Fourth, model limitations should be evaluated to provide a reasonable range of prediction uncertainties. This step involves the evaluation of uncertainties in the model input parameters, such as geologic layering (Johnson and Friedel 2009) and water chemistry. For example, the integrity of the conﬁning zone should be evaluated based on any uncertainty in geologic logs and can be tested using multiple geologic conceptual models. In addition, any predictions of long-term contaminant transport should provide adequate prediction uncertainties based on the uncertainties of the input data. Multiple conceptual models provide a range of potential groundwater quality impacts. This provides valuable feedback for the collection of additional data, which can assist in reducing uncertainty in future models.

Reactive Transport Modeling for the Proposed Dewey Burdock Uranium In-Situ Recovery Mine, Edgemont, South Dakota, USA (Johnson 2011) was presented at the IMWA 11[th] International Mine Water Association Congress, "Mine Water  -  Managing the Challenges." This paper provides an initial reactive-transport simulation, which supports a conceptual understanding of uranium roll-front formation, current ground water conditions, mining geochemistry, restoration geochemistry, and long-term ground water quality at a uranium ISR site. As the author states, this is the starting point for additional refinements to improve the model.

The reactive-transport modeling discussed in the paper used PHAST (Parkhurst et al. 2010), a relatively simple ground water ﬂow code coupled with PHREEQC (Parkhurst and Appelo 1999), to calculate geochemical conditions at each time step. For this work, generic values were assigned to the ground water ﬂow velocities and mass balances for the solid phase. Site-specific data are expected to be added in the future. Even so, some simulations were conducted to simulate pre-mining conditions. In one simulation, ground water with no dissolved oxygen was transported through the model domain. The result was a solid phase uraninite roll-front associated with pyrite on the solid-phase reduced side; uranium and dissolved oxygen were not found in solution.

Uranium in-situ mining was simulated using a leach solution fortified with oxygen and carbon dioxide. The resulting oxidation made uranium soluble, and the carbon dioxide created a complexing agent. A ﬁve-spot well pattern was simulated with a center pumping well and four surrounding injection wells. In the ore zone, the result was elevated concentrations of uranium in the ground water where the ore zone was being mined.

During the restoration phase, the existing wellﬁeld was used to ﬂush out the mining solutions from the ground water. This process was simulated as water with low dissolved constituent concentrations, but with 50 parts per billion (ppb) residual uranium. In one simulation, oxygen was left in the restoration ﬂuids, and in another simulation, oxygen was kept at zero. At this stage, reductant addition to help precipitate uranium could be simulated, but that simulation was not completed for this paper. As noted, the model will be refined as site-specific data are incorporated.

Ongoing Research

To assist decisionmaking about ISR design, operations, restoration, closure and monitoring will come from the application of reactive transport models. Many such models are already in use to support the ISR mining and new developments in this field (e.g., PHREEQC, Geochemist's Workbench[(R)]), although there are a few sophisticated codes that allow the reactive transport processes to be coupled to the ground water flow regime, such as PHAST or PHT3D. To further investigate data needs, data collection, model applicability and potential modeling approaches, the EPA has entered into a corporative agreement with the U.S. Geological Survey under a Regional Applied Research Effort (RARE).

Demonstrating Long-term Stability of Restored ISR Wellfields  -  Long-Term Monitoring and Geochemical Modeling

After wellfield restoration efforts have stopped, the restored wellfield is typically monitored for periods of six months to several years, with the intent of demonstrating that the ground water chemistry in the wellfield has reached a "steady state" at compositions as close as possible to the pre-mining background (baseline) levels. While this post-restoration monitoring will provide some confidence that the pre-mining geochemical conditions in the wellfield have returned, the situation may not persist over the long term. The initial injection of chemicals to oxidize and mobilize the uranium ore will most likely also oxidize the chemical reducing agents (electron acceptors) originally present in the aquifer that acted to sequester the uranium in the ore body. These materials are thought to consist of iron sulfide minerals and organic material. If the amounts of these reducing agents have been decreased sufficiently by the mining process, the continual movement of oxidizing waters into the ore zone from up gradient may be too strong for the remnants of the reducing agents to maintain chemically reducing conditions. In that case, the uranium chemically reduced by the restoration process will be re-oxidized and migrate down gradient out of the wellfield over time. In such a situation, the restoration efforts are simply palliative, since they may not restore the underlying mechanism(s) responsible for sequestering the uranium. The long-term prospect is that radionuclides and perhaps toxic metals will move out of the depleted ore zone and into surrounding aquifer areas, because the underlying mechanism for sequestering them has been weakened or totally removed.

To detect this possibility, two approaches are possible. In one approach, the post-restoration monitoring could be continued for periods that may be tens of years in duration to detect any deterioration of the chemically reducing conditions in the wellfield, at which point, if detected, remediation efforts could be developed. Such a long monitoring process is necessary, because the kinetics of a re-oxidation process in the field can be slow. Section 7.7.2 examines statistical measures for determining stability in a sequence of measurements considering the influence of natural variability variations and rates of change in the measured parameter. Table 7-20 and Table 7-21 show how many samples (assuming quarterly sampling) are needed to attain high levels of confidence (95% and 99%) that a trend can be detected for various assumed levels of natural variability and parameter change rates. For relatively low natural variability levels and rates of change, the number of samples, and consequently the post-restoration monitoring period, significantly exceed typical post-restoration sampling periods used in practice. A 30-year post-restoration monitoring period proposed in the rulemaking establishes a monitoring period consistent with RCRA regulations.

Another alternative is to use geochemical modeling to demonstrate that the restored wellfield and the down gradient geochemical conditions in the exempted aquifer area are sufficient to maintain chemically reducing conditions over the long-term. The modeling used to address the post-restoration possibilities involves relatively simple aqueous speciation modeling, as well as more complex process modeling and perhaps coupled flow and contaminant transport models. As described above, these types of models are readily available and typically used to design the wellfield geometry and optimize production, as well as help design restoration efforts. Their use in assessing post-restoration behavior at an ISR site is discussed further below. The intent of the modeling from a regulatory perspective is to answer two questions:

   * Will the ground water chemistry in the restored wellfield keep uranium and other mobilized toxic metals in place?
   * Does the down gradient portion of the exempted aquifer have the reducing capacity to remove uranium from ground waters that migrate into the area from the up gradient wellfield area?

As a first step in geochemical modeling, it is important to assess the chemical reducing capacity of the ore zone and down gradient portion of the exempted aquifer before the mining is initiated and after the restoration efforts have ceased. This requires information on the nature and amount of chemical reducing agents present in the ore zone before and after the ISR process, as well as down gradient of the ore zone in the remainder of the exempted aquifer area. Concentrations of pyrite (and other iron sulfide minerals) and organic material, as well as other electron acceptor species in the ore zone, should be measured before ISR operations begin and after restoration efforts end. Aqueous indicators of chemically reducing conditions (such as various redox sensitive couples, e.g., sulfide/sulfate, ferric/ferrous iron, redox state measurements, other oxidizing agents such as dissolved oxygen, and nitrate levels) should also be measured in the ground waters. Information on solid-phase electron acceptors can be gathered from analyses of cores taken within the exempted aquifer before operations begin and after restoration efforts stop. Section three of this document contains tabulations of aqueous species that should be monitored for geochemical modeling applications.

With the information on concentrations of chemically reducing agents in the ground water and solids within the wellfield and in the down gradient portion of the exempted aquifer, mass balance calculations and process modeling exercises can be done to determine if the reducing capacity of the restored wellfield and down gradient area is sufficient to keep the uranium remaining after the mining in a reduced state, minimizing the potential for migration. While such modeling evaluates the potential for maintaining chemically reducing conditions, kinetic data would allow more sophisticated modeling of reaction progress within the exempted aquifer, including coupling of reaction path and ground water flow modeling. This modeling would examine the reaction rates and evolution of changes in aqueous chemistry as ground waters bearing oxygen and uranium (VI) enter the wellfield area from the up gradient direction, interact with the "restored" wellfield chemistry and move down gradient into the un-mined portion of the exempted aquifer and eventually into the non-exempt portion of the aquifer. Kinetic data to support such modeling is limited (largely to laboratory data with limited field data), but the research described above should significantly increase the field database and understanding of these processes.

With sufficiently robust geochemical modeling demonstrating that the system can maintain chemically reducing conditions over the long-term and contain any uranium migrating out of the restored wellfield area within the down gradient portion of the exempted aquifer, the appropriate regulatory authorities may allow a shorter than 30-year post-restoration modeling period. This modeling demonstration is also important for regulatory decisions about the need for alternative concentration levels and the protection of ground water resources in the down gradient area outside the boundary of the exempted aquifer.
Active/Existing ISR Facilities: Monitoring Issues

The standards in 40 CFR 192.32 refer to several sections of RCRA 40 CFR Part 264, Subpart F, which describe EPA's regulatory approach for releases to ground water from waste management units that store, treat, and dispose of hazardous waste. Although §264.97 is not specifically cited in §192.32, it provides some useful guidance regarding general requirements that could be considered for establishing a suitable ground water baseline:

      (g) In detection monitoring or where appropriate in compliance monitoring, data on each hazardous constituent specified in the permit will be collected from background wells and wells at the compliance point(s). The number and kinds of samples collected to establish background shall be appropriate for the form of statistical test employed, following generally accepted statistical principles. The sample size shall be as large as necessary to ensure with reasonable confidence that a contaminant release to ground water from a facility will be detected. The owner or operator will determine an appropriate sampling procedure and interval for each hazardous constituent listed in the facility permit which shall be specified in the unit permit upon approval by the Regional Administrator. This sampling procedure shall be:
         (1) A sequence of at least four samples, taken at an interval that assures, to the greatest extent technically feasible, that an independent sample is obtained, by reference to the uppermost aquifer's effective porosity, hydraulic conductivity, and hydraulic gradient, and the fate and transport characteristics of the potential contaminants, or
         (2) An alternate sampling procedure proposed by the owner or operator and approved by the Regional Administrator.

         In practice, the procedures for establishing the ground water baseline are site specific and are included in the facility license issued by the NRC or Agreement State.
In practice, the procedures for establishing the ground water baseline are site specific and are included in the facility license issued by the NRC or Agreement State.

Ground Water Baseline: Case Studies

The requirements for baseline monitoring vary from state to state. (See Section 3.5 for additional details.) In Texas, 26 chemical constituents are measured before mining to establish a baseline, as shown in Table 5-1. This is example data from Production Authorization Area (PAA) No. 1 at the Zamzow ISR facility. Baseline values represent the highest average concentration from either the production or mine area, and are commonly selected as initial restoration goals (Hall 2009). In Table 5-1, the mine area is defined by a line through a ring of monitor wells in the production zone and the production area is defined by a line generally through the outer perimeter of injection and recovery wells. TCEQ regulations require a minimum of five baseline wells or one well per four acres, whichever is greater (TCEQ Title 30, Rule §331.104). Sampling frequency and number of samples are not specified by the TCEQ regulations.

Uranium Energy Corporation filed a production area authorization (PAA-1) application for the Goliad Uranium Project with the TCEQ on August 27, 2008, and this application was amended on March 27, 2009, to include data from additional baseline wells (Sass 2011). The amended application included 18 baseline wells from the 36-acre production area (i.e., 0.5 wells/acre). The average uranium content for these 18 wells was used to establish a baseline uranium value of 0.115 mg/L. The initial sampling of the 18 wells was conducted over a period of about 11 months (4 wells about July 2007, 6 wells in April 2008, and 8 wells in July 2008). The amended PAA-1 application did not include data from subsequent sampling of all 18 wells in July 2009 and again in November 2009. The averaged uranium assay results for these subsequent periods were 0.029 mg/L and 0.005 mg/L, suggesting that the initial samples were not indicative of geochemical equilibrium. Had the full time series of samples been included in the PAA-1 application, a more rigorous baseline standard would have been set against which to measure restoration.

Table 5-1.	Baseline Water Quality Data for Zamzow PAA-1

                                   Parameter
                                     Unit
                                Production Zone

                                  Mine Area**
                                Production Area

                                      Low
                                    Average
                                     High
                                      Low
                                    Average
                                     High
                                       1
Cadmium
                                     mg/l
                                      122
                                      317
                                      552
                                      195
                                      269
                                      390
                                       2
Magnesium
                                     mg/l
                                      15
                                     38.4
                                     84.2
                                      3.0
                                     21.1
                                      40
                                       3
Sodium
                                     mg/l
                                      239
                                      387
                                      750
                                      235
                                      383
                                      466
                                       4
Potassium
                                     mg/l
                                      19
                                     30.3
                                      49
                                     18.9
                                     26.7
                                      90
                                       5
Carbonate
                                     mg/l
                                       0
                                       0
                                       0
                                       0
                                       0
                                       0
                                       6
Bicarbonate
                                     mg/l
                                      128
                                      297
                                      400
                                      157
                                      269
                                      346
                                       7
Sulfate
                                     mg/l
                                      454
                                      793
                                     1,520
                                      441
                                      601
                                      940
                                       8
Chloride
                                     mg/l
                                      350
                                      503
                                      936
                                      394
                                      538
                                      662
                                       9
Fluoride
                                     mg/l
                                     0.16
                                     0.54
                                     1.19
                                     0.01
                                     0.36
                                     0.50
                                      10
Nitrate  -  N
                                     mg/l
                                   <0.01
                                     0.16
                                      0.9
                                   <0.01
                                     0.14
                                     0.49
                                      11
Silica
                                     mg/l
                                      31
                                     51.6
                                      85
                                      11
                                     43.9
                                      74
                                      12
pH
                                  Std. units
                                      6.6
                                      7.0
                                     7.66
                                     6.68
                                      7.0
                                     7.45
                                      13
TDS
                                     mg/l
                                     1,697
                                     2,289
                                     3,220
                                     1,810
                                     2,037
                                     2,360
                                      14
Conductivity
                                    umhos
                                     2,720
                                     3,204
                                     4,300
                                     2,680
                                     3,049
                                     3,430
                                      15
Alkalinity
                                  Std. units
                                      105
                                      275
                                      400
                                      206
                                      238
                                      204
                                      16
Arsenic
                                     mg/l
                                   <0.001
                                     0.009
                                     0.03
                                   <0.001
                                     0.006
                                     0.044
                                      17
Cadmium
                                     mg/l
                                  <0.0001
                                     0.001
                                     0.007
                                  <0.0004
                                     0.001
                                    0.0013
                                      18
Iron
                                     mg/l
                                     0.01
                                     0.915
                                      8.0
                                     0.03
                                     0.075
                                     0.26
                                      19
Lead
                                     mg/l
                                   <0.001
                                     0.001
                                     0.006
                                   <0.001
                                     0.004
                                     0.02
                                      20
Manganese
                                     mg/l
                                     0.009
                                     0.224
                                     0.82
                                     0.01
                                     0.118
                                     0.19
                                      21
Mercury
                                     mg/l
                                  <0.0001
                                    0.0004
                                    0.0018
                                    0.0001
                                    0.0006
                                     0.001
                                      22
Selenium
                                     mg/l
                                   <0.001
                                     0.01
                                     0.01
                                   <0.001
                                     0.004
                                     0.01
                                      23
Ammonia
                                     mg/l
                                   <0.01
                                     0.374
                                      1.4
                                   <0.01
                                     0.298
                                     0.78
                                      24
Uranium
                                     mg/l
                                   <0.001
                                     0.171
                                      1.7
                                   <0.001
                                     0.039
                                     0.432
                                      25
Molybdenum
                                     mg/l
                                   <0.001
                                     0.03
                                     0.95
                                   <0.001
                                     0.226
                                      2.1
                                      26
Radium-226
                                     pCi/l
                                      1.5
                                      155
                                      959
                                      6.5
                                      152
                                      744
** Monitor wells
Source: Hall 2009

In its license application for the Moore Ranch Uranium Project in Campbell County, Wyoming, Energy Metals Corporation (2007) proposed to establish the wellfield baseline by sampling production zone wells four times, with a minimum of 2 weeks between samplings (NRC 2010, Section <IP_ADDRESS>). Energy Metals also proposed to sample one well for each 3 acres of mine unit. Data for each sampled parameter would be averaged and used to calculate restoration goals. The average and range of baseline values in the production zone would then be used to assess the effectiveness of subsequent ground water restoration.

In another example, at Mine Unit 4 of the Christensen Ranch Project in Wyoming, the wellfield covered about 12 acres and, consequently, 12 injection or production wells were used to establish baseline ground water conditions within the ore zone, which in turn set the restoration goals (COGEMA 1994). The number of baseline wells was based on NRC guidance in NUREG-1569 (NRC 2003, p. 5-39) of one well per acre.

Commercial-scale uranium ISR facilities usually have more than one wellfield. For example, the Crow Butte facility in Dawes County, Nebraska, has constructed 10 wellfields since 1991 (Crow Butte 2007). The locations and boundaries for each wellfield are adjusted as more detailed data on the subsurface stratigraphy and distribution of uranium mineralization are collected during wellfield construction.

Wellfield Restoration

Wellfield restoration is defined as those actions taken to ensure that the uranium extraction process will not adversely affect the quality of the ground water adjacent to the ISR wellfields (NRC 2001). This requires returning the wellfield water quality parameters to meet the restoration goals included in the facility license issued by NRC or the Agreement State. Based on pre-mining monitoring, the operator establishes baseline values for the ground water quality. The regulator then uses these baseline values to set restoration goals in the wellfield license.

The portion of the aquifer undergoing uranium extraction is exempt from EPA regulatory protection under the SDWA (specifically the UIC Program at 40 CFR Part 144). However, ground water adjacent to the exempted portion of the aquifer must still be protected, and ground water protection provisions for this water are in effect. Similar to the NRC Agreement State provisions, an EPA Primacy State may impose more stringent requirements for ground water restoration than the federal program (NRC 2003). Ground water restoration requirements may vary from state to state. Of particular importance is underground injection and point source discharge into surface waters. Currently, UIC programs are administered (as authorized by EPA) in Wyoming, Nebraska, and New Mexico. South Dakota administers the program jointly with EPA.

It should be noted that UMTRCA  gives NRC the authority to require  licensees to restore ground water in the mined aquifer, and EPA to set the standards for that restoration. UMTRCA operates separately from SDWA UIC rules, and requires restoration of the ground water in the mined aquifer, even though the water may not be considered potable under SDWA. EPA's UIC regulations provide protections for non-exempt portions of the aquifer, but generally do not require restoration of the exempt portion unless it may affect drinking water outside its boundaries. State rules under UIC delegation are generally weaker than EPA's and NRC's UMTRCA requirements.

Wellfield Restoration: Case Study

Table 5-2 summarizes restoration results from 22 PAAs in Texas (Hall 2009). It is apparent that, for all of the PAAs, post-restoration analyses exceeded the baseline for some of the parameters tested. Similar information on restoration of sites in other states was extracted from NRC 2009 and is included as Attachment C. Table 5-2 also shows that all of the post-restoration parameters exceeded the baseline in some wellfields. This illustrates the difficulty of restoring wellfields to baseline conditions. However, for most of the species with quantitative limits set by MCLs or secondary drinking water standards, the quantitative limits were below the baseline. Exceptions were fluoride, nitrate, and sulfate where the baseline limits were below recommended standards for all 22 PAAs.

Table 5-2.	Ground Water Chemistry of Texas In-Situ Uranium Production Authorization Areas
                                    Analyte
                 EPA and TCEQ Drinking Water Standards (mg/l)
                                Baseline Range
                            Post-restoration Range
             PAAs with Baseline Above MCL or Recommended Standards
      PAAs with Post-restoration Water Above MCL or Recommended Standards
             PAAs Where Post-restoration Analyses Exceed Baseline
            PAAs Where Post-restoration Analyses are Below Baseline
EPA and TCEQ Primary Maximum Contaminant Levels (MCLs):
Arsenic
                                     0.01
                                  .004 - 0.23
                                  .002 - .323
                                      77%
                                      55%
                                      18%
                                      82%
Cadmium
                                     0.005
                                0.0001 - 0.0126
                                 0.0001 - 0.01
                                      45%
                                      23%
                                      27%
                                      73%
Fluoride
                                       4
                                  0.21 - 1.8
                                  0.29 - 1.6
                                      0%
                                      0%
                                      31%
                                      69%
Lead
                                     0.02
                                 0.003 - 1.97
                                 0.001 - 0.05
                                      81%
                                      18%
                                      9%
                                      91%
Mercury
                                     0.002
                                0.0001 - 0.445
                                 0.0001 - 0.01
                                      9%
                                      0%
                                      22%
                                      64%
Nitrate
                                      10
                                 0.031 - 10.0
                                  0.001 - 2.8
                                      0%
                                      0%
                                      4%
                                      96%
Selenium
                                     0.05
                                 0.001 - 0.049
                                 0.001 - 0.102
                                      18%
                                      4%
                                      54%
                                      45%
Radium (226 and 228 Ra: pCi/l)
                                    5 pCi/l
                                 9.36 - 429.8
                                   5.2 - 149
                                     100%
                                     100%
                                      4%
                                      96%
Uranium
                                     0.03
                                  0.025 - 2.0
                                 0.013 - 3.02
                                      95%
                                      86%
                                      68%
                                      32%
TCEQ Secondary Recommended Standards:
Sulfate
                                      300
                                  15.8 - 250
                                   78 - 3881
                                      0%
                                      18%
                                      86%
                                      14%
Chloride
                                      300
                                 196.9 - 3505
                                  138 - 3326
                                      86%
                                      86%
                                      22%
                                      78%
Total Dissolved Solids
                                     1000
                                 785.7 - 6349
                                 706.3 - 6155
                                      81%
                                      77%
                                      31%
                                      55%
Iron
                                      0.3
                                  0.04 - 5.49
                                  0.01 - 2.7
                                      54%
                                      9%
                                      4%
                                      96%
Manganese
                                     0.05
                                  0.01 - 0.41
                                  0.01 - 0.84
                                      77%
                                      50%
                                      40%
                                      60%
No Established MCL or Secondary Standards
Calcium
                                       -
                                  4.13 - 241
                                  14.7 - 191

                                      77%
                                      23%
Magnesium
                                       -
                                  0.477 - 125
                                   2.27 - 53

                                      72%
                                      28%
Sodium
                                       -
                                  200 - 2356
                                  169 - 2247

                                      31%
                                      65%
Potassium
                                       -
                                  6.38 - 101
                                   6.1 - 70

                                      14%
                                      86%
Carbonate
                                       -
                                  0.1 - 17.9
                                   0 - 14.6

                                      50%
                                      30%
Bicarbonate
                                       -
                                   160 - 500
                                   160 - 500

                                      66%
                                      25%
Silica
                                       -
                                   16.3 - 76
                                  13.4 - 77.6

                                      19%
                                      81%
Conductivity (μmhos/cm)
                                       -
                                 1310 - 11160
                                  1429 - 3697

                                      76%
                                      24%
Alkalinity (as CaCO3)
                                       -
                                   134 - 349
                                   145 - 408

                                      81%
                                      10%
Molybdenum
                                       -
                                  0.01 - 0.2
                                 0.0001 - 3.38

                                      42%
                                      54%
Ammonia-N
                                       -
                                  0.01 - 7.49
                                  0.04 - 120

                                      76%
                                      24%
Baseline and post-restoration data were available for all 22 PAAs with the exception of Ra, Mo, K, Si, Bicarbonate, Ammonia (21), Conductivity (14), Alkalinity (11), & Carbonate (10)
Source: Hall 2009

Issues Associated with Establishment of Post-restoration Steady State

During restoration, the operator monitors progress by periodic sampling of the ground water constituents until steady-state conditions are attained. Establishment of steady state requires that the ground water potentiometric surface be restored, to the extent practicable, to its preleaching status, so that the flow regime is similar to that existing before mining. In addition, constituents in the ground water must be in compliance with restoration goals and remain at those levels for a sufficient period to demonstrate that the results are not trending upwards to higher concentration levels. EPA describes a "steady state" as having the following relevant components (EPA 1992, Chapter 7):

         1.a	After treatment, the water levels and water flow, and the corresponding variability associated with these parameters (e.g., seasonal patterns), should be essentially the same as for those from comparable periods of time prior to the remediation effort.
      or
         1.b. 	In cases where the treatment technology has resulted in permanent changes in the ground water system, such as the placement of slurry wells, the hydrologic conditions may not return to their previous state. Nevertheless, they should achieve a state of stability which is likely to reflect future conditions expected at the site. For this steady state, the residual effects of the treatment will be small compared to seasonal changes.
      2.          The pollutant levels should have statistical characteristics (e.g., a mean and standard deviation), which will be similar to those of future periods.

The first of these components addresses the general behavior and characteristics of the ground water at the site. The second is more judgmental and requires projection of future contamination, based on available current information. These projections cannot be made with certainty; however, various criteria can be used in determining whether a steady state has been reached.

Section 7.8 of this report discusses statistical tests for measuring attainment of steady state. When the regulator is satisfied that steady state has been achieved, the operator is authorized to undertake long-term post-restoration stability monitoring.

Post-restoration Stability Monitoring

Once the operator concludes that restoration has been completed and has obtained concurrence from the regulator(s) that a steady state has been established, post-restoration stability monitoring begins. The purpose of the stability monitoring is to demonstrate that the aquifer conditions established at the end of restoration are sustainable over time. Currently, the duration of stability monitoring is a site-specific period of time established in the license(s). In the past, the license-established restoration period typically has been about 6 months (see case histories in Attachment B). More recently, the trend has been to increase the monitoring period established in the license. In practice, the actual period of stabilization may be several years, based on iterative analyses of additional samples requested by the regulators see Table 6-1). If the sandstone in the aquifer is heterogeneous, extended restoration times may be required to ensure that ground water in slow pathways is addressed.

Table 6-1.	Post-restoration and Stability Monitoring Periods
                                 Facility Name
                                     State
                   Projected or Estimated Restoration Period
           Projected or Estimated Post-restoration Monitoring Period
                               Comment/Reference
Moore Ranch
Wellfield 1
Campbell County, Wyoming
                                   3.5 years
                              1 year (quarterly)
                            NUREG-1910 Supplement 1
Moore Ranch
Wellfield 2
Campbell County, Wyoming
                                  5.25 years
                              1 year (quarterly)
                            NUREG-1910 Supplement 1
Nichols Ranch
Campbell and Johnson Counties, Wyoming
                                 1 to 5 years
                              1 year (quarterly)
                            NUREG-1910 Supplement 2
Lost Creek
Sweetwater, Wyoming
                                    2 years
                              6 months (monthly)
                           NUREG-1910 Supplement 3
Ruth Test Site
Johnson, Wyoming
                                   12 months
                                   12 months
                                 Schmidt 1989

Factors That Affect Time Frames for Post-mining Monitoring

This section summarizes the factors that must be understood to determine when the impacted aquifer has reached a steady-state condition.

Fate and Transport Processes

The monitored time frame is dependent on mass-balance estimates of how much extraction fluid remains in the aquifer. Monitoring during operations needs to determine a mass balance of the total volume of lixiviant injected into the system and the volume withdrawn. The lixiviant used to extract the uranium can mask baseline constituents and affect reaction kinetics. Knowing how much lixiviant remains in the aquifer will aid in understanding whether some reactants are still in the system, have migrated outside the monitored area, or have been temporarily sequestered in low-permeability zones, or are undergoing incomplete or slow reactions that may later release constituents.

Speciation

The environmental chemistry of uranium is largely dictated by its oxidation state (e.g., Fanghänel and Neck 2002). Under ambient oxidizing conditions, the predominant uranium oxidation state is U(VI). Where oxygen is limited, U(IV) may dominate. The metallic form, U(0), does not occur naturally, and is readily oxidized to U(IV) and eventually U(VI), upon exposure to oxidizing conditions. The mechanisms for the oxidation of U(0) and U(IV) to U(VI) are well established (e.g., NRC 2007). It is rare to find other oxidation states of uranium [e.g., U(V) and U(III)] under natural conditions, due to their instability. However, stable U(V) has been found on mica surfaces (Ilton et al. 2004, Ilton et al. 2005, Ilton et al. 2008).

In general, the solubility and therefore the mobility of uranium are greatest when it is in the U(VI) state. Complexation of U(VI) by inorganic anions, such as carbonate, fluoride, and phosphate, may enhance the solubility and mobility of this species. When reducing conditions are present, U(IV) is generally immobile and found either as an insoluble oxide (uraninite) or a silicate (coffinite). Under oxidizing conditions and near neutral pHs, U(VI) species dominate aqueous uranium concentrations. These highly soluble species are generally either hydroxy or carbonate complexes of the uranyl (UO2[2+]) cation, although elevated concentrations of potential inorganic ligands near the ISR target zone may exert greater influence on U(VI) speciation (e.g., phosphate).

A detailed discussion provided by Demuth and Schramke (2006) describes the geochemical processes controlling the fate and transport of uranium, radium, selenium, arsenic, molybdenum, sulfate, iron and manganese. The authors summarize their findings by reiterating the importance of redox conditions on influencing the mobility of uranium, selenium, arsenic, molybdenum, and sulfur. Although these constituents are likely to be more mobile under relatively oxidizing conditions, adsorption and desorption reactions between uranium, selenium, arsenic, and molybdenum with iron oxide surfaces are particularly important controlling reactions, because iron oxides are widespread in the hydrogeologic environment as coatings on other solids. The precipitation of sulfate phases (e.g., barite, celestite, gypsum) is likely to attenuate sulfate and radium-226 by solid solution. Radium-226 is also strongly attenuated by adsorption onto clay minerals. Under oxidizing conditions, iron and manganese tend to form relatively immobile iron oxyhydroxides and manganese oxides. These oxides may provide adsorption sites for many trace metals, such as uranium, arsenic and molybdenum. Under reducing conditions, particularly as sulfate is consumed and the sulfur is converted to sulfide, concentrations of dissolved metals such as molybdenum decrease as solid-phase metal sulfides are formed.

Calcium (or other alkaline earth metals, such as magnesium) and inorganic carbon in ground water tend to dominate the aqueous speciation of U(VI) under near neutral pH conditions. The presence of these species is common in many natural ground water systems (Hem 1985), and as noted below, these speciation characteristics also influence the degree to which U(VI) will adsorb onto aquifer solids. Under reducing conditions, U(IV) species, primarily the uranyl cation and its complexes, predominate, but because of the very low solubility of U(IV) minerals, reach maximum concentrations on the order of 10 nanomolar (2.4 micrograms U/L). For all practical purposes, therefore, only U(VI) aqueous species are at sufficient concentrations to be of environmental concern.

Chemical reaction kinetic equations or equilibrium thermodynamic equations can be used to describe chemical interactions among dissolved chemical species, the dissolution of immobile solid phases, or the formation and precipitation of new, immobile solid phases. Geochemical modeling is often performed at ISR facilities to gain a better understanding of thermodynamically controlled processes that include mineral dissolution/precipitation, oxidation/reduction, and adsorption/desorption.

Most of the available computer codes assume thermodynamic equilibrium and do not have a method of calculating reaction rates (i.e., kinetics). If a mineral forms or dissolves slowly in a system, the model developed from these codes will not account for these kinetic effects. This is not a major limitation for most aquifer systems, where residence times are measured in years; however, kinetic effects can become more important in modeling reactions anticipated to occur during applied remediation methods, such as the injection of reactants into an aquifer. Ground water flow rates in aquifers undergoing ISR are typically slow, on the order of feet to tens of feet per year, suggesting that the development of geochemical models for these environments may not be severly limited due to a lack of site-specific knowledge of kinetic effects for chemical reactions that occur during and after restoration efforts.

Speciation: Case Study

The experience with iron at the Crow Butte ISR facility is illustrative of speciation problems. Crow Butte Resources (CBR) experienced difficulty in reaching desired iron levels during wellfield restoration. During the initial stabilization monitoring period in 1999, the iron concentration averaged 0.089 mg/L. Subsequent testing in the summer of 2002 showed an average iron content of 0.278 mg/L. The operator attributed this to speciation initiated by the original injection of lixiviant, with subsequent transitory solubility increases resulting from the selected restoration method. As CBR stated (Crow Butte 2002):

      CBR believes that the elevated iron concentrations are due to the restoration process and will ultimately decrease to concentrations well below the restoration standard. During the in situ mining process, when the groundwater is oxygenated and the Eh is positive, the iron contained in pyrites is oxidized to ferric iron and forms ferric oxyhydroxides. The ferric oxyhydroxides are extremely insoluble, which explains the very low concentrations of iron in solution during mining, indicated by the end of mining values which, with the exception of one restoration well (PR-19), were below the detection limit of 0.05 mg/L. During the active restoration process, however, sodium sulfide is used as a reductant to decrease the Eh of the groundwater. As the Eh drops, the stable solid iron phase is reduced from ferric iron to ferrous iron, which is more soluble. During the transition from ferric to ferrous iron, the iron concentration in the groundwater increases significantly. This increase in the iron concentration is transitory and, as the Eh continues to decrease, iron sulfide minerals will be the dominant iron phase. Because of the relative insolubility of these iron sulfide minerals, this will cause a significant decrease in the iron concentration in solution. Based on these mechanisms, CBR expects that the elevated concentrations of iron at the current time will ultimately decrease.

Without greater insight into the mineralogy and flow and transport processes within the aquifer, however, there is no reliable means to test CBR's hypothesis. Active research is being conducted to better understand the processes controlling aquifer restoration. For example, Cameco Resources is currently seeking permission from the Wyoming Department of Environmental Quality to conduct a series of aquifer restoration experiments at Smith Ranch mine, which include: (1) tracer tests to determine the hydrologic pathways between injector and recovery wells; (2) bio-stimulation tests to determine the viability of using naturally occurring bacteria to re-precipitate uranium and other redox-sensitive species; and (3) natural attenuation tests to determine the ability of the aquifer down gradient of the mining zone to immobilize contaminants (Cameco 2012).

Solubility

Under most natural conditions, the thermodynamically stable uranium solid phases will be either U(VI) or U(IV) compounds. The most stable U(VI) compounds are the phosphates and vanadates, but their formation is often limited by the relatively low concentrations of these two anions, and thus more soluble U(VI) oxides, such as schoepite,  are often seen if any U(VI) solid phases are present. A significant fraction of the solid-phase U(VI) will be adsorbed to iron (hydr)oxide surfaces, to the edges of clay minerals, and to organic matter, rather than precipitated as discrete uranium phases. Maximum solubility of uranium is seen in oxidizing, phosphate-free, carbonate-rich solutions, and consequently, carbonates (or bicarbonates) and oxygen or hydrogen peroxide are the principal reagents used for ISR mining.

Under reducing conditions, the stable U(IV) solid phases are uraninite and, if high amounts of dissolved silica are present, coffinite. Organic complexes of U(IV) associated with humic material may also retain U(IV) in the solid phase. The solubility of the U(IV) phases is extremely low, and thus the presence of reducing conditions effectively halts or slows the movement of uranium in soils and sediments, provided that colloidal-sized uranium-bearing particles are not formed and transported. The most common uranium ore-forming process involves reductive precipitation of U(IV) phases as a result of microbiological activity to form a roll-front deposit (Langmuir 1997). The stability fields for U(VI) and U(IV) as a function of pH and Eh for various water compositions suggest that a wide variety of uranium-bearing precipitates are possible, especially in complex ground water systems that invariably contain silica, carbonate/bicarbonate, calcium/magnesium, sodium, and sometimes phosphate. Furthermore, it may be difficult to predict associations of uranium in the solid phase based on analysis of aqueous chemical data and solubility predictions from thermodynamic chemical data. In the absence of confirmatory solid-phase characterization data, equilibrium model projections indicate only the possible formation of specific uranium-bearing precipitates.

Natural Attenuation Processes

Natural attenuation processes include a variety of physical, chemical, and biological processes that can act to reduce the mass, mobility, volume, or concentration of contaminants in ground water. Attenuation processes important at ISR sites include pH buffering and acid neutralization, adsorption at the mineral-water interface, mineral precipitation, and dilution/dispersion.

Adsorption

Adsorption processes are typically categorized by the relative "strength" of the interaction between the adsorbate (species in solution) and the surface or adsorbent. If water molecules are positioned between the cation or anion and the surface, the adsorption complex is referred to as outer sphere and is considered to be weak. Conversely, if upon adsorption, the adsorbate loses waters of hydration such that no water molecules are positioned between the cation or anion and the surface, the adsorption complex is referred to as inner sphere and is considered to be strong.

Adsorption of uranium typically involves inner-sphere complexation of uranyl (i.e., UO2[2+]) species by oxygen ligands at the surfaces of iron oxyhydroxides, phosphates, and layered silicates. Uranyl species exhibit a high affinity for iron oxyhydroxide surfaces and for both basal and edge sites on layered aluminosilicates, such as the clays smectite and vermiculite. Adsorption of U(VI) to the aluminosilicate mineral, muscovite, has been observed in aquifer sediments at the Hanford Site in Richland, Washington (McKinley et al. 2007). Complexation of U(VI) by organic ligands in solid humic materials (primarily carboxylic-acid and phenolic groups) may also serve to remove uranium in shallow ground water systems (Sowder et al. 2003).

A compilation of published Kd values for U(VI) sorption onto soils/sediments is documented in EPA 1999a. However, the authors of that compilation recognized that there are major limitations to the application of published Kds for site-specific applications where either the ground water chemistry or the aquifer matrix differs significantly from the conditions under which a Kd was determined (Ochs et al. 2006). One interesting study examined uranium sorption in a sandstone aquifer under site-specific conditions by back-calculating a Kd for the observed uranium distribution after developing a validated ground water flow model for the aquifer using flow rates and carbon isotope data for the ground water system (Pearson et al. 1983).  Areas of chemically oxidizing and reducing conditions in the aquifer were observed, indicating that uranium reduction processes were active similar to those involved in the formation of roll-front deposits. A Kd of 6 was proposed for uranium sorpion on the ssandstones based on the field data and validated hydrologic model.  Most of the information on uranium sorption reported in the literature is derived from laboratory measurements, in contrast to the the study referenced above.

Davis et al. (2004) document a Surface Complexation Model (SCM) alternative to the constant K approach. As described by Davis et al. (2004), there are two major methods for applying the SCM concept; the Component Activity (CA) and the Generalized Composite (GC) approaches. In the CA approach, it is assumed that a mineral assemblage is composed of a mixture of one or more reference phases, whose surface chemical reactions are known from independent studies of each phase. Next, based on a measurement of the relative amounts or surface areas of each mineral present in the soil or sediment, adsorption by the mixture of phases can be predicted by an equilibrium calculation, without any fitting of experimental data for the mixture. In the GC approach, the surface of the mineral assemblage is considered too complex to be quantified in terms of the contributions of individual phases to adsorption. In the GC approach, it is assumed that adsorption can be described by mass laws written with "generic" surface functional groups, with the stoichiometry and formation constants for each mass law determined by fitting experimental data for the mineral assemblage as a whole.
This SCM approach incorporates the important influence of uranium solution speciation, while avoiding the need to model the influence of individual mineral components (and their respective surface charging behavior). While this approach still requires site-specific data, it provides a means for projecting the influence of changes in ground water chemistry on uranium sorption. The chemistry of ground water may be influenced by reaction with aquifer solids and/or external recharge/infiltration from atmospheric precipitation or surface water. As previously noted, alkalinity influences the aqueous speciation of U(VI), and it also influences the degree of sorption of U(VI) onto iron oxyhydroxides and aquifer solids in which these minerals control uranium partitioning (e.g., Um et al. 2007). It has been demonstrated that changes in ground water chemistry influence the transport of U(VI) through an aquifer (Yabusaki et al. 2008). Alternatively, transition from oxidizing to reducing conditions along the transport pathway may be accompanied by a shift from adsorption of U(VI) species to precipitation of U(IV)-bearing solids (Davis et al. 2006). Reactive-transport models used to project subsurface uranium mobility directly incorporate the influence of major ion chemistry and redox conditions on the chemical speciation of uranium.
Field evidence shows that adsorption of uranium to mineral surfaces within an aquifer may be an intermediate step to the formation of uranium-bearing precipitates. Murakami et al. (2005) have observed the association of nanoparticulate U(VI)-phosphate precipitates with iron oxyhydroxides in the weathering zone down gradient from a uranium ore deposit. The U(VI) mineral was identified as metatorbernite, which was present in ground water that was undersaturated with respect to precipitation of this mineral. Characterization of the textural associations between the nanocrystalline metatorbernite and iron oxyhydroxides present as fissure fillings, clay coatings, and nodules, along with compositional relationships between copper, phosphorus, and uranium (Sato et al. 1997), indicated that the formation of uranium precipitates was a secondary step following initial adsorption of these constituents onto iron oxyhydroxide mineral surfaces (Murakami et al. 2005). As summarized by Payne and Airey (2006), the observations in this subsurface system provide a point of reference for designing site characterization strategies and developing both conceptual and analytical models for interpreting and projecting uranium mobility in ground water.

O'Loughlin et al. (2003) believe that mixed ferrous/ferric hydroxides (i.e., green rust) play a central role in the biogeochemistry of iron. The authors conclude that their experimental results clearly indicate that U(VI) (as the soluble uranyl ion) is readily reduced by green rust to U(IV) in the form of relatively insoluble UO2 nanoparticles, suggesting that the presence of green rusts in the subsurface may have significant effects on the mobility of uranium, particularly under iron-reducing conditions. Lee et al. (2010) found that biogenic UO2 (uraninite) nanocrystals may be formed as a product of a microbial reduction process in uranium-rich environments. These results will extend the limited knowledge of microbial uraniferous mineralization and may provide new insights into the fate of aqueous uranium complexes.

Role of Secondary Minerals

The oxidation of iron sulfides in the host rock results in the release of iron, sulfate, acidity, and metals to solution. High aluminum and silica concentrations are also commonly encountered in mine effluents and are the result of weathering of aluminosilicate minerals at low pH. Oxidation and hydrolysis reactions can subsequently lead to the precipitation of a wide array of hydroxide, sulfate, and/or hydroxysulfate minerals, depending on geochemical and biogeochemical conditions (Nordstrom and Alpers 1999). These secondary minerals play important roles in attenuating contaminants in the ground water.

Secondary precipitates can remove contaminants from affected waters through adsorption and/or coprecipitation reactions. The extent to which dissolved contaminants will sorb onto secondary precipitates as outer sphere or inner sphere complexes will vary as a function of the contaminant species, the secondary precipitate, pH, particle size and surface area, and the presence of other sorbing species that may compete for adsorption sites.

Inorganic contaminants may be removed from solution due to precipitation of an insoluble phase in which the contaminant represents a major or minor component within the solid. Examples of secondary precipitates that form in impacted sites include oxyhydroxides [e.g., FeOOH(s)], hydroxysulfates [e.g., Fe8O8(OH)6(SO4)(s)], sulfates [e.g., PbSO4(s)], and sulfides [e.g., ZnS(s)]. For each of these minerals, there will be a limited compositional range of ground water chemistry over which precipitation could occur, and formation of these precipitates may compete with other removal processes, such as adsorption.

The potential for contaminant precipitation can be estimated by evaluating the saturation state of the ground water with respect to possible precipitate phases using a saturation-state modeling approach. To evaluate whether ground water is oversaturated, undersaturated, or at equilibrium with a particular phase, computer geochemical speciation models are of practical use. As an example, consider the solubility expression for lead sulfate (anglesite). The mass-action expression that applies to the equilibrium is:

                          PbSO4(s) = Pb[2+] + SO4[2-]

A natural water may or may not be at saturation with respect to anglesite, depending on whether the phase is actually present, available surface area, residence time of water, and kinetic factors that may impede dissolution and/or precipitation. If equilibrium is assumed between water and anglesite, then the ion activity product, Q, should be the same as the equilibrium constant, Kr:

where the activity, a, of PbSO4(s) is taken to be 1. Because ion activity products may vary by orders of magnitude, it is often more convenient to take the logarithm of the ratio; that is, to compute the saturation index, SI:

where SI = 0 at equilibrium. If the water is oversaturated in a particular phase, then the SI is positive, and there is a thermodynamic driving force for precipitation to occur. If the water is undersaturated, then the SI is negative, and the mineral, if present, will tend to dissolve:
                          SI > 0 if oversaturated
                                      and
                          SI < 0 if undersaturated

As previously indicated, the ground water chemistry will dictate the stability of a precipitate. Contaminant remobilization will occur as a result of dissolution of the precipitate phase, for example, when log Q/Kr < 0. Precipitate dissolution may occur due to ground water acidification, oxidation/reduction of precipitate components, dilution, or complexation of the precipitate component(s) with dissolved species that form more stable compounds. A key point is that attenuation processes involving inorganic contaminants are reversible (e.g., Gault et al. 2005; Moncur et al. 2005). Metals taken up at the mineral-water interface can be released back into solution. Geochemical modeling of mineral stability and contaminant adsorption/desorption behavior can provide insight into contaminant remobilization potential due to future changes in geochemical conditions. However, it must be noted that thermodynamic databases are often incomplete, and thermodynamic constants for specific compounds may vary from database to database. Thus, results from geochemical models must be carefully reviewed. In addition, the method outlined above assumes equilibrium conditions and ignores rates (i.e., kinetics) of mineral dissolution and precipitation. However, data are often lacking on the kinetics of biogeochemical processes responsible for contaminant uptake and remobilization, especially data that can be applied in field systems to predict the long-term behavior of contaminants.

With respect to predicting geochemical interactions at ISR facilities, several concerns raised by a reviewer of the geochemical modeling of an ISR facility and presented in NUREG-6820 (NRC 2007) illustrate the potential impacts from these types of limitations. The reviewer noted that since the applied model is nonkinetic, any bacterial influences from naturally occurring Desulfovibria and Thiobacillus are eliminated from consideration. The comment also noted that these influences may be as (or more) important to long-term stability than the addition of reductant during restoration. In addition, the role of pyrite during both restoration and stabilization was a concern, and the reviewer noted that a kinetic approach might result in simulations that more closely compared with observed conditions.

Role of Biological Processes

The purpose of the stabilization phase of aquifer restoration is to establish a chemical environment that reduces the solubility of dissolved constituents, such as uranium, arsenic, and selenium. An important part of stabilization during aquifer restoration is metals reduction (NRC 2007). During uranium recovery, if the oxidized (more soluble) state is allowed to persist after uranium recovery is complete, metals and other constituents such as arsenic, selenium, molybdenum, uranium, and vanadium may continue to leach and remain at elevated levels. To stabilize metals concentrations, the pre-operational oxidation state in the ore production zone should be re-established to the extent possible. This may be achieved by adding an oxygen scavenger or reducing agent, such as hydrogen sulfide (H2S), or through bioremediation (NRC 2007).

Bioremediation of uranium contamination has been under field and laboratory investigation since originally proposed in the early 1990s, with most of the efforts occurring in the last 10 to 12-years (see Abdessalem et al. 1999 for a listing of references on the subject). In bioremediation, the added bacteria use organic materials or other electron donor species in the ground water to generate electrons that are then transferred to an electron acceptor [ideally U(VI)] as the end product of the bacterial metabolic process. For bioremediation, bacteria are used in combination with electron donor materials (e.g., acetate or lactate ions) to reduce U(VI) to U(IV) in an effort to immobilize the uranium and prevent its migration away from the initial contaminant source area into surrounding ground waters.  Once uranium is chemically reduced, it is anticipated that the uranium will precipitate in the host rock pore space or be adsorbed on the surface of minerals in the host rock and remain sequestered within the treated area.

Laboratory studies of bioremediation illustrate that various bacteria and electron donor materials in contact with aqueous U(VI) result in a reduction of uranium (see Long et al. 2008 and Yabusaki et al. 2010 for extensive summaries of these studies). In addition to bioremediation using added organic electron donor species, reduction of U(IV) with nitrate, sulfate, and ferrous iron species added to ground water has been reported (Moon et al. 2009,  Jeon et al. 2005). Sulfate-reducing bacteria have the ability to attenuate the movement of metals through the precipitation of sulfide minerals (e.g., Gammons et al. 2005) and by raising the pH of the water. This process is recognized in the restoration of ISR sites and also occurs in the natural environment (Church et al. 2007). These results have greatly increased interest in using bioremediation techniques and led to some field applications.

Most of the field studies testing uranium contamination bioremediation have involved situations somewhat different than those presented for ISR operations. The source of the uranium contamination is the introduction of U(VI) into the subsurface from activities on the surface, such as the disposal of mill tailings or uranium wastes spilled on the ground surface or disposed of by shallow land burial methods. For these situations, U(VI) enters the vadose zone and penetrates to the water table, where it can migrate to contaminate surrounding ground water. In field studies of these situations, the ground water is injected with bacteria and other chemical additives to initiate the uranium reduction and immobilization process. For an ISR site, the uranium is initially present in the subsurface in a chemically reduced state from the mechanisms that originally deposited the ore body. In the ISR process, the reduced uranium is oxidized by chemicals pumped into the ore zone and later reduced by the addition of other chemicals during the aquifer restoration phase, with the expectation that the more mobile U(VI) will precipitate and remain in the wellfield. Bioremediation field studies for the ISR application have not been reported on extensively.

While laboratory results for bioremediation of U(VI) contamination were very promising, additional studies and field testing have not met with unqualified success (Charbonneau 2009). Uranium (VI) adsorbed on minerals in the subsurface is significantly less available kinetically for reduction by bacteria (Liu et al. 2009, Ortiz-Bernard et al. 2004). This finding may be particularly relevant to ISR applications, since the oxidizing conditions during operations may result in sorption of U(VI) on the host rock and subsequent resistance of the oxidized uranium to be reduced and stay immobile in the changing chemical environment during and after ground water restoration. Re-oxidation of U(IV) has been observed due to reaction with sulfate, nitrate, and ferric iron during chemical reduction of these species by other bacteria (Moon et al. 2009). For the ISR situation, the introduction of sulfate and ferric iron species to promote chemically reducing conditions, as well as the presence of elevated nitrate levels in some near-surface aquifer systems, may in fact work against the long-term immobilization of uranium in a restored ISR wellfield. During the initial oxidation process, iron sulfide minerals will also be oxidized by the lixiviant and ferric iron remaining in the wellfield ground water during restoration would compete with remaining U(VI) for reduction to iron sulfides again. A study by Wan et al. (2005) showed that the microbially mediated reduction of U(VI) to U(IV) can be transient, even under sustained reducing conditions. These authors found that uranium was reduced during the first 80 days, but after 100 to 500 days, it was re-oxidized and solubilized, even though the microbial community capable of reducing U(VI) was sustained.

In addition, the ISR restoration process involves injection and withdrawal of chemicals into the oxidized wellfield under a strong flow gradient. For bioremediation efforts, this relatively rapid flushing of ground water through the wellfield would probably severely limit the effectiveness of bioremediation by simply removing the bacteria before they have opportunity to work effectively. For bioremediation attempts, it may be more productive to "inoculate" a restored wellfield with the appropriate bacteria after the typical restoration efforts are stopped and the pumping in the wellfield ceases. The longer-term monitoring periods proposed in the rulemaking would then allow the progress of a bioremediation step to be monitored over a longer time frame, since only the natural flow of ground waters through the wellfield would affect the bacteria population.

The net effect of these processes may result in a slow oxidation of uranium left in a "restored"
ISR wellfield over the long-term, supporting the use of a longer post-restoration monitoring period than has been used previously in the industry. Additional field studies involving restored wellfields would allow the mechanisms mentioned above to be assessed to determine their long-term effects under actual field conditions. These studies would be welcomed and may significantly elevate confidence that a "restored" ISR wellfield will remain that way for the long-term.

Cameco Resources is considering bio-stimulation tests to determine the viability of naturally occurring bacteria to re-precipitate uranium and other redox sensitive species. Prior studies by Cameco at Smith Ranch showed that a mixture of safflower oil and ethanol or cheese whey alone resulted in rapid reductions of uranium, arsenic, and selenium in the ground water, but these amendments caused pump plugging problems (Cameco 2012). Cameco is proposing additional testing using other organic molecules to promote bio-stimulation. Cameco noted that studies at various DOE sites involved ground water with substantially different geochemistry than that at ISR facilities. In addition, the proposed Smith Ranch studies involved deeper-lying waters that are less susceptible to variations in oxygen contamination than the near-surface waters involved in the DOE studies.

Geochemically Based Restoration Techniques

Another component of aquifer restoration is accomplished by establishing a chemical environment that alters the solubility of dissolved constituents, such as uranium, arsenic, and selenium. These methods typically invoke chemical reactions in which the valence state of elements is either oxidized to a higher valence state or reduced to a lower valence state.

During uranium recovery, if the oxidized (more soluble) state is allowed to persist after uranium recovery is complete, metals and other constituents such as arsenic, selenium, molybdenum, uranium, and vanadium may continue to leach and remain at elevated levels. For example, if arsenic concentrations in mildly oxidizing water down gradient from an ISR facility must be lowered, then either increasing the redox potential to precipitate a less soluble arsenic oxide or reducing the redox potential and adding sulfide to form a less soluble sulfide mineral might be considered. Some of the issues to consider in the applied redox approach are the type and amount of reactant, means of emplacement, reaction kinetics, unwanted byproducts, solubility of contaminant-containing minerals, and geochemical stability of the imposed barrier environment.

As noted above, another method used to stabilize metals by the re-establishment of their pre-operational oxidation states is to add an oxygen scavenger or reducing agent [such as hydrogen sulfide (H2S)] or a biodegradable organic compound (such as ethanol) into the uranium production zone during the later stages of recirculation (NRC 2007).
As described in the case studies summarized in NRC (2007), sampling at some sites after H2S injection indicated that although reducing conditions were apparently achieved, they were not maintained over the longer term. For example, as a field test of ground water stabilization during aquifer restoration, hydrogen sulfide gas was injected as a reductant into the Ruth ISR research and development facility in Campbell County, Wyoming. After 6 weeks of hydrogen sulfide injection, the pH dropped relatively quickly from 8.6 to 6.3, and the sulfate concentration increased from 28 parts per million to 91 parts per million, indicating that the sulfide reductant was being consumed (NRC 2007). Concentrations of dissolved uranium, selenium, arsenic, and vanadium decreased by at least one order of magnitude. After 1 year of monitoring, however, reducing conditions were not maintained, and uranium, arsenic, and radium concentrations began to increase, suggesting that the amount of hydrogen sulfide injected was not sufficient to fully reduce all the material oxidized during the mining phase.

Based on the available field data from aquifer restoration, NRC (2007) concluded that aquifer restoration is complex and results can be influenced by several site-specific hydrological and geochemical characteristics, such as pre-operational baseline water quality, lixiviant chemistry, aquitard thickness and continuity, aquifer mineralogy, porosity, and permeability. In some cases, such as at Bison Basin and Reno Creek, the aquifer was restored in a relatively short time. In other cases, restoration required much more time and treatment than was initially estimated (e.g., the A- and C-Wellfields at the Highland ISR facility).

Monitored Natural Attenuation

MNA refers to the reliance on natural attenuation processes to achieve site-specific remediation objectives within a reasonable time frame. Natural attenuation processes include a variety of physical, chemical, and/or biological processes that act without human intervention to reduce the mass or concentration of contaminants in soil and ground water. These in-situ processes include biodegradation, dispersion, dilution, sorption, and volatilization; radioactive decay; and chemical or biological stabilization, transformation, or destruction of contaminants (EPA 1999a).

The overall impact of natural attenuation processes at a given site can be assessed by evaluating the rate at which contaminant concentrations are decreasing either spatially or temporally. Guidelines included in Office of Solid Waste and Emergency Response (OSWER) Directive 9200.4-17P (EPA 1999a) and by the American Society for Testing and Materials (ASTM 1998) have endorsed the use of site-specific attenuation rate constants for evaluating natural attenuation processes in ground water. The EPA directive on the use of MNA at Superfund, RCRA, and underground storage tank sites (EPA 1999a) includes several references to the application of attenuation rates:

      Once site characterization data have been collected and a conceptual model developed, the next step is to evaluate the potential efficacy of MNA as a remedial alternative. This involves collection of site-specific data sufficient to estimate with an acceptable level of confidence both the rate of attenuation processes and the anticipated time required to achieve remediation objectives. At a minimum, the monitoring program should be sufficient to enable a determination of the rate(s) of attenuation and how that rate is changing with time.
Site characterization (and monitoring) data are typically used for estimating attenuation rates. The ASTM Standard Guide for Remediation of Ground Water by Natural Attenuation at Petroleum Release Sites (ASTM 1998) also identifies site-specific attenuation rates as a secondary line of evidence of the occurrence and rate of natural attenuation.

The 1999 OSWER Directive also provides some general guidelines for use of MNA as a remedial approach for inorganic contaminants (EPA 1999a). The key policy concerns are that the specific mechanisms responsible for attenuation of inorganic contaminants should be known at a particular site, and the stability of the process should be evaluated and shown to be irreversible. The specific policy language is as follows:
      MNA may, under certain conditions (e.g., through sorption or oxidation-reduction reactions), effectively reduce the dissolved concentrations and/or toxic forms of inorganic contaminants in groundwater and soil. Both metals and non-metals (including radionuclides) may be attenuated by sorption reactions such as precipitation, adsorption on the surfaces of soil minerals, absorption into the matrix of soil minerals, or partitioning into organic matter. Oxidation-reduction (redox) reactions can transform the valence states of some inorganic contaminants to less soluble and thus less mobile forms (e.g., hexavalent uranium to tetravalent uranium) and/or to less toxic forms (e.g., hexavalent chromium to trivalent chromium). Sorption and redox reactions are the dominant mechanisms responsible for the reduction of mobility, toxicity, or bioavailability of inorganic contaminants. It is necessary to know what specific mechanism (type of sorption or redox reaction) is responsible for the attenuation of inorganics so that the stability of the mechanism can be evaluated. For example, precipitation reactions and absorption into a soil's solid structure (e.g., cesium into specific clay minerals) are generally stable, whereas surface adsorption (e.g., uranium on iron-oxide minerals) and organic partitioning (complexation reactions) are more reversible. Complexation of metals or radionuclides with carrier (chelating) agents (e.g., trivalent chromium with EDTA) may increase their concentrations in water and thus enhance their mobility. Changes in a contaminant's concentration, pH, redox potential, and chemical speciation may reduce a contaminant's stability at a site and release it into the environment. Determining the existence, and demonstrating the irreversibility, of these mechanisms is important to show that a MNA remedy is sufficiently protective.

Tiered Approach to Assessing Suitability of Monitored Natural Attenuation

EPA's Office of Research and Development has prepared a technical resource document for the application of MNA to inorganic contaminants in ground water (Reisinger et al. 2005; EPA 2007a and 2007b). The technical resource document presents a four-tiered assessment of MNA as a viable response action for selected metal, metalloid, and radionuclide contaminants encountered in ground water at a particular location. Components of the approach common to each tier include (1) demonstrating contaminant sequestration mechanisms, (2) estimating attenuation rates, (3) estimating attenuation capacity of aquifer solids, and (4) evaluating potential reversibility issues. EPA expects that users of this document will include EPA and state cleanup program managers and their contractors, especially those individuals responsible for evaluating alternative cleanup methods for a given site or facility. A decision-making approach is provided for evaluating MNA as a possible response action for contaminated ground water. Emphasis is placed on developing a more complete understanding of the site through development of a conceptual site model that includes an understanding of the attenuation mechanisms, the geochemical conditions governing these mechanisms, and indicators that can be used to monitor attenuation progress (EPA 2007a).

EPA judges this tiered decision-making approach to be an appropriate and cost-effective way to screen out sites unsuitable for MNA, while collecting the most relevant data at sites that might be amenable to this approach. Conceptually, a tiered assessment of MNA seeks to progressively reduce site uncertainty as MNA-specific data are collected. MNA for inorganics and radionuclides is most effectively implemented through four tiers that require progressively more information on which to assess the reasonableness of MNA:

   * Tier I. The plume is not threatening public health, is stable, and some direct evidence of contaminant attenuation exists.
   * Tier II. The attenuation capacity of the site exceeds the estimated mass of contaminant at the site.
   * Tier III. There is strong evidence that attenuation mechanism(s) will prevail over long periods of time.
   * Tier IV. A record of decision, including a long-term monitoring plan and other site closure considerations, is developed.

First-Order Attenuation Rate Determination

First-order attenuation rate constant calculations are an important consideration for evaluating natural attenuation processes at ground water contamination sites. Specific applications identified in EPA guidelines (EPA 1999a) include use in characterization of plume trends (shrinking, expanding, or showing relatively little change), as well as estimation of the time required to achieve remediation goals. As described by Newell et al. (2002), the use of the attenuation rate data for these purposes is complicated, as different types of first-order rate constants represent very different attenuation processes:
Concentration versus time rate constants are used for estimating how quickly remediation goals will be met at a site. In units of inverse time (e.g., per day), they are derived as the slope of the natural log concentration versus time curve measured at a selected monitoring location.

Concentration versus distance bulk attenuation rate constants are used for estimating whether a plume is expanding, showing relatively little change, or shrinking because of the combined effects of dispersion, biodegradation, and other attenuation processes. The attenuation rate constant, in units of inverse time (e.g., per day), is derived by plotting the natural log of the concentration versus distance and (if determined to match a first-order pattern) calculating the rate as the product of the slope of the transformed data plot and the ground water seepage velocity contaminant transport versus transport of a tracer, or more commonly, calibration of a solute transport model to field data.
To interpret the past behavior of plumes, and to predict their future behavior, it is necessary to describe the behavior of the plume in both space and time. Therefore, the collection of long-term monitoring data from wells that are distributed throughout the plume is important. Concentration versus time rate constants describe the behavior of the plume at one point in space, while concentration versus distance rate constants describe the behavior of the entire plume at one point in time. Under appropriate conditions, each of these constants can assist in site-specific evaluation and quantification of natural attenuation processes. Each of these terms is identified as an "attenuation rate." Because the rate constants differ in their purpose and relevance, it is important to understand their proper application, as summarized below.
Concentration versus Time Rate Constants: A rate constant derived from a concentration versus time (C vs. T) plot at a single monitoring location provides information regarding the potential plume longevity at that location, but that information cannot be used to evaluate the distribution of contaminant mass within the ground water system. The C vs. T rate constant at a location within the source zone represents the persistence in source strength over time and can be used to estimate the time required to reach a remediation goal at that particular location. To adequately assess an entire plume, monitoring wells must be available that adequately delineate the entire plume, and an adequate record of monitoring data must be available to calculate a C vs. T plot for each well. At most sites, the rate of attenuation in the source area is slower than the rate of attenuation of materials in ground water, and plumes tend to shrink back towards the source over time. In this circumstance, the life cycle of the plume is controlled by the rate of attenuation of the source and can be predicted by the C vs. T plots in the most contaminated wells. At some sites, however, the rate of attenuation of the source is rapid compared to the rate of attenuation in ground water. This pattern is most common when contaminants are readily soluble in ground water and when contaminants are not biodegraded in ground water. In this case, the rate of attenuation of the source as predicted by a C vs. T plot will underestimate the lifetime of the plume. This behavior would be expected at ISR sites, following the remediation of the source.

Concentration versus Distance Rate Constants: Attenuation rate constants derived from concentration versus distance (C vs. D) plots serve to characterize the distribution of contaminant mass within space at a given point in time. A single C vs. D plot provides no information with regard to the variation of dissolved contaminant mass over time and, therefore, cannot be employed to estimate the time required for the dissolved plume concentrations to be reduced to a specified remediation goal. This rate constant incorporates all attenuation parameters (sorption, dispersion, biodegradation) for dissolved constituents after they leave the source. Use of the rate constant derived from a C vs. D plot (i.e., characterization of contaminant mass over space) to characterize contaminant mass over time will provide erroneous results. The rate constant based on C vs. D indicates how quickly dissolved contaminants are attenuated once they leave the source, but provides no information on how quickly a residual source zone is being attenuated. Most sites will have some type of continuing residual source zone, even after active remediation, making the C vs. D rate constant inappropriate for estimating plume lifetimes for most sites.

In assessing the potential for long-term stability of a restored wellfield, and the potential in the exempted aquifer to contain any re-mobilized uranium from migrating into portions of the non-exempted aquifer, development of a geochemical model for the site is of critical importance. Natural attenuation mechanisms can play a role in limiting the potential for mobilized uranium, and other contaminant species, from escaping the exempted aquifer and potentially compromising potable water resources. In the absence of a geochemical model which demonstrates an adequate reducing capacity in the restored wellfield and down-gradient portion of the exempted aquifer, re-mobilization of uranium may be possible if the original mining process removed the mechanisms that were responsible for sequestering the uranium initially. For such a situation, restoration and monitoring may not detect the slow degradation of the restored wellfield during only a limited period of post-restoration monitoring.  The 30-year proposed post-restoration monitoring period allows an opportunity to detect a slow degradation and institute remediation measures through the regulatory process. If the degradation process occurs after the 30-year period, remediation efforts and its costs would fall on the taxpayer. To avoid that situation, demonstrating the reducing capacity of the restored exempted aquifer is of particular importance.
Statistical Analyses to Compare Pre- and Post-ISR Conditions

Statistical techniques and measures are frequently used to determine compliance with regulatory requirements. Because of the inherent variations in natural conditions and media properties in any particular location, confidence intervals appropriate to these specific statistical measures and available databases are also employed to allow a degree of flexibility in regulatory decisions accounting for natural variability. We have adopted this confidence measure approach in the Part 192 rulemaking for ISR operations. The statistical techniques selected for regulatory compliance demonstrations are dependent on the quality and quantity of field data available for the analyses. Selection of appropriate statistical techniques must be justified by considerations of the available data for building a defensible compliance compliance case. This chapter examines the applicability of various statistical techniques and measures to address various issues involved in ISR operations and regulatory decisions.

      Note: The statistical discussions in this chapter include traditional usage of many symbols like M and m, N and n, P and p, T and t, α and β, and S and σ. The definition of these symbols differs from section to section. The meaning of each symbol should be clear from the context of the discussion.

The statistical methods discussed in this chapter provide tools for answering the following basic questions of particular relevance to regulatory decisions for ISR operations:

   (1) What is the baseline at this site?
   (2) Has the site been restored to baseline?
   (3) Are there trends that indicate the site may not stay at baseline levels?
   (4) If one or more wells exceed the baseline, do trend analysis and/or modeling suggest the site will return to baseline [or an alternate concentration limit (ACL) as specified in 40 CFR 264.94(b)]?

The answers to these questions should be based on data sufficient for the purpose. This chapter presents methods for determining what is a sufficient number of samples to answer questions such as these. The chapter also presents statistical methods for analyzing the data to reach a conclusion.

Due to sampling variability, decisions such as these may be difficult to make without the use of hypothesis testing. Hypothesis testing provides a framework for controlling the frequency of decision errors. The procedures in this document favor protection of the environment and human health. If uncertainty is large or the sampling inadequate, these methods conclude that the sampled area does not attain the cleanup standard.

Hypothesis testing is used as a statistical tool for deciding when the ground water has reached steady state and for comparing post-restoration conditions with predetermined restoration goals. The statistical tests are based on measurements of baseline and post-restoration water quality conditions at the site. These measurements include a wide variety of water quality parameters. Usually, the measured parameter is a concentration of an analyte in a specific well at a given time, but other water quality properties may also be analyzed using the methods in this chapter.

Statistical tests are required to make decisions due to spatial and temporal variability in the sample values. Two types of statistical hypothesis tests are employed: two-sample comparisons of post-restoration and baseline data are used to determine if restoration goals have been attained, and trend analysis is used to determine if stability is achieved. Several two-sample statistical tests are described for comparing pre- and post-conditions in individual wells. A heterogeneity test then is used to determine if the test results are consistent across all wells in the unit. Once restoration goals are attained, subsequent monitoring is required to demonstrate stability. Several statistical trend tests are used to determine if stability is maintained. The trend tests address temporal variability when determining if restoration goals are maintained.
Although statistical analyses are used in all phases of the ISR process shown in Figure 3-1, three phases employ formal statistical methods to characterize baseline conditions, to determine when compliance with the baseline has been achieved, and when long-term stability has been demonstrated.

   * Phase 1  -  Measure baseline ground water concentrations to within established precision objectives and establish restoration goals based on statistical procedures that address temporal and spatial variability.
   * Phase 4  -  Establish wellfield restoration. At the end of this phase, the ground water potentiometric surface will have returned to baseline conditions, and statistical tests for significant differences are used to verify restoration.
   * Phase 5  -  Conduct long-term stability monitoring. At the end of this phase, statistical tests for trends are used to show that concentration of the monitored parameter is not increasing (or, in some cases, decreasing) with time.

Procedures used to combine data from separate wells to determine whether the site as a whole attains the restoration goals are discussed. Testing the samples from individual wells or groups of wells is also discussed.

Table 7-1 shows an outline of the statistical procedures used in Phase 1, Phase 4, and Phase 5. The first step in each phase requires estimation of the number of samples required for the task. The number of samples are determined by site conditions and the Data Quality Objectives (DQOs) established for each task.

Each phase has a data collection step. Data collection should be governed by appropriate quality control guidelines. 40 CFR 146 (Subpart D) specifies construction, operation, monitoring, and reporting requirements for injection wells (Class III). The Agency-wide EPA program requirements for quality assurance are described in EPA 2000c. EPA 2000d provides guidance for establishing data quality objectives (DQOs). EPA 2001 and EPA 2002d provide guidance for developing a Quality Assurance Project Plan (QAPP). EPA 2002e and EPA 2006b provide additional guidance for data collection activities. A detailed sampling plan should be submitted and approved prior to beginning data collection activities. All data collected in each phase of sampling are to be retained and submitted in standard digital format, such as a spreadsheet or database. The data should include well identification, sample collection date and time, analysis date and time, well location and screening interval, units of measurement, detection limits, and identification of outliers and nondetects. As many of the statistical tests require modifications when there are a large number of ties, unnecessary rounding of sample values should be avoided.

Table 7-1.	Outline of the Statistical Procedures used in Phases 1, 4, and 5

Statistical hypothesis tests are recommended for comparing post-restoration conditions with baseline conditions and for demonstrating stability of the site after restoration. Several parametric and nonparametric statistical tests are presented for the comparison with baseline conditions. These tests are used in Phase 4 to determine if the restoration goals have been met. The two-sample t-test and prediction limits (PLs) for a future mean are two parametric methods used in the comparison with baseline conditions, assuming that both data sets are stationary. The nonparametric Wilcoxon Rank Sum (WRS) test is also used to compare post-restoration well conditions with baseline values. The two-sample t-test and the WRS test are recommended for comparing baseline and post-remedial wells (EPA 2006a). The RCRA Unified Guidance (EPA 2009) recommends the PL method.

Linear regression and the nonparametric Mann-Kendall trend tests are recommended for trend detection in EPA 2006a and EPA 2009. The linear regression trend test relies on a variety of assumptions (e.g., normality) that need to be verified. The Mann-Kendall trend tests may be used with any series of four or more independent samples to test for trends in well parameters. The trend tests are used in Phase 1 to check for unexpected trends in baseline samples, and in Phase 5 to establish long-term stability. As an example of unexpected variations, sampling before operations in the ore zone and up gradient along with continued sampling up gradient of the production zone during ISR operations and during restoration can supply important information about seasonal variations in the ground water chemistry for shallow aquifer ore deposits. The Mann-Kendall trend test has been applied in ground water monitoring at RCRA sites (HydroGeo Logic 2005).

Several EPA sources were used as the bases for the statistical tests. Although these sources do not recommend procedures for ISR sites in particular, the sources are either general in nature or address related issues. The sources include guidance for applying the DQOs at remediated Comprehensive Environmental Response Compensation and Liability Act (CERCLA) sites (EPA 2002a), guidance for conducting the statistical tests in the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) (EPA 2000a), guidance for statistical analysis of ground water monitoring data at RCRA facilities (EPA 1989, EPA 2009), and general guidance for the application of nonparametric statistical tests found in Data Quality Assessment: Statistical Methods for Practitioners, EPA QA/G-9S (EPA 2006a). Many of the procedures for conducting the tests cited here were adapted from the EPA QA/G-9S document and are considered in more detail in Attachment D. We are not mandating the use of specific statistical methods in the Part 192 rulemaking, but we must emphasize that whatever statistical methods are selected by the operator for preparing a compliance case must be rigorously justified on the basis of the quantity and quality of the database collected. It is the responsibility of the appropriate regulatory authority to make the judgement on the reliability and defensibility of the statistical analyses presented by the ISR operator for regulatory approval.

In summary, the statistical approaches for each phase are:

Phase 1 Baseline Sampling

   * Estimate required number of baseline samples (Section 7.1)
   * Adjust measured data for seasonality if required (Section 7.7.1 and Attachment D, Section D.1)
   * Use regression trend test or Mann-Kendall test to check for unexpected trends (Section 7.7.2 and Attachment D, Sections D.2 and D.3)

Phase 4 Establish Compliance with Baseline

   * Estimate required number of monitoring wells (Section 7.2)
   * Adjust measured individual well data for seasonality, if required (Section 7.7.1 and Attachment D, Section D.1).
   * Use the two-sample t-test (Section <IP_ADDRESS>), PLs (Section <IP_ADDRESS>) or the WRS test (Section <IP_ADDRESS>) to compare baseline to post-restoration conditions for each well or for pooled wells. (Attachment D, Section D.4)
   * For multiple wells, first test wells for homogeneity. If the hypothesis of homogeneity across all wells is accepted, then test to confirm compliance of all wells with restoration goals. (Section <IP_ADDRESS> and Attachment D, Section D.5)
   * If steady-state data are from different wells than the baseline data and trends are not detected, the two-sample t-test or the WRS test may be used to compare baseline to steady-state measurements for statistical differences for the pooled data of all wells combined, which are treated as a single well. (Sections 7.9.1 and 7.9.2, and Attachment D, Section D.4)

Phase 5 Long-term Stability Monitoring

   * Determine sampling frequency and duration (Section 7.7.2)
   * Adjust measured data for each well for seasonality if required (Section 7.7.1 and Attachment D, Section D.1)
   * Use Mann-Kendall or regression trend test to test for trends for each well or in the pooled wells (Section 7.7.2 and Attachment D, Sections D.2 and D.3)
   * If trend is detected, use linear regression or Theil-Sen test to assess trend magnitude (Section 7.7.2)
   * Repeat for each well
   * If the multiple wells are evaluated, use the pooled-regression trend test or Mann-Kendall test for multiple locations (Section 7.7.2)

Post-restoration samples are expected to have a higher degree of variability and trend than found in baseline samples. Accordingly, periodic measurements for each contaminant [see proposed 40 CFR 192.52] and other species needed for supporting assessments, such as geochemical modeling] should be taken from each well over the initial post-restoration period. It is anticipated that the sampling will be quarterly, with four samples per year at each well. Quarterly sampling permits analysis of the data for seasonal variations to determine if variations in measurements reflect normal seasonal variability and not an increase in contaminants. Quarterly sampling is typical practice in the industry and this assumption was used in performing the illustrative calculations presented in this document. Analyses of quarterly sampling and assumptions about natural variability (Tables 7-19 to 7-21, Section <IP_ADDRESS>) suggest that quarterly sampling to reach the required level of confidence about the presence or absence of trends may require very long periods for post-restoration monitoring. More frequent sampling would reduce the monitoring period in these situations; however, more frequent sampling must still assure that the individual samples are independent, as discussed further below.

Determine Baseline Characteristics

The baseline characterization provides the frame of reference against which post-operational ground water remediation is judged. The goal of baseline sampling is to establish a zone-specific statistical distribution of baseline concentrations for key constituents and other hydrogeological parameters. Current guidelines require these distributions be based on independent and representative water samples collected from zones in which baseline wells are located by a statistically valid sampling design. It is emphasized here that baseline sampling refers to sampling within an ore zone that will be mined and subsequently restored. It should not be confused with background sampling, which defines water quality over a broader area and includes, for example, up gradient and down gradient wells or sampling points located above or below the mined zone.

Leachable uranium deposits are found in sandstones that have been deposited in basins, along mountain fronts, and in near-shore marine and deltaic environments. The rock sequence containing the ore bodies can be composed of a number of water-bearing units separated by confining units. The water-bearing unit containing an ore body is separated (at least locally) from other water-bearing units above and below.

The ore zones follow the general trend of drainage channels as shown in Figure 7-1. The shapes vary from almost linear to serpentine, reflecting the local draining. Individual ore bodies in sandstone lenses rarely exceed a few hundred meters in length. These are typically "roll-front" deposits, with reducing conditions on the down gradient side and oxidizing conditions on the up gradient side (see Figure 4-5 in Section 4.1.1). Uranium is continuously dissolved by oxygenated ground water and displaced further down gradient. As the uranium comes in contact with the reducing conditions down gradient, an economically recoverable deposit of uranium may eventually be formed. The term "roll front" is used because over time, the redox interface rolls down gradient as more oxygen is transported into the aquifer. The inner contact of ore and altered sandstone is generally sharp, whereas the uranium concentration on the reduced side of the interface is gradational.

Although leaching mobilizes and removes a portion of the uranium in the ore body, a large fraction of the uranium remains within the host rock after economically feasible extraction is completed. The concentration of uranium appearing in water samples from the baseline and post-restoration monitoring wells is strongly influenced by redox conditions in the ore body at the time of sampling. Other analytes have unique ground water chemistries also, with many inter-relationships between the analyte concentrations, host rock, hydrological parameters and redox conditions. It is possible that wells exhibiting higher radionuclide concentrations (uranium and radium particularly) in the pre-operational time frame may show significantly lower concentrations after production and, conversely, lower concentrations in some wells may become higher due to the non-homogeneous characteristics within the wellfield (local effective porosities, flow paths, changed geochemical conditions in the pre- and post-operational time frames). A major purpose of the baseline characterization is to determine the presence and contaminant concentrations of "hot spots," so that these data can be used in developing restoration goals. A rigorous baseline characterization would avoid problems later during restoration that might occur if the range of baseline contaminant concentrations were too low because the "hot spots" were not sampled initially.

The current state-of-the-art for analyzing spatial variability is the use of geostatistical methods. Although a full geostatistical analysis is not required, these methods provide a way to visualize the sample data using 2- and 3-D graphical representations of the entire ore zone. Integration of additional sample data from areas outside the ore zone and in over- and under-lying strata provides insight into the differences that may exist between baseline characteristics within the ore zone and baseline characteristics in surrounding regions. For those operators with an understanding of geostatistical software and analytical procedures, these procedures may provide better insight into baseline characteristics and the inter-relationships of the analytes and hydrogeological parameters under baseline conditions.

Baseline monitoring wells should be spaced sufficiently far apart that the ground water chemistry measurements in a well are not influenced by adjacent wells. NRC's current expectations regarding baseline sampling in the ore zone are set out in their Standard Review Plan for In Situ Leach Uranium Extraction License Applications (NRC 2003, p. 5-39), which specifies that one well per acre should be sampled. A single sample from each baseline monitoring well is insufficient to determine whether water-quality parameters are stable and representative of the ground water at the sample location. Baseline chemistry is established based on a statistical analysis of ground water data from a sufficiently large set of wells sampled over a period of time. The RCRA requirements (40 CFR Part 264 Subpart F) for the frequency of sampling are applicable for this purpose: A sequence of at least four samples, taken at an interval that assures that an independent sample is obtained, by reference to the uppermost aquifer's effective porosity, hydraulic conductivity, and hydraulic gradient, and the fate and transport characteristics of the potential contaminants. Calculations of the length of time or distance required to ensure samples will be independent should be provided in support of the well locations and sampling dates proposed in the baseline sampling plan.

The baseline data provide the technical basis for establishing restoration goals for the post-restoration monitoring phase. As such, it is critical that the baseline data represent the natural variability of each analyte, unbiased by variability resulting from residual effects of drilling, construction and development. Under some conditions, residual impacts from drilling can dominate the concentrations of some ground water constituents (particularly trace metals) in the vicinity of the well screen for months (if not years).

The re-equilibration time of baseline wells should be confirmed prior to sampling. The statistical tests for trends found in Section 7.3.4 may be applied to demonstrate steady-state geochemical conditions at baseline wells. Additional guidance on best practices for baseline ground water sampling is found in Puls and Barcelona (1989) and Yeskis and Zavala (2002). These documents give emphasis to the following technical aspects of baseline sampling:

   * Documenting the volume of water purged before sample collection and field parameter data measured during purging (e.g., pH, Eh, conductivity, turbidity) to provide a basis for assessing whether the ground water sample is representative of predrilling conditions.

   * Collecting additional water quality samples during purging that may provide additional insights on well performance issues. To this purpose, a time series of water quality samples during purging may be analyzed for major ions, trace metals and nonmetals, and total organic carbon. These data would then be evaluated for trends that might indicate that residual drilling or construction products remain, that mixing of ground waters from different hydrologic zones has occurred, or disequilibrium is evident in formation mineralogy.

   * Other approaches for evaluating representativeness of baseline well data include plots of redox-couple data on phase diagrams and use of geochemical modeling to determine the extent to which measured baseline water quality parameters are in equilibrium or disequilibrium with mineral phases known to be present in the formation.

An adaptive approach to baseline characterization is recommended that builds on information previously collected. For example:

   * A need for additional background well locations may be determined by the level of uncertainty in the range and spatial variability of ground water constituents;

   * A need for additional data from a particular well (or the need to resample or replace a well) may be determined by the consistency of the sample data with concentrations predicted from nearby wells; and

   * A need to continue sampling an individual well may be determined by testing for trends in the data indicating the extent to which the well has recovered from drilling and construction activities.

After samples have been collected and analyzed by the laboratory, the data should be inspected for unusual values that may unduly influence estimates of the baseline conditions. In many cases, laboratory results may indicate that the concentration is below the detection limit for the analytical method applied. These samples are commonly called "nondetects." Nondetects should be included in the calculation of baseline concentration distribution and in post-restoration comparisons with the baseline. If the nonparameteric methods recommended in this chapter are applied, nondetects may be included with no modifications. For parametric procedures, a value equal to one-half of the level of detection may be used for the nondetect samples.

Design for Baseline Sampling

Baseline conditions are characterized by the distribution of baseline samples collected in the ore zone. It is important to ensure that the baseline sampling program provides samples that are representative of ore zone conditions. The location of the baseline wells is based on a statistically valid sampling design developed following the DQO process, as described in Section 7.1.3. A random selection of wells from a systematic grid is one example of a statistically valid approach for locating baseline wells.

The design and implementation of a baseline characterization program will be driven by a variety of site-specific factors. The design for the baseline sampling program should include the number, location, and density of baseline monitoring wells and the timing of the samples. The density of monitoring locations will depend on the spatial variability of the analytes to be monitored: greater variability requires higher monitoring density. In this regard, each ISR site will have unique characteristics; hence, a flexible approach to designing the baseline sampling program is required. Although randomness requirements will serve to avoid bias, it is important to look for other complicating geological factors that do not conform to randomness. Complicating factors include, but are not limited to:

   * Intersecting or adjoining deposits near mine leases
   * Ground water contamination in adjacent abandoned mine shafts and tunnels
   * Dewatering effects of old mine workings in or near a proposed ISR operation
   * Limited knowledge about site mineralogy, particularly related to trace metals
   * Changes occurring in the ground water environment unrelated to the mine itself

As sampling progresses, the baseline sampling plan may be modified to adapt to unexpected spatial and temporal variability by increasing either the density of monitoring locations or the period of time each location is sampled.

Selection of Baseline Monitoring Wells

The methods discussed in Sections 7.1.3 and 7.1.4 will be used to determine the required number of baseline monitoring wells. It is also necessary to specify the locations of these wells. There are three cases to consider when selecting the baseline monitoring wells:

   (1) All baseline monitoring wells will be selected from pre-existing wells;
   (2) Some or all of the pre-existing wells in the wellfield area will serve as baseline monitoring wells, but additional monitoring wells are required; and
   (3) No pre-existing wells will be used as baseline monitoring wells, and new locations within the wellfield area are to be selected.

If the wellfield has a simple geometry, then a rectangular grid with the appropriate number of sampling locations is designed to cover the wellfield as uniformly as is technically feasible. The grid method is easy to apply, but requires modification when applied in Cases 1 and 2, or in Case 3 with a complex wellfield geometry.

In Cases 1 and 2, some of the wells already in place are to be selected as baseline monitoring wells, and prior information is available for these wells. This situation has both advantages and disadvantages. One advantage is that data from these wells may be used to estimate the variability of each analyte, which is used in Section 7.1.3 to determine the number of baseline monitoring wells. However, a serious disadvantage is that availability of prior information allows for the possibility of selection bias in characterizing baseline conditions by the purposeful selection of pre-existing wells, which tend to exhibit high (or low) readings for certain characteristics. In these cases, use of a randomized or systematic grid procedure for selecting pre-existing wells to serve as baseline monitoring wells is recommended to avoid the possibility of selection bias.

In Case 3, the geometry of the site is of greatest concern. For wellfields with simple geometries, a systematic grid is recommended. Creation of a systematic grid may be more difficult for wellfields with complex geometry of the type shown for Christensen Ranch ISR Mine Unit 6 in Figure 7.1. The collection of narrow and tortuous wellfield subareas in this unit make it difficult to design a grid of representative sampling locations covering the wellfield. The following alternative designs may be useful in certain cases.

Random Design for Case 1

In this design, all baseline monitoring wells will be selected randomly from a list of N qualified pre-existing wells located in the wellfield area. In this design, each pre-existing well (j=1,...,N) is assigned a random number (xj) between 0 and 1. The list is then sorted in ascending order using the random numbers x1,...,xN as the sorting variable. If n0 baseline monitoring wells are required, then the first n0 pre-existing wells in the list are selected as baseline monitoring wells. The main advantage of the random design is that it is easy to implement. The main disadvantage is that there may be large "gaps" between monitoring wells in some regions of the wellfield due to the random selection process, while in other areas the monitoring wells may be clustered too close together to provide independent samples. Use of a systematic sampling grid design minimizes these disadvantages.

Systematic Design for all Cases

The following approach uses a systematic sampling grid for locating baseline monitoring wells. The grid sampling approach may be useful for all three cases described above. A systematic sampling grid is applied over a broad area surrounding the wellfield, but only grid points falling within the wellfield outline are used as baseline monitoring locations. Grid points which do not fall within the wellfield outline are ignored. In this design, a rectangular area is defined enclosing the entire outline of the wellfield and excursion monitoring wells. The rectangle is filled with a rectangular sampling grid of sufficient density that the wellfield area will contain the required number of baseline monitoring well locations with high probability.

One baseline monitoring well is selected for each grid point lying within the wellfield outline. In Cases 1 and 2, the pre-existing well nearest to each grid point is selected as a representative baseline monitoring well for that grid point, but only if the pre-existing well is no further than a specified maximum distance (see below) from the grid point, and if the pre-existing well is qualified by meeting all baseline monitoring well requirements for construction, screening depth and sampling procedure. A pre-existing well can only represent one sampling grid location. If a grid point has several nearby pre-existing wells, only the one nearest to the grid point is used. In this situation, the remaining pre-existing wells are not used as baseline monitoring wells. (If the density or spatial distribution of pre-existing wells in Case 1 is not sufficient to assign a qualified pre-existing well to each grid point, then Case 2 applies.)

In Case 2, the procedure for Case 1 is applied until no more qualified pre-existing wells are available for assignment. All remaining grid points with no qualified pre-existing well assigned will require new baseline monitoring wells be constructed at or near each grid point. In Case 3, no pre-existing wells are available, and a new baseline monitoring well is located at or near each grid point. If the wellfield is similar in shape and size to the enclosing rectangle, then Case 3 reduces to the same design problem addressed in Sections 7.1.3 and 7.1.4.

Application to Christensen Mine Unit 6:

The Christensen Ranch Mine Unit 6 wellfield area (the highlighted area in Figure 7-1) and the surrounding excursion monitoring wells are enclosed by a rectangle of approximate size L = 1,300 yards by W = 1,100 yards, covering an area of approximately 295 acres. The wellfield area itself is complex in shape and encompasses approximately A0 = 42 acres ≈ 200,000 sq. yd. (Table 7.4). This is approximately 14.2% of the area of the enclosing rectangle. In this example, we will use the NRC rule of thumb of 1 baseline monitoring well per acre of wellfield, so at least n0 = 42 wells are required within the wellfield. In Case 1, some or all of these 42 wells would be pre-existing wells.

A sampling grid is constructed over the entire rectangular area using a rectangular grid. The sampling grid is an array of rectangles with shape proportional to the enclosing rectangle. The required number of grid rectangles (m) is given by:
                                       ,
where p = A0/LW = 0.142, q = 1-p, and zα is the 100∙(1-α) percentage point of the standard normal distribution.

This formula is based on the assumption that the actual number of grid points inside the wellfield follows a binomial distribution with a relatively large number of grid points and probability p. Due to the tortuous shape of the wellfield area, there is a chance that the wellfield may not actually contain the expected number of grid points within its boundaries. The parameter α determines the probability that the constructed grid will have less than n0 grid points inside the wellfield. For α = 0.05 use zα = 1.645. When α = 0.50, zα = 0 and m = n0/p. Using this value of m, the expected number of grid points falling inside the wellfield will be 42, but the actual number has approximately a 50% chance of being smaller than 42.

For the Christensen MU6 unit, m = 373 = (19.3)(19.3). Rounding to next highest integers, a 20 x 20 grid with 400 rectangular grid elements of size 65 yards by 55 yards is required for 95% assurance that the wellfield area will contain at least 42 grid points for baseline monitoring well locations. A grid sampling point is located at the center of each grid rectangle. With these dimensions, the greatest distance between grid sampling points is approximately 85 yards. This value would be used as the maximum distance that a pre-existing well can be from its assigned sampling grid point in Cases 1 and 2.

Determining the Number of Baseline Samples

The selection and location of the baseline sampling wells was discussed in the previous section. This section provides a statistical approach for determining the required number of baseline samples. A sufficient number of wells should be sampled to ensure that the baseline is adequately characterized. In this document, adequacy of the baseline characterization is measured by the precision of the estimates of the baseline parameter values.

The goal of the baseline sampling design is to estimate the mean baseline concentrations of all analytes of concern to within a specified level of precision. The required level of precision is obtained by having a sufficient number of samples to reduce the relative standard error of the mean to within acceptable levels. If there is a large degree of heterogeneity in the baseline concentrations in different sections of the production unit, it may not be possible to reduce the relative error of the mean to the specified level. In that case, it may be necessary to divide the unit into several survey units comprised of contiguous areas with similar baseline characteristics for the analytes of greatest concern. In this case, the decisions on compliance with baseline levels would be developed separately for each survey unit.

Relative Standard Error of the Mean

The population coefficient of variation cv = S/M is useful because the population standard deviation  must always be considered in the context of the population mean M. The value of the coefficient of variation is independent of the dimensions of the measurement and is a dimensionless number. By convention, the coefficient of variation is expressed as a percentage, where cv = 100% is equivalent to cv = 1.0. For comparison between analytes with different units or widely different means, the coefficient of variation provides a standardized measure of variability of the populations.