542R04001a
ฎEPA
United States
Environmental Protection
Agenoy
Demonstration of
Two Long-Term Groundwater Monitoring
Optimization Approaches
Report
Compliance boundary
Groundwater flow direction
* +
The nearest
downgradient
receptor
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NOTICE
This report was prepared by Mitretek Systems (Mitretek) for the U.S. Environmental Protection
Agency (U.S. EPA) under U.S. EPA Requisition #B4T024, QT-DC-04-000504, and summarizes
the results of demonstration projects completed by The Parsons Corporation (Parsons) under Air
Force Center for Environmental Excellence (AFCEE) contract (Contract No. F41624-00-D-8024,
Task Order No. 0024), and by Groundwater Services, Inc. (GSI), also under an AFCEE contract
(Contract No. F41624-98-C-8024). Reference to trade names, commercial products, process, or
service does not constitute or imply endorsement, recommendation for use, or favoring by the
United States Government or any agency thereof. The views and opinions of the authors
expressed herein do not necessarily state or reflect those of the United States Government or any
agency thereof.
This document, with its appendices (542-R-04-001b) or without its appendices (542-R-04-001a),
may be downloaded from U.S. EPA's Clean Up Information (CLUIN) System at http://www.clu-
jn.org. A limited number of hard copies of each version also are available free of charge from the
National Service Center for Environmental Publications (NSCEP) at the following address:
U.S. EPA National Service Center for Environmental Publications
P.O. Box 42419
Cincinnati, OH 45242-2419
Phone: (800)490-9198 or (513)489-8190
Fax: (513)489-8695
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PREFACE
This report summarizes the results of a demonstration in which optimization techniques were
used to improve the design of long-term groundwater monitoring programs. Two different
approaches to optimizing groundwater monitoring programs were used in the demonstration:
The Monitoring and Remediation Optimization System (MAROS) software tool,
developed by GSI for AFCEE (2000 and 2002), and
A three-tiered approach applied by Parsons.
The report discusses the results of application of the two approaches to the evaluation and
optimization of groundwater monitoring programs at three sites (the Fort Lewis Logistics Center,
Washington, the Long Prairie Groundwater Contamination Superfund Site in Minnesota, and
Operable Unit D, McClellan Air Force Base, California), and examines the overall results
obtained using the two monitoring program optimization approaches. The primary goals of this
demonstration were to highlight current strategies for applying optimization techniques to
existing long-term monitoring programs, and to assist site managers in understanding the
potential benefits associated with monitoring program optimization. The demonstration was
conducted as part of an assessment of long-term monitoring optimization approaches, initiated by
the U.S. Environmental Protection Agency's Office of Superfund Remediation and Technology
Innovation (USEPA/OSRTI) and AFCEE.
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ACKNOWLEDGEMENTS
This report summarizes the results of a demonstration of two different approaches to optimizing
long-term groundwater monitoring programs. The demonstration projects summarized herein
were completed by The Parsons Corporation (Parsons), Dr. Carolyn Nobel as principal
investigator, and Groundwater Services, Inc. (GSI), Ms. Julia Aziz as principal investigator; the
two teams are commended for the quality of their work, and the principal investigators are
thanked for their helpful cooperation through the course of this project.
This project would not have been possible without the cooperation of the facilities whose
monitoring programs were the subjects of the demonstration:
Fort Lewis, Washington - Richard W. Smith, U.S. Army Corps of Engineers, Seattle District,
Point of Contact
Long Prairie Superfund Site, Minnesota - Mark Elliott, Minnesota Pollution Control Agency, and
Eric Gabrielson, Barr Engineering, Points of Contact
Former McClellan Air Force Base, California - Brenda Callan, URS Corporation, and Diane H.
Kiyota, Air Force Real Property Agency (AFRPA), Points of Contact
The authors also wish to acknowledge the reviewers who have improved this document with their
productive comments. Their advice and assistance during the project are greatly appreciated.
The following agencies or individuals can be contacted for additional information:
U.S. Environmental Protection Agency,
Office of Superfund Remediation and Technology Innovation (U.S. EPA/OSRTI)
MS5102G
1200 Pennsylvania Avenue NW
Washington, D.C. 20460
(703)603-9910
John W. Anthony
Lead Hydrologist
Mitretek Systems
7720 E. Belleview Avenue, Suite BG6
Greenwood Village, Colorado 80111
john.anthony@mitretek.org
Carolyn Nobel, Ph.D.
Senior Scientist
The Parsons Corporation
1700 Broadway, Suite 900
Denver, Colorado 80290
carolyn.nobel@parsons.com
E. Kinzie Gordon
Lead Scientist/Regulatory Specialist
Mitretek Systems
7720 E. Belleview Avenue, Suite BG6
Greenwood Village, Colorado 80111
kinzie.gordon@mitretek.org
Julia J. Aziz
Senior Scientist
Groundwater Services, Inc.
2211 Norfolk Street, Suite 1000
Houston, Texas 77098
i aziz@gsi-net.com
111
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EXECUTIVE SUMMARY
This report summarizes the results of a demonstration in which optimization techniques were used to
improve the design of several long-term groundwater monitoring programs. Two different
approaches to optimizing groundwater monitoring programs were applied in the demonstration:
The Monitoring and Remediation Optimization System (MAROS) software tool, developed
by Groundwater Services, Inc. (GSI) for AFCEE (2000 and 2002), and
A three-tiered approach applied by The Parsons Corporation (Parsons).
The report discusses the results of application of the two approaches to the evaluation and
optimization of groundwater monitoring programs at three sites (the Fort Lewis Logistics Center,
Washington, the Long Prairie Groundwater Contamination Superfund Site in Minnesota, and
Operable Unit D, former McClellan Air Force Base, California), and examines the overall results
obtained using the two long-term monitoring optimization (LTMO) approaches. The primary goals
of this demonstration were to highlight current strategies for applying optimization techniques to
existing long-term monitoring (LTM) programs, and to assist site managers in understanding the
potential benefits associated with monitoring program optimization. The demonstration was
conducted as part of an assessment of LTMO approaches, initiated by the U.S. Environmental
Protection Agency's Office of Superfund Remediation and Technology Innovation (USEPA/OSRTI)
and the Air Force Center for Environmental Excellence (AFCEE).
The MAROS tool is a public-domain software package that operates in conjunction with an electronic
database environment (Microsoft Accessฎ 2000) and performs certain mathematical and/or statistical
functions appropriate to completing qualitative, temporal, and spatial-statistical evaluations of a
groundwater monitoring program, using data that have been loaded into the database (AFCEE, 2000
and 2002). MAROS utilizes parametric temporal analyses (using linear regression) and non-
parametric trend analyses (using the Mann-Kendall test for trends) to assess the statistical
significance of temporal trends in concentrations of contaminants of concern (COCs). MAROS then
uses the results of the temporal-trend analyses to develop recommendations regarding optimal
sampling frequency at each sampling point in a monitoring program by applying a modified Cost-
Effective Sampling algorithm, to assess the feasibility of reducing the frequency of sampling at
individual sampling points. Although the MAROS tool primarily is used to evaluate temporal data, it
also incorporates a spatial statistical algorithm, based on a ranking system that utilizes a weighted
"area-of-influence" approach (implemented using Delaunay triangulation) to assess the relative value
of data generated during monitoring, and to identify the optimal locations of monitoring points.
Formal decision logic and methods of incorporating user-defined secondary lines of evidence
(empirical or modeling results) also are provided, and can be used to further evaluate monitoring data
and make recommendations for adjustments to sampling frequency, monitoring locations, and the
density of the monitoring network.
In the three-tiered LTMO approach, the monitoring-program evaluation is conducted in stages to
address each of the objectives and considerations of monitoring: a qualitative evaluation first is
completed, followed in succession by temporal and spatial evaluations. At the conclusion of each
stage (or "tier") in the evaluation, recommendations are generated regarding potential changes in the
temporal frequency of monitoring, and/or whether to retain or remove each monitoring point
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considered in the evaluation. After all three stages have been completed, the results of all of the
analyses are combined and interpreted, using a decision algorithm, to generate final recommendations
for an effective and efficient LTM program.
Application of the two approaches to the optimization of LTM programs at each of the three case-
study example sites generated recommendations for reductions in sampling frequency and changes in
the numbers and locations of monitoring points that are sampled. Implementation of the optimization
recommendations could lead to reductions ranging from only a few percent to more than 50 percent
in the numbers of samples collected and analyzed annually at particular sites (Table ES.l). The
median recommended reduction in the annual number of samples collected, generated during the
optimization demonstration, was 39 percent. Although available information regarding monitoring-
program costs at each of the three case-study example sites is not directly comparable, it is projected
that depending upon the scale of the particular LTM program, and the nature of the optimization
recommendations, adoption of optimized monitoring programs at each of the case-study sites could
lead to annual cost savings ranging from a few hundred dollars (using the recommendations
generated by MAROS for the monitoring program at Operable Unit D [OU D], former McClellan Air
Force Base [AFB]) to approximately $36,500 (using the results generated by the three-tiered
approach for the monitoring program at the Fort Lewis Logistics Center Area). The results of the
evaluations also demonstrate that each of the optimized monitoring programs remains adequate to
address the primary objectives of monitoring at the sites. Although the general characteristics of each
of the three case-study example sites are similar (chlorinated solvent contaminants in groundwater,
occurring at relatively shallow depth in unconsolidated sediments), the assumptions underlying the
two approaches, and the procedures that are followed in conducting the evaluations are applicable to
a much broader range of conditions (e.g., dissolved metals in groundwater, or contaminants in a
fractured bedrock system).
Table ES.l: Summary of Results of LTMO Demonstrations
Feature of Monitoring Program
Total number of samples (per year) in
current program
Rangeb/ of total number of samples
(per year) in refined program
Percent reduction in number of
samples collected per year
Projected range of cost savings0' (per
year)
Example Site*
Fort Lewis
180
107-113
37-40
$33,500 - $36,500
Long Prairie
51
22-36
29-51
$4,200 -$8,1 00
McClellan AFB OU D
34
17-32
6-50
$300 - $2,550
" Information regarding site characteristics and the site-specific monitoring programs of the three example sites is presented
in Section 3 (Fort Lewis), Section 4 (Long Prairie) and Section 5 (McClellan AFB OU D), and in Appendices C and D.
bl Ranges of total numbers of samples collected annually in refined programs, percentage reductions in numbers of samples
collected, and associated potential annual cost savings, reflect the results of the evaluations conducted using MAROS and
the three-tiered approach.
d Estimates of potential annual cost savings were based on information regarding monitoring program costs provided by
facility personnel. Costs associated with monitoring include cost of sample collection, sample analyses, data compilation
and reporting, and management of investigation-derived waste (e.g., purge water).
Prior to initiating an LTMO evaluation, it is of critical importance that the monitoring objectives of
the program to be optimized be clearly articulated, with all stakeholders agreeing to the stated
objectives, so that the program can be optimized in terms of recognized (and agreed-upon) objectives,
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using decision rules and procedures that are acceptable to all stakeholders. The decisions regarding
whether to conduct an LTMO evaluation, which approach to use, and the degree of regulatory-agency
involvement in the LTMO evaluation and implementation of optimization recommendations, must be
made on a site-specific basis. Factors to be considered in deciding whether to proceed with an
LTMO evaluation include:
The projected level of effort necessary to conduct the evaluation;
The resources available for the evaluation (e.g., quality and quantity of data, staff having the
appropriate technical capabilities);
The anticipated degree of difficulty in implementing optimization recommendations; and
. The potential benefits (e.g., cost savings) that could result from an optimized monitoring
program.
Optimization of a monitoring program should be considered for most sites having LTM programs that
are based on sampling of characterization monitoring points, or for sites where more than about 50
samples are collected and analyzed on an annual basis. Because it is likely that monitoring programs
can benefit from periodic evaluation as environmental programs evolve, monitoring program
optimization also should be undertaken periodically, rather than being regarded as a one-time event.
Overall site conditions should be relatively stable, with no large changes in remediation approaches
occurring or anticipated. Furthermore, successful application of either approach to the site-specific
evaluation of a monitoring program is directly dependent upon the amount and quality of the
available data - results from a minimum of four to six separate sampling events are necessary to
support a temporal analysis, and results collected at a minimum of about six (for a MAROS
evaluation) to 15 (for a three-tiered evaluation) separate monitoring points are necessary to support a
spatial analysis. It also is necessary to develop an adequate conceptual site model (CSM) describing
site-specific conditions prior to applying either approach. In particular, the extent of contaminants in
the subsurface at the site must be adequately delineated before the monitoring program can be
optimized.
Although the MAROS tool is capable of being applied by an individual with little formal statistical
training, interpretation of the results generated by either approach requires a relatively sophisticated
understanding of hydrogeology, statistics, and the processes governing the movement and fate of
contaminants in the environment. Although many of the basic assumptions and techniques
underlying both optimization approaches are similar, and both optimization approaches utilize
qualitative, temporal, and spatial analyses, there are several differences between the two approaches,
which can cause one optimization approach (e.g., the three-tiered approach) to generate results that
are not completely consistent with the results obtained using the other approach (e.g., MAROS).
Nevertheless, each approach is capable of generating sound and defensible recommendations for
optimizing LTM programs.
The most significant advantage conferred by both optimization approaches is the fact that both
approaches apply consistent, well-documented procedures, which incorporate formal decision logic,
to the process of evaluating and optimizing groundwater monitoring programs. However, the process
of data preparation, screening, processing, and evaluation can be extremely time-consuming for either
approach. Both approaches could benefit from further development efforts to address current
limitations; and continued development of both approaches is contemplated or in progress.
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Typically, a program manager should anticipate incurring costs on the order of $6,000 to $10,000 to
complete an LTMO evaluation at the level of detail of the case-study examples described in this
demonstration. Consequently, an LTMO evaluation may be cost-prohibitive for smaller monitoring
programs. However, an LTMO evaluation that can be used to reduce the total number of samples
collected at a site by about 5 to 10 samples per annum should be cost-effective.
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TABLE OF CONTENTS
EXECUTIVE SUMMARY iv
LIST OF ACRONYMS AND ABBREVIATIONS xii
1.0 INTRODUCTION 1
1.1 PROJECT DESIGN 1
1.2 CASE-STUDY EXAMPLES 2
1.3 PURPOSES OF GROUNDWATER MONITORING 2
1.4 LONG-TERM GROUNDWATER MONITORING PROGRAM OPTIMIZATION 4
1.5 REPORT ORGANIZATION 6
2.0 EVALUATION AND OPTIMIZATION OF LONG-TERM
MONITORING PROGRAMS 7
2. l CONCEPTS IN GROUNDWATER MONITORING 7
2.2 METHODS FOR DESIGNING, EVALUATING, AND OPTIMIZING MONITORING
PROGRAMS 9
2.3 DESCRIPTION OF MAROS SOFTWARE TOOL 10
2.4 DESCRIPTION OF THREE-TIERED APPROACH 14
2.5 CASE-STUDY EXAMPLES 16
3.0 SUMMARY OF DEMONSTRATIONS AT LOGISTICS CENTER AREA,
FORT LEWIS, WASHINGTON 17
3.1 FEATURES OF FORT LEWIS LOGISTICS CENTER 17
3.2 RESULTS OF LTMO EVALUATION COMPLETED USING MAROS TOOL 19
3.3 RESULTS OF LTMO EVALUATION COMPLETED USING
THREE-TIERED APPROACH 20
4.0 SUMMARY OF DEMONSTRATIONS AT LONG PRAIRIE GROUNDWATER
CONTAMINATION SUPERFUND SITE, MINNESOTA 23
4.1 FEATURES OF LONG PRAIRIE SITE 23
4.2 RESULTS OF LTMO EVALUATION COMPLETED USING MAROS TOOL 25
4.3 RESULTS OF LTMO EVALUATION COMPLETED USING
THREE-TIERED APPROACH 26
5.0 SUMMARY OF DEMONSTRATIONS AT McCLELLAN AFB OU D,
CALIFORNIA 28
5.1 FEATURES OF MCCLELLAN AFB OU D 28
5.2 RESULTS OF LTMO EVALUATION COMPLETED USING MAROS TOOL 30
5.3 SUMMARY OF LTMO EVALUATION COMPLETED USING
THREE-TIERED APPROACH 31
6.0 CONCLUSIONS AND RECOMMENDATIONS 33
6.1 SUMMARY OF RESULTS OF MAROS EVALUATIONS AND
THREE-TIERED APPROACH 33
6.2 OTHER ISSUES 41
6.3 CONCLUSIONS 41
7.0 REFERENCES 44
Vlll
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LIST OF FIGURES
No. Title Page
3.1 Features of Fort Lewis Logistics Center Area 18
4.1 Features of Long Prairie Groundwater Contamination Superfund Site 24
5.1 Features of McClellan AFB OU D 29
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LIST OF TABLES
No- Title Page
1.1 Characteristics of Monitoring Programs at Three Example Sites Used in
Long-Term Monitoring Program Optimization Demonstrations 2
2.1 Primary Features of MAROS 12
2.2 Primary Features of Three-Tiered LTMO Approach 15
3.1 Results of Optimization Demonstrations at Logistics Center Area,
Fort Lewis, Washington 21
4.1 Results of Optimization Demonstrations at Long Prairie
Groundwater Contamination Superfund Site, Minnesota 26
5.1 Results of Optimization Demonstrations at McClellan AFB OU D, California 31
6.1 Summary of Optimization of Monitoring Program at
Fort Lewis Logistics Center Area 33
6.2 Summary of Optimization of Monitoring Program at
Long Prairie Groundwater Contamination Superfund Site 36
6.3 Summary of Optimization of Monitoring Program at McClellan AFB OU D 38
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LIST OF APPENDICES (included in EPA 540-R-04-001b)
Appendix A - Concepts and Practices in Monitoring Optimization
Appendix B - Description of MAROS Tool and Three-Tiered Optimization Approach
Appendix C - Synopses of Case-Study Examples
Appendix D - Original Monitoring Program Optimization Reports by Groundwater
Services, Inc. and Parsons
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LIST OF ACRONYMS AND ABBREVIATIONS
a
AFB
AFCEE/ERT
AFRPA
AR
ASCE
P
bgs
BRAC
CAM
CERCLA
CES
CFR
COC
COV
CR
CSM
CT
DCA
DCE
DNAPL
DQO
EGDY
ERPIMS
ESR1
ETD
EW
FS
ft/day
ft/yr
GC
GeoEAS
GIS
GWMP
gpm
GSI
GTS
GWOU
ID
IDW
IROD
LOGRAM
LTM
statistical confidence level
Air Force Base
Air Force Center for Environmental Excellence/Technology Transfer
Division
Air Force Real Property Agency
area ratio (calculated by MAROS)
American Society of Civil Engineering
statistical power
below ground surface
Base Realignment and Closure Act
chlorinated aliphatic hydrocarbon compound
Comprehensive Environmental Response, Compensation, and Liability Act
cost-effective sampling
Code of Federal Regulations
contaminant of concern
coefficient of variation
concentration ratio (calculated by MAROS)
conceptual site model
concentration trend (calculated by MAROS)
dichloroethane
dichloroethene
dense, non-aqueous-phase liquid
data-quality objective
East Gate Disposal Yard
(US Air Force) Environmental Restoration Program Information
Management System
Environmental Systems Research Institute, Inc.
extraction, treatment, and discharge
extraction well
feasibility study
feet per day
feet per year
gas chromatograph
Geostatistical Environmental Exposure Software
geographic information system
Groundwater Monitoring Plan
gallon(s) per minute
Groundwater Services, Inc.
Geostatistical Temporal/Spatial optimization algorithm
Groundwater Operable Unit
identifier
investigation-derived waste
Interim Record of Decision
revised Logistics Center monitoring program
long-term monitoring
XH
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LIST OF ACRONYMS AND ABBREVIATIONS (continued)
LTMO
LTMP
MAROS
MCL
Mitretek
MMR
MPCA
MS
NPL
NRC
O&M
ORP
OU
Parsons
PCE
POL
QA
QC
RAO
RCRA
RI
ROC
ROD
S
SF
SVE
TCA
TCE
OSRTI
US
USAGE
U.S. EPA
VOC
long-term monitoring optimization
long-term monitoring program
microgram(s) per liter
Monitoring and Remediation Optimization System
maximum contaminant level
Mitretek Systems
Massachusetts Military Reservation
Minnesota Pollution Control Agency
mass spectrometer
National Priorities List
National Research Council
operations and maintenance
oxidation-reduction potential
operable unit
The Parsons Corporation
tetrachloroethene
petroleum, oils, and lubricants
quality assurance
quality control
remedial action objective
Resource Conservation and Recovery Act
remedial investigation
rate-of-change parameter (calculated by MAROS)
record of decision
Mann-Kendall test statistic
slope factor (calculated by MAROS)
soil-vapor extraction
trichloroethane
trichloroethene
U.S. EPA's Office of Superfund Remediation and Technology Innovation
United States
U.S. Army Corps of Engineers
U.S. Environmental Protection Agency
volatile organic compound
Xlll
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1.0 INTRODUCTION
This report describes the results of a demonstration in which optimization techniques were used to
improve the design of long-term groundwater monitoring programs. The primary objectives of
optimizing the particular monitoring programs addressed in this study were to assess the optimal
frequency of monitoring implemented in each program, and to evaluate the spatial distribution of the
components of each monitoring network. Two different long-term monitoring optimization (LTMO)
approaches were used in the demonstration:
1. The Monitoring and Remediation Optimization System (MAROS) software tool, developed
by Groundwater Services, Inc. (GSI) for the Air Force Center for Environmental Excellence
(AFCEE) (2000 and 2002); and
2. A three-tiered approach applied by The Parsons Corporation (Parsons).
The primary goals of this demonstration were to highlight current strategies for applying optimization
techniques to existing long-term monitoring (LTM) programs, and to assist site managers in
understanding the potential benefits associated with monitoring program optimization. The report
also presents the basic concepts underlying environmental monitoring and monitoring optimization,
so that the discussion of particular procedures can be understood in terms of an overall monitoring
approach. The work presented in this document was commissioned by the U.S. Environmental
Protection Agency's (U.S. EPA's) Office of Superfund Remediation and Technology Innovation
(OSRTI).
1.1 PROJECT DESIGN
This project was conducted to demonstrate and assess two different LTMO approaches that can be
used to identify opportunities for streamlining groundwater monitoring programs. The project was
designed as follows:
Three sites having existing long-term groundwater monitoring programs were selected as
case-study examples for this demonstration project. The sites were required to meet minimum
screening criteria to ensure that the available monitoring data were sufficient for the LTMO
evaluations (refer to Sections 3, 4, and 5, and Appendix C of this report for detailed site
information).
GSI and Parsons evaluated groundwater monitoring data from each of the three sites using
their respective approaches, to assess whether the monitoring programs could be streamlined
without significant loss of information. GSI and Parsons then prepared reports summarizing
the results of their evaluations.
The summary reports then were provided to Mitretek Systems (Mitretek) for review. Using
those summary reports, Mitretek prepared this document, which summarizes the LTMO
evaluations and examines the results.
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1.2
CASE-STUDY EXAMPLES
The current LTM programs at the Fort Lewis Logistics Center, Washington (Fort Lewis), the Long
Prairie Groundwater Contamination Superfund Site in Minnesota (Long Prairie), and Operable Unit
(OU) D, McClellan Air Force Base (AFB), California (McClellan AFB OU D), were selected as case-
study example programs, because the numbers and spatial coverage of wells, and length of the
monitoring history at each site, were judged to be adequate to generate meaningful results. The
primary characteristics of the monitoring programs at each of the three sites are presented in Table
1.1.
Table 1.1: Characteristics of Monitoring Programs at Three Example Sites
Used in Long-Term Monitoring Program Optimization Demonstrations
Monitoring-Program
Characteristic
Number of distinct water-
bearing units or monitoring
zones addressed by the
monitoring program
Principal contaminants'1'
Total number of wells
included in program
Total number of samples
collected (per year)
Total costc/ of monitoring
(per year)
Example Site"
Fort Lewis
2 (Upper Vashon and
Lower Vashon)
cซ-l,2-DCE,PCE,
1,1,1-TCA,TCE, VC
21 extraction wells
40 upper Vashon
monitoring wells
1 1 lower Vashon
monitoring wells
180
$90,000
Long Prairie
3 (water table [Zone A], base
of upper glacial outwash
[Zone B], lower glacial
outwash [Zone C])
cw-l,2-DCE,PCE,TCE
2 municipal supply wells
6 extraction wells
12 Zone A monitoring wells
1 5 Zone B monitoring wells
8 Zone C monitoring wells
51
$14,280
McClellan AFB OU D
2 (Zones A and B)
1,2-DCA, cw-l,2-DCE,
PCE, TCE
6 extraction wells
32 Zone A monitoring wells
1 3 Zone B monitoring wells
34
Information not provided
^ Information regarding site characteristics and the site-specific monitoring programs of the three example sites is
presented in Section 3 (Fort Lewis), Section 4 (Long Prairie) and Section 5 (McClellan AFB OU D), and in
Appendices C and D.
b/ DCA = dichloroethane; DCE = dichloroethene; PCE = tetrachloroethene;
TCA = trichloroethane; TCE = trichloroethene; VC = vinyl chloride.
c/ Information regarding annual monitoring program costs was provided by facility personnel. Costs associated with
monitoring include cost of sample collection, sample analyses, data compilation and reporting, and management of
investigation-derived waste (e.g., purge water).
1.3
PURPOSES OF GROUNDWATER MONITORING
The U.S. EPA (2004) defines monitoring to be
"... the collection and analysis of data (chemical, physical, and/or biological) over a sufficient
period of time and frequency to determine the status and/or trend in one or more
environmental parameters or characteristics. Monitoring should not produce a 'snapshot in
time' measurement, but rather should involve repeated sampling over time in order to define
the trends in the parameters of interest relative to clearly-defined management objectives.
Monitoring may collect abiotic and/or biotic data using well-defined methods and/or
endpoints. These data, methods, and endpoints should be directly related to the management
objectives for the site in question."
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Monitoring of groundwater systems has been practiced for decades. Monitoring activities have
expanded significantly in recent years, to assess and address the problems associated with
groundwater contamination and its environmental consequences, because the processes active within
a groundwater system, and the interactions of a groundwater system with the rest of the environment,
can be assessed only through monitoring (Zhou, 1996).
There are statutory requirements establishing the necessity for monitoring, and governing the types of
monitoring that must be conducted under particular circumstances. Passage of the Resource
Conservation and Recovery Act (RCRA) in 1976, and subsequent promulgation of the first
regulations authorized under RCRA in 1980, resulted in significant expansion of the role of
groundwater monitoring. RCRA and subsequent amendments include provisions for establishing
groundwater monitoring programs at all of the hazardous-waste treatment, storage, and disposal
facilities, at all of the solid-waste landfills, and at many underground storage tank facilities in the
United States. In December 1980, the Comprehensive Environmental Response, Compensation, and
Liability Act (CERCLA) was passed, in part to address potential threats posed by "uncontrolled"
hazardous waste sites. CERCLA statutory authority regarding monitoring gives U.S. EPA the
authority to undertake monitoring to identify threats (42 USC ง9604[b]), and defines removal and
remedial actions as inclusive of any monitoring reasonably required to ensure that such actions
protect the public health, welfare, and the environment (42 USC ง9601 [23] and 42 USC ง9601 [24],
respectively). Therefore, response actions at such sites require that monitoring programs be
developed and implemented to investigate the extent of environmental contamination and to monitor
the progress of cleanup activities (Makeig, 1991).
Four inherently different types of groundwater monitoring programs can be distinguished (U.S. EPA,
2004):
Characterization monitoring;
Detection monitoring;
Compliance monitoring; and
Long-term monitoring.
Characterization monitoring is initiated in an area where contaminants are known or suspected to be
present in environmental media (soil, air, surface water, groundwater) as a consequence of a release
of hazardous substances. Site characterization involves delineating the nature, extent, and fate of
potential contaminants in the environment, identifying human populations or other biota ("receptors")
that could be adversely affected by exposure to those contaminants, and assessing the possibility that
the contaminants could migrate to a location where a potential receptor could come into contact with
the contaminant(s) ("exposure point"). Groundwater sampling is a critical element of site
characterization, as it is necessary to establish whether site-related contaminants are migrating in
groundwater to potential exposure points.
Detection monitoring and compliance monitoring generally are required for facilities that are
regulated under RCRA. A groundwater-quality monitoring program designed for detection
monitoring consists of a network of monitoring points (wells) in an uncontaminated water-bearing
unit that is at risk of contamination from an overlying waste facility. If the results of periodic
sampling conducted during detection monitoring indicate that a release may have occurred, the owner
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or operator of the facility must implement the next phase of groundwater monitoring - compliance
monitoring. During compliance monitoring, groundwater samples are collected from locations
designated as compliance points, and are analyzed for constituents that are known or suspected to
have been released. After it has been established that a release of the type and magnitude suspected
has occurred, a corrective-action program must be implemented (Makeig, 1991).
During a corrective action, the owner or operator of a facility must remove, control, and/or treat the
wastes that have caused the release, so that groundwater quality can be brought into compliance with
established groundwater protection criteria. (Additional characterization monitoring may be
necessary during the selection of a corrective action, so that the actual extent and fate of contaminants
in the subsurface can be assessed to the extent necessary to support remedy decisions.) Groundwater
cleanup criteria usually are established by the individual states, or on a site-specific basis within a
state. In all cases, the cleanup criteria must be as stringent as, or more stringent than, various
standards established by the federal government, unless such requirements are waived. After a
remedy has been selected and put in place, groundwater monitoring also is used in evaluating the
degree to which the remedial measure achieves its objectives (e.g., abatement of groundwater
contaminants, restoration of groundwater quality, etc.). This type of monitoring - known as LTM -
typically is initiated only after a remedy has been selected and implemented, in conjunction with
some type of corrective-action program. It usually is assumed that after a site enters the LTM phase
of remediation, site characterization is essentially complete, and the existing monitoring network can
be adapted, as necessary, to achieve the objectives of the LTM program (Reed et al, 2000).
Optimization techniques have been applied to the design of monitoring networks for site
characterization, detection monitoring, and compliance monitoring (Loaiciga et al., 1992). In
practice, however, optimization techniques usually are applied only to LTM programs, as these
programs typically provide well-defined spatial coverage of the area monitored, and have been
implemented for a period of time sufficient to generate a relatively comprehensive monitoring
history.
1.4 LONG-TERM GROUNDWATER MONITORING PROGRAM OPTIMIZATION
As of 1993, the National Research Council (NRC, 1993) estimated that groundwater had been
contaminated at between 300,000 and 400,000 sites in the United States. As a consequence of the
identification of certain technology limitations and recognition of the potentially significant costs for
remediating all of these sites (approximately $500 billion to $1 trillion), the paradigm for
groundwater remediation recently has shifted to some degree, from resource restoration to long-term
risk management. This strategy change is expected to result in more contaminants being left in place
for longer periods of time, thereby requiring long-term monitoring (NRC, 1999). At many sites,
LTM can require decades of expensive sampling of monitoring networks, ranging in size from tens to
hundreds of sampling locations, and resulting in costs of hundreds of thousands to millions of dollars
per year for sampling and data management (Reed et al., 2000). Development of cost-effective
monitoring programs, or optimization of existing programs, can produce significant cost savings over
the life of particular remediation projects. As a consequence of the resources required to maintain a
monitoring program for a long period of time, most monitoring optimization efforts, including the
monitoring optimization evaluations described in this report, have focused on LTM.
It is critical that the objectives of monitoring be developed and clearly articulated prior to initiating a
monitoring program (Bartram and Balance, 1996), or during the process of evaluating and optimizing
an existing program. Monitoring program objectives are dependent upon the types of information
that will be generated, and the intended uses of that information. The exact information needs of
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particular monitoring programs usually must be established by considering the program objectives
during the planning stages or during periodic LTM program reviews. Clearly articulated program
objectives will establish the end-uses of monitoring data, which in turn will clarify those data that
must be collected. The connection between the data collected by monitoring and the uses to which
those data are applied is an important element in the success of any water-quality monitoring
program. Without carefully connecting the acquisition of data with the production and use of
information contained within the data, there is a high probability that data collection will become an
end in itself (Ward et al., 1990). Because site conditions, particularly in saturated media, can be
expected to change through time, the objectives of any LTM program should be revisited and refined
as necessary during the course of the program.
Monitoring objectives fall into four general categories (U.S. EPA, 1994b and 2004; Gibbons, 1994):
Identify changes in ambient conditions;
Detect the movement and monitor the physico-chemical fate of environmental constituents of
interest (COCs, dissolved oxygen, etc.) from one location to another;
Demonstrate compliance with regulatory requirements; and
Demonstrate the effectiveness of a particular response activity or action.
As is clear from the discussion in Section 1.3, the two primary objectives of long-term groundwater
monitoring programs are a subset of these general objectives, and can be expressed as follow:
Evaluate the long-term temporal state of contaminant concentrations at one or more points
within or outside of the remediation zone, as a means of monitoring the performance of the
remedial measure (temporal objective); and
Evaluate the extent to which contaminant migration is occurring, particularly if a potential
exposure point for a susceptible receptor exists (spatial objective).
Ultimately, the relative success of any remediation system and its components (including the
monitoring program) must be judged based on the degree to which they achieve their stated
objectives. The most important components of a groundwater monitoring program are the network
density (the number of monitoring wells and their relative locations) and the sampling frequency (the
number of observations or samples per unit time) (Zhou, 1996). Designing an effective groundwater
monitoring program involves locating monitoring points and developing a site-specific strategy for
groundwater sampling and analysis in order to maximize the amount of relevant information
(information required to effectively address the temporal and spatial objectives of monitoring) that
can be obtained, while minimizing incremental costs. The efficiency of a monitoring program is
considered to be optimal if it is effectively achieving its objectives at the lowest total cost, and/or
with the fewest possible number of monitoring locations (Reed et al., 2000).
While several different LTMO methods have been developed and applied in recent years, this
evaluation examines the results obtained by investigators applying two approaches in current use.
The MAROS software tool, developed and applied by GSI, uses parametric and non-parametric trend
analyses to assess temporal chemical concentration trends and recommend optimal sampling
frequency, and also uses spatial statistical techniques to identify monitoring points that potentially are
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generating redundant information. The MAROS software then combines the results of the temporal
trend analysis and spatial statistical analysis, and uses the combined results to generate
recommendations regarding the frequency of monitoring and spatial distribution of the components of
the monitoring network. Parsons has applied a three-tiered approach consisting of a qualitative
evaluation, a statistical evaluation of temporal trends in contaminant concentrations, and a spatial-
statistical analysis, to assess the degree to which the monitoring program addresses each of the two
primary objectives of monitoring, and also to address other potentially-important considerations. The
results of the three evaluations then are combined and used to assess the optimal frequency of
monitoring and the spatial distribution of the various components of the monitoring network.
1.5 REPORT ORGANIZATION
The main body of this report is organized into seven sections, including this introduction:
Concepts in groundwater monitoring and techniques for evaluating monitoring programs are
discussed in Section 2; ways in which some of these techniques are implemented in the
MAROS software tool and in the three-tiered approach also are described briefly.
Background information relevant to the current groundwater monitoring programs at the Fort
Lewis Logistics Center, the Long Prairie Groundwater Contamination Superfund Site, and OU
D, McClellan AFB is reviewed in Sections 3, 4, and 5, respectively; and the summary results
of the MAROS and three-tiered evaluations of each monitoring program are presented in
those Sections.
Section 6 examines the results of the MAROS and three-tiered evaluations of the three
monitoring programs, and presents recommendations for implementing program
improvements.
References cited in this document are listed in Section 7.
Readers interested in a summary description of the demonstration project, and its results, will find
this information in the main body of this report (EPA 542-R-04-001a). Readers interested in more
detailed discussions can find supporting information contained in four appendices:
Concepts and practices in groundwater monitoring, and in monitoring optimization, are
discussed in detail in Appendix A.
Features of the MAROS tool and the three-tiered LTMO approach are described in
Appendix B.
Synopses of the MAROS and three-tiered LTMO evaluations of the three monitoring
programs are included in Appendix C.
The detailed results of the MAROS and three-tiered LTMO evaluations of the three
monitoring programs, as described in reports originally generated by GSI and Parsons, are
presented in Appendix D.
The main body of the report, together with the appendices, comprise EPA 542-R-04-001b.
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2.0 EVALUATION AND OPTIMIZATION OF
LONG-TERM MONITORING PROGRAMS
2.1 CONCEPTS IN GROUNDWATER MONITORING
Designing an effective groundwater-quality monitoring program involves selecting a set of sampling
sites, suite of analytes, and a sampling schedule based upon one or more monitoring-program
objectives (Hudak et al., 1993). An effective monitoring program will provide information regarding
contaminant migration and changes in chemical suites and concentrations through time at appropriate
locations, thereby enabling decision-makers to verify that contaminants are not endangering potential
receptors', and that remediation is occurring at rates sufficient to achieve remedial action objectives
(RAOs) in a reasonable timeframe. The design of the monitoring program therefore should address
existing receptor exposure pathways, as well as exposure pathways arising from potential future use
of the groundwater.
The U.S. EPA (2004) defines six steps that should be followed in developing and implementing a
groundwater monitoring program:
1. Identify monitoring program objectives.
2. Develop monitoring plan hypotheses (a conceptual site model, or CSM).
3. Formulate monitoring decision rules.
4. Design the monitoring plan.
5. Conduct monitoring, and evaluate and characterize the results.
6. Establish the management decision.
In this paradigm, a monitoring program is founded on the current understanding of site conditions as
documented in the CSM, and monitoring is conducted to validate (or refute) the hypotheses regarding
site conditions that are contained in the CSM. Thus, monitoring results are used to refine the CSM by
tracking changes in site conditions through time. All monitoring-program activities are undertaken to
support a management decision, established as an integral part of the monitoring program (e.g., assess
whether a selected response action is/is not achieving its objectives).
Most past efforts in developing or evaluating monitoring programs have addressed only the design of
the monitoring plan (Step 4 in the six-step process outlined above). The process of designing a
groundwater monitoring plan involves four principal tasks (Franke, 1997):
1. Identify the volume and characteristics of the earth material targeted for sampling.
2. Select the target parameters and analytes, including field parameters/analytes and
laboratory analytes.
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3. Define the spatial and temporal sampling strategy, including the number of wells necessary
to be sampled to meet program objectives, and the schedule for repetitive sampling of
selected wells.
4. Select the wells to be sampled.
However, this procedure considers only the physical and chemical data that the monitoring plan is
intended to generate, and does not completely take into account the objectives that the monitoring
data are intended to address (Step 1, above), the decision(s) that the monitoring program is(are)
intended to support (Step 6), or the means by which a decision will be selected (Step 3). All of the
six steps outlined by the U.S. EPA (2004) should be considered during the development or evaluation
of a monitoring program, if that program is to be effective and efficient, and also should be
considered during optimization of existing programs.
Most monitoring programs have been designed and evaluated based on qualitative insight into the
characteristics of the hydrologic system, and using professional judgment (Zhou, 1996). However,
groundwater systems by nature are highly variable in space and through time, and it is difficult or
impossible to account for much of the existing variability using qualitative techniques. More
recently, other, more quantitative approaches have been developed, arising from the recognition that
the results obtained from a monitoring program are used to make inferences about conditions in the
subsurface on the basis of samples, and on the need to account for natural variability. The process of
making inferences on the basis of samples, while simultaneously evaluating the associated variability,
is the province of statistics; and to a large degree, the temporal and spatial variability of water-quality
data currently are addressed through the application of statistical methods of evaluation, which enable
large quantities of data to be managed and interpreted effectively, while the variability of the data
also is quantified and managed (Ward et al., 1990).
All approaches to the design, evaluation, and optimization of effective groundwater monitoring
programs must acknowledge and account for the dynamic nature of groundwater systems, as affected
by natural phenomena and anthropogenic changes (Everett, 1980). This means that in order to assess
the degree to which a particular program is achieving the temporal and spatial objectives of
monitoring (Section 1.4), a monitoring-program evaluation must address the temporal and spatial
characteristics of groundwater-quality data. Temporal and spatial data generally are evaluated using
temporal and spatial-statistical techniques, respectively. In addition, there may be other
considerations that best are addressed through qualitative evaluation.
In a qualitative evaluation, the relative performance of the monitoring program is assessed from
calculations and judgments made without the use of quantitative mathematical methods (Hudak et al.,
1993). Multiple factors may be considered qualitatively in developing recommendations for
continuation or cessation of monitoring at each monitoring point. Qualitative approaches to the
evaluation of a monitoring program range from relatively simple to complex, but often are highly
subjective. Furthermore, the degree to which the program satisfies LTM objectives may not be
readily evaluated by qualitative methods.
Temporal data (chemical concentrations measured at different points in time) provide a means of
quantitatively assessing conditions in a groundwater system (Wiedemeier and Haas, 1999), and
evaluating the performance of a groundwater remedy and its associated monitoring program. If
attenuation or removal of contaminant mass is occurring in the subsurface as a consequence of
natural processes or operation of an engineered remediation system, attenuation or mass removal will
be apparent as a decrease in contaminant concentrations through time at a particular sampling
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location, as a decrease in contaminant concentrations with increasing distance from chemical source
areas, and/or as a change in the suite of chemicals through time or with increasing migration distance.
Conversely, if a persistent source is contributing to groundwater contaminant plumes or if
contaminant migration is occurring, this may be apparent as an increase in contaminant
concentrations through time at a particular sampling location, or as an increase in contaminant
concentrations through time with increasing distance from contaminant source areas.
The temporal objective of long-term monitoring (evaluate contaminant concentrations in groundwater
through time; Section 1.4) can be addressed by defining trends in contaminant concentrations, by
identifying periodic fluctuations in concentrations, and by estimating long-term average ("mean")
values of concentrations (Zhou, 1996). The frequency of sampling necessary to achieve the temporal
objective then can be based on trend detection, accuracy of estimation of periodic fluctuations, and
accuracy of estimation of long-term mean concentrations. Concentration trends, periodicity, and
long-term mean concentrations typically are evaluated using statistical methods - in particular, tests
for trends, including the Student's t-test (Zhou, 1996), regression analyses, Sen's (1968) non-
parametric estimator of trend slope, and the Mann-Kendall test, are widely applied (Hirsch et al.,
1991).
Spatial techniques that can be applied to the design and evaluation of monitoring programs fall into
two general categories - simulation approaches and ranking approaches (Hudak et al., 1993).
Simulation approaches utilize computer models to simulate the evolution of contaminant plumes.
The results then are incorporated into an optimization model which derives an optimal monitoring
network configuration (Reed et al., 2000). Ranking approaches utilize weighting schemes that
express the relative value to the monitoring program of candidate sampling sites distributed
throughout a sampling domain (Hudak et al., 1993). The relative value of a potential monitoring site
can be ranked by assessing its spatial position relative to areas such as contaminant sources, receptor
locations, or probable zones of contaminant migration. Ranking approaches commonly use
geostatistical methods to assist in the design, evaluation, or optimization of a monitoring network
(American Society of Civil Engineering [ASCE], 1990a and 1990b). General concepts in
groundwater monitoring, and techniques used in the design/optimization of monitoring programs, are
discussed further in Appendix A.
2.2 METHODS FOR DESIGNING, EVALUATING, AND OPTIMIZING MONITORING PROGRAMS
Although monitoring network design has been studied extensively in the past, most previous studies
have addressed one of two problems (Reed et al., 2000):
1. Application of numerical simulation and formal mathematical optimization techniques to
screen monitoring plans for detection monitoring at landfills and hazardous-waste sites; or
2. Application of ranking methods, including geostatistics, to augment or design monitoring
networks for site-characterization purposes.
A number of studies (Appendix A) have addressed detection monitoring by applying global
approaches to the design of new monitoring networks. In contrast, few investigators have formally
addressed the evaluation and optimization of LTM programs at sites having extensive monitoring
networks that were installed during site characterization. The primary goal of optimization efforts at
such sites is to reduce sampling costs by eliminating data redundancy to the extent possible. This
type of optimization usually is not intended to identify locations for new monitoring wells, and it is
assumed during optimization that the existing monitoring network sufficiently characterizes the
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concentrations and distribution of contaminants being monitored. It also is not intended for use in
optimizing detection monitoring. Two approaches to evaluating monitoring networks - the MAROS
tool and the three-tiered evaluation approach - were developed specifically for use in optimizing
existing monitoring programs. (Although formal mathematical optimization techniques have been
applied to the problem of optimizing monitoring programs [Appendix A], neither the MAROS tool
nor the three-tiered approach incorporates mathematical optimization in the strict sense. Rather, in
subsequent discussion, "optimization" refers to the application of rule-based procedures,
incorporating statistical analysis and professional judgment, to identify possible improvements to a
monitoring program that will continue to be effective at meeting the two objectives of monitoring
while addressing qualitative constraints and minimizing the necessary incremental resources.) The
principal features of these two approaches are discussed in the following sections, and are described
in detail in Appendix B.
2.3 DESCRIPTION OF MAROS SOFTWARE TOOL
The MAROS software originally was developed primarily for use as a tool to assist non-technical
personnel (e.g., facility environmental managers) in evaluating and optimizing long-term monitoring
programs (AFCEE, 2000). As an added benefit, the MAROS tool provides a convenient platform for
the organization, preliminary evaluation, and presentation of monitoring data in graphical or tabular
formats. In the years since its development, the performance of the MAROS software tool has been
assessed critically ("beta tested") by applying the tool to the evaluation and optimization of actual
monitoring programs at a number of U.S. Air Force facilities (e.g., Parsons, 2000 and 2003a). In
response to recommendations for modifications to the MAROS software, generated as a consequence
of the beta testing, GSI developed MAROS Version 2, which was issued by AFCEE (2002) for
additional testing in 2002. The public-domain software and accompanying documentation are
available free of charge for download on the AFCEE website at http://www.afcee.brooks.af.mil/er/
rpo.htm . All case-study example monitoring programs examined in the current demonstration
project were evaluated and optimized using MAROS Version 2 (Sections 3.2, 4.2, and 5.2 of this
report).
The MAROS tool consists of a software package that operates in conjunction with an electronic
database environment (Microsoft Accessฎ 2000) and performs certain mathematical and/or statistical
functions appropriate to completing qualitative, temporal, and spatial-statistical evaluations of a
monitoring program, using data that have been loaded into the database (AFCEE, 2002). MAROS
utilizes parametric temporal analyses (using linear regression) and non-parametric trend analyses
(using the Mann-Kendall test for trends) to assess the statistical significance of temporal trends in
concentrations of contaminants of concern (COCs) (Appendix B). MAROS then uses the results of
the temporal-trend analyses to develop recommendations regarding sampling frequency at each
sampling point in a monitoring program by applying a modified Cost-Effective Sampling (CES)
algorithm, based on the CES method developed at Lawrence Livermore National Laboratory (Ridley
et al, 1995). The modified CES method uses recent and historical COC measurements to determine
optimal sampling frequency.
Although the MAROS tool primarily is used to evaluate temporal data, it also incorporates a spatial
statistical algorithm, based on a ranking system that utilizes a weighted "area-of-influence" approach
(implemented using Delaunay triangulation) to assess the relative value of data generated during
monitoring, and to identify the optimal locations of monitoring points. Formal decision logic and
methods of incorporating user-defined secondary lines of evidence (empirical or modeling results)
also are provided, and can be used to further evaluate monitoring data and generate recommendations
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for adjustments to sampling frequency, monitoring locations, and the density of the monitoring
network. Additional features (moment analyses) allow the user to evaluate conditions and the
adequacy of the monitoring network across a contaminated site (rather than just at individual
monitoring locations.)
MAROS is intended to assist users in establishing practical and cost-effective LTM goals for a
specific site, by
Identifying the COCs at the site;
Determining whether temporal trends in groundwater COC concentration data are statistically
significant;
Using identified temporal trends to evaluate and optimize the frequency of sample collection;
Assessing the extent to which contaminant migration is occurring, using temporal-trend and
moment analyses;
Evaluating the relative importance of each well in a monitoring network, for the purpose of
identifying potentially-redundant monitoring points;
Identifying those wells that are statistically most relevant to the current sampling program;
Evaluating whether additional monitoring points are needed to achieve monitoring objectives;
Providing indications of the overall performance of the site remediation approach; and
Assessing whether the monitoring program is sufficient to achieve program objectives on
local or site-wide scales.
As with any approach to LTM program optimization, successful application of the MAROS tool to
the site-specific evaluation of a monitoring program is completely dependent upon the amount and
quality of the available data (e.g., data requirements for a temporal trend analysis include a suggested
minimum of six separate sampling events at an individual sampling point, and a spatial analysis
requires sampling results from a minimum of six different sampling locations). It also is necessary to
develop an adequate CSM (Section 2.1), describing site-specific conditions (e.g., direction and rate of
groundwater movement, locations of contaminant sources and potential receptor exposure points)
prior to applying the MAROS tool. In particular, the nature and extent of contaminants in the
subsurface at the site must be adequately characterized and delineated before the monitoring program
can be optimized.
MAROS is designed to accept data in any of three formats: text files in U.S. Air Force
Environmental Restoration Program Information Management System (ERPIMS) format, Microsoft
Accessฎ files, or Microsoft EXCELฎ files. Prior to conducting a monitoring-program evaluation,
spatial and temporal data are loaded into a database, to include well identifiers (IDs), the sampling
date(s) for each well, COCs, COC concentrations detected at each well sampled on each sampling
date, laboratory detection limits for each COC, and any quality assurance/quality control (QA/QC)
qualifiers associated with sample collection or analyses. The spatial analysis also requires that
geographic coordinates (northings and eastings, referenced to some common datum) be supplied for
each well.
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Because MAROS can be used to evaluate the spatial and temporal characteristics of a maximum of
five COCs in a single simulation, one or more COCs must be removed from data sets containing
more than five COCs, or the data set must be split, so that only five COCs are included in a single
simulation. MAROS is capable of evaluating a maximum of 200 monitoring points in each
simulation. Prior to applying MAROS to the evaluation of a monitoring network comprising more
than 200 monitoring points, those monitoring locations providing relatively little information (or
information that is not compatible with the other points in the network) can be identified using
qualitative methods and eliminated from the evaluation. As an alternative, a monitoring network
comprising more than 200 monitoring points could be divided into subsets, each subset of the
network could be evaluated using MAROS, and the results of the evaluations then could be combined
to generate recommendations for the entire network.
After COCs have been identified, and the monitoring points in the network to be used in the
evaluation have been selected, the MAROS evaluation and optimization of a monitoring program is
completed in two stages:
A preliminary evaluation of plume stability is completed for the monitoring network, and
general recommendations for improving the monitoring program are produced; and
More-detailed temporal and spatial evaluations then are completed for individual monitoring
wells, and for the complete monitoring network.
In general, the MAROS tool is intended for use in evaluating single-layer groundwater systems
having relatively simple hydrogeologic characteristics (GSI, 2003a). However, for a multi-layer
groundwater system, the user could analyze those components of the monitoring network completed
in individual layers, during separate evaluations.
The primary features of MAROS, and the ways in which it addresses the qualitative, temporal, and
spatial aspects of environmental monitoring data, are summarized in Table 2.1. Additional details
regarding the MAROS software tool, its functionality, capabilities, and methods of application, are
presented in Appendix B. Details regarding specific examples of its application are presented in
Appendix D.
Table 2.1: Primary Features of MAROS
Infrastructure
The MAROS tool is a public-domain software package that operates in conjunction with an electronic
database environment (Microsoft Accessฎ 2000) and performs certain mathematical and/or
statistical functions appropriate to completing qualitative, temporal, and spatial-statistical evaluations
of a monitoring program, using data that have been loaded into the database.
The MAROS software, and accompanying documentation, are available for download free of charge
from the AFCEE website.
Although relatively sophisticated applications of the MAROS tool are possible, many of the steps in
the evaluation are straightforward, and can be completed by a user unfamiliar with statistical concepts
and practice. In such instances, the recommendations generated by application of the software should
be reviewed by a more experienced individual.
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Table 2.1: Primary Features of MAROS
Qualitative Evaluation
Qualitative information is used to make preliminary recommendations for the entire monitoring
program rather than for individual wells. Qualitative considerations also may be applied to develop
recommendations regarding sampling frequency at various stages throughout the evaluation,
depending upon whether the available data are sufficient to be used reliably by the MAROS statistical
tools.
Temporal Evaluation
MAROS includes a linear-regression analysis and a Mann-Kendall test to determine whether COC
concentrations at a particular well display a statistically-significant temporal trend. MAROS also
calculates the coefficient of variation (COV) for each statistical test, for use in evaluating whether
COC concentrations displaying no trend at a particular well have a large degree of "scatter" or can be
considered "Stable."
MAROS requires the results of a minimum of six sampling events to complete a temporal analysis at
an individual well.
MAROS uses the results of the temporal-trend analyses to develop recommendations regarding
optimal sampling frequency at each sampling location, by applying a modified CES algorithm.
MAROS uses the results of moment analyses to assess the overall stability of a plume, and can
perform a data-sufficiency analysis, to assess whether RAOs have been/are being achieved at
individual wells and at designated compliance points.
MAROS assigns the value of the reporting limit (or some fraction thereof) to samples having a
constituent concentration below the reporting limit.
Spatial Evaluation
MAROS uses an inverse-distance weighting algorithm to estimate the concentrations of COCs at
individual monitoring locations.
MAROS uses a "slope factor", calculated based on the standardized difference between the measured
and estimated concentrations at a particular location, together with the average concentration ratio
and area ratio, to determine the relative value of information obtained at individual monitoring points.
MAROS requires sampling results from a minimum of six different sampling locations to complete a
spatial analysis.
The spatial-evaluation algorithm implemented in MAROS can be used to assess the spatial
distribution of multiple COCs simultaneously.
Overall
MAROS uses the results of the temporal evaluation to generate recommendations regarding
monitoring frequency, and uses the results of the spatial evaluation to identify potentially redundant
monitoring points. Qualitative information is considered only during the preliminary evaluation of
the monitoring program. A MAROS evaluation can be conducted using a maximum of five
constituents.
A monitoring program evaluation completed using MAROS may cost in the range of $6,000 to
$10,000. depending upon the size of the monitoring program.
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2.4 DESCRIPTION OF THREE-TIERED APPROACH
As described by Parsons (2003b, 2003c, and 2003d), a three-tiered LTMO evaluation is conducted in
stages to address each of the objectives and considerations of monitoring: a qualitative evaluation
first is completed, followed in succession by temporal and spatial evaluations. At the conclusion of
each stage (or "tier") in the evaluation, recommendations are generated regarding potential changes in
the temporal frequency of monitoring, and/or whether to retain or remove each monitoring point
considered in the evaluation. After all three stages of evaluation have been completed, the results of
all of the analyses are combined and interpreted, using a decision algorithm, to generate final
recommendations for an effective and efficient LTM program.
In the qualitative evaluation, the primary elements of the monitoring program (numbers and locations
of wells, frequency of sample collection, analytes specified in the program) are examined, in the
context of site-specific conditions, to ensure that the program is capable of generating appropriate and
sufficient information regarding plume migration and changes in chemical concentrations through
time. Criteria used in the qualitative evaluation are discussed in detail in Appendix B, and examples
of application of these criteria are presented in the detailed case-history examples (Appendices D-l,
D-2, and D-3). In the temporal evaluation, the historical monitoring data for every sampling point in
the monitoring program are examined for temporal trends in COC concentrations, using the Mann-
Kendall test (Appendices A and B).
After the Mann-Kendall test for trends has been completed for all COCs at all monitoring points, the
spatial distribution of temporal trends in COC concentrations is used to evaluate the relative value of
information obtained from periodic monitoring at each monitoring well by considering the location of
the well within (or outside of) the horizontal extent of the contaminant plume, the location of the well
with respect to potential receptor exposure points, and the presence or absence of temporal trends in
contaminant concentrations in samples collected from the well. In the third stage of the three-tiered
evaluation, spatial statistical techniques are used to assess the relative value of information (in the
spatial sense) generated by sampling at each monitoring point in the network. COC concentration
data collected during a single sampling event are used to identify those areas having the greatest
uncertainty associated with the estimated extent and concentrations of COCs in groundwater. At the
conclusion of the spatial-statistical evaluations, each well is ranked, from those providing the least
information to those providing the most information, based on the amount of information the well
contributed toward describing the spatial distribution of the COC being examined. Wells providing
the least amount of information represent possible candidates for removal from the monitoring
program, while wells providing the greatest amount of information represent sampling points that
probably should be retained in any refined version of the monitoring program.
At each stage in the three-tiered evaluation, monitoring points that provide relatively greater amounts
of information regarding the occurrence and distribution of COCs in groundwater are identified, and
are distinguished from those monitoring points that provided relatively lesser amounts of information.
After all three stages have been completed, the results of the three stages are combined to generate a
refined monitoring program that potentially can provide information sufficient to address the primary
objectives of monitoring at the site, at reduced cost.
The qualitative evaluation can be completed by a competent hydrogeologist. The temporal evaluation
can be completed using commercially-available statistical software packages having the capability of
using non-parametric methods (e.g., the Mann-Kendall test) to examine time-series data for trends.
The spatial-statistical evaluation can be completed by a user familiar with geostatistical concepts, and
having access to a standard geostatistical software package (e.g., the Geostatistical Environmental
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Exposure Software [GeoEAS; Englund and Sparks, 1992], GSLIB [Deutsch and Joumel, 1998] or
similar package). In practice, data manipulation, temporal and spatial analyses, and graphical
presentation of results are simplified, and the quality of the results is enhanced, if a commercially
available geographic information system (GIS) software package (e.g., ArcViewฎ GIS)
(Environmental Systems Research Institute, Inc. [ESRI], 2001) with spatial-statistical capabilities
(e.g., Geostatistical Analyst, an extension to the ArcViewฎ GIS software package) is utilized in the
LTMO evaluation.
As with the MAROS tool, the site-specific evaluation of a monitoring program using the three-tiered
approach is directly dependent upon the amount and quality of the available data. The primary
features of the three-tiered approach, and the ways in which it addresses the qualitative, temporal, and
spatial aspects of environmental monitoring data, are summarized in Table 2.2. Additional details
regarding the three-tiered approach, its functionality, capabilities, and methods of application, are
presented in Appendix B. Details regarding specific examples of its application are presented in
Appendix D.
Table 2.2: Primary Features of Three-Tiered LTMO Approach
Infrastructure
A three-.tiered LTMO evaluation is conducted in stages to address each of the objectives and
considerations of monitoring: a qualitative evaluation first is completed, followed in succession by
temporal and spatial evaluations. At the conclusion of each stage (or "tier") in the evaluation,
recommendations are generated to retain or remove each monitoring point considered in the
evaluation. After all three stages have been completed, the results of all of the analyses are combined
and interpreted, using a decision algorithm, to generate final recommendations for an effective and
efficient LTM program.
No software is required for the qualitative evaluation. The temporal evaluation can be completed
using commercially-available statistical software packages having the capability of using non-
parametric methods to examine time-series data for trends. The spatial-statistical evaluation can be
completed using a standard geostatistical software package. Data manipulation, temporal and spatial
analyses, and graphical presentation of results are simplified, and the quality of the results is
enhanced, if a commercially-available GIS software package with spatial-statistical capabilities is
used.
Completion of the qualitative evaluation requires a competent hydrogeologist and an adequate CSM.
The temporal and spatial-statistical evaluations require a user familiar with non-parametric statistical
and geostatistical concepts, having access to appropriate software.
Qualitative Evaluation
Qualitative information is evaluated to determine optimal sampling frequency and removal/inclusion
of each well in the monitoring program based on all historical monitoring results.
Temporal Evaluation
The three-tiered temporal statistical analysis includes classifications for wells at which a particular
COC has never been detected at a concentration greater than the reporting limit ("Not Detected") and
for wells at which a particular COC consistently has been detected at concentrations less than the
practical quantitation limit ("< PQL").
The three-tiered approach requires the results of a minimum of four sampling events (if seasonal
effects are not present) to complete a temporal analysis at an individual well.
15
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Table 2.2: Primary Features of Three-Tiered LTMO Approach
Temporal Evaluation (continued)
The three-tiered approach uses the results of the temporal evaluation to develop recommendations
regarding sampling frequency, and to identify wells to be retained in or removed from the program.
The approach uses a formal decision framework to develop these recommendations.
The three-tiered approach uses the results of the temporal evaluation to assess trends only at
individual monitoring points.
The three-tiered approach assumes that monitoring points having historical results with "No Trend"
are of limited value, while MAROS treats a monitoring point having "No Trend" in COC
concentrations similar to a monitoring point having an "Increasing Trend" in concentrations.
Spatial Evaluation
The three-tiered approach applies geostatistics to estimate the spatial distribution of COCs.
Application of this procedure depends upon the development of an appropriate semi-variogram.
The three-tiered approach uses changes in the median kriging error generated during different
realizations to rank the relative value of information obtained at individual monitoring points. The
relative ranking (from "Provides Most Information" to "Provides Least Information") is used to
develop recommendations regarding which wells should be retained in or removed from the
monitoring program. ^^
The three-tiered approach requires sampling results from a minimum of 15 different sampling
locations to complete a spatial analysis.
Currently, only a single "indicator COC" (typically, the COC that has been detected at the greatest
number of separate monitoring locations) is used in the three-tiered spatial evaluation.
Overall
The three-tiered approach combines the results of the qualitative, temporal, and spatial evaluations to
generate overall recommendations regarding optimal sampling frequency and number of monitoring
points in a monitoring program. Although the spatial evaluation stage is restricted to a single
constituent, the qualitative and temporal stages of the evaluation can be applied to an unlimited
number of constituents.
A monitoring program evaluation completed using the three-tiered approach may cost in the range of
$6,000 to $10,000, depending upon the size of the monitoring program.
2.5 CASE-STUDY EXAMPLES
The MAROS tool and the three-tiered approach each were applied to the evaluation and optimization
of existing groundwater monitoring programs at three different sites - the Logistics Center at Fort
Lewis, Washington, the Long Prairie Groundwater Contamination Superfund Site in Minnesota, and
OU D at the former McClellan AFB, California. Pertinent features of the groundwater monitoring
programs for each site, and the results of the MAROS evaluation and the three-tiered evaluation of
the monitoring program at each site, are summarized in the following sections.
16
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3.0 SUMMARY OF DEMONSTRATIONS AT LOGISTICS CENTER
AREA, FORT LEWIS, WASHINGTON
An overview of features pertinent to the groundwater monitoring program at the Logistics Center
area, Fort Lewis, Washington is provided in this section, together with a summary of the results of the
LTMO demonstrations. The features of the site, and of the monitoring-program evaluations that were
completed using the MAROS tool and the three-tiered approach, are summarized in Appendix C, and
are described in detail in Appendix D-l.
3.1 FEATURES OF FORT LEWIS LOGISTICS CENTER
The Fort Lewis Military Reservation is located near the southern end of Puget Sound in Pierce
County, Washington, approximately 11 miles south of Tacoma and 17 miles northeast of Olympia.
The Logistics Center occupies approximately 650 acres of the Fort Lewis Military Reservation.
Process wastes were disposed of at several on- and off-installation locations, including the East Gate
Disposal Yard (EGDY), located southeast of the Logistics Center. Between 1946 and 1960, waste
solvents (primarily trichloroethene [TCE]) and petroleum, oils, and lubricants (POL) generated
during cleaning, degreasing, and maintenance operations were disposed of in trenches at the EGDY,
resulting in the introduction of contaminants to soils and groundwater at and downgradient from this
former landfill. The dissolved chlorinated solvent plume that originates at the EDGY extends
downgradient across the entire width of the Logistics Center, and beyond the northwestern facility
boundary to the southeastern shore of American Lake (Figure 3.1). The program that was developed
to monitor the concentrations and extent of contaminants in groundwater in the vicinity of, and
downgradient from the EDGY, and to assess the performance of remedial systems installed to address
contaminants in groundwater, was the subject of the MAROS and three-tiered evaluations
(Appendices C and D).
TCE has been identified as the primary COC in groundwater beneath the Logistics Center, based on
its widespread detection in wells across the site. Other COCs in groundwater include cis-1,2-
dichloroethene (DCE), tetrachloroethene (PCE), 1,1,1-trichloroethane (TCA), and vinyl chloride
(VC). TCE, DCE, and TCA have been detected consistently in many wells, while PCE and VC have
been detected only sporadically, in a few wells. The former waste-disposal trenches at the EGDY are
the apparent source of these chlorinated aliphatic hydrocarbon compounds (CAHs) in groundwater
beneath and downgradient from the Logistics Center.
Beginning in December 1995, groundwater monitoring was conducted at the Logistics Center on a
quarterly basis. Under the monitoring program, 38 monitoring wells and 21 groundwater extraction
wells were sampled, resulting in 236 primary samples per year (59 wells each sampled four times per
year) (Appendices C and D). The primary objectives of the monitoring program, as expressed in the
monitoring plan, are to confirm that the groundwater extraction systems are preventing the continued
migration of contaminants in groundwater to downgradient locations, to evaluate potential reductions
in contaminant concentrations through time, to assess temporal changes in the lateral and vertical
extent of contaminants in groundwater, and to assess the rate of removal of contaminant mass from
the subsurface.
17
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American Lake
ป. U!*'W4&73
*% LC-111b02U VUU.fH
" - LX-1 10, f1*-3 "
* eTix.? 11
FL3-C11 ป ป _ '
-.. ปIW !"ซ
- .^^^ *
^^\ ป '
1-15 2.3 *~ *\
12* 0 2U I .JI.C-16 9 3
LC.132 MO ,LC<6. ซ-
ft \ ซLC-19.. ITO--5?
1240 7 '
LX.16:150 ซ /Lr'137ซ 'Vf
LX-19.
LC-136a;
, LC-136b: 130-
LX-17:2100
LCป53 190 LC-64a: 290(
LC-51 160
Legend
Vashon Aquifer Wells
o Extraction Well
Upper Vashon Monitoring Well
Lower Vashon Monitoring Well
[>.. ',,-,] East Gate Disposal Yard
! J Approximate extent of Upper Vashon TCE plume (5ug/L isopleth)
ป Approxhrale direction ot groundivater flow In Vashon Aquifer
Groundwater sink
(TCE plume boundaries from USAGE. 2001)
II
A
0 500 1,000 2,000 3ฃ00
eet
Figure 3.1: Features of Fort Lewis Logistics Center Area (after Parsons, 2003b)
18
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Two distinct monitoring zones are recognized in the groundwater system beneath the Logistics
Center area. Most groundwater monitoring wells are completed in the upper monitoring zone (the
"Upper Vashon" zone); relatively few monitoring wells are completed in the lower monitoring zone
(the "Lower Vashon" zone). An LTMO evaluation of the groundwater extraction system and
associated monitoring network at the Logistics Center was completed by the Fort Lewis project team
in May 2001 (Appendices C and D); the refined monitoring program generated as a result of this
evaluation is known as the LOGRAM program. Based on the results of the LOGRAM LTMO
evaluation, 24 monitoring wells were added to the Logistics Center monitoring program, and 11
previously sampled monitoring wells were removed from the program (a net increase of 13
monitoring wells); sampling frequencies generally were reduced. The revised Logistics Center
monitoring program (LOGRAM), which was initiated in December 2001, includes 72 wells -- 51
monitoring wells (29 wells sampled quarterly, 3 wells sampled semi-annually, and 19 wells sampled
annually), and 21 extraction wells (6 wells sampled quarterly and 15 wells sampled annually). The
reduction in sampling frequency at a number of wells produced a net reduction in the total number of
primary samples collected and analyzed per year, from 236 samples to 180 samples. All samples
from the monitoring and extraction wells are analyzed for volatile organic compounds (VOCs) using
U.S. EPA Method SW8260B.
3.2 RESULTS OF LTMO EVALUATION COMPLETED USING MAROS TOOL
Because extensive historical data were not available for the new wells installed during
implementation of the current LOGRAM monitoring program, the MAROS tool was used to evaluate
data from the 59 wells that remained in the monitoring program in September 2001 (21 extraction
wells and 38 groundwater monitoring wells; Appendix C) included in the original monitoring
program, and was not used to evaluate the LOGRAM program. The detailed results of the MAROS
evaluation of the groundwater monitoring program at the Fort Lewis Logistics Center area are
presented in Appendices C (Section C1.5) and D-l, and are summarized in this subsection.
Prior to the evaluation, five wells that potentially would provide "redundant" information were
identified on the basis of qualitative considerations (Appendices C and D-l); these were not included
in the moment analysis or in the spatial evaluation. Historic monitoring results from all monitoring
and extraction wells were included in the temporal evaluation. However, results from groundwater
extraction wells were not used in the spatial evaluation; and the results from two monitoring wells
completed in the lower part of the Lower Vashon subunit also were excluded from the spatial
evaluation, because these two wells were considered to be within a different monitoring zone than the
other monitoring wells (Appendix D-l).
Application of the Mann-Kendall and linear-regression temporal trend evaluation methods
(Appendices B and C) indicated that the extent and concentrations of TCE in groundwater at the
Logistics Center source area (the EGDY) probably are decreasing (GSI, 2003a). TCE concentrations
in groundwater at most of the extraction wells located northwest of the EGDY source area also are
probably decreasing. The results of the moment analysis indicated that the location of the center of
mass of the plume has remained essentially unchanged, and that the extent of TCE in groundwater
has decreased over time, providing further evidence that the plume is stable under current conditions.
The evaluation of overall plume stability indicated that the extent of TCE in groundwater is stable or
decreasing, resulting in the recommendation that a monitoring strategy appropriate for a "Moderate"
design category be adopted (Appendices C and D).
19
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The results of detailed spatial analyses using the Delaunay method (Appendices C and D) indicated
that 8 monitoring wells could be removed from the original monitoring program (which included 38
monitoring wells) without significant loss of information. However, the accompanying well-
sufficiency analysis indicated that there is a high degree of uncertainty in predicted TCE
concentrations in six areas within the network where the available historical sampling information
may be inadequate; new monitoring wells were recommended for installation in these six areas (GSI,
2003a). These six locations recommended for installation of new wells correspond to six wells that
had been installed and were being monitored in conjunction with the LOGRAM program (Appendix
C). All groundwater extraction wells were recommended for retention in the refined monitoring
program. The results of the sampling-frequency optimization analysis completed using MAROS
(Appendices C and D) indicated that most wells in the monitoring network could be sampled less
frequently than in the current (LOGRAM) monitoring program. The results of the data-sufficiency
evaluation, completed using power-analysis methods, indicated that RAO concentrations of TCE in
groundwater have nearly been achieved at the compliance boundary.
The optimized monitoring program generated using the MAROS tool includes 57 wells, with 19
sampled quarterly, 2 sampled semiannually, 30 sampled annually, and 6 sampled biennially
(Appendices C and D). Adoption of the optimized program would result in collection and analysis of
.113 samples per year, as compared with collection and analysis of 180 samples per year in the current
LOGRAM monitoring program (Table 3.1) and 236 samples per year in the original sampling
program. Implementing these recommendations could lead to a 37-percent reduction in the number
of samples collected and analyzed annually, as compared with the current LOGRAM program, or a
52-percent reduction in the number of samples collected and analyzed, as compared with the original
program (Table 3.1). Assuming a cost per sample of $500 for collection and chemical analyses
(based on information provided by the U.S. Army Corps of Engineers [USAGE, 2001]), adoption of
the monitoring program as optimized using the MAROS tool is projected to result in savings of
approximately $33,500 per year as compared with the LOGRAM program (Table 3.1). (The
estimated cost per sample is based on information provided by facility personnel in conjunction with
efforts to estimate potential cost savings resulting from optimization of the monitoring program, and
includes costs associated with sample collection and analysis, data compilation and reporting, and
handling of materials generated as investigation-derived waste [IDW] during sample collection [e.g.,
purge water].) The optimized program remains adequate to delineate the extent of TCE in
groundwater, and to monitor changes in the plume over time (GSI, 2003a).
3.3 RESULTS OF LTMO EVALUATION COMPLETED USING THREE-TIERED APPROACH
The three-tiered approach was used to evaluate the original monitoring program at the Logistics
Center area (which included 59 wells), and also was used to evaluate the current LOGRAM program
(which includes 72 wells). Because extensive historical data were not available for the new wells
included in the LOGRAM program, temporal analyses were not used in evaluating the new
LOGRAM wells - only qualitative and spatial evaluations of that program were completed for these
wells, and as a consequence, the results of evaluation of the two programs are not directly
comparable. The detailed results of the three-tiered evaluation of the groundwater monitoring
programs at the Fort Lewis Logistics Center area are presented in Appendices C (Section C1.6) and D
(Appendix D-l), and are summarized in this subsection.
20
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Table 3.1: Results of Optimization Demonstrations at
Logistics Center Area Fort Lewis, Washington
Monitoring-Program Feature
Wells sampled quarterly
Wells sampled semi-annually
Wells sampled annually
Wells sampled biennially
Wells sampled every 3 years
Total wells included in LTM program
Total number of samples (per year)
Annual costh/ of LTM program
Monitoring
Original
(prior to
December
2001)
59
~
~
59
236
$118,000
Current
(LOGRAM,
after December
2001)
35
3
34
~
72
180
$90,000
ป Program"'
Original
Refined using
MAROS
19
2
30
6
-
57
113
$56,500
Refined using
3-Tiered
Approach
16
7
16
14
15
69
107
$53,500
Details regarding site characteristics and the site-specific monitoring programs at the Logistics Center area, Fort Lewis,
Washington, are presented in Appendices C and D-l.
w Information regarding annual monitoring program costs was provided by facility personnel. Costs associated with
monitoring include cost of sample collection, sample analyses, data compilation and reporting, and management of
investigation-derived waste (e.g., purge water).
The primary COCs (TCE, PCE, cw-l,2-DCE, and VC) were considered in the qualitative and
temporal stages of the three-tiered evaluation; however, because TCE has been the most frequently
detected COC in groundwater at the Fort Lewis Logistics Center area, the spatial-statistical stage of
the three-tiered evaluation of the monitoring program used only the results of analyses for TCE in
groundwater samples. Furthermore, because the Upper Vashon and Lower Vashon subunits are
considered to be separate monitoring zones (Section 3.1), and the results of only a single water-
bearing unit or monitoring zone can be considered in the spatial-statistical evaluation, the spatial-
statistical evaluation was conducted using the sampling results from those monitoring wells
completed in the Upper Vashon subunit only. Sampling results from groundwater extraction wells
were not used in the spatial-statistical evaluation; however, sampling results from all wells
(groundwater extraction wells, and groundwater monitoring wells completed in the Upper Vashon
and Lower Vashon subunits) were used in the qualitative and temporal evaluations.
The results of the three-tiered evaluation indicated that 6 of the 72 existing wells could be removed
from the LOGRAM groundwater LTM program with little loss of information (Parsons, 2003b), but
also indicated that 2 existing wells that are not currently sampled should be included in the program,
and that one new well should be installed and monitored. A refined monitoring program (Appendices
C and D), consisting of 69 wells, with 16 wells sampled quarterly, 7 wells sampled semi-annually, 17
wells sampled annually, 14 wells sampled biennially, and 15 of the extraction wells sampled every 3
years (Table 3.1), would be adequate to address the two primary objectives of monitoring. If this
refined monitoring program were adopted, 107 samples per year would be collected and analyzed, as
compared with the collection and analysis of 180 samples per year in the current LOGRAM
monitoring program and 236 samples per year in the original sampling program. This would
represent a 40-percent reduction in the number of samples collected and analyzed annually, as
compared with the LOGRAM program, or a 55-percent reduction in the number of samples collected
and analyzed, as compared with the original program. Assuming a cost per sample of $500 for
21
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collection and chemical analyses, adoption of the monitoring program as optimized using the three-
tiered approach is projected to result in savings of approximately $36,500 per year as compared with
the LOGRAM program, or $64,500 per year as compared with the original monitoring program
(Table 3.1). Additional cost savings potentially could be realized if groundwater samples collected
from select wells (e.g., upgradient wells, and wells along the lateral plume margins) were analyzed
for a short list of halogenated VOCs using U.S. EPA Method SW8021B instead of U S EPA Method
SW8260B (Parsons, 2003b).
22
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4.0 SUMMARY OF DEMONSTRATIONS AT LONG PRAIRIE
GROUNDWATER CONTAMINATION SUPERFUND SITE,
MINNESOTA
An overview of features pertinent to the groundwater monitoring program at the Long Prairie
Groundwater Contamination Superfund Site, Minnesota (Long Prairie site) is provided in this section,
together with a summary of the results of the LTMO demonstrations. The features of the site, and of
the monitoring-program evaluations that were completed using the MAROS tool and the three-tiered
approach, are summarized in Appendix C, and are described in detail in Appendix D-2.
4.1 FEATURES OF LONG PRAIRIE SITE
The town of Long Prairie, Minnesota is a small farming community located on the east bank of the
Long Prairie River in central Minnesota. The Long Prairie site comprises a 0.16-acre source area of
contaminated soil that has generated a plume of dissolved CAHs in the drinking-water aquifer
underlying the north-central part of town. The source of contaminants in groundwater was a dry-
cleaning establishment, which operated from 1949 through 1984 in the town's commercial district.
Spent dry-cleaning solvents, primarily PCE, were discharged into the subsurface via a french drain.
The subsequent migration of contaminants through the vadose zone to groundwater produced a
dissolved CAM plume that has migrated to the north a distance of at least 3,600 feet from the source
area, extending beneath a residential neighborhood and to within 500 feet of the Long Prairie River.
The plume of contaminated groundwater currently is being addressed by extraction of CAH-
contaminated groundwater via nine extraction wells, treatment of the extracted water, and discharge
of treated water to the Long Prairie River. The performance of the groundwater extraction system is
monitored by means of periodic sampling of monitoring wells and water-supply wells, and routine
operations and maintenance (O&M) monitoring of the extraction and treatment systems. The
program that was established to monitor the concentrations and extent of contaminants in
groundwater in the vicinity of, and downgradient from the PCE source area, and to assess the
performance of the OU1 groundwater extraction, treatment, and discharge (ETD) system, was the
subject of the MAROS and three-tiered evaluations (Appendices C and D).
PCE and its daughter products TCE and cis-l,2-DCE are the primary COCs at the Long Prairie site,
and have been detected through a volume of groundwater about 1,000 feet wide, which extended (in
October 2002) from the source area, approximately 3,200 feet downgradient to the northwest (Figure
4.1). VC also has been detected in groundwater samples, although at few locations and at lower
concentrations than other CAHs.
Groundwater conditions are monitored periodically at the Long Prairie site, to evaluate whether the
groundwater ETD system is effectively preventing the continued migration of CAH contaminants in
groundwater to downgradient locations, and to confirm that contaminants are not migrating to the
water-supply wells of the municipality of Long Prairie. Several of the monitoring locations include
wells installed in clusters, with each well in a cluster completed at a different depth. Groundwater
monitoring wells, extraction wells, and municipal water-supply wells are included in the monitoring
program. A total of 44 wells in the Long Prairie area were sampled during the most recent
23
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'18A: ND
: 38
*
WW1A: ND
RW1B:
MW10A:
MW5B: ND
:22
MW13 flow direction under non-
pumping conditions (Barr, 2002)
Oct 2002 PCE concentrations in ug/L
ND = PCE not detected at well
I Feet
250 500 1,000
1 inch equals 500 feet
N
A
Figure 4.1: Features of Long Prairie Groundwater Contamination Superfund Site (after Parsons, 2003c)
24
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monitoring event (October 2002) for which sampling results are available. Approximately one-half
of the wells sampled during October 2002 are sampled routinely in conjunction with the groundwater
monitoring program. The "current" (2002) 27-well monitoring program at the Long Prairie site
includes the 18 monitoring wells, 6 active groundwater extraction wells, and one inactive extraction
well sampled during scheduled monitoring events in 2000 and 2001, together with two nearby
municipal-supply wells (Appendices C and D). All samples from the monitoring and extraction wells
are analyzed for VOCs using U.S. EPA Method SW8021B.
4.2 RESULTS OF LTMO EVALUATION COMPLETED USING MAROS TOOL
The detailed results of the MAROS evaluation of the groundwater monitoring program at the Long
Prairie site are presented in Appendix C (Section C2.6) and D (Appendix D-2), and are summarized
in this subsection.
Application of the Mann-Kendall and linear-regression temporal trend evaluation methods
(Appendices B and C) indicated that the extent and concentrations of PCE in groundwater at the Long
Prairie source area probably are decreasing (GSI, 2003b). PCE concentrations in groundwater at 24
of 27 wells downgradient of the source area also are probably decreasing under current conditions.
The results of the moment analysis indicated that the mass of PCE in groundwater is relatively stable,
and that although the location of the center of mass of the plume has moved downgradient over time,
the extent of PCE in groundwater has decreased through time. Overall, the results of trend analyses
and moment analyses indicated that the extent of PCE in groundwater is stable or decreasing,
resulting in a recommendation that a monitoring strategy appropriate for a "Moderate" design
category be adopted (Appendices C and D).
Seventeen of the 44 wells in the existing monitoring network were included in the detailed spatial
analysis (Appendices C and D); the results indicated that none of the 17 wells evaluated was
redundant. Other wells in the monitoring network were examined qualitatively; and the results of
qualitative considerations (GSI, 2003b) indicated that nine monitoring wells could be removed from
the monitoring network without significant loss of information. Using similar qualitative analyses,
three extraction wells in the source area were identified as candidates for removal from service,
because concentrations of COCs in effluent from these wells historically have been below reporting
limits (GSI, 2003b). However, six wells that currently are not routinely sampled were recommended
for inclusion in the monitoring program. These changes in the monitoring network were projected to
have a negligible effect on the degree of characterization of the extent of PCE in groundwater. The
accompanying well-sufficiency analysis indicated that there is only a moderate degree of uncertainty
in predicted PCE concentrations throughout the network, so that no new monitoring wells were
recommended for installation (GSI, 2003b). The results of the sampling-frequency optimization
analysis completed using MAROS (Appendices C and D) indicated that most wells in the monitoring
network could be sampled less frequently than in the current monitoring program. The results of the
data-sufficiency evaluation, completed using power-analysis methods (Appendices B and C) suggest
that the monitoring program is adequate to evaluate the extent of PCE in groundwater relative to
compliance points through time (GSI, 2003b).
The optimized monitoring program generated using the MAROS tool includes 32 wells, with 10
monitoring wells and 5 extraction wells sampled annually, and 13 monitoring wells, two extraction
wells, and two municipal wells sampled biennially (Appendices C and D). Adoption of the optimized
program would result in collection and analysis of 22 samples per year, as compared with collection
and analysis of 51 samples per year in the current monitoring program (Table 4.1). Implementing
25
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these recommendations could lead to a 51-percent reduction in the number of samples collected and
analyzed annually, as compared with the current program. Assuming a cost per sample in the range
of $100 to $280 for collection and chemical analyses, adoption of the monitoring program as
optimized using the MAROS tool is projected to result in savings ranging from approximately $2,900
to $8,120 per year. (The estimated range of costs per sample is based on information provided by
facility personnel in conjunction with efforts to estimate potential cost savings resulting from
optimization of the monitoring program, and includes costs associated with sample collection and
analysis, data compilation and reporting, and handling of IDW [e.g., purge water].) The optimized
program remains adequate to delineate the extent of COCs in groundwater, and to monitor changes in
the plume over time (GSI, 2003b).
Table 4.1: Results of Optimization Demonstrations at
Long Prairie Groundwater Contamination Superfund Site, Minnesota
Monitoring-Program Feature
Wells sampled quarterly
Wells sampled semi-annually
Wells sampled annually
Wells sampled biennially
Total wells included in LTM program
Total number of samples (per year)
Annual costb/ of LTM program
Monitoring Program"7
Actual
(October 2002)
8
-
19
-
27
51
$14,280
Refined using
MAROS
16
16
32
22
$6,160
Refined using
3-Tiered Approach
2
6
14
4
26
36
$10,080
Details regarding site characteristics and the site-specific monitoring programs at the Long Prairie Groundwater
Contamination Superfund Site are presented in Appendices C and D-2.
Information regarding annual monitoring program costs was provided by facility personnel. The cost of monitoring is
assumed to be $280 dollars per sample; costs associated with monitoring include cost of sample collection, sample
analyses, data compilation and reporting, and management of investigation-derived waste (e.g., purge water).
4.3
RESULTS OF LTMO EVALUATION COMPLETED USING THREE-TIERED APPROACH
The detailed results of the three-tiered evaluation of the groundwater monitoring program at the Long
Prairie site are presented in Appendices C (Section C2.6) and D (Appendix D-2), and are summarized
in this subsection.
The results of the three-tiered evaluation indicated that 18 of the 44 existing wells could be removed
from the groundwater monitoring network with little loss of information (Parsons, 2003c). The
results further suggested that the current monitoring program (18 monitoring wells, 6 active
extraction wells, one inactive extraction well, and 2 municipal water-supply wells included in the
2002 sampling program) could be further refined by removing 4 of the 27 wells now in the LTM
program, and adding three wells not currently included in the program. If this refined monitoring
program, consisting of 26 wells (2 wells to be sampled quarterly, 6 wells to be sampled semi-
annually, 14 wells to be sampled annually, and 4 wells to be sampled biennially) were adopted, an
average of 36 samples per year would be collected and analyzed, as compared with the collection and
analysis of 51 samples per year in the current (2001/2002) monitoring program (Table 4.1) - a
reduction of about 29 percent. Assuming a cost per sample ranging from $100 to $280 for collection
and chemical analyses, adoption of the monitoring program as optimized using the three-tiered
26
-------
approach is projected to result in savings ranging from about $1,500 per year to about $4,200 per year
(Table 4.1), as compared with the current program (Parsons, 2003c).
27
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5.0 SUMMARY OF DEMONSTRATIONS AT McCLELLAN AFB OU
D, CALIFORNIA
An overview of features pertinent to the groundwater monitoring program at OU D, McClellan AFB,
California, is provided in this section, together with a summary of the results of the LTMO
demonstrations. The features of the site, and of the monitoring-program evaluations that were
completed using the MAROS tool and the three-tiered approach, are summarized in Appendix C, and
are described in detail in Appendix D-3.
5.1 FEATURES OF MCCLELLAN AFB OU D
The former McClellan AFB is located approximately 7 miles northeast of downtown Sacramento,
California, and covers approximately 3,000 acres. OU D consists of contaminated groundwater
beneath and downgradient from contaminant source areas in the northwestern part of McClellan
AFB, and occupies approximately 192 acres. Through most of its operational history, McClellan
AFB was engaged in a wide variety of military/industrial operations involving the use, storage, and
disposal of hazardous materials, including industrial solvents, caustic cleaners, electroplating
chemicals, metals, polychlorinated biphenyls, low-level radioactive wastes, and a variety of fuel oils
and lubricants.
The COCs in groundwater targeted by the current LTM program at OU D are exclusively CAHs,
including PCE, TCE, cw-l,2-DCE, and 1,2-dichloroethane (DCA), with 1,1-DCA, 1,1-DCE, 1,1,1-
TCA, and VC also detected, but at lower concentrations and/or lower frequencies. Dissolved CAHs
originating at sources near former disposal areas at OU D have migrated with regional groundwater
flow to the south and southwest, and historically extended off-base, to the west of OU D. Currently,
VOCs (primarily TCE) are present in groundwater primarily in the central and southwestern parts of
OU D (Figure 5.1). The remediation systems currently operating to address CAH contaminants in
groundwater at OU D include a groundwater ETD system, and the associated monitoring network.
In accordance with the requirements of the basewide groundwater monitoring plan, wells in the OU D
area are sampled during the first quarter of each year. In the OU D area, groundwater sampling is
conducted to monitor areas where dissolved VOC concentrations exceed their respective maximum
contaminant levels (MCLs) in monitoring zones A and B. Groundwater monitoring data also are
used to evaluate contaminant mass-removal rates. Because the extent of COCs in groundwater at OU
D is relatively well defined, and COCs appear to be contained by the groundwater extraction system,
the wells associated with the OU D plume are sampled relatively infrequently (annually or
biennially). Currently, 22 of the 32 wells that monitor the upper part (Zone A) of the groundwater
system at OU D are sampled biennially, and 10 are sampled annually. Twelve of the 13 wells that
monitor a deeper part (Zone B) of the groundwater system are sampled biennially, and the remaining
well is sampled annually. The six extraction wells (EWs) are sampled annually. Historically,
however, the sampling schedule for wells at OU D was irregular, so that some monitoring wells at
OU D have been sampled as few as five times through the historic monitoring from the monitoring
and extraction wells are analyzed for VOCs by U.S. EPA Method SW8260B.
28
-------
MW-1027j
MW-102J
MW-1026'
1Q02 5ug/LA-Zone TCE IsqDleth
(based on non-lAB wells)
Legend
OUD Area Well
& Zone A
ฉ Zone A (previously IAB)
, ฉ Zone B
j *, 0 Zone B (previously IAB)
4- Extraction Well
CuTFent Sampling Frequency
O Annual "1
@ Biennial
fe GiouTndwatei Flow Direction
'-350j [
Figure 5.1: Features of McClellan AFB OU D (after Parsons, 2003d)
29
-------
5.2 RESULTS OF LTMO EVALUATION COMPLETED USING MAROS TOOL
The detailed results of the MAROS evaluation of the groundwater monitoring program at McClellan
AFB OU D are presented in Appendices C (Section C3.5) and D-3, and are summarized in this
subsection.
Application of the Mann-Kendall and linear-regression temporal trend evaluation methods
(Appendices B and C) indicated that the extent and concentrations of TCE in groundwater at the OU
D source area probably are decreasing (GSI, 2003c). However, the absence of identifiable trends in
TCE concentrations at many locations downgradient of the plume may be a consequence of less-
frequent sampling in these areas than occurs near the OU D source area (GSI, 2003c). The results of
the moment analysis indicated that the mass of TCE in groundwater is relatively stable, with
occasional fluctuations suggesting increases or decreases in TCE mass. The location of the center of
mass of the plume also appears to be relatively stable, with periodic temporal fluctuations in
concentrations tending to cause the center of TCE mass to appear to move in the upgradient or
downgradient directions. The lateral extent of TCE in groundwater has been variable, suggesting that
TCE concentrations in wells used to evaluate conditions over large, off-axis areas of the plume have
varied considerably through time, or that the wells have not been sampled consistently enough for a
clear trend in TCE concentrations to emerge. Temporal fluctuations in the apparent mass of TCE in
groundwater (calculated using the zero* moment), the center of mass of TCE (calculated using the
first moment), and the lateral extent of TCE (calculated using the second moment) likely are due to
long-term variability in locations sampled, resulting from an inconsistent monitoring program
through time (GSI, 2003c). The evaluation of overall plume stability indicated that the extent of TCE
in groundwater at OU D is stable or slightly decreasing, resulting in a recommendation that a
monitoring strategy appropriate for a "Moderate" design category be adopted (Appendices C and D).
The results of the detailed spatial analysis, supplemented with a qualitative evaluation (Appendices C
and D), identified five monitoring wells as candidates for removal from the monitoring network.
Removal of the recommended five wells would result in an 11 percent reduction in the number of
wells in the monitoring network, with negligible effect on the degree of characterization of the extent
of TCE in groundwater. The possibility of removing additional monitoring wells on the periphery of
OU D also was examined qualitatively, and it was concluded (GSI, 2003c) that the decision to stop
sampling the periphery wells should be made in accordance with non-statistical considerations,
including regulatory requirements, community concerns, and/or public health issues. Non-statistical
considerations may indicate that continued sampling of the periphery wells is warranted. The
accompanying well-sufficiency analysis indicated that there is only a low to moderate degree of
uncertainty in predicted TCE concentrations throughout the network, so that no new monitoring wells
were recommended for installation (GSI, 2003c). In nearly all instances, the results of the sampling-
frequency optimization analyses at McClellan AFB OU D were adversely affected by the lack of
consistent temporal monitoring data (Appendices C and D). Accordingly, all recommendations
generated by MAROS were examined qualitatively, after the temporal statistical evaluations had been
completed, to generate recommendations regarding sampling frequency (GSI, 2003c). The results of
the data-sufficiency evaluation, completed using power-analysis methods, indicate that the
monitoring program is more than sufficient to evaluate the extent of TCE in groundwater relative to
the compliance boundary through time, assuming continued operation of the extraction system (GSI,
2003c).
The optimized monitoring program generated using the MAROS tool includes 29 A-zone wells, 11
B-zone wells, and 6 groundwater extraction wells, with 11 monitoring wells and 6 extraction wells
30
-------
sampled annually, and 29 monitoring wells sampled biennially (Appendices C and D). Adoption of
the optimized program would result in collection and analysis of 32 samples per year, as compared
with collection and analysis of 34 samples per year in the current monitoring program (Table 5.1).
Implementing these recommendations could lead to an approximately 6-percent reduction, in the
number of samples collected and analyzed annually, as compared with the current program.
Adoption of the monitoring program as optimized using the MAROS tool is projected (GSI, 2003c)
to result in savings of approximately $300 per year (Table 5.1). (Estimated annual cost savings were
provided by facility personnel; however, specific information regarding the estimated annual cost of
the LTM program at McClellan AFB OU D, and the total cost per sample is not available; and the
means used to derive the estimated cost savings are uncertain.) The optimized program remains
adequate to delineate the extent of COCs in groundwater, and to monitor changes in the condition of
the plume over time (GSI, 2003c).
Table 5.1: Results of Optimization Demonstrations at McClellan AFB OU D, California
Monitoring-Program Feature
Wells sampled annually
Wells sampled biennially
Total wells in LTM program
Total number of samples (per year)
Annual costb/ of LTM program
Monitoring Program3'
Actual
(October 2002)
17
34
51
34
--
Refined using
MAROS
17
29
46
32
c/
Refined using
3-Tiered Approach
13
8
21
17
C/
^ Details regarding site characteristics and the site-specific monitoring programs at McClellan AFB OU D are
presented in Appendices C and D-3.
w No information regarding annual monitoring program costs was provided by facility personnel.
c/ Total costs associated with refined monitoring programs cannot be estimated; no information available.
5.3
SUMMARY OF LTMO EVALUATION COMPLETED USING THREE-TIERED APPROACH
The detailed results of the three-tiered evaluation of the groundwater monitoring program at
McClellan AFB OU D are presented in Appendices C (Section C3.6) and D (Appendix D-3), and are
summarized in this subsection.
The results of the three-tiered evaluation (Parsons, 2003d) indicated that 30 of the 51 existing wells
could be removed from the groundwater monitoring program with comparatively little loss of
information (Parsons, 2003d). Most of the wells recommended for removal from the monitoring
program are wells peripheral to the OU D plume, which also were identified as possible candidates
for removal during the MAROS evaluation. If this refined monitoring program (Appendices C and
D), consisting of 21 wells (13 wells to be sampled annually, and 8 wells to be sampled biennially)
were adopted, an average of 17 samples per year would be collected and analyzed, as compared with
the collection and analysis of 34 samples per year in the current monitoring program - a reduction of
50 percent in the number of samples collected and analyzed annually, as compared with the current
program. Although information regarding the annual costs associated with the LTM program at
McClellan AFB OU D including the estimated total cost per sample is not available, based on
analytical costs alone, and assuming a cost per sample of $150 for chemical analyses (analyses for
VOCs only), adoption of the monitoring program as optimized using the three-tiered approach is
projected to result in savings of about $2,550 per year as compared with the current program
31
-------
(Parsons, 2003d). Additional cost savings could be realized if groundwater samples collected from
select wells (e.g., upgradient wells, and wells along the lateral plume margins) were analyzed for a
short list of halogenated VOCs using U.S. EPA Method SW8021B instead of U S EPA Method
SW8260B (Parsons, 2003d).
32
-------
6.0 CONCLUSIONS AND RECOMMENDATIONS
A software tool (MAROS) developed for AFCEE, and a three-tiered approach applied by Parsons,
were used to evaluate and optimize groundwater monitoring programs at the Fort Lewis Logistics
Center, Washington, the Long Prairie Groundwater Contamination Superfund Site in Minnesota, and
OU D, McClellan AFB, California. Although many of the basic assumptions and techniques
underlying both optimization approaches are similar, and both approaches utilize qualitative,
temporal, and spatial analyses, there are several differences in the details of implementation in the
two approaches, which can cause one optimization approach (e.g., the three-tiered approach) to
generate results that are not completely consistent with the results obtained using the other approach
(e.g., MAROS). As a consequence of structural differences in approaches to the evaluation and
optimization of monitoring programs, the results generated by any. optimization approach should be
expected to differ slightly from the results generated by other approaches; however, the results of any
optimization approach should be defensible, if the decision logic on which the approach has been
based is sound.
6.1 SUMMARY OF RESULTS OF MAROS EVALUATIONS AND THREE-TIERED APPROACH
The results of the MAROS optimization and three-tiered evaluation of the monitoring program at the
Fort Lewis Logistics Center are summarized in Table 6.1. "Final" recommendations for the entire
program could be developed by considering together the results of the three-tiered evaluation and of
the MAROS evaluation for each well. Example composite recommendations are provided in Column
5 of Table 6.1.
Table 6.1: Summary of Optimization of Groundwater Monitoring Program at
Fort Lewis Logistics Center Area"'
Well ID
Current11'
Sampling
Frequency
Recommendations
Generated Using
MAROS Tool
Recommendations
Generated Using
Three-Tiered
Approach
Example Composite0
Recommendations
Monitoring Wells Completed in Upper Vashon Subunit
FL2 (new"")
FL3 (new)
FL4B (new)
FL6 (new)
LC-03
LC-05
LC-06
LC-14a
LC-16(new)
LC-19a
LC-19b
LC-19c
LC-20 (new)
LC-24 (new)
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Semi-Annual
Annual
Quarterly
Quarterly
..tf
Quarterly
Quarterly
Not Considered"'
Quarterly
Not Considered
Not Considered
Annual
Quarterly
Quarterly
Annual
Quarterly
Annual
Remove
Remove
Quarterly
Not Considered
Annual
Removef/
Biennial
Biennial
Biennial
Remove
Annual
Annual
Remove
Annual
Remove
Remove
Biennial
Biennial
Annual
Quarterly
Biennial
Biennial
Annual
Annual
Semi-Annual
Annual
Quarterly
Annual
Remove
Remove
Quarterly
Biennial
33
-------
Table 6.1: Summary of Optimization of Groundwater Monitoring Program at
Fort Lewis Logistics Center Area
Well ID
Current
Sampling
Frequency
Recommendations
Generated Using
MAROS Tool
Recommendations
Generated Using
Three-Tiered
Approach
Example Composite
Recommendations
Monitoring Wells Completed in Upper Vashon Subunit (continued)
LC-26
LC-34 (new)
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-57 (new)
LC-61b(new)
LC-64a
LC-66a
LC-66b
LC-73a
LC-108
LC-132
LC-136a
LC-136b
LC-137a
LC-1375
LC-149c
LC-149d
LC-165
LC-167 (new)
LC-180
NEW-1 (new)
NEW-2 (new)
NEW-3 (new)
NEW-4 (new)
NEW-5 (new)
NEW-6 (new)
PA-381
PA-383
T-04
T-06 (new)
T-08
T-llb(new)
T-12b
T-13b
Annual
Quarterly
Annual
-
Annual
-
Annual
Quarterly
Quarterly
Quarterly
Annual
-
~
~
Quarterly
Annual
-
Quarterly
Annual
-
Quarterly
Annual
Not Considered
Quarterly
Remove
Semi-Annual
Remove
Quarterly
Not Considered
Not Considered
Quarterly
Remove
Annual
Biennial
Annual
Quarterly
Quarterly
Remove
Remove
Quarterly
Biennial
Remove
Biennial
Quarterly
Proposed for installation using 3-tiered
approach'1'
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Quarterly
Semi-Annual
Quarterly
Quarterly
Semi-Annual
Not Considered
Not Considered
Quarterly
Not Considered
Quarterly
Not Considered
Annual
Biennial
Annual
Not Considered
Annual
Not Considered
Annual
Annual
Remove
Biennial
Annual
Remove
Annual
Remove
Annual
Biennial
Semi-Annual
Quarterly
Remove
Annual
Remove
Remove
Annual
Quarterly
Annual
Remove
Remove
Biennial
Biennial
Remove
Semi-Annual
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Biennial
Biennial
Annual
Quarterly
Semi-Annual
Quarterly
Biennial
Semi-Annual
Annual
Biennial
Annual
Remove
Annual
Remove
Annual
Biennial
Semi-Annual
Quarterly
Remove
Annual
Remove
Remove
Annual
Quarterly
Annual
Remove
Quarterly
Biennial
Biennial
Biennial
Quarterly
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Biennial
Annual
Quarterly
Semi-Annual
Quarterly
Biennial
Semi-Annual
34
-------
Table 6.1: Summary of Optimization of Groundwater Monitoring Program at
Fort Lewis Logistics Center Area
Well ID
Current
Sampling
Frequency
Recommendations
Generated Using
MAROS Tool
Recommendations
Generated Using
Three-Tiered
Approach
Monitoring Wells Completed in Lower Vashon Subunit
FL4a (new)
LC-41b (new)
LC-64b
LC-lllb
LC-116b
LC-122b
LC-128
LC-137c
MAMC 1
MAMC 6 (new)
T- 10 (new)
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
Not Considered
Not Considered
Annual
Biennial
Semi-Annual
Biennial
Annual
Annual
Not Considered
Not Considered
Not Considered
Biennial
Annual
Annual
Biennial
Annual
Remove
Annual
Annual
Quarterly
Quarterly
Semi-Annual
Example Composite
Recommendations
Biennial
Annual
Annual
Biennial
Annual
Biennial
Annual
Annual
Quarterly
Quarterly
Semi-Annual
Groundwater Extraction Wells
LX-1
LX-2
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
LX-10
LX-11
LX-1 2
LX-1 3
LX-1 4
LX-1 5
LX-1 6
LX-1 7
LX-1 8
LX-1 9
LX-21
RW-1
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Quarterly
Every 3 years
Every 3 years
Every 3 years
Every 3 years
Every 3 years
Every 3 years
Every 3 years
Every 3 years
Every 3 years
Every 3 years
Every 3 years
Every 3 years
Every 3 years
Every 3 years
Every 3 years
Semi-Annual
Quarterly
Quarterly
Quarterly
Quarterly
Semi-Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
el
0
Information from GS1 (2003a) and Parsons (2003b).
"Current" monitoring program was initiated in December 2001 (Section 3.1).
"Composite" recommendations generated considering the current monitoring program, and recommendations
generated by MAROS tool and three-tiered approach.
"new" = the well was not included in the monitoring program prior to December 2001.
"Not Considered" = the well was not included in the MAROS evaluation.
"Remove" indicates that the well is recommended for removal from the monitoring program.
A dash (-) indicates that the well is not included in the current or refined monitoring program.
"Proposed for installation" indicates that a location for an additional monitoring well was identified on the basis of the
evaluation.
35
-------
A well was not selected for removal from the program in the example "composite" recommendations,
unless that well was recommended for removal in both the MAROS and three-tiered evaluations, or
unless that well was recommended for removal in one of the evaluations, and was not included in the
monitoring program that was initiated in December 2001. The frequency of sampling provided in the
"composite" recommendations was the frequency of sampling specified in the recommendations
generated in the MAROS and three-tiered evaluations, if those recommendations were in agreement.
If the frequencies recommended in the MAROS and three-tiered evaluations did not agree, but one of
the recommended frequencies was the same as the current sampling frequency, the current sampling
frequency was retained in the example "composite" recommendations. If the frequency of sampling
at a particular well, specified in the recommendations generated in the three-tiered evaluation, did not
agree with the frequency of sampling at that well in the current monitoring program, and the MAROS
evaluation did not consider that well, the frequency of sampling recommended in the three-tiered
evaluation was specified in the "composite" recommendations. If none of the current, and
recommended, sampling frequencies were in agreement, the intermediate sampling frequency was
specified in the "composite" recommendations. This example represents a "conservative" approach
to LTMO for the program at the Fort Lewis Logistics Center area, because it considers
recommendations generated using two different approaches, in addition to giving weight to currently-
accepted monitoring practice at the site, by also considering the current monitoring program.
Adoption of the example "composite" monitoring program would result in removal of eight wells
from the current monitoring program at the Fort Lewis Logistics Center area, together with
adjustment of the frequency of sampling to less-frequent events at most locations. Of course, more
aggressive approaches to a "composite" optimization scheme also could be applied.
The results of the MAROS optimization and the three-tiered evaluation, including recommendations
for removal of wells and adjustments to sampling frequency, were fully consistent for approximately
40 percent of the wells in the Fort Lewis Logistics Center monitoring program. (Wells that MAROS
did not consider are not included in this comparison.)
The results of the three-tiered evaluation and MAROS optimization of the monitoring program at the
Long Prairie Groundwater Contamination Superfund Site are summarized in Table 6.2. Example
composite recommendations also are provided in Column 5 of Table 6.2.
Table 6.2: Summary of Optimization of Groundwater Monitoring Program at
Long Prairie Groundwater Contamination Superfund Site8'
Well ID
Current1"7
Sampling
Frequency
Recommendations
Generated Using
MAROS Tool
Recommendations
Generated Using
Three-Tiered
Approach
Example Composite07
Recommendations
Monitoring Wells
BAL2B
BAL2C
MW1A
MW1B
MW2A
MW2B
MW2C
MW3A
MW3B
d/
-
Annual
Annual
Annual
-
Biennial
Biennial
Remove
Biennial
. Remove
Annual
Annual
Remove
Biennial
Remove67
Remove
Remove
Remove
Remove
Annual
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Annual
Annual
Remove
Remove
36
-------
Table 6.2: Summary of Optimization of Groundwater Monitoring Program at
Long Prairie Groundwater Contamination Superfund Site
Well ID
Current
Sampling
Frequency
Recommendations
Generated Using
MAROS Tool
Recommendations
Generated Using
Three-Tiered
Approach
Example Composite
Recommendations
Monitoring Wells (continued)
MW4A
MW4B
MW4C
MW5A
MW5B
MW6A
MW6B
MW6C
MW10A
MW11A
MW11B
MW11C
MW13C
MW14B
MW14C
MW15A
MW15B
MW16A
MW16B
MW17B
MW18A
MW18B
MW19B
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Remove
Annual
Annual
Remove
Biennial
Remove
Annual
Annual
Annual
Remove
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Remove
Annual
Annual
Remove
Biennial
Biennial
Remove
Annual
Annual
Remove
Annual
Remove
Annual
Annual
Annual
Remove
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Remove
Annual
Annual
Remove
Biennial
Biennial
Remove
Annual
Annual
Remove
Biennial
Remove
Annual
Annual
Annual
Remove
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Remove
Annual
Annual
Remove
Biennial
Biennial
Groundwater Extraction Wells
RW1A
RW1B
RW1C
RW3
RW4
RW5
RW6
RW7
RW8
RW9
RW7
RW8
RW9
Quarterly
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Remove
Remove
Remove
Annual
Biennial
Annual
Annual
Annual
Annual
Biennial
Annual
Annual
Biennial
Remove
Remove
Remove
Annual
Biennial
Annual
Annual
Annual
Annual
Biennial
Annual
Annual
Biennial
Remove
Remove
Remove
Annual
Biennial
Annual
Annual
Annual
Annual
Biennial
Annual
Annual
Biennial
37
-------
a/
b/
Table 6.2: Summary of Optimization of Groundwater Monitoring Program at
Long Prairie Groundwater Contamination Superfund Site
Well ID
Current^
Sampling
Frequency
Recommendations
Generated Using
MAROS Tool
Recommendations
Generated Using
Three-Tiered
Approach
Example Composite
Recommendations
Municipal Water-Supply Wells
CW3
CW6
Quarterly
Quarterly
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Information from GSI (2003b) and Parsons (2003c).
"Current" monitoring program was in effect in 2002.
"Composite" recommendations generated considering the current monitoring program, and recommendations
generated by MAROS tool and three-tiered approach.
A dash () indicates that the well is not included in the current monitoring program.
"Remove" indicates that the well is recommended for removal from the monitoring program.
The results of the MAROS optimization and the three-tiered evaluation, including recommendations
for removal of wells and adjustments to sampling frequency, were fully consistent for nearly 90
percent of the wells in the monitoring program at the Long Prairie site. Adoption of the example
"composite" monitoring program would result in removal of 16 wells from the current monitoring
network at the Long Prairie site, together with adjustment of the frequency of sampling to less-
frequent events at several locations.
The results of the three-tiered evaluation and MAROS optimization of the monitoring program at
McClellan AFB OU D are summarized in Table 6.3. Example composite recommendations also are
provided in Column 5 of Table 6.3.
Table 6.3: Summary of Optimization of Groundwater Monitoring Program at
McClellan AFB OU D"'
Well ID
Currentb/
Sampling
Frequency
Recommendations
Generated Using
MAROS Tool
Recommendations
Generated Using
Three-Tiered
Approach
Example Composite'
Recommendations
Zone A Monitoring Wells
MW-10
MW-11
MW-12
MW-14
MW-15
MW-38D
MW-52
MW-53
MW-55
MW-70
Annual
Annual
Annual
Biennial
Annual
Annual
Biennial
Biennial
Biennial
Biennial
Annual
Annual
Annual
Remove*"
Annual
Annual
Biennial
Biennial
Biennial
Biennial
Annual
Annual
Annual
Biennial
Annual
Annual
Remove
Remove
Biennial
Remove
Annual
Annual
Annual
Biennial
Annual
Annual
Biennial
Biennial
Biennial
Biennial
38
-------
Table 6.3: Summary of Optimization of Groundwater Monitoring Program at
McClellan AFB OU D
Well ID
Current
Sampling
Frequency
Recommendations
Generated Using
MAROS Tool
Recommendations
Generated Using
Three-Tiered
Approach
Example Composite
Recommendations
Zone A Monitoring Wells (continued)
MW-72
MW-74
MW-76
MW-88
MW-89
MW-90
MW-91
MW-92
MW-237
MW-240
MW-241
MW-242
MW-350
MW-351
MW-412
MW-458
MW-1004
MW-1026
MW-1041
MW-1042
MW-1064
MW-1073
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Annual
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Annual
Annual
Annual
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Zone B Monitoring Wells
MW-19D
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39
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Table 6.3: Summary of Optimization of Groundwater Monitoring Program at
McCIellan AFB OU D
Well ID
Current
Sampling
Frequency
Recommendations
Generated Using
MAROS Tool
Recommendations
Generated Using
Three-Tiered
Approach
Example Composite
Recommendations
Groundwater Extraction Wells
a/
b/
c/
d/
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Information from GSI (2003c) and Parsons (2003d).
"Current" monitoring program was in effect in 2002.
"Composite" recommendations generated considering the current monitoring program, and recommendations
generated by MAROS tool and three-tiered approach.
"Remove" indicates that the well is recommended for removal from the monitoring program.
The results of the MAROS optimization and the three-tiered evaluation, including recommendations
for removal of wells and adjustments to sampling frequency, were fully consistent for approximately
50 percent of the wells in the monitoring program at McCIellan AFB OU D. Application of the
three-tiered approach to the monitoring program generated considerably more recommendations for
well-removal from the program than did the MAROS evaluation, primarily on the basis of the
qualitative evaluation, which recommended the removal of wells at the periphery of OU D, that
historically have had no detections (or few detections at low concentrations) of COCs in
groundwater. Even though the example "composite" program represents a conservative approach to
program optimization, adoption of the example "composite" monitoring program would result in
removal of four wells from the current monitoring program at OU D, together with adjustment of the
frequency of sampling to less-frequent events at several locations.
Application of the two approaches to the optimization of long-term monitoring programs at each of
the three case-study example sites generated recommendations for reductions in sampling frequency
and changes in the numbers and locations of monitoring points that are sampled. Implementation of
the optimization recommendations could lead to reductions ranging from only a few percent (using
MAROS at McCIellan AFB OU D) to more than 50 percent (using MAROS at the Long Prairie site
and the three-tiered approach at McCIellan AFB OU D) in the numbers of samples collected and
analyzed annually at particular sites. The median recommended reduction in the annual number of
samples collected, generated during the optimization demonstration, was 39 percent. Depending
upon the scale of the particular long-term monitoring program, and the nature of the optimization
recommendations, adoption of an optimized monitoring program could lead to annual cost savings
ranging from a few hundred dollars (using MAROS at McCIellan AFB OU D) to approximately
$36,500 (using the three-tiered approach at the Fort Lewis Logistics Center Area). The results of the
evaluations also demonstrate that each of the optimized monitoring programs remains adequate to
address the primary objectives of monitoring.
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6.2 OTHER ISSUES
The procedures used in the LTMO evaluations were discussed with various stakeholders (the
environmental coordinators, responsible parties, and regulatory-agency personnel) through the entire
course of the project. After the evaluations had been completed, the results were presented to
stakeholder groups at each facility. Presenting the results to regulators at the three facilities raised
questions that had to do more with the data quality objectives (DQOs) than with the approaches
themselves. It became clear that every monitoring location that was recommended for removal, or
for a change in sampling frequency, had a non-quantifiable, subjective value that depended on the
person making the optimization decision. Much discussion revolved around the necessity of
monitoring to a degree sufficient to incontrovertibly document plume capture. Other questions were
raised regarding whether changes to monitoring programs would require modifications to existing
Records of Decision (RODs).
Based on those discussions, it is clear that before any optimization recommendation is accepted, there
must be a careful and thorough presentation of the long-term groundwater monitoring DQOs from the
viewpoint of all the stakeholders, followed by stakeholder agreement on DQOs, possibly for every
groundwater monitoring location. After the objectives have been defined, and consensus has been
reached, the results of the optimization analyses can be examined, and a decision made to accept or
reject recommendations. Note that there may be intangible costs associated with the development
and presentation of recommendations to reduce the spatial density or temporal frequency of
monitoring, including resistance of stakeholders and changes in public perception.
Depending upon the degree of difficulty in arriving at stakeholder concurrence with LTMO
recommendations, the tangible and intangible costs associated with conducting and implementing an
LTMO evaluation may outweigh the dollar cost savings that might be realized from an optimized
program. This possibility must be addressed on a site-specific basis.
6.3 CONCLUSIONS
The most significant advantage conferred by the optimization approaches is the fact that both
approaches apply consistent, well-documented procedures, which incorporate formal decision logic,
to the process of evaluating and optimizing monitoring programs. However, there are certain
limitations to each approach to monitoring program optimization. The primary limitation of MAROS
is associated with the way in which the tool deals with COC concentrations that are below the
reporting limit - MAROS assigns the value of the reporting limit (or some fraction thereof) to
samples having a constituent concentration below the reporting limit (Appendix B). This can lead to
identification of spurious temporal trends in concentrations, or to incorrectly concluding that reported
concentrations are unstable through time. Identification of spurious trends, in turn, will affect the
recommendations regarding the optimal frequency of sampling. The primary limitation of the three-
tiered approach is that the spatial-statistical stage of the evaluation generally is completed using
sampling results for only one constituent (Appendix B). The fact that the spatial evaluation currently
is conducted in two spatial dimensions (rather than three) represents a limitation of both approaches.
For either approach, the process of becoming familiar with the pertinent characteristics of a site,
identifying those data appropriate for the intended application, and transferring those data to the
appropriate format (even if the data are available in an electronic database), can be time-consuming
and labor-intensive, and represents a significant up-front investment of time and resources. Both
approaches could benefit from further development efforts to address these limitations; continued
development of both approaches is contemplated or in progress.
41
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Experience obtained during the demonstrations indicates that although the MAROS tool is capable of
being applied by an individual with little formal statistical training, interpretation of the results
generated by either approach requires a relatively sophisticated understanding of hydrogeology,
statistics, and the processes governing the movement and fate of contaminants in the environment.
The two approaches differ primarily in the procedures used to select a sampling frequency. MAROS
utilizes a relatively rigorous, statistical approach based on identification of temporal trends in COC
concentrations, while the three-tiered approach depends primarily upon qualitative considerations,
applied using detailed knowledge of the local hydrogeologic system, with support from the results of
the temporal and spatial-statistical evaluations. However, if the assumptions underlying the MAROS
statistical approach are violated (e.g., the number of separate monitoring events is not sufficient to
identify a trend), application of MAROS to develop recommendations regarding monitoring
frequency also will depend on qualitative considerations (e.g., GSI, 2003c). Both approaches use a
ranking approach to identify potentially-unnecessary monitoring locations, although the spatial-
statistical procedures used to implement the ranking approach are somewhat different.
In general, the recommendations generated by MAROS regarding spatial redundancy and sampling
frequency were more conservative than the recommendations generated during the three-tiered
evaluation (e.g., MAROS may recommend semi-annual sampling at a particular monitoring location,
while the three-tiered evaluation may recommend annual sampling at the same location). In addition,
the three-tiered approach tends to generate recommendations for removing a larger proportion of
wells from a monitoring program than does MAROS, because the three-tiered approach considers the
results of qualitative, temporal, and spatial analyses together to determine whether a particular well
should be retained or removed from the monitoring program, while MAROS will recommend a well
for removal from the program only if it is classified as redundant for all COCs based on the results of
the spatial evaluation alone. It is possible that the more rigorous qualitative evaluation in the three-
tiered approach justifies less-conservative recommendations than are generated using the MAROS
approach. For example, the three-tiered evaluation generated a recommendation for biennial
sampling at well LC-149c in the optimized Fort Lewis Logistics Center monitoring program, because
the qualitative review in the three-tiered evaluation identified well LC-149c as having no historical
detections of COCs throughout a monitoring history comprising 24 sampling events. By contrast, the
temporal-statistical evaluation algorithm in MAROS originally generated a recommendation for
annual sampling at that well. (The recommendation for annual sampling later was revised by
applying qualitative considerations during subsequent stages of the MAROS evaluation.)
The general characteristics of each of the three case-study example sites addressed in this
demonstration project are similar, comprising chlorinated solvent contaminants in groundwater,
occurring at relatively shallow depth in unconsolidated sediments. However, the assumptions
underlying the two approaches, and the procedures that are followed in conducting the evaluations,
are applicable to a much broader range of conditions (e.g., dissolved metals in groundwater, or
contaminants in a fractured bedrock system). In summary, either the MAROS tool or the three-tiered
approach can be used to generate sound and defensible recommendations for optimizing a long-term
monitoring program, under a wide range of site conditions.
Prior to initiating an LTMO evaluation, it is of critical importance that the monitoring objectives of
the program to be optimized and the DQOs for individual monitoring points be clearly articulated,
with all stakeholders agreeing to the stated objectives, decision rules, and procedures, so that the
program can be optimized in terms of recognized objectives, using decision rules and procedures that
are acceptable to all stakeholders. The decisions regarding whether to conduct an LTMO evaluation,
which approach to use, and the degree of regulatory-agency involvement in the LTMO evaluation
42
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and subsequent implementation of optimization recommendations, must be made on a site-specific
basis. Factors to be considered in deciding whether to proceed with an LTMO evaluation include:
The projected level of effort necessary to conduct the evaluation;
The resources available for the evaluation (e.g., quality and quantity of data, staff having the
appropriate technical capabilities);
The anticipated degree of difficulty in implementing optimization recommendations; and
The potential benefits (e.g., cost savings) that could result from an optimized monitoring
program.
Experience suggests that optimization of a monitoring program should be considered for most sites
where the LTM programs are based on monitoring points and/or sampling frequencies that were
established during site characterization, or for sites where more than about 50 samples are collected
and analyzed on an annual basis. Because it is likely that monitoring programs can benefit from
periodic evaluation as environmental programs evolve, monitoring program optimization also should
be undertaken periodically, rather than being regarded as a one-time event. Overall site conditions
should be relatively stable, with no large changes in remediation approaches occurring or anticipated.
For sites at which response decisions are being validated or refined (e.g., during periodic remedy-
performance reviews), optimization of the LTM program should be postponed until adjustments to
the response have been implemented and evaluated. Successful application of either LTMO approach
to the site-specific evaluation of a monitoring program is directly dependent upon the amount and
quality of the available data - results from a minimum of four to six separate sampling events are
necessary to support a temporal analysis, and results collected at a minimum of about six (for a
MAROS evaluation) to 15 (for a three-tiered evaluation) separate monitoring points are necessary to
support a spatial analysis. It also is necessary to develop an adequate CSM, describing site-specific
conditions (e.g., direction and rate of groundwater movement, locations of contaminant sources and
potential receptor exposure points) prior to applying either approach; the extent of contaminants in
the subsurface at the site also must be adequately delineated before the monitoring program can be
optimized.
Typically, a program manager should anticipate incurring costs on the order of $6,000 to $10,000 to
complete an LTMO evaluation using one of the two approaches presented in this demonstration, at
the level of detail of the case-study examples used in the demonstration (Sections 3, 4, and 5; and
Appendices C and D). Consequently, an LTMO evaluation may be cost-prohibitive for smaller
monitoring programs. Assuming a payback period of three years, potential cost savings of
approximately $2,000 to $3,300 per year must be realized if optimization of a monitoring program is
to be cost-effective. Because the costs associated with collection and analysis of a groundwater
sample (including prorated mobilization costs, and costs for field sampling, management of water
produced during sampling, laboratory analyses, QA/QC, and reporting) using conventional sampling
technologies (bailer or purge pump) can range from about $200 per sample to more than $500 per
sample (U.S. Air Force, 2004), an LTMO evaluation that can be used to reduce the total number of
samples collected at a site by about 5 to 10 samples per annum should be cost-effective.
43
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7.0 REFERENCES
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Franke, O.L. (ed.) 1997. Conceptual Frameworks for Ground-Water-Quality Monitoring. Ground-
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Parsons Corporation (Parsons). 2000. Remedial Process Optimization Report for Operable Unit 1,
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Office of Solid Waste and
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September 2004
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