (row 3). 

Aqueous Uranium Sensitivity: Dispersivity

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low Dispersivity

Row 2 Low Dispersivity

Row 3 Low Dispersivity

Row 1 High Dispersivity

Row 2 High Dispersivity

Row 3 High Dispersivity

Aqueous Uranium Sensitivity: Initial Biomass

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low Biomass

Row 2 Low Biomass

Row 3 Low Biomass

Row 1 High Biomass

Row 2 High Biomass

Row 3 High Biomass
A.3

Figure A.5. Time-dependent aqueous uranium sensitivity to initial SRB biomass decay at 2.5 m 

(row 1), 5.0 m (row 2), and 8.5 m (row 3). 

Figure A.6. Time-dependent aqueous uranium sensitivity to uranium bioreduction rate at 2.5 m 

(row 1), 5.0 m (row 2), and 8.5 m (row 3). 

Aqueous Uranium Sensitivity: Biomass Decay Rate

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low Biomass Decay

Row 2 Low Biomass Decay

Row 3 Low Biomass Decay

Row 1 High Biomass Decay

Row 2 High Biomass Decay

Row 3 High Biomass Decay

Aqueous Uranium Sensitivity: U Rate

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low U Rate

Row 2 Low U Rate

Row 3 Low U Rate

Row 1 High U Rate

Row 2 High U Rate

Row 3 High U Rate
A.4

Figure A.7. Time-dependent aqueous uranium sensitivity to sulfate bioreduction rate at 2.5 m 

(row 1), 5.0 m (row 2), and 8.5 m (row 3). 

Figure A.8. Time-dependent aqueous uranium sensitivity to initial aqueous uranium 

concentration at 2.5 m (row 1), 5.0 m (row 2), and 8.5 m (row 3). 

Aqueous Uranium Sensitivity: SO4 Rate

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low SO4 Rate

Row 2 Low SO4 Rate

Row 3 Low SO4 Rate

Row 1 High SO4 Rate

Row 2 High SO4 Rate

Row 3 High SO4 Rate

Aqueous Uranium Sensitivity: Initial Uranium

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low Initial U

Row 2 Low Initial U

Row 3 Low Initial U

Row 1 High Initial U

Row 2 High Initial U

Row 3 High Initial U
A.5

Figure A.9. Time-dependent aqueous uranium sensitivity to uranium surface complexation site 

density at 2.5 m (row 1), 5.0 m (row 2), and 8.5 m (row 3). 

Figure A.10. Time-dependent aqueous uranium sensitivity to pH at 2.5 m (row 1), 5.0 m (row 

2), and 8.5 m (row 3). 

Aqueous Uranium Sensitivity: Site Density

0.00E+00

2.00E-01

4.00E-01

6.00E-01

8.00E-01

1.00E+00

1.20E+00

1.40E+00

1.60E+00

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low Site Density

Row 2 Low Site Density

Row 3 Low Site Density

Row 1 High Site Density

Row 2 High Site Density

Row 3 High Site Density

Aqueous Uranium Sensitivity: pH

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0 10 20 30 40 50 60

Time (d)

Concentration (uM)

Row 1 Low pH

Row 2 Low pH

Row 3 Low pH

Row 1 High pH

Row 2 High pH

Row 3 High pH
A.6