Draft Technical Report
CONSIDERATIONS RELATED TO POST-CLOSURE MONITORING OF
URANIUM IN-SITU LEACH/IN-SITU RECOVERY (ISL/ISR) SITES
Radiation Protection Division
Office of Air and Radiation
U.S. Environmental Protection Agency
June 2011
This report provides background information to assist the SAB in performing its advisory
review. To do that, the information in this report is grouped into nine sections, split between two
parts. Part 1 details a draft overall approach; Part 2 discusses specific issues and case studies
associated with the draft overall approach. Additional detailed information is provided in five
attachments. EPA believes it is important to provide the SAB with the context so that the SAB
may relate the technical questions to the complex physical situations in which they might be
applied. We also believe it is important for the SAB to understand the statutory basis governing
our regulatory approach, i.e., EPA's standards must be consistent with RCRA requirements, but
those standards are implemented and enforced by NRC or its Agreement States through its
licensing requirements. It should therefore be understood that while EPA is requesting advice on
the technical aspects to be considered in a rulemaking that will establish standards applicable to
ISL/ISR facilities, EPA is not requesting advice on either the form or content of those standards.
EPA's regulatory proposal will be informed, in part, by the technical advice of the SAB, and will
be developed in a manner that is consistent with EPA's UMTRCA standard-setting authority
while taking into account the Agency's broader groundwater protection and risk management
policies.
In its charge to the SAB, EPA requested that the SAB address the following with respect to
ISL/ISR extraction processes:
(1) Comment on the technical areas described in this report and their relative importance for
designing and implementing a monitoring network. Identify any technical considerations
that have been omitted or mischaracterized.
(2) Comment on the proposed approaches for characterizing baseline groundwater chemical
conditions in the pre-mining phase and proposed approaches for determining the duration
of such monitoring to establish baseline conditions.
(3) Comment on the approaches considered for monitoring in the post-mining/restoration
phase and the approaches considered for determining when groundwater chemistry has
reached a "stable" level.
(4) Comment on statistical techniques about which you are aware that have been used in
other applications, as well as the subsequent data requirements for their use relative to
ISL mining applications (particularly for the areas in items 2 and 3 above).
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TABLE OF CONTENTS
Overview 1
Part 1
1.0 Introduction 4
2.0 Resource Conservation and Recovery Act 6
2.1 Summary 6
2.2 Groundwater Monitoring Requirements for Treatment Storage and Disposal Facilities
(TSDFs) 7
2.2.1 Overview 7
2.2.2 Permitted Facilities 7
2.2.3 Detection Monitoring 8
2.2.4 Compliance Monitoring 9
2.2.5 Corrective Action 9
2.3 Application to ISL/ISR Facilities 10
3.0 Groundwater Monitoring at ISL/ISR Facilities 11
3.1 Overview 11
3.2 Pre-Operational Monitoring (Phase 1) 12
3.3 The ISL/ISR Leaching Process (Phase 2) 12
3.4 Post-Operational Monitoring (Phases 3 through 5) 15
4.0 Technical Considerations for ISL/ISR Groundwater Monitoring 16
4.1 Uranium Geology 16
4.2 Establishing Baseline Conditions 17
4.3 Extraction Operations Phase 18
4.4 Post-Extraction Phase 19
4.5 Factors Affecting Post-Mining Timeframes and Wellfield Stability 19
4.6 Modeling 21
5.0 Statistical Analyses to Compare Pre- and Post-ISL/ISR Conditions 23
Part 2
Overview to Part 2 25
6.0 Active/Existing ISL/ISR Facilities: Monitoring Issues 25
6.1 Groundwater Baseline: Case Studies 26
6.2 Wellfield Restoration 27
6.3 Wellfield Restoration: Case Study 28
7.0 Issues Associated with Establishment of Post-Restoration Steady State 30
7.1 Post-Restoration Stability Monitoring 30
7.1.1 ISL/ISR Extraction Phase 30
7.2 Factors that Affect Post-Mining Monitoring Timeframes 31
7.2.1 Fate and Transport Process 31
7.2.1.1 Speciation 31
7.2.1.2 Speciation: Case Study 32
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7.2.1.3 Solubility 33
7.2.2 Natural Attenuation Processes 33
7.2.2.1 Adsorption 34
7.2.2.2 Role of Secondary Minerals 35
7.2.2.3 Role of Biological Processes 37
7.2.2.4 Case Study 38
7.3 Geochemically-Based Restoration Techniques 38
7.4 Monitored Natural Attenuati on 39
7.4.1 Tiered Approach to Assessing Suitability of MNA 40
7.4.2 First-Order Attenuation Rate Determination 41
7.5 Fate and Transport Modeling to Support ISL/ISR Compliance Activities 43
7.5.1 Modeling Objectives 43
7.5.2 Development of the Conceptual Model 44
7.5.3 Basic Aspects of Fate and Transport Modeling 44
8.0 Details on Statistical Analyses to Compare Pre-and Post-ISL/ISR Conditions 46
8.1 Hypothesis Testing and Data Quality Objectives 47
8.2 Decision Errors and Confidence Levels 49
8.3 Statistical Methods for Trends and Seasonality 55
8.3.1 Adjusting for Seasonality 55
8.3.2 Using Trend Tests to Determine Stability 57
8.3.2.1 A Nonparametric Statistical Test for Detecting Trends 58
8.3.3 Testing Multiple Wells for Trends 59
8.3.3.1 Multiple Observations per Time Period for Multiple Wells 60
8.4 Verify that Contaminants and Hazardous Constituent Concentrations are Below Required
Restoration Levels 60
8.4.1 Nonparametric Test for Comparing Baseline and Post-Restoration Conditions...61
8.4.1.1 Comparing One Well to the Baseline 62
8.4.1.2 Comparing Multiple Wells Testing for Homogeneity and Overall
Compliance to the Baseline 63
8.5 Summary of Statistical Approached 64
9.0 Summary of Post-Closure Performance Issues 67
9.1 Designing the Monitoring Program to Allow Reliable Baseline Conditions to be
Established Prior to Active Mining 67
9.2 Determining that the Groundwater Chemistry has Reached Steady State and Restoration
Processes Can be Discontinued 68
9.3 Post-Restoration Stability Monitoring 69
10.0 References 71
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ATTACHMENTS
Attachment A: Development of Groundwater Baseline for Dewey-Burdock ISL/ISR Site in
South Dakota
Attachment B: Post-Restoration Stability Monitoring Case Histories
Attachment C: Aquifer Restoration (Extracted from NRC 2009, Section 2.11.5)
Attachment D: Instructions and Examples for Statistical Calculations
Attachment E: Statistical Tables
LIST OF TABLES
Table 6-1. Baseline Water Quality Data for Zamzow PAA-1 27
Table 6-2. Groundwater Chemistry of Texas In-Situ Uranium Production Authorization
Areas (PAAs) 29
Table 7-1. Post Restoration and Stability Monitoring Periods 38
Table 8-1. Hypothesis Testing: Type I and Type II Errors 51
Table 8-2 Required Sample Size for Selected Values of o 53
Table 8-3 Achievable Values of a=P for Selected Values of n=m with MDD/ o =1 53
Table 8-4 Minimum Sample Size for Selected Values of MDD/ o with a=0.10 and
[5 0.10 54
LIST OF FIGURES
Figure 3-1. Variation of Typical Groundwater Constituent Over Time 12
Figure 3-2. Idealized Schematic Cross Section to Illustrate Ore-Zone Geology and Lixiviant
Migration from an Injection Well to a Production Well 13
Figure 3-3. Schematic Diagram of a Wellfield Showing Typical Injection/Production Well
Patterns, Monitoring Wells, Manifold Buildings, and Pipelines 14
Figure 3-4. Wellheads and Header House, Smith Ranch, Converse County, Wyoming 15
Figure 8-1. Test Resolution (MDD/ o) versus Total Sample Size (N) 55
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ACRONYMS AND ABBREVIATIONS
ANOVA
Analysis of Variance
ASTM
American Society for Testing and Materials
BPT
Best Practicable Technology
C vs. D
concentration vs. distance
C vs. T
concentration vs. time
CBR
Crow Butte Resources
CERCLA
Comprehensive Environmental Response Compensation and Liability Act
CFR
Code of Federal Regulations
COGEMA
Cogema Mining, Inc.
DBS&A
Daniel B. Stephens and Associates, Inc.
DENR
Department of Environment and Natural Resources (South Dakota)
DEQ
Department of Environmental Quality
DQO
data quality objective
EDTA
Ethylenediaminetetraacetic Acid
Eh
oxidation-reduction potential
EPA
Environmental Protection Agency (U.S.)
GCGCD
Goliad County Groundwater Conservation District
gpm
gallons per minute
GWS
groundwater sweep
IAEA
International Atomic Energy Agency
ISL
in-situ leach
ISR
in-situ recovery
Kd
partition or distribution coefficient
LQD
Low-Q Diffractometer
MARS SIM
Multi-Agency Radiation Survey and Site Investigation Manual
MCL
maximum contaminant level
MDD
minimum detectable difference
mg/L
milligram per liter
MNA
monitored natural attenuation
MU
Mine Unit
NDEQ
Nebraska Department of Environmental Quality
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NEM
non-electrostatic surface complexation model
NRC
Nuclear Regulatory Commission (U.S.)
OSWER
Office of Solid Waste and Emergency Response
PAA
Production Authorization Area or Proposed Action Area
pH
measure of acidity of a solution
pCi/L
picocurie per liter
ppm
parts per million
PQL
practical quantitation limit
PRI
Power Resources, Inc.
QA
quality assurance
RAI
Request for Additional Information
RCRA
Resource Conservation Recovery Act
RO
reverse osmosis
SAB
Science Advisory Board
SI
saturation index
SRB
sulfate-reducing bacteria
TCEQ
Texas Commission on Environmental Quality
TDS
Total Dissolved Solids
TENORM
Technologically Enhanced Naturally Occurring Radioactive Material
UIC
Underground Injection Control
UMTRCA
Uranium Mill Tailings Radiation Control Act
USGS
U.S. Geological Survey
UST
underground storage tank
WDEQ
Wyoming Department of Environmental Quality
WQD
Water Quality Division
WRS
Wilcoxon Rank Sum
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OVERVIEW
Background
In accordance with the Uranium Mill Tailings Radiation Control Act of 1978 (UMTRCA)
section 206, the U.S. Environmental Protection Agency (EPA) is authorized to develop standards
for the protection of public health, safety, and the environment from radiological and non-
radiological hazards associated with residual radioactive materials at inactive uranium mill
tailings sites and with the processing, possession, transfer, and disposal of byproduct material
(tailings or wastes) at sites where ores are processed primarily for their uranium content or used
for disposal of byproduct material. UMTRCA requires EPA to develop health and
environmental standards for both Title I inactive mill sites administered by the U.S. Department
of Energy (DOE) and Title II (present and future) operations licensed by the U.S. Nuclear
Regulatory Commission (NRC) or its Agreement States.
In 1983, EPA promulgated regulations at 40 CFR Part 192 - Health and Environmental
Protection Standards for Uranium and Thorium Mill Tailings in response to the statutory
requirements of UMTRCA. When the Agency promulgated 40 CFR Part 192, uranium recovery
from ore was based almost exclusively on the conventional milling process, where a few pounds
of uranium were recovered for each ton of ore mined and processed. The residues from the
milling process (the tailings or byproduct material) accumulated in large piles on the surface at
the mill site. Concern that these tailings piles would be a continuing source of radiation
exposure unless properly reclaimed was the driving force behind the passage of UMTRCA.
Because the major environmental risk at that time was perceived to come from the conventional
uranium mill tailings, other uranium recovery operations, such as heap leaching and in-situ
leaching (ISL), received little attention.
The EPA last revised its regulations for uranium and thorium milling in 1995, and currently is
reviewing them to determine if revisions are necessary to bring them up-to-date. Since 40 CFR
Part 192 was promulgated, there has been a shift in uranium recovery from conventional milling
to ISL where, in a sense, a portion of the milling process is conducted underground. Where the
ore body is amenable to use of the ISL technology, uranium can be recovered economically
without extensive surface facilities, large waste volumes, or expectations of long-term site
maintenance associated with conventional milling. In the ISL process, also referred to as in-situ
recovery (ISR), chemical solutions are pumped underground through an array of wells into the
ore body, where the uranium is dissolved in place. The uranium-rich solutions are pumped to the
surface, where the uranium is extracted. The solutions are then chemically refortified and
pumped back into the ore body to recover additional uranium.
EPA's standards must address non-radiological, as well as radiological, constituents. Therefore,
for Title I sites, UMTRCA states that the standards shall, "... to the maximum extent practicable,
be consistent with the requirements of the Solid Waste Disposal Act, as amended," now known
as the Resource Conservation and Recovery Act (RCRA). For Title II sites, the non-radiological
standards shall be "... consistent with the standards required under subtitle C of the Solid Waste
Disposal Act, as amended, which are applicable to such hazards."
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EPA's current standards in 40 CFR Part 192 incorporate the RCRA groundwater monitoring
requirements for hazardous waste units specified in 40 CFR Part 264, including statistical
techniques applicable to determining when monitoring requirements have been achieved. A key
question in amending 40 CFR Part 192 is whether, and to what extent, it is appropriate to apply
these technical approaches, developed to address releases to groundwater from engineered units
such as landfills, impoundments, and tanks, to ISL/ISR facilities, where the regulated "unit" is a
defined portion of an aquifer.
Draft Technical Report Contents Overview
With ISL/ISR operations expected to be the most common type of new uranium extraction
facility in the U.S., and the potential for these facilities to affect groundwater, EPA is
considering how to address groundwater monitoring as a component of the regulatory standards
specifically applicable to these facilities in its revision of 40 CFR Part 192. To support a request
for technical advice from the Agency's Science Advisory Board (SAB), EPA has prepared this
draft Technical Report, Considerations Related to Post-Closure Monitoring of Uranium In-Situ
Leach/In-Situ Recovery (ISL/ISR) Sites, to address considerations involved in establishing
groundwater monitoring systems around uranium ISL/ISR operations (e.g., sampling protocols,
time frames, statistical tools and techniques).
There are several objectives for monitoring an ISL/ISR uranium extraction operation,
specifically: to establish baseline (pre-operational) groundwater chemical compositions; to detect
excursions of the injected and mobilized components beyond the well field; and to determine
when the post-operational/restoration phase groundwater chemistry has "stabilized," i.e., reached
concentration levels that are expected to remain constant over time.
EPA's regulatory effort will focus on establishing requirements applicable to ISL/ISR facilities.
Because the "milling" of uranium ore is performed within the aquifer by injection of mobilizing
agents, ISL/ISR facilities present challenges for groundwater protection that are distinct from
those posed by conventional mills. Further, the intent of ISL/ISR operators is to release the site
after additional processing of ore is no longer economically viable, making it available for other
uses. Given the disruption of the aquifer inherent in ISL/ISR technology and the foreseeable
desire for a relatively short period of post-operational institutional control, groundwater
protection will be of central importance in amendments to 40 CFR Part 192.
As noted above, one purpose of monitoring is to demonstrate that the aquifer conditions (i.e.,
contaminant concentrations or geochemical characteristics) established at the end of restoration
are sustainable, or stable, over time. Currently, the duration of stability monitoring is a site-
specific period of time established in the license(s) required by the NRC or the appropriate
Agreement State. In the past, the license-established restoration period frequently has been
about six months. More recently, the trend has been to increase the monitoring period
established in the license to at least one year. In practice, the actual period necessary for
contaminant concentrations to stabilize may be several years, based on iterative analyses of
additional samples required by the regulators.
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The draft Technical Report is intended to support the SAB's technical consideration of issues
associated with establishing the groundwater baseline for new facilities, demonstrating that the
restored groundwater has reached steady state, and post-restoration stability monitoring to ensure
that the groundwater quality is not deteriorating over time after restoration.
Organizationally, the draft Technical Report addresses two main topic areas. The report initially
focuses on the process and considerations associated with the overall approach. Specifically, the
first section of the report provides an outline of the technical requirements associated with
monitoring of ISL/ISRs and includes: a summary of UMTRCA; a summary of relevant
components of RCRA; background information on the ISL/ISR process; a discussion of the
purposes of a groundwater monitoring system; factors affecting the timeframe and ability to
restore an ISL/ISR wellfield to baseline conditions; and discussion of various statistical
techniques and approaches to measure achievement of post-operational restoration goals.
Second, the report focuses on specific issues associated with ISL/ISR facilities and groundwater
monitoring. This latter discussion provides case studies, identifies key issues associated with
post-closure monitoring, and summarizes performance issues regarding groundwater monitoring
at ISL/ISR facilities.
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PART 1
1.0 INTRODUCTION
In 1983, the U.S. Environmental Protection Agency (EPA) promulgated regulations at 40 CFR
Part 192 - Health and Environmental Protection Standards for Uranium and Thorium Mill
Tailings in response to the statutory requirements of the Uranium Mill Tailings Radiation
Control Act (UMTRCA) of 1978. UMTRCA amended AEA by directing EPA to set generally
applicable health and environmental standards to govern the stabilization, restoration, disposal,
and control of effluents and emissions at both active and inactive mill tailings sites.
Title I of the Act covers inactive uranium mill tailing sites, depository sites, and vicinity
properties. In addition to EPA's standard-setting responsibilities, Title I designated the U.S.
Department of Energy (DOE) as the agency responsible for implementing EPA's standards for
the tailings piles (residual radioactive material) and vicinity properties and for providing long-
term stewardship of some properties. In addition, the U.S. Nuclear Regulatory Commission
(NRC) was designated to review completed site cleanups for compliance with EPA standards
and to license sites to the state or DOE for long-term stewardship, as necessary.
Title II of the Act covers operating uranium processing sites licensed by the NRC. EPA was
directed to promulgate standards for the processing, possession, transfer, and disposal of uranium
mill tailings (byproduct material). NRC, or its Agreement States, was required to implement and
enforce these standards at Title II sites.
40 CFR Part 192 thus establishes standards for active and closed mill sites, including
groundwater, soil, and building clean-up requirements. These standards are applicable to
uranium and thorium extraction facility licensing, operations, sites, and wastes and are
implemented and enforced by the NRC and its Agreement States, and DOE. Part 192 applies to
residual radioactive material and byproduct material from conventional mills, in situ
leach/recovery (ISL/ISR) facilities, and heap leach facilities, but not conventional mines (open
pit or underground).
Since 40 CFR Parti92 was promulgated, there has been a shift in emphasis in uranium recovery
methods from conventional milling to ISL/ISR, which is considered to be "underground
milling."1 In the ISL/ISR process, chemical solutions (i.e., lixiviants) are pumped underground
through an array of wells into the ore body, where the uranium is dissolved. The lixiviants are
then pumped to the surface, where the uranium is extracted.
In response to this shift in production technology, EPA announced on May 27, 2010, that they
planned to review 40 CFR Part 192. In support of the review, EPA is requesting guidance from
the Science Advisory Board (SAB) on selected issues related to explicitly incorporating
1 Like conventional mills, ISL/ISR operations are regulated by NRC as a form of uranium processing.
However, the injection-extraction technology is also applied to the recovery of other minerals, where it is broadly
known as "solution mining." Where this draft Technical Report uses the term "mining," which may be more
familiar to the general public, it is referring to the ISL/ISR extraction method.
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standards for in-situ leaching facilities into 40 CFR Part 192. These issues center on
groundwater monitoring and stability, which are the subject of this draft Technical Report.
Groundwater monitoring within and in the vicinity of an ISL site serves vital functions that are
necessary for efficient uranium recovery with minimal adverse environmental impacts. Proper
monitor well placement and data collection from these wells assures that the aquifer constituents
are detected, and then restored, to pre-mining levels. Without adequate monitoring well
placement and proper data collection, including consideration of sample frequency and sampling
timeframe, mine operators and regulators (1) may not detect excursions of lixiviant outside the
mining area during operations, and (2) may not be able to confidently determine whether the
impacted aquifer needs further restoration or has been restored to its pre-mining state or another
suitable condition that satisfies regulatory requirements.
EPA's standards in 40 CFR Part 192 are required by statute to address non-radiological, as well
as radiological, constituents, and to provide for the "protection of human health and the
environment consistent with the standards required under Subtitle C of the Solid Waste Disposal
Act...." (UMTRCA sec. 206(b)(2)). In particular, for Title I sites, UMTRCA states that the
standards shall, "... to the maximum extent practicable, be consistent with the requirements of the
Solid Waste Disposal Act, as amended," now known as the Resource Conservation and Recovery
Act (RCRA). For Title II and future NRC licensed sites, the standards shall be "... consistent
with the standards required under subtitle C of the Solid Waste Disposal Act, as amended, which
are applicable to such hazards."
The existing standards incorporate groundwater protection requirements applicable to hazardous
waste management units. These requirements are specified in 40 CFR Part 264, Subpart F
(Releases from Solid Waste Management Units). These requirements also provide a reasonable
basis for a proposal to address post-operational groundwater monitoring and restoration at
ISL/ISR facilities, while also providing the flexibility for site-specific, performance-based
implementation by the regulatory authority (NRC or Agreement State).
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2.0 RESOURCE CONSERVATION AND RECOVERY ACT
42 U.S.C. §6901 et seq. (1976)
The purpose of this section is to provide an overview of the Resource Conservation and
Recovery Act (RCRA) program. Provisions specifically relevant to ISL/ISR facility
licensing/oversight are discussed in detail. These include: Subtitle C facilities; groundwater
monitoring requirements; and Treatment Storage and Disposal Facilities.
2.1 Summary
The Resource Conservation and Recovery Act (RCRA) was passed in 1976, as an amendment to
the Solid Waste Disposal Act of 1965, to ensure that solid wastes are managed in an
environmentally sound manner. RCRA gives EPA the authority to control hazardous waste from
the "cradle-to-grave." This includes the generation, transportation, treatment, storage, and
disposal of hazardous waste (Subtitle C). RCRA also set forth a framework for the management
of non-hazardous solid wastes (Subtitle D). RCRA has been further amended to extend its
application; for example, the 1986 amendments to RCRA enabled EPA to address environmental
problems that could result from underground tanks storing petroleum and other hazardous
substances.
RCRA is a key component of EPA's UMTRCA standards in 40 CFR Part 192. As noted in
Chapter 1, Congress specified that EPA's standards were to address non-radiological, as well as
radiological, constituents. Therefore, for Title I sites, UMTRCA states that the standards shall,
"... to the maximum extent practicable, be consistent with the requirements of the Solid Waste
Disposal Act, as amended," now known as RCRA. For Title II and future NRC licensed sites,
the standards shall be "... consistent with the standards required under subtitle C of the Solid
Waste Disposal Act, as amended, which are applicable to such hazards." UMTRCA Section
206(a)
EPA's current standards in 40 CFR Part 192 incorporate the RCRA groundwater monitoring
requirements for hazardous waste units specified in 40 CFR Part 264, including statistical
techniques applicable for determining when standards have been achieved. A key question in
this advisory is whether, and to what extent, it is appropriate to apply these techniques, which
were developed to address releases to ground water from engineered hazardous waste units such
as landfills, impoundments, and tanks, to in situ leach uranium recovery facilities, where the
regulated "unit" is a defined portion of an aquifer.
The RCRA approach to protecting groundwater represents a reasonable starting point for
developing criteria and standards specific to ISL/ISR facilities. The remainder of this chapter
provides additional detail on the RCRA requirements and discusses technical challenges in
applying those requirements to ISL/ISR facilities. Part 2 of this document describes technical
approaches for consideration by the SAB, including potential modifications, extensions, and
additions to the RCRA requirements.
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2.2 Groundwater Monitoring Requirements for Treatment, Storage, and Disposal
Facilities (TSDFs)
2.2.1 Overview
The groundwater monitoring requirements for hazardous waste treatment, storage, and disposal
facilities (TSDFs) are an important aspect of the RCRA hazardous waste management strategy
for protecting human health and the environment from accidental releases of hazardous
constituents. While land disposal restrictions and unit specific standards seek to reduce the
toxicity of waste and prevent releases, respectively, the groundwater monitoring requirements
represent the last line of defense by ensuring that any releases are detected and remediated in a
timely manner.
TSDFs that manage hazardous waste in landfills, surface impoundments, land treatment units,
and some waste piles (referred to as "regulated units" in the regulations) are required to
implement a groundwater monitoring program to detect the release of hazardous constituents to
the underlying ground water. The regulations for permitted facilities are found at 40 CFR Part
264, "Standards for Owners and Operators of Hazardous Waste Treatment, Storage, and
Disposal Facilities." Specifically, Subpart F addresses "Releases from Solid Waste Management
Units" and includes elements of a monitoring program such as:
Groundwater protection standard;
Hazardous constituents;
Concentration limits;
Point of compliance;
Compliance period;
General monitoring requirements;
Detection monitoring;
Compliance monitoring; and
Corrective action.
The overall goal of these requirements is to protect the ground water in the uppermost aquifer
from contamination by the hazardous constituents managed at the TSDF.
2.2.2 Permitted Facilities
For permitted TSDFs, a groundwater monitoring program consists of three phases: detection
monitoring (§264.98), compliance monitoring (§264.99), and corrective action (§264.100). The
phases are sequential, with a facility able to move back and forth between phases as certain
criteria are met. The regulations are written as performance standards that require each facility's
groundwater monitoring program to have a sufficient number of wells installed at the appropriate
locations and depths that can yield representative samples of background conditions and water
quality at the point of compliance in the uppermost aquifer (defined as the geological formation
nearest the natural surface that is capable of yielding significant quantities of ground water to
wells or springs).
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To meet these standards, each facility must design, install, and operate a groundwater monitoring
program based upon the site's specific geology and hydrology, as well as the type of waste
management unit and the characteristics of the waste being managed. The monitoring wells must
be appropriately designed and installed and consistent sampling and analytical procedures must
be implemented to ensure accurate and representative samples are taken. The specific sampling
requirements and procedures (including frequency of sampling) are specified in the facility's
hazardous waste permit.
2.2.3 Detection Monitoring
Detection monitoring is phase one of the groundwater monitoring program. Under this phase,
facilities are monitoring to detect and characterize any releases of hazardous constituents into the
uppermost aquifer. Samples are taken from the monitoring wells and analyzed for specific
indicator parameters and any other waste constituents or reaction products that indicate that a
release might have occurred. The facility's permit identifies the specific constituents and
parameters to be monitored and establishes the frequency of sampling. At a minimum, four
samples must be taken from each well semi-annually.
Samples taken from the point of compliance {i.e., the wells downgradient of the waste
management unit) are compared to the background samples taken from the upgradient well(s).
These samples are analyzed to determine if a statistically significant increase (SSI) in the levels
of any of the monitored constituents has occurred. When analyzing the samples, facility
owner/operators may use one of the following five methods:
Parametric analysis of variance.
Nonparametric analysis of variance based on ranks.
Tolerance or prediction interval procedure.
A control chart approach.
Another statistical test method approved by the EPA Regional Administrator.
If an SSI is detected, the facility must switch to a compliance monitoring program, unless the
owner/operators can demonstrate that the SSI was due to a sampling, analysis, or statistical
analysis error; or is due to natural variations in the groundwater chemistry. If unable to make this
demonstration, the owner/operators must:
Notify the EPA Regional Administrator of the SSI within 7 days.
Immediately sample all wells for hazardous constituents listed in Part 264 Appendix IX.
Determine which Part 264 Appendix IX constituents are present and at what levels.
Submit a permit modification application within 90 days to begin a compliance
monitoring program.
Submit an engineering feasibility plan for a corrective action program within 180 days.
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2.2.4 Compliance Monitoring
The purpose of a compliance monitoring program is to ascertain whether the constituents
released to the uppermost aquifer are exceeding acceptable concentration levels and threatening
human health and the environment. The first step in this process is establishing a groundwater
protection standard (GWPS). As stated above, a facility must submit a permit modification
application to switch from detection monitoring to compliance monitoring when an SSI is
detected. As part of this modified permit, the EPA Regional Administrator specifies the GWPS
for the facility. The GWPS establishes:
The list of hazardous constituents for which to monitor (from Part 261, Appendix VIII).
The concentration limits for each of the listed constituents based either on background
levels, Safe Drinking Water Act (SDWA) Maximum Contaminant Levels (MCLs), or
alternate concentration levels (ACLs) determined by the EPA Regional Administrator.
The point of compliance, which is the vertical surface at which the facility must monitor
the uppermost aquifer to determine if the GWPS is being exceeded.
The compliance period during which the GWPS applies and compliance monitoring must
be continued.
During compliance monitoring, samples are taken at each well located at the point of compliance
(four samples from each well) and compared to the GWPS. The frequency of sampling is
determined by the EPA Regional Administrator and specified in the modified facility permit. At
a minimum, samples must be taken at least semi-annually. The facility must also analyze
samples for Part 264 Appendix IX constituents at least annually. If any new constituents are
found to have an SSI, then they also must be added to the GWPS list of constituents.
If the level of any of the constituents exceeds the GWPS, the owner/operators must notify the
EPA Regional Administrator in writing within 7 days. The owner/operators also must submit a
permit modification application to establish a corrective action program. Compliance monitoring
must be continued during this period.
2.2.5 Corrective Action
Once an exceedance of the groundwater protection standard (GWPS) has been detected, the
facility must take action to bring the constituent concentration levels back into compliance with
the GWPS. To achieve this, the owner/operator must either remove the hazardous constituents or
treat them in place. The EPA Regional Administrator will approve the facility's selected
corrective action method and specify the time frame in which it must take place. Any hazardous
constituents that have migrated beyond the point of compliance also must be remediated. The
facility must continue corrective action until the GWPS has not been exceeded for three
consecutive years. At that point, the facility may return to compliance monitoring.
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2.3 Application to ISL/ISR Facilities
While the application of the RCRA groundwater monitoring requirements to conventional mills
and tailings impoundments is relatively straightforward, the ISL/ISR technology presents
additional technical challenges for post-operational monitoring. First, the technology is applied
within the aquifer by intentionally altering its chemical characteristics to facilitate transport of
uranium. Thus, in the RCRA framework, contaminants have already been released into the
environment and are no longer contained within the engineered hazardous waste unit (that is, a
surface impoundment). This suggests that the situation could be viewed as a corrective action
from the time operations cease.
The intent of the operator to release the site for unrestricted use presents the more significant
challenge. Unlike conventional tailings impoundments, which are subject to long-term
stewardship requirements, ISL/ISR facilities will leave no significant surface facilities or waste
behind. Restoration of the groundwater will therefore need to be achieved throughout the well
field, within which there may be significant heterogeneity. Further, from a corrective action
standpoint, the "source" of contamination cannot necessarily be identified to a specific location
within the affected area. It is therefore particularly important that an appropriate monitoring
program be developed, including an appropriate number of wells in the right locations, to
determine, with the appropriate level of confidence, that restoration and stability have been
achieved. As discussed in this document, there may be technical approaches that can be used to
modify or extend the RCRA requirements. Additionally, there may be better-suited technical
approaches for these particular types of facilities.
This situation has been further complicated for operating ISL/ISR facilities by the fact that
permits for lixiviant injection wells must be obtained from EPA's Underground Injection Control
(UIC) program developed pursuant to the SDWA (in some cases, authority to issue UIC permits
has been delegated to states). In issuing the UIC permit, the regulatory authority must exempt
the portion of the aquifer affected by the activity. The primary concern is that there be no
transport of contaminants beyond the exempted portion of the aquifer ("excursion") into an
Underground Source of Drinking Water (USDW). Requirements for restoration of the exempted
portion of the aquifer under the UIC program are limited compared to the requirements of 40
CFR Part 192. Failure to recognize the applicability of 40 CFR Part 192 to all groundwater at an
ISL/ISR facility {i.e., in the well field) has led to a situation in which operators at some ISL/ISR
facilities have not been held to the more stringent standards in 40 CFR Part 192 (see case studies
included in this document). Further, in some cases the appropriate baseline conditions may not
have been recorded. Advice on handling these cases is also needed.
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3.0 GROUNDWATER MONITORING AT ISL/ISR FACILITIES
3.1 Overview
The lifecycle of an ISL/ISR facility includes the following:
Exploration and development to establish that a commercially viable operation is possible
Establishment of site baseline conditions for in-situ leaching (mining) of the ore body
Recovery of uranium from the ore body
Restoration of the groundwater to predetermined conditions
Demonstration that restored groundwater has reached steady state
Post-restoration stability monitoring of the groundwater
Decommissioning of mined area and surface facilities
This draft Technical Report is most concerned with the pre- and post-operational aspects of
groundwater monitoring, specifically establishment of the groundwater baseline, demonstration
that the restored groundwater has reached steady state, and post-restoration stability monitoring
to ensure that the groundwater quality is not deteriorating over time after restoration. Figure 3-1
is a graphic representing an evolution of a groundwater component of interest during the phases
described below.
The five phases of groundwater monitoring during the life of the ISL/ISR facility are:
Phase 1 - Measure baseline groundwater concentrations and establish regulatory
approved restoration goals based on statistical procedures that embrace pre-mining
temporal and spatial variability.
Phase 2 - Conduct in-situ mining. Detect lixiviant excursions outside the mining area if
they occur. Determine the groundwater chemistry at the end of ISL/ISR operations.
Phase 3 - Conduct wellfield restoration. Monitor the progress of restoration through
groundwater sampling.
Phase 4 - Establish wellfield steady state. At the end of this phase, the groundwater
potentiometric surface will have returned to baseline conditions (to the extent practicable)
and statistical tests show that groundwater chemistry is stable.
Phase 5 - Conduct long-term stability monitoring. At the end of this phase, use statistical
tests to show that concentration of the monitored species is not increasing with time and
that concentration is not statistically different from baseline conditions, or if baseline
conditions are unachievable, that the concentration is not statistically different from
approved restoration goals.
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Begin
End
End
Mining
Mining
Treatment
i /
Begin Stability
Monitoring
End
Monitoring
Measured
Ground
Water
Concentration
Restor
ation
Goal
Date
Phase 1 - Measure baseline groundwater concentrations
and establish regulatory restoration values
Phase 2 - Conduct in-situ mining
Phase 3 - Conduct wellfield restoration
Phase 4 - Establish wellfield steady state
Phase 5 - Conduct long-term stability monitoring
Figure 3-1. Variation of Typical Groundwater Constituent Over Time
3.2 Pre-Operational Monitoring (Phase 1)
The key to any baseline monitoring program is to adequately characterize groundwater temporal
and spatial variations before mining begins. In order to provide the basis of comparison for
assessing progress in restoring the wellfield after mining has been completed, the breadth of pre-
operational groundwater monitoring needs to be sufficiently robust for adequate comparisons
with post-operational monitoring.
3.3 The ISL/ISR Leaching Process (Phase 2)
During typical ISL/ISR operations, chemicals such as sodium carbonate/bicarbonate, gaseous
oxygen, and hydrogen peroxide are added to the groundwater to produce a concentrated oxygen-
rich leaching solution called the lixiviant. The lixiviant is injected into the production zone to
create groundwater oxidizing conditions which mobilize the uranium from the uranium rich
geologic zone. This mobilized uranium is pumped back to the surface for extraction at a
processing plant (Figure 3-2).
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Injection Well~
Production Well
Perforations I
I Potentiometric Surface (Exaggerated)
~ Less Permeable Strata'
Ore Bearing Sand
!V
I Perforations
; Less Permeable Strataj
Figure 3-2. Idealized Schematic Cross Section to Illustrate Ore-Zone Geology and
Lixiviant Migration from an Injection Well to a Production Well (NRC 2009)
The most common injection/pumping patterns are five- and seven-spot (NRC 2003). The shape
of the mineralized ore body and surface topography, however, may give rise to other patterns
(NRC 1997). A typical five-spot pattern contains four injection wells and one recovery well.
The dimensions of the pattern vary depending on the mineralized zone, but the injection wells
are generally between 40 to 150 feet apart. In order to effectively recover the uranium and also
to complete the groundwater restoration, the wells are often completed so that they can be used
as either injection or recovery wells. During mining operations, a slightly greater volume of
water will be recovered from the mineralized zone aquifer than was injected, in order to create a
cone of depression or a flow gradient towards the recovery wells. This practice is intended to
minimize excursions of leachate outside the production area. Groundwater monitoring is
necessary to detect any excursions of lixiviant outside the mining area during operations. A
typical well arrangement using five- and seven-spot patterns is shown in Figure 3-3. Figure 3-4
illustrates a typical wellfield. Piping connecting the individuals to the header house is typically
run underground.
Ore body size and geometry will also influence the number of wells in a wellfield.
For example, at the Crow Butte ISL facilities in Dawes County, Nebraska, the number
of injection and production wells varied from about 190 in the first wellfield (MU-1)
to about 900 in later wellfields (MU-5 and MU-6) (NRC 1998).
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Three types of wells predominate at uranium ISL/ISR facilities during the operational (leaching)
phase (see Figure 3-3):
(1) Injection wells for introducing solutions into the uranium mineralization
(2) Production wells for extracting uranium-enriched solutions
(3) Perimeter monitoring wells for assessing containment of leachate within the wellfield
(the ore zone monitor wells in Figure 3-3)
a a
A
A
A
Injector Recovery Wells
A Ore Zone Monitor Wells
O Shallow Zone Monitor Wells
(One Per 4 Acres)
Figure 3-3. Schematic Diagram of a Wellfield Showing Typical Injection/Production Well
Patterns, Monitoring Wells, Manifold Buildings, and Pipelines (NRC 2009)
Recovery Trunxhne
Injection Tnmkline
.Oulline ol
Pattern
Injection We i Recovery Well
(Located at Each (Located at Each
Gnd Intersection) Gnd Center)
Patterns Repeal Through Wellfield
7-Spot Pattern
Edge
Wellfield Area
Wollf'Old Building
5-Spot Pattern
Groundwater Flow
Inset;
X
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Figure 3-4. Wellheads and Header House, Smith Ranch, Converse County, Wyoming
(NRC 2009, Figure 2.1-4)
3.4 Post-Operational Monitoring (Phases 3 through 5)
The intent of restoration efforts is to establish hydrologic and geochemical conditions in the
mined areas that will maintain steady-state conditions in all potentially affected aquifers (i.e.,
overlying, underlying, and adjacent aquifers) and assure no degradation of water quality from
pre-mining conditions. During restoration, the operator monitors progress by periodic sampling
of the groundwater constituents and analysis in an effort to determine when steady-state
conditions are attained. Establishment of steady-state conditions requires that the groundwater
potentiometric surface be restored, to the extent practicable, to its pre-leaching status, so that the
flow regime is similar to that existing before mining. In addition, constituents in the
groundwater must be returned to the predetermined restoration goal and remain at that level for a
sufficient period to demonstrate that the results are not trending upwards to higher concentration
levels.
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 the stability monitoring period is site-specific, with the period established in the license(s). In
the past, the license-established restoration period typically has been about 6 months. 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.
A key question associated with this issue is: Is the use of a confidence level an appropriate
potential metric for determining when the aquifer can be considered stable?
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4.0 TECHNICAL CONSIDERATIONS FOR ISL/ISR GROUNDWATER MONITORING
Monitoring wells within an in-situ mining area and site vicinity serve vital functions necessary
for efficient uranium recovery with minimal adverse environmental impacts. Proper monitor
well placement and data collection from these wells assure that the aquifer constituents are
detected and then restored to pre-mining levels. Without adequate monitoring well placement
and proper data collection, which includes consideration of sample frequency and sampling
timeframe, mine operators and regulators (1) may not detect excursions of lixiviant outside the
mining area during operations; and (2) may not be able to confidently determine whether the
impacted aquifer needs further restoration or has been restored to its pre-mining state or
predetermined conditions specified by regulators.
This section focuses on technical considerations for groundwater monitoring through all phases
of an ISL/ISR facility. Because the monitoring goals and practices are dependent on the
characteristics of the ore body, this section begins with a discussion of geographic, geologic, and
chemical characteristics typical of uranium deposits suitable for leaching.
4.1 Uranium Geology
The principal regions of uranium recovery by ISL/ISR are the Wyoming basin, the Colorado
Plateau and the Gulf Coastal Plain of Texas. The southern Black Hills in South Dakota and
northeast Colorado/western Nebraska within the Great Plains region also contain sedimentary
uranium deposits amenable to ISL/ISR.
Leachable uranium deposits are found in sandstones that have been deposited in intermontane
basins, along mountain fronts, and in near-shore marine and deltaic environments. The deposited
sediments were created as a complex and heterogeneous rock sequence that may be greater than
2,000 meters thick (Rojas 1989). This rock sequence can be made up of a number of water-
bearing units separated by confining units. The water-bearing unit containing the ore body is
separated (at least locally) from other water-bearing units above and below.
Zones of uranium mineralization follow a general trend of drainage channels. Individual ore
bodies in sandstone lenses rarely exceed a few hundred yards in length (Rojas 1989). These are
typically "roll-front" deposits that are formed when oxygenated water enters the sandstone
aquifer by local recharge dissolving the uranium. Deeper into the aquifer, the oxygen becomes
depleted and typically a convex curved redox interface is formed, with reducing conditions on
the downgradient side and oxidizing conditions on the upgradient side. Reducing conditions can
be caused by contact with carbonaceous material and pyrite.
Freshly precipitated uranium along with uranium in the arkosic sandstone minerals is
continuously dissolved by oxygenated groundwater and displaced further downgradient (Rojas
1989). As the uranium comes in contact with the reducing conditions downgradient, an
economically recoverable deposit of uranium may eventually be formed. The term "roll front" is
used because over time, the redox interface rolls downgradient as more oxygen is transported
into the aquifer. The inner contact of ore and altered sandstone are generally sharp, whereas the
uranium concentration on the reduced side of the interface is gradational.
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4.2 Establishing Baseline Conditions
Prior to initiating the ISL/ISR activities, knowledge of the aquifer baseline characteristics is
needed to help determine restoration goals for the post-mining phase. Pre-mining monitoring
and testing wells are installed to collect data that define the groundwater flow regime through the
extraction zone and surrounding areas and determine the chemical characteristics of the
groundwater. Monitoring wells should be installed at well locations upgradient, downgradient,
and tangential to the proposed ISL/ISR field, as well as within the "ore-zone." Well placement
should be designed to measure all potential "escape" pathways for introduced constituents and
mobilized metals, as well as to provide data to determine the choice and effectiveness of aquifer
restoration actions. The design of the monitoring network is largely a site-specific decision
predicated on a thorough knowledge of the groundwater flow regime and the effects of the
injection and withdrawal rates on the flow system behavior. A system of wells should be
emplaced to monitor the horizontal and vertical groundwater velocity and flow paths,
groundwater chemical conditions, and the potential for hazardous constituents to migrate beyond
the ISL/ISR mine field, both within the mined aquifer and through transmission of contamination
to overlying and underlying aquifers. These areas beyond the ISL/ISR may experience
contamination from the mined area beneath them.
The following components and parameters need to be considered in establishing baseline site
characteristics (more details can be found in Part 2):
(1) Hydro-geochemical Conditions - Eh (including redox sensitive couples), dissolved
oxygen, pH, major ions, total dissolved solids (TDS), carbonate alkalinity, pCC>2,
radioactive constituents, colloids, organic constituents, hydrogen sulfide, trace elements
(to be compared against post-restoration measurements).
(2) Concentrations of those constituents listed in 40 CFR Part 192 - Arsenic, Barium,
Cadmium, Chromium, Lead, Molybdenum, Nickel, Radium-226 and -228, Selenium,
Silver, Thorium, Uranium, etc.
(3) Uranium Ore Deposit Types and Oxidation States - The site-specific and varied diagenic
processes that formed the uranium deposits will determine how baseline conditions will
be affected by ISL/ISR operations and which restoration approach is likely to be most
effective. Knowledge of these processes can be used as a framework in estimating the
timeframe needed for the aquifer to reach baseline conditions once post-mining
restoration and monitoring are initiated.
(4) Hydro-geologic Setting - Pre-mining groundwater velocities (un-stressed), flow paths,
and solute transport timeframes. A reliable and defensible characterization survey of the
ISL/ISR site requires thorough core and water sampling from all monitoring wells and
exploration boreholes. Sufficient data must be collected before the mining activity to
understand when baseline levels have been reached after mining. Aquifer pump/stress
tests and core sample analysis will determine aquifer characteristics within and
surrounding the ore body and be used to determine:
a. Host rock and ore zone permeability, porosity, storativity, thickness
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b. Whether more monitoring wells are needed for post-closure activities and to
assess the timeframe of post-closure monitoring
c. Timeframe estimates after mining has ceased, in order for the system to reach pre-
ISL/ISR conditions
d. Recharge/discharge points
e. Impermeable layers above and below ore zone
f. Proximity to groundwater barriers
g. Proximity to surface water bodies - natural or manmade
Sampling the groundwater may require special sample collection techniques, depending on the
chemical constituents of concern. For major ions and some other chemical species, sampling
may be relatively simple, in that these species are not susceptible to change upon exposure to
atmospheric conditions. For species that are susceptible to re-equilibration in response to
atmospheric conditions, particularly redox-sensitive species and the carbonate-bicarbonate
system, water sampling may require that the sampled interval be "packed off' within the well
and water samples taken in containers, which were placed within the sealed intervals prior to the
"packing-off' and left to equilibrate in the flowing groundwater for a period of time prior to
removal. Redox-sensitive couples typically examined include ferrous (II)/ferric (III) iron, and
the arsenic (III) /arsenic (V) couple. In addition to dissolved oxygen levels, these couples can
produce important characterization of the redox conditions in the production zone prior to,
during, and after the leaching process, and can also be important in determining the effectiveness
of various aquifer restoration processes.
In addition, uranium speciation is strongly affected by pH and carbonate concentrations in the
groundwater, which, in turn, are a function of the pCC>2 in the groundwater. Exposure of the
groundwater sample to the atmosphere can result in the escape of CO2 and re-equilibration of the
uranium-carbonate system due to the out-gassing. The uranium concentrations in the re-
equilibrated water would not reflect the actual speciation in-situ, and, consequently, could result
in misleading calculations of uranium speciation and solubility constraints in the subsurface
waters. Because of these effects and their relative importance to characterizing the in-situ
groundwater chemistry, monitoring water chemistry in and around the "ore body" may well
require differing sampling methods.
4.3 Extraction Operations Phase
During the ISL/ISR mining operations phase, wells are placed in the active ISL/ISR-treatment
zone, fringe zone (wells at the ISL/ISR-mine boundary), and outside the impacted areas. The
functions of a monitoring system during the extraction phase include:
(1) Monitoring the extraction process to determine uranium recovery rates within the mining
zone
(2) Assessing the mass-balance of the lixiviant fluids
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(3) Monitoring excursions beyond the ore zone (both within the ore-bearing aquifer and in
overlying and underlying aquifers)
(4) Monitoring groundwater chemical composition in wells surrounding and downgradient of
the extraction field
(5) Monitoring the chemical composition of groundwater upgradient of the extraction field to
determine if these waters are chemically stable over the course of the extraction effort
4.4 Post-Extraction Phase
The post-extraction monitoring system should be designed to assess the effectiveness of the
remediation process, assess when final remediation objectives have been met, and assure that the
impacted aquifer is at steady state and the site is ready for decommissioning. A system of wells
located in the active treatment zone, as well as outside the boundary of the impacted area, is
required to monitor the horizontal and vertical groundwater velocity and flow paths within and
around the vicinity of the ISL/ISR site. The functions of a post-mining monitoring system
include:
(1) Measuring downgradient groundwater chemical constituents to determine if and/or when
the groundwater chemistry has returned to pre-ISL/ISR compositions (baseline)
(2) Determining if additional chemical components have been added to the groundwater as a
product of the extraction process (e.g., metals mobilized with the uranium)
(3) Demonstrating when the groundwater chemistry has reached "stable" levels (i.e.,
statistically equivalent compositions over an extended time period)
(4) Determining if post-mining restoration levels for groundwater constituents have been met
4.5 Factors Affecting Post-Mining Timeframes and Wellfield Stability
Post-restoration monitoring must be of sufficient duration to assure that once groundwater
chemistry appears to have reached acceptable restoration levels, these levels are at steady state
and the groundwater system is at equilibrium. Steady-state restoration levels are not just for
uranium, but include other hazardous constituents that may have been mobilized by ISL/ISR
operations, such as radium, manganese, and selenium. Chemical speciation and solubility, as
well as natural attenuation processes, must be understood to determine when the impacted
aquifer has reached a steady-state condition.
Aquifer restoration is complex and results can be influenced by a number of site-
specific hydrological and geochemical characteristics. 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 ISL facility).
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The environmental chemistry of uranium is largely dictated by its oxidation state, with the
solubility, and therefore mobility, of uranium the greatest when it is in the U(VI) state. Because
different chemicals may be used during the restoration process than were used during ISL/ISR
operation, the chemical form of uranium or other hazardous constituents may differ during
restoration. Since most of the available computer codes do not have a method of calculating
reaction rates, these reactions may be unexpected, and the duration of the monitoring program
must be long enough to accommodate such unexpected conditions.
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
groundwater. Attenuation processes important at ISL/ISR sites include pH buffering and acid
neutralization, adsorption at the mineral-water interface, mineral precipitation, dilution, and
biological processes.
Another factor affecting the post-monitoring timeframe and wellfield stability is the form of
remediation utilized. Pump and treat and geochemically-based techniques are commonly applied
remediation approached. Monitored natural attenuation is another response action that may be
effective in certain situations.
Pump and Treat
Alternative approaches included in pump and treat remediation are:
Groundwater Transfer - This involves transferring groundwater between the wellfield
starting restoration and another where uranium leach operations are beginning. No liquid
effluents are generated as water is transferred between one wellfield and another.
Groundwater Sweep - Injection of lixiviant is stopped and the contaminated liquid is
pumped from the leaching zone via all the injection and production wells. Fresh
groundwater flows into the leaching zone from the outside, which displaces lixiviant in
the pore spaces. Typically, an ion-exchange system is used to process the sweep water,
which is disposed of either in evaporation ponds or via deep well injection in accordance
with the site permit. The pumping rates are site specific, and the duration and volume of
water removed depends on the aquifer affected by the ISL/ISR. Due to heterogeneities in
the aquifers, groundwater sweep alone is insufficient and uneconomical for complete
restoration. In addition, groundwater sweep may cause oxic conditions from upgradient
waters to enter the ore zone, making it more difficult to re-establish chemically reducing
conditions.
Reverse Osmosis (RO) - To return groundwater to baseline conditions, it is usually
necessary to remove contamination from the mined zone water while minimizing
disposal of waste liquids. Reverse osmosis, which involves passing the water being
restored through pressurized, semi-permeable membranes, is a common way of treating
groundwater. The RO treatment results in clean water or permeate that can be re-injected
into the aquifer and brine that is water with concentrated ions. The brine is usually sent
to an evaporation pond, injected into deep disposal wells, or dried (using an evaporator)
for disposal at a licensed facility.
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Permeate Injection - Many aquifers are characterized by porosity where groundwater
with decreased mobility resides in regions of moderate to low permeability. It is very
difficult to remove all of the lixiviant and associated contamination from this portion of
the groundwater, which will act as a source of contaminants, even after long periods of
pumping and treating. Chemicals may be added to injection water in the latter stages of
restoration to assist in re-establishing baseline conditions. This includes reducing the
mobility of many of the metal species that make up contaminants of concern, including
uranium, selenium, molybdenum, and arsenic.
Geochemically-Based 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 are either oxidized to a higher valence state or reduced to a lower valence state.
Monitored Natural Attenuation
Monitored natural attenuation (MNA) refers to the reliance on natural processes to achieve site-
specific remediation objectives within a reasonable timeframe. These processes include
biodegradation, dispersion, dilution, sorption, and volatilization; radioactive decay; and chemical
or biological stabilization, transformation, or destruction of contaminants. The overall impact of
MNA at a given site can be assessed by evaluating the rate at which contaminant concentrations
are decreasing either spatially or temporally. EPA has prepared a technical resource document
(EPA 2007a and 2007b) that presents a four-tiered assessment of MNA as a viable response
action for selected metal, metalloid, and radionuclide contaminants encountered in groundwater
and involves the following: (1) demonstrating contaminant sequestration mechanisms; (2)
estimating attenuation rates; (3) estimating attenuation capacity of aquifer solids; and (4)
evaluating potential reversibility issues. Additional details on MNA can be found in Part 2,
section 7.4.
4.6 Modeling
Groundwater fate and transport modeling is often utilized to reduce the uncertainty regarding the
spatial and temporal behavior of the contaminant plume(s). For example, groundwater modeling
is commonly implemented at ISL/ISR facilities to assist in meeting the following objectives:
Optimize the monitoring well spacing to detect injection fluid excursions into non-mined
aquifer zone(s)
Estimate the number of pore volumes needed during site remediation activities to
adequately reduce contaminant concentrations
Establish a specific period of monitoring for ISL/ISR facilities once uranium extraction
operations are completed
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A conceptual model that summarizes the theoretical understanding of the primary conditions that
affect groundwater flow and chemical transport and fate is first developed. Then, to solve the
general model, a computer code is used. Computer codes frequently used to meet the modeling
objectives at ISL/ISR facilities include three types: (1) groundwater flow, (2) particle tracking;
and (3) transport codes.
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5.0 STATISTICAL ANALYSES TO COMPARE PRE- AND POST-ISL/ISR
CONDITIONS
Although statistical analyses are used in all phases of the ISL/ISR process described above,
statistical hypothesis tests are specifically used to establish baseline monitoring requirements
(Phase 1), to determine when restoration is complete (Phase 4), and when long-term stability has
been demonstrated (Phase 5). Hypothesis testing is a statistical tool for deciding when the
groundwater has reached steady state, and for the comparison of 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 a possible contaminant in a specific well at
a given time, although other water quality parameters may also be analyzed using the methods in
this section.
Both linear regression and the nonparametric Mann-Kendall trend test are recommended as
viable alternatives in EPA 2006 and EPA 2009. Linear regression relies on a variety of
assumptions, for example, normality which needs to be tested. The Mann-Kendall trend test may
be used with any series of four or more independent samples to test for trends in well parameters.
The test can be employed in Phase 1 to check for unexpected trends in baseline samples, in Phase
4 to determine when steady state is reached, and particularly in Phase 5 to establish long-term
stability. The Wilcoxon Rank Sum test (also known as the Mann-Whitney or Wilcoxon-Mann-
Whitney test) can be applied in Phase 5 to compare post-restoration well parameters with
baseline parameters, assuming that both datasets are stationary. The Wilcoxon Rank Sum
(WRS) test is recommended for comparing baseline and post-remedial wells in EPA 2006.
It is essential that sufficient data be collected to support a statistical comparison of baseline and
post-restoration conditions. Under ideal conditions, the dataset would include a complete time
series of measurements systematically collected at each well at equally spaced times using the
same measurement device with a very low limit of detection. In reality, such datasets exist only
in textbook examples.
In summary, the preferred statistical approaches for each phase are:
Phase 1 Baseline Sampling
Estimate required number of samples
Adjust measured data for seasonality, if required
Use Mann-Kendall test to check for unexpected trends
Phase 4 Determination of Steady State
Adjust measured individual well data for seasonality, if required.
Use Mann-Kendall test for individual well trends.
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2
If a trend is detected, use linear regression or Theil-Sen test to assess trend magnitude.
If trends not detected, use WRS test to compare baseline to steady-state measurements for
statistical differences for a single well. Repeat for all wells.
For multiple wells, when trends are not detected, first test wells for homogeneity. If test
results confirm homogeneity, then test to confirm compliance of all wells with restoration
goals.
If steady-state data are from different wells than the baseline data and trends are not
detected, use WRS test 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.
Phase 5 Long-term Stability Monitoring
Adjust measured data for each well for seasonality, if required.
Use Mann-Kendall test for trends for each well.
If trend is detected, use linear regression or Theil-Sen test to assess trend magnitude.
If trends not detected, use WRS test to compare baseline to stability monitoring results
for a single well. Repeat for each well.
If the before/after comparison is made between multiple wells, first test all wells for
homogeneity using chi-squared approach, then test to confirm compliance of all wells
with restoration goals.
If post-restoration data are from different wells than baseline data and trends are not
detected, use WRS test to compare baseline to stability monitoring results for the pooled
data of all wells combined.
Statistical tests for trends are recommended for demonstrating stability of the site after
restoration. Statistical tests are also recommended for comparing post-restoration conditions
with baseline conditions after stability has been reached. Several EPA sources were used as the
bases for the statistical tests. Although these sources do not recommend procedures for ISL/ISR
sites in particular, the sources are either general in nature or address related issues. These
sources include guidance for applying the Data Quality Objectives (DQOs) at remediated
CERCLA sites (EPA 2002a), guidance for conducting the statistical tests in the Multi-Agency
Radiation Survey and Site Investigation Manual (MARS SIM) (EPA 2000), guidance for
statistical analysis of groundwater monitoring data at RCRA facilities (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 2006). Many of the procedures for
conducting the tests discussed above, in section 8, and explained in detail in Attachment D were
adapted from the EPA QA/G9S document.
2 Theil-Sen test is a nonparametric alternative to linear regression and is often used when constructing
trends on data sets containing non-detects. The Theil-Sen line estimates the change in median concentration over
time and not the mean as in linear regression.
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PART 2
Overview to Part 2
Part 1 of this document provided basic background and context to frame issues related to
groundwater monitoring at ISL/ISR sites. Part 2 provides additional technical detail specific to
the questions of establishing baseline conditions, post-operational stability monitoring, and
statistical approaches that can be applied to determine that the restoration performance objectives
have been achieved.
EPA believes it is important to provide the SAB with the context so that the SAB may relate the
technical questions to the complex physical situations in which they might be applied. We also
believe it is important for the SAB to understand the statutory basis governing our regulatory
approach, i.e., EPA's standards must be consistent with RCRA requirements, but those standards
are implemented and enforced by NRC or its Agreement States through its licensing
requirements. It should therefore be understood that while EPA is requesting advice on the
technical aspects to be considered in a rulemaking that will establish standards applicable to
ISL/ISR facilities, EPA is not requesting advice on either the form or content of those standards.
EPA's regulatory proposal will be informed, in part, by the technical advice of the SAB, and will
be developed in a manner that is consistent with EPA's UMTRCA standard-setting authority
while taking into account the Agency's broader groundwater protection and risk management
policies.
EPA recognizes that setting standards involves both policy and technical elements and that it can
be difficult to clearly separate the two. For example, defining technical criteria that would
indicate stability of post-restoration conditions naturally raises the question of how long such
monitoring should be conducted. As a technical matter, EPA is requesting advice from the SAB
to account for influences such as the size of the well field and seasonal variation. As a policy
matter, EPA will determine whether a monitoring period should be specified and, if so, what that
period should be.
Similarly, this document addresses statistical approaches such as confidence levels and specific
tests that can be applied to determine restoration goals and whether those goals have been
achieved. EPA is requesting advice from the SAB regarding the validity of these approaches,
whether other approaches might be equally valid or more suitable for the situation, and what
factors may affect their application (e.g., the amount of data required). EPA will determine how
to incorporate these considerations into our standards, which will be developed through notice-
and-comment rulemaking.
6.0 ACTIVE/EXISTING ISL/ISR FACILITIES: MONITORING ISSUES
Many of the standards in 40 CFR 192.32 refer to RCRA 40 CFR 264 Part, Subpart F, which
describe EPA's regulatory approach for releases to groundwater 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 groundwater baseline:
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(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.
Issue: In practice, the procedures for establishing the groundwater baseline are site-
specific and are included in the facility license issued by the NRC or Agreement
State.
6.1 Groundwater Baseline: Case Studies
There is some variation among states in the requirements for baseline monitoring. An example
of the development of the groundwater baseline for the proposed Dewey-Burdock ISL/ISR
operation in South Dakota is included in Attachment A. In Texas, 26 chemical constituents are
measured before mining to establish a baseline, as shown in Table 6-1. This is example data
from Production Authorization Area (PAA) No. 1 at the Zamzow ISL/ISR facility. Baseline
values shown in the table represent the highest average concentration from either the production
or mine area, which are commonly selected as initial restoration goals (Hall 2009).
In its license application for the Moore Ranch Uranium Project in Campbell County, Wyoming,
Energy Metals Corporation proposed that the wellfield baseline would be established by
sampling production zone wells 4 times, with a minimum of 2 weeks between samplings (NRC
2010, Section 6.3.1.1) Energy Metals also proposed that the number of wells sampled would be
1 well for each 3 acres of mine unit. Data for each sampled parameter are to be averaged and
used to calculate restoration goals. The average and range of baseline values in the production
zone are then used to assess the effectiveness of subsequent groundwater restoration.
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Table 6-1. 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/1
122
317
552
195
269
390
2
Magnesium
mg/1
15
38.4
84.2
3.0
21.1
40
3
Sodium
mg/1
239
387
750
235
383
466
4
Potassium
mg/1
19
30.3
49
18.9
26.7
90
5
Carbonate
mg/1
0
0
0
0
0
0
6
Bicarbonate
mg/1
128
297
400
157
269
346
7
Sulfate
mg/1
454
793
1,520
441
601
940
8
Chloride
mg/1
350
503
936
394
538
662
9
Fluoride
mg/1
0.16
0.54
1.19
0.01
0.36
0.50
10
Nitrate - N
mg/1
<0.01
0.16
0.9
<0.01
0.14
0.49
11
Silica
mg/1
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/1
1,697
2,289
3,220
1,810
2,037
2,360
14
Conductivity
|imhos
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/1
<0.001
0.009
0.03
<0.001
0.006
0.044
17
Cadmium
mg/1
<0.0001
0.001
0.007
<0.0004
0.001
0.0013
18
Iron
mg/1
0.01
0.915
8.0
0.03
0.075
0.26
19
Lead
mg/1
<0.001
0.001
0.006
<0.001
0.004
0.02
20
Manganese
mg/1
0.009
0.224
0.82
0.01
0.118
0.19
21
Mercury
mg/1
<0.0001
0.0004
0.0018
0.0001
0.0006
0.001
22
Selenium
mg/1
<0.001
0.01
0.01
<0.001
0.004
0.01
23
Ammonia
mg/1
<0.01
0.374
1.4
<0.01
0.298
0.78
24
Uranium
mg/1
<0.001
0.171
1.7
<0.001
0.039
0.432
25
Molybdenum
mg/1
<0.001
0.03
0.95
<0.001
0.226
2.1
26
Radium-226
pCi/1
1.5
155
959
6.5
152
744
** - Monitor wells
Source: Hall 2009
In another example, Mine Unit 4 of the Christensen Ranch Project located in Wyoming, the
wellfield covered about 12 acres and, consequently, 12 injection or production wells were used
to establish baseline groundwater conditions within the ore zone, which in turn set the restoration
goals (Cogema 1994).
Commercial-scale uranium ISL/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 uranium mineralization distribution are collected
during wellfield construction.
6.2 Wellfield Restoration
Wellfield restoration is defined as those actions taken to assure that the quality of the
groundwater adjacent to the ISL/ISR wellfields will not be adversely affected by the uranium
extraction process (NRC 2001). This requires returning the wellfield water quality parameters to
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meet the restoration goals included in the facility license by NRC or the Agreement State. Based
on pre-mining monitoring, the operator establishes baseline values for the groundwater quality.
The regulator then uses these baseline values to set restoration goals in the wellfield license.
It should be noted that the portion of the aquifer undergoing uranium extraction is exempt from
EPA regulatory protection under the Safe Drinking Water Act (specifically the UIC Program at
40 CFR Part 144). However, groundwater adjacent to the exempted portion of the aquifer must
still be protected, and groundwater protection provisions for this water are in effect. Similar to
"3
the NRC Agreement State provisions, the EPA Primacy State may impose more stringent
requirements for groundwater restoration than the federal program (NRC 2003). Groundwater
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.
6.3 Wellfield Restoration: Case Study
Restoration results from 22 PAAs in Texas are summarized in Table 6-2 (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.
3 Texas, Colorado, and Utah operate as Agreement States under NRC regulations in establishing state-
specific ISL regulations, while Wyoming, New Mexico, and South Dakota are directly regulated by NRC. Nebraska
is also an Agreement State, but since it does not have specific ISL regulations, these facilities are regulated by the
NRC.
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Table 6-2. Groundwater Chemistry of Texas In-Situ Uranium Production
Authorization Areas (PAAs)
Analyte
EPA and TCEQ
Drinking Water
Standards
(mg/1)
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 &
228 Ra: pCi/1)
5 pCi/1
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
(|jmhos/cm)
-
1310-11160
1429-3697
76%
24%
Alkalinity (as
CaC03)
-
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 date was available for all 22 PAAs with the exception of Ra, Mo, K, Si, Bicarbonate, Ammonia (21), Conductivity
(14), Alkalinity (11), & Carbonate (10)
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7.0 ISSUES ASSOCIATED WITH ESTABLISHMENT OF POST-RESTORATION
STEADY STATE
During restoration, the operator monitors progress by periodic sampling of the groundwater
constituents until steady-state conditions are attained. Establishment of steady state requires that
the groundwater potentiometric surface be restored, to the extent practicable, to its pre-leaching
status, so that the flow regime is similar to that existing before mining. In addition, constituents
in the groundwater 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 defines a "steady state," which is characterized by the following relevant
components (EPA 1992, Chapter 7):
(1) 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.
(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 provides the general behavior and characteristics of the
groundwater at the site. The second is more judgmental and projects future contamination, based
on available current information. These projections cannot be made with certainty; however,
there are various criteria that can be used in determining whether a steady state has been reached.
Statistical tests for measuring attainment of steady state are discussed in Sections 8.3 and 8.4.
When the regulator is satisfied that steady state has been achieved, the operator is authorized to
undertake long-term post-restoration stability monitoring.
7.1 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. If the sandstone in the
aquifer is heterogeneous, extended restoration times may be required to insure that groundwater
in slow pathways is addressed.
7.1.1 ISL/ISR Extraction Phase
During the ISL/ISR mining phase (Phase 2, Figure 3-1, wells are placed in the active ISL/ISR-
treatment zone, fringe zone (wells at the ISL/ISR-mine boundary), and outside the impacted
areas (see Figure 2-4). Parameters that need to be measured are site-specific. Basic
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measurements include Eh, pH, major ions, TDS, carbonate species, radioactive constituents,
colloids, organic constituents, and trace elements compared with pre-ISL/ISR measurements.
The measurement frequency for each monitoring well is dependent on ISL/ISR injection-
extraction cycle and groundwater flow and transport times moving across ISL/ISR field. This
report is not concerned with the detection and correction of excursions during the leaching
operations. However, monitoring wells used to detect excursions during operation may also be
used to collect data for post-mining groundwater evaluation.
7.2 Factors that Affect Post-Mining Monitoring Timeframes
A number of factors must be understood to determine when the impacted aquifer has reached a
steady-state condition. This section summarizes these factors.
7.2.1 Fate and Transport Process
The monitored timeframe is dependent on mass balance estimates of how much extraction fluid
remains in the aquifer. A mass balance of the total volume of lixiviant injected into the system
and the volume withdrawn needs to be determined by the monitoring during operations. 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, if some have migrated outside the monitored area, been
temporarily sequestered in low permeability zones, or are undergoing incomplete or slow
reaction kinetics that may release constituents later on.
7.2.1.1 Speciation
The environmental chemistry of uranium is largely dictated by its oxidation state (e.g.,
Fanghanel 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.
In general, the solubility and therefore the mobility of uranium is 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 carbonato
complexes of the uranyl (UO22 ) cation, although elevated concentrations of potential inorganic
ligands near the ISL/ISR target zone may exert greater influence on U(VI) speciation (e.g.,
phosphate).
Calcium (or other alkaline earth metals, such as magnesium) and inorganic carbon in
groundwater tend to dominate the aqueous speciation of U(VI) under near neutral pH conditions.
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The presence of these species is common in many natural groundwater 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 due to the very low solubility of U(IV) minerals,
reach maximum concentrations on the order of 10 nM (2.4 |ig U/L). For all practical purposes,
therefore, only U(VI) aqueous species are at sufficient concentrations to be of environmental
concern. Under oxidizing conditions and neutral pHs, U(VI) species dominate aqueous uranium
concentrations.
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 ISL/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.
7.2.1.2 Speciation: Case Study
Illustrative of speciation problems is experience with iron at the Crow Butte ISL/ISR facility.
Crow Butte Resources (CBR) experienced difficulty in restoring 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 stated in 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 oxyhydr oxides. 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
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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.
7.2.1.3 Solubility
In 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, which is bright yellow in color,
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, the edges of clay minerals, and to organic
matter, rather than precipitated as discrete U 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 ISL/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 phases 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 groundwater 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 upon analysis of aqueous
chemical data and solubility predictions from thermodynamic chemical data. In the absence of
confirmatory solid phase characterization data, equilibrium model projections only indicate the
possible formation of specific uranium-bearing precipitates.
7.2.2 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
groundwater. Attenuation processes important at ISL/ISR sites include pH buffering and acid
neutralization, adsorption at the mineral-water interface, mineral precipitation, and dilution/
dispersion.
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7.2.2.1 Adsorption
Adsorption of uranium typically involves inner-sphere complexation of uranyl (i.e., those
containing UO22 ) 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 groundwater systems
(Sowder et al. 2003).
A compilation of published Kd values for U(VI) sorption onto soils/sediments is documented in
EPA 1999. However, as recognized by the authors of that compilation, there are significant
limitations to the application of published KdS for site-specific applications where either the
groundwater chemistry or the aquifer matrix differs significantly from the conditions under
which a Kd was determined (Ochs et al. 2006). Davis et al. (2004) document an alternative
approach, whereby a site-specific Kd value is modeled through the use of a non-electrostatic
surface complexation model (NEM) developed as a function of site geochemistry for aquifer
sediments. This 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 groundwater chemistry on uranium
sorption. The chemistry of groundwater 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
groundwater 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.
There is field evidence 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 downgradient from a uranium ore deposit. The U(VI)
mineral was identified as metatorbernite, which was present in groundwater that was under-
saturated 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, phosphorous, 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
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characterization strategies, and developing both conceptual and analytical models for interpreting
and projecting uranium mobility in groundwater.
7.2.2.2 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 groundwater.
Secondary precipitates can remove contaminants from impacted waters through adsorption
and/or coprecipitation reactions. 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 there are no water molecules
positioned between the cation or anion and the surface, the adsorption complex is referred to as
inner sphere and is considered to be strong. 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., FegC^OH^SC^Xs)], sulfates [e.g., PbSO^s)], and sulfides [e.g., ZnS(s)].
For each of these minerals, there will be a limited compositional range of groundwater 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 groundwater with respect to possible precipitate phases using a saturation-state modeling
approach. In order to evaluate whether a groundwater 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:
PbS04(s) = Pb2+ + S042"
aPl>S04(s)
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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:
G - a a , =K =10"7-*
Rb"' SO|~ r
where the activity (a) of PbS04(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:
H
S/ = los= 0
*Kr
where SI = 0 at equilibrium. If a 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 stability of a precipitate will be dictated by the groundwater
chemistry. Contaminant remobilization will occur as a result of dissolution of the precipitate
phase, for example, when log QIKr < 0. Precipitate dissolution may occur due to groundwater
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. Data, however, are often lacking on the kinetics of bio-
geochemical 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 ISL/ISR facilities, the potential impacts
from these types of limitations are illustrated by several concerns raised by a reviewer of the
geochemical modeling of an ISL/ISR facility and presented in NUREG-6820 (NRC 2007). The
reviewer noted that since the applied model is a non-kinetic model, any bacterial influences from
naturally occurring Desulfovibria and Thiobacillus are eliminated from consideration. The
comment further noted that these influences may be as (or more) important to long-term stability
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than the addition of reductant during restoration. In addition, the role of pyrite during both
restoration and stabilization was also a concern, and the reviewer noted that a kinetic approach
might result in simulations that more closely compared with observed conditions.
7.2.2.3 Role of Biological Processes
Microbial processes can play a role in both mobilizing and attenuating inorganic contaminants at
ISL/ISR sites. For example, Macur et al. (2001) showed that microbial reduction of arsenate
[As(V)] to arsenite [As(III)] occurred over relatively short time scales and resulted in enhanced
arsenic mobilization in mine tailings pore water. In addition, iron-reducing bacteria may cause
contaminant dissociation from aquifer solids as a consequence of iron oxide dissolution. Metals
and metalloid species associated with secondary iron-bearing precipitates may be released via the
activity of bacteria under certain conditions (Herbel and Fendorf 2006).
Sulfate-reducing bacteria (SRB), however, 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 ISL/ISR sites and also occurs in the
natural environment (Church et al. 2007). The overall sulfate-reduction process can be described
by the reaction:
2CH20 + S042" + 2H+ = H2S + C02 + H20
where CH2O represents organic matter, either in the solid or aqueous phase. The resulting
dissolved hydrogen sulfide can precipitate with divalent metals in, for example (M = Cd, Cu, Fe,
Ni, Pb, or Zn):
H2S + M2+(aq) = MS(s) + 2H+
The mass concentration of reactants involved in sulfate reduction is usually much larger than the
mass concentration of metals involved in secondary precipitation reactions; hence, these
combined reactions can lead to an increase in alkalinity and the pH of the water, while
simultaneously attenuating divalent metals. Alkalinity produced during the sulfate reduction
process can also drive the precipitation of carbonate minerals, such as calcite and siderite
(Paktunc and Dave 2002), and can help neutralize acidity in the groundwater.
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 is achieved by adding 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).
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7.2.2.4 Case Study
Table 7-1 presents projections of monitoring periods for a few facilities seeking licenses. The
monitoring periods were taken from their respective Environmental Impact Statements. It is
evident that restoration may take a period of a few years, while post-restoration usually is
estimated for 1 year.
Table7-1. 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 &
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
7.3 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 are 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 downgradient from an ISL/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
contain i nant-contai ni ng minerals, and geochemical stability of the imposed barrier environment.
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
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maintained over the longer term. For example, as a field test of groundwater stabilization during
aquifer restoration, hydrogen sulfide gas was injected as a reductant into the Ruth ISL/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 ppm to 91 ppm, 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 could be influenced by a number of 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 ISL/ISR facility).
7.4 Monitored Natural Attenuation
Monitored natural attenuation (MNA) refers to the reliance on natural attenuation processes to
achieve site-specific remediation objectives within a reasonable timeframe. 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 groundwater.
These in-situ processes include biodegradation, dispersion, dilution, sorption, and volatilization;
radioactive decay; and chemical or biological stabilization, transformation, or destruction of
contaminants (EPA 1999).
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 OSWER Directive 9200.4-17P (EPA 1999) and by the American Society
for Testing and Materials (ASTM 1998a) have endorsed the use of site-specific attenuation rate
constants for evaluating natural attenuation processes in groundwater. The EPA directive on the
use of Monitored Natural Attenuation (MNA) at Superfund, RCRA, and UST sites (EPA 1999)
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.
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Site characterization (and monitoring) data are typically used for estimating attenuation rates.
The ASTM Standard Guide for Remediation of Groundwater by Natural Attenuation at
Petroleum Release Sites (ASTM 1998a) 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. 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 concentra-
tion, 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.
7.4.1 Tiered Approach to Assessing Suitability of MNA
EPA's Office of Research and Development has prepared a technical resource document for the
application of MNA to inorganic contaminants in groundwater (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 groundwater 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 po-
tential 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
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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 groundwater.
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).
This tiered decision-making approach is judged by EPA 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
greater 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.
7.4.2 First-Order Attenuation Rate Determination
First-order attenuation rate constant calculations are an important consideration for evaluating
natural attenuation processes at groundwater contamination sites. Specific applications
identified in EPA guidelines (EPA 1999) include use in characterization of plume trends
(shrinking, expanding, or showing relatively little change), as well as estimation of the time
required for achieving 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 vs. Time Rate Constants are used for estimating how quickly remediation goals
will be met at a site; and, in units of inverse time (e.g., per day), are derived as the slope of the
natural log concentration vs. time curve measured at a selected monitoring location.
Concentration vs. Distance Bulk Attenuation Rate Constants are used for estimating whether a
plume is expanding, showing relatively little change, or shrinking due to 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 vs.
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 groundwater seepage velocity contaminant
transport vs. transport of a tracer, or more commonly, calibration of solute transport model to
field data.
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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, it is important to collect
long-term monitoring data from wells that are distributed throughout the plume. Concentration
vs. Time Rate Constants describes the behavior of the plume at one point in space; while
Concentration vs. Distance Rate Constants describes 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 vs. Time Rate Constants: A rate constant derived from a concentration
vs. 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 groundwater 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 groundwater, and plumes tend to shrink back towards the source over time. In this
circumstance, the lifecycle 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 groundwater. This pattern is most common when contaminants are readily
soluble in groundwater and when contaminants are not biodegraded in groundwater. 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 ISL/ISR
sites, following the remediation of the source.
Concentration vs. Distance Rate Constants: Attenuation rate constants derived from
concentration vs. 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) for this purpose {i.e.,
to characterize contaminant mass over time) will provide erroneous results. The C vs. D-
based rate constant 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. Note that 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.
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7.5 Fate and Transport Modeling to Support ISL/ISR Compliance Activities
7.5.1 Modeling Objectives
Groundwater fate and transport modeling is often utilized to reduce the uncertainty regarding the
spatial and temporal behavior of the contaminant plume(s). For example, groundwater modeling
is commonly implemented at ISL/ISR facilities to assist in meeting the following objectives:
Optimize the monitoring well spacing to detect injection fluid excursions into non-mined
aquifer zone(s)
Estimate the number of pore volumes needed during site remediation activities to
adequately reduce contaminant concentrations
Establish a specific period of monitoring for ISL/ISR facilities once uranium extraction
operations are completed
7.5.2 Development of the Conceptual Model
Because computer codes are generic in nature and must be adapted to actual field conditions in
order to be useful, a clear understanding of the existing physical system (a conceptual model) is
required. The hydrogeologist develops a conceptual model of the hydrogeologic environment
based on field experience and available literature. A conceptual model generally summarizes the
theoretical understanding of the primary conditions that affect groundwater flow and chemical
transport and fate.
As contaminant plumes move downgradient from the mined area, they tend to spread laterally
and vertically, thereby lowering the average contaminant concentration as the plume expands.
The shape taken by an individual plume varies, primarily depending on the nature of the geologic
materials making up the aquifer, but also on the rate of groundwater flow. In fine-grained
unconsolidated sediments, such as sands and silts, plumes tend to spread out laterally in a fan
shape as they move downgradient. This process is called dispersion. Vertical flow also occurs
and is controlled by the uniformity of the sediments, as well as the vertical hydraulic gradient.
When all the aquifer materials are of essentially the same size and are well-rounded, vertical
flow can easily take place assuming a vertical hydraulic gradient exists. Fine-grained layers of
sediments such as clays and silts in an otherwise coarse-grained aquifer prevent or retard
downward (or upward) vertical flow. Groundwater flowing at a moderate to fast rate tends to
minimize both horizontal and vertical dispersion, while slower flow (normally in fine-grained
materials) allows greater dispersion. All of these processes, however, will be complicated by the
effects caused by the injection and withdrawal of water during the active and remedial phases of
the ISL/ISR mining.
Contaminant plumes extend downgradient from the mined area over time until a steady-state
condition is reached, based on the concentration of contaminants in the groundwater and the
degree of chemical attenuation taking place within the aquifer. Contaminant concentrations
decline as downgradient flow occurs, because processes such as dispersion, adsorption, and
chemical transformation are constantly taking place in the aquifer. The length of a plume will
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depend on (1) how rapidly these processes work, (2) the rate of groundwater flow, (3) the rate of
chemical releases to the aquifer, and (4) other environmental factors, such as temperature and the
basic chemistry of the groundwater. Ultimately, even with a constant source of contamination to
the aquifer, any plume will reach a point beyond which it can no longer expand and will more or
less stabilize. This stabilization, or steady-state condition, occurs when attenuation processes in
the aquifer remove as much contaminant mass as is being released to the aquifer in the source
area.
If the source of the contamination is cut off (for example, by pump and treat extraction wells), a
reduction in chemical concentrations will occur downgradient of the mined area and will be
especially noticeable along the axis of the plume. Over time, the reduction in plume
concentrations will be propagated farther downgradient consistent with the hydraulic
conductivity of the aquifer. Subsequently, the plume will begin to contract in extent.
7.5.3 Basic Aspects of Fate and Transport Modeling
The objectives related to assessing the potential impacts of ISL/ISR mining activities are
frequently satisfied by completing the following steps during the modeling process:
1. Adopting a conceptual model to guide creation of model elements
2. Choosing an appropriate computer code for the analysis
3. Establishing the time period represented by the model and the duration of subdivisions of
this period (time steps) required for modeling
4. Selecting a suitable model domain and determining the dimensional (horizontal and
vertical) limits of the analysis
5. Establishing the model structure by determining the number of model layers and the grid
spacing requirements for the flow analysis
6. Incorporating hydraulic boundaries and features, including the shape and characteristics
of constant-head boundaries, rivers, precipitation/recharge, and pumping/injection
7. Assigning hydraulic parameters consisting of hydraulic conductivity, porosity, and
specific storage (for transient analyses)
8. Specifying initial head values (groundwater surface elevation)
9. Selecting hydraulic calibration targets {i.e., water levels)
10. Evaluating and assigning appropriate model computational characteristics (for example,
solution method, iteration limits, and convergence criteria) to enhance model stability,
computational efficiency, and solution accuracy
11. Running the model and adjusting assigned model parameters within predetermined limits
to achieve the closest fit between model results (hydraulic heads) and calibration targets
12. Evaluating the sensitivity of model results to changes in model parameters
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13. Placing particles within the model to determine groundwater flow directions and capture
zone characteristics
14. Establishing the model structure, including determining the number of model layers and
the grid spacing requirements for the transport analysis
15. Assigning the characteristics of chemical sources (e.g., leaks, spills) consisting of
dimensions, locations, concentrations, and time dependency
16. Assigning transport parameters, including the distribution coefficient (which defines
contaminant adsorption to soil/rock and affects transport by retarding the rate of
contaminant movement) and the decay coefficient (which relates to the rate of chemical
decay or 'half-life' in the groundwater system)
17. Selecting chemical calibration targets
18. Running the model and adjusting assigned model parameters within predetermined limits
to achieve the closest fit between model results and calibration targets
19. Conducting chemical transport scenarios
Completion of these steps is necessary to create a model representing anticipated field conditions
as accurately as possible within the constraints of practicality and data availability.
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8.0 DETAILS ON STATISTICAL ANALYSES TO COMPARE PRE- AND POST
ISL/ISR CONDITIONS
Although statistical analyses are used in all phases of the ISL/ISR process shown in Figure 3-1,
three phases use procedures based on statistical hypothesis tests:
Phase 1 - Measure baseline groundwater concentrations and establish restoration goals
based on statistical procedures that embrace temporal and spatial variability.
Phase 4 - Establish wellfield steady state. At the end of this phase, the groundwater
potentiometric surface will have returned to baseline conditions, and statistical tests for
significant trends are used to verify stability.
Phase 5 - Conduct long-term stability monitoring. At the end of this phase, statistical
tests for trends are again used to show that concentration of the monitored parameter is
not increasing (or, in some cases, decreasing) with time. Other statistical tests for
comparing post-restoration data with baseline conditions are used to determine when pre-
ISL/ISR conditions are achieved. The trend test and comparison with baseline conditions
first are conducted well-by-well. If the wells exhibit homogeneous dynamics, the well-
by-well statistics may be combined for a wellfield analysis,
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 a possible contaminant in a specific well at
a given time, although other water quality parameters may also be analyzed using the methods in
this section.
Both linear regression and the nonparametric Mann-Kendall trend test are recommended as
viable alternatives in EPA 2006 and EPA 2009. Linear regression relies on a variety of
assumptions, for example, normality, which need to be tested. The Mann-Kendall trend test has
been applied in groundwater monitoring at RCRA sites.4 The Mann-Kendall trend test may be
used with any series of four or more independent samples to test for trends in well parameters.
The test is employed in Phase 1 to check for unexpected trends in baseline samples, in Phase 4 to
determine when steady state is reached, and particularly in Phase 5 to establish long-term
stability. The Wilcoxon Rank Sum test (WRS) (also known as the Mann-Whitney or Wilcoxon-
Mann-Whitney test) is applied in Phase 5 to compare post-restoration well parameters with
baseline parameters, assuming that both datasets are stationary. (EPA 2006)
It is necessary that sufficient data be collected to support a statistical comparison of baseline and
post-restoration conditions. Under ideal conditions, the dataset would include a complete time
series of 12 measurements per year systematically collected at each well at equally-spaced times
using the same measurement device with a very low limit of detection compared to the level of
the parameter under pre- and post-restoration conditions. In reality, such datasets exist only in
textbook examples. Given an ideal dataset spanning 50 to 100 time periods, a multivariate time
series analysis of the type described by Anderson (1994) and Box and Jenkins (2008) would be
4 See, for example, HydroGeoLogic, Inc. (2005). OU-1 Annual Groundwater Monitoring Report - Former
Fort Ord, California, Appendix D: Mann Kendall Analysis.
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appropriate. The multivariate nature of the problem extends over both wells and parameters,
with many possible temporal and cross-correlations that would require attention in this type of
analysis.
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 [per 40 CFR
264.97(h)] 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.
8.1 Hypothesis Testing and Data Quality Objectives
Hypothesis testing is a statistical tool for deciding when the groundwater has reached steady
state, and for the comparison of post-restoration conditions with baseline conditions. The
hypothesis tests are conducted for individual wells and, when wells exhibit homogenous
dynamics, for all wells combined.
The first step in developing a hypothesis test is to transform the problem into statistical
terminology by formulating a null hypothesis and an alternative hypothesis. These hypotheses
form the two alternative decisions that the hypothesis test will evaluate. When a well is
compared with the baseline, the unknown parameter of interest (S) is the amount by which the
post-restoration distribution exceeds the baseline distribution. Delta (S) is an unknown value,
and statistical tests may be used to evaluate hypotheses relating to its possible values. A
hypothesis test is designed to reject or not reject hypotheses about S based on test statistics
computed from the sample data.
At its core, this is another example of the "How clean is clean?" problem. The action level for
baseline comparisons is the largest difference in the two distributions that is acceptable to the
decision maker. In this report, the action level for this difference is defined as a substantial
difference (A), which may be zero or a positive value based on the risk assessment, an applicable
regulation, a screening level, or guidance.
This document does not establish a specific value for a substantial difference A, since the value
will vary from parameter to parameter and from site to site. Therefore, specific values for A
should be considered on a case-by-case basis. In many cases, the minimum feasible value of A is
determined by the normal variability in that parameter during pre-ISL/ISR phase. The selection
of a value for A is discussed further in Appendix A of EPA 2002a. The determination of A for
each parameter of interest should be considered during the development of a Quality Assurance
Project Plan as part of the planning process for the site evaluation.
Hypothesis testing is a quantitative method to determine whether a specific statement concerning
the unknown difference 8 (a statement known as the null hypothesis) can be rejected based on the
data at hand. Decisions concerning the true value of 8 (e. g., is 8 > 0?) reduce to a choice
between "Yes" or "No." When viewed in this way, two types of incorrect decisions, or decision
errors, may occur:
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Incorrectly deciding the answer is "Yes" when the true answer is "No"
Incorrectly deciding the answer is "No" when the true answer is "Yes"
While the possibility of decision errors can never be totally eliminated, it can be controlled to
acceptable levels. To control decision errors, it is necessary to control the uncertainty in the
estimate of 8. Uncertainty arises from three sources:
Sampling error
Measurement error
Natural variability
The decision maker has some control over the first two sources of uncertainty. For example, a
larger number of samples may lead to fewer decision errors because the probability of a decision
error decreases as the number of samples increases. Use of more precise measurement
techniques or duplicate measurements can reduce measurement error, thus minimizing the
likelihood of a decision error. The third source of uncertainty is more difficult to control.
Natural variability arises from the uneven distribution of chemical concentrations and conditions
at the site.
Natural variability is measured by the true standard deviation (er) of the distribution. A large
value of g indicates that a large number of measurements will be needed to achieve a desired
limit on decision errors. Since post-restoration variability is usually higher than in the baseline,
post-restoration data collected on the site ideally would be used to estimate o.
It is advisable to overestimate o rather than underestimate the true variability. A very crude
approximation for o may be made by dividing the anticipated range (maximum-minimum) by 6
(EPA 2002a, Section 3.1). It is important that overly optimistic estimates for o be avoided,
because this may result in a sample size that fails to generate data with sufficient power for the
decision.
The minimum detectable difference (MDD) for a statistical test indicates that differences smaller
than the MDD cannot be detected reliably. If the test is used to decide if post-restoration
concentrations exceed the baseline concentrations by more than A, it is necessary to ensure that
MDD for the test is less than A. In the planning stage, this requirement is met by designing a
sampling plan with sufficient power to detect differences as small as A (MDD < A). If data were
collected without the benefit of a sampling plan, retrospective calculation of the power of the test
may be necessary before making a decision.
In the planning stage, the absolute size of the MDD is of less importance than the ratio of the
MDD to the natural variability of the post-restoration concentrations. This ratio is termed the
relative difference defined as MDD/o, where o is an estimate of the standard deviation of the
post-restoration distribution. The relative difference expresses the power of resolution of the
statistical test (MDD) in units of uncertainty (o). Relative differences much less than one
standard deviation (MDD/o « 1) are more difficult to resolve unless a larger number of
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measurements are available. Relative differences of more than three standard deviations
(MDD/o > 3) are easier to resolve.
8.2 Decision Errors and Confidence Levels
A key step in developing a sampling and analysis plan is to establish the level of precision
required of the data used for decision-making. These requirements will determine the required
sample size. An increased number of samples generally increases the level of precision. Due to
the uncertainties that result from sampling variation, decisions will be subject to errors. There
are two ways to err when analyzing data (Table 8-1):
Type I Error: Based on the observed data, the test may reject the null hypothesis when,
in fact, the null hypothesis is true (a false positive). This is a Type I error. The
probability of making a Type I error is a (alpha).
Type IIError: On the other hand, the test may fail to reject the null hypothesis when the
null hypothesis is, in fact, false (a false negative). This is a Type II error. The
probability of making a Type II error is P (beta).
The acceptable level of decision error associated with hypothesis testing is defined by two key
parameters; confidence level and power (see Box 8-2). These parameters are closely related to
the two error probabilities, a and p.
Confidence level: 100(l-a)%. As the confidence level is lowered (or
alternatively, as a is increased), the likelihood of committing a Type I error
increases.
Power: 100(1-^)%. As the power is lowered {i.e., as P is increased), the
likelihood of committing a Type II error increases.
The selection of appropriate levels for decision errors and the resulting number of samples is a
critical component of the data quality objectives (DQO) process that should concern all
stakeholders.
Because there is an inherent tradeoff between the probability of committing a Type I or Type II
error, a simultaneous reduction in both types can only occur by increasing the number of
samples. If the probability of committing a false positive is reduced by increasing the level of
confidence of the test (in other words, by decreasing a), the probability of committing a false
negative is increased, because the power of the test is reduced (increasing P).
When the site is sampled for a number of species, the selection of appropriate data quality
objectives for each contaminant will be influenced by the relative health risks and costs of
control for each species. If a single contaminant is the major focus of concern, the data quality
objectives (a and P) may be based on this species. If more than one species is a matter of
concern, then the Bonferroni correction5 is a simple approach for addressing the problem. If the
5 Bonferroni correction is a statistical method used to address the problem of multiple comparisons. It
helps control the probability of Type I errors (i.e., false positives).
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species are of equal concern, the nominal significance level for each test (a) is divided by the
number of contaminants that are to be tested. Similarly, adjustments may be made when the
species have different levels of concern using a different a for each species. However, the
sample size calculations (described below) show that this reduction in the significance level
requires a significant increase in the amount of data to be collected. The issue of multiple
comparisons is beyond the intended scope of this document. A complete discussion of
Bonferroni confidence intervals and newer alternative approaches to the multiple-comparison
problem is presented in Bickel and Doksum (2006).
A Type 1 error occurs if the null hypothesis is rejected when it is true. Since the null hypothesis
states that the post-restoration values exceed the baseline by more than A, a Type I error means
that the site was incorrectly determined to be in compliance with restoration goals. A point to be
made is that the null hypothesis depends on what the "working assumption" is for each
monitoring phase, and, perhaps more generally, what has already occurred. For post-restoration,
one "assumes" that values exceed the baseline by delta, and compliance can only be shown by
rejection of the null hypothesis, i.e. "proving" the alternative. For this phase, a regulator would
be primarily concerned with occurrence of Type I Errors (showing compliance when not
justified). However, for showing that the site is stable, the null hypothesis would be a statement
of no trend. For that phase of the process, one would be concerned with Type 2 errors, and the
discussion of MDDs and minimum sample sizes would be relevant6.
A Type 2 error occurs if the null hypothesis is accepted when it is not true. A Type 2 error
means that the site was incorrectly determined to require further restoration. From a human
health perspective, a Type 1 error is more serious than Type 2 error. Hence, it is reasonable that
the Type 1 error rate (a) should be smaller than the Type 2 error rate (P). In almost all scientific
studies, a is selected to be either 0.05 or 0.10, limiting the chance of a Type 1 decision error to
5% or 10%, respectively. Once a is selected, a higher value of P will reduce the required number
of samples, but there will a greater likelihood that the site is incorrectly determined to be out of
compliance. In this case, the site operator faces a trade-off and may select to reduce the value of
P (at the expense of a greater number of samples) and increase the power of the test in order to
avoid the possibility of a Type 2 error.
For the purposes of this report, minimum recommended performance measures are:
Confidence level at least 90% (a < 0.10) and power at least 80% (fi < 0.20).
[EPA 2002a, Section 3.2]
6 Documents such as EPA (2009) "Statistical Analysis of Groundwater Monitoring Data at RCRA
Facilities" describe processes involving several phases, and the null hypothesis depends on the phase of the process
and/or what may have occurred previously. For some stages, the null hypothesis would be a statement that "all is
well", e.g. there is no trend for a particular contaminant at a particular well monitoring location. For other stages,
the null hypothesis is just the opposite, e.g. the site is out of compliance with respect to a particular contaminant.
For the former, rejection of the null hypothesis in effect "proves" that the site is not stable, and regulators would be
primarily concerned with the occurrence of what statisticians refer to as Type 2 Errors (we are unable to detect a
worrisome trend when such trend exists). For the latter, the primary concern would be the occurrence of a Type I
Error (falsely concluding the site is finally in compliance when it isn't).
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These performance standards are described in more detail in Box 8-2.
Table 8-1. Hypothesis Testing: Type I and Type II Errors
Decision Based on
Sample Data
Actual Site Condition
H0 is True
H0 is not True
H0 is not rejected
Correct Decision: (1 -a)
Type II Error: False
Negative ((3)
H0 is rejected
Type I Error: False
Positive (a)
Correct Decision: (1 -(3)
Box 8-1. Definitions
S (delta): The true difference between the post-restoration distribution and the baseline
distribution of parameter X. Delta is an unknown value that describes the true state of nature.
Hypotheses about its value are evaluated using statistical hypothesis tests. In principle, we can
select any specific value for S and then test if this difference is statistically significant or not with
a given confidence and power.
A (a substantial difference): A difference between the two distributions that is sufficiently large
to warrant additional interest based on health or ecological information. A is the investigation
level. If 8 exceeds A, the difference in concentrations is judged to be sufficiently large to be of
concern for the purpose of the analysis. A hypothesis test uses baseline and post-restoration
measurements to determine if 8 exceeds A.
MDD (minimum detectable difference): The smallest difference that the statistical test can
resolve. The MDD depends on sample-to-sample variability, the number of samples, and the
power of the statistical test. The MDD is a property of the survey design.
Box 8-2. Interpretation of the Statistical Measures
Confidence level = 90%: On average, in 90 out of 100 cases, post-restoration concentrations are
correctly identified as exceeding baseline concentrations by more than A, while in 10 out of 100
cases, post-restoration concentrations will be incorrectly identified as not exceeding baseline
concentrations by more than A when, in fact, they do.
Power = 80%: On average, in 80 out of 100 cases, post-restoration concentrations will be
correctly identified as not exceeding baseline concentrations by more than A, while in 20 out of
100 cases, post-restoration concentrations will be incorrectly identified as exceeding baseline
concentrations by more than A when, in fact, they don't.
Adopting hypothesis tests and a DQO approach described in EPAQA/G9S (EPA 2006,
Section 3.4) can help control the probability of making decision errors. However, incorrect use
of hypothesis tests can lead to erratic decisions. Each type of hypothesis test is based on a set of
assumptions that should be verified to confirm proper use of the test. Procedures for verifying
the selection and proper use of parametric tests, such as the t-tests, are provided in EPA 2006
(Chapter 4). The tests recommended in this document for verifying stability and determining
when the site has met the remedial goals are nonparametric tests. Nonparametric tests generally
have fewer assumptions to verify.
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The minimum sample size (N ) required in each phase for these tests may be obtained using the
n
approximate formula:
N*=1.16 [(0.25) z2i.+ 2 (zi_+ zi.p)2 o2/ (MDD)2]
th 8
Here zp is the (100 p) percentile of the standard normal distribution. Power and sample size
calculations tend to be much more difficult for nonparametric procedures than for
parametric procedures. Nonparametric procedures usually have less statistical power than
parametric tests when the data follow a known distribution. The sample size formula above
includes an adjustment factor of 1.16 to account for the possible loss of efficiency when
nonparametric procedures are used. In addition, MARRSIM (EPA 2000, Section 5.5.2.4)
recommends increasing N by 20% to account for possible underestimation of o and to prepare
for unplanned events that result in missing or unusable data. With this added safety margin, the
recommended sample size is n = m = 1.2-N (rounded up to the next integer). Values of m and n
represent the number of baseline samples and the number of post-restoration samples,
respectively.
The number of measurements required to achieve the desired decision error rates has a strong
inverse relationship with MDD/o. Smaller values of a and P (leading to larger values for the z
terms) magnify the strength of this inverse relationship. Hence, a tradeoff exists between cost
(number of samples required) and benefit (better power of resolution of the test). The value of n
is tabulated for a variety of o values in Table 8-2 for hypothetical values of a = P = 0.10 and
MDD = 50 mg/1. Note the dramatic increase in the minimally sufficient sample size as MDD/o
is lowered from 1 to 0.25. This document does not recommend a specific sample size, since each
site will have different variability (a) and DQO parameters (a and P).
Achievable levels of a (and P) for selected sample sizes of m in the baseline and n in the post-
restoration period with m = n and a hypothetical value of MDD/o=l are shown in Table 8-3. A
complete set of sample size estimates for m + n for a = 0.01/0.025/0.05/0.10/0.20 and for P =
0.01/0.025/0.05/0.10/0.20 are tabulated for a range of the MDD/o ratio in Table E-4 in
Attachment E. The sample sizes in Tables 8.2, 8.3 and Attachment E include the added safety
margin of 1.2.
7 See EPA QA/G-9S (EPA 2006, Section 3.3.2.1.1 and Box 3-32, Step 6 of that document) and EPA 2002a
(Chapter 3).
8 The value of zp may be calculated in Excel using the spreadsheet function: zp = NORMSINV(p).
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Table 8-2. Required Sample Size for Selected Values of o
(a = P = 0.10 and MDD = 50 mg/1)
o (mg/1)
MDD/o
N*
n=m*
25
2
4.29
6
50
1
15.72
19
75
0.67
34.77
42
100
0.50
61.44
74
125
0.40
95.73
115
150
0.33
137.6
166
175
0.29
187.2
225
200
0.25
244.3
294
Note: *m is the number of baseline samples and n is the number of post-
restoration samples.
Table 8-3. Achievable Values of a = p for Selected Values of n=m with MDD/o =1
n=m
N*
Z(l-a)
a
II
¦CO
10
8.3
0.933
0.175
11
9.2
0.979
0.164
12
10.0
1.022
0.153
13
10.8
1.064
0.144
14
11.7
1.104
0.135
15
12.5
1.143
0.127
16
13.3
1.180
0.119
17
14.2
1.217
0.112
18
15.0
1.252
0.105
19
15.8
1.286
0.099
20
16.7
1.320
0.093
22
18.3
1.384
0.083
24
20.0
1.446
0.074
26
21.7
1.505
0.066
28
23.3
1.561
0.059
30
25.0
1.616
0.053
35
29.2
1.746
0.040
40
33.3
1.866
0.031
45
37.5
1.980
0.024
52
43.3
2.128
0.017
Referring to Table 8.3, it can be seen that 38 samples (m + n) are required to have a 90%
confidence limit (1-a) and a 90% power of the test (1-P), if MDD/o=l.
The information contained in Table E-4 may be used several ways. If values for a, P, A, MDD,
and g have been determined, then the table may be used to estimate the number of baseline and
post-restoration samples required to achieve the targeted values. Alternatively, the table may be
used to determine the maximum resolution that is obtainable with a fixed number of baseline and
post-restoration samples for the selected DQO parameters a and p.
For example, consider an ISL/ISR site with 10 wells and 8 baseline samples per well, collected 1
per quarter over a period of 2 years. For post-restoration sampling, it is proposed to collect 12
additional samples from the same wells, 1 per quarter over a 3-year period. In this example, a
total of 200 samples are collected, 20 quarterly samples from each of 10 wells. If a = 0.10 and
P = 0.10, then the corresponding column is selected from Table E-4. This column is reproduced
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in Table 8-4. These values are plotted in Figure 8-1. Note that increases in sample size generate
increasingly smaller increments of resolution for the site-wide post-restoration versus baseline
comparison when the sample size is increased beyond 200. However, for individual wells with
20 samples per well, increases in the sample size will generate relatively larger increases in
resolution, due to the steepness of the curve on the far left.
With the proposed site-wide sample size of 200 samples, resolutions (MDD/o) of approximately
0.5o are achievable. With this sample size, differences of a/2 or larger between overall baseline
and post-restoration conditions at the site are resolvable with the desired level of confidence and
power. There are 20 samples, 8 in the baseline plus 12 in the post-restoration period, for each
well. For individual wells, differences of approximately 1.4a or larger between baseline and
post-restoration conditions are resolvable at the desired level of confidence and power. If only 5
wells were sampled in each period, the resolution for each well would remain the same.
However, the site-wide total sample size is now 100. This would reduce the resolution of the
site-wide comparison to approximately 0.7a (a reduction in resolution of approximately 40%).
Table 8-4. Minimum Sample Size for Selected Values of MDD/o with a = 0.10 and P = 0.10
(Table shows values of m + n.)
a = 0.10
MDD/o
¦CO
II
©
O
0.1
3,660
0.2
916
0.3
408
0.4
230
0.5
148
0.6
103
0.7
76
0.8
59
0.9
47
1
38
1.1
32
1.2
27
1.3
23
1.4
1.5
20
-
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1.25
D
q 0.75
0.5
0.25
0
200
400
600
800
1,000
N=n+m
Figure 8-1. Test Resolution (MDD/o) versus Total Sample Size (N)
8.3 Statistical Methods for Trends and Seasonality
The first step in analyzing measurements in one or more wells is to plot the data as a time series.
Examples of such plots are shown in the example discussed in Attachment D. Plots of the data
may reveal patterns such as seasonality and/or the existence of outliers or blunders in the data.
Outliers are values that appear to be unusually high or low when compared to the other values.
Outliers may be valid data or may arise from unusual circumstances unrelated to the process
being measured. Blunders are outright errors made in recording the data, transcription, or
calculations. A common blunder is a mistake in the units of measure. Plotting is used to detect
these situations, but does not provide for an explanation or resolution for the unusual value. If a
value is identified as erroneous, it should be removed from the dataset. In cases of doubt, the
value should be retained. The nonparametric statistical tests discussed in this section were
selected due to their robustness. The statistical term "robust" is loosely defined as resistant to
the effects of outliers and blunders in the data.
8.3.1 Adjusting for Seasonality
Seasonality may occur in baseline samples in Phase 1, while the site is reaching steady state in
Phase 4, and/or in Phase 5, where seasonality may affect decisions concerning long-term stability
and whether target remediation values are attained.
Seasonality is a pattern that repeats periodically in a cycle. An annual seasonal pattern has a
cycle which can span 12 months or 4 quarters. A seasonal index measures how far the average
for a particular period is above (or below) the average for all periods. The unified RCRA
Guidance (EPA 2009) provides the following recommendations concerning seasonality:
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Seasonal fluctuations in intrawell background can be treated in one of two ways.
A seasonal Mann-Kendall trend test built to accommodate such fluctuations can
be employed (Section 14.3.4). Otherwise, the seasonal pattern can be estimated
and removedfrom the background data, leaving a set of seasonally-adjusted data
to be analyzed with either a prediction limit or control chart. In this latter
approach, the same seasonal pattern needs to be extrapolated beyond the current
background to more recent measurements from the compliance well being tested.
These later observations also need to be seasonally-adjusted prior to comparison
against the adjusted background, even if there is not enough compliance data yet
collected to observe the same seasonal cycles.
However, the following caveat is added.
Corrections for seasonality should be used cautiously, as they represent
extrapolation into the future. There should be a good physical explanation for the
seasonal fluctuation as well as good empirical evidence for seasonality before
corrections are made. Higher than average rainfall for two or three Augusts in a
row does not justify the belief that there will never be a drought in August, and
this idea extends directly to groundwater quality. At least three complete cycles
of the seasonal pattern should be observed on a time series plot before attempting
the adjustment below. If seasonality is suspected but the pattern is complicated,
the user should seek the help of a professional statistician.
The seasonal Mann-Kendall test is a variation of the Mann-Kendall test for trends described
below in Section 8.3.2. The test is described in detail in EPA 2009 in Section 14.3.4.
Seasonal adjustment procedures are commonly applied to ecological and economic data to
account for seasonal patterns. The process of deseasonalizing the data removes these periodic
seasonal variations to reveal the underlying longer-term pattern. The P seasonal component (Q,)
is defined as the deviation of the seasonal mean (Y;) from the overall mean (Ym): Qi = Y, -Ym.
The deseasonalized time series (X) is obtained by subtracting the seasonal means from the
original data series: Xt,i = Ytj; - Q; (EPA 2009, Eq. 14.23). The deseasonalized data series has
the short-term seasonal variations removed; longer-term trends remain in the data. Plots of the
seasonally adjusted data series are useful for determining when suspected outliers in sample
values reflect the normal variability of monitored parameters after adjusting for the seasonal
variations.
When there are four quarterly measurements in each year, the data may be seasonally adjusted by
the procedure described in Section D. 1 in Attachment D. Appropriate modifications must be
made for periodic variations based on other timeframes. Some parameters may require seasonal
adjustment and others not. Formal tests for the presence of seasonality across several wells are
based on an Analysis of Variance (ANOVA). This procedure is described in EPA 2002b
(Sections 14.2.2 and 14.3.3).
The seasonal adjustment procedures are applicable to data that are approximately symmetric and
normally distributed. For highly skewed lognormal data series, the calculations above would be
applied to the logarithms of the measurements. This is equivalent to using the ratio of the
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quarterly mean to the overall mean (Q i=Y;/ Ym) as the seasonality index in place of the additive
index above. If this index is 1.2, this means that on average the period (season) is 20% higher
than average. In this case, the seasonally adjusted data series is obtained by dividing the original
data series by the seasonal index: Xy = Yy/Q ;.
We have assumed that there is a complete set of quarterly measurements for 3 years with no
missing or "non-detect" values. If one or two non-detects occur in the data series, one should
replace those values with the limit of detection. If there is at most one missing data value, these
methods may be applied using the averages of the available data to compute the seasonal index.
If more than one value is missing, the appropriateness of adjusting for seasonal variation should
be discussed with a statistician familiar with environmental sampling.
Unless otherwise noted, in the remaining sections of this chapter the term
"data" refers to the seasonally adjusted data series Xtj.
8.3.2 Using Trend Tests to Determine Stability
Trend tests may be used with any series of four or more independent samples to test for trends in
well parameters. The test is employed in Phase 1 to check for unexpected trends in baseline
samples, in Phase 4 to determine when steady state is reached, and particularly in Phase 5 to
affirm long-term stability.
The following text was excerpted from EPA QA/G-9S (EPA 2006):
4.3.2.1 Estimating a Trend Using the Slope of the Regression Line
The classic procedures for assessing linear trends involve regression. Linear
regression is a commonly used procedure in which calculations are performed on
a data set containing pairs of observations (Xu Yj), so as to obtain the slope and
intercept of a line that best fits the data. For temporal data, the X, values
represent time and the Y, values represent the observations. An estimate of the
magnitude of trend can be obtained by performing a regression of the data versus
time and using the slope of the regression line as the measure of the strength of
the trend.
Regression procedures are easy to apply. All statistical software packages and
spreadsheet programs will calculate the slope and intercept of the best fitting line,
as well as the correlation coefficient r (see Section 2.2.4). However, regression
entails several limitations and assumptions. First of all, simple linear regression
(the most commonly used method) is designed to detect linear relationships
between two variables; other types of regression models are generally needed to
detect non-linear relationships such as cyclical or non-monotonic trends.
Regression is very sensitive to outliers and presents difficulties in handling data
below the detection limit, which are commonly encountered in environmental
studies. Hypothesis testing for linear regression also relies on two key
assumptions: normally distributed errors, and constant variance. It may be
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difficult or burdensome to verify these assumptions in practice, so the accuracy of
the slope estimate may be suspect. Moreover, the analyst must ensure that time
plots of the data show no cyclical pattern; outlier tests show no extreme data
values; and data validation reports indicate that nearly all the measurements
were above detection limits. Due to these drawbacks, linear regression is not
recommended as a general tool for estimating and detecting trends, although it
may be useful as an informal and quick screening tool for identifying strong
linear trends. [Emphasis added.]
Due to the drawbacks of using regression to detect a trend, a nonparametric test (Mann-Kendall)
for trends is used in this document to detect a trend. However, the Mann-Kendall test does not
provide an estimate of the magnitude of the trend. Once a trend has been detected using the
Mann-Kendall test, an estimate of the magnitude of the trend may be required. In this regard,
linear regression may be used to estimate the trend, provided that the assumptions required for
linear regression are met.9
The assumptions concerning outliers and non-detects may preclude the use of linear regression
for estimating the magnitude of the trend. If there are outliers and/or non-detects in the dataset, a
nonparametric method (the Theil-Sen trend line estimator10) may be used to estimate the
magnitude of the trend.
8.3.2.1 A Nonparametric Statistical Test for Detecting Trends
The Mann-Kendall test is recommended to detect trends in the data series. The Mann-Kendall
test is a nonparametric statistical test. One need not assume that the data are normally
distributed, and the test accommodates outliers and values below the detection limit. The test is
applied to the data series for each well. Test results for a set of wells may be combined to test
for a common trend across all wells (see Section 8.3.3).
The Mann-Kendall test may be used with any series of four or more independent samples to test
for trends. The test is employed in Phase 1 to check for unexpected trends in baseline samples,
in Phase 4 to determine when steady state is reached, and particularly in Phase 5 to establish
long-term stability.
As noted in EPA 2006:
4.3.4.1 One Observation per Time Period for One Sampling Location
The Mann-Kendall test involves computing a statistic S, which is the difference
between the number of pairwise differences that are positive minus the number
that are negative. IfS is a large positive value, then there is evidence of an
increasing trend in the data. IfS is a large negative value, then there is evidence
of a decreasing trend in the data. The null hypothesis or baseline condition for
9 A complete discussion of linear regression techniques for assessing trends and projecting probable future
levels is found in EPA 1992, Chapter 6.
10 The Theil-Sen trend estimator is described in detail in EPA 2009 in Section 17.3.3. The Mann Kendall
test (discussed in the next section) is also described in EPA 2009 in Section 17.3.2.
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this test is that there is no temporal trend in the data values. The alternative
hypothesis is that of either an upward trend or a downward trend.
The Mann-Kendall test is applied to the post-restoration data to verify stability, where stability is
defined as the lack of a trend. Consider measurements yk(t) of a single parameter in K wells
(k=l,... ,K) at times t spanning two stationary time periods. There are mk samples from well k
(t = 1, ... , mk) in the baseline period and nk samples from well k in the post-restoration period
(t = mk+l, , mk+nk).
The Mann-Kendall statistic Sk is used in testing well k for trend in a single parameter in post-
restoration period (t > mk):
mk+nk t-1
Sk = y y sign\x>:(t) - xk(t - / )]
t=mk+2 z=l
If Sk is sufficiently large, the null hypothesis of no trend in well k is rejected in favor of the
alternative and a trend has been detected. Detailed instructions for performing the Mann-Kendall
test for a single well are shown in Attachment D in Boxes D-l, D-2 and D-3. For additional
information on the Mann-Kendall test that is accessible to non-statisticians, see Gilbert 1987
(Chapter 16).
EPA 1992 suggests the following "rule-of-thumb:"
7.4.2 A Test for Trends Based on Charts
The charts described here provide a simple way of identifying trends. If six
consecutive data points are increasing (or decreasing) - sometimes stated as "5
consecutive intervals of data " so that it is understood that the first point in the
string is to be counted - then there is evidence that the variable being monitored
(e.g., water levels or flows, or contaminant concentrations) has changed (exhibits
a trend).
The Mann-Kendall statistic can also be used to detect short-term trends in the stabilization period
following restoration. Critical values of the Mann-Kendall statistic S are tabulated in Table E-l
in Attachment E for values of n from 4 to 40 for a = 0.01, 0.05 and 0.10.
8.3.3 Testing Multiple Wells for Trends
The Mann-Kendall test is useful for analyzing the trend in data from a single well. If the data
were collected systematically across the site at approximately the same sampling times, the
Mann-Kendall test statistics Sk for all wells may be combined to make an overall summary for
the entire set of wells. In this approach, the statistics Sk are used as a summary measure of the
trend in each well. There must be consistency in the data series across wells to make a
determination of trend that is valid across all wells.
A single statement applicable to trends across all wells is valid if the wells exhibit approximately
steady trends in the same direction (upward or downward), with roughly comparable slopes.
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Formal statistical tests for the comparability of the data series across wells and for a common
trend are described in EPA QA/G9S (EPA 2006) in the text below. Both tests are based on the
chi squared distribution. The two tests are designed to be implemented sequentially, first testing
for comparability of slopes, then for a significant common trend across wells.
The hypothesis tests described in EPA 2006 are:
Comparability of stations. Hq,: Similar dynamics affect all K stations vs. Ha: At
least two stations exhibit different dynamics.
Testing for overall monotonic trend. Ho*: Contaminant levels do not change
over time us. Ha*: There is an increasing (or decreasing) trend consistent across
all stations.
Therefore, the analyst must first test for homogeneity of stations, and then, if
homogeneity is confirmed, test for an overall monotonic trend. Directions for the
test are contained in Box 4-11 and ideally, the stations in Box 4-11 should have
equal sample sizes. However, the numbers of observations at the stations can
differ slightly, because of isolated missing values, but the overall time periods
spanned must be similar. This guidance recommends that for less than 3 time
periods, an equal number of observations (a balanced design) are required. For
4 or more time periods, up to 1 missing value per sampling location may be
tolerated.
Plots of the measurements from all wells using a different symbol for each well are examined to
assess the consistency across wells. Examples of these plots are shown in Attachment D.
Detailed instructions for performing the Mann-Kendall test for multiple wells are shown in
Attachment D in Boxes D-4, D-5 and D-6.
8.3.3.1 Multiple Observations per Time Period for Multiple Wells
If multiple measurements are taken at various times and stations, then the previous approaches
are still applicable. However, the variance of the statistic Sk must be calculated using a different
equation for calculating V(S). Details of this calculation are provided in Sections 4.3.4.2 and
4.3.4.3 of EPA 2006.
8.4 Verify that Contaminants and Hazardous Constituent Concentrations are Below
Required Restoration Levels
The hypothesis testing framework described in Section 8.1 is used to verify that contaminants
and hazardous constituent concentrations are below required restoration levels. A hypothesis test
is used to compare the post-restoration conditions to the baseline. The comparison may be based
on a statistical parameter (e.g., a mean or median) of a probability distribution selected to best
represent the population, or it may be a distribution-free comparison of the two populations.
With small sample sizes, it is difficult to demonstrate conclusively that a particular distribution
represents both populations adequately. Tests that do not assume a known family of probability
distributions (e.g., normal or lognormal) to represent the populations are called distribution-free
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or nonparametric tests. A nonparametric statistical test may be more useful for comparing two
populations than one which assumes a specific distribution, because the nonparametric tests are
less sensitive to deviations from the assumed distribution.
When the exact same sampling wells are used for baseline and post-restoration samples, then the
two sets of samples are paired and not independent. In this case, the statistical test is applied to
the differences in contaminant levels for each well. In the paired approach, contaminant levels in
each well after restoration are compared to contaminant levels from the same well before
treatment. The differences are then compared to a threshold value using a two-sample statistical
test for differences.
The threshold value may be zero, in which case, the comparison is used to determine whether the
post-restoration well values are less than baseline levels, or threshold value may be a positive
number representing the maximum allowable difference between the two populations. This
threshold A is defined as a "substantial difference.' It is anticipated that A will be different for
each parameter.
When the baseline and post-restoration samples are not collected from the same wells, the test
involves a comparison of two independent populations.
8.4.1 Nonparametric Tests for Comparing Baseline and Post-Restoration Conditions
A comparison of post-restoration with baseline samples is conducted in Phase 4 to assess steady-
state conditions, and in Phase 5 to determine if post-restoration values have achieved targeted
remediation levels. In these comparisons, the statistical approach adopted will depend on the
type of data collected. If the baseline and post-restoration samples are from the same wells, then
the paired nature of the data is used in the analysis and the wells are analyzed separately; then
results are combined to conduct an analysis of the entire site. If the baseline and post-restoration
samples are from the different wells, then the baseline and post-restoration data are pooled into
two datasets (before and after) and the comparison method described for a single well is applied
to conduct a site-wide analysis of the pooled data.
The statistical tests are designed to compare post-restoration parameter values with baseline well
parameters, assuming that both datasets were collected under stable conditions. It is likely that
the baseline well data will meet this condition, except for possible seasonal effects. Before
proceeding with the test for comparing baseline samples with post-restoration samples, it is first
necessary to conduct the test for homogeneity of trends and for existence of a monotonic trend as
described in Section 8.3.3 and in Attachment D in Boxes D-4, D-5 and D-6. These prior steps
are applied to the post-restoration data to affirm stability. If the test for homogeneity of trend
across wells is not met, then the individual wells should be tested for trends as described in
Section 8.3.2.1 and in Attachment D in Boxes D-l, D-2 and D-3. In this case, the following
procedures for determining if remediation goals are met are applicable only to the set of wells
with demonstrated stability.
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8.4.1.1 Comparing One Well to the Baseline
The comparison of baseline and post-restoration samples from stable wells is made using the
Wilcoxon Rank Sum (WRS) test (also called the Mann-Whitney or Wilcoxon-Mann-Whitney
test). The advantage of the nonparametric WRS test is that the data need not have a known
distribution. Given the small sample sizes, it would be difficult to determine this distribution
empirically. The WRS test also allows for non-detect measurements to be present in the baseline
and/or post-restoration samples. As a general rule, the WRS test can be used with up to 40%
"less than" measurements in either dataset. Two assumptions underlying this test are:
(1) Samples from the baseline and post-restoration periods are independent,
identically distributed random samples
(2) Each measurement is independent of every other measurement, regardless of the
set of samples from which it came
The null hypothesis is that the post-restoration data exceed the baseline by a substantial
difference. The null hypothesis is formulated for the express purpose of being rejected if the
data provide support for the alternative:
The null hypothesis (H0): The post-restoration distribution exceeds the baseline by more
than A. Symbolically, the null hypothesis is written as Ho: 8 > A.
The alternative hypothesis (HA): The post-restoration distribution does not exceed the
baseline by more than A (Ha: 5 < A).
Here, A is the investigation level. The investigation level is determined on a case-by-case basis.
The hypothesis test is structured so that the post-restoration data must provide evidence that the
site is within acceptable limits. This test assumes that any difference between the baseline and
post-restoration sample value distributions is due to a shift in the distribution of sample values to
higher values in the post-restoration period. The hypotheses to be tested using the WRS test
have the following definition.
Null Hypothesis Hr>: The post-restoration distribution exceeds the baseline
distribution by more than a substantial difference delta (A);
versus the:
Alternative Hypothesis HThe post-restoration distribution is lower than the
baseline distribution or exceeds the baseline distribution by no more than A.
The null hypothesis is assumed to be true unless the statistical test indicates that it should
be rejected in favor of the alternative.
A two-sample statistical is a test for differences between the distributions of two independent
samples. The post-restoration samples from well k are compared with the baseline samples from
the same well to determine if remediation goals have been met. The WRS test is a test based on
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the relative rank of the post-restoration samples versus the baseline samples. The WRS statistic
for well k is defined as:
Wk = Uk + m(m + \)l2
Here, C4 is the Mann-Whitney statistic for well k. 14 is equal to the number of positive
differences in the set of all n\-n\ possible differences between the (possibly augmented) baseline
data and the post-restoration data for well k.
flk l)ll\
Uk = ££/[(.»(/) + A) - xk{r + /)]
j=\ i=i
Here, the indicator function I[y] equals 1 if y>0 and equals 0 otherwise. Box D-7 in
Attachment D has detailed instructions for calculating the statistics C4 and Wu. For additional
information on the Wilcoxon-Mann-Whitney test that is useful to non-statisticians, see Conover
1998 (Chapter 5).
To determine if well k has met the remediation goal, the test statistic Wk is compared with the
critical value for the WRS test for sample sizes mk and «k in Attachment E in Tables E-5, E-6,
E-7, and E-8 for a =0.01, 0.025, 0.05, 0.10, respectively. If the test statistic exceeds the critical
value from the table, the null hypothesis is rejected and we conclude that the parameter values in
the post-restoration period are below the baseline or exceed the baseline by no more than A.
8.4.1.2 Comparing Multiple Wells Testing for Homogeneity and Overall Compliance to the
Baseline
The WRS test described above is useful for analyzing the data from a single well. The WRS
statistic Wk for all wells may be combined to make an overall summary for the entire set of wells.
In this approach, the statistics Wk are used as a summary measure of compliance in each well.
However, there must be consistency across wells in the relative levels of the baseline and post-
restoration data to make a determination of compliance that is valid across all wells.
The procedures described in Section 8.3.3 for conducting an overall test for a trend using the
summary Mann-Kendall statistics for each well may be modified to construct an overall test for
determining when remediation goals are met. Two tests are used; first a test for homogeneity
across wells and then a test for overall compliance. Again, both tests are based on the chi
squared distribution. The two tests are designed to be implemented sequentially, testing first for
homogeneity, then for compliance across wells as follows:
Step 1. Test for comparability of wells for compliance determination
Ho: Similar dynamics affect allK wells vs.
Ha: At least two wells exhibit different dynamics
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Step 2. Test for overall compliance
Ho : Baseline values are exceeded by more than a substantial difference
A at one or more wells vs.
Ha : Post-restoration values are lower than baseline values or exceed
baseline values by no more than a substantial difference A.
The expected value and variance of Sunder the null distribution are:
Ek = E(fVk) = mk(rik + mk +1)/ 2
Vk = Var(lVk) = nmik{nk + nik + l)/12
The standardized form of the test statistic W\: is zk = (W/, - Ek)/*JVk . If z\{ is sufficiently large,
there is evidence that this well has met the remediation goal.
To perform the test for homogeneity (or comparability), first calculate the average of the
2 2
standardized test statistics Zm = Y^klK. The homogeneity chi-square statistic is x h = (Y7- k) ~
KZ2m. Using the chi-squared table in Table E-3 of Attachment E, find the critical value for %
with (K-l) degrees of freedom at significance level a. For example, with a significance level of
5% and 5 degrees of freedom, y2(5) = 11.07, i.e., 11.07, is the cut point, which puts 5% of the
2 2
probability in the upper tail of a chi-square variable with 5 degrees of freedom. If x h < X (K-l),
there are comparable test statistics across wells at significance level a. If x h > X (K-i), the wells
are not homogeneous at the significance level a. In this case, individual a -level WRS tests
should be conducted at each well using the methods presented in Box D-7.
If the hypothesis of homogeneity across wells is accepted in Step 1, use Step 2 to affirm the
compliance of all wells with the remediation goals. The chi-squared table in Table E-3 of
Attachment E is used to find the critical value for y with 1 degree of freedom at significance
* 2 2 2 2 *
level a . Calculate the overall compliance test statistic x c = KZ M- If X c > X (i)> reject H0 and
conclude that the site appears to be below baseline conditions or no more than A higher than
baseline conditions. If x2c < X2(i)' there is not sufficient evidence (at the a significance level)
that all wells are in compliance with the remediation goals. In this case, additional remediation
may be required.
8.5 Summary of Statistical Approaches
The preferred statistical approaches outlined in the previous sections are summarized here.
Phase 1 Baseline Sampling
Estimate required number of samples (Section 8.2, Tables 8.1 and 8.2; Attachment E,
Table E-4)
Adjust measured data for seasonality if required (Section 8.3.1 and Attachment D,
Section D.l)
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Use Mann-Kendall test to check for unexpected trends (Section 8.3.2, 8.3.3 and
Attachment D, Sections D.2 and D.3)
Phase 4 Determination of Steady State
Adjust measured individual well data for seasonality if required (Section 8.3.1 and
Attachment D, Section D.l)
Use Mann-Kendall test for individual well trends (Section 8.3.2, 8.3.3 and Attachment D,
Sections D.2 and D.3)
If a trend is detected, use linear regression or Theil-Sen test to assess trend magnitude
(Section 8.3.2)
If trends not detected, use WRS test to compare baseline to steady-state measurements for
statistical differences for a single well. Repeat for all wells. (Section 8.4.1.1 and
Attachment D, Section D.4)
For multiple wells, when trends are not detected, first test wells for homogeneity. If test
results confirm homogeneity, if hypothesis of homogeneity across all wells is accepted,
then test to confirm compliance of all wells with restoration goals. (Section 8.4.1.2 and
Attachment D, Section D.5)
If steady-state data are from different wells than the baseline data and trends are not
detected; use WRS test 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.
(Section 8.4.1.1 and Attachment D, Section D.4)
Phase 5 Long-term Stability Monitoring
Adjust measured data for each well for seasonality if required (Section 8.3.1 and
Attachment D, Section D.l)
Use Mann-Kendall test for trends for each well (Section 8.3.2, 8.3.3 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 8.3.2)
If trends not detected, use WRS test to compare baseline to stability monitoring results
for a single well. Repeat for each well. (Section 8.4.1.1 and Attachment D, Section D.4)
If the before/after comparison is made between multiple wells, first test all wells for
homogeneity using chi-squared approach, then test to confirm compliance of all wells
with restoration goals (Section 8.4.1.2 and Attachment D, Section D.5)
If post-restoration data are from different wells than baseline data and trends are not
detected, use WRS test to compare baseline to stability monitoring results for the pooled
data of all wells combined (Section 8.4.1.1 and Attachment D, Section D.4)
Gilbert 1987 contains extensive discussions of the issues concerning use of statistics in
environmental and groundwater monitoring. For a detailed discussion of the tests mentioned in
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this chapter, as well as step-by-step guidance on calculations for the various types of
comparisons, see also EPA 2000 and EPA 2006.
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9.0 SUMMARY OF POST-CLOSURE PERFORMANCE ISSUES
This section provides a synopsis of the topics discussed in the previous sections that are
important to designing a monitoring network and demonstrating acceptable post-closure
performance of an in-situ mining operation.
9.1 Designing the Monitoring Program to Allow Reliable Baseline Conditions to be
Established Prior to Active Mining
A meaningful interpretation of post-closure monitoring results relies on the accurate
characterization of baseline groundwater conditions before active mining (leaching) begins. The
baseline monitoring program must capture both temporal and spatial variability in groundwater
chemistry. Considerations for this performance issue include:
Placement of monitoring wells (both within and beyond the influence of the injection-
withdrawal field) and well construction (e.g., screened intervals)
Chemical constituents to be monitored, including sampling techniques, and frequencies
Duration of sampling to determine natural variations in pre-mining groundwater
chemistry
Statistical methods for assessing variations in data and confidence measures for these
data and subsequent decisions about baseline conditions (e.g., temporal variations in
"background" levels and how much data are sufficient for decision-making)
The placement and number of monitoring wells in and around an in-situ mining operation is
strongly, if not totally, dependent on the site-specific hydrogeologic setting. The flow
characteristics of the ore bearing aquifer, the injection and withdrawal rates and spacing of these
wells will dictate the placement of monitoring wells to not only assess baseline conditions in the
aquifer, but to enable the detection of excursions of the treated groundwaters beyond the
withdrawal wells.
Extensive experience in collecting and analyzing groundwater chemical components exists
within the technical community concerned with fate and transport of pollutants. In addition,
there is a reasonable experience base from previous investigations and restoration efforts at in-
situ mining operations. Sampling protocols are reasonably well developed and can be reliably
adapted to the in-situ mining application. The mining and post-mining restoration efforts involve
actively altering the chemical environment. Although reaction kinetics ultimately dictate how
and over what timeframes the groundwater chemistry will respond, the uncertainties introduced
by the heterogeneities in the ore-bearing zone are too complex and locally variable to allow
reliable predictive modeling of the system response. Statistical assessments of groundwater
chemistry in monitoring well samples are still the best tools for assessing the achievement of
steady-state conditions.
Constituents to be monitored should be established on a site-specific basis. Currently, 40 CFR
Part 192 requires that molybdenum and uranium be added to the list of hazardous constituents in
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40 CFR 264.93,11 and Ra-226+Ra-228 (5 pCi/L) and gross alpha (15 pCi/L) are added to the
concentration limits provided in 40 CFR 264.94. These, together with several additional
parameters, are listed by NRC in its guidance for ISL/ISR license review (Standard Review
Plan).
To insure that temporal variability is captured, monitoring should be conducted over a period
sufficient to capture seasonal variations. Both the EPA and NRC have recommended that at least
four quarterly sets of samples be taken (NRC 2003 and 40 CFR 264.97) to establish the baseline.
Since this approach only covers one set of seasons, a larger number of samples may be required
to obtain adequate statistics if seasonal variations are anticipated. If significant seasonal
variations are anticipated, longer timeframes for collecting samples sufficient to cover a number
of seasonal cycles would be appropriate to establish confidence in the baseline characterization.
Monitoring for spatial variability within the permit area for mining should include wells
upgradient, downgradient, laterally adjacent to, and within the proposed leach area, sufficient to
identify high and low permeability zones. Monitoring should also include overlying and
underlying aquifers, which could become contaminated from leaching activities. Offsite wells in
the vicinity, such as drinking water wells and stock water wells, should also be monitored. In its
Standard Review Plan for ISL/ISRs, NRC defines an acceptable set of samples as including all
wellfield perimeter monitor wells, all upper and lower aquifer monitor wells, and at least one
production/injection well per acre in each wellfield, except that the requirement of one
production well per acre can be reduced for very large wellfields. It is difficult to define
minimum well spacing without detailed characterization of the flow system and
injection/withdrawal rates and configuration of the mining wellfield.
9.2 Determining that the Groundwater Chemistry has Reached Steady State and
Restoration Processes Can be Discontinued
Sufficient information must be provided to the regulator so that a determination can be made that
restoration is complete and steady-state conditions have been achieved prior to initiating post-
restoration stability monitoring, or to indicate that additional restoration efforts are necessary.
As noted in EPA 1992 (Section 7.5):
Finding that the ground water has returned to a steady state after terminating
remediation efforts is an essential step in the establishment of a meaningful test of
whether or not the cleanup standards have been attained. There are uncertainties
in the process, and to some extent it is judgmental. However, if an adequate
amount of data are carefully gathered prior to beginning remediation and after
ceasing remediation, reasonable decisions can be made as to whether or not the
ground water can be considered to have reached a state of stability.
The decision on whether the ground water has reached steady state will be based
on a combination of statistical calculations, plots of data, ground water modeling,
11 40 CFR 264.93 lists the following inorganic species: Ag, As, Ba, Cd, Cr, Hg, Pb, and Se.
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use ofpredictive models, and expert advice from hydrogeologists familiar with the
site.
In addition to groundwater chemistry, attention must also be directed to site hydrology to
establish that the potentiometric surface has returned to baseline conditions.
It is anticipated that restoration will take several years (see Table 7-1). During this time,
groundwater sampling will be used to follow the progress of the restoration process.
Considerations for this performance issue include:
Placement of monitoring wells in and surrounding the injection-extraction field
(proximity to the extraction field), sampling frequency, and sampling techniques
(particularly if they differ from the pre-mining techniques)
Chemical constituents to be examined (mobilized species) and constituents that may have
been added to the groundwater in attempts to restore pre-mining conditions (e.g.,
chemical reducing agents or other chemicals to sequester or inhibit movement of
mobilized metals)
Statistical tools necessary to determine when steady-state post-mining conditions are
established (data demands and consequent uncertainty levels)
The statistical tools for assessing "steady-state" conditions have a well-established record of
application in other contaminant remediation efforts and are easily adapted to the in-situ leaching
application. Care must be exercised in the application of these tools to assure that the database
for the site is detailed enough to allow clear application and interpretation of the results.
Statistical tools required to determine steady-state conditions using the Mann-Kendall test (i.e.,
absence of trends) are described in Section 8.3.2 and 8.3.3. Statistical tools that can be used to
compare the restored groundwater to the baseline using the WRS test are discussed for single
wells and multiple wells in Sections 8.4.1.1 and 8.4.1.2, respectively. If the monitoring period is
too short, divergent data reflecting slower flow paths through the ore zone, and still active
chemical processes, could be missed and an incorrect assessment of the aquifer's chemical state
could result.
9.3 Post-Restoration Stability Monitoring
After the regulators have judged that the restoration process is complete, the period of long-term
stability monitoring begins. In the past, the stability monitoring period has been set as a license
condition at about 6 months, but more recently, this has been increased to a minimum of 1 year
(Table 7-1). Field experience suggests that 1 year may not be adequate. In some cases, the
actual stability monitoring period has extended over several years to insure that stability has been
achieved (see Attachment B). Uranium in-situ leaching locations are typically in fluvial
sandstone deposits, which characteristically exhibit lithologic heterogeneities reflecting the
original depositional environments of the deposits. The formation of the uranium deposits in
these sediments also introduces changes in the porosity and permeability of the ore zone in
contrast to the surrounding aquifer. The mining and post-mining restoration activities would
further alter the local flow regime in the ore body. In such systems, groundwater flow paths
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through the ore-body would be anticipated to differ significantly from the surrounding media,
strongly suggesting that post-mining monitoring timeframes should be longer than sometimes
applied, in order to capture the effects of locally variable flow fields.
Considerations for this performance issue include:
Chemical constituents in pre- and post-mining waters are examined to determine if
aquifer water quality has been degraded by the leaching operations
Statistical measures needed to insure that the groundwater remains stable over several
years (i.e., concentrations are not trending upward)
Statistical measures needed to make decisions on whether the aquifer has achieved
restoration goals
The same statistical tools can be used for post-restoration stability monitoring as described in
Section 9.2. As mentioned above, quantitative prediction of the groundwater system's chemical
evolution is extremely difficult, and statistical measures to assess "steady-state" attainment
remain the primary tool for evaluating the success of post-mining restoration efforts.
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10.0 REFERENCES
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Draft Technical Report
76
June 2011
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ATTACHMENT A: DEVELOPMENT OF GROUNDWATER BASELINE FOR
DEWEY-BURDOCKISL SITE IN SOUTH DAKOTA
Powertech (USA) Inc., is seeking an NRC source material license to operate an ISL facility in
South Dakota (the Dewey-Burdock site) (Powertech 2009). The license application was
resubmitted to the NRC in October 2009 and technical review is ongoing. The Proposed
Action Area (PAA) encompasses about 10,520 acres. Startup of the Dewey and Burdock
operations will commence upon completion of construction and will continue for approximately
7 to 20 years or more, during which time additional wellfields will be completed along the roll-
fronts at both Dewey and Burdock sites. It is planned that groundwater restoration can be
accomplished within NRC requirements for timeliness in decommissioning (10 CFR § 40.42);
however, in the event restoration cannot be accomplished within this timeframe, Powertech
(USA) will seek NRC approval for an alternate schedule (Powertech 2009).
Baseline groundwater sampling was conducted in general accordance with NRC Regulatory
Guide 4.14 as appropriate to ISL operations (NRC 1980). For the baseline study for the NRC
permit application, 19 groundwater wells (14 existing and 5 newly drilled) were selected in
response to an NRC direction to characterize point of contact water quality and water within
overlying, production, and underlying aquifers. The existing wells selected for sampling include
eight domestic wells and six stock watering wells. The subset includes wells within the Fall
River Formation (4), Lakota Formation (7), Inyan Kara Group (Fall River or Lakota) (2),
Sundance Formation (1), and alluvium (5). Initial baseline sampling of these wells was
conducted quarterly from July 2007 through June 2008.
As required by the South Dakota Department of Environment and Natural Resources (DENR),
an additional 12 wells were sampled monthly beginning in March 2008 and continuing through
February 2009. Of these 12 wells, 6 wells are in the Dewey area and 6 wells are near Burdock.
At Dewey, a set of Fall River and Lakota wells were sampled at three places; upgradient, within,
and downgradient of the proposed operations. Near the Burdock area, the same well
arrangement applies with two wells each upgradient, within, and downgradient of the proposed
operations. In addition, one water quality sample was collected from each of the monitor wells
used during the May 2008 aquifer pump tests.
A groundwater quality constituent list was developed based on NUREG-1569 (NRC 2003)
groundwater parameters, NRC Regulatory Guide 4.14 parameters (NRC 1980), and added
parameters based on a constituent list review with South Dakota DENR. Table A-l lists
constituents analyzed for in groundwater samples, the number of samples analyzed for each
constituent, the analytical method, and the Practical Quantitation Limit (PQL).
12 See http://www.iirc.gov/materials/iiramum-recoverv/license-apps/ur-proiects-list-public.pdf.
Draft Technical Report
A-l
June 2011
-------
Table A-l. Dewey-Burdock Site - Number of Groundwater Samples Collected,
Analytical Method, and PQL by Constituent
Constituent, Unit
Number of Samples
Analyzed
Analytical
Method
PQL1
Major Cations and Anions
Anions (meq/L)
140
A1030E
Bicarbonate as HC03 (mg/L)
140
A2320B
5
Carbonate as C03 (mg/L)
140
A2320B
5
Sulfate (mg/L)
140
E300.0
36
Chloride (mg/L)
140
E300.0
1
Fluoride (mg/L)
140
E300.0
0.1
Nitrogen, Nitrite as N (mg/L)
140
E300.0
0.1
Nitrogen, Nitrate as N (mg/L)
140
E300.0
0.1
Cations (meq/L)
140
A1030E
Ammonia (mg/L)
140
A4500-NH3 G
1
Sodium-Dissolved (mg/L)
140
E200.7
0.8
Calcium-Dissolved (mg/L)
140
E200.7
0.5
Magnesium-Dissolved (mg/L)
140
E200.7
0.5
Potassium-Dissolved (mg/L)
140
E200.7
0.5
Silica-Dissolved (mg/L)
140
E200.7
0.5
General Water Quality Indicators
Alkalinity-Total as CaC03 (mg/L)
140
A2320B
5
Anion/Cation Balance (± 5) (%)
280
A1030E
Conductivity a 25 C (|imhos/cm)
140
A2310B
5
Oxidation-Reduction Potential (mV)
118
A2580B
pH
140
A4500-H B
0.01
Sodium Adsorption Ratio (meq/L)
120
Calculation
0.1
Solids-Total Dissolved TDS (mg/L)
140
A2540C
5
Solids-Total Dissolved , Calc. (mg/L)
140
Calculation
5
TDS Balance (0.80 - 1.20) (dec.%)
140
A1030E
Methods, Dissolved
Aluminum-Dissolved (mg/L)
140
E200.8
0.1
Arsenic-Dissolved (mg/L)
140
E200.8
0.001
Barium-Dissolved (mg/L)
140
E200.8
0.1
Boron-Dissolved (mg/L)
140
E200.7
0.1
Cadmium-Dissolved (mg/L)
140
E200.8
0.005
Chromium-Dissolved (mg/L)
140
E200.8
0.05
Cooper-Dissolved (mg/L)
140
E200.8
0.01
Iron-Dissolved (mg/L)
140
E200.7
0.03
Lead-Dissolved (mg/L)
140
E200.8
0.001
Manganese-Dissolved (mg/L)
140
E200.8
0.01
Mercury-Dissolved (mg/L)
140
E200.8
0.001
Molybdenum-Dissolved (mg/L)
140
E200.8
0.1
Nickel-Dissolved (mg/L)
140
E200.8
0.05
Selenium-Dissolved (mg/L)
140
A3114B
0.001
Selenium-IV-Dissolved (mg/L)
118
A3114B
0.001
Selenium-VI-Dissolved (mg/L)
118
A3114B
0.001
Silver-Dissolved (mg/L)
140
E200.8
0.005
Thorium 232-Dissolved (mg/L)
140
E200.8
0.005
Uranium-Dissolved (mg/L)
140
E200.8
0.003
Vanadium-Dissolved (mg/L)
140
E200.8
0.1
Zinc-Dissolved (mg/L)
140
E200.8
0.01
Draft Technical Report
A-2
June 2011
-------
Table A-l. Dewey-Burdock Site - Number of Groundwater Samples Collected,
Analytical Method, and PQL by Constituent
Constituent, Unit
Number of Samples
Analyzed
Analytical
Method
PQL1
Metals, Suspended
Uranium-Suspended (mg/L)
138
E200.8
0.0003
Metals, Total
Antimony-Total (mg/L)
95
E200.8
0.003
Arsenic-Total (mg/L)
95
E200.8
0.001
Barium-Total (mg/L)
95
E200.8
0.1
Beryllium-Total (mg/L)
95
E200.8
0.001
Boron-Total (mg/L)
95
E200.7
0.2
Cadmium-Total (mg/L)
95
E200.8
0.005
Chromium-Total (mg/L)
95
E200.8
0.05
Cooper-Total (mg/L)
95
E200.8
0.01
Iron-Total (mg/L)
95
E200.7
0.03
Lead-Total (mg/L)
95
E200.8
0.001
Manganese-Total (mg/L)
95
E200.8
0.01
Mercury-Total (mg/L)
163
E200.8
0.001
Molybdenum-Total (mg/L)
95
E200.8
0.1
Nickel-Total (mg/L)
95
E200.8
0.05
Selenium-Total (mg/L)
95
E200.8
0.002
Silver-Total (mg/L)
95
E200.8
0.005
Strontium-Total (mg/L)
95
E200.8
0.1
Thallium-Total (mg/L)
95
E200.8
0.001
Uranium-Total (mg/L)
99
E200.8
0.0003
Zinc-Total (mg/L)
95
E200.8
0.01
Radionuclides
Gross Alpha-Dissolved (pCi/L)
140
E900.0
1
Gross Beta-Dissolved (pCi/L)
140
E900.0
2
Gross Gamma-Dissolved (pCi/L)
140
E901.1
20
Lead-210-Dissolved (pCi/L)
140
E909.0M
1
Lead-210-Suspended (pCi/L)
138
E909.0M
1
Lead-210-Total (pCi/L)
20
E909.0M
1
Polonium 210-Dissolved (pCi/L)
140
RMO-3008
1
Po Ionium 210-Suspended (pCi/L)
138
RMO-3008
1
Polonium 210-Total (pCi/L)
20
RMO-3008
1
Radium 226-Dissolved (pCi/L)
134
E903.0
0.2
Radium 226-Suspended (pCi/L)
133
E903.0
0.2
Radium 226-Total (pCi/L)
90
E903.0
0.2
Radon 222-Total (pCi/L)
120
D5072-92
100
Thorium 230-Dissolved (pCi/L)
140
E907.0
0.2
Thorium 230-Suspended (pCi/L)
138
E907.0
0.2
Thorium 230-Total (pCi/L)
20
E907.0
0.2
PQL = Practical Quantitation Limit. The concentration that can be reliably measured within specified limits
during routine laboratory operating conditions, below which results are reported as "less than reporting limit."
The contracting laboratory uses the PQL as the reporting limit.
Source: Powertech 2009, Table 2.7-30
Draft Technical Report
A-3
June 2011
-------
Table A-2 lists current National Primary and Secondary Drinking Water Standards as regulated
by EPA, together with the number of samples analyzed for each constituent, the total number of
detections above the reporting limit, and the total number of detections equal to or above the
Maximum Contaminant Level (MCL) for each constituent. These standards or MCLs are
enforced by the EPA on public drinking water systems, but only serve as a guide for private
water systems. Private water systems, as defined by the EPA, serve less than 25 people and have
less than 15 service connections; all other systems are defined as public water systems. All
drinking water wells within the Production Authorization Area (PAA) are private water systems.
Table A-2. Dewey-Burdock Baseline Water Quality Sampling Statistics with Water
Quality Regulatory Limits for Public Drinking Water Supply Systems
Test
Analyte/Parameter
EPA Maximum
Number of
Number of
Detections
Number of
Detections
Equal to or
Above MCL
Units
Contaminant
Level (MCL)
Samples
Analyzed*
BULK PROPERTIES
pH
pH Units
6.5-8.5 [1]
141
141
6
Total Dissolved Solids (TDS)
mg/L
500 [1]
141
141
141
CATION S/ANION S
Sodium, Na
mg/L
200 [1]
141
141
63
Chloride, CI
mg/L
250 [1]
141
141
4
Fluoride, F
mg/L
4; 2 [1]
141
136
0
Sulfate, S04
mg/L
250 [1]
141
141
141
Nitrate (as Nitrogen)
mg/L
10
141
29
0
Nitrite (as Nitrogen)
mg/L
1
141
0
0
Nitrate and Nitrite
(Combined)
mg/L
10
141
29
0
TRACE METALS (total)
Antimony, Sb
mg/L
0.006
98
0
0
Aluminum, A1
mg/L
0.05-0.2 [1]
141
0
0
Arsenic, As
mg/L
0.01
98
80
11
Barium, Ba
mg/L
2
98
6
0
Beryllium, Be
mg/L
0.004
98
2
0
Boron, B
mg/L
1.4 [2]
98
29
3
Cadmium, Cd
mg/L
0.005
98
0
0
Chromium, Cr
mg/L
0.1
98
1
0
Copper, Cu
mg/L
1.0 [1]; 1.3 [3]
98
5
0
Iron, Fe
mg/L
0.3 [1]; 5 [4]
98
95
2 [1]; 1 [4]
Mercury, Hg
mg/L
0.002
170
1
0
Manganese, Mn
mg/L
0.05 [1]; 0.8 [4]
98
98
89 [1]; 19 [4]
Molybdenum, Mo
mg/L
0.04 [2]
98
8
2
Nickel, Ni
mg/L
0.1 [2]
98
1
1
Lead, Pb
mg/L
0.015 [3]
98
18
8
Selenium, Se
mg/L
0.05
98
26
0
Silver, Ag
mg/L
0.1 [1], [2]
98
0
0
Strontium, Sr
mg/L
4 [2]
98
97
37
Thallium, T1
mg/L
0.002
98
0
0
Uranium, U
mg/L
0.030
102
77
18
Zinc, Zn
mg/L
5 [1]; 2 [2]
98
35
0
Draft Technical Report
A-4
June 2011
-------
Table A-2. Dewey-Burdock Baseline Water Quality Sampling Statistics with Water
Quality Regulatory Limits for Public Drinking Water Supply Systems
Test
Analyte/Parameter
Units
EPA Maximum
Contaminant
Level (MCL)
Number of
Samples
Analyzed*
Number of
Detections
Number of
Detections
Equal to or
Above MCL
RADIONUCLIDES
Beta Particles and Photons
(Combined)
mRem/
Year
4
141
137
N/A
Radium 226 and 228
(Combined)
pCi/L
5
135
119
59
Radon-222 (total)
pCi/L
300 [5]
121
121
105
Notes:
[1] Secondary guideline value above which use of water may give complaints by consumers.
[2] Health Advisory - Lifetime
[3] Action level which if exceeded triggers treatment.
[4] Region 8 Permit Limit
[5] Proposed MCL
N/A - Not available
* - Number of samples includes results for only those wells that were sampled quarterly or monthly as part of the
baseline sampling plan
Source: Powertech 2009, Table 2.7-35
Attachment A References
NRC (U.S. Nuclear Regulatory Commission) 1980. Radiological Effluent and Environmental
Monitoring at Uranium Mills. Regulatory Guide 4.14, Revision 1.
NRC (U. S. Nuclear Regulatory Commission) 2003. Standard Review Plan for In-Situ Leach
Uranium Extraction License ApplicationsFinal Report. NUREG-1569. Washington, DC.
June 2003.
Powertech 2009. Dewey-Burdock Project Application for NRC Uranium Recovery License, Fall
River and Custer Counties, South Dakota Technical Report. February 2009. ML092870295.
Draft Technical Report
A-5
June 2011
-------
ATTACHMENT B: POST-RESTORATION STABILITY MONITORING
CASE HISTORIES
Power Resources Inc. (PRD, Smith Ranch - Highland Uranium Project: A-Wellfield
Most of the following information was extracted from Power Resources 2004. The A-Wellfield
was mined from January 1988 through July 1991 using a lixiviant formed by adding gaseous
carbon dioxide and oxygen to the natural groundwater. Restoration began in July 1991 and was
completed in October 1998. The Wyoming Department of Environmental Quality (WDEQ)
mine permit and the NRC license required that post-restoration stability monitoring be conducted
over a period of 6 months. However, PRI initially collected stability data from February 1999
through April 2000 (14 months). Additional stability data on a limited suite of parameters
(chloride, bicarbonate, conductivity, and uranium) were collected through November 2003. In
November 2003, the WDEQ concluded that stability had been demonstrated, but
decommissioning could not begin until an additional monitoring plan related to natural
attenuation was approved.
During restoration, PRI applied Best Practicable Technology (BPT) and returned the
groundwater to a quality of use equal to, and consistent with, uses for which the water was
suitable prior to in-situ leaching. Restoration involved groundwater sweep, reverse osmosis
treatment, and use of a chemical reductant. All of the groundwater parameters except iron,
manganese, selenium, and radium were restored to baseline or to a condition within the
WDEQ/WQD Class I classification (Domestic Use Suitability). The baseline for radium was
100 times the WDEQ/WQD upper limit for domestic or agricultural use and 30 times higher than
13
the EPA treatability limit. The only acceptable use for the water was WDEQ/WQD Class V -
Commercial - Mineral (e.g., uranium mining).
Baseline values were established via five monitoring wells. Table B-l shows the average
baseline values for the 35 monitored parameters (WDEQ Guideline 8 parameters) together with
values at the end of mining, during restoration prior to the introduction of H2S as a reductant, and
at the end of restoration. The final column lists WDEQ standards for Class I water. PRI noted
that the post-mining values of pH and HCO3" were based on laboratory measurements, where
degassing of the samples inevitably occurs. In-situ values were expected to be about 6.0 for pH
and 1,200 mg/L for HCO3.
Initial stability period sampling involved measuring the 35 parameters from five baseline
monitor wells on February 23, 1999; August 18, 1999, and October 20, 1999. In addition,
conductivity, water level elevation, HCO3", CI", and Unat were sampled more frequently (up to 9
samples during the 8-month period). On the basis of the monitoring results, PRI concluded that
wellfield stability had been achieved (March 31, 2000). Presumably WDEQ did not concur,
since PRI subsequently measured the 35 parameters on April 26, 2000. As noted above,
sampling of the five baseline wells for conductivity, HCO3", CI", and Unat continued through
November 2003.
13 According to the EPA, existing technology for the safe treatment of potable water containing more than
20 pCi/L is impractical for populations of less than 10,000 people.
Draft Technical Report
B-l
June 2011
-------
On May 5, 2003, the WDEQ requested that additional samples be provided on uranium and
selenium from three mining zone wells. The requested data, together with data from five
monitor wells and five production wells that had been collected since March 31, 2000 (the date
PRI stated that stability had been achieved), were submitted to WDEQ on May 23, 2003.
PRI subsequently conducted fate and transport modeling to show that natural attenuation would
prevent endangerment of adjacent groundwater. (Details of these calculations are not available
electronically.) On November 25, 2003, the Wyoming Land Quality Division determined that,
even though for some elements the groundwater had not been returned to baseline quality, the
A-Wellfield met statutory and regulatory requirements. Nevertheless, the WDEQ requested that
additional monitoring be conducted to support the modeling calculations (November 25, 2003).
NRC concurred with WDEQ that restoration had been completed, but that additional long-term
monitoring should be conducted to demonstrate that natural attenuation is effective (NRC 2004).
Based on an April 26, 2011, telephone conversation with Steve Ingle (307-777-7064) at the
WDEQ, the attenuation modeling involved both groundwater flow modeling with MODFLOW
and PHREEQC geochemical modeling. The calculations showed that a maximum of 15 years
would be required to achieve the full benefits of natural attenuation. To support the modeling
results, the operator was required perform semi-annual monitoring of four wells (a "hot spot"
well with elevated levels of U and Se, an upgradient well, a downgradient well, and a lateral
well) beginning in 2004. According to Ingle, the results are stable, but not declining as would be
expected from natural attenuation. He also noted that the WDEQ is moving toward a minimum
of 1 year for stability monitoring, with sufficient sampling to statistically characterize trends.
Draft Technical Report
B-2
June 2011
-------
Table B-l. A-Wellfield, Average Water Quality at Wells MP-1 through MP-5
(All values in mg/L, except pH, conductivity in ^mhos/cm, and Ra in pCi/L)
BASELINE
END MINING
pre-h2s
END REST
CLASS 1
(Aug. 1987)
(July 1991)
(May 1995)
(Feb. 1999)
(*see below)
Ca
44.1
313.4
68.6
73.4
Mg
9.0
59.5
12.4
13.5
Na
55.0
80.8
37.4
42.2
K
8.0
13.4
4.7
4.4
C03
0.0
0.0
0.0
0.0
HC03
215.0
720.2
242.2
256.6
S04
91.0
380.6
83.9
127.2
250.0
CI
4.7
212.6
14.4
18.0
250.0
NH4
0.1
0.7
0.2
0.29
N02
0.0
0.1
0.1
0.1
N03
0.0
0.2
0.1
0.1
F
0.2
0.2
0.1
0.15
Si02
16.0
20.5
12.6
11.9
TDS
330
1507
342
410
COND
525
2390
579
647
ALK
177
591
199
211
pH
8.00
6.78
7.25
7.31
A1
0.1
0.1
0.1
0.1
As
0.001
0.001
0.010
0.030
0.050
Ba
0.1
0.1
0.1
0.1
B
0.1
0.1
0.1
0.1
Cd
0.01
0.03
0.005
0.005
Cr
0.05
0.05
0.05
0.05
Cu
0.01
0.02
0.03
0.01
Fe
0.05
0.05
1.32
1.30
0.30
Pb
0.05
0.05
0.05
0.05
Mn
0.03
0.66
0.41
0.49
0.05
Hg
0.001
0.001
0.001
0.001
Mo
0.10
0.10
0.10
0.10
N1
0.05
0.08
0.05
0.05
Se
0.001
0.990
0.160
0.070
V
0.10
0.19
0.10
0.10
Zn
0.01
0.04
0.01
0.01
U
0.05
40.19
3.00
3.53
5.00
Ra
675
3286
1056
1153
5
Class 1 Domestic Use Suitability Standard, Chapter VIII of the WDEQ, Water Quality Division Rules and
Regulations.
Highland A-Wellfield References
Power Resources 2004. Letter to Gary Janoskco, NRC, from W.F. Kearney, Power Resources,
Inc., dated January 15, 2004. Subject: Smith Ranch - Highland Uranium Project, Docket 40-
8964, SUA-1548, A-Wellfield Groundwater Restoration Information. (ML040300369).
Draft Technical Report
B-3
June 2011
-------
NRC 2004. Letter to W.F. Kearney, Power Resources, Inc., from Gary Janosko, NRC, dated
June 29, 2004. Subject: Review Of Power Resources, Inc.'s A-Wellfield Ground Water
Restoration Report for the Smith Ranch-Highland Uranium Proj ect. (ML041840470).
Crow Butte Mine Unit 1
On September 3, 1999, Crow Butte Resources (CBR) submitted Mine Unit 1 Restoration Report
to the Nebraska Department of Environmental Quality (NDEQ) for approval. On November 18,
1999, NDEQ accepted restoration of Mine Unit 1 as completed. Then on January 10, 2000, CBR
submitted Mine Unit 1 Restoration Report to NRC requesting approval of groundwater
restoration by the Commission (Crow Butte 2000). The report covered both restoration activities
and post-restoration stability monitoring results over a 6-month period. On June 26, 2001, NRC
requested additional information on efforts made to achieve primary restoration goals, efforts
made to ensure restoration of wellfield flare, and additional data on stability monitoring. CBR
provided the requested information on August 24, 2001. On March 29, 2002, NRC denied
approval of restoration based on concerns about the stability of six groundwater parameters,
which NRC felt showed increasing trendsammonium, Se, TDS, U, Fe, and Ra-226. CBR was
directed by NRC to resume stability monitoring.
The proposed supplemental monitoring plan submitted by CBR to NRC involved measuring the
6 groundwater parameters in 6 wells with a minimum of 3 samples for each parameter collected
over a 3-month period. The additional monitoring results were supplied to the NRC on October
11, 2002 (Crow Butte 2002). A comparison of results from the 1999 and 2002 stability
monitoring results is presented in Table B-2.
Table B-2. Comparison of Stability Monitoring Results for 1999 and 2002 at
Crow Butte Mine Unit 1
Species
1999
2002
NDEQ
Restoration
Standard
Baseline
Range
Average
Range
Average
Uranium (mg/L)
1.09-2.33
1.73
1.6-1.8
1.66
5
N/A
Radium-226 (pCi/L)
216-385
303
298-330
314
584
230
Ammonium (mg/L)
0.07-0.18
0.12
0.05-0.06
0.05
10
0.37
Selenium (mg/L)
0.001-0.003
0.002
0.0013-0.002
0.0016
0.05
0.003
Iron (mg/L)
0.049-0.127
0.089
0.24-0.31
0.278
0.3
0.44
TDS (mg/L)
1026-1153
1094
1078-1089
1084
1218
1170
Figures B-l and B-2 show trend lines for uranium and iron.
Draft Technical Report
B-4
June 2011
-------
Mine Unit 1 Average Uranium Concentration
6.0
5.0
4.0
Average Uranium
NDEQ Standard
2.8
S
3
a
2.0
2.0
0.0
\ \ \B \ \n % % \ V \
'¦%> '¦%> -%> s-%> °o °o o "Oa
<*>
-------
Comparison of the 1999 and 2002 stability monitoring in Table B-2 shows that, with the
exception of iron, stability was realized. In the case of iron, as stated in 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.
No discussion was provided on the expected timeframe for the postulated decrease.
Restoration of Mine Unit 1 was approved by NRC on February 12, 2003 (NRC 2003).
Crow Butte References
Crow Butte 2000. Mine Unit 1 Restoration Report, Crow Butte Uranium Project. Submitted to NRC
January 10, 2000. (ML003677938).
Crow Butte 2002. Mine Unit 1 Groundwater Stability Data, Source Materials License SUA-1534,
Docket Number 40-8943. Letter from Michael Griffin, Crow Butte Resources, Inc., to Daniel
Griffen, NRC, dated October 11, 2002. ML022980095. This reference includes a report entitled
"Additional Stability Monitoring Data for Mine Unit 1 Groundwater Restoration."
NRC 2003. Letter from Daniel M Gillen, NRC, to Michael L Griffin, dated February 12, 2003.
License Amendment 15, Crow Butte Resources In Situ Leach Facility License No. SUA-1534,
Wellfield #1 Restoration Acceptance (TAC No. L52491).
Christensen Ranch
The Christensen Ranch uranium in-situ leach project is located in Johnson and Campbell
Counties, Wyoming. Wellfield restoration operations were initiated at Mine Units 2, 3, and 4
(MU2, MU3, and M4, respectively) in 1997, and in Mine Units 5 and 6 (MU5 and MU6) in
2000. Restoration of all MUs, including stability monitoring, was completed by 2006. An
average of 10.1 pore volumes of water were treated for the five Christensen Ranch MUs during
Draft Technical Report
B-6
June 2011
-------
restoration activities. Groundwater within the production zone has been restored to the pre-
mining class of use, using Best Practicable Technology (BPT), as required by the WDEQ
(Cogema 2008).
Table B-3 indicates parameters that were not restored to desired values.
Table B-3. Parameters Exceeding Remediation Goals and WDEQ or EPA Standards
Mine Unit
TDS
Fe
Mn
Se
u
Ra
MU2
X
X
X
MU3
X
MU4
X
X
X
X
X
X
MU5
X
X
X
MU6
X
X
X
X - Parameter exceeds remediation goals and WDEQ Class 1 drinking water use or EPA maximum concentration
limit.
Baseline water quality was determined for each MU prior to commencement of production.
Baseline water quality was measured within the production zone of the MU, on the perimeter of
the production zone, and in the overlying and underlying aquifers. Ore zone baseline water
quality was established by sampling designated restoration wells 4 times, separated by a
minimum of 2 weeks. The restoration well density was one well per acre of wellfield.
Consider stability monitoring of MU2 for illustrative purposes. Post-restoration stability
monitoring involved sampling each of the 25 restoration monitoring wells 4 times over a
9-month period. Summary results based on wellfield averages are included in Table B-4,
together with results at various points in the restoration process (after mining, after groundwater
sweep [GWS], after remote osmosis treatment [RO], and after reductantfH^S] addition).
Table B-4. Restoration and Stability Monitoring Water Quality Results, Mine Unit 2,
Christensen Ranch, Wyoming
Active Restoration Monitoring
Stability Monitoring
Post
Mining
Post
GWS
Post RO
Post
Reductant
Round 1
Round 2
Round 3
Round 4
Major Ions mg/1:
Ca
285.8
160.0
36.4
32.3
52.6
64.6
65.7
63.3
Mg
53.1
33.7
6.7
3.9
5.7
7.5
8.0
7.9
Na
696.4
522.6
140.7
65.2
88.7
105.5
106.4
109.2
K
9.4
6.5
2.0
1.2
1.2
1.4
1.4
1.5
C03
1.0
1.0
1.0
1.0
1.2
1.2
1.0
1.0
HC03
1898.8
1376.0
365.3
172.4
210.1
237.5
260.5
273.0
S04
784.1
504.9
108.8
78.4
155.6
194.2
191.8
175.7
CI
122.9
77.1
15.0
7.4
7.8
8.5
8.5
8.6
NH4
0.52
0.35
0.08
0.17
0.14
0.10
0.10
0.10
N02 (N)
0.12
0.10
0.11
0.10
0.10
0.10
0.10
0.10
N03 (N)
0.22
0.39
0.10
0.10
0.10
0.31
0.10
0.10
F
0.10
0.12
0.12
0.10
0.10
0.10
0.10
0.10
Si02
12.6
7.8
6.2
7.4
4.8
10.8
10.5
10.8
TDS
3054.6
2143.6
509.4
297.5
435.4
542.4
569.2
548.4
Cond. (|imho/cm)
4007.8
3032.2
806.8
464.4
627.8
796.9
786.0
792.6
Alk. (as CaC03)
1484.9
1128.4
302.0
143.8
170.9
195.0
213.7
224.2
Draft Technical Report B-7 June 2011
-------
Table B-4. Restoration and Stability Monitoring Water Quality Results, Mine Unit 2,
Christensen Ranch, Wyoming
Active Restoration Monitoring
Stability Monitoring
Post
Mining
Post
GWS
Post RO
Post
Reductant
Round 1
Round 2
Round 3
Round 4
pH (units)
7.51
7.90
7.85
7.69
7.51
7.77
7.82
7.76
Trace Metals mg/1:
A1
0.10
0.10
0.13
0.18
0.10
0.10
0.10
0.10
As
0.12
0.09
0.01
0.02
0.01
0.01
0.01
0.01
Ba
0.10
0.10
0.12
0.37
0.49
0.50
0.50
0.50
B
0.10
0.11
0.10
0.07
0.06
0.04
0.06
0.06
Cd
0.010
0.005
0.005
0.003
0.002
0.002
0.002
0.002
Cr
0.05
0.05
0.05
0.02
0.01
0.01
0.01
0.01
Cu
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
Fe
0.14
0.40
0.14
0.43
1.19
1.06
0.66
0.57
Pb
0.05
0.05
0.05
0.03
0.02
0.02
0.02
0.02
Mn
0.66
0.37
0.17
0.27
0.38
0.41
0.39
0.34
Hg
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
Mo
0.10
0.11
0.10
0.05
0.02
0.02
0.02
0.02
Ni
0.12
0.05
0.05
0.03
0.01
0.01
0.01
0.01
Se
6.33
2.40
1.29
0.01
0.01
0.01
0.01
0.01
V
0.24
0.10
0.10
0.10
0.10
0.10
0.09
0.10
Zn
0.05
0.02
0.01
0.01
0.01
0.01
0.01
0.01
Radiometric:
U (mg/1)
11.75
12.58
3.33
0.76
0.28
0.26
0.27
0.36
Ra 226 (pCi/1)
257.7
191.4
161.2
219.6
228.2
351.3
295.3
223.9
Numeric values represent the mean of all designated restoration wells for the specified phase of restoration.
On April 8, 2008, Cogema requested thatNRC approve restoration of MU2 through MU6
(Hargrove 2008). On February 19, 2009, the NRC stated that (Linton 2009):
By letter to the U.S. Nuclear Regulatory Commission (NRC) dated April 8, 2008,
(ADAMS Accession Package No. ML081060155), COGEMA Mining, Inc.
(COGEMA) submitted a Wellfield Restoration Report for mine units 2 through 6
at its Christensen Ranch facility. NRC conditionally accepted the application for
review in a letter to COGEMA dated May 13, 2008 (ADAMS Accession No.
ML081330021) requesting that COGEA4A confirm groundwater class of use
designation from the Wyoming Department of Environmental Quality (WDEQ).
COGEMA confirmed the groundwater class of use with the WDEQ and forwarded
its response to the NRC in a letter dated July 28, 2008. NRC staff has completed
a safety review of COGEMA 's Wellfield Restoration Report. NRC staff requires
additional information from COGEA4A in order to complete its assessment of the
license renewal application.
The NRC raised a large number of questions about the Wellfield Restoration Report. A
recurring theme was concerns about establishment of reducing conditions and the role of natural
attenuation:
Section 8 of the Report states, "The reestablishment of long-term reducing
conditions in the restored aquifer is an important factor that can serve to limit the
Draft Technical Report
B-8
June 2011
-------
migration of constituents affected by ISR mining because reducing conditions
have a major effect on the mobility of many constituents associated with uranium
roll front deposits, including U, Se, As, Mo, S. " In Section 9 the Report states,
"significant attenuation of uranium will occur as groundwater from the wellfields
moves into the down gradient reducing portions of the aquifer. " Demonstrate the
basis for these comments by providing information that reducing conditions have
been reestablished within the wellfields or exist at monitoring well ring wells
down gradient of the wellfields such that reducing conditions would likely limit
the movement of monitored constituents.
Cogema provided responses on December 31, 2009 (Hargrove 2009). On June 8, 2010, the NRC
advised the operator (now Uranium One Americas, Inc.) as follows (Linton 2010):
While the Wellfield Restoration Report review is not complete at this time, NRC's
preliminary review of the RAI responses indicates that monitoring well 5MW66
upper control limit concentrations have continued to increase and uranium
concentrations are reported as several times higher than background. Cogema,
and now Uranium One, has been sampling 5MW66, as per Cogema's proposed
recommendations to the Wyoming Department of Environmental Quality, dated
December 9, 2004, and agreed to by the NRC. However, with the current
excursion status of this well, the increasing trends in all upper control limit
concentrations and confirmation of uranium several times above background,
NRC has determined that corrective action is required consistent with NRC
License SUA-1341, License Condition 11.2.
Although the excursion status on well 5MW66 was resolved on April 19, 2011 (Arbogast 2011),
NRC has not made a determination regarding restoration of MU2 through MU6.
Christensen Ranch References
Arbogast 2011. Letter from Larry Arbogast, Uranium One Americas, Inc., to Document Control
Desk (Keith McConnell), NRC, dated April 19,2011. Subject: Termination of the excursion
status of monitor well 5MW 66. ML11116A144.
Cogema 2008. Wellfield Restoration Report Christensen Ranch Project, Wyoming. March 5,
2008, Cogema Mining, Inc, and Petrotek Engineering Corp. (ML081060131). http://www.wise-
uranium.org/udusail.html#CHRISTENS
Hargrove 2008. Letter from Tom Hargrove, Cogema Mining Inc., to Ron Linton, NRC, dated
April 8, 2008. Ref: Docket No 040-08502, Source Material License SUA-1341, Wellfield
Restoration Report, Christensen Ranch Project. ML081060129.
Hargrove 2009. Letter from Tom Hargrove, Cogema Mining Inc. to Document Control Desk,
NRC, dated December 30, 2009, Ref: Docket No 040-08502, License No. SUA-1341, Request
for Additional Information. Wellfield Restoration Report, Christensen Ranch Project.
ML100131020.
Draft Technical Report
B-9
June 2011
-------
Linton 2009. Letter from Ron Linton, NRC, to Tom Hardgrove, Cogema Mining Inc., dated
February 19, 2009. Subject: Request for Additional Information, Wellfield Restoration Report,
Christensen Ranch Project, Cogema Mining Inc., Irigaray and Christensen Ranch In Situ
Uraniun Recovery Project, Source Materials License SUA-1341 (TAC J00563). ML090360478.
Linton 2010. Letter from Ron Linton, NRC, to Jon Winter, Uranium One Americas Inc., dated
June 8, 2010. Subject: Uranium One Exploration U.S.A., Inc., (Cogema Mining Inc.) Irigaray
and Christensen Ranch Project, Campbell and Johnson Counties, Wyoming, Source Materials
License SUA-1341, Groundwater Restoration Report Review, Christensen Ranch MU2-6 (TAC
J00563). ML101580441.
Draft Technical Report
B-10
June 2011
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ATTACHMENT C: AQUIFER RESTORATION (EXTRACTED FROM NRC 2009,
SECTION 2.11.5)
Operational history at NRC-licensed ISL facilities is available to examine aquifer restoration at
the wellfield scale. Table 2.11-4 shows a summary of restoration data for a 12-ha [30-acre] area
covered by Production Units 1-9 at the commercial-scale Cogema Irigaray ISL facility (Cogema
2006a and 2006b). A comparison of the baseline and post-restoration stability monitoring
groundwater analytical data determined that for the water quality in the production zone, the
individual restoration and stabilization data fell within the baseline ranges for all constituents
except for calcium, magnesium, sodium, carbonate, chlorine, ammonium, total dissolved solids,
conductivity, alkalinity, lead, barium, manganese, and radium-226. These data showed that,
when comparing pre-mining baseline ranges to post-mining stabilization ranges, several
constituents did not meet the pre-mining baseline concentration levels. Additionally, post-
mining mean concentrations for nearly half of the constituents exceeded the pre-mining baseline
mean concentrations for the same constituents in Production Units 1-9 (Cogema 2006a and
2006b).
Table 2.11-4. Irigaray Water Quality Summary for Designated Aquifer
Restoration Wells*
Constituents
Mine Units 1-9 Baseline
Mine Units 1-9 Round Four
Restoration Results
Samples
Exceeding
Baseline
Range
Minimum
Maximum
Mean
Minimum
Maximum
Mean
Major Ions (mg/L)
Calcium
1.6
27.1
7.8
11.6
65
28.8
17
Magnesium
0.02
9
0.9
2.8
13
7.0
7
Sodium
95
248
125
107
275
185.6
2
Potassium
0.92
17.5
2.4
1.1
4.9
2.9
0
Carbonate
0
98
13.2
<1.0
<1.0
0.8
0
Bicarbonate
5
144
88.3
5.1
631
409
31
Sulfate
136
504
188.1
62.8
237
132.0
0
Chloride
5.3
15.1
11.3
0.1
117
39.4
32
Ammonia
0.05
1.88
0.3
0.05
36.1
8.5
13
Nitrogen Dioxide
<0.1
1
<0.4
<0.1
<0.1
<0.1
0
Nitrate
0.2
1
0.9
<0.1
0.12
0.1
0
Fluoride
0.11
0.68
0.29
0.1
0.22
0.12
0
Silica Dioxide
3.2
17.2
8.3
2.5
7.3
4.99
0
Total Dissolved
Solids
308
784
404
343
968
626
5
Specific
Conductivity
535
1,343
658
604
1,970
1094
5
Alkalinity
67.8
232
104
127
518
345
30
pH
6.6
11.0
9.00
7.07
8.40
7.76
0
Trace Metals (mg/L)
Aluminum
0.05
4.25
0.160
<0.1
0.140
0.102
0
Arsenic
<0.001
0.105
0.007
<0.001
0.029
0.005
0
Barium
<0.01
0.12
0.060
0.03
0.200
0.095
1
Boron
<0.01
0.225
0.110
<0.05
0.100
0.088
0
Cadmium
<0.002
0.013
0.005
<0.002
0.005
0.004
0
Chromium
<0.002
0.063
0.020
<0.005
0.050
0.039
0
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June 2011
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Table 2.11-4. Irigaray Water Quality Summary for Designated Aquifer
Restoration Wells*
Constituents
Mine Units 1-9 Baseline
Mine Units 1-9 Round Four
Restoration Results
Samples
Exceeding
Baseline
Range
Minimum
Maximum
Mean
Minimum
Maximum
Mean
Copper
<0.002
0.04
0.011
<0.01
0.020
0.010
0
Iron
0.019
11.8
0.477
<0.03
0.500
0.113
0
Lead
<0.002
0.05
0.020
<0.001
0.090
0.039
1
Manganese
<0.005
0.19
0.014
0.060
0.950
0.215
13
Mercury
<0.0002
0.001
0.0004
<0.0002
<0.001
<0.001
0
Molybdenum
<0.02
0.1
0.060
<0.01
<0.1
0.069
0
Nickel
<0.01
0.2
0.100
<0.05
<0.05
<0.05
0
Selenium
<0.001
0.416
0.013
<0.001
0.086
0.019
0
Vanadium
<0.05
0.55
0.070
<0.05
<0.1
0.088
0
Zinc
0.009
0.07
0.016
<0.01
<0.01
<0.01
0
Radiometric (pCi/L)
Uranium
0.0003
18.60
0.52
0.08
6.03
1.83
0
Radium-226
0
247.7
39.6
23.50
521.0
130.7
3
*Wichers, D.L. "Re: Request: Summary Table Irigaray Mine Unit Restoration RAI Response." E-mail to R.
Linton (August 11), NRC. Mills, Wyoming: Cogema Mining, Inc. 2006.
Catchpole et al. (1992a and 1992b) provide an early discussion of small-scale restoration efforts
for research and development of ISL uranium recovery facilities in Wyoming. These include the
Bison Basin facility in Fremont County (described in NRC 1981), the Reno Creek project in
Campbell County, and the Leuenberger Project in Converse County. Restoration activities
required treatment of water from nine pore volumes at Bison Basin and five pore volumes at
Reno Creek. In all cases, most water quality parameters were returned to within a statistical
range of baseline values with the exception of uranium (Bison Basin and Reno Creek) and
radium-226 (Leuenberger). For these parameters, Catchpole et al. (1992a and 1992b) report that
water in the well field was returned to the same class of use.
NRC (2007) detailed available information on aquifer restoration at ISL uranium recovery
facilities. These include a pilot scale study by Rio Algom for the Smith Ranch facility in
Converse County, Wyoming (Rio Algom Mining Corporation 2001); the proposed Crownpoint
ISL facility near Crownpoint, New Mexico (NRC 1997); the commercial-scale A-Well Field at
the Highland Uranium Project in Converse County, Wyoming (Power Resources, Inc. 2004); and
the commercial-scale Crow Butte Mine Unit No. 1 in Dawes County, Nebraska (NRC 2002,
2003). Rock core laboratory studies that Hydro Resources Inc. conducted for the Crownpoint
facility (NRC 1997) also provide useful insights to water quality parameters that may present
challenges for aquifer restorations.
NRC (2007) generally concluded that for the sites and data they examined, aquifer restoration
took longer and required more pore volumes than originally planned. For example, at the A-
Well Field at the Highland Uranium Project, the licensee's original plan anticipated that
restoration would last from four to seven years and require treating 5-7 pore volumes of
groundwater. When uranium recovery in the well field ended in 1991, the baseline and class of
use were not restored in the well field until 2004 (Table 2.11-5), and more than 15 pore volumes
of water were involved (NRC 2006, 2004). Similarly, WDEQ has noted that the C-Well field at
Draft Technical Report
C-2
June 2011
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the Smith Ranch-Highland Uranium Project has been undergoing restoration for 10 years
(WDEQ 2008). At the Crow Butte Mine Unit No. 1, more than 9.85 pore volumes of
groundwater were used in all the stages of aquifer restoration over approximately 5 years, as
compared to the 8 pore volumes estimated before restoration (NRC 2002, 2003). Crow Butte
Resources extracted uranium from an additional 26 pore volumes using ion exchange, without
lixiviant injection, prior to active restoration.
Table 2.11-5. Baseline Groundwater Conditions, Aquifer Restoration Goals, and Actual
Final Restoration Values the U.S. Nuclear Regulatory Commission Approved for the
Q-Sand Pilot Well Field, Smith Ranch, Wyoming*!
Parameter (units)
Range
Mean
Restoration
Goal
Actual
Restoration
Arsenic (mg/L) J
0.001-0013
0.004
0.05
0.008
Boron (mg/L)
0.002-0.70
0.15
0.54
0.14
Calcium (mg/L)
24-171
72
120
78
Iron (mg/L)
0.01-0.27
0.025
0.3
0.24
Magnesium (mg/L)
3-22
16
0.092
0.06
Manganese (mg/L)
0.01-0.077
0.023
Not applicable
0.1
Selenium (mg/L)
0.001-0.024
0.004
0.029
0.003
Uranium (mg/L)
0.001-3.1
0.28
3.7
1.45
Chloride (mg/L)
4-65
18
250
15
Bicarbonate (HC03) (mg/L)
129-245
199
294
254
Carbonate (C03) (mg/L)
Nondetectible-75
18
15
Nondetectible
Nitrate (mg/L)
0.1-1.0
0.4
Not applicable
0.13
Potassium (mg/L)
7-34
12
23
8
Sodium (mg/L)
19-87
28
41
38
Sulfate (mg/L)
100-200
124
250
128
Total dissolved solids (mg/L)
155-673
388
571
443
Specific conductivity
((imhos/cm)
518-689
582
827
642
pH (standard units)
7.5-9.4
8.0
6.5-8.6
7.0
Radium-226 (pCi/1)
6-1132
340
923
477
Thorium-230 (pCi/1)
0.027-4.65
1.03
5.62
3.4
*NRC. "Environmental Assessment for the Addition of the Reynolds Ranch Mining Area to Power Resources,
Inc.'s Smith Ranch/Highlands Uranium Project Converse County, Wyoming." Source Material License No. SUA-
1548. Docket No. 40-8964. Washington, DC: NRC. 2006.
f Sequoyah Fuels Corporation. "Re: License Application, Smith Ranch Project, Converse County, Wyoming."
ML8805160068. Glenrock, Wyoming: Sequoyah Fuels Corporation. 1988.
{1 mg/L = 1 ppm
As a field test of groundwater stabilization during aquifer restoration, hydrogen sulfide gas was
injected as a reductant into the Ruth ISL research and development facility in Campbell County,
Wyoming. After 6 weeks of hydrogen sulfide injection, pH dropped relatively quickly from 8.6
to 6.3, and sulfate concentration increased from 28 ppm to 91 ppm, indicating a more reducing
environment (Schmidt 1989; 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.
Draft Technical Report
C-3
June 2011
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Based on the available field data from aquifer restoration, NRC (2007) concluded that aquifer
restoration is complex and results could be influenced by a number of site-specific hydrological
and geochemical characteristics, such as preoperational 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- Well Fields at the Highland ISL facility).
Attachment C References
Catchpole, G., R. Garling, and M. Neumann 1992a. "Groundwater Restoration at Wyoming
Uranium Solution Mining Sites." The Mining Claim. Cheyenne, Wyoming: Wyoming Mining
Association, pp. 6-9.
Catchpole, G., R. Garling, and M. Neumann 1992b. "Groundwater Restoration at Wyoming
Uranium Solution Mining Sites. Part II." The Mining Claim. Cheyenne, Wyoming: Wyoming
Mining Association, pp. 14-19.
Cogema Mining, Inc. 2006a. "Response to NRC RAI, COGEMA Irigaray Mine Restoration
Report." Letter (June 21) to S. Cohen, NRC. Docket No. 40-8502. Mills, Wyoming: Cogema
Mining, Inc.
Cogema Mining, Inc. 2006b. "Response to NRC RAI, COGEMA Irigaray Mine Restoration
Report, Summary Table." E-mail (August 11) to R. Linton, NRC. Docket No. 40-8502. Mills,
Wyoming: Cogema Mining, Inc.
Davis, J. A. and G.P. Curtis 2007. "Consideration of Geochemical Issues in Groundwater
Restoration at Uranium In-Situ Leaching Mining Facilities." Nuclear Regulatory Commission.
NUREG/CR-6870. Washington, DC. January 2007.
NRC 1981. "Final Environmental Statement Related to the Operation of Bison Basin Project."
Docket No. 40-8745. Nuclear Regulatory Commission. Washington, DC.
NRC 1997. "Final Environmental Impact Statement To Construct and Operate the
Crownpoint Uranium Solution Mining Project, Crownpoint, New Mexico." NUREG-1508.
Nuclear Regulatory Commission. Washington, DC. February 1997.
NRC 2002. "Denial, Well Field Unit 1 Ground-Water Restoration Approval, Crow Butte
Resources In-Situ Leach Facility, License No. SUA-1534." ML020930087. Nuclear Regulatory
Commission. Washington, DC.
NRC 2003. "License Amendment 15, Crow Butte Resources In Situ Leach Facility, License
No. SUA-1534, Well Field #1 Restoration Acceptance." ML030440055. Nuclear Regulatory
Commission. Washington, DC.
Draft Technical Report
C-4
June 2011
-------
NRC 2004. "Review of Power Resources, Inc.'s A-Well Field Ground Water Restoration Report
for the Smith Ranch-Highland Uranium Project." ML041840470. Nuclear Regulatory
Commission. Washington, DC: NRC.
NRC 2006. "Environmental Assessment for the Addition of the Reynolds Ranch Mining Area to
Power Resources, Inc.'s Smith Ranch/Highlands Uranium Project Converse County, Wyoming."
Source Material License No. SUA-1548. Docket No. 40-8964. Nuclear Regulatory
Commission. Washington, DC: NRC.
Power Resources, Inc. 2004. "Smith Ranch-Highland Uranium Project, A-Well Field
Groundwater Restoration Information." ML040300369. Glenrock, Wyoming: Power
Resources, Inc.
Rio Algom Mining Corporation 2001. "Amendment 1 to Source Material License SUA-1548."
ML020220040. Washington, DC: NRC.
Schmidt, C. 1989. "Groundwater Restoration and Stabilization at the Ruth ISL Test Site in
Wyoming, USA." In-Situ Leaching of Uranium: Technical, Environmental, and Economic
Aspects. IA E A-T EC DOC-492. Vienna, Austria: International Atomic Energy Agency, pp. 97-
126.
Sequoyah Fuels Corporation 1988. "Re: License Application, Smith Ranch Project, Converse
County, Wyoming." Glenrock, Wyoming: Sequoyah Fuels Corporation. ML8805160068.
WDEQ 2008. "In-Situ Uranium Permits 603 and 633, Notice of Violation, Docket No. 4231-
08." Cheyenne, Wyoming: WDEQ, Land Quality Division. March 10, 2008.
Draft Technical Report
C-5
June 2011
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ATTACHMENT D: INSTRUCTIONS AND EXAMPLES FOR STATISTICAL
CALCULATIONS
This section demonstrates the application of the statistical methods discussed in Section 5. The
tests are designed to show whether data are stable or trending upward, and for comparing post-
restoration data with baseline conditions to determine when pre-ISL conditions are achieved.
Three topics are addressed in this section:
(1) Seasonal adjustment
(2) Mann-Kendall test for trends
(3) Wilcoxon Rank Sum (WRS) test for comparisons with baseline
The three types of analyses are meant to be applied sequentially, first adjusting for seasonality,
then testing the post-restoration data from each well for trends using the Mann-Kendall test. If
the trend tests indicate that the wells have reached stability, then the WRS test is used for
comparing the post-restoration samples with baseline samples in each well. If (and only if) the
well-specific summary statistics for the Mann-Kendall and WRS tests indicate that the wells
exhibit homogenous dynamics, the summary statistics may be combined into a wellfield
assessment for each parameter.
D.l Instructions for Seasonal Adjustment
Let Yt,i represent the measured concentration in year t and season i. An array of values for
3 years and 4 seasons are shown in Table A-l. Also shown are the quarterly averages Y0j at the
bottom of the table, the annual averages Ytjo at the right, and the overall average (mean) denoted
by Ym. The time series is plotted in Figure D-l. It has peaks at quarters 2, 6, and 10, and valleys
at quarters 4, 8, and 12. This pattern of regularly spaced peaks and valleys indicates there is a
strong seasonal component in the time series.
The seasonal component in each quarter (Q;) is defined as the deviation of the seasonal mean
from the overall mean: Q;; = Y0;i -YM. The plot of the seasonal component in Figure D-2 shows
the repeated pattern of seasonal component of the time series.
The "deseasonalized" time series (X) is obtained by subtracting the seasonal means from the
original data: Xtj; = Ytj; - Q;. The two series are compared in Figure D-3. The seasonally
adjusted series has the same mean, but a lower variance. If the time series contains only a small
seasonal component, the Q; values will be small relative to the original data, and the seasonal
adjustment procedure will not significantly affect the data. A formal statistical test for the
existence of a significant seasonal component in the time series is based on an Analysis of
Variance (ANOVA).14
14 This procedure is described in EPA 2002, Sections 14.2.2 and 14.3.3.
Draft Technical Report D-l
June 2011
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Table D-l. Array Used for Seasonal Adjustments
Quarter
Year
1
2
3
4
Annual Averages
1
Yu
Yu
Yu
Yl,4
Y1.0
2
Y2,i
Yo,2
Y23
Y2,4
Y2.0
3
Y3,i
Y3 2
Y3 3
Y3 4
Y3.0
Quarterly Averages
Yo,l
Y;i,2
Yo,3
Yo,4
Ym
7
6
<5
4
C
a>
o
c
o
o
3
2
1
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Quarter
Figure D-l. Data Series Showing Seasonal Variation
n
J
_
m
¦
¦
1
1 2 3 4 5 6 7 8 9 10 11 12
Quarter
Figure D-2. Seasonal Components (QO
Draft Technical Report
D-2
June 2011
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7
6
5
4
3
2
1
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Quarter
0- - Original Data
¦ Deseasonalized Data
AMean
Trend
Figure D-3. Comparison of the Original and Seasonally Adjusted Data Series
D.2 Performing the Mann-Kendall Trend Test
The Mann-Kendall test is applied to test the post-restoration data from each well for trends. This
test is applied after seasonal adjustment of the data and before post-restoration conditions are
compared with baseline conditions. Before wells are combined for a summary analysis of the
wellfield, the post-restoration samples are analyzed for trends well by well.
If the summary statistics for the trend test on each well show no unusual trends and indicate that
the wells exhibit similar dynamics, the summary statistics from each well may be combined into
a wellfield assessment. If the trend tests indicate that the wells have reached stability, then the
WRS test discussed in Section D.4 is used for comparing the post-restoration samples with
baseline samples in each well.
Instructions for performing the Mann-Kendall test are shown in Boxes D-l and D-2. The test is
performed by calculating the sample value of S and comparing this value to the critical value for
the test (for a series with 40 or fewer measurements) found in Table E-l in Attachment E. If the
post-restoration sample size (n) exceeds 40, then the normal approximation shown in Box D-3
may be used. If all measurements in a series have the same limit of detection, non-detect values
are assigned a common value equal to the limit of detection or one-half the limit of detection.
(Both choices will result in the same test statistic.) If there are two or more detection limits for
the parameter in question, use the highest detection limit for all non-detects.
Draft Technical Report
D-3
June 2011
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Box D-l. Triangular Array of Differences
t
t-1
2
3
4 5
6
7
1
X2-Xj
Xj-Xj
X,-X, Xs-X,
Xfi-Xj
x-x,
2
x3-x2
x4-x2 x,-x2
x6-x2
x7-x2
3
X4-X3 x,-x3
x6-x3
X7-X3
4
X5-X4
x6-x4
x7-x4
5
x6-x5
x7-x5
6
x7-x6
Signs of Differences
t
t-1
2
3
4 5
6
7
1
+
+
+ +
+
+
2
+
+
+
+
3
+
-
+
4
+
-
+
5
-
+
6
+
Scoring of Differences
t
t-1
2
3
4 5
6
7
1
1
1
1 1
1
1
2
1
-1 1
1
1
3
-1 1
-1
1
4
1
-1
1
5
-1
1
6
S = 11
Box D-2. Directions for the Mann-Kendall Trend Test
STEP 1: List the data in the order collected over time: X,. X2 XM. where Xt is the datum at time t.
Assign a value of DL/2 to values reported as below the detection limit (DL). Construct a "Data Matrix"
similar to that at the top of Box D-l.
STEP 2: Compute the sign of all possible differences as shown in the middle and bottom portion of
Box D-l.
STEP 3: Compute the Mann-Kendall statistic S, which is the number of positive signs minus the number of
negative signs in the triangular table: S = (number of + signs) - (number of - signs).
The absolute value of the Mann Kendall test statistic S is compared with the critical values in
Table E-l of Attachment E to determine if there is a significant trend. The critical values in
Table E-l are used to determine whether the time-series data is increasing, decreasing, or stable.
For example, if n = 7, the critical values of S are S = 13 for a test with significance a = 0.05 and
S = 11 for a test with significance a = 0.10. Under the null hypothesis assumption of No Trend,
the absolute value of the test statistic |S| would equal or exceed critical value of S* = 11 in less
than 10% of repeated trials, and would exceed S* = 13 in less than 5% of the trials. If the test
statistic does exceed the critical value, the null hypothesis is rejected.
Draft Technical Report
D-4
June 2011
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Values of |S| greater than or equal to S will occur at a rate less than or equal to a when the true
trend is 0. To test the null hypothesis of no trend against Hi (upward trend), reject H0 if S > 0
and if |S| > S . For testing the null hypothesis of no trend against H2 (downward trend), reject Ho
if S < 0 and |S| > S . In the example in Box D-l, the sum S is 11. Thus, the null hypothesis of
No Trend can be rejected at the a = 0.10 significance level, but not at the a = 0.05 significance
level. Referring to the more detailed Table A.21 in Hollander and Wolfe (1999), the exact
probability under the null distribution that S > 11 when n = 7 is 0.068. Thus, there is borderline
evidence of a positive trend in this well.
If the sample size is more than 40, a normal approximation to the Mann-Kendall procedure may
be used to test for a significant trend. In this approach, the value of S calculated from the data
series is standardized using the expected value and variance of the sampling distribution of S
under the null hypothesis. The standardized value of S is used as the test statistic in the normal
approximation as described in Box D-3.
Box D-3. Directions for the Mann-Kendall Procedure Using Normal Approximation
STEP 1: Complete steps 1, 2, and 3 of BoxD-2.
STEP 2: Calculate the variance of S: V(S) = n(n-l)(2n+5)/18.
If ties occur, let g represent the number of tied groups and wp represent the number of data points in the plh
group. The variance of S is:
V(S) = [n(n-l)(2n+5) - £p wp (wp-l)(2wp+5)]/18
STEP 3: Calculate Z = (S-l) / [V(S)]1/2 if S > 0, Z= 0 if S = 0, orZ = (S+l) / [V(S)]1/2 if S < 0.
STEP 4: Use Table E-2 of Attachment E to find the critical value z\_ a such that
100(l-a)% of the normal distribution is below zi_ a . For example, if a =0.05 then zi_a =1.645.
STEP 5: For testing the hypothesis, H0 (no trend) against
(1) H1 (an upward trend) - reject H0 if Z is greater than a, or
(2) H2 (a downward trend) - reject H0 if Z < 0 and the absolute value of Z is greater than zUa.
D.3 Instructions for Performing the Mann-Kendall Trend Test for Multiple Wells
When one measurement is taken for each time period for each well, a generalization of the
Mann-Kendall statistic is used to test for a trend across all wells. This procedure is described in
Box D-4.
Box D-4. Data for Multiple Times and Multiple Wells
Let i = 1, 2,..., n represent time, k = 1, 2,..., K represent wells, and represent the post-restoration
measurements at time i from well k. This data can be summarized in matrix form, as shown in Box D-5,
where Sk = Mann-Kendall statistic for well k (see STEP 3, Box D-2), V(Sk) = variance for S statistic for
well k (see STEP 2, Box D-3), and Zk=Sk/VAR(Sk).
Draft Technical Report
D-5
June 2011
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Box D-5. Array for Multiple Wells
Wells
Time
1
2
3
4
1
xu
xL2
Xl,3
xM
2
Xo.l
Xo,2
X23
X2.4
3
X3J
X3 2
X3,3
X3 4
4
X4J
X4 2
X43
X4 4
5
X5,1
X5 2
X^,3
X5 4
6
X6.1
X6.2
X6.3
X6.4
sk
Si
S2
S3
s4
Var(Su)
Var(Si)
Var(So)
Var(S3)
Var(S4)
zk
Zi
z2
z3
z4
Box D-6. Testing for Comparability of Wells and an Overall Monotonic Trend
Let i = 1, 2,.... n represent time, k = 1, 2,.... K represent sampling wells, and represent the
measurement at time i from well k. Let a represent the significance level for testing homogeneity and a*
represent the significance level for testing for an overall trend.
STEP 1: Calculate the Mann-Kendall statistic Sk and its variance V(Sk) for each of the K wells using the
method in Box D-4.
STEP 2: For each of the K wells, calculate Z.k = Sk /11 '(.S'/.).|12
STEP 3: Calculate the average ZM = X Z* IK
STEP 4: Calculate the homogeneity chi-square statistic y_2h = (Y/2i¦)- K Z2M
STEP 5: Using a clii-squared table (Table E-3 of Attachment E), find the critical value for yj with (K-l)
degrees of freedom at significance level a. For example, for a significance level of 5% and 5 degrees of
freedom y2,^, = 11.07, i.e., 11.07, is the cut point, which puts 5% of the probability in the upper tail of a
chi-square variable with 5 degrees of freedom.
STEP 6: Ifx2h X2( K-i ! the wells are not homogeneous (i.e.. different dynamics at different wells) at the
significance level a. Therefore, individual a*-level Mann-Kendall tests should be conducted at each well
using the methods presented in Box D-2.
STEP 7: Using a clii-squared table (Table E-3 of Attaclunent E), find the critical value for yj with 1 degree
of freedom at significance level a. If KZ2M > y;ah reject H0* and conclude that there is a significant
(upward or downward) monotonic trend across all wells at significance level a*. The signs of the Sk
indicate whether increasing or decreasing trends are present. If KZ2M < x2(i >, there is not significant
evidence of a monotonic trend across all wells. That is, the wells appear approximately stable over time.
D.4 Performing the Wilcoxon Rank Sum Test
Comparison of post-restoration with baseline samples is required in Phase 4 to assess steady-
state conditions, and in Phase 5 to determine if post-restoration values have achieved targeted
remediation levels. In these comparisons, the statistical approach adopted will depend on the
type of data collected. Before a comparison is attempted, the post-restoration samples are
Draft Technical Report
D-6
June 2011
-------
examined for trends using the procedures in Section D.2 to demonstrate that the wells have
reached a stable condition.
If the baseline and post-restoration samples are paired {i.e., from the same well), then the paired
nature of the data is used in the analysis and the wells are analyzed separately. If (and only if) all
individual wells exhibit similar dynamics, the results of tests on individual wells are then
combined to conduct an analysis of the entire wellfield. Before wells are combined for a
summary analysis of the wellfield, the post-restoration samples are compared well-for-well with
the baseline samples using WRS test.
If some or all of the baseline and post-restoration samples are from unmatched wells, then the
paired wells are treated individually using the procedure described above and the baseline and
post-restoration data for the unpaired wells are pooled into two datasets (before and after), and
the WRS comparison method described for a single well is applied to the set of unmatched wells.
Instructions for conducting the WRS test are outlined in the five steps shown in Box A-7 and the
calculations for a single well are illustrated in Box A-8a. In this example, a value of A = 3 was
selected, approximately 10% of the baseline average. The hypothetical baseline and post-
restoration data in Box D-8a for Well 1 are plotted in Figure D-4. The values of the baseline
plus A are also plotted in this figure. For a test with a significance level of a = 0.10, the critical
value given in Table B-8 of Appendix B for n = 6 and m = 4 is 28. Since W\ = 29 is greater than
the critical value for the test, we reject the null hypothesis that the post-restoration distribution
exceeds the baseline distribution by more than a substantial difference A. It appears that the
post-restoration conditions in this well are below or no more than A above baseline conditions.
Box D-7. Test for a Substantial Difference between Baseline and
Post-Restoration Conditions
STEP 1: Create an array with the (seasonally adjusted) baseline values A, across the top and the post-
restoration values down the left side as shown in Box D-8a.
STEP 2: Obtain the m adjusted baseline measurements X, by adding the substantial difference A to each
baseline measurement: X, = A , + A.
STEP 3: Fill the matrix of all possible comparisons by assigning a value of+1 when the adjusted baseline
measurement exceeds post-restoration measurement; otherwise assign a value of 0.
STEP 4: Calculate the sum of the sum of the matrix entries. This sum is the Mann-Whitney form of the
test statistic. Add the quantity m(m+l)/2 to the array sum. This is the WRS statistic for well k (II)).
STEP 5: Compare II ) with the critical value given in Attachment E in Table E-5 (a=0.01), Table E-6
(a=0.025), Table E-7 (a=0.05) or Table E-8 (a=0.10) for the appropriate values of n, m. If II) is greater
than the tabulated value, reject the hypothesis that the post-restoration distribution exceeds the baseline
distribution by more than a substantial difference A. If the null hypothesis is rejected, it appears that the
post-restoration conditions in this well are not significantly different from baseline conditions.
Draft Technical Report
D-7
June 2011
-------
Box D-8a. Calculating the WRS Statistic for Well 1
Delta=
3.0
Baseline Samples (m=4)
Post-
1
2
3
4
Remedial
X
32.6
30.0
30.5
33.1
Samples
x+A
35.6
33.0
33.5
36.1
1
33.7
1
0
0
1
2
34.0
1
0
0
1
3
18.4
1
1
1
1
4
33.4
1
0
1
1
5
30.4
1
1
1
1
6
25.8
1
1
1
1
Sum
19
m(m+l)/2
10
Wj
29
Box D-8b. Calculating the WRS Statistic for Well 2
Delta=
3.0
Baseline Samples (m=6)
Post-
1
2 3
4 5
6
Remedial
X
34.4
34.1 34.5
23.7 34.1
29.6
Samples
x+A
37.4
37.1 37.5
26.7 37.1
32.6
1
34.2
1
1 1
0 1
0
2
18.8
1
1 1
1 1
1
3
35.4
1
1 1
0 1
0
4
43.2
0
0 0
0
5
22.6
1
1 1
1 1
1
6
28.0
1
1 1
0 1
1
7
21.1
1
1 1
1 1
1
8
28.7
1
1 1
0 1
1
9
23.1
1
1 1
1 1
1
10
29.2
1
1 1
0 1
1
11
31.5
1
1 1
0 1
1
12
32.0
1
1 1
0 1
1
Sum
57
m(m+l)/2
21
w2
78
Box D-8c. Calculating the WRS Statistic for Well 3
Delta=
3.0
Baseline Samples (m=8)
Post-
1
2
3
4
5
6
7
8
Remedial
X
33.4
41.7
28.6
33.3
30.7
25.2
24.6
23.6
Samples
x+A
36.4
44.7
31.6
36.3
33.7
28.2
27.6
26.6
1
24.0
1
1
1
1
1
1
1
1
2
40.0
0
1
0
0
0
0
0
0
3
36.6
0
1
0
0
0
0
0
0
4
30.7
1
1
1
1
1
0
0
0
5
36.6
0
1
0
0
0
0
0
0
6
34.4
1
1
0
1
0
0
0
0
7
27.2
1
1
1
1
1
1
1
0
8
30.2
1
1
1
1
1
0
0
0
9
31.1
1
1
1
1
1
0
0
0
10
29.9
1
1
1
1
1
0
0
0
Sum
41
m(m+l)/2
36
W3
77
Draft Technical Report
D-8
June 2011
-------
40
30
o
H
<
a:
g 20
LU
o
z
o
° 10
10
20
30
Post-restoration
TIME
¦ Baseline
Baseline+A
Figure D-4. Hypothetical Baseline and Post-restoration Data for Well 1
» Post-restoration <¦ Baseline a Baseline+A
Figure D-5. Hypothetical Baseline and Post-restoration Data for Well 2
Draft Technical Report
D-9
June 2011
-------
O 20
Post-restoration Basehne ±-Basehne+A
Figure D-6. Hypothetical Baseline and Post-restoration Data for Well 3
D.5 Instructions for Performing the Wilcoxon Rank Sum Test for Multiple Wells
The comparison for multiple wells is based on the WRS statistics Wk for each well calculated
using the instructions in Box D-7. Tables for calculating Wk for three wells are shown in Boxes
D-8a, D-8b and D-8c.
To test for multiple wells, first compute the mean and variance of Wk under the null distribution
as shown in Box D-9. The standardized form of the test statistic W\: is zk = (Wk - Ek)/y[Vk .
Instructions for conducting the comparability test and the test for overall compliance with
remediation goals are shown in Box D-9. The calculations for the three wells in Boxes D-8a,
D-8b, and D-8c are illustrated in Box D-10. The hypothetical baseline and post-restoration data
for Wells 1, 2 and 3 are plotted in Figures D-4, D-5 and D-6. The values of the baseline plus A
are also plotted in these figures.
Box D-9. Testing for Comparability of Wells and Overall Compliance with
Remedial Goals
Let k = 1, 2,.... K represent sampling wells, and Xlk represent the measurement at time i from well k. Let a
represent the significance level for testing homogeneity and a* represent the significance level for testing
for an overall compliance.
STEP 1: For each of the K wells, calculate the WRS statistic Wk using the instructions in Box D-7, and its
expected value E(Wk) and variance V(Wk) using the equations below.
Ek = E(Wk) = nu(iik + nu- + 1)/2
Vk = Var(Wk) = mnu-(ni- + nu- + l)/12
Draft Technical Report
D-10
June 2011
-------
STEP 2: For each of the K wells, calculate the standardized test statistic
Zk = (iWk-Ek)/JVk
STEP 3: Calculate the average ZM = £ Zk IK
STEP 4: Calculate the homogeneity chi-square statistic y_2h = C£/.2k) - KZ2U
STEP 5: Using a chi-squared table (Table E-3 of Attachment E), find the critical value for x2 with (K-l)
degrees of freedom at significance level a. For a significance level of 5% and 5 degrees of freedom, x%) =
11.07, i.e., 11.07, is the cut point, which puts 5% of the probability in the upper tail of a chi-square
variable with 5 degrees of freedom.
STEP 6: Ifx2h x2(k-i ), the wells are not homogeneous (i.e.. different dynamics at different wells) at the
significance level a. Therefore, individual a*-level WRS tests should be conducted at each well using the
methods presented in Box D-7.
STEP 7: Using a chi-squared table (Table E-3 of Attachment E), find the critical value for x2 with 1 degree
of freedom at significance level a*. If x2c =KZ2M > x2(i), reject H0* and conclude the site appears to be
below baseline conditions or no more than A higher than baseline conditions. If K/2M < x2(i), there is not
significant evidence at the a* level that all wells are in compliance with the remediation goals.
Box D-10. Tests for Homogeneity and Overall Compliance
k
mk
nk
wk
Ek
vk
Zk
(zk)2
1
4
6
29
22
22.0
1.492
2.227
2
6
12
78
57
114.0
1.967
3.868
3
8
10
77
76
126.7
0.089
0.008
Zm
1.183
6.104
I2 h
1.907
I2 c
4.196
2
Comparing the value of x h (1.907) in Box D-10 to the critical value from Table E-3 (a=0.05,
DF=2) of 5.991, we conclude that there are comparable dynamics across the wells (see Box D-9,
Step 6). Similarly, comparing x\ = 4.196 with the critical value from Table E-3 (a=0.05, DF=1)
of 3.841, we conclude that, since > x2 , the restoration values are no more than A higher than
the baseline (Box D-9, Step 7).
D.6 A Real-life Example
Figures D-7 through D-12 show plots of the post-restoration measurements of six groundwater
parameters taken in six wells at the Crow Butte ISL site in Nebraska (see Attachment E for
details.). The six parameters evaluated are total dissolved solids (TDS), radium, selenium, iron,
ammonium, and uranium. A separate analysis was done for a time series consisting of the
across-well average in each period. Values below the limit of detection have been replaced with
a value equal to the limit of detection. The data were analyzed for trends in individual wells
(Section 5.3.2 and Section D-2 of this attachment) and for a common trend across all wells
(Section 5.3.3 and Section D-3 of this attachment). Separate analyses were conducted for each
parameter. An error rate of a=0.10 was used for all tests.
Draft Technical Report
D-ll
June 2011
-------
Results of the analysis are shown in Table D-2. The table shows the value of the Mann-Kendall
test statistic, number of data points, standard deviation and standardized test statistic for each
parameter in each well. The test for homogeneity of trend across wells shows that the trend is
not homogeneous for two parameters: TSD and radium. The test for monotonic trend across all
wells is reported for the remaining four parameters. (The test results for TDS and radium are
darkened, because the test for homogeneity of trend indicates these are not meaningful.) The test
for a monotonic trend indicates that selenium and ammonium show no trend, while iron and
uranium do show significant upward trends at the 0.10 level of confidence.
A summary of trend test results by well is shown at the bottom of Table D-2 for TDS and
radium, which showed no homogeneity of trend across wells, and for iron and uranium, which
showed significant upward trends. Individual wells generally have upward trends for TDS, iron,
and uranium, while results are mixed for radium. All four parameters show upward trends for
the average well at the 0.10 significance level. From an implementation perspective, the results
for radium and TDS suggest that continued restoration and monitoring would be advisable to
determine if these parameters attain steady-state values over an extended time period. TDS is
typically considered an indicator of active changes in the geochemical state, i.e., solutes are
being actively introduced into or removed from the groundwater by some mechanism(s).
Radium concentrations may be controlled by solubility constraints that may also still be evolving
in the system, as suggested by the TDS behavior.
TDS
130
90
2 3 4 5 6 7 8 9 10 11 12
Round of Testing
-~PR-15
IJ-25
I Average Well
*IJ-13
¦*IJ-28
Trend of Average
PR-8
IJ-45
Figure D-7. Total Dissolved Solids (TSD)
Draft Technical Report
D-12
June 2011
-------
Radium
300
50
1 2 3
4
5 6 7 8
Round of Testing
9 10 11 12
~PR-15
¦IJ-13
* PR-8
IJ-25
*IJ-28
IJ-45
Trend of Averaqe
Figure D-8. Total Dissolved Solids (TSD)
Selenium
450
1 2 3
4
5 6 7 8
Round of Testing
9 10 11 12
~PR-15
¦IJ-13
± PR-8
IJ-25
*IJ-28
IJ-45
Trend of Averaqe
Figure D-9. Selenium
Draft Technical Report
D-13
June 2011
-------
Iron
6000
5000
4000
o
c o
n ^
3000
>i
ra
Q
2000
1000
1
2
3
4
5
6
7
8
9
10 11
12 13
Round of Testing
~PR-15
¦IJ-13
A PR-8
IJ-25
*IJ-28
IJ-45
Trend of Averaqe
Figure D-10. Iron
Ammonium
700
600
500
O
c o 400
o
15 ii
ST 300
Q
200
100
1
2
3
4
5
6
7
8
9
10
11
12
Round of Testing
~PR-15
¦ IJ-13
PR-8
IJ-25
*IJ-28
IJ-45
Trend of Averaqe
Figure D-ll. Ammonium
Draft Technical Report D-14 June 2011
-------
Uranium
1 1000
9 10 11 12 13 14 15 16 17 18 19 20
Round of Testing
-~ PR-15
IJ-25
I Average Well
-¦IJ-13
-XIJ-28
Trend of Average
PR-8
- IJ-45
Figure D-12. Uranium
Draft Technical Report
D-15
June 2011
-------
Table D-2. Analysis for Trends of Six Parameters in Six Wells
S = Count
Well
TDS
Radium
Selenium
Iron
Ammonium
Uranium
PR-15
26
-13
-15
19
-3
7
IJ-13
26
22
8
29
1
32
PR-8
19
31
-5
38
-10
-19
IJ-25
17
-17
11
29
-11
26
IJ-28
19
-1
-19
34
-5
24
IJ-45
-12
-7
-4
12
-3
22
Average
22
19
-11
40
-9
52
Well
N = Sample Size
Well
TDS
Radium
Selenium
Iron
Ammonium
Uranium
PR-15
9
10
10
10
9
16
IJ-13
9
10
10
10
9
17
PR-8
9
10
10
10
9
16
IJ-25
9
10
10
10
9
10
IJ-28
9
10
10
10
9
10
IJ-45
9
10
10
10
9
10
Average
9
10
10
10
9
17
Well
SD(S) = Standard Deviation of S
Well
TDS
Radium
Selenium
Iron
Ammonium
Uranium
PR-15
9.59
11.18
11.18
11.18
9.59
22.21
IJ-13
9.59
11.18
11.18
11.18
9.59
24.28
PR-8
9.59
11.18
11.18
11.18
9.59
22.21
IJ-25
9.59
11.18
11.18
11.18
9.59
11.18
IJ-28
9.59
11.18
11.18
11.18
9.59
11.18
IJ-45
9.59
11.18
11.18
11.18
9.59
11.18
Average
9.59
11.18
11.18
11.18
9.59
24.28
Well
z(S) = Normal Score for S
Well
TDS
Radium
Selenium
Iron
Ammonium
Uranium
PR-15
2.61
-1.07
-1.25
1.61
-0.21
0.27
IJ-13
2.61
1.88
0.63
2.50
0.00
1.28
PR-8
1.88
2.68
-0.36
3.31
-0.94
-0.81
IJ-25
1.67
-1.43
0.89
2.50
-1.04
2.24
IJ-28
1.88
0.00
-1.61
2.95
-0.42
2.06
IJ-45
-1.15
-0.54
-0.27
0.98
-0.21
1.88
Average
2.19
1.61
-0.89
3.49
-0.83
2.10
Well
p(S) = Probability{Z>|z(S)|}
Well
TDS
Radium
Selenium
Iron
Ammonium
Uranium
PR-15
0.005
0.142
0.105
0.054
0.417
0.394
IJ-13
0.005
0.030
0.266
0.006
0.500
0.101
PR-8
0.030
0.004
0.360
0.001
0.174
0.209
IJ-25
0.048
0.076
0.186
0.006
0.149
0.013
IJ-28
0.030
0.500
0.054
0.002
0.338
0.020
IJ-45
0.126
0.296
0.394
0.163
0.417
0.030
Average
0.014
0.054
0.186
0.000
0.202
0.018
Well
Draft Technical Report
D-16
June 2011
-------
Table D-2. Analysis for Trends of Six Parameters in Six Wells
Summary
Statistic
TDS
Radium
Selenium
Iron
Ammonium
Uranium
Mean(z)
1.58
0.25
-0.33
2.31
-0.47
1.15
Range(z)
3.75
4.11
2.5
2.33
1.04
3.05
Test for Homogeneity of Trend Across Wells
Statistic
TDS
Radium
Selenium
Iron
Ammonium
Uranium
XTh
9.73
13.83
4.91
3.73
0.91
7.17
Result
Fail
Fail
Pass
Pass
Pass
Pass
Test for Monotonic Trend Across All Wells
Statistic
TDS
Radium
Selenium
Iron
Ammonium
Uranium
XTi
15
0.39
0.65
32.03
1.32
7.95
Result
Up
Flat
Flat
Up
Flat
Up
Significant Trends (p(S)< 0.10 )
Well
TDS
Radium
Selenium
Iron
Ammonium
Uranium
PR-15
UP
-
UP
-
IJ-13
UP
UP
-
UP
-
-
PR-8
UP
UP
-
UP
-
-
IJ-25
UP
DOWN
-
UP
-
UP
IJ-28
UP
-
DOWN
UP
-
UP
IJ-45
-
-
-
-
-
UP
Average
UP
UP
-
UP
-
UP
Well
The two highlighted entries in the under the row titled "Test for Homogeneity of Trend Across
Wells" indicate that the wells cannot be lumped together for TDS and radium. The entries in the
"Test for Monotonic Trend Across All Wells" rows are darkened because the test is not
appropriate for these parameters. The summary of individual well results at the bottom of the
table indicates that almost all wells show up-trends for TDS and two wells show up-trends for
radium.
The remaining four parameters pass the test for homogeneity of trend. The results of the test for
a monotonic trend are only meaningful for these four parameters. Of the four, uranium and iron
show monotonic up-trends across all wells. This is confirmed by the summary of individual well
results at the bottom of the table.
Only two parameters demonstrate stability in the post-restoration period; selenium and
ammonium.
Draft Technical Report
D-17
June 2011
-------
ATTACHMENT E: STATISTICAL TABLES
Table E-l. Critical Values of S in Mann-Kendall Trend Test for a Series of Size N
Alpha
Alpha
N
0.01
0.05
0.10
N
0.01
0.05
0.10
4
-
6
6
23
89
65
51
5
10
8
8
24
94
68
54
6
13
11
9
25
102
72
58
7
17
13
11
26
105
77
61
8
20
16
12
27
111
81
63
9
24
18
14
28
118
86
68
10
27
21
17
29
124
90
70
11
31
23
19
30
131
95
75
12
36
26
20
31
137
99
77
13
40
28
24
32
144
104
82
14
45
31
25
33
150
108
86
15
49
35
29
34
157
113
89
16
54
38
30
35
165
119
93
17
58
42
34
36
172
124
96
18
63
45
37
37
178
128
100
19
69
49
39
38
185
133
105
20
72
52
42
39
193
139
109
21
78
56
44
40
200
142
112
22
83
61
47
Source: Adapted from Hollander and Wolfe 1999, Table A.21
Draft Technical Report
E-l
June 2011
-------
Tables E-2. Normal Distribution Table
(Cumulative probabilities for positive z-values are shown in table)
z
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.0
0.5000
0.5040
0.5080
0.5120
0.5160
0.5199
0.5239
0.5279
0.5319
0.5359
0.1
0.5398
0.5438
0.5478
0.5517
0.5557
0.5596
0.5636
0.5675
0.5714
0.5753
0.2
0.5793
0.5832
0.5871
0.5910
0.5948
0.5987
0.6026
0.6064
0.6103
0.6141
0.3
0.6179
0.6217
0.6255
0.6293
0.6331
0.6368
0.6406
0.6443
0.6480
0.6517
0.4
0.6554
0.6591
0.6628
0.6664
0.6700
0.6736
0.6772
0.6808
0.6844
0.6879
0.5
0.6915
0.6950
0.6985
0.7019
0.7054
0.7088
0.7123
0.7157
0.7190
0.7224
0.6
0.7257
0.7291
0.7324
0.7357
0.7389
0.7422
0.7454
0.7486
0.7517
0.7549
0.7
0.7580
0.7611
0.7642
0.7673
0.7704
0.7734
0.7764
0.7794
0.7823
0.7852
0.8
0.7881
0.7910
0.7939
0.7967
0.7995
0.8023
0.8051
0.8078
0.8106
0.8133
0.9
0.8159
0.8186
0.8212
0.8238
0.8264
0.8289
0.8315
0.8340
0.8365
0.8389
1.0
0.8413
0.8438
0.8461
0.8485
0.8508
0.8531
0.8554
0.8577
0.8599
0.8621
1.1
0.8643
0.8665
0.8686
0.8708
0.8729
0.8749
0.8770
0.8790
0.8810
0.8830
1.2
0.8849
0.8869
0.8888
0.8907
0.8925
0.8944
0.8962
0.8980
0.8997
0.9015
1.3
0.9032
0.9049
0.9066
0.9082
0.9099
0.9115
0.9131
0.9147
0.9162
0.9177
1.4
0.9192
0.9207
0.9222
0.9236
0.9251
0.9265
0.9279
0.9292
0.9306
0.9319
1.5
0.9332
0.9345
0.9357
0.9370
0.9382
0.9394
0.9406
0.9418
0.9429
0.9441
1.6
0.9452
0.9463
0.9474
0.9484
0.9495
0.9505
0.9515
0.9525
0.9535
0.9545
1.7
0.9554
0.9564
0.9573
0.9582
0.9591
0.9599
0.9608
0.9616
0.9625
0.9633
1.8
0.9641
0.9649
0.9656
0.9664
0.9671
0.9678
0.9686
0.9693
0.9699
0.9706
1.9
0.9713
0.9719
0.9726
0.9732
0.9738
0.9744
0.9750
0.9756
0.9761
0.9767
2.0
0.9772
0.9778
0.9783
0.9788
0.9793
0.9798
0.9803
0.9808
0.9812
0.9817
2.1
0.9821
0.9826
0.9830
0.9834
0.9838
0.9842
0.9846
0.9850
0.9854
0.9857
2.2
0.9861
0.9864
0.9868
0.9871
0.9875
0.9878
0.9881
0.9884
0.9887
0.9890
2.3
0.9893
0.9896
0.9898
0.9901
0.9904
0.9906
0.9909
0.9911
0.9913
0.9916
2.4
0.9918
0.9920
0.9922
0.9925
0.9927
0.9929
0.9931
0.9932
0.9934
0.9936
2.5
0.9938
0.9940
0.9941
0.9943
0.9945
0.9946
0.9948
0.9949
0.9951
0.9952
2.6
0.9953
0.9955
0.9956
0.9957
0.9959
0.9960
0.9961
0.9962
0.9963
0.9964
2.7
0.9965
0.9966
0.9967
0.9968
0.9969
0.9970
0.9971
0.9972
0.9973
0.9974
2.8
0.9974
0.9975
0.9976
0.9977
0.9977
0.9978
0.9979
0.9979
0.9980
0.9981
2.9
0.9981
0.9982
0.9982
0.9983
0.9984
0.9984
0.9985
0.9985
0.9986
0.9986
3.0
0.9987
0.9987
0.9987
0.9988
0.9988
0.9989
0.9989
0.9989
0.9990
0.9990
3.1
0.9990
0.9991
0.9991
0.9991
0.9992
0.9992
0.9992
0.9992
0.9993
0.9993
3.2
0.9993
0.9993
0.9994
0.9994
0.9994
0.9994
0.9994
0.9995
0.9995
0.9995
3.3
0.9995
0.9995
0.9995
0.9996
0.9996
0.9996
0.9996
0.9996
0.9996
0.9997
3.4
0.9997
0.9997
0.9997
0.9997
0.9997
0.9997
0.9997
0.9997
0.9997
0.9998
Draft Technical Report
E-2
June 2011
-------
Table E-3. Chi Squared Distribution Table
(Table shows values of x where probability(x2> x}= a)
a
DF
0.2
0.1
0.05
0.025
0.02
0.01
0.005
0.002
0.001
1
1.642
2.706
3.841
5.024
5.412
6.635
7.879
9.550
10.828
2
3.219
4.605
5.991
7.378
7.824
9.210
10.597
12.429
13.816
3
4.642
6.251
7.815
9.348
9.837
11.345
12.838
14.796
16.266
4
5.989
7.779
9.488
11.143
11.668
13.277
14.860
16.924
18.467
5
7.289
9.236
11.070
12.833
13.388
15.086
16.750
18.907
20.515
6
8.558
10.645
12.592
14.449
15.033
16.812
18.548
20.791
22.458
7
9.803
12.017
14.067
16.013
16.622
18.475
20.278
22.601
24.322
8
11.030
13.362
15.507
17.535
18.168
20.090
21.955
24.352
26.124
9
12.242
14.684
16.919
19.023
19.679
21.666
23.589
26.056
27.877
10
13.442
15.987
18.307
20.483
21.161
23.209
25.188
27.722
29.588
11
14.631
17.275
19.675
21.920
22.618
24.725
26.757
29.354
31.264
12
15.812
18.549
21.026
23.337
24.054
26.217
28.300
30.957
32.909
13
16.985
19.812
22.362
24.736
25.472
27.688
29.819
32.535
34.528
14
18.151
21.064
23.685
26.119
26.873
29.141
31.319
34.091
36.123
15
19.311
22.307
24.996
27.488
28.259
30.578
32.801
35.628
37.697
16
20.465
23.542
26.296
28.845
29.633
32.000
34.267
37.146
39.252
17
21.615
24.769
27.587
30.191
30.995
33.409
35.718
38.648
40.790
18
22.760
25.989
28.869
31.526
32.346
34.805
37.156
40.136
42.312
19
23.900
27.204
30.144
32.852
33.687
36.191
38.582
41.610
43.820
20
25.038
28.412
31.410
34.170
35.020
37.566
39.997
43.072
45.315
21
26.171
29.615
32.671
35.479
36.343
38.932
41.401
44.522
46.797
22
27.301
30.813
33.924
36.781
37.659
40.289
42.796
45.962
48.268
23
28.429
32.007
35.172
38.076
38.968
41.638
44.181
47.391
49.728
24
29.553
33.196
36.415
39.364
40.270
42.980
45.559
48.812
51.179
25
30.675
34.382
37.652
40.646
41.566
44.314
46.928
50.223
52.620
26
31.795
35.563
38.885
41.923
42.856
45.642
48.290
51.627
54.052
27
32.912
36.741
40.113
43.195
44.140
46.963
49.645
53.023
55.476
28
34.027
37.916
41.337
44.461
45.419
48.278
50.993
54.411
56.892
29
35.139
39.087
42.557
45.722
46.693
49.588
52.336
55.792
58.301
30
36.250
40.256
43.773
46.979
47.962
50.892
53.672
57.167
59.703
31
37.359
41.422
44.985
48.232
49.226
52.191
55.003
58.536
61.098
32
38.466
42.585
46.194
49.480
50.487
53.486
56.328
59.899
62.487
33
39.572
43.745
47.400
50.725
51.743
54.776
57.648
61.256
63.870
34
40.676
44.903
48.602
51.966
52.995
56.061
58.964
62.608
65.247
35
41.778
46.059
49.802
53.203
54.244
57.342
60.275
63.955
66.619
36
42.879
47.212
50.998
54.437
55.489
58.619
61.581
65.296
67.985
37
43.978
48.363
52.192
55.668
56.730
59.893
62.883
66.633
69.346
38
45.076
49.513
53.384
56.896
57.969
61.162
64.181
67.966
70.703
39
46.173
50.660
54.572
58.120
59.204
62.428
65.476
69.294
72.055
40
47.269
51.805
55.758
59.342
60.436
63.691
66.766
70.618
73.402
41
48.363
52.949
56.942
60.561
61.665
64.950
68.053
71.938
74.745
42
49.456
54.090
58.124
61.777
62.892
66.206
69.336
73.254
76.084
43
50.548
55.230
59.304
62.990
64.116
67.459
70.616
74.566
77.419
44
51.639
56.369
60.481
64.201
65.337
68.710
71.893
75.874
78.750
45
52.729
57.505
61.656
65.410
66.555
69.957
73.166
77.179
80.077
46
53.818
58.641
62.830
66.617
67.771
71.201
74.437
78.481
81.400
47
54.906
59.774
64.001
67.821
68.985
72.443
75.704
79.780
82.720
48
55.993
60.907
65.171
69.023
70.197
73.683
76.969
81.075
84.037
49
57.079
62.038
66.339
70.222
71.406
74.919
78.231
82.367
85.351
50
58.164
63.167
67.505
71.420
72.613
76.154
79.490
83.657
86.661
Source: http://www.medcalc.org/manual/clii-sauare-table.php
Draft Technical Report
E-3
June 2011
-------
Table E-4. Sample Sizes for the Wilcoxon Rank Sum Test
(Table shows values of m + n)
a=0.01
a
=0.025
cf0.05
a=0.10
cf0.20
MDD/ct
3=0.01
LO
O
o
CO-
LO
CM
O
O
CO.
3=0.10
3=0.20
3=0.01
LO
CM
O
O
CO.
LO
O
O
CO.
3=0.10
3=0.20
3=0.01
LO
CM
O
O
CO.
LO
O
o
CO.
3=0.10
3=0.20
3=0.01
LO
CM
O
O
CO.
LO
O
o
CO.
3=0.10
3=0.20
3=0.01
LO
CM
O
O
CO.
LO
O
o
CO.
3=0.10
3=0.20
0.1
12058
10234
8785
7252
5592
10233
8559
7239
5854
4373
8783
7238
6028
4771
3445
7249
5852
4770
3660
2512
5589
4371
3443
2511
1579
0.2
3018
2562
2200
1816
1401
2561
2142
1812
1466
1096
2198
1811
1509
1194
863
1814
1464
1194
916
629
1398
1094
862
628
395
0.3
1344
1141
980
810
625
1140
954
807
653
489
978
806
672
532
385
807
652
531
408
281
622
487
383
280
176
0.4
758
644
553
457
354
643
538
455
369
276
551
455
379
300
218
455
367
300
230
159
350
274
216
158
100
0.5
486
413
356
294
228
412
345
293
237
178
354
292
243
193
140
292
236
192
148
102
225
176
139
101
64
0.6
339
288
248
206
159
287
241
204
166
125
246
203
170
135
98
203
164
134
103
71
156
122
97
71
45
0.7
250
213
183
152
118
212
178
151
123
92
182
150
125
100
73
150
121
99
76
53
115
90
71
52
33
0.8
193
164
141
118
92
163
137
116
95
71
140
115
97
77
56
115
93
76
59
41
88
69
55
40
26
0.9
153
131
113
94
73
129
109
93
75
57
111
92
77
61
45
91
74
61
47
33
70
55
43
32
20
1
125
107
92
77
60
105
89
76
62
47
90
75
63
50
37
74
60
49
38
27
57
45
35
26
-
1.1
104
89
77
64
50
88
74
63
52
39
75
62
52
42
31
62
50
41
32
22
47
37
29
22
-
1.2
88
75
65
55
43
74
63
53
44
34
63
53
44
35
26
52
42
35
27
-
40
31
25
-
-
1.3
76
65
56
47
37
64
54
46
38
29
54
45
38
31
23
45
36
30
23
-
34
27
21
-
-
1.4
66
56
49
41
33
55
47
40
33
25
47
39
33
27
20
39
31
26
20
-
30
23
-
-
-
1.5
58
50
43
36
29
49
41
35
29
23
41
35
29
24
-
34
28
23
-
-
26
20
-
-
-
1.6
51
44
39
33
26
43
37
31
26
20
37
31
26
21
-
30
24
20
-
-
23
-
-
-
-
1.8
41
36
31
27
22
35
30
26
21
-
29
25
21
-
-
24
20
-
-
-
-
-
-
-
-
2
34
30
26
22
-
29
25
21
-
-
24
20
-
-
-
20
-
-
-
-
-
-
-
-
-
2.25
28
24
22
-
-
23
20
-
-
-
20
-
-
-
-
-
-
-
-
-
-
-
-
-
-
2.5
24
21
-
-
-
20
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Notes: Shaded region shows resolutions (MDD/c) obtainable with a combined sample size of 144 = (6)(3)(4)(2): 6 wells with 3 years of quarterly measurements per well in
baseline and post-remedial periods.
The upper highlighted entry for a=0.05 and (3=0.05 indicates that differences smaller than 0.7c are not resolvable with a sample size of 144 at this level of confidence, but
larger differences are resolvable.
In individual wells (with 24 observations) differences of 1.8c are resolvable, but smaller differences are not (lower highlight)
- Sample size estimates based on normal approximation are not reliable if m or n is less than 10.
Draft Technical Report
E-4
June 2011
-------
Table E-5. Critical Value for WRS Test for a=0.01
2
3
4
5
6
7
8
9
n
10
11
12
13
14
15
16
17
18
19
20
m 2
7
9
11
13
15
17
19
21
23
25
27
28
30
32
34
36
38
39
41
3
12
15
18
21
24
26
29
31
34
37
39
42
45
47
50
52
55
58
60
4
18
22
26
29
32
36
39
42
46
49
52
56
59
62
66
69
72
76
79
5
25
30
34
38
42
46
50
54
58
62
66
70
74
78
82
86
90
94
98
6
33
39
43
48
53
58
62
67
72
77
81
86
91
95
100
104
109
114
118
7
42
48
54
59
65
70
76
81
86
92
97
102
108
113
118
123
129
134
139
8
52
59
65
71
77
84
90
96
102
108
114
120
125
131
137
143
149
155
161
9
63
70
77
84
91
98
105
111
118
125
131
138
144
151
157
164
170
177
184
10
75
83
91
98
106
113
121
128
135
142
150
157
164
171
178
186
193
200
207
11
88
97
105
113
122
130
138
146
153
161
169
177
185
193
200
208
216
224
232
12
102
111
120
129
138
147
156
164
173
181
190
198
207
215
223
232
240
249
257
13
116
127
137
146
156
165
174
184
193
202
211
220
229
238
247
256
265
274
283
14
132
144
154
164
175
185
194
204
214
224
234
243
253
263
272
282
291
301
311
15
149
161
172
183
194
205
215
226
236
247
257
267
278
288
298
308
319
329
339
16
167
180
192
203
215
226
237
248
259
270
281
292
303
314
325
336
347
357
368
17
186
199
212
224
236
248
260
272
284
295
307
318
330
341
353
364
376
387
399
18
206
220
233
246
259
272
284
296
309
321
333
345
357
370
382
394
406
418
430
19
226
242
256
269
283
296
309
322
335
348
361
373
386
399
411
424
437
449
462
20
248
264
279
293
307
321
335
349
362
376
389
402
416
429
442
456
469
482
495
Note: m is the number of baseline samples and n is the number of post-restoration samples.
Source: MARSSIM, Appendix I (EPA 2000).
Draft Technical Report
E-5
June 2011
-------
Table E-6. Critical Value for WRS Test for a=0.025
2
3
4
5
6
7
8
9
n
10
11
12
13
14
15
16
17
18
19
20
m 2
7
9
11
13
15
17
18
20
22
23
25
27
29
31
33
34
36
38
40
3
12
15
18
20
22
25
27
30
32
35
37
40
42
45
47
50
52
55
57
4
18
22
25
28
31
34
37
41
44
47
50
53
56
59
62
66
69
72
75
5
25
29
33
37
41
44
48
52
56
60
63
67
71
75
79
82
86
90
94
6
33
37
42
47
51
56
60
64
69
73
78
82
87
91
95
100
104
109
113
7
42
47
52
57
63
68
73
78
83
88
93
98
103
108
113
118
123
128
133
8
51
57
63
69
75
81
86
92
98
104
109
115
121
126
132
137
143
149
154
9
62
69
76
82
88
95
101
108
114
120
126
133
139
145
151
158
164
170
176
10
74
81
89
96
103
110
117
124
131
138
145
151
158
165
172
179
186
192
199
11
87
95
103
111
118
126
134
141
149
156
164
171
179
186
194
201
208
216
223
12
100
109
118
126
135
143
151
159
168
176
184
192
200
208
216
224
232
240
248
13
115
125
134
143
152
161
170
179
187
196
205
214
222
231
239
248
257
265
274
14
131
141
151
161
171
180
190
199
208
218
227
236
245
255
264
273
282
292
301
15
148
159
169
180
190
200
210
220
230
240
250
260
270
280
289
299
309
319
329
16
166
177
188
200
210
221
232
242
253
264
274
284
295
305
316
326
337
347
357
17
184
197
209
220
232
243
254
266
277
288
299
310
321
332
343
354
365
376
387
18
204
217
230
242
254
266
278
290
302
313
325
337
348
360
372
383
395
406
418
19
225
239
252
265
278
290
303
315
327
340
352
364
377
389
401
413
425
437
450
20
247
261
275
289
302
315
329
341
354
367
380
393
406
419
431
444
457
470
482
Note: m is the number of baseline samples and n is the number of post-restoration samples.
Source: MARSSIM, Appendix I (EPA 2000).
Draft Technical Report
E-6
June 2011
-------
Table E-7. Critical Value for WRS Test for a=0.05
2
3
4
5
6
7
8
9
n
10
11
12
13
14
15
16
17
18
19
20
m 2
7
9
11
12
14
16
17
19
21
23
24
26
27
29
31
33
34
36
38
3
12
14
17
19
21
24
26
28
31
33
36
38
40
43
45
47
50
52
54
4
18
21
24
27
30
33
36
39
42
45
48
51
54
57
59
62
65
68
71
5
24
28
32
35
39
43
46
50
53
57
61
64
68
71
75
79
82
86
89
6
32
36
41
45
49
54
58
62
66
70
75
79
83
87
91
96
100
104
108
7
41
46
51
56
61
65
70
75
80
85
90
94
99
104
109
113
118
123
128
8
50
56
62
67
73
78
84
89
95
100
105
111
116
122
127
132
138
143
148
9
61
67
74
80
86
92
98
104
110
116
122
128
134
140
146
152
158
164
170
10
73
80
87
93
100
107
114
120
127
133
140
147
153
160
166
173
179
186
192
11
86
93
101
108
115
123
130
137
144
152
159
166
173
180
187
195
202
209
216
12
99
108
116
124
132
140
147
155
165
171
179
186
194
202
209
217
225
233
240
13
114
123
132
140
149
157
166
174
183
191
199
208
216
224
233
241
249
257
266
14
129
139
149
158
167
176
185
194
203
212
221
230
239
248
257
265
274
283
292
15
146
157
167
176
186
196
206
215
225
234
244
253
263
272
282
291
301
310
319
16
164
175
185
196
206
217
227
237
247
257
267
278
288
298
308
318
328
338
348
17
183
194
205
217
228
238
249
260
271
282
292
303
313
324
335
345
356
366
377
18
202
215
226
238
250
261
273
284
295
307
318
329
340
352
363
374
385
396
407
19
223
236
248
261
273
285
297
309
321
333
345
356
368
380
392
403
415
427
439
20
245
258
271
284
297
310
322
335
347
360
372
385
397
409
422
434
446
459
471
Note: m is the number of baseline samples and n is the number of post-restoration samples.
Source: MARSSIM, Appendix I (EPA 2000).
Draft Technical Report
E-7
June 2011
-------
Table E-8. Critical Value for WRS Test for a=0.10
2
3
4
5
6
7
8
9
n
10
11
12
13
14
15
16
17
18
19
20
m 2
7
8
10
11
13
15
16
18
19
21
22
24
26
27
29
30
32
33
35
3
11
13
16
18
20
22
24
27
29
31
33
35
37
40
42
44
46
48
50
4
17
20
22
25
28
31
34
36
39
42
45
48
50
53
56
59
61
64
67
5
23
27
30
34
37
41
44
47
51
54
57
61
64
67
71
74
77
81
84
6
31
35
39
43
47
51
55
59
63
67
71
75
79
83
87
91
94
98
102
7
40
44
49
54
58
63
67
72
76
81
85
90
94
99
103
108
112
117
121
8
49
54
60
65
70
75
80
85
91
96
101
106
111
116
121
126
131
136
141
9
60
66
71
77
83
89
94
100
106
112
117
123
129
134
140
145
151
157
162
10
71
78
84
91
97
103
110
116
122
128
135
141
147
153
160
166
172
178
184
11
84
91
98
105
112
119
126
133
139
146
153
160
167
173
180
187
194
201
207
12
97
105
113
120
128
135
143
150
158
165
172
180
187
194
202
209
216
224
231
13
112
120
129
137
145
153
161
169
177
185
193
201
209
217
224
232
240
248
256
14
128
136
145
154
163
171
180
189
197
206
214
223
231
240
248
257
265
273
282
15
144
154
163
172
182
191
200
209
218
227
236
246
255
264
273
282
291
300
309
16
162
172
182
192
202
211
221
231
241
250
260
269
279
289
298
308
317
327
336
17
180
191
202
212
223
233
243
253
264
274
284
294
305
315
325
335
345
355
365
18
200
211
222
233
244
255
266
277
288
299
309
320
331
342
352
363
374
384
395
19
220
232
244
256
267
279
290
302
313
325
336
347
358
370
381
392
403
415
426
20
242
254
267
279
291
303
315
327
339
351
363
375
387
399
410
422
434
446
458
Note: m is the number of baseline samples and n is the number of post-restoration samples.
Source: MARSSIM, Appendix I (EPA 2000).
Draft Technical Report
E-8
June 2011
------- |